(PDF) CoSiM Conference Proceeding No. 3 of 7th international online Conference on contemporary Studies in Management (CoSiM) 2023 - CoSiM Journal No 3 - Special Issue on Intuition, ISSN 2943-9019

Markus A. Launer

CoSiM Journal No. 3

Conference Proceedings

Special Issue on Intuitive Decision-Making

7th international Conference on Contemporary

Studies in Management (CoSiM)

Markus A. Launer

December 2023

A publication of the

Institut für gemeinnützige Dienstleistungen gGmbH, Suderburg, Germany

(independent non-profit organization for social projects)

ISSN 2943-9019 DOI: 10.13140/RG.2.2.14158.88641

Editor:

Prof. Dr. Markus A. Launer

Bötzow Str. 12

10407 Berlin

Orcid No. 0000-0001-9384-0807

Prof. Dr. Markus A. Launer is Professor of Business Administration and Service Management at the

a German University of Applied Sciences. Before he was lecturer at the Fresenius University,

Hamburg School of Business Administration (HSBA), and International School of Management

(ISM). He has over 20 years of domestic and international industry experience in large, medium and

small businesses as well as a consultant in investor relations and capital markets communications,

including 9 years in the United States. The last 6 years he was working closely with Universities in

Asia and Latin America. Launer is founder and managing director of the independent Institut für

gemeinnützige Dienstleistungen gGmbH (non-profit, charity organization).

Publisher:

Institut für gemeinnützige Dienstleistungen gGmbH

Hauptstrasse 28

29556 Suderburg, Germany

Launer@InstitutfuerDienstleistungen.com

Bibliographische Informationen (in German)

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insbesondere für Vervielfältigung, Übersetzung, Mikroverfilmung und die Einspeicherung,

Verarbeitung und Übermittlung in elektronischen Systemen.

Citation example: Launer, M. (2023). CoSiM Conference Proceeding No. 7 / 2023 Special Issue

on Intuitive Decisiion-Making, in: Launer, M. (ed). Conference Proceeding of 7th international

online Conference on contemporary Studies in Management (CoSiM), in: CoSiM Journal No

3, Suderburg, Germany, p. 1-10, Orcid No 0000-0001-9384-0807

https://institutfuerdienstleistungen.com/en/journal/

Support

Lukas Alvermann (student)

Markus A. Launer Special Issue Intuition 2023

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Acknowledgement of Editorial Board, Review Team & Session Chairs

Collaborators and Editorial Board

Prof. Dr. Joanna Paliszkiewicz, Warsaw University of Life Sciences, Poland

Prof. Dr. Fatih Çetin, Baskent University, Turkiye

Prof. Dr. Mohammad Daus Ali, University of Haripur, Pakistan

Prof. Dr. Erik Capistrano, University of the Philippines, Dilliman, Manila, Philippines

Prof. Dr. Dave Marcial, Silliman University, Philippines

Prof. Dr. Kandappan Balasubramanian, Taylor`s University, CRiT Institute, Malaysia

Prof. Dr. Kuanchin Chen, Western Michigan University, USA

Prof. Dr. Bo Aquila Yang, Beijing Open University, China

Prof. Dr. Joeffrey Maddatu Calimag, Kyungsung University, Korea

Prof. Dr. Marja Nesterova, Dragomanov Ukrainian State University, Kiev, Ukraine

Prof. Dr. Frithiof Svenson, Germany

Review Team

Prof. Dr. Fatih Çetin, Baskent University, Turkiye

Prof. Dr. Frithiof Svenson, Germany

Prof. Dr. Erik Capistrano, University of the Philippines, Manila

Prof. Dr. Dave Marcial, Silliman University, Philippines

Dr. Joanna Rosak-Szyrocka, Czestochowa University of Technology, Poland

Acknowledgement of our Key Note Speakers

Dr. Marta Sinclair, Senior Lecturer in the Department of Business Strategy and Innovation, and

member of the Griffith Asia Institute, Australia. Editor of the Handbook of Research Methods on

Intuition.

Prof. Dr. Eugene Sadler-Smith, Professor of Organizational Behaviour, University of Surrey, UK.

https://www.surrey.ac.uk/people/eugene-sadler-smith

Prof. Dr. Jay Liebowitz, Columbia University’s Data Science Institute, USA

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Alphabetical List of Authors

Agnieszka Tul-Krzyszczuk, Warsaw University of Life Science, the Management Institute, Poland

Anna Jasiulewicz, Warsaw University of Life Science, the Management Institute, Poland

Azra Tahjizi, Magreb University, Iran

Barbara Wyrzykowska. Warsaw University of Life Science, the Management Institute, Poland

Eoghan Jennings, IT Expert, Ireland

Jasveen Kaur, Business School-UBS, Guru Nanak Dev University, India

Joanna Rosack-Szyrocka, Czestochowa University of Technology, Poland

Joefrrey Maddatu Calimag, Kyungsun University, Korea

Ketevan Devadze, Batumi Shota Rustaveli State University, Georgia,

Konrad Michalski, Warsaw University of Life Science, Poland

Marcus T Anthony, Beijing Institute of Technology, Zhuhai, China

Marta Mendel, Warsaw University of Life Science, Poland

Mohammad Atiq Rafique Khattak, Institute of Management Sciences, University of Haripur, Pakistan

Mohammad Daud Ali, Institute of Management Sciences, University of Haripur, Pakistan

Muhammad Umair, Auyub Medical College Abbottabad, Pakistan

Natsuko Uchida, Ferris University, Japan

Markus A. Launer, Ostfalia University and Institut für gemeinnützige Dienstleistungen gGmbH

Independent (Non-proft Organization), Germany

Olena Kulykovets, Warsaw University of Life Science, the Management Institute, Poland

Piotr Pietrzak, Warsaw University of Life Science, the Management Institute, Poland

Shailja Vasudeva, Shaheed Captain Vikram Batra Government Degree College, India

Simon Zalimben, University de Catholoica de Ascuncion, Paraguay

Susan Jamieson, Company Light In Life

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Table of Content

Acknowledgement of Editorial Board, Review Team & Session Chairs ....................... II

Acknowledgement of our Key Note Speakers ................................................................. II

Alphabetical List of Authors ............................................................................................ III

Extended Abstract with double-blind Peer Review ........................................................ 1

Acknowledgment of our global Research Network on “Intuitive Decision-Making” ........... 1

The Impact of Trauma on Intuition Development in Children ............................................... 2

Intuition in an interpersonal clinical Setting........................................................................... 6

Integrated Intelligence, Digital Wisdom and the Futures of the Internet ............................ 10

Item Selection Study for measuring rational and intuitive Decision-Making ..................... 12

Three Rational & nine Intuitive Decision-Making Styles (RIDMS) ....................................... 25

Interoception and Intuitive Decision-Making ........................................................................ 45

Intuition in Educational Management ................................................................................... 57

Study on rational and intuitive Decision-Making in Tourism .............................................. 78

Concept Papers ............................................................................................................. 102

Rational and Intuitive Decision-Making in Latin America .................................................. 104

The Emotion Wheel for measuring Mood as an intuitive Decision-Making Style ............ 140

Emotional Intelligence and rational and intuitive Decision-Making .................................. 155

Developing a Concept for Measuring Rational and Intuitive Decision-Making based on

modern Technologies .......................................................................................................... 175

The Gut Feeling based on Human Gut Microbiome and ENS System .............................. 197

Intuitive and Rational and Decision-Making in Marketing ................................................. 206

Anticipation in Intuition Research ....................................................................................... 225

Rational and Intuitive Decision Making in the Healthcare Sector ..................................... 247

A new Concept for Digital Intuition ..................................................................................... 265

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Job Complexity and Rational and Intuitive Decision-Making ............................................ 287

Rational and intuitive decision-making in the area of sustainability ................................ 297

Perceived Organizational Performance and Rational & Intuitive Decision-Making ......... 306

Speed and Timing of intuitive Decision-Making ................................................................. 316

Uncertainty Avoidance and Rational and Intuitive Decision-Making ................................ 328

Intuition on Feelings, Mood and Emotional State .......................................................... - 339 -

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Extended Abstract with double-blind Peer Review

Acknowledgment of our global Research Network on “Intuitive Decision-

Making”

The following extended abstracts were presented at the international online Conference on

Contemporary Studies in Management 2023 (CoSiM).

Contemporary Studies in Management (CoSiM)

Key topic of this conference are the latest contemporary studies in Management such as

Digitalization and Digital Transformation, Intuitive Management and Decision Making, modern

Education Management, Sustainability, Intercultural Integration & Diversity. Special topic is

this year “Circular Raw Material Management (Recycling)”.

36 hours Concept

This years CoSiM Conference will be around the clock in different time zones. After the

presentation by researchers from Europe, Latin America and the US, we will continue in Asia.

With this around the clock concept we are able to bring in more participants at a convenient

time. Therefore, please check carefully the time zones.

Rational and intuitive decision-making @ the workplace

One of the key topcs in a special session was rational and intuitive decision-making at @ the

workplace.

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The Impact of Trauma on Intuition Development in Children

Ketevan Devadze

Batumi Shota Rustaveli State University, Georgia, Orcid No 0000-0001-5967-0816

Extended Abstract

Purpose:

This research is dedicated to a comprehensive investigation of the intricate relationship

between childhood trauma and the development of intuition in children aged 8-16. Recognizing

the pressing concern of childhood trauma worldwide, this study endeavors to shed light on how

traumatic experiences may shape a child's intuitive abilities and influence their decision-

making processes.

Theoretical Framework:

The theoretical foundation of this study is rooted in the amalgamation of child psychology,

trauma studies, and cognitive science. This multidisciplinary approach provides a

comprehensive framework for understanding the complex interplay between childhood trauma

and the development of intuition in children aged 8-16.

Child psychology forms the cornerstone of our theoretical framework, recognizing that

childhood is a critical period for cognitive and emotional development. The field posits that

children's minds are inherently adaptable and influenced by their experiences. Childhood

trauma, such as abuse, neglect, and adverse events, has been extensively studied in child

psychology and is known to shape various aspects of child development. Trauma can lead to

emotional dysregulation, cognitive alterations, and hypervigilance, which significantly impact a

child's emotional well-being, relationships, and mental health.

The trauma studies perspective acknowledges the profound and lasting impact of traumatic

experiences on individuals, emphasizing the importance of understanding the effects of

trauma, particularly in children. Trauma studies encompass research on the immediate and

long-term consequences of trauma, detailing the enduring imprint of trauma on various facets

of a survivor's life. This perspective highlights the need to explore how trauma-induced

changes may extend to cognitive processes, including the development of intuition.

Cognitive science provides insights into the cognitive processes that underlie human decision-

making, including intuition. Intuition, often described as the ability to understand or know

something without conscious reasoning, is recognized as a vital aspect of human cognition. It

is intertwined with cognitive and emotional development, playing a pivotal role in how children

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perceive and interact with the world around them. Cognitive science posits that intuition can

be influenced by cognitive and emotional factors, making it a suitable lens for understanding

how trauma might shape a child's intuitive abilities.

Methodology

The research employs a multidisciplinary approach, intertwining various disciplines to gain a

comprehensive understanding. Data collection involves surveys and interviews administered

to children aged 8-16 who have experienced trauma, particularly those who have sought

support from psychosocial centers, as well as children who have not experienced trauma.

Additionally, advanced neuroimaging techniques are utilized to explore potential neurological

correlates of trauma-induced changes in intuition. The research process unfolds in multiple

stages, including recruitment and selection, data collection, neuroimaging, data analysis, and

interpretation.

Discussion

The preliminary findings of this ongoing research shed light on the intricate relationship

between childhood trauma and the development of intuition in children aged 8-16. The data

collected through the International Trauma Questionnaire for Children, the Child Intuition

Questionnaire, and neuroimaging techniques offer a multifaceted perspective on how trauma

can influence a child's intuitive abilities and their self-perception of intuition. The discovery that

some children with trauma histories report a heightened sense of intuition raises questions

about the underlying mechanisms and has significant implications for both clinical practice and

the broader understanding of child development in the context of trauma.

These findings have significant implications for both clinical practice and the broader

understanding of child development in the context of trauma. First and foremost, they

underscore the need for a nuanced and individualized approach to trauma-informed care.

Understanding that some children with trauma histories may possess heightened intuition calls

for tailored therapeutic interventions that leverage this innate ability to foster resilience and

recovery.

Moreover, the research highlights the importance of considering intuitive abilities in the

assessment and treatment of children who have experienced trauma. Intuition can serve as a

valuable resource for children to navigate complex emotional and interpersonal challenges,

and recognizing and validating this skill can contribute to their overall well-being.

From an academic perspective, these findings contribute to the evolving discourse on the

effects of trauma on child development. They challenge the prevailing narrative that trauma

universally leads to deficits and suggest that it may also cultivate unique strengths in some

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individuals. This, in turn, encourages a more holistic approach to trauma studies that explores

both the negative and positive outcomes of trauma experiences.

While the preliminary findings are promising, it is crucial to acknowledge the ongoing nature of

this research. Further analysis, a larger sample size, and the integration of neuroimaging data

are necessary to draw comprehensive conclusions. The complexity of trauma and intuition

development requires a meticulous examination, and this study is committed to providing a

more profound understanding of these dynamics.

Conclusion

In conclusion, the research illuminates the complex relationship between childhood trauma

and the development of intuition in children. It underscores the need for tailored, trauma-

informed care that recognizes and harnesses the potential strengths of children with trauma

histories. It also contributes to the broader academic discourse by challenging conventional

narratives and encouraging a more comprehensive understanding of the impact of trauma on

child development. As this study progresses, it aspires to offer further insights and guidance

for the benefit of children who have experienced trauma and the professionals who support

them.

Keywords: Trauma, Intuition Development, Child Psychology, Cognitive Science, Trauma

Studies, Psychosocial Support.

References:

Stanton, S. D. (2016). Intuition: A Silver Lining for Clinicians with Complex Trauma.

Pretz, J. E., Brookings, J. B., Carlson, L. A., Humbert, T. K., Roy, M., Jones, M., & Memmert,

D. (2014). Development and validation of a new measure of intuition: The types of

intuition scale. Journal of Behavioral Decision Making, 27(5), 454-467.

Cummins, J. (1980). Psychological assessment of immigrant children: Logic or intuition?.

Journal of Multilingual & Multicultural Development, 1(2), 97-111.

Singer, F. M., & Voica, C. (2008). Between perception and intuition: Learning about infinity.

The Journal of Mathematical Behavior, 27(3), 188-205.

Kelemen, D. (2004). Are children “intuitive theists”? Reasoning about purpose and design in

nature. Psychological science, 15(5), 295-301.

Fischbein, E. (2002). Intuition and experience. Intuition in science and mathematics: an

educational approach, 85-96.

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Perry, B. D. (2007). Stress, trauma and post-traumatic stress disorders in children. The Child

Trauma Academy, 17, 42-57.

Shaw, J. A. (2000). Children, adolescents and trauma. Psychiatric Quarterly, 71, 227-243.

Zilberstein, K. (2014). Neurocognitive considerations in the treatment of attachment and

complex trauma in children. Clinical Child Psychology and Psychiatry, 19(3), 336-354.

Campbell, C., Roberts, Y., Synder, F., Papp, J., Strambler, M., & Crusto, C. (2016). The

assessment of early trauma exposure on social-emotional health of young children.

Children and youth services review, 71, 308-314.

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Intuition in an interpersonal clinical Setting

Susan Jamieson

Company- Light In Life

Abstract

The primary argument of this paper is that human beings are inherently attuned to a

quantum process and interpret/interface with these surrounding energies unconsciously on a

daily basis. The scientific and biological/physiological basis of this, is that our bodies are

regulated by and communicate through a light network on a quantum neurobiological - DNA,

cellular and enzyme basis.3’4’5’6 If we are connected in a web of quantum information (I refer

to as ‘Light’), criss-crossing and connected like a spider’s web, human beings would indeed

be able to pick up more information (instantly, as this is largely a non-local phenomena). This

may be the basis of a skill which we call, ‘Intuition.’ This paper proposes that this information

exisits in the quantum light fields connecting all things.

Keywords: intuition; heuristic; medical intuition; transpersonal

Introduction

A study in Harvard Business Review(Maidique, 2011) stated that 85% of major decisions

by Chief Executive Officers involved intuition as a major determining factor. Further, Einstein

famously said, ' The intuitive mind is a sacred gift and the rational mind is a faithful servant.

We have created a society the honours the servant and has forgotten the gift’. For decades,

research has widely agreed that over 50% of our communication is conveyed in a non-verbal

way, using all of the senses. Previously thought to be over 90%,( Albert Mehrabian, 1967),

it’s still a huge consideration.

As a medical doctor with 30 years experience, The Author defines this ability of ‘the senses’.

Not only useful in the therapy arena, it is an essential tool to enhance communication and

decision-making ability. Also, honing this innate ability has an added benefit, in enhancing

resilience – so important in an increasingly stressful world.

Intuition is defined by the Merriam-Webster dictionary as, 'Immediate apprehension or

cognition'. A paper in the journal Nature (Nalliah, 2016) focussing on dentists, has shown that

what they call ‘domain expertise’ is not sufficient. ‘Domain expertise’ means that to be good

at intuitive skills it is necessary to be actually qualified and have specialist knowledge. For

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example, a specialized person such as a business consultant or lawyer would have spent

years studying. However, the study (Nalliah, 2016) demonstrated that in addition, the domain

expert requires a minimum of five years of additional work experience to hone this intuition

skill . They then have stored information in subconscious frameworks, enabling quick

extraction of this data without conscious thought. As an example of such domain expertise is

a fireman/woman trying to make instant life -and -death decisions, and coping with many

fires, multidirectional winds, few hoses and trapped people.

Many business executives would resonate with the necessity for quick decisions that have to

be correct. We need a skill to use, especially in decisions that have time constraints, or

problems that are complex or ambiguous in nature. Perhaps more so when there is a lack of

scientific evidence for decision making, we need tools that enable us to bring a hat out of the

box. Therefore we need a sophisticated, highly complex cognitive structure that gives fast

and accurate responses. Fortunately, we have this - in our brain and consciousness, which

are perfectly made for this type of job.

The author argues that in her area of expertise, as a physician for 30 years, using specific

techniques (which she teaches) grants her ‘Medical Intuition’, a tool useful in any type of

patient-centered therapy as a form of non-verbal communication. The situation is similar for

professionals such as phycologists, naturopaths and other therapists, as well as any trained

experienced professionals.

Indeed, having over 30 years practical experience, The Author has much empirical evidence

of this. As an example, when consulting a healthy fit 30 year old for an unrelated problem,

the words, ‘High blood pressure’, suddenly come to her mind. The woman had no indications

or reasons for hypertension, no logical reason to suspect it. However when checked, she

was found to have life - threateningly high blood pressure. Many doctors have similar

examples, however don’t like to share lest they are judged, ‘less than scientific’.

Undoubtably, learning to be more intuitive helps the professional to be quicker at getting to

the root of a problem, and also quicker to plot out the best solutions. In addition, there are

added benefit of enhancing resilience, as with enhanced intuition paths are smoothed in both

business and personal lives. Choosing easier paths naturally leads to greater job satisfaction

and lower stress levels.

In the following part of the paper, the author argues that whilst the above studied

prerequisites of expert training and experience are hardly a surprise, to gain more knowledge

we can take advantage of what some of the most scientific minds have to say about Intuition.

Aside from Einstein, there was Tesla, who allegedly said, ‘My brain is only the receiver. In

the Universe, there is a core from which we obtain knowledge.’

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Great minds in different countries may come up with the same revolutionary ideas at the

same time. It could be said that this phenomenon involves similar processes to that of expert

intuition. The famous psychotherapist Carl Fung had similar beliefs to Tesla. He taught that,

‘Consciousness is could be compared to small island in the ocean of the unconscious, while

the unconscious is part of the primordial condition of humankind’. This implies that somehow,

if we had the skills, we could tap into any form of information.

Nobel Laureate Roger Penrose, with anaesthesiologist Hamerhoff,3 showed in the 1990s that

microtubles in neurons were capable of quantum computing. In their “Orch OR” theory, it’s

stated that, ‘OR is related to the fundamentals of quantum and space-time geometry, so

Orch OR suggests that there is a connection between the brain's biomolecular processes

and the basic structure of the universe. In the physical body and our also with our

consciousness, it could be said that this subtle intuition skill lies in the area of quantum-

neurobiology, and in the realms of what is now being called Energy Medicine, or Vibrational

Medicine. As exciting areas of new research emerge regarding the quantum nature of our

bodies, at the DNA and cellular physics levels3’4’5, more possibilities and explanations are

appearing.

In the past decade, DNA, at the core of our being, are thought to be resonating oscillators,

and sensitive to Electromagnetic Freqencies4 (Polesskaya et al, 2017) Many studies show

the electrical properties of brain neurons, electrical polarity of both cells and organs, biologist

assert (Kanev et al 2013)5, ‘Chromosomes should be regarded not only as vehicles for

carrying genes and inheritance, but also as generators, transformers, conductors,

condensers, switchers, transmitters and receivers in electric circuits, capable of operating

electric currents or moving charges.”

So it can be seen that in addition to Electro-magnetic radiation the body, in keeping with

present day laws of physics, has to exhibit the physical phenomena of inductance, are

upgraded. In the past century the biology and chemistry, rather than quantum physics, were

revolutionary, as an example as Nobel Prize-winner biochemist Albery Szent Gory

discovered that the body’s enzyme systems were more sensitive to one colour

(electromagnetic frequency) over another.6’7 Now, as we learn more complex physics, as

previously described, we find the body exhibits all phenomena.

Conclusion

In conclusion, the author proposes that on an unconscious basis we interface with these

light energies as well as those of the natural world, connecting to the light of the Earth’s

magnetic field. Forever bathed in this energy, like fish in water, we are unaware of the

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currents and tides with which we interact. Energy medicine recognizes that our thoughts and

emotions, even though difficult to measure using traditional science, obviously exist and can

be accessed. Human beings need to understand and consciously work with these energies,

accepting the interconnectedness of our physical and nonphysical beings, and the

resonances that can develop. In former ages, physicians recognized that true wholeness and

healing could only be reached through embracing our own wholeness and acknowledging

the innate interconnectedness of all things.

“In every culture and in every medical tradition before ours, healing was accomplished by

moving energy.”6 Albert Szent-Györgyi, MD and Nobel Laureate in Medicine.

References

Maidique, M. A. (2011, August 15). Decoding Intuition for More Effective Decision-Making.

Harvard Business Review. https://hbr.org/2011/08/decoding-intuition-for-more-ef

Nalliah, R. (2016). Clinical decision making – choosing between intuition, experience and

scientific evidence. Br Dent J 221, 752–754 (2016).

Hameroff S, Penrose R. Consciousness in the universe: a review of the 'Orch OR' theory.

Phys Life Rev. 2014 Mar;11(1):39-78. doi: 10.1016/j.plrev.2013.08.002. Epub 2013

Aug 20. PMID: 24070914.

Oksana Polesskaya, Vadim Guschin, Nikolai Kondratev, Irina Garanina, Olga Nazarenko,

Nelli Zyryanova, Alexey Tovmash, Abraham Mara, Tatiana Shapiro, Elena

Erdyneeva, Yue Zhao, Eugenia Kananykhina, Max Myakishev-Rempel,On possible

role of DNA electrodynamics in chromatin regulation,Progress in Biophysics and

Molecular Biology,Volume 134,2018,Pages 50-54,ISSN 0079-6107,

Ivan Kanev, Wai-Ning Mei, Akira Mizuno, Kristi DeHaai, Jennifer Sanmann, Michelle Hess,

Lois Starr, Jennifer Grove, Bhavana Dave, Warren Sanger (2013). Computational

and Structural Biotechnology Journal, Volume 6, Issue 7,2013,e201303007,ISSN

2001-0370, Computational and Structural Biotechnology Journal,

Szent-Györgyi A. Introduction to a Submolecular Biology. Academic Press; New York, NY,

USA: 1960

Martinek K, Berezin IV. Artificial light-sensitive enzymatic systems as chemical amplifiers of

weak light signals. Photochem Photobiol. 1979 Mar;29(3):637-49. doi:

10.1111/j.1751-1097.1979.tb07104.x. PMID: 375252.

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Integrated Intelligence, Digital Wisdom and the Futures of the Internet

Marcus T Anthony, PhD

Beijing Institute of Technology, Zhuhai, China

ORCID No. 0000-0003-0211-2770

In the era of information overload and the crisis in sensemaking, the concept of Digital Wisdom

represents a potentially useful framework to strategize for the future of the internet, the digital

society and the internet in general. In this paper, futurist Marcus T Anthony introduces the key

concepts of Digital Wisdom (Anthony 2022, 2023a,b; 2022) and Integrated Intelligence

(Anthony 2008, 2015, 2023a,b), highlighting their significance in addressing the current

challenges faced as we enter the age of generative AI. This paper draws upon key concepts

in the discipline of Foresight, and Inayatullah’s (2018) analytical Futures Studies method of

Causal Layered Analysis. Firstly, Anthony defines Digital Wisdom as a transformative concept

that transcends mere digital literacy, emphasizing the cultivation of inner wisdom and

introspection. Integrated Intelligence refers to the harmonious integration of human

intelligence, and incorporates an understanding of both personal and collective aspects of

mind – both rational and intuitive cognitive functions. The paper explores the urgent need to

establish an Authentic Self in individuals, considering the prevailing meaning crisis and the

fragmentation that have emerged in the digital age. After briefly summarising the empirical and

report-based evidence for Integrated Intelligence, Anthony introduces the concept of Digital

Wisdom, and outlines its three key domains: knowing yourself, knowing your fellow humans,

and knowing technological systems. This paper primarily focuses on the first domain, “know

yourself,” as it is the foundation of Digital Wisdom. It shall be argued that in order to foster

Digital Wisdom in society requires a re-valuing of introspection and intuitive insight. Anthony

stresses the importance of instilling these values and skills in parenting, education and work

environments (including management), while emphasizing their potential to revolutionize how

we navigate the digital landscape and our personal lives, work and education. In summary,

this paper outlines the concepts of Digital Wisdom and Integrated Intelligence, elucidating their

potentially vital role in transforming the digital society. By prioritizing inner wisdom and self-

understanding, we can foster Digital Wisdom and pave the way for a more enlightened world

that honors the human spirit.

Keywords: digital wisdom, integrated intelligence, meaning crisis, authentic self, introspection,

intuition, artificial intelligence, information technology, disinformation and misinformation

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References

Anthony, M.T. (2008). Integrated Intelligence: Classical & Contemporary Depictions of

Intelligence & Their Educational Implications. Sense Publishers.

Anthony, M.T. (2015). “Classical Intuition and Critical Futures.” Journal of Futures Studies,

September 2015, 20(1): 131-138.

Anthony, M.T. (2022). “A Critical Futures Studies Perspective on Embodiment and the Crisis

in Sensemaking.” In: Crisis Management - Principles, Roles and Application.

10.5772/intechopen.107776.

Anthony, M.T. (2023a). Digital Wisdom and Embodied Presence as Enhancers of Pervasive

Learning in the Metaverse and Beyond. In Proceedings of the International Conference

on Learning and Teaching for Future Readiness, ICLT 2023 (pp. 41-46). 17 - 19 May

2023.

Anthony, M.T. (2023b). Power and Presence. Reclaiming Your Authentic Self in a Digitized

World. MindFutures.

Inayatullah, S. (2018). What Works: Case Studies in Foresight. Tamkang University Press.

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Item Selection Study for measuring rational and intuitive Decision-Making

Markus A. Launer

Ostfalia University and Institut für gemeinnützige Dienstleistungen gGmbH independent

(Non-proft Organization), Germany

Abstract

This concept paper is an analysis of item for measuring rational and intuitive Decision-Making.

It shows a a summary of all important measurement instruments on intuitive decision-making.

All items with factor loading large than 0,7 were summarized. The result is a comprehensive

catalogue on items to measure rational and intuitive decision-making. The results will be used

in the new globald study RIDSMS by Launer and Cetin.

Introduction

This concept paper summarizes all important measurement instruments on rational and

intuitive decision-making such as:

 CEST = Cognitive-Experiential Self-Theory (Epstein, 1994)

 REI = Rational Experiential Inventory (Pacini & Epstein, 1999);

 PMPI = Perceived Modes of Processing Inventory (Burns & D’Zurilla, 1999);

 GDMS = General Decision Making Style inventory (Scott & Bruce, 1995);

 PID = Preference for Intuition and Deliberation scale (Betsch, 2004),

 CoSI = Cognitive Style Indicator (Cools & Van den Broeck, 2007).

 TIntS = Types of Intuition Scale (Pretz et al, 2014)

 USID = Unified Scale to Assess Individual Differences in Intuition and Deliberation

(Pachur and Spaar, 2015)

 BEM = Feeling the future (Bem et al., 2015)

 RHIA = Rationality Heuristic Intuition Anticipation (Launer and Svenson, 2022)

 RIDMS-E = Rational and intuitive Decision-Making Style (Launer and Cetin, 2023)

All rational and intuitive decision-making styles combined in an overview

Hamilton, Shih & Mohammed (2016) developed a table on the Dimensions and measures of

decision style.

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Launer and Cetin (2023) provide a summary of all measurement instruments on rational and

intuitive decision-making.

Analysis of Scales and Items

The analysis of the scales and the items will be analyzed according to the structure of Launer

and Cetin (2023). All items with a factor load larger than 0,7 were analyzed. The items marked

black and bold were part of then original questionnaire of Launer and Cetin (2023). Marked in

red and bold are the results after the analysis with Crohnbach`s ASlpha and factor analysis. Irt

reüptresents the optimal short version of the measurement of rational and intuitive decision-

making style.

CEST 1994

GDMS 1995

REI 1999

PMPI 1999

PID 2004

CoSI 2007

TIntS 2014

USID 2015

2023

Epstein Scott /

Bruce

Pacini /

Epstein

Burns /

D´Zurilla

Betsch Cools / van

den Broek

Pretz et al Pachur / Spaar Launer &

Cetin

Rational Analytical Cognitive

system

Rational:

Analytical

Rational:

Thinking

Rational

Processing:

Thinking fact-

based

Deliberation /

Analytical

Knowing Cognitive

Knowing

Deliberation:

Knowing

Planning Deliberation /

Planning

Cognitive

Planning

Deliberation:

Planning

Intuition Emotional Intuition:

Emotinal /

Feelings /

Instincts

Experiential:

Feelings /

Instinct

Emotional

processing:

Feelings /

Instincts

Intuition:

Feelings

Affective:

Feelings

Affective:

Feeling

Emotional

Body Impulses Experiential:

Gut Feeling /

Heart

Emotional

Processing:

Gut Feling

Intuition: Gut

Feeling

Affective:

Heart / Gut

Feeling

Affective:

Heart

Heart, Skin,

Gut feeling

Mood Mood

Holistic Holistic

Abstract and

Big Picture

Holistic

Spontaneous Automatic

Processing:

Swift Decisions

Spontaneous Spontaneous

Experince-based

heuritics

Experiential:

Associative,

Automatic

Learning

Automatic

Processing:

Experience

Intuition: Life

experience,

human

understanding

Inferential:

experince-

based

Affective: Life

experience,

human

understanding

Heuristics

Anticipation Experiential:

Hunches

Emotional

Hunches

Affective:

Hunches

Affective

Hunches

Anticipation

Dependent

(Support by

Others)

Dependent Support by

Others

Unconscipous

Thoughts

Slow

Unconscious

Other Avoidant Avoidant

Creating Creating

New Support by

Technology

Digital

Intuition

Digital Intuition Digital

Intuition

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Rational: Analytical Decision-Making Style

Rational: Planning Style

Study and Factor

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Rational: Analyzing Style

Theoretical Basis

18 PMPI (F 0,818)

Instead of acting on the first idea that comes to mind, I care fully consider all

my options.

Rational Processing Scale

1 PID (F 0,754) Before I make decisions, I usually think carefully first. Deliberation

GDMS (F 0,61 & 0,73) I make decisions in a logical and systematic way Rational GDMS

GDMS (F 0,73 & 0,76) My decision making requires careful thoughts Rational GDMS

K2 CoSI (F -0,9)

I like to analyze problems

Knowing Style

K3 CoSI (F -0,61) I makes detailed analysis Knowing Style

23 TIntS (F 0,76) I prefer to follow my head rather than my heart Rational TIntS

28 TIntS (F 0,73) It is foolish to base important decisions on feelings Rational TIntS

20 TIntS (F 0,72) I generally don`t depend on my feelings to help me make decisions Rational TIntS

8 PMPI( F 0,707) I usually think of as many alternative ways of coping as possible be fore I de

cide what I am going to do. Rational Processing Scale

15 PMPI (F 0,754)

I usually try to ge t all the facts that I can be fore deciding how to cope .

19 PMPI (F 0,897) Before I attempt to cope , I think of all my options and care fully consider

the pros and cons of each one. Rational Processing Scale

24 PMPI (F 0,83)

When I am attempting to cope , one of the first things I do is gather as many

facts about the situation as possible so that I will be able to understand what it

is all about.

Rational Processing Scale

REI (F 0,75) I try to avoid situations that require thinking in depth about something Rational Scale

REI (F 0,74) I am not that good at figuring out complicated problems Rational Scale

REI (F 0,72)

I enjoy intellectual challenges

Rational Scale

REI (F 0,7) I don`t like to have to do a lot of thinking Rational Scale

13 PID (F 0,591) When I have a problem I first analyze the facts and details before I decide Deliberation

14 PID (F 0,556) I think first before I act. Deliberation

10 PID (F 0,401) I am a perfectionist Deliberation

6 PID (F 0,520) I am think about myself Deliberation

11 PID (F 0,419) If I have to justify a decision, I think particularly carefully beforehand Deliberation

GDMS (F0,53 bis 0,63) I double-check my information sources to be sure I have the right facts before

making decisions Rational GDMS

GDMS (F 0,52 bis 0,75) When making a decision, I consider various options in terms of a specific goal Rational GDMS

REI (F 0,64) I am a very analytical thinker

Study and Factor

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Rational: Planning Style

Theoretical Basis

RIDMS Launer Cetin Following clear goals is very important to me Planning Style RIDMS

P1 CoSI (F 0,7) Developing a clear plan is very important to me Planning Style CoSI

P3 CoSI (F 0,77)

I like detailed action plans

Planning Style CoSI

P5 CoSI (F 0,64) I prefer well-prepared meetings with a clear agenda and strict time

management Planning Style CoSI

USID When I make decisions, I proceed step-by-step Deliberation

REI I usually have clear, explainable reasons for my decisions Rational Thinking

RHIA Launer Svenson A good task is a well planned task. Planning Style

3 PID (F 0,611) Before making decisions I usualy think about the goals I want to achieve Deliberation

P7 CoSI (F 0,65) A good task is a well prepared task Planning Style

P2 CoSI (F0,54)

I always want to know what should be done when.

Planning Style CoSI

P4 CoSI (F 0,67) I prefer clear structures to do my job Planning Style CoSI

P6 CoSI (F 0,62) I make definite engagements, and I follow up meticulously. Planning Style CoSI

7 PID (F 0,487) I prefer making detailed plans rather than leaving things to chance. Deliberation

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Rational: Knowing Style

Unconscious Big Picture and Holistic Intuition

Fast spontaneous intuitive Decisions

Fast experience-based heuristically Intuition

Study and Factor

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Rational: Knowing Style

Theoretical Basis

K1 CoSI (F -0,56) I want to have a full understanding of all problems Knowing Style

K4 CoSI (F -0,69) I study every problem until I understand the underlying logic Knowing Style

REI

I enjoy solving problems that require hard thinking

Rational Thinking

REI I prefer complex problems to simple problems Rational Thinking

REI I have no problem thinking things through carefully Rational Thinking

REI I enjoy intellectual challenges Rational Thinking

REI I enjoy thinking in abstract terms Rational Thinking

Study and Factor

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Unconsious Holistic Intuition

Theoretical Basis

18 PID (F 0,736) I am an intuitive person Intuition PID

REI (F0,66) GDMS (F 0,72

bis 0,75) 26 Carl (F

0,483)

When I makes decisions, I tend to rely on my intuition (impression) Emotional Feelings & Instincts,

Experiental, Intuition GDMS

29 TIntS (F 0,8) I am a big picture person Big picture TIntS

26 TIntS (F 0,74) I try to keep in mind the big picture Big picture TIntS

18 TIntS (F 0,75) I prefer concrete facts over abstract theories. (R) Holistic Abstract TIntS

RHIA Launer Svenson I always think in interconnections among elements with another Unconsious Intuition

RHIA Launer Svenson I use my general thoughts of whole rather than details when to decide Unconsious Intuition

RHIA Launer Svenson I always use big picture perspective when to decide Unconsious Intuition

RHIA Launer Svenson Before I decide, I try the understand the big picture of the problem Unconsious Intuition

RHIA Launer Svenson I always use big picture before I decide Unconsious Intuition

11 TIntS (F 0,69) 23 Carl

(F0,529) I would rather think in terms of theories than facts. Holistic Abstract TIntS

24 TIntS (F 0,66) 19 Carl

(F0,554) I enjoy thinking in abstract terms Holistic Abstract TIntS

REI (F 0,65) I don`t have a very good sense of intuition ( R ) Experiental scale

14 TIntS F (0,66) When working on a complex problem or decision I tend to focus on the details

and lose sight of the big picture ( R ) Big picture TIntS

1 TIntS (F 0,64) When tackling a new project, I concentrate on big ideas rather than the details Big picture TIntS

5 TIntS (F 0,43)) It is better to break a problem into parts than to focus on the big picture. (R) Big picture TIntS

13 Carl (F0,447) When I get stuck working on a problem, the answer frequently comes to me

suddenly at some later point in time. Abstract Holistic Carl

37 Carl (F 0,412) Intuition is an accurate and reliable shortcut for problems that would

otherwise require a lot of analysis. Abstract Holistic Carl

10 Carl (F 0,405) Ambiguity makes me very uncomfortable. Abstract Holistic Carl

Study and Factor

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Fast Spontaneous Decisions

Theoretical Basis

GDMS (F 0,85 bis 0,87)

I generally make snap decisions

Spontaneous GDMS

GDMS (F 0,73 bis 0,75

I make quick decisions

Spontaneous GDMS

GDMS (F 0,72 bis 0,78)

I often make decisions on the spur of the moment

Spontaneous GDMS

GDMS (F 0,7) I often make impulsive decisions Spontaneous GDMS

17 PMPI (F 0,682)

I typically figure out the way to decide swiftly

Automated Processing Scale

13 PMPI (F 0,635)

The right way to decide usually comes to mind almost immediately

Automated Processing Scale

21 PMPI (F 0,599) I quickly do the right thing when deciding because I’ve often faced almost

the same thing before

Automated Processing Scale

29 PMPI (F 0,660)

How to decide usually becomes quickly apparent

Automated Processing Scale

31 PMPI (F 0,687) When a stressful situation occurs I know right away what I ne ed to do to

cope with

Automated Processing Scale

10 TIntS (F 0,38) 25 Carl

(F 0,46)

My intuitions come to me very quickly. Inferential intuition TIntS

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Slow Unconscious Thoughts with Incubation Time

Study and Factor

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Fast Experience-Based Heuristic

Inferential Intuition

Theoretical Basis

RIDMS Launer/Cetin

For quick decisions, I go through a decision tree with yes / no questions.

Gigerenzer Heuristics

RIDMS Launer/Cetin I have experience in my job and can make decisions very quickly. Gigerenzer Heuristics

22 TIntS (F 0,76) 21 Carl

(F0,541) If I have to, I can usually give reasons fo my intuitions Inferential intuition TIntS

27 TIntS (F 0,75) When I make intuitive decisions, I can usually explain the logic behind my

decision Inferential intuition TIntS

RHIA Launer Svenson 8

PID (F 0,597) I often make quick and spontaneous decisions based on my life experience. Gigerenzer Heuristics

RHIA Launer Svenson 8

PID (F 0,597)

I often make quick and spontaneous decisions based on my knowledge of

human nature. Gigerenzer Heuristics

10 PMPI F 0,649 I’ve had enough experience to just know what I need to do most of the time

without trying to figure it out every time Automated Processing Scales

6 TIntS (F 0,48) 35 Carl (F

0,55) There is a logical justification for most of my intuitive judgments Inferential intuition TIntS

19 TIntS (F 0,51) When making a quick decision in my area of expertise, I can justify the decision

logically Inferential intuition TIntS

4 TIntS (F 0,44)

Familiar problems can often be solved intuitively

Inferential intuition TIntS

2 TIntS (F 0,42) I trust my intuitions, especially in familiar situations. Inferential intuition TIntS

12 TintS (F0,38 ) My intuitions are based on my experience. Inferential intuition TIntS

16 Carl (F 0,48) I am not very good at keeping in mind the big picture when working on a

problem.

Inferential intuition Carl

8 Carl (F 0,445)

When I have much experience or knowledge about a problem I almost always

trust my intuitions. Inferential intuition Carl

Study and Factor

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Slow Unconscious Thinking or Incubation

Theoretical Basis

RHIA Launer Svenson I never make decisions immediately, I just wait a while. Unconscious Thoughts

RHIA Launer Svenson Before I make a decision, I first distract myself with other activities. Unconscious Thoughts

RHIA Launer Svenson In my job, you don't have to make a decision quickly, I think over every

decision over a long period of time. Unconscious Thoughts

RHIA Launer Svenson Over time, I process many different influences on my decision. Unconscious Thoughts

RHIA Launer Svenson I need time and inspiration to make decisions Unconscious Thoughts

16 PMPI (F 0,804) I usually set aside enough time to think things through care fully and figure

out what is the be thing to do. Emotional processing

1 Carl (F 0,539) When I make decisions, I always sleep over it for a night. Incubation Carl

14 Carl (F0,454) My instincts in my areas of expertise are much better than in areas I do not

know well Incubation Carl

4 Carl (F 0,632) After working on a problem for a long time, I like to set it aside for a while

before making a final decision Incubation Carl

7 Carl (F 0,546)

When working on a problem, I prefer to work slowly so that there is time for all

the pieces to come together Incubation Carl

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The Ability to feel, Emotional Intuition or Experiental Provessing

Interoception: Affective Intuition and Body Impulses

Study and Factor

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Emotional Feelings, Ability to feel

Theoretical Basis

20 PMPI F 0,727 Emotions are usually more useful than thoughts for coping. Experiental Processing Style

RHIA Feelings play a big role in my decisions. RHIA Emotion

GDMS (F 0,51 bis 0,7) I generally make decisions that feel right to me Intuitive GDMS

18 PID (F 0,736) I am a very intuitive person Intuition (PID)

28 TIntS (F0,73) 9 Carl (F

0,694)

It is foolish to base important decisions on feelings. (R) Affective Intuition TIntS

20 TIntS (F0,72) 6 Carl (F

0,682) I generally don’t depend on my feelings to help me make decisions. (R) Affective Intuition TIntS

7 TIntS (F 0,67) 18 Carl (F

0,588

I rarely allow my emotional reactions to override logic. (R) Affective Intuition TIntS

RHIA Launer Svenson When choosing the right decision I get a feeling of power Damasio Somatic Marker

GDMS (F 0,49 bis 0,57) When I make a decision, it is more important for me to feel the decision is

right than to have a rational reason for it Intuitive GDMS

4 PID (F 0,516) RHIA

In most decisions, it makes sense to rely on your feelings

RHIA / PID Intuition

2 PID F (0,526) RHIA I follow my feelings when deciding. Intuition (PID)

TIntS (no) Carl (F0,675)

When making decisions, I value my feelings and hunches just as much as I value

facts. Affective Intuition TIntS

5 PID (F 0,507) I don’t like situations that require me to rely on my intuition Inferential Intuition

15 PID (F 0,539) I prefer emotional people. Intuition (PID)

19 PID (F 0,593) I like emotional situations, discussions, and movies. Intuition (PID)

Study and Factor

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Interoception: Affective Intuition or Body

Impulses

Theoretical Basis

GDMS (F 0,69 or 0,79) When I make a decision, I trust my inner body feeling and somatic reactions Intuition GDMS

RIDMS E Launer Cetin I tend to use my skin feeling for my decisions Body Impulses

REI (F 0,9) 9 TIntS (F

0,73) Carl (F 0,708)

I tend to use my heart as a guide for my actions Affective Intuition REI TIntS

23 TIntS (F 0,786) 15 Carl

(F 0,73) I prefer to follow my head rather than my heart. (R) Inferential Intuition TIntS

5 PMPI (F 0,744)

When I am trying to decide how to cope , I usually go with my gut feeling

Emotional processing

30 PMPI (F 0,738) When I am attempting to cope I can usually trust my gut feelings to tell me

what to do Emotional processing

12 PMPI (F 0,816) Gut feelings are more important to me than logic and evidence when I have to

cope . Emotional processing

8 PID (F 0,597) I prefer drawing conclusions based on my feelings, my knowledge of

human nature, and my experience of life Intuition

12 PID (F 0,427) When it comes to trusting people, I can usually rely on my gut feelings.

REI (F 0,5) I tend to use my gut feeling for my decisions Affective Intuition

REI (F 0,65) Using my gut feelings usually works well for me in figuring out problems in

my life Affective Intuition

13 TIntS (F0,6) Carl (F

0,609)

I often make decisions based on my gut feelings, even when the decision is

contrary to objective information. Affective Intuition TIntS

36 Carl (F0,465) almost always trust my intuition because I think it is a bad idea to analyze

everything Affective Intuition Carl

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Anticipation: Hunches, Pre-Cognitions and Premonitions

Dependent: Support by Others

Avoidance of decision-Making

The creating style

Questions from the Cognitive Style Indicator

1. In my experience, rational thought is the only realistic basis for making decisions.

2. To solve a problem, I have to study each part of it in detail.

3. I am most effective when my work involves a clear sequence of tasks to be performed.

Study and Factor

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Anticipation Decisions, Pre-Cognition

Theoretical Basis

RHIA I often have a premonition of what is going to happen. Antizipation

RHIA I can often foresee the outcome of a process. Antizipation

7 PMPI (F 0,577)

I foresee how to decide before I review all aspects

Automated Processing Scales

14 PMPI (F 0,759) REI (F

0,64) TInT (F0,63) Carl

(F0,647)

I believe in trusting my hunches Experiental Processing Scale,

Affective Intuition TIntS

PSI, Bem

I sense information that cannot be explained physiologically or biologically.

PSI Premonition

PSI, Bem I can perceive information from my immediate and wider environment. PSI Premonition

PSI, Bem I can perceive information outside of the typical human senses. PSI Premonition

GDMS (F 0,72 bis 0,82 When I make decisions, I rely on upon my instincts Intuition GDMS

3 TIntS (F 0,63) I prefer to use my emotional hunches to deal with a problem, rather than

thinking about it Affective Intuition TIntS

Carl (F 0,608) Rather than spend my time a problem situation, I prefer to use my emotional

hunches Affective Intuition TIntS

Study and Factor

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Dependent: Support by Others

Theoretical Basis

GDMS F 0,66 bis 0,74 I often need assistance of other people when making important decisions Dependent

GDMS (F 0,51 bis 0,66) If I have support by others, it is easier for me to make important decisions Dependent

GDMS (F 0,44 bis 0,7) I like to have someone to steer me in the right directionwhen I am faced

with important decisions Dependent

GDMS F 0,61 bis 0,79 I rarely make important decisions without consulting other people Dependent

GDMS (F0,5 bis 0,69)

I use the advice of other people in making my important decisions.

Dependent

Study and Factor

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Avoidant

Theoretical Basis

GDMS (F 0,82 bis 0,89) I avoid making important decisions until the pressure is on Avoidant GDMS

GDMS (F0,85 bis 0,94) I postpone decision making whenever possible Avoidant GDMS

GDMS (F 0,82 bis 0,86) I often procrastinate when it comes to making important decisions Avoidant GDMS

GDMS (F 0,80 bis 0,84) I generally make important decisions at the last minute Avoidant GDMS

GDMS (F 0,72 bis 0,77) I put off making many decisions because thinking about them makes me

uneasy Avoidant GDMS

Study and Factor

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Creating Style

Theoretical Basis

C5 CoSI (F 0,81) New ideas attract me more Creating Style

C6 CoSI (F 0,7) I like to extend boundaries Creating Style

C3 CoSI (F 0,69) I am motivated by ongoing innovation. Creating Style

C1 CoSI (F 0,53)

I like to contribute to innovative solutions

Creating Style

C2 CoSI (F 0,52) I prefer to look for creative solutions. Creating Style

C4 CoSI (F0,62) I like much variety in my life Creating Style

C7 CoSI (F 0,5) I try to avoid routine Creating Style

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4. I have difficulty working with people who ‘dive in at the deep end’ without considering the

finer aspects of the problem.

5. I am careful to follow rules and regulations at work.

6. I avoid taking a course of action if the odds are against its success.

7. I am inclined to scan through reports rather than read them in detail.

8. My understanding of a problem tends to come more from thorough analysis than flashes

of insight.

9. I try to keep to a regular routine in my work.

10. The kind of work I like best is that which requires a logical, step-by-step approach.

11. I rarely make ‘off the top of the head’ decisions.

12. I prefer chaotic action to orderly inaction.

13. Given enough time, I would consider every situation from all angles.

14. To be successful in my work, I find that it is important to avoid hurting people’s feelings.

15. The best way for me to understand a problem is to break it down into its constituent parts.

16. I find that to adopt a careful, analytical approach to making decisions takes too long.

17. I make most progress when I take calculated risks.

18. I find that it is possible to be too organised when performing certain kinds of task.

19. I always pay attention to detail before I reach a conclusion.

20. I make many of my decisions on the basis of intuition.

21. My philosophy is that it is better to be safe than risk being sorry.

22. When making a decision, I take my time and thoroughly consider all relevant factors.

23. I get on best with quiet, thoughtful people

24. I would rather that my life was unpredictable than that it followed a regular pattern.

25. Most people regard me as a logical thinker.

26. To fully understand the facts I need a good theory.

27. I work best with people who are spontaneous.

28. I find detailed, methodical work satisfying.

29. My approach to solving a problem is to focus on one part at a time.

30. I am constantly on the lookout for new experiences.

31. In meetings, I have more to say than most.

32. My ‘gut feeling’ is just as good a basis for decision making as careful analysis.

33. I am the kind of person who casts caution to the wind.

34. I make decisions and get on with things rather than analyse every last detail.

35. I am always prepared to take a gamble.

36. Formal plans are more of a hindrance than a help in my work.

37. I am more at home with ideas rather than facts and figures.

38. I find that ‘too much analysis results in paralysis’.

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The Measurement Instrument by Launer and Cetin 2023

The measurement instrument of Launer and Cetin (2023) measures 3 rational and 9 different

type of intuitive decision-making.

To what extend which you would agree that that statement is true for you at your current job?

from 1-Definitely false to 5-Definitely true

Analytical

1. Before I make decisions, I usually think carefully first.

2. Instead of acting on the first idea that comes to mind, I carefully consider all my

options.

3. I make decisions in a logical and systematic way

Planning

4. I like detailed action plans

5. Following a clear plan in very important to me

6. A good task is a well-planned task

Knowing

7. I study every problem until I understand the underlying logic

8. I enjoy solving problems that require hard thinking

9. I prefer complex problems to simple problems

Holistic unconscious

10. I use my general thought of whole rather the details when to decide

11. Before I decide, I try the understand the big picture of the problem

12. I always use big picture perspective when to decide

Spontaneous

13. I generally make snap decisions

14. I make quick decisions

15. I typically figure out the way to decide swiftly

Heuristic

16. I make decisions based on my knowledge of human nature.

17. I make decisions based on my life experience.

18. I’ve had enough experience to just know what I need to do most of the time

without trying to figure it out every time

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Slow unconscious

19. When I make decisions, I always sleep over it for a night.

20. Over time, I process many different influences on my decision.

21. I usually set aside enough time to think things through carefully and figure out

what is the be thing to do.

Emotional

22. Feelings play a big role in my decisions.

23. I follow my feelings when deciding.

24. Emotions are usually more useful than thoughts for coping.

Body impulses

25. When I make a decision, I trust my inner body feeling and somatic reactions

26. I prefer drawing conclusions based on my feelings, my knowledge of human

nature, and my experience of life

27. I tend to use my gut feeling for my decisions

Mood

28. When I have to take decisions, I feel afraid and/or curiosity in me

29. When I have to make decisions, I feel anger and/or serenity inside me.

30. When I have to decide I feel anger and/or relief in me

Anticipation (Pre-Cognition)

31. I have a premonition of what is going to happen.

32. I can foresee the outcome of a process.

33. I foresee how to decide before I review all aspects

Support by others

34. I need assistance of other people when making important decisions

35. If I have support by others, it is easier for me to make important decisions

36. I like to have someone to steer me in the right direction when I am faced with

important decisions

Conclusion

The study provides an overview of the most important items for measuring rational and intuitive

dercision-making.

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Three Rational & nine Intuitive Decision-Making Styles (RIDMS)

Markus A. Launer

Ostfalia University and Institut für gemeinnützige Dienstleistungen gGmbH (independent

non-profit organization), Germany

In teamwork with Faith Cetin, Baskent University, Turkiye, and Frithiof Svenson, Ostfalia

University

Abstract

The research program of Launer on rational and intuitive decision-making lead to many

publications (Anselm & Launer, 2022). As a result three rational and nine different decision-

making styxles could be identified. This articles provides an overview and the theoretical basis

to the studies by Launmer & Svenson (2022) and Launer and Cetin (2023).

This article combines the different approaches on intuition GDMS, REI, PMPI, CEST, TIntS,

PID, and USID. However, many types of intuition that are well known, e.g. anticipation, different

body impulses and moods, are not academically researched yet. In addition, the time-delayed

intuition and the dependence on colleagues is missing in theory. There is a lack of an

integrated framework that combines and completes all approaches in a practical application of

intuitive decision-making. Launer, Svenson, and Cetin provide a new combinational approach

RIEHUAD. As a result, they developed a multidimensional, multidisciplinary measurement tool

to apply to all kinds of decision-making. The tool leads to 12 independent dimensions on

intuition: Analytical, Knowing, Planning, Holistic, Spontaneous, experienced-based Heuristics,

affective (feelings) like Emotions, Body Impulses, Mood as well as Anticipation, Unconscious

Thinking and the Dependence on colleagues. The dimensions were tested according to all

standard testing methods. Therefore it is a robust, valid and reliable measurement tool.

Introduction

Today, intuition is an important decision-making theory across various disciplines, e.g

management, sociology, psychology and philosophy combined (Sinclair & Ashkanasy, 2005;

Hodgkinson et al., 2008; Dane & Prat, 2009; Hogarth, 2010) as well as in neuroscience

(LeDoux 1996; Barais et al, 2015, 2017, 2018; Craig, 2002; Damasio, 1999; Korteling and

Toet, 2020), behavioural sciences (Hodgkinson et al., 2008) para-psyachology (Bem, 2011;

Bem et al., 2015, Radin, 2017) as well as medicine and health sciences (Glatzer et al., 2020;

Chlupsa et al., 2021) or engineering and design (Cash & Maier, 2021; de Rooij et al., 2021).

Intuition is described in various ways in management (Simon, 1987; Agor, 1989; Behling &

Eckel, 1991; Shapiro & Spence, 1997; Burke & Miller, 1999; Andersen, 2000; Akinci & Sadler-

Smith, 2011; Gore & Sadler-Smith, 2011; Hodgkinson & Sadler-Smith, 2018; Cristofaro, 2019;

Sadler-Smith, 2022; Paliszkiewicz, Çetin, Launer, 2023), strategic decision-making (Wally &

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Baum, 1997; Brockmann & Anthony, 1998; Hodgkinson et al., 2009a; Callabnretty et al.,

2017;), in different industries (Launer, Çetin, Svenson, Ohler, 2021), supply chain

management (Carter et al., 2017), as well as different management level (Paliszkiewicz, Çetin,

Launer, 2021).

Theoretical Foundation based on existing Studies

Dual Process Approaches

The basic, historical approach is the dual process theory distinguishing between rational

decision-making (Deliberation) and intuition. Several frameworks in psychology assume a

dual-process (Chaiken & Trope, 1999; Epstein, 2008; Hammond, 1996; Ham & Van den Bos,

2011; Kahneman, 2011; Mukherjee, 2010; Sloman, 1996; Stanovich & West, 2000; Evans &

Stanovich, 2013; Evans, 2008; Keck & Tang, 2020). There are two perspectives within the dual

process theory: the unitary view postulates that cognition and intuition are opposite poles of a

single dimension, whereas the dual-process view proposes that they are independent

constructs (Hodgkinson et al., 2009b). There are two major studies with a dual approach that

develop scales and items.

The study Rational-Experiental Inventory (REI) by Epstein, Pacini & Norris (1998) and the new

version by Pacini & Epstein (1999) was based on the Cognitigve-Experiental Self-Theory

(CEST) by Epstein, Pacini, Denes-Raj & Heier (1996). They describe decision-making with a

Rationality Scale (Need for Cognition or Analytical-Rational Thinking) and an Intuitive-

Experiental Scale or faith for intuition. In principal, they relate to Jung (1964/1968), natural

decisions by Tversky & Kahneman (1983), automatic decisions by Bargh (1989) and Higgins

(1989), heuristic (Chaiken, 1980; Fiske & Taylor, 1991), schematic (Leventhal, 1984),

prototypical (Rosch, 1983), narrative (Bruner, 1986), implicit (Weinberger & McClelland, 1991),

imagistic-nonverbal (Bucci, 1985; Paivio, 1986), experiential (Epstein, 1983), and mythos

(Labouvie-Vief, 1990). Pacini and Epstein (1999) relate their inventory to the big five theory

researched by D. W. Fiske (1949), and later expanded upon by others, including Norman

(1967), Smith (1967), Goldberg (1981), and McCrae & Costa (1987).

They describe intuition in an Experiental Scale or faith for intuition or Intuitive-Experiental. They

describe intuition based on the so-called gut feeling, hunches, instincts, feelings, snap

judgement, heart (Buck, 1985; Leventhal, 1984; Jung, 1964/1968), deliberative-effortful-

intentional-systematic (Bargh, 1989; Chaiken, 1980; Higgins, 1989), explicit (Weinberger &

McClelland, 1991), extensional (Tversky & Kahneman, 1983), verbal (Bucci, 1985; Paivio,

1986), and logos (Labouvie-Vief, 1990).

The second key study using a dual approach is the Preference for Intuition or Deliberation

according to Betsch (2014, PID) based on Epstein et al (1996). She distinguishes into

Deliberation or Analytical and Planning (Cacioppo & Petty, 1982) and Affective Intuition (Jung,

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1962; Slovic, Finucane, Peters, & MacGregor, 2001, Loewenstein, Weber, Hsee, & Welch,

2001; Myers & McCaulley, 1986; Keller et al. 2000). She bases her theory on the concept of

Interoception (Wilson & Schooler, 1991; Wilson, Lisle, Schooler, Hodges, Klaaren, & LaFleur,

1993), routinized decision making (Betsch, Haberstroh, Molter, Glöckner, 2004; Betsch,

Haberstroh, Hohle, 2002), implicit attitude formation (Betsch, Plessner, Schwieren, & Gütig,

2001), predictive behavior (Epstein, 1983), the processes, contents, and correlates of intuition

(Hogarth, 2001); reasoning (Sloman, 1996), the context of discovery (Bowers, Regher,

Balthazard, & Parker, 1990), and behavioral interests, personality, and experiences (Langan-

Fox & Shirley, 2003).

More and more, theories view the relationship between the rationality and intuition as more

complex (Thompson et al., 2009). Krajbich et al. (2015), De Neys and Pennycook (2019) and

De Neys, (2021) show a revised dual-process models comparing fast and slow intuition. Bago

and De Neys (2017) sketch a revised dual process model in which the relative strength of

different types of intuitions determines reasoning performance. Pennycook et al. (2015)

showed a three-stage model to explain what causes analytic thinking to occur. Therefore, the

concept of rationality needs to be described more comprehensively.

Rational Decision-Making

Rational decision-making is often described as an Information processing (Epstein, 1990) or

cognitive style (Messick, 1984; Riding & Rayner, 1998; Antonietti, 2003; Pachur & Bröder,

2013). Scott & Bruce (GDMS, 1995) describe the Analytical Style as a search & evaluation

process using a logic and systematic analysis and evaluation in terms of specific goals (Keen,

1974; Mitrof, 1983). This can also be based on Allinson and Hayes’ theory (1996) or Riding’s

(1997) analytic style. Burns & D`Zurilla, (PMPI, 1999) describe the rational processing style as

a structured thinking process, fact-based, goal-oriented, and evaluating alternatives based on

stress (Aldwin, 1994; Lazarus & Folkman, 1984) and problem solving (D’Zurilla & Goldfrid,

1971; D’Zurilla & Nezu, 1990, Mayde u-Olivare s & D’Zurilla, 1996).

Cools, & van den Broek (CoSI, 2007) and Pachur & Spaar (USID, 2015) describe two different

rational decision-making styles mainly based on education and experimental psychology

(Grigorenko & Sternberg, 1995; Rayner & Riding, 1997, 1998), Messick, 1984; Miller, 1987;

Hunt, Krzystofiak, Meindl, & Yousry, 1989; Riding & Cheema, 1991), perception, learning,

problem solving, decision making, communication, and creativity in important ways (Hayes &

Allinson, 1994; Kirton, 2003), field-dependent and field-independent (Witkin, Moore,

Goodenough & Cox, 1977); information processing (Shipman & Shipman, 1985), learning and

innovation (Sadler-Smith & Badger, 1998), and industrial, work, and organizational psychology

(Hodgkinson, 2003) and management (Hodgkinson & Sadler-Smith, 2003).

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One dimension is the Knowing Style based on facts, details, logical, reflective, objective,

impersonal, rational, precise, methodical decisions (Allinson & Hayes, 1996; Myers &

McCaulley, Quenk, & Hammer, 2003. Miller, 1987. Riding & Cheema, 1991) which is

empirically related to the REI study (Pacini & Epstein, 1999). Cools, & van den Broek (2007)

found this style to be similar to existing conceptualizations of the analytic pole, e.g. Allinson

and Hayes’ theory (1996) or Riding’s (1997) analytic style.

On the other hand is the Planning Style described as sequential, structured, conventional,

conformity, planned, organized, systematic, routine-based (Allinson & Hayes, 1996; Miller,

1987; Riding, Cheema, 1991). The planning style is empirically related to the Adaptiveness

pole of the KAI (Kirton, 1994) and the REI study by Pacini and Epstein (1999). Cools, & van

den Broek (CoSI, 2007) mention the Creating Style based on Myers, McCaulley, Quenk, &

Hammer (2003) which was not used in this study. This style is related to existing

conceptualizations of the intuitive pole (Cools, & van den Broek (2007), such as intuition in

Allinson and Hayes’ theory (1996) or the innovativeness pole of Kirton (1994).

On the way to a multidimensional approach of intuition

Today, researchers in the field of intuition more and more follow a multi-dimensional and

interdisciplinary approach (Shirley & Langan-Fox, Sadler-Smith & Shefy, 2007, 1996;

Cristofaro, 2019; Sinclair, 2011, 2014, 2020). Based on Dane & Pratt`s and Sinclair`s

constructs, many scholars followed developed a broader theory on intuition (Hodgkinson et al.,

2008, 2009a, 2009b; Sadler-Smith, 2010, 2015, 2016; Blume and Covin, 2011; Akinci and

Sadler-Smith, 2012, 2013, 2019; Baldacchino, 2013, 2019; Baldacchino et al., 2015; Healey

et al., 2015; Sadler-Smith et al., 2021; Okoli et al., 2021). Gore and Sadler-Smith (2011)

disaggregate intuition by discriminating between domain-general mechanisms and domain-

specific processes, primary and secondary types of intuition. Cristofaro (2020) describes in

depth an Affect-Cognitive Theory. But there is still a need for comprehensive model due to the

lack of synergies between scholars from different disciplines (Adinolfi & Loia, 2022).

Intuition is not a homogeneous concept, but a label used for different cognitive mechanisms

(Glöckner & Witteman, 2010; Hogarth, 2010; Pratt & Crosina, 2016). There were conceptual

shortcomings stemming from the tendency to ignore the philosophical heritage of intuition or

to dismiss the relevance of this heritage to contemporary theory (Osbeck, 1999, 2001).

Multi-dimensional Approaches of Intuitive Decision-Making

There are five multidimensional studies with a more detailed, structured dimensions on

intuition. Intuition according to Scott & Bruce (1995, GDMS) was decribed in four styles based

on the items by Bruce (1991). The first style is intuitive-based (Hunt et al, 1989; Harren, 1979),

based on feelings (Keen, 1973), and a learned habit (Driver, 1979; Driver et al.,1990). The

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second style was dependent decisions (Harren, 1979; Phililips, Pazienza & Ferrin, 1984). This

was also described by Simon (1987) as intuition based on interpersonal interaction or women`s

intuition (Snodgrass, 1985) and lately in neurobiology (Marks-Tarlow, 2014). Later Lieberman

(2007) goes even beyond describing dependent decision based on social cognitive

neuroscience in: (a) understanding others, (b) understanding oneself, (c) controlling oneself,

and (d) the processes that occur at the interface of self and others. The third subdimension

Avoidant was not used in this study (Driver, 1970; Behling, Gifford & Tolliver, 1980; Driver et

al, 1990). In their stiudy they found the fourth dimension called Spontaneous.

Burns & D`Zurilla (1999, PMPI) describe intuitive decision-making designed to assess a

person’s awareness and perception of his or her dominant mode of processing across stressful

situations (Aldwin, 1994; Folkman & Lazarus, 1980; Pearlin & Schooler, 1987; Carver, Scheier,

& Weintraub, 1989; Tobin et al., 1989) and the cognitive -experiential self-theory (CEST) by

Epstein (1990, 1994). The CEST theory described intuition as an experiential intuition focusing

on such qualities as the speed and effciency of processing (minimal time and mental effort);

the reliance on feelings, vibes, hunches, and instincts) and the recall of past coping

experiences and familiar coping responses (Burns & D`Zurilla, 1999). Based on a content

analysis of the item clusters, exploratory and confirmatory factor analyses, the three factors

were named rational processing, emotional processing, and automatic processing. The

Automated Processing is described as quickly and efficiently, swiftly, aware, repetitive and

experience-based (Burns & D`Zurilla, 1999). In the literature, it was described as fast and

efficient, outside of awareness, unintentional, and uncontrolled (Bargh, 1994; Smith, 1994;

Shiffrin & Schneider, 1977) based on expertise (Carter et al., 2017). Logan (1988, 1989)

described it as an automatic memory retrieval, Bargh (1994) as a goal-dependent automaticity

and for Smith (1994) it was all about speed and efficiency. It is an immediate knowing of how

to cope based on past coping experiences (Burns & D`Zurilla, 1999). The Emotional

Processing described as instincts, feelings, vibes, gut feeling, hunches, and emotions (Burns

& D`Zurilla, 1999). People with a preferfernce to emotional processing aren more extraverted,

preferring emotional and interpersonal relationships, and are more adaptive for emotion-

focused coping, expressing emotions and seeking social support. Later Miller and Ireland

(2005) describe strategic decision making based on holistic hunches and automated expertise.

Pretz et al (2014, TIntS; Denin et al., 2022) described intuitive decision-making in three

dimensions based on the literature review by Pretz & Totz (2007). Intuition has a holistic nature

of intuition (Jung, 1971; Hammond, 1996) described as knowing without being able to explain

how we know (Vaughan, 1979). The first sub dimension is Affective Intuition based on feelings

(Bastick, 1982), a feeling of certainty (Hogarth, 2001), or emotional processing (Epstein, 1998;

Bechara, Damasio, & Damasio, 2000). Affective intuition was described as body impulses incl.

heart-based, emotions, hunches (anticipation), and gut feeling decisions (Pretz et al., 2014).

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The second type of intuition is Inferential Intuition (Hill, 1987) as an automated (Vaughan,

1979) and heuristical (Wescott, 1968; Forgas, 1994) type of intuition in an implicit judgmental

sense (Greenwald & Banaji, 1995). It is also described as experience-based, quick, familiar

decisions with reasoning, logic (Klein, 1998, Sternberg et al., 2001). Third type of intuition is a

Holistic Style (Jung, 1926; Hammond, 1996) or holistic mechanism (Bowers, Regehr,

Balthazard, & Parker, 1990; Dijksterhuis, 2004; Wilson & Schooler, 1991) which was divided

by an factor analysis into a Holistic Big Picture Intuition and a Holistic Abstract Intuition (Pretz

et al., 2014). The holistic-associative view of intuition is acknowledged also by psychology

researchers (Agor, 1986; Kihlstrom, 1987; Shapiro & Spence, 1997; Betsch & Glöckner, 2010;

Glöckner & Witteman, 2010) as well as management scholars (Dreyfus & Dreyfus, 1986;

Simon, 1987; Prietula & Simon, 1989; Kahneman & Tversky, 2000;) and lately by Adinolfi &

Loia (2022).

Pachur and Spaar (2015, USID) distinguish in domain-specific perspective based on previous

studies e.g. PID, REI, GDMS, CoSI, PMPI) two major dimensions. First, the quick Spontaneous

Intuition and Experience-based Style described as experienced (Boucouvalas, 1997),

immediate, swiftly, quick, snap decisions, awareness, experience, repetitive decisions and

heuristics (Gigerenzer et al., 2011) by experts (Pachur, 1986 & Marinello, 2013). The

importance of experience has been researched best by Klein (1998) in his recognition-primed

decision model. Pachur and Marinello (2013) described that experts are more likely to rely on

a lexicographic heuristic, whereas the non-experts used a more complex strategy, that

aggregates across different cues (Garcia-Retamero & Dhami, 2009).

Second is the Affective Intuition based on feelings, body impulsess, and hunches, inner

reactions, knowledge of human nature, life experience, gut feeling, hunches, heart (Burns &

D`Zurilla, 1999; Pretz et al., 2014; Betsch, 2014). Affective intuition is still a rather broad

descrition of many different feelings, body impulses, and moods.

New Dimensions for intuitive Decision-Making

Body Impulses

Different kind of feelings are a source of intuitive decision-making (Bonabeau, 2003; Burke &

Miller, 1999; Dane, Pratt, 2006; Klein, 2003; Sinclair, Ashkanasy. 2005) and relief or certitude

(Cappon, 1994; Petitmengin-Peugeot, 1999). Results of the collection of senses in the internal

state of the body (interoception or body Impulsess) from neurology and medicine (LeDoux

1996; Barais et al, 2015, 2017, 2018; Craig, 2002; Cameron, 2002; 2009; Barrett, Simmons,

2015; Khalsa, Lapidus, 2016; Damasio, 2008; Damasio, Tranel & Damasio, 1991) showed that

emotional processes guide (or bias) decision-making, e.g. in the homoestatic sensory activity

(Craig, 2002, 2009). The concept of gut feeling needs to described newly from a broad and

unspecific term to a more differentiated approach based on feelings in the stomach, colon and

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the visceral sensory system (Gershon, 2001; Hooper et al, 2001: Barbosa, Rescigno, 2010;

Mayer, 2001; Arumugam et al 2011; Brandtzaeg, 2011; Cryan, Dinan, 2012; Haller,

Hörmannsperger, 2013; Schemann, 2020). The interoception and somatic markers of the heart

beating rate influences decision-making (Schandry, 1981; Polatos, Schandry, 2004; Dunn et

al, 2007; Pollatos, Herbert, B. M., Matthias, Schandry, 2007; Garfinkel et al, 2015; Schulz,

2016) and skin arousals (Loggia, Juneau, Bushnell, 2011; Breimhiorst et al, 2011).

Mood

The mood is an affective emotional intuition type influencing the intuitive decision-making

(Boltte et al., 2003; Ekman 2007, Frijda 1988, Rottenberg, 2005; Gilbert 2006, Keltner et al.

2014, Keltner & Lerner 2010, Lazarus 1991, Loewenstein et al. 2001, Scherer & Ekman 1984;

Lerner, Li, Valdesolo, Kassam 2015, Sinclair, 2020) and affevtive actions (Bechara, Damasio,

& Damasio, 2000; Bower, 1991; Clore, Schwarz, & Conway, 1994; Fredrickson, 2000; Lerner

& Keltner, 2000). Positive and negative moods are accompanied by qualitatively different

information processing modes (Gray, 2001; Isen, 1999; Kuhl, 1983, 2000). Forgas (2001)

describes in the the affective infusion model (AIM) for empirical findings in the areas of mood-

congruent memory, mood-congruent judgments, mood effects on planning and executing

strategic social behaviors, mood effects on processing style, and mood effects on creative and

flexible processing styles are examined. Paige et al. (2021) examined the effects of mood on

quality and feasibility of design outcomes.

Anticipation

The described scales on intuition describe an affective type of decisions based on hunches

(Scott, Bruce, 1995; Pacini, Epstein, 1999; Pretz et al 2014; Pachur, Sppar, 2015). In this study

we enlarge this characteristics to an own dimension called Anticipation (Launer, XXX). The

received information in this regard comes from outside the body (Sinclair, 2011, 2014). Many

researchers try to explain atypical or paranormal decision making (Honorton, Ferrari, 1989),

anticipation of solutions, e.g. presentiments of future emotions (Radin, 2004), precognition

(conscious cognitive awareness), premonition (affective apprehension) according to Bem et

al. (2015), extrasensory perception (ESP) by Thalbourne and Haraldsson (1980) paranormal

belief and experiences (Lange, Thalbourne, 2002), or automatic evaluation (Ferguson, Zayas,

2009). In sports, the concept of anticipating future moves by people is also called heuristics

(Grush, 2004; Williams, Ward, 2007; Schultz, 2013).

Unconscious Thoughts

In a study by Carlson (2008) based on the TIntS by Pretz and Totz (2007), he included the

dimension incubation based on the theory by Dijksterhuis (2004). Decisions can not only be

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made fast but also after a period of time and (unconscious) reflection and activation (Bowers

et al., 1990; Waroquier et al, 2010), incubation (Wallas, 1920; Shirley & Langan-Fox, 1996),

unconscious thinking (Dijksterhuis and Nordgren (2006), distraction (Kohler, 1969), removal of

blockages (Duncker, 1945), completion of schemes (Mayer, 1996), or in intuitive step-ups

(Nicholson, 2000). Despite the many critics on the quality of the decision (González-Vallejo et

al., 2008; Srinivasan et al, 2013; Newell & Shanks, 2014; Čavojová, Mikušková, 2014; Abbott,

2015; Nieuwenstein et al., 2015) slow decision-making is the usual process in management

(Pachur & Aebi Forrer, 2013).

Integrated, multi-dimensional and multi-disciplinary Framework

When combing all approaches on how to measure intuition in an integrated, multi-dimensional

and multi-disciplinary framework, a rather broad definition on intuition is needed. Intuition

seems to be an unconscious, spontaneous inferential or slow decision making process based

on holistic abstract or big picture (Holistic), experience-learned heuristics, affective and

emotional feelings, body impulses and moods, perception without awareness, environmental

influences by people as well as the capability for pre-cognition based on hunches (Launer et

al., 2020).

Discussion

Launer, Svenson, and Cetin developed 12 independent dimensions to measure rational and

intuitive decision-making for the general use in various fields of research and practice. The

new instrument covers all existing dimensions from the original studies such as Rational or

Deliberation (Analytic, Planning, and Knowing), Holistic Unconscious decisions (Abstract and

Big Picture), Fast decisions (Spontaneous and Heuristic), Slow Unconscious decisions

(Incubation) and Advice by Others, and Emotional decisions. The dimension Emotional

decisions was deeper analyzed in the additional dimension Emotions, Body Impulses, and

Moods, as wel as a new dimension Anticipation (hunches). All 12 items were fully independent.

This provides an multidimensional, interdisciplinary integrated framework for all kind of

decisions.

The correlation table showed, all three rational types of decision making were correlated with

each other. This confirms the result of the previous studies of a dual approach, a clear

distinction between rationality and intuition (planning, knowing, and analytical style compared

to emotions, body impulses, mood and anticipation). The deeper analysis of the affective type

intuition (feelings) into emotions, body impulses, and mood showed a close correlation as well.

This proves that the affective intuition (feeling) can be better described in depth. The confusing

term of gut feeling is now precisely described. Highly correlated with the three affective feeling

types is the dimension of Anticipation (hunches). The result is in line with all previous studies,

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however, it shows a new and separate dimension of intuition. It could also be proved that fast

holistic unconscious and slow unconscious thoughts are two separate dimensions of intuition.

The spontaneous and experience-based heuristcal intuitive decision making could be

described more clear in two separate items. The support bay others, which did not correlate

well in the GDMS study now had a higher factor load describing another intuition item.

Limitations and future research

The RIEHUAD approach has several limitations, this is why still caution is needed when

interpreting the findings at this stage in its development. These limitations are related to the

self-report format and the exclusive use of self-report criterion measures to evaluate the validity

(Burns & D´Zurilla, 1999; Hodgkinson, & Sadler-Smith, 2011). The 12 independent dimensions

for rational and intuitive decision-making do not describe a process of decision-making

(Topolinski, 2011; Remmers det al., 2015; Volz & von Cramon, 2006). Intuition research also

systematically blinds us to the full universe of problems our minds spontaneously solve,

restricting our attention instead to a minute class of unrepresentative “high-level” problems ().

Still missing are more dimensions describing the intuition based on the context and

environment (Elsbach and Barr, 1999; Palmer, 1998; Vaughan, 1979) and Instincts (Sun &

Wilson, 2014; Boyd & Heney, 2017).

The concept of interpersonal intuition has to be deeper researched. Our approach covered the

intuition by consulting others. However, the intuition needs to be researched when talking to

others, presenting or teaching. Specially the so called teacher intuition should be a new

dimension (Akinbode, 2013; Thorbjörn. Kroksmark, 2019, Sipman, Thölke, Martens,

McKenney, 2019) in relation to their context (Koffeman, Snoek, 2019) based on generative

power of intuitive pedagogy (Markauskaite, Goodyear, 2014). Therefore, the socio affective

intuition as an interpersonal intuition is lacking (Raidl, Lubart, 2000). This study also does not

describe so called wise decisions (Sadler-Smith, 2012), a combination of rational and intuitive

decisions (Eling, Langerak, & Griffin, 2015; Thanos, 2022)

In their study, the success of a decision has not been reflected (Sinclair & Ashkanasy, 2005).

Rational choices not always leads to perfect decision-making, sometimes it is imperfect and

problematical (Watkins, 1970; Elster, 1983) and has ambiguity (March, 1978; March, Olsen,

1976). Intuitive decisions also do not always lead to good decisions (Burke & Miller, 1999;

Kinatta et al, 2021) It was not intended to assess the quality of the respective decision style

(…). We did not measure the frequency and speed of the decision-making. An additional

determinant of perceived expertise maybe the amount of information employees typically

acquire and/or need before making a decision in the respective dimension (domain).

With the new questionnaire, they measured the preferences of the participants. Some

dimensions need to be deeper tested in qualitative experiments.

Markus A. Launer Special Issue Intuition 2023

34

Conclusion

This study introduces an Integrated multidisciplinary multidimensional framework based on

existing, widely accepted studies and empirical studies by Launer, Svenson, and Cetin. They

provide a comprehensive collection of all dimensions for rational and intuitive decision-making

and four additional dimensions for the emotional decision-making style. It is usable for all kind

of decision-making in the broad research field.

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Interoception and Intuitive Decision-Making

Mohammad Daud Ali 1), Mohammad Atiq Rafique Khattak 1), Muhammad Umair 2)

and Markus A. Launer 3)

1) Institute of Management Sciences, University of Haripur, Pakistan

2) Auyub Medical College Abbottabad, Pakistan

3) Ostfalia University of Applied Sciences and Institut für Dienstleistungen (independent non-profit

organization), Germany

Abstract

The objective of this desk study is to explore the connection of interoception and intuition, with

a special focus on sensations associated with the skin and heart. Previous interdisciplinary

research by Launer & Svenson (2022; EFRE-research project RHIA) has underscored the

importance of bodily sensations, particularly affective or emotional intuition, as a key factor in

intuitive decision-making. This non-systematic literature review aims to investigate how

sensations related to heartbeat and skin influence intuitive decision-making. Additionally, we

aim to identify and adapt measurement tools, such as questionnaires, to assess interoception

in a manner that aligns with participants' preferences and the needs of intuition research. We

utilized the Body Perception Questionnaire Autonomic (BPQ-20 ANS) by Cabrera et al. (2018)

and the Multidimensional Assessment of Interoceptive Awareness (MAIA) by Mehling et al.

(2012) as item inventories. Research findings by Launer & Cetin (2023) highlight the

relationship between interoception and intuition, providing a new and practical item pool for

intuition research. These findings necessitate for extensive research to better comprehend

the interplay between interoception and decision-making. The results of this study contribute

to the development of a new comprehensive intuition model in the workplace through a global,

interdisciplinary study.

Keywords: Interoception, Intuition, Decision-Making, Heartbeat, Skin Feeling.

Introduction

Emotional experiences are closely linked to the perception of internal bodily processes (Schandry,

1981). This involves sensing one's internal state, including visceral sensations (Schulz, 2016). It refers

to the awareness of internal bodily states, contrasting with exteroception, which involves sensing

external stimuli, and proprioception, which involves sensing the position and posture of body parts

(Sherrington, 2023). Humans perceive feelings from the body that inform them about their physical

condition, influencing mood and emotional states.

Traditionally, distinct feelings like itch, temperature, and pain are linked to the somatosensory

system(exteroceptive), while less distinctive visceral feelings such as hunger, vasomotor activity, and

thirst are associated with the interoceptive system (Craig, 2003). Recent findings suggest a conceptual

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shift, showing that all bodily feelings are signified in a phylogenetically innovative system in primates,

which developed from an ancient homeostatic system responsible to maintains bodily integrity. These

bodily sensations represent the physiological situation of the whole body, redefining 'interoception'

(Craig, 2003).

Lately, the role of interoception and the perception of inner bodily states (Craig, 2002; Khalsa et al.,

2017) in higher order cognitive functions, for instance emotional abilities, learning, and subsequent

decision-making, gained recognition (Barrett & Simmons, 2015; Critchley & Harrison, 2013; Damasio,

1994; Heyes & Bird, 2008; Khalsa & Lapidus, 2016; Murphy, Brewer, Catmur, & Bird, 2017; Quattrocki

& Friston, 2014; Seth, 2013). Although theoretical models have elegantly described how interoception

may influence various cognitive processes, research examining individual differences in interoception

and their impact on abilities like decision-making (Sokol-Hessner, Hartley, Hamilton & Phelps, 2015,

Dunn et al., 2010), emotion recognizing emotion (Terasawa, Moriguchi, Tochizawa, & Umeda, 2014),

theory of mind (Shah, Catmur, & Bird, 2017), and memory (Garfinkel et al., 2013) is still relatively limited

but growing.

Ceunen et al. (2016) trace the evolution of the concept of interoception.

Over the past century, its meaning has expanded from a restrictive to an inclusive sense, making it

relevant to various aspects of life such as emotion, decision-making, time perception, health, and pain.

The concept's development underscores the need for a broad understanding of interoception (Ceunen

et al., 2016).

Many experiemtnal tecqhniques have been used to unearth the mechanism of information processing

in the inner body. These in retun have paved way to knowledge developement and how they interelate

to the behaviour and human experience. Researches have shown the vital nature of pulmonary,

neuromuscular, and gastrointestinal process, and the of the cardiovascular afferent inputs (Vaitl, 1996).

According to Lin et al. (2023), recent studies have underlined the necessity of distinguishing between

several features of interoception in self-report trials, for example attentiveness and subjective

interoceptive accuracy. Some of the most popular theories claim that emotions arise from the various

physiological differences in the body. The dispensing, signaling, and psychological display of internal

bodily signs are all contained within in interoception (Critchley & Garfinkel, 2017).

Interoception: Theory

Contrary to the processing of external cues, such as hearing, touch, smell, and sight, interoception is

the capability of the inner body state. It involves afferent pathways that manage internal physiological

functions (Tsakiris & Critchley, 2016). Research indicates that primates have a specialized cortical area

for homeostatic afferent activity, which mirrors the body's overall physiological condition.

This interoceptive system, connected to autonomic motor control, contrasts with the exteroceptive

system, which directs somatic motor functions. The primary interoceptive representation located in the

dorsal posterior insula generates specific and clear bodily sensations, such as pain, temperature, itch,

sensual touch, muscular and visceral sensations, vasomotor activity, hunger, thirst, and breathlessness.

In humans, this initial interoceptive activity is further processed in the right anterior insula, forming a

meta-representation that underpins self-awareness and emotional consciousness (Craig, 2003).

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Interoception impacts more than homeostatic and allostatic reflexes; it is essential for motivation,

emotion, social cognition, and self-awareness. From early development, the ongoing integration of

biological data from the body establishes the foundation for conscious awareness and the subjective

sense of being a distinct individual (Tsakiris & Critchley, 2016).

Interoception and Emotions

Emotions are frequently experienced through bodily sensations, and somatosensory feedback is

believed to play a role in triggering conscious emotional experiences (Nummenmaa, Glerean, Hari, &

Hietanen, 2014). Human emotions involve distinct feeling states that rely on interoception—the

processing and representation of internal bodily signals (Tsakiris & Critchley, 2016). Emotions represent

psychophysiological patterns that allocate physiological and psychological resources to adapt behavior.

The expression of emotions involves changes within internal organ systems driven by autonomic

responses, often occurring without conscious control. Interoceptive signals contribute to homeostatic

reflexes and allostatic regulation, including feedback about physiological changes caused by emotions.

Individuals commonly reference internal sensations while describing emotional experiences

(Nummenmaa et al., 2014; Critchley & Garfinkel, 2017). While emotions regulate behavior and

physiological states during both survival-relevant situations and enjoyable interactions, the mechanisms

underlying these subjective sensations remain not fully understood (Nummenmaa et al., 2014).

Interoception and Heartbeat

Individuals with having clearer perception of heart activity exhibit superior levels of momentary emotional

experiences, such as anxiety, and score higher on traits like emotional capability. This facet of cardiac

consciousness focuses on heartbeat perception (Schandry, 1981). Non-invasive measures of heartbeat

detection accuracy are frequently used to index interoceptive sensitivity (Brener & Ring, 2016).

Interoception focusing on heart has received certain attention owing its role in decision-making, clinical

disorders and emotional experience like depression and anxiety (Schulz, 2016). Schulz's meta-analysis

revealed an extensive network linked to heart-focused interoceptive attentiveness, which includes the

posterior insula, precentral gyrus, right claustrum, and medial frontal gyrus.

The right-hemispheric dominance highlights the processing of non-verbal information, with the posterior

insula serving as the primary gateway for cardioception. Increased heart-focused interoceptive accuracy

correlates with heightened emotional intensity, improved memory, and more adaptive decision-making

(Garfinkel et al., 2013; Werner et al., 2009).

Paulus & Stein (2010) suggested that interoception essentially cause anxiety and mood disrders. The

claim has been strengthened by the work of (Domschke et al., 2010) who related anxiety disorders to

interoception whereas Wiebking et al. (2010) termed depression the outcome of lack of interoception.

Interoceptive signals can be harmful, and confusing for those people who are under panic disorders,

adding to their difficulties to make intuitive decisions (Wölk et al., 2014).

Interoception and Skin Feeling

The C-tactile(CT) affernts in hairy skin, which respond to the slightest of touch like cares, have attracted

attention in the literature. This is skin-mediated interoceptive processes similar to pain and tactile

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pleasures (Crucianelli & Morrison, 2023). In order to investigate the brain underpinnings of mindfulness,

touch, and interoception, Casals-Gutierrez & Abbey's research (2020) studied MRI operations

researches to thoroughly understand the underpinnings of brain’s interoception, touch, and mindfulness

which revealed that there may be some possible regions where these modalities interconnect. Skin-

mediated signals like pain, temperature, and affective touch have been redefined as interoceptive

(Crucianelli & Ehrsson, 2023). Neurophysiology and functional neuroimaging suggest that social,

affective touch is a prominent category of tangible experience, operating mainly in social interactions

and relationships, and playing a role in physiological regulation during stress and challenges (Morrison,

2016).

Touch can safeguard detrimental physiological effects of maladaptive reactions. Inter-individual touch

can sack negative affect while evoking strong moods of pleasure, though context and internal state can

alter the hedonic value of touch (Ellingsen et al., 2016). While hostile somatosensations remain well-

characterized in terms of peripheral signaling, pleasant tactile sensations may be mediated by

specialized peripheral tactile afferents like C-tactile fibers, which respond to light and slow stroking and

are related only in hairy skin (Vallbo et al., 1999; Löken et al., 2009; Zaman et al., 2020). Soviet studies

of interoceptive conditioning found it to be unconscious, slower to establish, and more resistant to

extinction than exteroceptive conditioning (Uno, 1970).

Measuring Interoception

Interoceptive Accuracy Scale (IAS)

Collecting evidence of interoception in an objective manner is always challenging. Dissimilar to

exteroception, the effective stimulus for interoception are frequently unidentified, and they are difficult to

control experimentally, even when identifiable (Brener & Ring, 2016). The Interoceptive Accuracy Scale

(IAS) is a self-reported tool used to measure accuracy and attention to interoceptive signals (Murphy,

Catmur, & Bird, 2019; Murphy, Brewer, Plans, Khalsa, Catmur & Bird, 2020).

This scale includes various items pertaining to bodily sensations that are classified as interoceptive

(Khalsa et al., 2017; Khalsa & Lapidus, 2016) or linked to activation in the insula, a brain region crucial

for processing interoceptive signals (e.g., Critchley & Harrison, 2013; Langer, Beeli, & Jäncke, 2010;

Mazzone, McLennan, McGovern, Egan, & Farrell, 2007; Craig, 2002; Khalsa et al., 2017). We sought

to include signals with objectively measurable accuracy, opting for specific examples like flatulence and

eructation rather than general terms such as "gastric sensations" for alimentary interoception.

Participants received detailed instructions and examples of accurate internal perception (see Figure 2).

The scale comprises 21 items, rated betwee strongly agree (5) and strongly disagree (1), resulting in

scores between 21 and 105. Greater self-reported interoceptive accuracy is indicated when scores are

higher. Similar to the ICQ, the IAS inquires participants to assess the accuracy of perceived

interoception. The issue is that IAS does not have specific examples such as“I can accuraltely sense

when i am cold or hot, always “or “I can sense precisely when I am hungry“. Unlike the ICQ which

comprised of examples such as I often forget to eat" and "Others find it uncomfortable when I adjust car

or room temperature".

The goal of the Interoceptive Accuracy Scale (IAS) was to measure the variety of personal encounters

with interior sensations. This design acknowledges that challenges with perceiving these sensations

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may manifest in ways distinct from those illustrated in the Interoceptive Confusion Questionnaire (ICQ)

cases. For example, a person who has difficulty sensing hunger may overeat instead of forgetting to

eat, according to Murphy et al (2020).

Body Perception Questionnaire (BPQ-SF)

In the Body Perception Questionnaire-Short Form (BPQ-SF), Cabrera et al. (2018) examine the factor

reliability, structure, and convergent validity of the Autonomic Reactivity subscales and Body

Awareness. Self-reported sensitivity to internal cues is used to test interoceptive sensibility, with an

emphasis on people's self-confidence in their interoceptive skills. This is exemplified by the awareness

subscale of the Body Perception Questionnaire (BPQ) (Porges, 1993).

Body Perception Questionnaire Autonomic: Symptoms Short Form (BPQ-20 ANS)

Autonomic Reactivity and Body Awareness subscales of the Body Perception Questionnaire-Short Form

(BPQ-SF) are examined by Cabrera et al. (2018) for factor structure, reliability, and convergent validity.

They use self-reported tests to measure interoceptive sensibility, or sensitivity to internal bodily signals,

and then investigate the individual's self-evaluated belief in their interoceptive ability. One such tool that

is utilized is the awareness subscale of the Body Perception Questionnaire (BPQ), which was created

by Porges (1993). For analogous evaluations, the Body Perception Questionnaire Autonomic:

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Symptoms Short Form (BPQ-20 ANS) is employed. By checking the option that most closely matches

their response, respondents are asked to score their awareness of each given attribute.

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Multidimensional Assessment of Interoceptive Awareness Questionnaire (MAIA)

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Mehling WE, Price C, Daubenmier JJ, Acree M, Bartmess E, Stewart A (2012) The Multidimensional

Assessment of Interoceptive Awareness (MAIA). PLoS ONE 7(11).

The MAIA is an extensive self-report instrument created to assess interoceptive body awareness. Its

development was based on a systematic mixed-methods approach, involving a thorough review of

existing literature, the formulation of a multidimensional conceptual framework, the assessment of pre-

existing instruments, the creation of new items, and the analysis of feedback from focus groups

consisting of instructors and patients participating in therapies aimed at enhancing body awareness.

Intuition Measurement Tool RIDMS-E by Launer and Cetin (2023)

Based on the research EFRE-project Rationality, Heuristics, Intuition, and Anticipation (RHIA) by Markus

Launer (2018-2021), a broader measurement instrument for intuition was initiated. Launer and Svenson

(2022) laid the theoretical basis and first empirical study to better measure emotions, affection, and

feelings than in the above-mentioned intuition studies.

Based on their analysis, the authors analyzed all items on interoception from the following studies:

General Decision Making Style inventory (Scott & Bruce, 1995, short GDMS), Rational Experiential

Inventory (Pacini & Epstein, 1999, shortb REI); Perceived Modes of Processing Inventory (Burns &

D’Zurilla, 1999, short PMPI), and Types of Intuition Scale (Pretz et al, 2014, short TIntS). Only the items

were used with a factor loading higher than 0.7.

Figure 2: Analysis of items from intuition studies (Launer, 2023)

Launer and Cetin (2023) deepened this measurement instrument and researched emotional intuition in

more depth, e.g. body impulses (interoception). Based on a Crohnbach`s Alpha and factor analysis the

best statistical results showed a summary of three items. These items on body impulses were used as

a short version:

 When I make a decision, I trust my inner body feeling and somatic reactions

 I prefer drawing conclusions based on my feelings, my knowledge of human nature, and my experience

of life

 I tend to use my gut feeling for my decisions

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In the statistical analysis, the heart beat and skin feeling did not lead to a better result of the overall item

pool. The authors are working on an international study in 2024 to better describe the item pool.

Discussion

Through the prism of the extant literature that we systematically reviewed, reflect that there is an

influence of skin-mediated interactions. These specifically include the skin pain and emotional

communication process. Emotional control and decision making is more improved with the ability the

inner body signals and intuitive acumen. People with more consciousness reflect it in their deliberate

decision making (Dunn et al.2010; Sokol-Hessner et al. 2015). The cognitive process can enhance the

stability and emotional states to reach too much informed decision-making when the sensory information

are possessed timely.

Implications for Decision-Making

It is evident from the scholastic work that these feeling affect the decision-making in a profound manner.

In the highly complex systems, the enhanced skin-mediated generated awareness can warrant

interventions responsible for interceptive skills. These kind of feelings can be improved by trainings that

aim physical awareness and mindfulness integration, capacitating emotional maturity and better

decision making. Interoception can play the function of well-rounded and informed choice seeking

through techniques of positive decision-making.

Future Research Directions

More research should be done on specialized brain systems connected to skin-mediated interoception

and decision-making. Investigating how various touch kinds and pain thresholds impact decision-making

in various contexts might lead to a deeper understanding. Morrison (2016) and Brener and Ring (2016)

suggest that different brain networks impacting cognitive and emotional outcomes are impacted by

sensory input. Mapping these pathways and their interactions with decision-making processes is crucial.

The long-term impact of treatments that enhance interoceptive awareness on decision-making can be

shown via longitudinal study. Scholars may also request for a positivist research paradigm in the future

to measure the quantifiable outcomes of these topics.

Conclusion

The importance of skin-mediated interoception in decision-making is highlighted in this work. By

enhancing interoceptive awareness, interventions that improve decision-making can result from an

understanding of these relationships. Improving one's ability to recognize internal cues may improve

one's ability to control emotions and make decisions. Since this field of study has applications in

psychology, neuroscience, business, and healthcare, more investigation is necessary to fully

understand how interoception and decision-making interact, particularly with regard to skin-mediated

sensory experiences.

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Intuition in Educational Management

Daria Suprun 1) and Markus A. Launer 2)

1) National University of Life and Environmental Sciences of Ukraine, Ukraine

2) Ostfalia University of Applied Sciences & Institut für Dienstleistungen (independent non-

prcofit organization), Germany

Abstract

Intuitive decision making is becoming an important part in daily management. The question is,

how can future managers be trained in goof intuitive management skills in education?

The purpose of this conceptual study is lay a theoretical basis for the empirical research for

Intuition in Educational management. The target is to develop a concept to improve intuitive

decision-making as leadership quality.

The study is based on the new approach by Launer and Cetin (2023) with 12 different types of

decision making styles: Analytical, Knowing, Planning, Holistic, Spontaneous, experienced-

based Heuristics, Affective (feelings) like Emotions, Body Impulses, Mood as well as

Anticipation, Unconscious Thinking and the Dependence on Colleagues. This concept

combines the different approaches on intuition by CEST, GDMS, REI, PMPI, TIntS, PID, and

USID. In this paper we add two more dimension of intuitive decision making: Support by

Technology and Creating Style.

Introduction

Intuition is a concept that has been studied across various disciplines of management,

sociology, psychology, and philosophy (Hodgkinson and Sadler-Smith, 2003; Sinclair &

Ashkanasy, 2005; Dane & Prat, 2009; Hogarth, 2010), behavioral sciences (Hodgkinson et al.,

2008), parapsychology (Bem et al., 2015; Radin, 2017), medicine, and health sciences

(Glatzer et al., 2020; Chlupsa et al., 2021), engineering (Cash & Maier, 2021; de Rooij et al.,

2021).

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Although interest in organizational learning has grown dramatically in recent years, a general

theory of organizational learning has remained elusive. But intuition is now seen as a part of

education and organizational learning (Crossan, Lane & White, 1999). Dane and Pratt (2007)

developed a model and propositions that incorporate the role of domain knowledge, implicit

and explicit learning, and task characteristics on intuition effectiveness. They suggest

directions for future research on intuition and its applications to managerial decision making

(Dane and Pratt, 2007). Intuitive knowledge is very complex. It should not be taken for granted

or otherwise discounted. In fact, educators should devote more time and energy to

understanding and improving their knowledge about intuition, they need to become more

reflective and more aware of their responses (Weimer, 2013). According to another approach

It has been established (Sopivnyk & Suprun, 2023) that effective leadership in student age is

provided by such features as the ability to self-discovery, self-assertion, pronounced

independence, the ability to self-determination, the desire for collectivity, public activity etc.

Thus, it’s age for open ability for development of rational and Intuitive Decision Making. Thus,

the aim of research is the development of professional managerial skills and thinking, specially

intuition as a component of rational and intuitive decision making in context of Educational

Management (Suprun, 2023). So, we can make manifestation of intuition as important aspect

in context of educational managerial competence. It is quite obvious that there is a need

for multi-faceted consideration of the above-mentioned issue in accordance with its

significance for educational management.

Theoretical Foundation

Definition of Intuition in Educational Management

Dörfler and Ackermann (2011) developed a set of six features which define intuitive knowledge,

resembling closely to those of others (Kahneman, 2003; Sadler-Smith, 2008). However, in this

study we enlarge this concept for intuition in Educational Management and we follow the

approach by Launer and Cetin (2023). Intuition is based on different feelings such as gut

feeling, skin feeling, or heart beat. But also the mood deciders are in play a vital role (Launer

et al, 2020b). They named and propose twelve types of styles as Analytic, Planning, Knowing,

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Holistic Unconscious, Spontaneous, Heuristic, Slow Unconscious, Emotions, Body Impulses,

Moods, Anticipation, and Support by Others. In this measurement instrument the intuitive style

creative is missing (Cools and van den Broek, 2007). It is Cools and van den Broek (2007)

who added creating into their measurement tool for intuition. Another problem with intuitive

decision-making is that the intuitor can feel confident about their intuition (with no apparent

reason in terms of evidence) or cannot indentify the feeling or message (Launer et al, 2020b).

Alongside this process of searching for the features of intuition, we have recognized that all

the reports, whether academic or practitioner, from a variety of fields, including management,

psychology and philosophy as well as reports from artists and scientists from diverse fields,

mention two major areas in educational management which intuition is used: namely decision

taking and creative problem solving (Dörfler and Ackermann, 2011).

The role of intuition in creativity

Many researchers focus on the creativity for and through intuition (e.g. Bergson, 1946;

Beveridge, 1957; Bruner, 1966; Hadamard, 1954; Hong, 2006b; Poincaré, 1914; Popper,

1968). There seems to be a general agreement that intuition is a necessary component of

creativity (see e.g. Polanyi, 1962, 1964, 1966); at least, the creation of any great novum (new

knowledge) appears to be based on intuition. Some of the management literature also

mentions and, occasionally, discusses in depth the role intuition plays in creativity (e.g.

Claxton, 1998; Dane and Pratt, 2009; Hodgkinson et al., 2009a; Sinclair, 2010); however, apart

from notable exceptions (Sinclair, 2010), intuition in creativity is still viewed as judgement.

Naturally, the creative process may involve intuitive judgements, for example judging which

path to pursue in the course of a research progress (Dörfler and Ackermann, 2012).

Intuition and Moral and Ethics

To demonstrate the value of ethical intuition to organizational scholars, we consider the

potential impact of moral intuition research in four areas of organizational studies especially

suited to insights from this research: leadership, organizational corruption, ethics training and

education, and divestiture socialization (Weaver, G. R., Reynolds & Brown, 2014). Intuition

based on moral judgment have broad implications for moral education. The “five foundations

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theory of intuitive ethics” is applied to explain a longstanding rift in moral education as an

ideological disagreement about which moral intuitions should be endorsed and cultivated

(Graham, Haidt & Rimm-Kaufman, 2008). Originally developed to explain cultural variation in

moral judgments, moral foundations theory (MFT) has become widely adopted as a theory of

political ideology (Smith et al, 2015; Vozzola, 2016; Graham et al, 2013). The Moral

Foundations Theory (MFT; Graham et al., 2013; Haidt & Joseph, 2004) was designed to

explain both the variety and universality of moral judgments. It makes four central claims about

morality (Graham et al, 2018; Haidt & Joseph, 2004; Haidt, Koller & Dias, 1993).

1. Care/harm

2. Fairness/cheating

3. Loyalty/betrayal

4. Authority/subversion

5. Purity/degradation (Haidt & Joseph, 2004; Haidt, Koller & Dias, 1993).

Intuitions comes before judgement. The MFT theory is an intuitionist theory that builds on the

Social Intuitionist Model (Haidt, 2001, 2012). Like other types of evaluations, moral judgments

happen quickly, often in less than one second of seeing an action or learning the facts of a

case (Haidt, 2001; Zajonc, 1980). These judgments are associative, automatic, relatively

effortless, rapid, and rely on heuristic processing; they occur by processes that many

researchers call “System 1” thinking (Bruner, 1960; Kahneman, 2011; Stanovich & West,

2000).

Social Intuition and Implicit Learning

Implicit learning is assumed to play a central role in various everyday behaviors (Norman, &

Price, 2012). One example is the learning of complex patterns of motor responses involved in

skills like playing musical instruments and driving, in which the details of the acquired

knowledge are not fully accessible to conscious awareness (Clegg, DiGirolamo, & Keele,

1998). Another example is the acquisition of grammatical rules of one’s native language, which

is claimed to occur largely independently of the conscious intent of the learner (Cleeremans,

Destrebecqz, & Boyer, 1998; Reber, 1967, 1989). Yet another category of everyday

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behaviours explained in terms of implicit learning is the encoding and decoding of social signals

in social interactions (Lieberman, 2000). According to Lieberman, social intuition involves

making rapid judgements about the emotions, personality, intentions, attitudes, and skills of

others (p. 111). Such judgements are often based on the perception of sequences of various

forms of nonverbal cues, including subtle facial expressions, body postures, and nonverbal

gestures. Lieberman refers to this process as the “learning of nonverbal decoding. Norman

and Price suggest five methodological criteria for increasing the relevance of implicit learning

experiments to situations of social intuition.

1. Learning should involve exposure to stimulus sequences that represent a dynamic

event.

2. The sequence should involve different states of one entity rather than a series of

different entities.

3. Learning episodes should consist of many separate exemplars of the sequential

regularity.

4. Repetition of exactly the same sequences should be minimized.

5. The task should include precise measurement of what information participants are

consciously aware of so that it is possible to discriminate between nonconscious implicit

learning, social intuition, and explicit rule awareness.

Sequence learning can be constructed in a manner that simulates the properties of real-world

social learning environments. Norman and Price (2012) have also found that the learning

obtained under these conditions appears to be based more on explicit rule knowledge when

sequence elements are letters, but based more on implicit intuitive feelings when elements are

images of body posture. Perhaps the most important implication of our findings is that

researchers of implicit learning may underestimate the possibility and real-world prevalence of

truly implicit learning if they restrict themselves to using stimuli such as letter sequences or

sequences of simple geometrical shapes (Dienes et al, 1995).

Implicit learning processes are the cognitive substrate of social intuition. This hypothesis is

supported by (a) the conceptual correspondence between implicit learning and social intuition

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(nonverbal communication) and (b) a review of relevant neuropsychological (Huntington's and

Parkinson's disease), neuroimaging, neurophysiological, and neuroanatomical data

(Lieberman, 2000). Hodgkinson, Langan‐Fox, and Sadler‐Smith (2008) provide a fundamental

bridge construct in the behavioural sciences.

Intution in Education

Teaching today is still dominated by a analytical step-by-step reasoning learning process, e.g.

comparing and contrasting alternatives, evaluating them, examining their characteristics, the

associated costs and benefits, etc.(Dörfler & Ackermann 2012). However, such step-by-step

reasoning is not the only way of knowing. Intuitive knowledge is often described by scientists

(see e.g. Beveridge, 1957; Hadamard, 1954; Koestler, 1971) and decision takers2 (see e.g.

Barnard, 1938; Campbell and Mintzberg, 1991; Sadler-Smith and Shefy, 2004; Simon, 1987).

They just ‘know’, in a moment without knowing how or why they ‘know’. Thus the knowledge

arrived at by means of intuiting we call intuitive knowledge (Dörfler & Ackermann, 2012).

Conceptualizing intuition as intuitive knowledge, although limiting the scope of the intuition

field, enables us to apply arguments originally developed for the domain of knowledge to the

domain of intuition (Dörfler & Ackermann, 2012).

Spinoza (1677: Part 2, Proposition 40, Scholium 42) distinguished three kinds of knowledge:

(1) opinion or imagination, (2) reason and (3) intuitive knowledge; and without much

explanation declared that intuitive knowledge is the most powerful of the three (Spinoza, 1677;

Dörfler & Ackermann, 2012). Jung (1921: §770) distinguished four psychological functions:

thinking, feeling, sensation and intuition. He was probably the first to emphasize the intrinsic

certainty and self-referential nature of intuitive knowledge (Dörfler & Ackermann, 2012).

Bergson (1911: 238, 239) sees the role of intuition as helping to arrive at new ideas, after which

we should abandon intuition and work on building the body of knowledge using the new

intuitively obtained knowledge. Bergson (1946: 33 ff.) argues for intuition as a method of

dynamic and abstract thinking, contrasting intuition to intellect (Dörfler & Ackermann, 2012).

Gerard (cited by Vaughan, 1979: 66–80) distinguishes four levels of intuitive awareness: the

physical, the emotional, the mental and the spiritual. Extending the examination of intuition to

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the other three faculties can foster a deeper understanding of intuition as well as explain the

somatic and affective charges often reported about intuition (Dörfler & Ackermann, 2012).

Intuition in Educational Process

Dialogue sharpens and extends individual understandings of intuition in coaching, professional

training, educational process; and expertise and developmental maturity facilitate more

choiceful and effective decisions about using intuitions. A model, ‘Working at the boundary’,

symbolises the potential in the moment between a coach noticing and responding to an

intuition (Sheldon, 2018). It captures four ways of working with intuition, mapping the impact

of these interventions on the coaching relationship. profess using intuition in their work (Soyez

& Dini, 2015). Practitioner literature positions intuition as a critical part of coaching practice

(e.g. Bluckert, 2006; Starr, 2011; Whitworth et al., 2007). Successful coaches are highly

intuitive’ (Skiffington & Zeus, 2000, p.164; Sheldon, 2018).

Although intuition is positioned as a critical part of successful coaching practice, there is

minimal empirical evidence to support such assertions. That which does exist (de Haan, 2008;

Mavor et al., 2010) provides useful markers, but leaves gaps in our understanding of how

intuition is used in coaching, professional training, common educational process; (Sheldon,

2018). Working at the boundary, a higher-level category and explanatory model. This maps

the four positions a coach might take when responding to an intuition, together with coaching

outcomes (Sheldon, 2018).

 Missing a chance and Taking a risk are less mature interventions with less effective

outcomes;

 Holding back and Allowing not-knowing are more mature and more supportive of the

coaching relationship.

 The place of expertise and Maturing as a coach (we should legitimise dialogue about

intuition in training, coaching)

 Working at the boundary (provides bandwidth for mature intuitive interventions).

Ability to Trust your Intuition and interpret it correctly

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The idea that decision making involves distinctive analytic and intuitive components resonates

with everyday experience. Intuition is a Challenge for Psychological Research on Decision

Making (Hogarth, 2010). When should people trust their intuition? The answers to these

question depend on informational variables, such as feedback quality and the consequences

of inferential errors (Hogarth, 2001; Kardes, 2006). Liebowitz, Paliszkiewicz, and Gołuchowski

(2017) researched intuition, trust, and analytics. intuition, analytics and trust should still be part

of the winning formula for making sound executive decisions (Liebowitz et al, 2019). The

question is how well do executives (specially in educational process) trust their intuition

(Liebowitz et al, 2018).

However this perception has changed and more researchers are now recognizing that the

deliberative conscious reasoning is not the only way of arriving at valid knowledge.

(Hodgkinson et al., 2009a: 279). Intuition also needs trust. Intuition is worthy of trust (Hogarth,

2001; Kahneman & Klein, 2009; Salas et al., 2010). Students and Managers need to trust their

intuition. There are researchers who have found intuition useful in their respective fields of

research (such as Keren, 1987; Burke and Miller, 1999: in management; Hayashi, 2001: in

leadership), Dörfler & Ackermann, 2012).

Expert and Entrepreneurial Intuition

Crossan et al. (1999) and Miller and Ireland (2005) consider expert intuition as particular type

of intuition. They (1999: 526) distinguish between expert and entrepreneurial intuition. They

argue that the experience is past pattern oriented; thus the experts ‘almost spontaneously’

apply their existing knowledge in a familiar or similar to familiar situation. This is described as

experienced-based intuition or heuristics. the contrary, the latter is supposedly future- and

change-oriented, thus the ability to make novel connections and discern possibilities. Miller

and Ireland (2005: 21) distinguish between ‘holistic hunch’ and ‘automated expertise’. The first

‘corresponds to judgement or choice made through a subconscious synthesis of information

drawn from diverse experiences’, whilst the second is ‘merely’ subconscious application of

learned rules. the holistic hunch to be able to synthesize information from diverse experiences,

that information needs to be there (Dörfler & Ackermann, 2012). This may lead to the intuition

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described by Pretz et al (2007 and 2014). It is also a well-known phenomenon that experts will

not only be able to handle situations they have already experienced or for what they have

learned rules (e.g. Sadler-Smith, 2008: 257) but will also be able to go beyond the existing

knowledge (Dörfler & Ackermann, 2012). Again, it needs trust to dollow its own intuition and

creaticity to find new ways of doing things (Cools and van den Broek, 2007).

Results

Decision-making and Creativity

Following these studies intuition is a complex, integrated, multi-dimensional and multi-

disciplinary concept. The main features of intuition are unconscious, spontaneous inferential

or slow decision making process based on holistic abstract or big picture (holistic), experience-

learned heuristics, affective and emotional feelings, body impulses and moods, perception

without awareness, environmental influences by people as well as the capability for pre-

cognition based on hunches (Launer et al., 2020b, 2022; Svenson et al., 2022).

One of the results by reviewing the literature on intuition is that most if not all accounts of

intuitive knowledge can be located in one of two areas: decision taking and creativity. To build

a conceptual model of the types of intuition, all parts need to be integrated: knowledge,

methods, and creativity. But there are more applications for intuition in management (Dane

and Pratt, 2009; Glöckner and Witteman, 2009; Gore and Sadler-Smith, 2011). Dane and Pratt

(2009) who distinguish problem solving, moral and creative intuitions; Glöckner and Witteman

(2009) who differentiate associative, matching, accumulative and constructive intuitions; and

Gore and Sadler-Smith (2011) who identify problem-solving, creative, social and moral

intuitions as primary types (the secondary types being composites of the primary types),

As a result, we summarize some of the most important areas of application of intuitive

capabilities in the context of educational management:

- Knowledge as a basis

- Problem-solving techniques

- Decision-Making

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- Creativity

- Moral and Ethics

- Social intuition incl. empathy

- Trust in your intuition and interpret it correctly

Discussion based on the the Approach by Launer and Cetin (2023)

Launer and Cetin (2023) assembled a comphrehensive set of rational and intuitive decision

styles. According to various theories and approaches from different fields, they combine or

divide styles from different studies, add new styles which is not much mentioned before, and

test styles for finding a comprehensive valid and reliable instrument. This might be usefull for

researching intuition in Educational Management. In this paper we add one more iontuitive

decision-making style: Creating style. This can be found in the study by Cools and van den

Broek (2007) who researched the Creating style in their study with the following items:

C1. I like to contribute to innovative solutions

C2. I prefer to look for creative solutions

C3. I am motivated by ongoing innovation

C4. I like much variety in my life

C5. New ideas attract me more than existing solutions

C6. I like to extend boundaries

C7. I try to avoid routine

Intuition, Moral and ethics should be researched based on the MTF theory (Haidt & Joseph,

2004; Haidt, Koller & Dias, 1993):

1. Care/harm

2. Fairness/cheating

3. Loyalty/betrayal

4. Authority/subversion

5. Purity/degradation

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Conclusion

In this extended abstract we used the 12 dimensions of rational and intuitive decision-making

style by Launer & Cetin (2023). We added one dimension of intuitive decision making by using

the items by Cools and van den Broek (2007). We also suggest questions about ethics and

moral based on the MTF theory. This should be a good basis for researching intuition in

educational management.

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Study on rational and intuitive Decision-Making in Tourism

Markus A. Launer

Ostfalia University of Applied Sciences and Institut für gemeinnützige Dienstleistungen

gGmbH (non-profit organization), Germany,

Abstract

In 2021, Launer and Cetin presented the theory and the item selection on the topic Intuition in

the hotel industry at the Gloserv Conference (Launer et al, 2021) and a presentation to the

Ph.D. class of the Taylor`s University with Kandappan Balasubramaniam (2023). This abstract

shows the key results in a summary. The purpose of this study is to lay a foundation for a

global study on intuition based on the extended measurement instrument by Launer and Cetin

(2023). The study also confirms once more the measurement instrument by Launer and Cetin

(2021) in a global analysis for hotel, restaurant, food processing, and wholesale industry

(tourism industry, n=278). An explanatory and confirmatory factor analysis was applied. In

addition, important information were analysed about the rational and intuitive decision-making

in the tourism industry.

Introduction

The hotel, restaurant, food processing, and wholesale industry (tourism industry) is one of the

most customer-oriented sectors. A customer-oriented decision-making style by all employees

in all supply chains expected (Polo pena et al, 2013; Cheng et al, 2023). Fast reactions, friendly

appearance, strategic thinking are important capabilities in this industry (Kim & Jang, 2023) as

well as sustainability (Mkono & Hughes, 2020; Abdou et al, 2023; Majeed & Kim, 2023), LGBT-

friendly (Zhu, 2023), corporate responsible (Tang, 2023), and customer value co-created

(Carvalho & Alves, 2023). Therefore, plenty of technology and digital systems are being

implemented to better serve customer needs (Hallin, C.A.; Øgaard, T.; Marnburg, E., 2009;

Koen, Bertels, Kleinschmidt, 2014; Tung, Au, 2018). Based on personal and technological

capabilities, different decision-making styles can be differentiated (Balasubramanian, K.;

Ragavan, N.A. (2019; Balasubramanian, Balraj, Kumar, 2015). Modern technologies like

Artificial Intelligence (AI), Augmented and Virtual Reality (AR and VR), Big Data, Robots,

Blockchain, and the Metaverse are day-to-day routines (Akdim et al, 2023; Khoshaim, 2023).

This study purposes to research the rational and different intuitive decision-making styles of

employees and the management in the hotel industry on different hierarchical levels.

Therefore, the short measurement instrument by Launer and Svenson (2020) was used, a

study to define different types of intuition. The theoretical basis were a short version of the key

measurement instruments by

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 CEST = Cognitive-experiential self-theory (Epstein, 1994)

 GDMS = General Decision Making Style inventory (Scott & Bruce, 1995)

 REI = RationalExperiential Inventory (Pacini & Epstein, 1999)

 PMPI = Perceived Modes of Processing Inventory (Burns & D’Zurilla, 1999)

 PID = Preference for Intuition and Deliberation scale (Betsch, 2004)

 CoSI = Cognitive Style Indicator (Cools & Van den Broeck, 2007).

 TIntS = The Types of Intuition Scale (Pretz et al., 2014)

 USID = Domain-specific preferences for intuition and deliberation in decision making

(Pachur & Spaar, 2015)

CEST 1994

GDMS

1995 REI 1999 PMPI 1999 CoSI 2007 PID 2004 TIntS 2014 USID 2015

Epstein

Scott /

Bruce

Pacini /

Epstein

Burns /

D´Zurilla

Cools / van

den Broek Betsch Pretz et al

Pachur /

Spaar

The study was based on one rational decision-making style (incl. knowing, planning and

analytical style; Launer & Cetin, 2023) and six intuitive decision-making styles: the intuitive

decision making style, holistic unconscious intuition, emotional intuition (gut feeling), fast

experience-based intuition (heuristics), slow and time-delayed unconscious intuition

(unconscious thinking) and anticipation (hunches).

Research Methodology

The questionnaire was part of the survey developed by Marcial and Launer (2019) to measure

digital trust and intuition at the workplace. Part of the survey were items for intuition based on

the existing studies and new questions. The new questions were pre-tested on a sample in

Germany (n=90) by Launer and Svenson (2020).

The questionnaire was pre-tested with the calculation of test-retest reliability coefficients and

the internal consistency of the proposed survey questionnaire. The measurement of the test-

retest reliability was done in Germany and the Philippines (n=82). The questionnaire’s internal

consistency was measured through the pretesting (n=376) of the survey (Launer et al., 2020

and Marcial & Launer, 2021).

The main study showed a total of 5,570 answers from over 30 different countries of all

continents. The electronic questionnaire was translated into 15 different languages and pre-

tested in each language with specialsts. A self-selection sampling method has been identified

to collect the data for this study. The invitation to participate in the online survey was partly

made via the personal network of the authors and researchers of the international network in

the tourism industry. The respondents were guaranteed that their data will be anonymized and

that the use of aggregated data complies with European data protection regulations. The data

collection for the main study was carried out between March 1 and September 30, 2020. Data

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sampling was extended as part of CoVid-19-related restrictions on social life. It took

participants 30 to 45 minutes to complete the entire survey based on the electronic

questionnaire hosted by SoSci Survey, Germany. All questions were translated into related

culture by field experts and checked for semantic loss.

Sample and Demographics of Participants

The present sub sample of 278 participants in the tourism industry stemed from a total of 28

countrie. Key Countries (above 5% of the sample) were Brazil (Latin America), China and

Japan (Asia), Germany, Romania, and Spain (Europe), Ghana and Kenya (Africa), and the

United States of America. The percentages of gender were 45% of female, 28% of LGBT-Q,

and 27% of male; civil status was 59% of single, 31% of married, 9% of separated or divorced,

and 1% of widowed. The majority has master’s degree (55%), bachelor’s (21%) or high school

(10%) diploma. The professional experience is ranged from 4 to 10 years (69%), less than 3

years (25%), and more than 11 years (6%). The employment status is mostly permanent or

regular (90%). The distribution of managerial positions was top management -CEO, President,

Board Members, Vice Presidents (21%), middle management -Department Heads, Branch

Managers (48%), first level management -Supervisors, Foreman, Office Managers (13%),

contributors -Salesmen, Clerical, Secretarial, Technical Employees (12%, called front line

managers), and Self-employed (5%).

Validated Measurement Instrument

The measurement instrument was validated by Launer and Cetin (2021) in a global analysis

across various industries based on 5,578 participants of the global study Digital Trust and

Intuition at the Workplace (Marcial & Launer, 2019). The present measurement instrument for

the hotel, restaurant, and tourism industry was based on 278 participants validated again by

an explanatory and confirmatory factor analysis.

There are 21 items measuring the six types of intuition styles, namely, rational, intuition,

emotional, fast heuristic, unconscious, and anticipation based on a literature review (Launer,

Svenson, Ohler, Ferwagner, Meyer, 2020). It was measured with a 4-point Likert scale from 1

(strongly disagree) to 4 (strongly agree). The higher scores indicate higher levels in the

decision making styles. The sample questions from each types are:

 “Rational; Before I make a decision, I usually think about it for quite some time” based

on the Rational Choice Theory (Simon, 1955; Braun & Gautschi, 2011),

 “Intuition; If I am supposed to determine whom I can trust, I make intuition-driven

decision” (Burke & Miller, 1999; Dane, Pratt, 2006; Simon, 1989)

 “Emotional; For most decisions, it makes sense to feel” (Bonabeau, 2003; Craig, 2008;

Sinclair, Ashkanasy. 2005, Damasio, 1996)

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 “Fast heuristic; I frequently make quick and spontaneous decisions based on my life

experience” (Gigerenzer, 2007, 2016; Klein, 1993, 2003),

 “Unconscious; I never make decisions right away, and I always wait for a while”

(Dijksterhuis, 2004; Dijksterhuis & Nordgren, 2006),

 “Anticipation (pre-cognition); I can often predict emotional events” (Radin, 2004; Radin,

Borges, 2009).

Since the questionnaire was developed from the related items in the literature (Betsch, 2004),

we have explored factorial structure of instrument with using explanatory factor analysis in

SPSS. The principal component analysis with varimax rotation technic revealed six factors

based on the initial eigenvalue criteria of 1. Then, with using the maximum likelihood estimating

method in Amos program, we confirmed the six-factor structure with 16 items, after excluding

inconsistent items in accordance with the modification suggestions of the program (X2/df=1.72,

TLI=.97, CFI=.96, RMSEA=.051, RMR= .050). After the validation, Cronbach’s Alpha

coefficients ranged between .78 and .89. The results indicate a valid and reliable instrument

for determining decision-making styles of managers in the hotel and tourism industry.

Since the sub-groups of managerial positions are not adequate and not equally distributed (top

managers n=59, middle level managers n=134, first level managers n=37, contributors n=33,

self-employments n=14), we have used non-parametric Kruskal Wallis test for determining the

significant differences among positions.

Two-factorial model based on PID (Betsch, 2004)

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After confirming the two-factorial PID, we have tested the structural patterns of emotional, quick

heuristic, unconscious, and anticipation decision-making styles with rational decision-making

style. The modified version of the construct confirmed the fit of five construct of four types of

intuition and rational decision-making. We also tested the modified and confirmed factorial

structure on the same three random subsamples selected from the total sample to increase

the generalizability.

The construct validation of rational and different types of intuition styles

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We then calculated Cronbach’s Alpha coefficients of confirmed factors for determining the

internal consistencies. The coefficients of the subdimensions of the modified five-factor

structure ranged from .76 to .85 for the total sample and all random samples. These results

presented reliability of the multifactorial structure.

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Validation of the complete Intuition Model

The confirmed factorial structure of multidimensional decision-making styles

 Rational Decisions

 Unconscious Intuition

 Emotional Intuition

 Fast heuristic Decisions

 Slow Unconscious Thoughts

 Anticipation / Prä-Cognition

The Explanatory and confirmatory factorial constructs have confirmed the fit of six different

thinking style dimensions. After excluding inconsistent items, a total of 16 questions described

six dimensions. The factorial construct also ensured the reliability of the instrument by

providing higher-level internal consistencies. All the findings have demonstrated that the

proposed instrument is valid and reliable for testing multidimensional intuition thinking styles

in the workplace.

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Confirmed factorial structure of multidimensional decision-making styles

Factor loadings from .62 to .84, Cronbach’s Alpha coefficients from .75 to .85.

Results

Second Study on the Tourism Industry

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Decision Making Averages in the Hotel Industry

High level intuitive decision styles in hotel industry is

 Fast Heuristics Decisions

 Emotional Decisions

 Intuitive Decisions

Rational Decisions in Hotel Industry

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First Level Managers are more rational than Middle & Top Managers

Front Line Contributors are more rational than Middle Managers

Holistic Unconscious Decisions in Hotel Industry

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Middle level Managers are more intuitive than Front Line Contributors

Top Level Managers are more intuitive than Front Line Contributors

Emotional Decisions in Hotel Industry

No significant differences

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Unconscious Thinking in Hotel Industry

Front Line Contributors are more unconscious than Middle Level Managers

First Level Managers are more unconscious than Middle Level Managers

Top level Managers are more unconscious than Middle Level Managers

Experienced-based Heuristic Decisions in Hotel Industry

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Middle Level Managers are more fast heuristic than Front Line Contributors

Top Level Managers are more fast heuristic than Front Line Contributors

Anticipation in Hotel Industry

First Level Managers are more anticipative than Middle Level Managers

First Level Managers are more anticipative than Top Level Managers

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Analysis of the hierarchical Level

Top Level Managers in Hotel Industry

Fast Heuristic, Intuitive, Emotional are significantly higher than Anticipation

Middle Level Managers in Hotel Industry

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Fast Heuristic, Intuitive, Emotional are significantly higher than Anticipation, Unconscious,

Rational

First Level Managers in Hotel Industry

First Level Managers. No significant Differences

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Front Line Contributors in Hotel Industry

Rational are significantly higher than Fast heuristic, Anticipation, Intuition

Unconscious are significantly higher than Fast heuristic

Self Employed in Hotel Industry

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No significant Differences!

The results showed that managers’ intuitive decision-making styles are significantly different

in all styles. The analysis by different management level also showed significant differences.

For the rational decision-making style there are significant differences among the top level and

first level managers (the mean ranks for top level is 143.3 and first level is 191.3); the middle

level and first level managers (the mean ranks for middle level is 108.6 and first level is 191.3);

the middle level and contributors (the mean ranks for middle level is 108.6 and contributor is

189.1). For the intuition decision-making style there are significant differences among the top-

level managers and contributors (the mean ranks for top level is 153.7 and contributor is

101.3); the middle level managers and contributors (the mean ranks for middle level is 150.8

and contributor is 101.3). For the emotional decision-making style there are not significant

differences among the level of managers. For the unconscious decision-making style there are

significant differences among the top level and middle level managers (the mean ranks for top

level is 163 and middle level is 105.9); the middle level and first level managers (the mean

ranks for middle level is 105.9 and first level is 173.4); the middle level and contributors (the

mean ranks for middle level is 105.9 and contributor is 187). For the fast heuristic decision-

making style there are significant differences among the top-level managers and contributors

(the mean ranks for top level is 166.4 and contributor is 96.2); the middle level managers and

contributors (the mean ranks for middle level is 141.6 and contributor is 96.2). For the

anticipation decision-making style there are significant differences among the top-level and

first level managers (the mean ranks for top level is 124 and first level is 200.7); the middle

Markus A. Launer Special Issue Intuition 2023

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level and first level managers (the mean ranks for middle level is 120.8 and contributor is

200.7).

Front Line Managers (Contributors)

 Rational are significantly higher than Fast heuristic, Anticipation, Intuition

o Direct contact with the customer – first touch point to raise the demands

o Every customer should be treated same – Interact with the customer – better

understanding of issues

o Unconscious are significantly higher than Fast heuristic

o Customer experience and engagement – top priority

 Unconscious Thinking

o Front Line Contributors are more unconscious than Middle Level Managers

o Changing demands and expectations – Be innovative and creative

o Thinking about next day events and booking – staff related issues

 Rational decision

o Front Line Contributors are more rational than Middle Managers

o Direct impact – customer expectations – service excellence

o First impression – value creation – brand ambassador – Increase the loyal

customer

First level Managers

 Anticipation decision

o First Level Managers are more anticipative than Middle and Top Level

Managers

o First point of contact - front line managers – address the customers needs and

demands

o Observe - customers physically - Knowing their cultural values, expectation

and emotions

o High chances – Interaction with the customer – explore to changing demands

among customer

 Unconscious Thinking

o First Level Managers are more unconscious than Middle Level Managers

o In hotel industry, the service innovation - very important and competitive edge

– focused always

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o Dominant effect of quick decision – impact – service – customer satisfaction –

revisit intention

o New ideas to attract the customer – products – service - experience

 Rational decision

o First Level Managers are more rational than Middle & Top Managers

o Direct impact – customer satisfaction – brand reputation

o Previous data – customer preference – customer-centric is kept in high place

Top-level Managers in the Tourism Industry

 Fast Heuristic, Intuitive, Emotional are significantly higher than Anticipation

o International Chain of hotels – corporate culture – head quarters protocols

o Handle with their diverse and rich experience – absence of context is

highlighted

 Fast heuristic decision

o Top Level Managers are more fast heuristic than Front Line Contributors

o Branding and reputations

o Empowerment – Corporate SOP

 Unconscious Thinking

o Top level Managers are more unconscious than Middle Level Managers

o Property demand – poor decision is done

o Very competitive world – Development of social media - digitalisation

 Intuitive decision

o Top Level Managers are more intuitive than Front Line Contributors

o Service Industry – customer centric

o Experience - Networking

Discussion

This paper aims to test a multidimensional intuitive decision-making styles instrument. It

researches the intuitive decision-making styles of employees in the hotel industry on different

management levels. The proposed measurement instrument for decision styles was valid

based on an explanatory and confirmatory factor analyses of 278 employees of the hotel,

restaurant, and tourism industry. Rational decisions, unconscious intuitive decisions, emotional

decision-making (gut feeling), slow unconscious thinking, fast heuristic decisions and

anticipation (pre-cognition) were significantly different. Based on different management level,

important differences in rational and intuitive decision style were confirmed. The management

level were contributors (front line managers), first, middle, and top-level management. The

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findings indicated that the intuitive decision making of top level and middle level managers

significantly different from some other managerial levels. Clearly, top level managers are

relatively less rational than first level; more intuitive than contributors; more unconscious than

second level; more fast heuristic than contributors; less anticipative than first level managers.

The middle level managers are relatively less rational than first level and contributors; more

intuitive than contributors; less unconscious than top level, first level managers and also

contributors; more fast heuristic than contributors; less anticipative than first level managers.

It was concluded that top level managers prefer to decide intuitively, have unconscious thinking

style, and use fast heuristics decision-making. Middle level managers prefer intuitive decision-

making and use unconscious thinking. First level managers are more rational deciding and use

anticipation (pre-cognition). The contributors (front line managers) prefer rational decisions and

use unconscious thinking. These results were firstly tested in the hotel, restaurant, and tourism

industry and need further research in a larger sample.

Limitations of the study

There are also some limitations concerning the results. First, focusing the multidimensional

intuitive decision-making styles and managerial positions, all these findings are firstly tested in

a hotel and tourism companies in the literature. For confirming and generalizing the findings

future studies with large-scaled samples from hotel industry, country, or region are needed.

Second, this primary study employing a relatively comparative design in which how and why

questions are not explored. For understanding the reasons for differences in decision-making

styles of managers, future studies should focus on possible individual, contextual and job-

related factors that explain these differences.

Conclusion

It could be shown that the rational and intuitiove decision-makinh in tourism is different than

anticipated. Front line managers decide more rational than emotional. Employees with high

emotional intuition seem to become top managers. Further research is needed in this regard.

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Appendix

Inventory by Launer & Svenson (2020) and Launer and Cetin (2021)

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Concept Papers

The followng chapter publishes concept papers on rational and intuitive decision-making @

the workplace from the Conference on Contemporary Studies in Management (CoSiM) in

2023. All papers were presented by Markus Launer and co-authors. The aim is to lay the

thereotical foundation for a global study in 2024 together with the Baskent University,

Warschau University of Life Sciences, and University of South Florida.

More information on the research program on intuitive decision-making you find at

https://www.ostfalia.de/cms/en/pws/launer/research-and-development/intuition/

Prof. Launer and his team have been researching the topic of intuition since 2018. Different

types of intuition are being examined and a new model and an intuition test is being developed.

 Project funded by the EU and the state of Lower Saxony: Rationality, Heuristics,

Intuition & Anticipation (RHIA) 2018-2022.

 Follow-up project for Germany in 2022-2023

 Global follow-up Study RIDMS in 20 countries in 2024-2025

 Call for Paper for a Special Issue on "Rational & Intuitive decision-making". Deadline

Octoberc31, 2024.

Rational and Intuitive Decision-Making (RIDMS)

The project RIDMS is an international follow-up project to the EU research project Rationality,

Heuristics, Intuition & Anticipation (RHIA) funded by the EU and the State of Lower Saxony,

Germany. The aim is in particular to research unconscious decision-making @ the Workplace.

The study targets various industries and all levels of management.

The follow-up project is self-financed in collaboration with

 Prof. Dr. Markus Launer, Ostfalia University,

Germany

 Prof. Dr. Fatih Cetin from Baskent University

(Turkiye)

 Prof. Dr. Joanna Paliszkiewicz from Warsaw

University of Life Science, The Management

Institute (Poland)

 Prof. Dr. Cihan Cobanoglu, University of South

Florida, USA.

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Call for Paper for a Special Issue on

“Rational and Intuitive Decision-Making”

INTERNATIONAL JOURNAL FOR INNOVATIVE RESEARCH IN MULTIDISCIPLINARY FIELD

(IJIRMF), ISSN: 2455-0620, https://www.ijirmf.com/

Sponsored by Ostfalia University od Applied Sciences Campus Suderburg, Germany, Prof.

Dr. Markus A. Launer (free of charge)

https://www.ostfalia.de/cms/en/pws/launer/research-and-development/intuition/

You are cordially invited to submit a full paper on the topic “Rational and Intuitive

Decision-Making” free of charge.

This Special Issue is based on a former research project funded by the European Union and

the State of Lower Saxony, Germany (Intuition RHIA).

The call is multidisciplinary for all kind of research from literature study to experimental and

empirical studies, from medical / neurology and psychology / sociology to business sciences.

The selection process is in a double-blind review. Intuition is defined in a broad term from

unconscious intuition (holistic big picture), emotional intuition (gut, skin, and heart feeling),

spontaneous intuition, experience-based expert intuition, anticipation or pre-cognition as well

as the unconscious thought theory, support by others (dependent style) or the creating style.

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Rational and Intuitive Decision-Making in Latin America

Simon Zalimben 1) and Markus Launer 2)

1) University de Catholoica de Ascuncion, Paraguay

2) Ostfalia University of Applied Sciences and Institut für gemeinnützige Dienstleistungen

gGmbH (independent non-profit organization), Germany,

Abstract

Rational and intuitive decision-making has to be seen in within its cultural context. In Latin

America, the culture has a high impact on decision-making. Very dominant is the family-

orientation and emotional impact. However, there are different types of intuition according to

Launer (2020), Launer and Svenson (2022) and Launer and Cetin (2023). The question is,

how do managers in Latin America take rational or intuitive decisions compared to employees

on other continents and if there are differences within Latin America. Therefore, this analysis

compares the rationality and intuition of managers by continents (Asia, Africa, Europe, North

America, and Latin America) and by four Latin American countries: Argentina, Brazil, Chile,

and Paraguay.

The problem is that intuitive decision-making is often times is not differentiated by different

styles. The proposed basic decision-making principles, forming the intuition model are rational,

classical intuition, emotional, fast heuristic, unconscious, and anticipation (Launer, 2020).

The purpose of this study is to explore the different rational and intuition types in Latin America.

This leads to a better understanding of rational and intuitive decision-making in general, and

at different workplaces in particular. Each industry or job function needs a special set of

decision-making capabilities.

The sample was from a global study with n=5,579 employees from different industries and

countries worldwide. For the validity, the explanatory and confirmatory factor analyses were

conducted for determining the latent factorial constructs (Launer / Cetin, 2021). Based on the

valid and reliable new model, the different intuition types of employees were tested in this

worldwide sample. The countries researched are Argentina, Brazil, Chile, and Paraguay. The

study is based on a sample of n = 742 out of a worldwide sample consisting of 5.575 employees

working in different countries and industries. The online survey methodology was used for

collecting data. 13 languages were offered in total incl. English, Spanish and Portuguese. The

results showed that the 50-item questionnaire is valid and reliable instrument for measuring

digital trust in Latin America.

The key results show that in Latin America compared to Africa, Asia, Europe, and Latin

America, the classical unconscious intuition and the emotional intuition is relatively high

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compared to other continents. In contrast, the rational decision making, unconscious thinking,

and fast heuristic decision making is relatively low as well as the anticipation (pre-cognition).

The analysis by Latin American countries shows significant differences in different intuition

style. In Argentina, intuition style showed a very low rational decision level as well as very low

heuristical intuition and anticipation. Therefore, the classical intuition and emotional styles as

well as the unconscious thought level were very high. Brazil showed a higher intuition style in

general. The rational decision was relatively high. All other intuition styles were on a mid-level.

Chile showed the highest level of rational, heuristically and Unconscious thought decision style

in Latin America. In opposite, Chile shows the lowest classical unconscious intuition, emotional

intuition, and anticipation. This shows a clear trend towards more objective thinking styles. It

seems, Chile´s managers are very analytically business oriented, and their decision are more

based on extensive learnings and trainings. In Paraguay, the classical intuition is relatively

high as well as emotional oriented decisions, fast heuristics and tze highest level of

anticipation. This shows a much more emotional and feeling oriented decision-making

orientation.

The theory is based on the two EFRE research projects “Digital Trust & Teamwork (DigVert)”

and “Intuition (RHIA)" of Launer et all (2020) financed by the European Union and the State of

Lower Saxony, Germany. The database was from the empirical follow-up project “Digital Trust

& Intuition at the Workplace” (n = 5.500) of Ostfalia University (Marcial / Launer, 2019). The

model was pre-tested with a tesol test, test retest (Germany and Philippines, n = 83), and pre-

test (n = 376) in nine languages by Launer / Marcial / Gaumann (2020), test retest (Marcial /

Launer (2021), and hypotheses derived in a pre-test (Launer / Svenson / Ohler, 2020).

Introduction

Research about intuitive decision making in Latin America shows a research gap in science.

Despite the presence of significant differences between countries1, intuitive management

studies have not been matched by others in regions such as Africa and the Arab Middle East2

or other regions. Klatt et al (2019) researched intuition and creativity of soccer coaches

between Brazil and Germany.3

1 Said, E.; Fadol, S. (2016). The role of context in intuitive decision-making, in: Journal of

Management & Organization , Cambridge University Press, Volume 22 , Issue 5 , September

2016 , pp. 642 – 661, DOI: https://doi.org/10.1017/jmo.2015.63

2 Elbanna, S., Di Benedetto, C. A., & Gherib, J. (2015). Do environment and intuition matter in

the relationship between decision politics and success?. Journal of Management &

Organization, 21(1), 60-81.

3 Klatt, S., Noël, B., Musculus, L., Werner, K., Laborde, S., Lopes, M. C., ... & Raab, M. (2019).

Creative and intuitive decision-making processes: A comparison of Brazilian and German

soccer coaches and players. Research Quarterly for Exercise and Sport, 90(4), 651-665.

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For a long time, Latin America culture was not well understood inside and outside of Latin

America.4 In the meantime, Latin American culture has changed due to contemporary5,

modernity and postmodernity influences.6 The culture has been researched in various fields

such as the organizational impact7, human resource management8, entrepreneurship9, video

gaming10, women and politics11, anti-corruption politics12, divergent modernities13, ethos

components14, the informal empires15, or the soul of Latin America16. La Cadena describes the

racism in Latin America.17

There is only a limited amount of cross-cultural studies. Inglehart and Carballo (1997)

described in across cultural analysis based on the coherent cultural regions, having people

with distinctive values and worldviews that make them think differently and behave differently

from people of other cultures.18 Lehman (1996) described the religious transformations

between Brazil compered to Latin America.19

Zalimben (2021)20 indicates that in 1955 Hebert Simon criticized most economic models that

assumed that economic agents were rational in their decision-making. Simon mainly criticized

that these models contemplated those economic agents had unlimited information processing

capacities. This is how Simon introduces the term limited rationality or approximate rationality,

which indicates that the economic man or administrative man has limited knowledge and skills.

(Simon, A Behavioral Model of Rational Choice, 1955)

Other authors describe the influence from other countries on Latin America, such as

Portuguese culture on Brazil21, a comparison of Latin American and North American legal

traditions22, the role of leadership and cultural contingencies in total quality management23 or

studies of outstanding Central American managers in Central America24. Valdés and Kadir

4 Gillin, J. (1946).

5 Yúdice, G., Flores, J., & Franco, J. (1992).

6 Brunner, J. J. (1993).

7 Osland, J. S., De Franco, S., & Osland, A. (1999).

8 Dávila, A., & Elvira, M. M. (2007).

9 Fernández-Serrano, J., & Liñán, F. (2014).

10 Penix-Tadsen, P. (2016).

11 Bergmann, E. L. (1990).

12 Husted, B. W. (2002).

13 Ramos, J. (2001).

14 Gillin, J. (1955).

15 Brown, M. (Ed.). (2009).

16 Wiarda, H. J. (2003).

17 La Cadena, M. D. (2001).

18 Inglehart, R., & Carballo, M. (1997).

19 Lehmann, D. (1996).

20 Zalimben, S (2021).

21 Cheke, M. (1953).

22 Rosenn, K. S. (1988)..

23 Osland, A. (1996)..

24 Osland, J. S. (1993).

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(2004) described the literary cultures of Latin America 25 Hewet et al (2006) described the

influence of National culture and industrial buyer-seller relationships in the United States and

Latin America.26 In (Moreno-Jiménez, 2014) He mentions three schools of thought in relation

to decision-making, the normative one where rationality prevails and indicates how decisions

are made and the methods used. Then the descriptive one with a procedural rationality in

decision-making and finally, the prescriptive or constructive one that is more pragmatic and

indicates how to improve decision-making processes.

Osland et al (1996) provides a good framework to research Latin American culture.27 Thereby

it is important to understand the history of Latin America.28 However, there is lack of literature

on the Latin American culture in sciences and a research gap on the cultural influence on

intuitive decision-making.

Theoretical Basis

Rational choice and deliberation

Cognitive psychology is researching how people perceive, think, plan, make decisions and

ultimately generate actions with an external stimulus and the following behavior [29 30]. These

include perception, attention, memory, language, thinking and problem solving, and

intelligence [31. Processes of information absorption (perception, attention), information

processing (thinking, decision-making) and knowledge (memory) a form the basis for sensorial

control of movements [32]. However, it is also referred to as an interdisciplinary science with

approaches from linguistics, computer science (artificial intelligence), philosophy, physics or

neurosciences, brain research and physiology [33] and economics [34] and the environment [35].

Models on rational behavior, judgment and decision-making [36] are standard for individual

behavior in economics [37], behavioral economics [38], business adm. [39], sociology [40], and

25 Valdés, M. J., & Kadir, D. (2004).

26 Hewett, K., Money, R. B., & Sharma, S. (2006).

27 Albert, R. D. (1996).

28 Eakin, M. C. (2007).

29 Simon, H. (1980).

30 Boudon, R. (2009).

31 Spering, M., & Schmidt, T. (2009); Gerrig, R. J. (2015).

32 Hänsel, S. D.; Baumgärtner, J. M.; Kornmann, F. Ennigkeit (2016).

33 Hagendorf, H., Krummenacher, J., Müller, H.-J., Schubert, T. (2011).

34 Simon, H. (1955).

35 Simon, H. (1956).

36 Binmore, K. (2008); Chater, N., Oaksford, M. (2006).

37 Kahneman, D. S.; Tversky, A. (ed.). (2009). Choices, values, and frames (10. printing).

Cambridge Univ. Press.

38 Simon, H. (1955); Camerer, C. (1999): Camerer, C., Loewenstein, G.; Rabin, M. (2011).

39 Hollis, M. (1979); Elster, J (1986, ed); Scott, J. (1999).

40 Lindenberg, S. (1985).

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politics [41]. Rational decision-making is described dependable on the environment [42], risk [43],

and uncertainty [44]. It is based on the so-called homo economicus [45], normative decision-

making [46], optimization theory [47], critical thinking [48], the calculus of uncertain reasoning

(probability theory) [49]. Rationality is fundamental to explain employee’s behavior [50] by

describing the information-processing by cognition [51]. All available information can therefore

be cognitively analyzed and processed [52]. In this regard, rational cognitive processes are

slow, deliberative, and conscious compared to fast, automatic, and unconscious [53] (see

heuristics, unconscious intuition and thinking). Conscious thought is generally considered to

lead to good decisions in easy situations [54]. However, because conscious thinking has a low

capacity to process multiple factors (The magical number seven, plus or minus two) [55],

conscious thought on an issue will lead to a poorer decision when applied to complex issues.

Therefore, the type of rational decision maker has been described very well in theory [56].

The classical intuition type, as decribed by Hill [57] is a holistic intuition type integrating diverse

sources of information in a Gestalt-like, non-analytical manner [58]. Epstein (1994) described

within his experiential system of the Cognitive-Experiential Self-Theory (CEST) [59] the items

preconscious, automatic, effortless, and holistic [60]. The Rational Experiential Inventory (REI)

has a holistic component as well [61]. Burns and D’Zurilla describe in their Perceived Modes of

Processing Inventory (PMPI) an automatic processing type as well [62].

41 Green, D.P., Shapiro, I. (1999).

42 Anderson, J. R.; Schooler, L. J. (1991). Aldrich, H. E.; Pfeffer, J. (1976); Aldrich, H.; Mindlin,

S. (1978); Simon, H. (1956).

43 Kahneman, D. S.; Tversky, A. (1979).

44 Kahneman, D.S., Slovic, P., & Tversky, A. (ed.) (1982). Kahneman, D. S., Slovic, P. &

Tversky, A. (Hg.). (1982).

45 Jolls, C., Sunstein, C. R.; Thaler, R. (1998); Wallacher, J. (2003).

46 Chater, N., Oaksford, M. (2012).

47 Anderson, J.R. (1990); Anderson, J.R. (1991).

48 Klein, G. A. (2011).

49 Oaksford, M.; Chater, N. (2007).

50 Bratman, M. (1987); Fodor, J. A. (1987); Payne, J. W., Bettman, J. R.; Johnson, E. J. (1992);

Payne, J. W., Bettman, J. R.; Johnson, E. J. (1992).

51 Anderson, J.R. (1991). Anderson, J.R. (1990); Oaksford, M.; Chater, N. (2007):

52 Chater, N., et al (2018). Binmore, K. (2008).

53 Evans, J. S. B.T. (2008).

54 Dijksterhuis, A., Bos, M.W., Nordgren, L.F., van Baaren, R.B. (2006).

55 Miller, G. A. (1956).

56 Schwartz, B. (2015).

57 Hill, O.W. (1981).

58 Pretz, E., et al. (2014).

59 Epstein, S. (1994).

60 Epstein, S., Pacini, R. (1999).

61 Epstein, S., Pacini, R. (1999).

62 Burns, L. R., D’Zurilla, T. J. (1999).

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Unconscious intuitive decision-making, as described by Pretz [63], is a kind of natural judgment

process that takes place without conscious thinking and without an explicit awareness or

knowledge base [64], it is just available [65]. It is a perception of patterns, meanings, structures

that are initially unconscious, but which nonetheless lead thinking to a certain decision [66]. It

is an affectively charged judgment that arise through quick, unconscious and holistic

associations [67] and difficult to verbalize [68].

The capability of fast unconscious decisions can be reached by (a) implicit learning produces

a tacit knowledge base that is abstract and representative of the structure of the environment

[69]; (b) such knowledge is optimally acquired independently of conscious efforts to learn [70];

(c) it can be used implicitly to solve problems and make accurate decisions about novel

stimulus circumstances [71], or (d) spontaneous, [72].

Spontaneous heuristic decisions

Another cognitive decision theory describe intuition as an implicit, heuristic, unconscious

knowledge of an individual [73] and employees in organizations [74]: Heuristics [75], best

described by Gigerenzer [76] and summary article by Chater et al [77]. Thus, intuition is a process

of pattern comparison based on so-called mental maps and action scripts [78]. These capabilities

are gained through experience and learning processes in the respective job training, by life or

job experiences, e.g. wisdom [79], unconsciously storing associations cognitively [80], and/or

knowledge-based intuition training [81]. Events that we remember very easily seem to be more

likely in decision-making than events that are more difficult to remember [82], but which leads

63 Pretz, J.E., Brookings, J.B. (2007). Pretz, J.E., Totz, K.S. (2007).

64 Reber, A. S. (1989b). Vaughan, F. E. (1979).

65 Reber, R. (2017).

66 Bowers, K. S., Regehr, G., Balthazard, C. & Parker, K. (1990).

67 Dane, E., Pratt, M. G. (2007).

68 Goleman, D. (1996). Goleman, D., Boyatzis, R., & McKee, A. (2002).

69 Reber, A. S. (1993). Reber, A. S. (1989a).

70 Reber, A. S. (1992).

71 Claxton, G. (1997).

72 Scott, S. G., Bruce, R. A. (1995).

73 Woolhouse, L. S., Bayne, R. (2000). Gigerenzer, G., Brighton, H. (2011).

74 Agor, W. H. (1989). Klein, G. A. (2003a).

75 Gigerenzer, G., Hertwig, R. & Pachur, T. (ed.). (2011). Gigerenzer, G. & Todd, P. M. (1999).

76 Gigerenzer, G. (2007). Gigerenzer, G. (2015). Gigerenzer, G. (2016).

77 Chater, N., et al. (2018).

78 Klein, G. A. (2003b).

79 Goldberg, E. (2005).

80 Kahneman, D. S., Slovic, P., Tversky, A. (ed.). (1982)..

81 Claxton, G. (2000)..

82 Reber, R. (2017).

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to errors, wrong predictions [83] and biases [84]. Heuristics refers to the art of arriving at

probable statements or practicable solutions with limited knowledge (incomplete information)

and little time [85]. Heuristics can be further structured in [86]: the availability heuristic [87], the

representativeness heuristic [88], the anchor heuristic [89], recognition heuristic [90], and the

judgment heuristic [91]. Pretz et al. describes heuristics as inferential intuition that is based on

previously analytical processes that have become automatic [92].

Klein describes a model of quick heuristic decisions based on experience where the decision

maker is assumed to generate a possible course of action, compare it to the constraints imposed

by the situation, and select the first course of action that is not rejected developed: the

"recognition primed decision-making model" (RPD model) [93] or the naturalistic decision

making approach [94]. It needs to be discussed if this is another item by itself.

Emotional decision-making

In intuition research, affective decisions are based on feelings, or the so-called gut feeling.

Emotions are not mentioned so far. Epstein [95], Pretz [96], and Betsch [97] described feelings

as an affective type of intuition, Burns and D`Zurilla described it as emotional processing [98].

This phenomenon, however, is best described in medicine and neurology, but so far without a

style inventory for empirical research. Neuroscience distinguishes the gut feeling [99], but also

heart rate feelings, and skin arousal [100], respiratory feedback [101], anger and aggression [102]

and others [103]. Researching feelings as valid forms of intuition is a form of unconscious

83 Kahneman, D. S. (2011).

84 Kahneman, D. S., Slovic, P. & Tversky, A. (ed.). (1982).

85 Tversky, A., & Kahneman, D.S. (1973). Gigerenzer, G. & Todd, P. M. (1999).

86 Kahneman, D.S., & Schmidt, T. (2016). Kahneman, D.S., Slovic, P., & Tversky, A. (1982).

87 Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991).

88 Grether, D.M. (1980). Brannon, L.A., Carson, K.L. (2003).

89 Tversky A., Kahneman D. (1974).

90 Goldstein, G., Gigerenzer, G. (2011).

91 Strack, F. & Deutsch, R. (2002).

92 Pretz, E., et al. (2014).

93 Klein, G. A. (1993).

94 Klein, G. A. (1998). Klein, G. A. (2003). Klein, G. A. (2008).

95 Epstein, S., Pacini, R. (1999):

96 Pretz, E., et al. (2014). 7

97 Betsch, C. (2004).

98 Burns, L. R., D’Zurilla, T. J. (1999).

99 Lerner, A. (2017).

100 Dunn, B. D., et al. (2010).

101 Philippot, P., Chapelle, G., Blairy, S. (2002).

102 LeDoux, J. (1996).

103 Dunn, B. D., et al. (2010).

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intuition but with a medical background [104]. Emotions can alert us to opportunities, and more

informed personal decision-making [105]. This can be considered a conscious decision. The

theory that feelings influence our intuitive thinking is still subjective and controversial. But the

approach can be seen as a valid approach for emotional, intuitive decision-making style [106

107].

Anticipation

For a long time, researchers try to explain unnormal or paranormal decision-making [108],

anticipation of solutions, e.g. presentiments of future emotions [109], precognition (conscious

cognitive awareness) and premonition (affective apprehension) [110]. extrasensory perception

(ESP) [111], paranormal belief and experiences [112], or automatic evaluation [113]. We

summarized all approaches in the term anticipation. The theoretical basis is parapsychological

research on pre-cognition: Recent meta-studies, which examined a total of up to 90

experiments and studies with anticipation (Bem et al., 2015).

Anticipation has not yet been accepted in business administration, psychology and other

sciences. Research also not shows any applications at the workplace. However, in esoteric

circles, anticipation is researched in depth and widely accepted. In interviews, may employees

assured us that they often just know what they should decide.

Slow unconscious thinking

Most professional decisions do not have to be taken immediately rather they can be done after

a period of time]. A longer time of distraction from the problem to decide on is not yet described

in intuition theory. Most scales are concentrating on spontaneous intuitive decisions. This why

we argue, another intuition theory needs to be added into a holistic research model. Until a

decision has to be taken, employees might be distracted from their task or decision-problem.

When the task is outside of the attention, unconscious thoughts occur automatically [114]. During

this time, many processes are influencing the decision-making process unconsciously without

104 Bonabeau, E. (2003). Burke, L.A., & Miller, M.K. (1999).

105 Goleman, D. (1996).

106 Rosanoff, N. (1999).

107 Simon, H. (1989a).

108 Honorton C., Ferrari D.C., (1989).

109 Radin, D. (2004).

110 Bem, D.J. (2011). Bem, D. Tressoldi, P., Rabeyron, T., Duggan, M. (2016). Mossbridge J,

Tressoldi P, Utts J. (2012). Mossbridge, J.A., Tressoldi, P., Utts, J., Ives, J.A., Radin, D., Jonas,

W.B. (2014).

111 Thalbourne, M., Haraldsson, E. (1980).

112 Lange, R., Thalbourne, M.A. (2002).

113 Ferguson M.J., Zayas, V. (2009).

114 Dijksterhuis, A., Nordgren, L. F. (2006).

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awareness, the brain works further on the solution while the deciders overtakes different tasks

[115]]. There might be a combined conscious and/or unconscious reflection or incubation [116],

associations [117], intuitive leaps [118], productive thoughts by removing mental blockages [119],

by completing a complexes schemes based on the GestaltPsychology [120], emotions and

feelings [121], or by expertise [122]. The research approach is based on neuroscience, cognitive

psychology, and social cognition [123]. Medically, this might be explainable with different

semantic nodes getting activated and connected, which can lead to the slow completion of the

decision.

Method

Instrument & Participants

The questionnaire in this study was part of the research project “Digital Trust @ the Workplace”

with 30 researchers from schools in Europe, the USA, Latin America, Africa, and Asia named

including 21 items on intuitive decision-making. An electronic questionnaire was used to collect

data with a snowball sampling method through the international personal network of authors.

In Latin America, the questionnaires were collected through partner Universities in Argentina

(Quilmes University), Chile (Universidad de Chile, and Paraguay (UCA University). In Brazil, a

country managers supported the data collection,

Complying with the European data protection rules, voluntariness and confidentiality were

used to invite individuals to participate in the online questionnaire. This was in particular

important in Latin America. The questionnaire was originally developed in English. Qualified

bilingual professionals were commissioned to translate the questionnaires into 14 different

languages including Spanish and Portuguese. Experts evaluated and tested these translations

in Chile and Brazil.

The questionnaire was pre-tested with the calculation of test-retest reliability coefficients and

the internal consistency of the proposed survey questionnaire. The measurement of the test-

retest reliability was done in Germany (n=51) and the Philippines (n=32). The questionnaire’s

internal consistency was measured through the pretesting (n=376) of the survey in China,

Japan, South Korea, Paraguay, Russia, Brazil, Thailand, USA, and the United Kingdom from

June to November 2019.

115 Dijksterhuis, A.Nordgren, L. F. (2006).

116 Wallas, G. (1920). The art of thought. Watts

117 Dijksterhuis, A. & Meurs, T. (2006).

118 Nicholson, N. (2000).

119 Duncker, K. (1945).

120 Mayer, R. E. (1996).

121 Dijksterhuis, A., Höhr, H., Roth, G. (2010).

122 Dijkstra, K.A., van der Pligt, J., van Kleef, G.A. (2012).

123 Dijksterhuis, A. Aarts, H. (2010).

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In the main study, the participants were 5.574 employees working in 43 different industries

from over 30 countries from the research project. Participation from Latin America was n=742

managers. This splits into Argentina (n= 115), Brazil (n= 253, Chile (n= 173), and Paraguay

(n= 201).

For the validity, the explanatory and confirmatory factor analyses were conducted for

determining the latent factorial constructs (Launer / Cetin, 2021). Based on the valid and

reliable new model, the different intuition types of employees were tested in this worldwide

sample. A General Linear Model was used to analyze the cross-cultural differences between

 the continents Europe, North America, Africa, Asia, and Latin America

 the four countries Argentina, Brazil, Chile, and Paraguay.

Between-Subjects Factors

Value Label N

CONTINENTS 1 Africa 491

2 Asia 2017

3 Europe 2051

4 Latin America 742

5 North America 277

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Results

4.1 Graphical Overview by Continents

Rational Decision-Making

Compared to Europe, North America, Africa, and Asia, Latin America shows a relatively low

level of rational decision-making-

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Like in Africa, Asia and Northern America, Latin America shows a relatively high level of

intuitive decision-making. In Europe, the level of intuitive decision-making was much lower.

The level of emotional decision-making in Africa, Asia, and Latin America was relatively high.

Emotional intuition in Europe and Northern America was much lower.

People on Asia seems to have a very level of unconscious decision-making. Latin America

showed the lowest level of unconscious thinking.

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Fast heuristic decision-making in Asia was very high, in North America rather low. Latin

America showed a mid-level of fast heuristics together with people from Africa and Europe.

The level of anticipative decision-making in Latin America was very low. In Asia, people seem

to have a very high level of anticipation.

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Graphical Overview by Latin American Countries

Rational Decision Making

The level of rational decision-making in Chile was the highest together with people from Brazil.

Paraguay showed a mid-level of rational decision-making and Argentina a low level.

Classical Intuition

Intuitive decision making in Chile was much lower compared to Argentina, Brazil, and

Paraguay.

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Emotional intuition

The emotional intuition level was very low in Chile compared to a high level in Argentina and

Paraguay. Brazil showed a mid-level of emotional decision-making.

Unconscious thoughts

The level of intuition based on unconscious thoughts were in Argentina and Chile on a high

level. Brazil showed a mid-level of unconscious decision making over time and Paraguay a

very low level.

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Fast heuristics

The level of fast heuristic decision-making in Chile and Paraguay was on a very high level.

Brazil showed a mid-level for fast heuristics and Argentina a very low level.

Anticipation / pre-cognition

Anticipation was on a very high level in Paraguay. Brazil showed a mid-level of anticipative

decision-making and Argentina and Chile a rather low level.

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Descriptive Statistics

CONTINENTS Mean Std. Deviation N

Rational Africa 2.8734 .94613 491

Asia 2.9086 .89339 2017

Europe 2.8105 .92777 2051

Latin America 2.4108 1.10663 742

North America 2.8649 .84937 277

Total 2.8010 .95243 5578

Intuition Africa 3.0678 .89659 491

Asia 3.0270 .78967 2017

Europe 2.7396 .95298 2051

Latin America 3.0116 .98803 742

North America 2.9206 .77845 277

Total 2.9176 .89928 5578

Emotional Africa 2.7661 .88764 491

Asia 2.9089 .81413 2017

Europe 2.4910 .92511 2051

Latin America 2.8605 1.03798 742

North America 2.5102 .83574 277

Total 2.7164 .91559 5578

Unconscious Africa 2.6326 .86209 491

Asia 2.7684 .85082 2017

Europe 2.6888 .86813 2051

Latin America 2.5122 .97922 742

North America 2.5939 .85457 277

Total 2.6844 .88017 5578

Fast Heuristic Africa 2.4677 .90758 491

Asia 2.6865 .81375 2017

Europe 2.5117 .87816 2051

Latin America 2.4890 .93619 742

North America 2.3911 .98411 277

Total 2.5620 .87696 5578

Anticipation Africa 2.4667 .83903 491

Asia 2.6532 .83979 2017

Europe 2.2919 .89680 2051

Latin America 2.0699 .96123 742

North America 2.1260 .77831 277

Total 2.4001 .90070 5578

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Box's Test of Equality of

Covariance Matricesa

Box's M 1344.042

F 15.934

df1 84

df2 5564223.568

Sig. .000

Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups.

a. Design: Intercept + CONTINENTS

Multivariate Tests

a

Effect Value F Hypothesis df Error df Sig.

Intercept Pillai's Trace .960 22007.833

b

6.000 5568.000 .000

Wilks' Lambda .040 22007.833

b

6.000 5568.000 .000

Hotelling's Trace 23.715 22007.833

b

6.000 5568.000 .000

Roy's Largest Root 23.715 22007.833

b

6.000 5568.000 .000

CONTINENTS Pillai's Trace .145 34.983 24.000 22284.000 .000

Wilks' Lambda .861 35.588 24.000 19425.641 .000

Hotelling's Trace .155 36.008 24.000 22266.000 .000

Roy's Largest Root .093 86.378

c

6.000 5571.000 .000

a. Design: Intercept + CONTINENTS

b. Exact statistic

c. The statistic is an upper bound on F that yields a lower bound on the significance level.

Levene's Test of Equality of Error Variances

a

F df1 df2 Sig.

Rational 41.306 4 5573 .000

Intuition 47.797 4 5573 .000

Emotional 38.780 4 5573 .000

Unconscious 11.653 4 5573 .000

FastHeuristic 19.023 4 5573 .000

Anticipation 13.691 4 5573 .000

Tests the null hypothesis that the error variance of the dependent variable is

equal across groups.

a. Design: Intercept + CONTINENTS

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Tests of Between-Subjects Effects

Source Dependent Variable

Type III Sum of

Squares df Mean Square F Sig.

Corrected

Model

Rational 140.233

a

4 35.058 39.721 .000

Intuition 106.769

b

4 26.692 33.782 .000

Emotional 207.384

c

4 51.846 64.670 .000

Unconscious 39.874

d

4 9.968 12.978 .000

FastHeuristic 52.861

e

4 13.215 17.386 .000

Anticipation 257.149

f

4 64.287 83.958 .000

Intercept Rational 24107.543 1 24107.543 27313.867 .000

Intuition 27331.976 1 27331.976 34591.563 .000

Emotional 22968.886 1 22968.886 28650.294 .000

Unconscious 21826.811 1 21826.811 28416.865 .000

FastHeuristic 19729.671 1 19729.671 25955.647 .000

Anticipation 16888.906 1 16888.906 22056.599 .000

CONTINENTS

Rational 140.233 4 35.058 39.721 .000

Intuition 106.769 4 26.692 33.782 .000

Emotional 207.384 4 51.846 64.670 .000

Unconscious 39.874 4 9.968 12.978 .000

FastHeuristic 52.861 4 13.215 17.386 .000

Anticipation 257.149 4 64.287 83.958 .000

Error Rational 4918.796 5573 .883

Intuition 4403.418 5573 .790

Emotional 4467.863 5573 .802

Unconscious 4280.586 5573 .768

FastHeuristic 4236.206 5573 .760

Anticipation 4267.289 5573 .766

Total Rational 48823.111 5578

Intuition 51990.931 5578

Emotional 45835.369 5578

Unconscious 44516.813 5578

FastHeuristic 40902.723 5578

Anticipation 36657.715 5578

Corrected Total

Rational 5059.029 5577

Intuition 4510.186 5577

Emotional 4675.247 5577

Unconscious 4320.459 5577

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FastHeuristic 4289.067 5577

Anticipation 4524.437 5577

a. R Squared = .028 (Adjusted R Squared = .027)

b. R Squared = .024 (Adjusted R Squared = .023)

c. R Squared = .044 (Adjusted R Squared = .044)

d. R Squared = .009 (Adjusted R Squared = .009)

e. R Squared = .012 (Adjusted R Squared = .012)

f. R Squared = .057 (Adjusted R Squared = .056)

Estimated Marginal Means

Grand Mean

Dependent Variable Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

Rational 2.774 .017 2.741 2.807

Intuition 2.953 .016 2.922 2.984

Emotional 2.707 .016 2.676 2.739

Unconscious 2.639 .016 2.608 2.670

FastHeuristic 2.509 .016 2.479 2.540

Anticipation 2.322 .016 2.291 2.352

Post Hoc Tests by CONTINENTS

Multiple Comparisons

Dependent Variable (I) CONTINENTS (J) CONTINENTS

Mean Difference (I-

J) Std. Error Sig.

Rational Tukey HSD Africa Asia -.0352 .04728 .946

Europe .0628 .04720 .671

Latin America .4626

*

.05465 .000

North America .0085 .07060 1.000

Asia Africa .0352 .04728 .946

Europe .0981

*

.02946 .008

Latin America .4978

*

.04034 .000

North America .0437 .06020 .951

Europe Africa -.0628 .04720 .671

Asia -.0981

*

.02946 .008

Latin America .3998

*

.04025 .000

North America -.0544 .06014 .895

Latin America Africa -.4626

*

.05465 .000

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Asia -.4978

*

.04034 .000

Europe -.3998

*

.04025 .000

North America -.4541

*

.06615 .000

North America Africa -.0085 .07060 1.000

Asia -.0437 .06020 .951

Europe .0544 .06014 .895

Latin America .4541

*

.06615 .000

Intuition Tukey HSD Africa Asia .0408 .04473 .892

Europe .3282

*

.04466 .000

Latin America .0562 .05171 .814

North America .1472 .06680 .178

Asia Africa -.0408 .04473 .892

Europe .2874

*

.02787 .000

Latin America .0154 .03817 .994

North America .1064 .05696 .335

Europe Africa -.3282

*

.04466 .000

Asia -.2874

*

.02787 .000

Latin America -.2720

*

.03808 .000

North America -.1810

*

.05690 .013

Latin America Africa -.0562 .05171 .814

Asia -.0154 .03817 .994

Europe .2720

*

.03808 .000

North America .0910 .06259 .592

North America Africa -.1472 .06680 .178

Asia -.1064 .05696 .335

Europe .1810

*

.05690 .013

Latin America -.0910 .06259 .592

Emotional Tukey HSD Africa Asia -.1429

*

.04506 .013

Europe .2751

*

.04499 .000

Latin America -.0944 .05209 .367

North America .2559

*

.06728 .001

Asia Africa .1429

*

.04506 .013

Europe .4180

*

.02808 .000

Latin America .0485 .03844 .715

North America .3987

*

.05737 .000

Europe Africa -.2751

*

.04499 .000

Asia -.4180

*

.02808 .000

Latin America -.3695

*

.03836 .000

North America -.0193 .05732 .997

Latin America Africa .0944 .05209 .367

Asia -.0485 .03844 .715

Europe .3695

*

.03836 .000

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North America .3502

*

.06305 .000

North America Africa -.2559

*

.06728 .001

Asia -.3987

*

.05737 .000

Europe .0193 .05732 .997

Latin America -.3502

*

.06305 .000

Unconscious Tukey HSD Africa Asia -.1359

*

.04410 .018

Europe -.0563 .04403 .705

Latin America .1204 .05099 .126

North America .0387 .06586 .977

Asia Africa .1359

*

.04410 .018

Europe .0796

*

.02748 .031

Latin America .2562

*

.03763 .000

North America .1746

*

.05616 .016

Europe Africa .0563 .04403 .705

Asia -.0796

*

.02748 .031

Latin America .1766

*

.03755 .000

North America .0950 .05610 .438

Latin America Africa -.1204 .05099 .126

Asia -.2562

*

.03763 .000

Europe -.1766

*

.03755 .000

North America -.0817 .06171 .676

North America Africa -.0387 .06586 .977

Asia -.1746

*

.05616 .016

Europe -.0950 .05610 .438

Latin America .0817 .06171 .676

FastHeuristic Tukey HSD Africa Asia -.2188

*

.04387 .000

Europe -.0441 .04380 .853

Latin America -.0213 .05072 .993

North America .0766 .06552 .769

Asia Africa .2188

*

.04387 .000

Europe .1748

*

.02734 .000

Latin America .1975

*

.03743 .000

North America .2954

*

.05587 .000

Europe Africa .0441 .04380 .853

Asia -.1748

*

.02734 .000

Latin America .0228 .03735 .974

North America .1206 .05581 .195

Latin America Africa .0213 .05072 .993

Asia -.1975

*

.03743 .000

Europe -.0228 .03735 .974

North America .0979 .06139 .501

North America Africa -.0766 .06552 .769

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Asia -.2954

*

.05587 .000

Europe -.1206 .05581 .195

Latin America -.0979 .06139 .501

Anticipation Tukey HSD Africa Asia -.1865

*

.04404 .000

Europe .1748

*

.04396 .001

Latin America .3968

*

.05091 .000

North America .3407

*

.06576 .000

Asia Africa .1865

*

.04404 .000

Europe .3614

*

.02744 .000

Latin America .5833

*

.03757 .000

North America .5273

*

.05607 .000

Europe Africa -.1748

*

.04396 .001

Asia -.3614

*

.02744 .000

Latin America .2219

*

.03749 .000

North America .1659

*

.05601 .026

Latin America Africa -.3968

*

.05091 .000

Asia -.5833

*

.03757 .000

Europe -.2219

*

.03749 .000

North America -.0561 .06161 .893

North America Africa -.3407

*

.06576 .000

Asia -.5273

*

.05607 .000

Europe -.1659

*

.05601 .026

Latin America .0561 .06161 .893

Based on observed means.

The error term is Mean Square(Error) = .766.

*. The mean difference is significant at the .05 level.

Homogeneous Subsets

Rational

CONTINENTS N

Subset

1 2

Tukey HSD

a,b,c

Latin America 742 2.4108

Europe 2051 2.8105

North America 277 2.8649

Africa 491 2.8734

Asia 2017 2.9086

Sig. 1.000 .346

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Tukey B

a,b,c

Latin America 742 2.4108

Europe 2051 2.8105

North America 277 2.8649

Africa 491 2.8734

Asia 2017 2.9086

Means for groups in homogeneous subsets are displayed.

Based on observed means.

The error term is Mean Square(Error) = .883.

a. Uses Harmonic Mean Sample Size = 626.737.

b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I

error levels are not guaranteed.

c. Alpha = .05.

Intuition

CONTINENTS N

Subset

1 2 3

Tukey HSD

a,b,c

Europe 2051 2.7396

North America 277 2.9206

Latin America 742 3.0116 3.0116

Asia 2017 3.0270 3.0270

Africa 491 3.0678

Sig. 1.000 .212 .797

Tukey B

a,b,c

Europe 2051 2.7396

North America 277 2.9206

Latin America 742 3.0116 3.0116

Asia 2017 3.0270 3.0270

Africa 491 3.0678

Means for groups in homogeneous subsets are displayed.

Based on observed means. The error term is Mean Square(Error) = .790.

a. Uses Harmonic Mean Sample Size = 626.737.

b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels

are not guaranteed.

c. Alpha = .05.

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Emotional

CONTINENTS N

Subset

1 2 3

Tukey HSD

a,b,c

Europe 2051 2.4910

North America 277 2.5102

Africa 491 2.7661

Latin America 742 2.8605 2.8605

Asia 2017 2.9089

Sig. .996 .336 .874

Tukey B

a,b,c

Europe 2051 2.4910

North America 277 2.5102

Africa 491 2.7661

Latin America 742 2.8605 2.8605

Asia 2017 2.9089

Means for groups in homogeneous subsets are displayed. Based on observed means.

The error

term is Mean Square(Error) = .802.

a. Uses Harmonic Mean Sample Size = 626.737. b)

The group sizes are unequal. The harmonic mean

of the group sizes is used. Type I error levels are not guaranteed. C) Alpha = .05.

Unconscious

CONTINENTS N

Subset

1 2 3

Tukey HSD

a,b,c

Latin America 742 2.5122

North America 277 2.5939 2.5939

Africa 491 2.6326 2.6326

Europe 2051 2.6888 2.6888

Asia 2017 2.7684

Sig. .107 .308 .493

Tukey B

a,b,c

Latin America 742 2.5122

North America 277 2.5939 2.5939

Africa 491 2.6326 2.6326

Europe 2051 2.6888 2.6888

Asia 2017 2.7684

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Means for groups in homogeneous subsets are displayed.

Based on observed means.

The error term is Mean Square(Error) = .768.

a. Uses Harmonic Mean Sample Size = 626.737.

b. The group sizes are

unequal. The harmonic mean of the group sizes is used. Type I error levels

are not guaranteed.

c. Alpha = .05.

Fast Heuristic

CONTINENTS N

Subset

1 2

Tukey HSD

a,b,c

North America 277 2.3911

Africa 491 2.4677

Latin America 742 2.4890

Europe 2051 2.5117

Asia 2017 2.6865

Sig. .103 1.000

Tukey B

a,b,c

North America 277 2.3911

Africa 491 2.4677

Latin America 742 2.4890

Europe 2051 2.5117

Asia 2017 2.6865

Means for groups in homogeneous subsets are displayed. Based on observed means.

The error term is Mean Square(Error) = .760.

a. Uses Harmonic Mean Sample Size = 626.737.

b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I

error levels are not guaranteed.

c. Alpha = .05.

Anticipation

CONTINENTS N

Subset

1 2 3 4

Tukey HSD

a,b,c

Latin America 742 2.0699

North America 277 2.1260

Europe 2051 2.2919

Africa 491 2.4667

Asia 2017 2.6532

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Sig. .789 1.000 1.000 1.000

Tukey B

a,b,c

Latin America 742 2.0699

North America 277 2.1260

Europe 2051 2.2919

Africa 491 2.4667

Asia 2017 2.6532

Means for groups in homogeneous subsets are displayed.

Based on observed means.

The error term is Mean Square(Error) = .766.

a. Uses Harmonic Mean Sample Size = 626.737.

b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not

guaranteed.

c. Alpha = .05.

Conclusions

The study showed a clear difference in intuitive decision-making between Latin America and

other continents. The study also showed significant differences between the Latin American

countries Argentina, Brazil, Chile, and Paraguay.

The key results show that in Latin America compared to Africa, Asia, Europe, and Latin

America, the classical unconscious intuition and the emotional intuition is relatively high

compared to other continents. In contrast, the rational decision making, unconscious thinking,

and fast heuristic decision making is relatively low as well as the anticipation (pre-cognition).

The analysis by Latin American countries shows significant differences in different intuition

style. In Argentina, intuition style showed a very low rational decision level as well as very low

heuristically intuition and anticipation. Therefore, the classical intuition and emotional styles as

well as the unconscious thought level were very high. Brazil showed a higher intuition style in

general. The rational decision was relatively high. All other intuition styles were on a mid-level.

Chile showed the highest level of rational, heuristically and Unconscious thought decision style

in Latin America. In opposite, Chile shows the lowest classical unconscious intuition, emotional

intuition, and anticipation. This shows a clear trend towards more objective thinking styles. It

seems, Chile´s managers are very analytically business oriented, and their decision are more

based on extensive learnings and trainings. In Paraguay, the classical intuition is relatively

high as well as emotional oriented decisions, fast heuristics and the highest level of

anticipation. This shows a much more emotional and feeling oriented decision-making

orientation.

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The Emotion Wheel for measuring Mood as an intuitive Decision-Making Style

Shailja Vasudeva 1) and Markus A. Launer 2)

1) Shaheed Captain Vikram Batra Government Degree College, India

2) Ostfalia University of Applied Sciences and Institut für gemeinnützige Dienstleistungen

gGmbH (independent non-profit organization), Germany

Abstract

This is a concept paper on decisiion-making based on different kind of moods. It is based on

theory on mood and the mood wheel.

Introduction

Emotions became a central topic in research (Ashkanasy & Cooper, 2008). Research

companion to emotion in organizations. Cheltenham, UK: Edward Elgar. The Purpose of this

study is to deepen the knowledge about intuitive decision-making based on the mood wheel.

This is important for the study of Launer and Cetin (2023) on nine different types of intuition.

This is a non-systematic literature study. The result is, the mood can be considered as an

intuitive decision-making style. It could be shown, the mood is an important and independent

style of intuitive decision-making.

Moods can play a significant role in the decision making (Zulfiqar & Islam, 2017). Everyday

experience and lay intuition suggest that we see the world as a better place when we feel

happy rather than sad (Schwarz, 2001, 2012). Social decisions are heavily influenced by

emotion (van Kleef et al, 2010). Numerous experimental studies confirm this intuition. In fact,

finding a dime is sufficient to increase an individual's general life-satisfaction for a limited time

(Schwarz, 2002). Nothing is more familiar to people than their moods and emotions

(Rottenberg, 2005). It is also researched well that emotions influence economic behavior.

Although there has been increasing interest in the role of affect in work settings, the impact of

moods and emotions in strategic decision making remains largely unexplored (Ashton-James

& Ashkanasy, 2008)

Bolte et al. (2003) explored how emotional states affect the ability to make intuitive judgments

about the semantic coherence of word triads. In their study, participants were shown triads of

words that either had a weak association with a common fourth concept (coherent triads) or

lacked a common associate (incoherent triads). In Experiment 1, participants in a neutral mood

were able to distinguish between coherent and incoherent triads better than chance, even

without consciously retrieving the solution word. Experiment 2 revealed that a positive mood

enhanced participants' ability to make intuitive coherence judgments, while those in a negative

mood performed at chance level. The study concluded that a positive mood facilitates the

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spread of activation to weaker or more distant memory associates, thereby improving intuitive

judgments. Conversely, a negative mood restricts activation to more immediate and dominant

word meanings, impairing intuitive coherence judgments.

Coleman (2022) addresses two prevailing tendencies in regard to political situations: the

dismissal of mood as an indistinct affective state and the attempt to quantify mood through

empirical measurement scales. His argument was that mood is a distinct phenomenon,

characterized by a perceptual blurring between objectivity and subjectivity, and by diffuse

affective sources and cumulative sensations, rather than by discrete, temporally bound events.

When he refers to the mood of a social situation, he acknowledged this ambiguous intersection

between subjective interpretation and objective constraint. Moods, which resemble persistent

background feelings, shape not only our immediate experiences but also our potential for future

thought and action. In this way, moods influence political agency, primarily through intuitive

rather than conventional cognitive processes (Coleman, 2022).

The study by Paige et al. (2021) explored how mood and thinking style (rational vs. intuitive)

impact the quality and feasibility of design solutions. The hypothesis was that positive moods

enhance intuitive thinking, leading to higher-quality and more feasible designs. Positive affect

was expected to boost creativity and practicality in problem-solving, resulting in better design

outcomes compared to neutral or negative moods. The feasibility of design solutions was found

to have a positive correlation with an exhausted mood in the Rational Thinking condition, while

it was negatively correlated with composed and relaxed moods in the CI condition. These

results contribute to a deeper understanding of how mood influences design outcomes in both

intuitive and analytical problem-solving contexts, which could have implications for design

practice (Paige et al., 2021).

Economic decision-making models are fundamentally consequentialist, positing that

individuals select among various actions by evaluating the desirability and probability of their

outcomes and then integrating this information through an expectation-based calculus (Rick &

Loewenstein, 2008). The desirability of an outcome is termed "utility" by economists, and the

goal of decision making is to maximize this utility. However, this framework does not suggest

that decision makers are devoid of emotions or unaffected by them. To clarify this, it's important

to distinguish between "expected" and "immediate" emotions (Loewenstein, Weber, Hsee, &

Welch, 2001; Loewenstein & Lerner, 2003). Expected emotions are those anticipated to arise

from the outcomes of different possible actions. For instance, if Laura, a potential investor,

were deciding (Rick & Loewenstein, 2008).

Theory

Subjective rationality, or the feeling of meaning, was identified by William James (1893) as a

central aspect of the non-sensory fringe of consciousness (Hicks et al., 2010). The study

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explored the role of metacognitions—thoughts about one's own cognitive processes—in

problem-solving behavior, particularly in distinguishing between different types of problems (:

insight problems, noninsight problems, and algebra problems. The key findings were (Metcalfe,

Wiebe, 1987):

1. Feeling of Knowing: Confidence in solving problems accurately predicted performance for

algebra but not insight problems, suggesting algebra problems are more amenable to

metacognitive evaluation.

2. Performance Expectations: Participants often overestimated their problem-solving abilities,

particularly with insight problems, highlighting a gap between perceived and actual

performance.

3. Normative vs. Self-Predictions: General problem-solving norms provided more accurate

performance estimates than participants' self-predictions, especially for insight problems.

4. Warmth Ratings: "Warmth" ratings, indicating closeness to a solution, increased steadily for

algebra and non-insight problems but spiked suddenly for insight problems, reflecting an "aha"

moment. These findings support the idea that insight and noninsight problems are governed

by different cognitive processes. Insight problems, often marked by sudden illumination and

unpredictability, are less accessible to metacognitive monitoring and prediction compared to

the more incremental, predictable processes involved in solving non-insight problems. The

suddenness and unpredictability of the solution experience in insight problems are proposed

as defining characteristics of insight itself.

There is increasing agreement that human self-representations are primarily formed through

intuitive processing (Greenwald & Banaji, 1995; Kuhl, 1994, 2000). Autonoetic memories,

which involve feelings associated with self-relevant experiences ("remembering what"), can

become distinct from conceptual memories ("knowing that") (Wheeler, Stuss, & Tulving, 1997).

Implicit representations can be assessed through spontaneous expressions, whether or not

they relate to motivational or self-related content (Klinger, 1999; Schultheiss & Brunstein,

1999), or even if they do not pertain to the self (Schacter, 1987). Experimental research

indicates that implicit self-representations and explicit self-concepts are mediated by different

processing systems: For instance, information related to self-relevant experiences (e.g., words

associated with feeling smart) does not influence the processing of self-conceptual information

(e.g., words describing self-attributes like "I am a smart person") and vice versa (Klein & Loftus,

1993). Similarly, neuropsychological evidence shows dissociations between explicit and

implicit self-representations. For example, a patient could describe personality changes on an

abstract level (e.g., from extroverted to introverted) but lost episodic memory, which is essential

for self-related feelings (Kihlstrom & Klein, 1997).

It has been suggested that access to implicit self-representations is hindered under stress and

negative affect conditions (Kuhl, 1994, 2000). This impairment likely extends beyond implicit

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self-representations to other forms of intuitive processing. In a previous study, we

demonstrated that inducing negative affect experimentally impairs intuitive judgments, while

positive affect enhances them, even when these judgments are unrelated to the self (Bolte,

Goschke, & Kuhl, 2002).

Positive affect consistently impacts performance across a range of cognitive tasks. A new

neuropsychological theory suggests that these effects can be explained by the association

between positive affect and elevated brain dopamine levels. This theory predicts or explains

how positive affect influences various cognitive functions, including olfaction, the consolidation

of long-term (episodic) memories, working memory, and creative problem-solving (Ashby et

al., 1999).

Bower (1981) proposes an associative network theory to explain these effects. According to

this theory, an emotion acts as a memory unit that forms associations with events occurring

simultaneously. The activation of this emotion unit facilitates the retrieval of events linked to it

and primes emotional themes for use in free association, fantasies, and perceptual

categorization.

Loewenstein et al. (2001) proposed a theoretical perspective on risk and emotion known as

the "risk-as-feelings" hypothesis, which emphasizes the influence of emotions experienced at

the moment of decision-making. Drawing from research across clinical, physiological, and

other psychological subfields, they demonstrate that emotional responses to risky situations

frequently differ from cognitive evaluations of those risks. In cases where this divergence

occurs, it is often the emotional reactions that predominantly drive behavior. The risk-as-

feelings hypothesis offers an explanation for a variety of phenomena that have been difficult

to interpret using purely cognitive or consequentialist frameworks (Loewenstein et al., 2001).

Mood and intuitive versus rational decision-making

Deliberative decision-making is a methodical process characterized by cognition, governed by

rules, analytical thinking, precision, and a slower pace. Individuals who engage in deliberative

decision-making take time to thoroughly evaluate the pros and cons of different options,

carefully weighing their choices before reaching a conclusion. This approach stands in contrast

to intuitive decision-making, where decisions are guided by an inherent sense of correctness

or preference for one option over another, often without a clear explanation for the source of

this "gut feeling" or intuition. Intuitive decision-making relies on these feelings as the basis for

making choices (Kahneman, 2003; Lieberman, 2000; De Vries, Holland & Witteman, 2008).

The degree to which individuals engage in deliberative or intuitive information processing has

been found to vary depending on their affective states. According to dual-process models of

information processing, mood can significantly influence how people approach decision-

making tasks. Research indicates that individuals in a sad mood are more likely to engage in

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deliberative, systematic processing compared to those in a happy mood (Clore, Schwarz, &

Conway, 1994; Martin & Clore, 2001, De Vries, Holland & Witteman, 2008).

For example, Fiedler (1988, 2001) demonstrated that people in a sad mood produce fewer

inconsistencies when performing a multi-attribute decision task than those in a happy mood.

This suggests that sadness promotes a more careful and thorough evaluation of information.

Additionally, studies have shown that mood can affect how individuals respond to argument

strength. Specifically, when in a sad mood, individuals are more likely to engage in systematic

elaboration, making them more responsive to strong arguments while discounting weak ones.

Conversely, happy individuals are moderately persuaded by both strong and weak arguments,

indicating a less rigorous processing style (see Bless & Schwarz, 1999, De Vries, Holland &

Witteman, 2008).

There is evidence suggesting that individuals in a happy mood tend to rely more on intuitive

processing than those in a sad mood (e.g., Bolte, Goschke, & Kuhl, 2003; Isen & Means, 1983).

For instance, mood has been shown to influence the ability to make intuitive judgments. In a

study by Bolte et al. (2003), participants in a happy mood outperformed those in a sad mood

when intuitively judging whether word triads (e.g., “playing, credit, report”) had a common weak

associate (“card”) that they were not consciously aware of. This suggests that a happy mood

fosters a heuristic, intuitive mindset, which is likely to align well with intuitive decision-making

processes (De Vries, Holland & Witteman, 2008).

In contrast, a sad mood tends to induce a more deliberative mindset, where decisions are more

likely to be made after thoroughly analyzing the pros and cons. Therefore, while a happy mood

may enhance intuitive decision-making, it may not be as conducive to situations requiring

careful, deliberative analysis. Conversely, a sad mood, which promotes a more systematic and

analytical approach, might be better suited to deliberate decision-making tasks (De Vries,

Holland & Witteman, 2008).

The mood state of a decision maker doesn’t always align with the decision-making strategy

they employ, which can seem counterintuitive at first glance. For instance, one might expect

that individuals in a happy mood would naturally rely on their initial feelings, while those in a

sad mood would engage in thorough deliberation. However, the reality is more complex.

Situational demands often necessitate the use of a decision-making strategy that doesn’t align

with the current mood state (De Vries, Holland & Witteman, 2008).

For example, a decision maker in a sad mood might not have sufficient time to thoroughly

analyze the pros and cons of various options, forcing them to make a quick decision based on

intuition. Conversely, a happy decision maker might need to engage in careful deliberation,

particularly if they are required to justify their decision to others. This mismatch between mood

and decision strategy can have significant implications for how the decision outcome is

perceived (De Vries, Holland & Witteman, 2008).

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Recent studies suggest that when there is a fit between mood and decision strategy,

individuals may experience greater satisfaction with their decisions. Conversely, a non-fit—

such as having to deliberate when in a happy mood or make an intuitive decision when in a

sad mood—might detract from the subjective value of the decision outcome. This dynamic

underscores the importance of situational factors in shaping decision-making processes and

outcomes (e.g., Avnet & Higgins, 2003; Higgins et al., 2003; De Vries, Holland & Witteman,

2008).

Recent studies have demonstrated that the "fit effect" can also emerge from a match between

situationally induced information-processing styles and the decision strategies that individuals

are instructed to use. For instance, Avnet & Higgins (2003) and Förster & Higgins (2005) found

that when a person’s information-processing style aligns with their decision-making strategy,

it can enhance the subjective value of their decision (De Vries, Holland & Witteman, 2008).

One example of this is the distinction between global and local processing. Global processing,

which involves focusing on the whole rather than the parts, aligns well with a decision-making

strategy that emphasizes gains. Conversely, local processing, which involves focusing on

specific details or parts, fits better with a decision-making strategy that focuses on avoiding

losses. When there is a fit between the information-processing style and the choice strategy—

such as global processing paired with a gain-focused strategy or local processing paired with

a loss-focused strategy—the chosen object tends to have a higher subjective value. In

contrast, a mismatch between these elements results in a lower subjective value of the

decision outcome (Förster & Higgins, 2005; De Vries, Holland & Witteman, 2008).

Several studies have demonstrated the effects of a "fit" between decision-making strategies

and individual preferences. However, the influence of mood on fit effects has not been

thoroughly explored, especially in the context of intuitive versus deliberative decision

strategies. This gap is surprising, given the significant role that the distinction between

decisions based on feelings (intuition) and those based on analytical thought plays in

psychology and decision-making research (e.g., Dijksterhuis, 2004; Wilson, 2002; De Vries,

Holland & Witteman, 2008).

Recently, Betsch and Kunz (2007) provided evidence for a fit effect between intuitive versus

deliberative decision strategies and individuals' dispositional preferences for these decision-

making styles. They found that intuitive decision-makers, who generally prefer to rely on their

feelings, and deliberative decision-makers, who prefer to thoroughly analyze pros and cons,

experience different outcomes based on whether their preferred decision mode aligns with the

decision strategy they are instructed to use (De Vries, Holland & Witteman, 2008). In their

study, individuals were categorized according to their preferences for intuitive or deliberative

decision-making. The results showed that when the decision mode that participants naturally

preferred (either intuitive or deliberative) matched the instructed decision strategy, the

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perceived value of the chosen object was higher. Conversely, when there was a mismatch

between preferred and instructed decision strategies, the perceived value was lower (see also

Avnet & Higgins, 2006). This finding underscores the importance of alignment between

decision-making strategies and individual preferences in influencing the subjective value of

decision outcomes (De Vries, Holland & Witteman, 2008).

Methodology to measure Mood

How to capture individual and social mood states is typically attempted through methods that

claim to represent affective dynamics via empirical measurement scales and normative

assessments (Coleman, 2022). For instance, various psychological tools designed to track,

measure, and even modify real-time mood fluctuations have become increasingly common in

the early twenty-first century. Commenting on the widespread use of these personalized

interfaces, William Davies (2017) notes that they transform users into subjects of assessment

and judgment, thereby blurring the distinction between representing and regulating their

experiences.

Another widely embraced contemporary approach to capturing mood is sentiment analysis,

which essentially serves as a macro-level version of individual mood tracking. The goal here

is metaphorically framed as identifying a "social pulse," quantified through the constant flow of

data on social media. By codifying the emotional tone of online expressions in relation to

specific issues or contexts, sentiment analysts claim to produce representative insights into

the overall public mood. However, these claims are subject to familiar criticisms: the sentiment

samples analyzed are often unrepresentative of broader populations (Jensen & Anstead, 2013;

Mellon & Prosser, 2017), and the interpretive codes used to classify the emotional meanings

of the often brief and contextually ambiguous online messages lack nuance and cultural

sensitivity. Furthermore, they rely heavily on the semantic positivism inherent in natural

language processing (Coleman et al., 2018).

Isen and Means (1983) explored how a positive emotional state impacts decision-making when

selecting an option from a set of alternatives. The analysis of decision-making strategies

revealed that the positive-affect group was more likely to employ "elimination by aspects," a

method where options are ruled out based on failing to meet a critical criterion. The results

suggest that positive emotions enhance decision-making efficiency by streamlining the

process (Isen and Means, 1983).

Huntsinger (2011) researched the prediction that affect and trust in intuition would interactively

shape implicit and explicit attitude correspondence to empirical assessment. In the study,

affect played a crucial role in shaping individuals' reliance on their intuitions, influencing the

correspondence between implicit and explicit attitudes. Positive affect served as a validation

cue, encouraging individuals to trust their intuitive judgments, while negative affect acted as

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an invalidation cue, leading individuals to question or distrust their intuitions. This dynamic, in

turn, influenced the degree to which implicit attitudes—those automatic, unconscious

evaluations—were reflected in explicit attitude reports, which are more deliberate and

conscious expressions of beliefs and feelings (Huntsinger, 2011). By integrating research on

affect as information with studies on the translation of implicit attitudes into explicit reports,

these experiments shed light on the often enigmatic processes that determine when and how

implicit attitudes become consciously recognized and articulated. The findings suggest that the

mood state of an individual can significantly impact whether they rely on their intuitive, gut-

level responses or engage in more deliberate, analytical processing when forming explicit

attitudes. Thus, positive and negative affect can serve as critical moderators in the alignment

or divergence between what people feel implicitly and what they consciously express explicitly

(Huntsinger, 2011).

Kuhl & Beckmann (1994) had a research program aiming at an analysis of the self and its

regulatory functions. According to personality systems interaction theory, a negative mood is

anticipated to limit access to extended semantic networks and negatively impact performance

on intuitive coherence judgments, particularly for individuals who have difficulty down-

regulating negative affect, i.e., state-oriented participants (Baumann & Kuhl, 2002). Their

findings on intuition align with research on self-infiltration, which suggests that state-oriented

participants are more likely to be influenced by social expectations and may misinterpret

external assignments as self-selected intentions when they are feeling sad (Kazén, Baumann,

& Kuhl, 2001).

Mood Wheel

A study by The Greater Good Science Center suggests there are 27 distinct emotions (Cowen

& Keltner, 2017; Keltner & Lernen, 2010; Lerner & Keltner, 2000; Keltner et al., 2014)).

Through years of studying emotions, American psychologist Robert Plutchik proposed that

there are eight primary emotions that serve as the foundation for all others: joy, sadness,

acceptance, disgust, fear, anger, surprise, and anticipation (Racine et al., 2016; Miao et al.,

2018, 2019).

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Watson and Tellegen (1985) suggest a consensual mood structure as a two-factor model of

affect (Tellegen et al, 1999).

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The somatic marker hypothesis offers a comprehensive neuroanatomical and cognitive

framework for understanding decision-making and its interplay with emotion. Central to this

hypothesis is the idea that decision-making is guided by "marker signals" that emerge from

bioregulatory processes, including those manifested as emotions and feelings. These marker

signals can influence decision-making at various levels, with some effects being conscious and

others operating non-consciously (Bechara, Damasio, & Damasio, 2000).

Ashton-James and Ashkanasy (2008) present a model of strategic decision-making that

emphasizes the influence of affective states on the cognitive processes crucial to decision

outcomes. This model is grounded in Affective Events Theory, which suggests that

environmental demands create "affective events" that trigger emotional responses in

organizational members. These emotional reactions subsequently shape the attitudes and

behaviors of those members, ultimately impacting the decision-making process.

Measuring the effect of mood on intuitive decisions by Launer & Cetin

In the study by Launer and Cetin (2023), the mood was integrated into a study on developing

a measurement instrument for rational and intuitive decision-making. As a result of their

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statistical analysis, the following three questions were used to measure the effct of mood on

intuitive, affective or emotional intuitive decision-making.

Mood (number of question)

1. When I have to take decisions, I feel afraid and/or curiosity in me

2. When I have to make decisions, I feel anger and/or serenity inside me.

3. When I have to decide I feel anger and/or relief in me

Conclusion

It could be shown that the mood is an important influence and guiding principle for intuitive

decision-making, as stated in Launer and Cetin (2003). The emotion wheel is a good basis to

explain intuitive decision-making based on the mood of a person. However. Launer and cetin

(2023) removed the mood dimension from their RIDMS approach due to a lack of theoretical

basis.

Keywords: Mood, mood wheel, intuition, decision-making

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Emotional Intelligence and rational and intuitive Decision-Making

Joefrrey Maddatu Calimag 1) and Markus A. Launer 2)

1) Kyungsun University, Korea

2) Ostfalia University and Institut für gemeinnützige Dienstleistungen gGmbH (independent

non-profit organization)

Abstract

This is a concept paper on emotional intelligence and rational and intuitive decision-making. It

describes the theory.

Introduction

As early as the 1920s, Thorndike (1920) explored the limitations of IQ's predictive power and

introduced the concept of "social intelligences" to explain aspects of success that IQ could not

account for. However, it wasn't until the early 1980s that Gardner (1993) renewed interest in

other factors beyond IQ that might influence individual success (Dulewicz, Higgs & Slaski

(2003).

In 1990, Peter Salovey and John Mayer published a seminal article that laid the groundwork

for academic research on Emotional Intelligence (EI). In this influential work, they integrated

the previously separate fields of emotion and intelligence into a cohesive theory, which has

since sparked extensive theoretical and empirical research. They defined EI as "the ability to

monitor one's own and others' feelings and emotions, to discriminate among them, and to use

this information to guide one's thinking and actions" (Brackett &Geher, 2013).

In this paper, the authors show the connection of EI and intuitive decision-making.

Theory

Conceptualizations of Emotional Intelligence

Over the past three decades, various conceptualizations of emotional intelligence have

emerged, primarily summarized into three models: ability, trait, and mixed (Bru-Luna et al.,

2021). These models have shaped the development of instruments used to measure EI. In the

ability model, developed by Mayer and Salovey, EI is regarded as a form of innate intelligence

comprising several capacities that affect how individuals perceive, understand, and manage

their own emotions and the emotions of others. These emotion-processing skills include

according to Bru-Luna et al. (2021; Mayer, Caruso, Salovey, 2000; Mayer & Salovey, 1997)

(1) perception, evaluation and expression of emotions,

(2) emotional facilitation of thought,

(3) understanding and analysis of emotions, and

(4) reflective regulation of emotions

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According to Bru-Luna et al. (2021), trait-based instruments typically consist of self-reported

measures and are designed as scales where there are no right or wrong answers. Instead,

individuals respond by selecting items that best reflect their behaviors (e.g., "Understanding

the needs and desires of others is not a problem for me"). These instruments are effective at

measuring typical behavior and are therefore good predictors of actual behaviors across

various situations. Trait EI is particularly useful for predicting effective coping strategies when

dealing with everyday stressors in both adults and children. As a result, these instruments are

commonly employed in contexts characterized by stressors, such as educational and

workplace environments (Bru-Luna et al., 2021; O’Connor, Hill, Kaya, Martin, 2019).

In this sense, trait emotional intelligence (trait EI) is defined as a constellation of emotion-

related self-perceptions that describes how an individual assesses their own emotional and

social effectiveness (Petrides, Pita, et al., 2007).

Questionnaires based on the mixed conceptualization of EI often assess a combination of

traits, social skills, competencies, and personality measures using a self-reported format

(O’Connor, Hill, Kaya, Martin, 2019). Some of these measures also employ 360-degree

assessments, which include self-reports alongside evaluations from supervisors, colleagues,

and subordinates. These instruments are generally used in workplace settings, as they are

designed to predict and enhance job performance by focusing on emotional competencies

linked to professional success. Despite the different approaches to conceptualizing EI, many

instruments share common features: they are hierarchical, providing both a total EI score and

scores for different dimensions, and they often overlap conceptually, including elements such

as emotional perception, emotional regulation, and the adaptive use of emotions (O’Connor,

Hill, Kaya, Martin, 2019.

Mayer et al (2024) describe the ability to model of emotional intelligence outlines four

interrelated abilities:

 perceiving emotions,

 using emotions to facilitate thought,

 understanding emotions, and

 managing emotions.

Several performance-based assessments have been created to evaluate these four abilities.

While some researchers have found empirical support for the existence of all four distinct

abilities, others have suggested that emotional intelligence may be better represented as three

abilities, two abilities, or even a single, unified ability (Legree et al., 2014; Palmer, Gignac,

Manocha, & Stough, 2005).

Therefore, Emotional intelligence can be defined as the “the ability to reason about emotions,

and of emotions to enhance thinking” (Mayer, Salovey, & Caruso, 2004, p. 197). Empirical

evidence suggests that when emotional intelligence is defined and measured as a set of

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abilities, it qualifies as a broad intelligence similar to verbal, spatial, and other types of

intelligences. It systematically correlates with these intelligences while maintaining partial

independence from them (Bryan & Mayer, 2021; MacCann, Joseph, Newman, & Roberts,

2014; Schlegel et al., 2019).

Studies on Emotional Intelligence and Decision-Making Styles in General

The book of Buontempo, G. (2005) describes the impact on judgment biases in the relationship

of emotional intelligence and decision making. Hess and Bacigalupo (2011). Study how the

behaviors associated with emotional intelligence may be practically applied to enhance both

individual and group decision‐making. Rehman and Waheed (2012) elaborate on trelationship

among transformational leadership style and decision-making styles. It also determines the

moderating role of emotional intelligence in predicting this relationship.

Di Fabio and Kenny (2012) describe the contribution of emotional intelligence to decisional

styles among Italian high school students. Di Fabio, A. (2012) further researches emotional

intelligence. He writes about a new variable in career decision-making. Rehman et al. (2012)

determine the impact of employee decision making styles on organizational performance.

Study also investigates the moderating role of emotional intelligence on the relationship among

decision making styles and organizational performance.

Avsec, A. (2012) researches if emotionally intelligent individuals use more adaptive decision-

making styles. Mayer (2013) describes a new field guide to emotional intelligence - emotional

intelligence in everyday life. Dua, Y. S. (2015) research emotional intelligence of entrepreneurs

and their decision-making style in regard to the role of vision. Hersing, W. S. (2017) describe

the managing cognitive bias in safety decision making in regard to an application of emotional

intelligence competencies. Baba and Siddiqi (2017) describe Emotional intelligence and

decision making effectiveness in an empirical study of institutions of higher learning. Zaki et al.

(2018) assess the effect of emotional intelligence program on decision making style for head

nurses.

Hutchinson et al. (2018) describe the use of emotional intelligence capabilities in clinical

reasoning and decision‐making in a qualitative, exploratory study. Grubb, Brown & Hall (2018)

describe emotionally intelligent officer. They explore decision-making style and emotional

intelligence in hostage and crisis negotiators and non-negotiator-trained police officers.

Vyatkin, Fomina and Shmeleva (2019) research the empathy, emotional intelligence and

decision-making among managers of agro-industrial complex. They explore the role of

tolerance for uncertainty in decision-making.

Dilaware (2019) examine whether, how, and when trait emotional intelligence (EI) influences

the relationships between operational stress and decision-making styles for personnel working

in highly stressful professions. El Othmann et al. (2020) seek to evaluate the influence of

personality traits on emotional intelligence (EI) and decision-making among medical students

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at Lebanese universities. The study also aims to examine the potential mediating role of

emotional intelligence between personality traits and decision-making styles within this group.

Ibrahim and Elsabahy (2020) link emotional intelligence and locus of control to decision making

styles of nursing managers.

Ramchandran, Tranel, Duster and Denburg (2020) research the role of emotional vs. cognitive

intelligence in economic decision-making amongst older adults. Ndawo, G. (2021).

Researches in a qualitative study the facilitation of emotional intelligence for the purpose of

decision-making and problem-solving among nursing students in an authentic learning

environment. Babu (2024) elaborates on the impact of Emotional Intelligence on the Decision-

Making Styles of Academic Leaders

Studies in Emotional Intelligence and Intuition

Today's managers are increasingly expected to make decisions using paradigms that diverge

from traditional models of rationality and information processing (Sayegh, Anthony and

Perrewé, 2004). This is especially true during crisis situations, where time is limited and there

is little information available to consider all options. While recent management literature has

provided growing empirical and theoretical support for the use of intuition and tacit knowledge

in decision-making, the role of emotions has not been prominently featured. The authors seek

to advance management decision theory by introducing a conceptual model that emphasizes

the importance of emotions in intuitive decision-making under crisis conditions (Sayegh,

Anthony and Perrewé, 2004).

Oblak and Lipušček (2003) describe the intuitive decision-making in a model of integral

decision-making scheme in twelve wood industry enterprises in Slovenia. They present a

model of integral decision-making scheme combining including intuitive decision-making. As a

result, they show that the process of decision-making in Slovene woodworking companies is

fairly conservative and that with additional training of the existing management the decision-

making processes could be substantially improved.

Pinizzotto, Davis and Miller (2004) describe Intuitive policing: Emotional and rational decision

making in law enforcement from a practical experience. Sayegh, Anthony and Perrewé (2004)

describe the managerial decision-making under crisis in regard to the role of emotion in an

intuitive decision process.

Downey, Papageorgiou and Stough (2006) examine the relationship between leadership,

emotional intelligence and intuition in senior female managers. Several authors have also

asserted that the effective use of emotional intelligence allows individuals to heighten intuition,

gain insight into complex challenges, and motivate themselves to act (Maier, 1999; Sosik and

Megerian, 1999; Reed‐Woodard and Clarke, 2000). The growing interest in research on the

role of emotions, particularly emotional intelligence (EI), and its connection to workplace

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success is driven by the idea that EI may influence various aspects of job performance that

are not explained by IQ or personality traits (Downey, Papageorgiou & Stough, 2006). The

idea that moods and emotions significantly influence cognitive processes and behavior,

particularly in workplace decision-making (George, 2000), suggests that effectively using these

otions intuitively may propel individuals to achieve top performance within their organizations

(Goleman, 1998a, 1998b; Reed‐Woodard and Clarke, 2000; Downey, Papageorgiou & Stough,

2006).

The connection between women and emotionally intelligence is often researched. Women are

often perceived as being more intuitive and empathetic compared to men, who are typically

viewed as analytical and logical problem solvers in the workplace (Brenner and Bromer, 1981;

Loden, 1985). Despite this perception, women are frequently underrepresented in

management roles, where an intuitive decision-making style is considered to be particularly

effective (Agor, 1989; (Downey, Papageorgiou & Stough, 2006). Látalová, V., & Pilárik, L.

(2015) predict career decision-making strategies in women. They explore the role of self-

determination and perceived emotional intelligence.

Burciu and Hapenciuc (2010) describe the non-rational thinking in the decision-making

process. Moghadam et al. (2011) deal with clarifying the relationship between Emotional

Intelligence (EI) and decision-making styles (rational, intuitive, dependant, spontaneous and

avoidant) of managers in Iranian oil industry. Chaffey, Unsworth and Fossey (2012) descrtibe

the relationship between intuition and emotional intelligence in occupational therapists in

mental health practice. They see Intuition appears to be influenced by awareness and

understanding of emotions. Huang (2012) researches in her doctoral thesis Intuition and

emotion. She examined two non-rational approaches in complex decision making. Campbell

(2000) exploring in his doctoral thesis the relationship between emotional intelligence, intuition,

and responsible risk-taking in organizations. Tabesh and Zare (2013) study the effect of

training emotional intelligence skills on rational, intuitive, avoidant, dependent and

spontaneous decision-making styles. Erenda, Meško and Bukovec (2013) research the

correlation between emotional intelligence and intuitive decision-making styles among top and

middle level managers in Slovenian automotive industry.

Julmi (2019) viewed general intuition research as a holistic form of information processing that

differs from analytical methods and can sometimes be more effective. To address the

inconsistencies in intuition research, he critically evaluates prevailing views on the

effectiveness of intuition and offers a re-conceptualization based on recent findings in the field.

He suggests that the structuredness of a decision problem is the key criterion for determining

when intuition is most effective and proposes using organizational information processing

theory to conceptually establish this connection. According to Julmi, it is not the uncertainty of

decision problems but their equivocality that necessitates an intuitive approach (Julmi, 2019).

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Jokić and Purić (2019). relate rational and experiential thinking styles with trait emotional

intelligence in broader personality space. Baillie, Toleman and Lukose (2000, January)

describe emotional intelligence for intuitive agents. This analysis shows that intuitive decision-

making and EI is strongly connected.

Measuring Emotional Intelligence

Emotional intelligence measures tend to use either a self-report personality-based approach,

an informant approach, or an ability-based assessment procedure. In his paper, the

measurement and psychometric properties of four of the major emotional intelligence

measures were discussed (Conte (2005). He reviewed and examined the comparability of

these measures.

 Emotional Competence Inventory,

 Emotional Quotient Inventory

 Multifactor Emotional Intelligence Scale

 Mayer–Salovey–Caruso Emotional Intelligence Test)

Bru-Luna (2021) conducted a systematic review of existing instruments used to assess

emotional intelligence (EI) in professionals, focusing on their characteristics and psychometric

properties, such as reliability and validity. A literature search was performed using the Web of

Science (WoS) database, resulting in 2,761 items that met the eligibility criteria. From these,

40 different instruments were identified and analyzed. Most of these instruments are based on

three primary models—skill-based, trait-based, and mixed—which differ in how they

conceptualize and measure EI. Each type of tool has its own inherent advantages and

disadvantages. The instruments most frequently reported in the studies include the

 Emotional Quotient Inventory (EQ-i),

 Schutte Self Report Inventory (SSRI),

 Mayer-Salovey-Caruso Emotional Intelligence Test 2.0 (MSCEIT 2.0),

 Trait Meta-Mood Scale (TMMS),

 Wong and Law’s Emotional Intelligence Scale (WLEIS), and

 Trait Emotional Intelligence Questionnaire (TEIQue).

Williams et al. (2009) examined the relationships between trait EI, objective measures of

emotional ability, and psychopathology, and the factor structure of five measures of emotional

skills. According to them, two distinct methods for measuring Emotional Intelligence (EI) are

now well established. The first method, self-report measures, involves questionnaires where

respondents indicate their level of agreement or disagreement with various statements (e.g.,

the Trait Emotional Intelligence Questionnaire – Adolescent Short Form [TEIQue – ASF]

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(Petrides et al., 2006). These measures can be susceptible to socially desirable responding,

as individuals may provide answers that present them in a favorable light (Conte, 2005).

The second method, ability-based EI, is assessed by having individuals complete tasks that

require elements of EI to perform well (e.g., the MSCEIT, Mayer, Salovey, & Caruso, 2000).

These tasks may involve identifying emotions from facial expressions or evaluating the

effectiveness of different strategies for managing emotions. However, the scoring methods for

some of these tasks have been criticized for lacking full objectivity (Conte, 2005). Additionally,

it has been argued that ability-based EI measures assess emotion-related knowledge rather

than actual performance and that there is limited evidence supporting the notion that ability EI

is a latent trait that can be reliably measured psychometrically (Brody, 2004; Williams et al.

(2009)).

Dulewicz and Higgs (2000b) found that, in a sample of general managers, an Emotional

Intelligence scale based on 16 relevant competencies demonstrated strong reliability and

predictive validity over a seven-year period. Building on these findings and a comprehensive

literature review, they aimed to move beyond competency assessment. By developing the

questionnaire, Cronbach alpha reliability co‐efficiency for each of the element scales ranged

from 0.6 to 0.8. The alpha for the overall EIQ score derived from the seven elements was 0.77

(Dulewicz & Higgs, 2000a).

They developed a tailored questionnaire, the Emotional Intelligence Questionnaire (EIQ),

designed to specifically assess seven elements of an individual's emotional intelligence

through self-report (Dulewicz and Higgs, 1999; 2000a; Dulewicz, Higgs & Slaski, 2003).

1. self‐awareness: being aware of one's feelings and managing them;

2. emotional resilience: being able to maintain one's performance when under

pressure;

3. motivation: having the drive and energy to attain challenging goals or targets;

4. inter‐personal sensitivity: showing sensitivity and empathy towards others;

5. influence: influencing and persuading others to accept one's views or proposals;

6. intuitiveness: making decisions using reason and intuition when appropriate; and

7. conscientiousness: being consistent in one's words and actions, and behaving

according to prevailing ethical standards.

The structure of the second tool, the EQ-i (Emotional Quotient Inventory), is grounded in the

existing literature and its author's experience as a clinical psychologist (Bar-On, 1997a). The

concept was developed by logically grouping variables and identifying key underlying factors

believed to influence effective and successful functioning and promote positive emotional

health (Bar-On, 1997b). The EQ-i provides a total EQ score, five composite scale scores, and

15 sub-scale scores, as defined by Bar-On (1997a). This structure, outlined in Table I, was

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empirically supported through several factor analyses, confirming the 1-5-15 framework of the

EQ-i. Thus, the EQ-i represents a hierarchical model of emotional intelligence (Bar-On, 1997a).

Accordeing to Zadorozhny et al. (2024) the Trait Emotional Intelligence Questionnaire

(TEIQue) is the only inventory that directly and comprehensively operationalizes the trait EI

theory (Austin et al., 2008; Petrides et al., 2016).

Finegan (1989) discussed emotional intelligence as a subset of social intelligence and

personal intelligences (Gardner, 1983), describing it as a mental ability that facilitates the

cognitive processing of emotions (Mayer and Salovey, 1993). Three studies are highlighted to

illustrate the concept of emotional intelligence and its measurement: (1) a study by Mayer,

DiPaolo and Salovey (1990) involving 139 undergraduates, which examined the ability to

recognize emotional content in visual stimuli; (2) a study by Salovey et al. (1995) with 86

participants, which focused on measuring individual differences in the ability to attend to,

clarify, and manage emotions; and (3) a study by Mayer and Geher (1996) with 40 participants,

which investigated the accurate identification of emotions. The implications of emotional

intelligence for achievement, emotional well-being, and cultural contexts are also discussed.

Emotional Intelligence was assessed by Downey, Papageorgiou and Stough (2006) byusing

the Swinburne University Emotional Intelligence Test (SUEIT; Palmer and Stough, 2001) for

the workplace. The workplace SUEIT is a 64-item self-report test designed to measure how an

individual typically thinks, feels, and behaves at work based on emotional information.

Participants rate each statement on a 5-point Likert-type scale (1 = never, 5 = always),

indicating the degree to which each statement reflects their usual thoughts, feelings, and

actions in a work setting. A second measure of Emotional Intelligence (EI) used by Downey,

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Papageorgiou and Stough (2006) n the study was the Trait Meta-Mood Scale (TMMS; Salovey

et al., 1995). The TMMS is a 30-item self-report instrument where participants respond on a 5-

point scale (1 = strongly disagree, 5 = strongly agree). The items are divided into three

subscales, based on factor analyses conducted by Salovey and his colleagues: attention to

feelings (e.g., "I pay a lot of attention to how I feel"), clarity of feelings (e.g., "I am usually very

clear about my feelings"), and mood repair (e.g., "Although I am sometimes sad, I have a

mostly optimistic outlook") (Downey, Papageorgiou & Stough, 2006). Preliminary psychometric

analysis of the TMMS by Salovey et al. (1995) indicates that this scale can serve as a reliable

and valid self-report measure of the ability to monitor and regulate emotions. Their findings

show that each subscale of the TMMS captures a coherent and internally consistent construct,

with reliability coefficients for attention to feelings for clarity of feelings, and for mood repair.

Additionally, the scale demonstrates evidence of both convergent and discriminant validity.

The results of Downey, Papageorgiou and Stough, (2006) were:

Tapia, M. (2001) measures emotional intelligence. Tapia (2001) (a) developed a measure of

emotional intelligence called the Emotional Intelligence Inventory and (b) identified the

underlying dimensions of this inventory by testing 111 high school students at a bilingual

college preparatory school. The original inventory consisted of 45 items, but after removing the

four weakest items, the reliability coefficient was α = 0.83. The revised 41-item inventory was

then administered to 319 junior and senior high school students at the same school, resulting

in a reliability coefficient of α = 0.81. A maximum likelihood factor analysis with varimax rotation

identified four factors: empathy, utilization of feelings, handling relationships, and self-control.

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The psychometric properties of the revised Emotional Intelligence Inventory were robust,

making it a suitable tool for investigating emotional intelligence (Tapia, 2001).

Caruso, Mayer and Salovey (2002) research the relation of an ability measure of emotional

intelligence to personality. Is emotional intelligence merely a simplistic theory of personality, or

does it represent a distinct form of intelligence? For emotional intelligence to be meaningful, it

must capture something unique that is not encompassed by standard personality traits. To

investigate this, the study examined an ability-based test of emotional intelligence and its

relationship to various personality test variables to assess the degree of overlap between these

constructs (Caruso, Mayer & Salovey, 2002). Dulewicz, Higgs and Slaski (2003) measure the

content, construct and criterion‐related validity of emotional intelligence. They summarize

existing information on the reliability and validity of two measures of EI, the Dulewicz and Higgs

EIQ and the Bar‐on EQ‐i.

Salovey, Mayer, Caruso and Lopes (2003) measure emotional intelligence as a set of abilities

with the Mayer-Salovey-Caruso Emotional Intelligence Test. Mayer,Salovey, Caruso and

Sitarenios (2003) measure emotional intelligence with the MSCEIT V2.0. Dulewicz, V., &

Higgs, M. (2000) researched emotional intelligence with a review and evaluation study. Schutte

et al. (1998) developmed and validated a measure of emotional intelligence. Personality and

individual differences. Schutte et al. (1998) developed a measure of emotional intelligence

based on the model proposed by Salovey and Mayer (1990) in their work Emotional

Intelligence. The initial pool of 62 items was designed to reflect the different dimensions of this

model. A factor analysis conducted on responses from 346 participants led to the creation of

a 33-item scale. Further studies demonstrated that the 33-item measure had strong internal

consistency and test-retest reliability. Validation studies revealed that scores on this measure

(a) correlated with eight out of nine theoretically related constructs, such as alexithymia,

attention to feelings, clarity of feelings, mood repair, optimism, and impulse control; (b)

predicted first-year college grades; (c) were significantly higher for therapists compared to

therapy clients or prisoners; (d) were significantly higher for females than males, aligning with

previous research on emotional skills; (e) showed no relation to cognitive ability; and (f) were

associated with the openness to experience trait within the Big Five personality dimensions.

Groves, Pat McEnrue and Shen (2008). primary measure is based on the Mayer and Salovey

(1997) model “Mayer-Salovey-Caruso Emotional Intelligence Test”, short MSCEIT9 (Mayer et

al., 2003). Existing research indicates that the MSCEIT has robust psychometric properties,

including strong construct, convergent, discriminant, and predictive validities, particularly when

compared to other emotional intelligence measures (Daus and Ashkanasy, 2005; McEnrue

and Groves, 2006; Day and Carroll, 2004; O'Conner and Little, 2003; Brackett and Mayer,

2003). Groves, Pat McEnrue and Shen (2008) specifically examined the measure for its utility

for management development applications.

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Mayer, J. D., Salovey, P., Caruso, D. R., & Sitarenios, G. (2001). Emotional intelligence as a

standard intelligence. Pérez, Petrides and Furnham (2005). Measure trait emotional

intelligence. Emotional intelligence. Groves, Pat McEnrue and Shen (2008) develope and

measures emotional intelligence of leaders. They empirically test whether it is possible to

deliberately develop emotional intelligence as conceptualized in the Mayer and Salovey model.

Roberts, Schulze and MacCann (2008) describe the status of measurements of emotional

intelligence in 2008.

The latest studies in 2024

Saikia, George, Unnikrishnan, Nayak and Ravishankar (2024) describe thirty years of

emotional intelligence. They provide a scoping review of emotional intelligence training

programme among nurses. They analyze the stressful environment of healthcare setting that

can be detrimental to nurses' mental and emotional health.

Zadorozhny et al (2024) determined the temporal stability of a construct is essential for

confirming its validity and usefulness in real-world settings. To date, there have been few

studies examining the test-retest reliability of trait emotional intelligence (trait EI), especially

over longer durations. The present study provides data from the Trait Emotional Intelligence

Questionnaire (TEIQue) over various intervals, ranging from 30 days (one month) to 1,444

days (approximately four years). The findings support trait EI theory, showing strong temporal

stability across all levels of the construct, including global, factor, and facet levels.

According to Zadorozhny et al (2024), there are currently few measures of Emotional

Intelligence (EI) that have demonstrated test-retest reliability, internal consistency, and validity

in terms of a robust factor structure and predictive ability (Davis & Wigelsworth, 2018). To date,

there have been limited studies on the test-retest reliability of the Trait Emotional Intelligence

Questionnaire (TEIQue) (Perazzo et al., 2021), and most of these studies have not adequately

assessed its temporal stability due to their use of short test-retest intervals (Costa & McCrae,

1998; Wood et al., 2022). It has been suggested that intervals of less than a year are

considered short-term for evaluating the stability of personality traits (Murray et al., 2003;

Schuerger et al., 1989). As a result, using shorter test-retest intervals limits the ability to

examine longitudinal changes and may introduce confounding factors such as memory effects

(Sovet et al., 2014; Zadorozhny et al (2024)).

Mayer et al (2024) research how many emotional intelligence abilities are there. They provide

an examination of four measures of emotional intelligence. Various measures of emotional

intelligence have been developed over nearly 25 years to assess the four-area model of

emotional intelligence:

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 the Multifactor Emotional Intelligence Scale, (MEIS, Mayer, Caruso, & Salovey,

1999),

 the Mayer-Caruso-Salovey Emotional Intelligence Test (MSCEIT, Mayer, Salovey, &

Caruso, 2002),

 the Youth Research Version (YRV) of the MSCEIT (MSCEIT-YRV, Mayer, Salovey, &

Caruso, 2014) and

 a forthcoming version (MSCEIT-2; Mayer et al., 2023).

According to Yousaf, Javed and Badshah (2024), the interdependent dynamics of innovative

work behavior (IWB), innovative culture (IC), employee inventive performance (EIP), and

emotional intelligence (EI) become apparent as key factors influencing the creative fabric of

firms.

Limitations

Among the various indicators of reliability and validity, test-retest reliability is particularly

important (McCrae, 2015; Oostrom et al., 2019; Zadorozhny et al (2024). However, many

studies on traits do not report test-retest reliability, even though "only test-retest reliability is

necessarily relevant to studies of longitudinal stability or change" (McCrae et al., 2011, p. 29).

In other words, for a personality measure to be accurate, it must consistently perform over time

(Dave et al., 2021; Davies et al., 2010). Furthermore, test-retest reliability offers an estimate

of the maximum potential correlation strength a measure can have with other variables, which

directly affects its construct validity (Assaad et al., 2022; Zadorozhny et al., 2024).

There are different measures and it needs to be shown which measure is the best describing

the relationship between Emotional Intelligence and intuitive decision-making.

Conclusion

It could be shown that Emotional Intelligence and intuitive decision-making is intercorrelated.

The authors recommend further research on this connection.

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No 17 / 93

Developing a Concept for Measuring Rational and Intuitive Decision-Making

based on modern Technologies

Joanna Rosack-Szyrocka 1) and Markus A. Launer 2)

1) Czestochowa University of Technology, Poland

2) Ostfalia University of Applied Sciences & Institut für gemeinnützige Dienstleistungen

gGmbH (independent non-profit organization), Germany

Abstract

Rational and Intuitive decision making is an important topic in the new environment of modern

technologies like AI, AR, VR, Blockchain, Big data, Metaverse etc. The modern technology

give support for rational and intuitive decision-making. The purpose of this conceptual study is

to develop a basic theory for a global study for users of modern technology. The framing of

modern technologies in the era of digitalization and industry 4.0 is based on theories by Rosak-

Szyrocka (Rosak-Szyrocka et al., 2022; Singh et al., 2023). A new model on intuition was used

developed by Launer and Svenson (2022) and Launer and Cetin (2023). This leads to up to

12 different types of decision-making styles: Analytical, Knowing, Planning, Holistic,

Spontaneous, experienced-based Heuristics, Affective (feelings) like Emotions, Body

Impulses, Mood as well as Anticipation, Unconscious Thinking and the Dependence on

colleagues (Launer, 2023). Based on an unpublished scale for rational and intuitive decision-

making digitally online a first item catalogue was developed. These items might be very useful

in international studies on rational and intuitive decision-making based on modern

technologies.

Introduction

Modern technologies have evolved over decades at an increasing pace. This revolution in

information technology has compelled companies to include modern technology as a key

component in their overall daily use, strategy, organization, and vision. Decisions in companies

are influenced by modern technology as well as in the IT industry (Selart, Johansen,

Holmesland, & Grønhaug, 2008). Thus, modern technologies changed the way decisions are

made. Intuitive decision making has begun to shift to become more data centric in decision-

making. Research in the areas of intuition and information technology are very limited. This

might be due to the inconsistency in defining intuition and the difficulty in measuring it

(Ramrathan & Sibanda, 2017). IT investment decisions made by the organizations have

uncertain value characteristics; maybe a higher risk than other capital investment. Decision

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making is here an important issue of managerial activity (Kusumawati & Subriadi, 2019;

Procuniar and Murphy, 2008).

Research on decision support systems began in the 1960s, with managers gaining advantages

from computer-based decision tools (McCosh & Morton, 1978; Power & Kaparthi, 2002).

Today, modern technology plays a crucial role in decision-making for many individuals (Longin,

Bahrami & Deroy, 2023; Walter, Wentzel & Raff, 2023; Jiang & Yang, 2022). In the business

world, managers often depend on control dashboards available on tablets or smartphones

(Himmelstein & Budescu, 2023). The advent of World Wide Web technologies has

revolutionized the design, development, implementation, and deployment of decision support

systems (Bhargava et al., 2007). Modern DSS offer users a wide range of functionalities,

enabling them to perform tasks such as information gathering, analysis, model building,

sensitivity analysis, collaboration, evaluation of alternatives, and decision execution (Bhargava

et al., 2007). Each technology brings its own unique features (Edwards & Fasolo, 2001).

However, decision-making in the early stages of technology-based service (TBS) innovation

projects remains challenging, with high failure rates despite significant investments in these

innovations (van Riel et al., 2011).

But what is the role of science in decision-making on science and technology-related issues,

and how can we identify the factors and reasoning involved in such decisions (Bell &

Lederman, 2003)? How does the use of modern technologies impact intuitive decision-

making? And what functions do different devices serve?

Now, in the twenty-first century, we have the prosumer, who is knowledgeable about the

products and services connected to a certain brand and who teaches others about it (Rosak-

Szyrocka et al., 2023). We now have a good understanding of the role that customers play in

dealing with manufacturers. Real market and organizational expectations have replaced the

awareness's formal system validation phase. Customers expect continuous improvements in

the products and services they purchase in today's ever more competitive market. It is

essential for an organization to exhibit that it is carrying out its social responsibilities and that

it is treating its internal and external clients—consumers and employees—fairly and equally.

Not only that, but you will also succeed financially. Businesses now need to consider the

interests, preferences, and changing lifestyles of their customers as a result of this knowledge

(Parra-Domínguez et al., 2023; Rosak-Szyrocka et al., 2024). Businesses are being forced to

alter their corporate procedures, organizational structures, and business models as a result of

a broad range of digital trends and technology (2014; 2020c; 2020d; Kraus et al., 2021).

Modern technology has evolved over the decades at an increasing pace. This revolution in

information technology has compelled companies to include modern technology as a key

component in their overall daily use, strategy, organization, and vision. Decisions in company

are influenced by modern technology as well as in the IT industry (1921; Selart et al., 2008;

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2020a). Modern technologies changed the way decisions are made. Intuitive decision making

has begun to shift to become more data centric in decision making. Research in the areas of

intuition and information technology are very limited. This might be due to the inconsistency in

defining intuition and the difficulty in measuring it (1000; Ramrathan* and Sibanda*, 2017;

Bouncken et al., 2021). IT investment decisions made by the organizations have uncertain

value characteristics; maybe a higher risk than other capital investment. Decision making is

here an important issue of managerial activity (Burns and D'Zurilla, 1999; 2008; Kusumawati

and Subriadi, 2019; Cooper, 2023).

Theory on Digitalization

Marcial and Launer (2019) developed a conceptual framework to study information technology,

e.g. digital trust. The framework includes six interconnected variables that could influence

decision-makers' levels of digital trust. This level of digital trust is evaluated across three key

components of the information systems workplace: people, technology, and process, each with

its own set of variables. For technology, digital trust is assessed based on the electronic

devices, hardware and software systems, and information systems utilized in the workplace.

Regarding the process, digital trust is evaluated through information systems operations, data

privacy and protection practices, and the use of the internet and social media. In terms of the

people component, digital trust is measured by the roles of management and other internal

organizational entities, IT and data support, and external entities directly impacting the

organization's operations. Additionally, the study aims to assess levels of trust by examining

the respondents' priorities, experiences, and attitudes. Over time, the study will also explore

the effects of digital trust in the workplace.

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Figure1. Theoretical Framework of the Study “Digital Trust in the Workplace”

Decision Making based on modern Technologies

Information technology (IT) may be defined as computer-based technology for the storage,

accessing, processing and communication of information (Molloy & Schwenk, 1995). IT

technology is developed in IT companies such as for hardware, software, as wel as new

technologies such as AI, AR, Big Data, Blockchain etc. In this study, IT is defined a very broad

sense. On the other side are the customers of the IT industry. IT departments buy and use IT

products in their organizations. Questions in an online survey need to include questions on

 People

 Technology

 Processes

Modern technology may be defined as computer- and internet-based technology for the

storage, accessing, processing and communication of information (Molloy & Schwenk, 1995).

Modern technology is developed in IT companies such as for hardware, software, as well as

new technologies such as AI, AR, Big Data, Blockchain etc. In this study, modern technologies

is defined a very broad sense. On the other side are the customers of the IT industry. IT

departments buy and use IT products in their organizations.

Following these studies intuition is a complex, integrated, multi-dimensional and multi-

disciplinary concept. The main features of intuition are unconscious, spontaneous inferential

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or slow decision-making process based on holistic abstract or big picture (holistic), experience-

learned heuristics, affective and emotional feelings, body impulses and moods, perception

without awareness, environmental influences by people as well as the capability for pre-

cognition based on hunches (Launer et al., 2020b, 2022; Svenson et al., 2022). Based on all

previous studies, Launer Cetin (2023) developed a comprehensive measuring instrument for

rational and intuitive decision making.

Decision Making based on modern Technologies

Modern technology may be defined as computer- and internet-based technology for the

storage, accessing, processing and communication of information (Molloy and Schwenk, 1995;

Bem, 2011; Colombari et al., 2023). Modern technology is developed in IT companies such as

for hardware, software, as well as new technologies such as AI, AR, Big Data, Blockchain etc.

In this study, modern technologies is defined a very broad sense (Hodgkinson and Ford, 2009;

Kahneman and Klein, 2009; Nadin, 2017). On the other side are the customers of the IT

industry. IT departments buy and use IT products in their organizations (Radin, 2017; Newton

et al., 2023; Patrick Rödel, 2023).

Artficial Intelligence

AI is defined as “a system’s ability to interpret external data correctly, to learn from such data,

and to use those learnings to achieve specific goals and tasks through flexible adaptation”

(Kaplan & Haenlein, 2019, p. 15). Put more simply, AI is intelligent machines that can think,

learn, and make decisions accordingly. A distinguishing feature of AI systems from other

computerized systems is that, similar to the human intellect, these nonhuman entities can not

only absorb and process (Vincent, 2021). Artificial intelligence (AI) has increased the ability of

organizations to accumulate tacit and explicit knowledge to inform management decision-

making (King & ChatGPT, 2023).

Duan et al. (2019) highlight the challenges linked to the adoption and impact of advanced AI-

based systems in decision-making, offering a series of research propositions for information

systems (IS) scholars. They examine AI's role in decision-making broadly, with a focus on the

specific challenges related to integrating AI to either support or replace human decision-

makers. The widespread reports on AI's potential to surpass human performance in various

tasks within a few years, as well as the tangible achievements in this area, have been well-

documented (McCorduck, 2004). Although the initial promises made for AI during the 1950s

and 1960s were arguably overly optimistic, consistent progress has been made over the past

four decades in the core areas of AI (Cantu-Ortiz, 2014).

Pomerol (1997) identifies two key aspects of decision-making with AI: diagnosis and look-

ahead. He explains that AI has a strong connection to diagnosis through expert systems, case-

based reasoning, and fuzzy and rough set theories. However, AI has largely overlooked look-

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ahead reasoning, which involves managing uncertainty and preferences. AI is even applied to

weather forecasting, influencing decisions in that domain (McGovern et al., 2017). Surgeons

face the challenge of making complex, high-stakes decisions under time pressure and

uncertainty, which significantly impact patient outcomes (Loftus, 2020). Decision-making in the

defense and security sectors has also been explored (Dear, 2019). Additionally, clinicians and

researchers rely on computer-assisted decision-making in complex clinical scenarios

(Shortliffe & Sepúlveda, 2018). Contreras and Vehi (2018) provide a literature review on

decision-making in diabetes treatment using medical devices, mobile computing, and sensor

technologies. Scherer (2019) discusses the use of AI in legal decision-making.

ChatGPT is a new cutting-edge AI chatbot technology that uses natural language processing

and machine learning to enable users to have conversational interactions with a virtual

assistant. Developed by OpenAI, ChatGPT is designed to be highly intelligent and intuitive,

with the ability to understand and respond to complex requests in a way that feels natural and

human-like. With its advanced capabilities, ChatGPT is revolutionizing the way we interact with

technology and is paving the way for a new era of intelligent, conversational AI (King &

ChatGPT, 2023; Poola & Božić, 2023).

A literature study on modern technologies structures the advance knowledge by (i) assessing

the current state of AI and BDA in supply chain literature, (ii) identifying the phases of supply

chain resilience (readiness, response, recovery, adaptability) that AI and BDA have been

reported to improve, and (iii) synthesising the reported benefits of AI and BDA in the context

of supply chain resilience.

Augmented Reality

Pantano et al. (2017) discuss the enhancement of the online decision-making process through

the use of augmented reality (Sangiorgio et al., 2021). They explore how augmented reality-

based decision-making (AR-DM) can support multi-criteria analysis in construction projects

(Sangiorgio et al., 2021). Additionally, Rodriguez-Abad et al. (2021) offer a literature review on

the application of AR in decision-making within the higher education sector, particularly in

health sciences. Kazmi et al. (2021) examine how augmented reality influences changes in

consumer behavior. This raises an important question: are decisions made using AR grounded

in rationality, or are they driven by intuition?

Big Data

Jeble et al. (2017) explore the role of decision-making with big data (Elgend & Elragal, 2016),

while Janson et al. (2017) focus on the quality of decisions made using big data. Big data has

the potential to significantly impact senior management by pushing directors to make faster

decisions and adapt their capabilities to respond to environmental changes (Merendino et al.,

2018; Kościelniak & Puto, 2015). It also plays a crucial role in decision-making within intelligent

manufacturing (Li, Chen, & Shang, 2022) and is utilized throughout the policy cycle, particularly

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in digital-era policy decision-making (Höchtl, Parycek, & Schöllhammer, 2016) and in

organizational business intelligence (Niu et al., 2021). This leads to the recurring question: Are

decisions based on big data driven by rational analysis (Power, 2014), or are they more

intuitive?

The Metaverse

In the metaverse, recent literature examines various aspects of decision-making, including

decision intelligence and modeling (Hawkins, 2022), fuzzy decision-making (Kou et al., 2023),

and predictive algorithms (Balica et al., 2022). Research also delves into decision-making

styles, particularly the effects of immersion and embodiment (Bampouni, Xi, & Hamari, 2023).

Huang, Shao, and Chen (2022) investigate control and decision theory within the metaverse

context. Additionally, Deveci et al. (2022) discuss sustainable urban transportation solutions in

the metaverse, while Zaidan (2023) focuses on uncertainty decision modeling using control

engineering tools to support industrial cyber-physical metaverse smart manufacturing systems

(ICPMSMSs). Michalikova (2022) explores virtual hiring and training processes in the

metaverse.

Intuition Theory

The intuition theory does not research intuitive decision-making based on modern technologies

yet. But intuitive decision-makers need to be informed about current affairs and understand

how cognitive schemes connect to holistic thinking (Shirley and Langan-Fox, 1996; Sinclair,

2013b; Calabretta et al., 2017). It is also thought that an abrupt realization of facts might have

an impact on the intuitive decision-making process (Sinclair, 2013a, 2014; Zhu et al., 2017).

Many intuition studies followed a dual process theory on intuition distinguishing between

rational and intuitive decision making (Epstein, 1973; Thanos, 2023), Pacini & Epstein (Pacini

and Epstein, 1999) also describing the ability to feel if a person is wrong or right (REI). For

modern technologies, it needs to be researched when and how decisions get taken intuitively

based on AI, AR, Blockchain, big data, and the metaverse.

The General Decision-Making Style (Scott and Bruce, 1995) proposes a dependent decision-

making styles. For the rational decision making style, Cools and van den Broek (Cools and

van den Broeck, 2007) propose Cognitive Style Indicator (CoSi) suggesting categories like

knowing, planning ang creating styles. Pachur and Spaar (Pachur and Spaar, 2015) combined

the different styles of REI, GDMS, CoSI, PMPI, PID into Unified Scale to Assess Individual

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Differences in Intuition and Deliberation (USID). They divided preference for intuition into

affective and spontaneous, the preference for deliberation into knowing and planning.

The following graph summarizes the main studies on intuition in an overview:

Fig. 1: An overview of different studies on intuition (Launer / Svenson, 2022)

Launer and Svenson (2022) and Launer and Cetin (2023) developed 12 independent types of

rational and intuitive decision making styles are defined. The new measurement instrument

describes the following dimensions:

Rationality

Analytic: A thorough, rational search for a logical evaluation of alternatives (GDMS), reliance

on and enjoyment of thinking in an analytical, logical manner and enjoying intellectual

challenges (REI), rational processing by problem solving (PMPI), rational processing by logical

reasoning and problem-solving techniques, gathering all necessary information and analyzing

all options (PMPI), deliberative thinking on its aims and solutions, facts and details (PID). For

modern technologies that means Users will analysze their decision while surfing, using AI, Big

Data, Blockchain, or the Metaverse. For Simon, problem solving was a “search through a vast

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maze of possibilities, a maze that describes the environment” (Simon, 1982h, p. 66).

Rationality is bounded rationality or limited by the vast maze of possibilities which is our

environment (Franz, 2003).

Planning: cognitive style based on sequential, structured, conventional, confirmative, planned,

organized, systematic routines (CoSI) deliberate, reflective, planning style (PID), or planning

style (USID). For modern technologies that means Users will plan before taking decision while

surfing, using AI, Big Data, Blockchain, or the Metaverse.

Knowing: rational by knowing the answer without having to understand the reasoning behind

(REI), cognitive style based on knowing facts, details, logical, reflective, objective, impersonal,

rational, precision, methodical (CoSI), or knowing style (USID). For modern technologies that

means Users will base their decision on facts and logic while surfing, using AI, Big Data,

Blockchain, or the Metaverse.

Intuition

Holistic Unconscious: Experiential decisions based on a higher order (CEST), experiential

ability to think in abstract terms (REI), holistic big picture and abstract types of intuition

integrating diverse sources of information in a Gestalt-like, non-analytical manner (TIntS). For

modern technologies that means Users will base their decision on holistic thinking while

surfing, using AI, Big Data, Blockchain, or the Metaverse. McCarthy and Hayes claim that

Philosophy and Artificial Intelligence have important relations. Philosophical problems about

the use of “intuition” in reasoning are related, via a concept of anlogical representation, to

problems in the simulation of perception, problem-solving and the generation of useful sets of

possibilities in considering how to act (Sloman, 19971)

Spontaneous intuition: a sense of immediacy and a desire to get through the decision-making

process as soon as possible (GDMS), spontaneous, speedy and efficient automated

processing (PMPI), intuitions come very quickly (TIntS), spontaneous, fast and swift decisions

(USID). For modern technologies that means Users will make fast spontaneous decision while

surfing, using AI, Big Data, Blockchain, or the Metaverse.

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Heuristic or experience-based intuition: Experiential, automatic learning system based on

experience according to the principles and attributes of associative learning system. It is

automatic, effortless, rapid, primarily non-verbal, holistic, concrete, minimally demanding of

cognitive resources. Associative learning includes association, contiguity, reinforcement,

extinction, and spontaneous recovery. (CEST), experience-based automated processing

(PMPI), experiential processing by coping based on experiences and familiar coping response

(PMPI), inferential intuition based on previously analytical processes and experiences that

have become automatic (TIntS), affective knowledge about humans and having life-experience

(PID), or knowledge on human behavior and life experience (USID). For modern technologies

that means Users will base their decision on experience and former training when surfing,

using AI, Big Data, Blockchain, or the Metaverse.

Slow Unconscious Thinking: Unconscious Thought Theory (Dijksterhuis, 2004). For modern

technologies that means Users will delay their decision on while surfing, using AI, Big Data,

Blockchain, or the Metaverse and do other things for distractio0n. They might wait some hours

or days to decide online.

Emotional or affective intuition: intuitive by relying on feelings (GDMS), experiential ability

referring to a high level of ability with respect to one's intuitive impressions and feelings (REI),

emotional processing (PMPI), affective intuitions based on feelings (TIntS), affective mode

(PID), affective decisions based on feelings being an intuitive person (PID), affective decisions

based on feelings (USID). For modern technologies that means Users will use feelings for their

decision while surfing, using AI, Big Data, Blockchain, or the Metaverse.

Body Impulses: Experiential ability to rely on gut feelings and using its heart for a guide (REI),

emotional processing based on (gut) feelings (PMPI), affective feelings based on the gut and

heart as a guide (TIntS), affective decisions based on the guts (PID), or affective decisions

based on gut feelings (USID). For modern technologies that means Users will base their

decision on gut feeling, heart beats or skin arousals while surfing, using AI, Big Data,

Blockchain, or the Metaverse.

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Moods: Positive and negative moods by qualitatively different information processing modes

(Bolte et al, 2003) according to the Affective Infusion Model (Forgas, 2001). For modern

technologies that means Users will base their decision on their mood swings while surfing,

using AI, Big Data, Blockchain, or the Metaverse.

Anticipation: intuitive by relying on hunches (GDMS), experiential based on hunches (REI),

emotional processing relying on vibes and hunches (PMPI), emotional hunches (TIntS),

affective trust on its hunches (USID). For modern technologies that means Users will base

their decision on their hunches while surfing, using AI, Big Data, Blockchain, or the Metaverse

Support by Others: Dependent meaning a search for advice and direction from others, feeling

a person is wrong or right (GDMS), feeling a person is wrong or right (REI). For modern

technologies that means Users will base their decision based on support by others while

surfing, using AI, Big Data, Blockchain, or the Metaverse.

Measuring intuitive decision-making based on modern technologies

Global Study by Launer and Svenson (2022)

Launer and Svenson (2022) developed a new measuring instrument for measuring intuition

based on the approach by Betsch (2004). The purpose of this study was to develop new

dimensions on intuition in more detail for a new measurement instrument usable in dynamic,

multi-cultural settings. This was important since most scale-development studies were

developed in a more national approach. In a first step, they tested the measurement instrument

for Perceived Intuition and Deliberation (PID) in a multicultural sample in over 30 countries and

more than 35 different industries with n=5570. Second, we develop new styles for intuition by

using adapted and new items. The proposed intuition styles are Unconscious Thoughts and

Anticipation (hunches) which are not represented in the existing measurement studies.

However, the items for heuristic style (based on experiences) is not researched in detail for

the multi-cultural and dynamic intuition.

The methods used were Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis

(CFA), Cronbach’s Alpha coefficients, Primary Component Analysis (PCA), Maximimum

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Reliability (Hancock`s H) as well as calculating Classical Testing Theories. The results were

robust, valid and reliable new and improved intuition decision-making styles for the use in

measurement instruments and practice. The items catalogue used was

Questions

R = Rational, E = Emotional, U = Unconcious Thoughts, H = Heuristics, A = Anticipation

01 Before I make a decision, I usually think about it for quite some time. (R)

02 I think more about my plans and goals than other people. (R)

03 I prefer to make elaborate plans rather than leave anything to chance. (R)

04 Emotions play a significant role in my decision-making patterns. (E)

05 For most decisions, it makes sense to feel. (E)

06 I carefully watch my innermost feelings. (E)

07 If I have to make a decision, I always sleep on it. (U)

08 I never make decisions right away, and I always wait for a while. (U)

09 I frequently make quick and spontaneous decisions based on my insights into humanity (H)

10 I frequently make quick and spontaneous decisions based on my life experience (H)

11 I make quick decisions by rules of thumb (H)

12 I frequently have a premonition as to what will happen. (A)

13 I can often predict emotional events. (A)

14 I can frequently predict the outcome of a transaction. (A)

National Study in Germany by Launer and Cetin (2023)

Launer and Cetin (2023) developed a new and comprehensive measurement instrument

embracing variety of styles by using existing and new items in the literature. Data were

collected via a convenience sampling method from employees (n= 212 for the Study 1 & n=

530 for the Study 2) working in different organizations in Germany. The explanatory and

confirmatory factor analyses, internal consistencies, concurrent and predictive validities, and

discriminant analysis were calculated for the validity and reliability of the measurement

instrument. The findings indicate that the 12-dimensional decision-making style (RIDMS-E)

serves as a valid and reliable measuring tool for assessing different individual tendencies in

the future studies.

Transferring the new measurement on intuition based on modern technologies

Launer and Cetin (2023) made a first attempt to measure intuitive decision-making based on

modern technologies (online decisions) in an unpublished research. Based on their

comprehensive model and measurement instrument they adapted the tested items to online

decisions. This might be a good basis for measuring intuition based on modern technologies.

Please indicate to what extent the following statements apply to you when working online at

your job in a digital environment (browsing through the internet or using digital appliances).

1 Online, I think first before I act.

2 Online, following a clear browsing plan in very important to me

3 I want to have a full understanding of all my browsing activities

4 Before I browse or use digital devices, I try the understand the big picture of the problem

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5 Online, I make quick decisions

6 I often make quick and spontaneous decisions based on my digital experience.

7 I need time and inspiration to make digital decisions

8 Feelings play a big role in my digital decisions.

9 I tend to use my gut feeling (skin or heart beat) for my digital decisions

10 My digital decisions depend on my current mood

11 I believe in trusting my hunches when working digitally

12 I often need assistance of other people when making digital decisions

First attempt to measure intuition based on modern Technology

It could be tried to use the decision-making inventory approach for preferences on rational or

intuitive decision-making. Questions could be formulated like this.

1. Do you use Artificial Intelligence for decision-making?

2. When you decide based on AI, do you decide more rational or intuitive

3. Do you use Augmented Reality for decision-making?

4. When you decide based on AR, do you decide more rational or intuitive

5. Do you use Bid data for decision-making?

6. When you decide based on BD, do you decide more rational analytically or intuitive

7. Do you use Blockchain technology for decision-making?

8. When you decide based on BC, do you decide more rational or intuitive

9. Do you use the Metaverse for decision-making?

10. When you decide based on the Metaverse, do you decide more rational or intuitive

Discussion

The approach by Launer and Cetin (2023) is a comprehensive measuring instrument for

measuring rational and intuitive decision-making. It is the most comprehensive instrument in

theory at present. Rationality and intuition are two categories into which decision-making

styles, which may occur at the individual and group levels, can be divided. When problems are

handled swiftly in spite of few resources or expertise, intuitive decision-making improves the

performance of the organization (Sinnaiah et al., 2023). For modern technologies, the

approach seems appropriate to use. The significance of intuition in decision-making is gaining

traction in the field of management decision research. Research has somewhat evolved from

its original emphasis on intuition's weaknesses in the decision-making process to its

advantages, suggesting that, in certain situations, intuition may be just as good as or even

better than analytical reasoning (Sadler-Smith, 2016). According to Michel Serres (Hunt,

2022), by handing over mental processing and synthesizing habits to digital technology,

millennials have refined cognitive circumstances that lead to a more "intuitive" way of being in

the world (Pedwell, 2019).

The item catalogues measuring rational and intuitive decisions online could be used to

measure intuition based on modern technologies. However, the results by Launer and cetin

are still unpublished. However, the items catalogue can be transferred to modern technologies

like AI, AR, VR, Big data, Metaverse, Blockchain, and other new technologies.

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Conclusion

This study discussed the use of modern technologies like AI, AR, VR, Big Data, Blockchain,

and the Metaverse for rational and intuitive decision-making. The authors discussed the dual

problem if decisions based on these technologies are rational analytic or intuitive. However,

rational and intuitive decision-making might be more complex than a dual approach. Therefore,

the authors used the more comprehensive approaches by Launer ands Svenson (2022) and

Launer and Cetin (2023). Based on these approaches a first measurement instrument ewas

developed to measure decisions made online digitally. However, this approach does not yet

describe rational and intuitive decision-making separately for AI, AR, VR, Big Data, Blockchain,

and the Metaverse.

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The Gut Feeling based on Human Gut Microbiome and ENS System

Muhammad Umair 1) and Markus A. Launer 2)

1) Auyub Medical College Abbottabad, Pakistan

2) Ostfalia University od Applied Sciences and Institut für gemeinnützige Dienstleistungen

gGmbH (independent non-profit organization), Germany

Abstract

Gut feeling is one of the most misunderstood terms. It can mean the intuition based on any

feeling (unconscious awareness, feelings, heuristics, anticipation) or the gut-brain-axis. A

major scientific breakthrough in understanding the interaction of the nervous system with the

digestive system occurred with the discovery of the so-called enteric nervous system (ENS) in

the mid-nineteenth century Jahrhunderts (Launer, Svenson et al, 2020). This study is also

important to better understand the intuition style feelings and body impulses (Launer & Cetin,

2023). This study summarizes non-systematic selected study on the gut-brain-interaction to

support the discussion on affective intuition. As a result, the knowledge supports further studies

in intuitive decision-making

Introduction

The notion of a "gut feeling" is deeply rooted in traditional beliefs, despite lacking scientific

evidence. This idea revolves around an instinctive sense or intuition when making decisions.

The concept dates back to ancient times when the stomach was considered the primary organ

responsible for intuitive choices. Over time, this idea evolved into the phrase "gut feeling,"

which is now associated with interoception—the biological process where internal organ

functions influence the brain. Our actions are shaped not only by external factors but also by

internal signals. The gut plays a pivotal role in this process, serving as the central hub for

complex interactions between our genes and the immune system's external impact on the

body, making it a key organ in environmental communication (Brandtzaeg, 2011).

The enteric nervous system (ENS), often referred to as the gut's autonomous nervous system,

has captivated scientists for over a century. True to its name, it operates autonomously,

managing complex tasks and regulating essential functions independently of external input.

Simultaneously, the ENS receives and processes a barrage of signals from various cells within

the gut wall and lumen, integrating these inputs (Annahazi & Schemann, 2020). Despite

remarkable scientific advancements in recent years that have deepened our understanding of

the communication between microbes and their hosts, the fundamental mechanisms behind

the microbiota-gut-brain connection remain elusive (Silva, Bernardi & Frozza, 2020).

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Theory

Human Gut Microbiome

Our knowledge of species and functional composition of the human gut microbiome is rapidly

increasing, but it is still based on very few cohorts and little is known about variation across

the world (Arumugam et al., (2011). However, the main stream of medical research is in

regards to bowel disease (Greenblum et al., 2012), the selective entry of nutrients (digestion)

the immune system (Barbarosa & Resigno, 2010), aging (Rampelli et al., 2023), metabolic

programming (Mischke & Plösch, 2013) as well as its role in obesity and insulin resistance

(Lee, Sears & Maruthur, 2020). Tan (2023) describes the microbiota-gut-brain axis in stress

and depression. Frontiers in Neuroscience, 17, 1151478. But, human individual is best

described as a super-individual in which a large number of different species (including Homo

sapiens) coexist. Fasano and Flaherty, (2022) describe the gut feelings and the microbiome in

relation to our health.

The primary function of the intestine is to absorb nutrients and water from the external

environment. To ensure that the body receives the necessary energy and essential nutrients,

it first requires the secretion of various digestive enzymes and functional components, such as

bile acids. This crucial exchange with the environment leads to the intestine’s unique role as a

semipermeable barrier, distinct from other body surfaces like the skin. The intestinal mucosa

acts as a highly selective barrier, permitting the absorption of specific luminal substances

(nutrients) through active transport or passive diffusion while effectively blocking the entry of

harmful agents such as viruses, bacteria, and parasites (Haller & Hörmannsperger, 2013).

In an adult human, the gut epithelium spans an estimated surface area of about 300 m² when

considering the villi, microvilli, crypts, and folds. Although this barrier consists of only a single

cell layer, making it inherently fragile, it is safeguarded by a range of chemical and physical

innate defense mechanisms that work closely with the local adaptive immune system

(Brandtzaeg, 2009a). The healthy gut is home to an immense population of beneficial bacteria,

or "symbionts," which outnumber the body's cells by about tenfold, comprising an estimated

10¹³ to 10¹⁴ microbial cells with a total weight of 1–2 kilograms (Neish, 2009). Numerous genes

govern both the innate and adaptive branches of the immune system. Over time, human

immunogenetics have evolved to detect and respond to "danger," shaped by the challenges of

a "dirty environment," even long after the hunter-gatherer era. Throughout this evolutionary

journey, the intestinal immune system has developed two adaptive anti-inflammatory

strategies: immune exclusion, facilitated by SIgA, to control microbial colonization on surfaces

and prevent the mucosal penetration of potentially harmful agents; and oral tolerance

(Brandtzaeg, 2011b).

Hooper et al. (2001) report findings that demonstrate how this commensal bacterium

influences the expression of genes associated with several critical intestinal functions, such as

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nutrient absorption, strengthening the mucosal barrier, xenobiotic metabolism, angiogenesis,

and postnatal intestinal development. These results underscore the fundamental importance

of the interactions between resident microorganisms and their hosts (Hooper et al., 2001).

The idea that the gut and brain are intricately connected, influencing not only gastrointestinal

function but also emotional states and intuitive decision-making, is deeply embedded in our

language. Recent neurobiological research into this gut-brain interaction has uncovered a

complex, bidirectional communication system. This system plays a crucial role in maintaining

gastrointestinal homeostasis and digestion and likely impacts emotions, motivation, and higher

cognitive functions, such as intuitive decision-making. Moreover, disruptions in this

communication network have been linked to various disorders, including functional and

inflammatory gastrointestinal issues, obesity, and eating disorders (Mayer, 2011; Pandey et

al., 2017; Launer et al., 2020).

The ENS System

Decades of work in animal models have demonstrated that the enteric nervous system (ENS)

plays a key role in controlling gut functions. Recent advances made it possible to extend such

studies to the ENS of man in health and even in disease. (Schemann & Neunlist, 2004;

Schemann et al., 2002). Our body is building a brain in the gut. Goldstein, Hofstra, & Burns

(2013) describe the development of the enteric nervous system (Baron et al, 2022).

The enteric nervous system (ENS), which forms the intrinsic neural network of the

gastrointestinal tract, comprises various types of neurons and glial cells. These are organized

within two intramuscular plexuses that run throughout the entire length of the gut, regulating

coordinated smooth muscle contractions and other essential gut functions (Sasselli, Pachnis,

& Burns, 2012).

The enteric nervous system (ENS) is the largest and most complex division of the peripheral

and autonomic nervous systems (PNS and ANS) in vertebrates. It comprises a vast array of

neurons—comparable in number to those in the spinal cord—and features a variety of

neurotransmitters and neuromodulators similar to those in the central nervous system (CNS).

The ENS is organized into an interconnected network of neurons and glial cells, grouped into

ganglia within two major plexuses (Sasselli, Pachnis & Burns, 2012). These components

create an integrated circuitry that governs intestinal motility, fluid exchange across the mucosal

surface, blood flow, and the secretion of gut hormones. While the gut also receives extrinsic

parasympathetic and sympathetic innervation, the intrinsic neuronal circuits of the ENS are

capable of generating reflexive gut contractile activity independently of CNS input (Sasselli,

Pachnis & Burns, 2012).

The neural crest origin of the enteric nervous system (ENS) was first demonstrated by Yntema

and Hammond, who found that when the vagal (hindbrain) region of the neural crest was

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ablated in avian embryos, enteric ganglia did not develop along the gastrointestinal tract

(Yntema and Hammond, 1954). This finding was later confirmed and expanded through

studies using isotopic and isochronic grafts of quail pre-migratory neural crest cells into chick

embryos (Le Douarin, 1973).

The gut-brain connection

The gut-brain connection involves complex cross-communication through multiple biological

networks, including the neural network, neuroendocrine system, immune system, and

metabolic pathways, facilitating bidirectional communication between the brain and gut (Gwak

& Chang, 2021; Ma et al., 2019). Alterations in gut microbiota can impact brain physiology and

cognitive functions (Morais, Schreiber & Mazmanian, 2021). There is increasing recognition of

the gut microbiota's role in modulating various neurochemical pathways via the highly

interconnected gut-brain axis (Silva, Bernardi & Frozza, 2020; Morais, Schreiber &

Mazmanian, 2021).

Neurobiological research into gut-brain communication has uncovered a complex, bidirectional

system that not only ensures the maintenance of gastrointestinal homeostasis and digestion

but also influences affect, motivation, and higher cognitive functions (Mayer, 2011). The

sympathetic and parasympathetic nervous systems modulate intestinal functions, potentially

mediating the emotion-related changes in motor, secretory, and possibly immune activities

within the gastrointestinal tract. Sensory information in the gut is encoded through three

primary mechanisms: by primary afferent neurons, immune cells, and enteroendocrine cells.

Both extrinsic and intrinsic primary afferents contribute to multiple reflex loops designed to

optimize gut function and preserve gastrointestinal homeostasis during internal disturbances

(Mayer, 2011).

Sylvia et al. (2020) indicate that the mechanisms by which short-chain fatty acids (SCFAs)

might affect brain physiology and behavior are not yet fully understood. Enteroendocrine cells

(EECs), specialized epithelial cells derived from the endoderm, are dispersed throughout the

gastrointestinal (GI) tract (Dalile, Van Oudenhove, Vervliet & Verbeke, 2019). These cells

constitute the body's largest endocrine organ and are crucial in regulating GI secretion and

motility, controlling food intake, and managing postprandial glucose levels and metabolism.

EECs detect luminal content and release signaling molecules that can enter the bloodstream

to function as hormones on distant targets, act locally on neighboring cells, or engage distinct

neuronal pathways, including those involving enteric and extrinsic neurons (Latorre et al.,

2016).

The saprophytic gut microbial flora plays a crucial role in modulating the gut-brain

communication pathway, now recognized as the "microbiota-gut-brain axis." The gut

microbiota is essential for maintaining homeostasis at local, systemic, and brain levels.

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Numerous neuroactive molecules, hormones, and metabolites facilitate this bidirectional

communication, enabling cross-talk between the gut and brain (Bistoletti, Bosi, Banfi, Giaroni,

& Baj, 2020). The dorsal vagal complex in the brainstem of the central nervous system (CNS)

organizes vagovagal reflexes and establishes connections between the CNS and the gut. This

complex effectively links the "CNS brain" with the "ENS brain," creating a brain-gut

connectome that provides reflexive adjustments to optimize digestion and nutrient and fluid

assimilation (Powley, 2021).

The connection between the gut environment and the brain can significantly influence host

mood and behavior. While the link between gut microbiota and the brain has been recognized

for some time, recent studies have begun to uncover the mechanisms by which gut microbiota

and the integrity of the gut barrier impact brain function and behavior (Gwak & Chang, 2021).

This interaction between the microbiota and the gut-brain axis (GBA) is bidirectional, involving

communication from gut microbiota to the brain and vice versa, mediated through neural,

endocrine, immune, and humoral pathways (Carabotti, Scirocco, Maselli & Severi, 2015; Ma

et al., 2019).

Microglia, the brain's resident immune cells, play a critical role in modulating neurogenesis,

influencing synaptic remodeling, and regulating neuroinflammation by constantly monitoring

the brain's microenvironment. Dysfunction in microglial activity has been linked to the onset

and progression of various neurodevelopmental and neurodegenerative diseases. However,

the complex array of factors and signals that influence microglial behavior remains to be fully

understood (Abdel-Haq, Schlachetzki, Glass & Mazmanian, 2019).

Akyildiz et al. (2019) explore minimally invasive, heterogeneous, and externally accessible

electrical and molecular communication channels to transmit information between devices

through the Microbiome-Gut-Brain Axis (MGBA), which includes the gut microbial community,

gut tissues, and the enteric nervous system. Gut microorganisms can activate the vagus nerve,

a process that plays a crucial role in influencing brain function and behavior. The vagus nerve

appears to distinguish between non-pathogenic and potentially pathogenic bacteria, even in

the absence of noticeable inflammation, and vagal pathways can mediate signals that lead to

either anxiogenic or anxiolytic effects, depending on the nature of the stimulus (Forsythe,

Bienenstock & Kunze, 2014).

Orexin-A is a key chemical mediator in the gut-brain axis, with hypothalamic orexin-A

influencing gastrointestinal motility and secretion, while peripheral orexin in the intestinal

mucosa can affect brain functions, potentially forming an orexinergic gut-brain network. Orexin-

A is thought to regulate nutritional processes, including short-term food intake, gastric acid

secretion, and motor activity during the cephalic phase of feeding. Additionally, orexin-A is

linked to stress systems and responses, particularly through its interaction with the

hypothalamic-pituitary-adrenal (HPA) axis (Mediavilla, 2020).

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Basal Ganglia

The basal ganglia play a crucial role in an attentional mechanism that helps connect sensory

input to motor output in the executive forebrain. This focused attention creates an automatic

link between voluntary effort, sensory input, and the activation of sequences of motor programs

or thoughts (Brown & Marsden, 1998; Haber & Gdowski, 2005; Mink, 2003; Smith, Bevan,

Shink & Bolam, 1998). As a major neural system, the basal ganglia receive inputs from various

cortical areas, process this information, and relay it back to the cortex through connections in

the midbrain and thalamus. Although inputs to the basal ganglia originate from multiple cortical

regions, including the frontal, parietal, temporal, and limbic cortices, the feedback from the

thalamus is primarily directed toward frontal cortical areas, such as the prefrontal, premotor,

and supplementary motor areas. This thalamic feedback, similar to the cerebellar connections

that ascend through the thalamus to the primary motor cortex, integrates the basal ganglia into

motor function (Gerfen & Wilson, 1996; Heimer & Heimer, 1983).

Conclusion

There is plenty of research on the gut-brain-axis. However, it still has a lot of uncertainties. The

key focus of these researching medical doctors are diseases and not intuitive decision-making.

This is why this abstract is important to connect the topics better.

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Intuitive and Rational and Decision-Making in Marketing

Anna Jasiulewicz 1) and Markus A. Launer 2)

1) Warsaw University of Life Science, the Management Institute, Poland

2) Ostfalia University of Applied Sciences and Institut für Dienstleistungen (independent non-

profit organization), Germany

Abstract

Rational and intuitive decision-making is important in the daily work of marketing professionals.

It is also the basis for consumer decision-making on products of the company. Thus, Ratinal

and intuitive decision-making is an important topic for marketing. However, there is only limited

research about the topic in literature. There are some fragmented papers covering parts of the

subject. This paper gathers all results on rational and intuitive decision-making styles, fills the

gaps and combines them to one comprehensive approach for marketing. The purpose is to

provide a basis for future empirical research. The result of this conceptual study are different

decision-making styles such as Analytic, Planning, Knowing, Holistic Unconscious,

Spontaneous, Heuristic, Slow Unconscious, Emotions, Body Impulses, Moods, Anticipation,

and Support by Others, and Support by Technology (Launer & cetin, 2023). These results go

along with all mayor studies on intuition.

Introduction

Therre are many studies and theories on how decisions are made by marketing professionals

as well as on consumer behavior. However, there is only limited information about rational and

intuitive decision-making in marketing.

As the world becomes increasingly mediated and social networks gain power, consumer

decisions are increasingly shaped by identity expression, social status, and personal branding.

Additionally, factors such as time constraints and the abundance of choices significantly impact

decision-making processes. These evolving dynamics affect the strategies consumers use to

make decisions (Willman-Iivarinen, 2017). However, there is evidence, that consumer behavior

is increasingly influenced by both intuitive and analytical reasoning (Anderson & Engelberg,

2006). Intuitive insights are now recognized as crucial components in marketing and

managerial expertise (Vanharanta & Easton, 2010).

Historically, intuition in strategic marketing was undervalued (Patterson et al., 2013). However,

recent shifts in the business landscape and educational focus have led to a greater emphasis

on core skills, including written and oral communication, intuition, creativity, and proficiency

with technology (Ships et al., 2015). Thus, intuitive decision-making becomes more important.

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But marketing managers decide rational or intuitive themselves in their marketing function. It

is expected that advertising managers might be more emotional intuitive than the marketing

analysts based on statistics. Patterson et al. (2013) investigates the ways in which the heuristic

of intuition is used in marketing managers for making strategic-level decisions. They explore

the extent to which intuitive insights are privileged over systematic, rational, and logical

evaluations. Intuition-led decision making becomes a powerful tool in instances where there is

a paucity of data, when options are manifold, when the future is uncertain and when the logic

of strategic choice needs to be confirmed (Patterson et al, 2013). Kucuk (2023) shows range

from abstract to intuitive decision-making in marketing. There seems to be a need to deeper

explore the decision-making styles of different marketing managers.

Theoretical Results from Literature

Marketing Mangers Decision-Making

The significance of intuitive decision-making in marketing management has gained increasing

recognition (Collinson & Shaw, 2001; Miller, 2000; Palmer & Miller, 2004; Vanharanta &

Easton, 2010). Marketing managers have to make decisions every day, tactical, as well as

strategically. The following summary describes the theoretical results in the literature on

rational and intuitive decision-making by Marketing managers in their job and in specific tasks.

In the literature, results can be found interms of decisions regarding market segmentation,

mental stimulations, and expert intuition of marketing managers. In particular, Kahneman and

Klein (2009) explore the contrasting views of intuition and expertise: heuristics and biases

versus naturalistic decision making. They argue that while professional intuition can sometimes

be highly effective, it can also be flawed. The Recognition-Primed Decision (RPD) model by

Klein (XXX) seems to be a good basic model. Klein also researched intuitive decision-making

for soldiers, fire fighters, and policemen (Klein, XXX).

Market Segmentation

The market segmentation is one of the key tasks in marketing. Wierenga (2006) explores the

relationship between consumers, channels, and intuition (Wierenga, 2006). In the realm of

management tools, market segmentation, particularly in industrial markets, exhibits a distinct

gap between theoretical models and practical application (Miller, 2000). Research on

segmentation often follows a common pattern: authors first list potential criteria for market

segmentation and then suggest an order for applying these criteria. For instance, Shapiro and

Bonoma's (1983) "nested approach" recommends starting with simpler criteria and progressing

to more complex ones. An initial segmentation draft can be rapidly developed through intuition,

providing a rough starting point rather than a polished final product. This preliminary draft

serves merely to establish initial classification poles before moving on to a more detailed

rationalization process (Miller, 2000; Palmer & Miller, 2004).

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Mental Stimulations in Marketing

Vanharanta and Easton (2010) provide empirical evidence demonstrating the application of

mental simulation as a heuristic in industrial networks. Their observations align with the

Recognition-Primed Decision (RPD) model (Klein, XXX), which suggests that intuitive thinking

leverages managerial experience to guide network actions without relying on a formal,

comparative decision process. The main business value of mental network simulations is their

ability to clarify unclear or partially known network situations, aid in developing coherent plans

and tactics, and mentally anticipate the outcomes of specific strategies. Essentially, these

simulations are valuable for navigating complex environmental challenges and facilitating

effective interactions between companies (Vanharanta & Easton, 2010).

Intuitive decision-making by marketing managers

The ability of managers to make quick, intuitive decisions is often seen as a key indicator of

their proficiency (Dreyfus & Dreyfus, 2005; Klein, 1999). Experienced marketers may rapidly

assess complex situations and identify suitable actions. However, for intuitive expertise to be

effective, it must be validated within its organizational context. Intuitive insights have a

substantial institutional and social dimension, influencing marketing management performance

by either aligning with or challenging institutional logic (Vanharanta et al., 2014).

Experienced business marketers often generate effective solutions to marketing problems

intuitively, quickly arriving at good decisions without the need for extensive comparison and

analysis (Dreyfus & Dreyfus, 2005; Klein, 1999; Vanharanta & Easton, 2010). This intuitive

proficiency is particularly valuable under time constraints and in dynamic, ambiguous

marketing situations (Klein, 1999).

However, intuitive decision-making cannot stand alone; most managerial tasks benefit from a

blend of intuition and deliberate analysis (Hayashi et al., 2021; Patton, 2003). For instance,

complex computations necessitate thorough analysis, while routine tasks under time pressure

are more suited to intuitive approaches (Klein, 1999). Propositional knowledge, decision rules,

and modeling also contribute significantly to marketing performance. Thus, it is crucial for

marketers to understand which tasks are best approached with intuition and how to integrate

it with other decision strategies.

Despite advancements in understanding the cognitive limits of intuitive decision-making (Dane

& Pratt, 2007; Klein, 1999, 2004, 2011, 2015), the role of inter-organizational and social factors

has been less explored. The influence of institutional mechanisms on intuitive decision-making

remains under-researched (Agor, 1986; Shapiro & Spence, 1997).

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In summary, there is still a research gap how marketing managers decide in dails business,

tactic, and strategically. In detail, different positions within marketing as well as specific tasks

need different types of rational and intuitive decision-making (Launer & Svenson, 2020)

Consumer´s Decision-Making

Consumers’ decision-making is influenced by various theories, including Decision Theory

(Willman-Iivarinen, 2017), Consumer Psychology, Media Research, Brand Theory, Mood

Management Theory (Zillman), Cost of Thinking (Shugan), Decision Goals and Heuristics

Theory (Bettman), Theory of Extended Selves (Belk), and Theory of Stuff and Identity

(Gosling). Although earlier research often relied on rational choice or non-cooperative game

theory to explain consumer behavior, these models are insufficient for understanding decision-

making in today’s socio-technological context (Gstrein & Teufel, 2014). Understanding

consumer rational and intuitive decision-making is both intricate and crucial (Haws et al., 2017).

Decision-making theorists, such as Tversky and Kahneman (1981) and Bettman et al. (1998),

generally agree on the process involved. It starts with identifying a need or motive. Following

this, a set of alternatives, or opportunity set, is established. After assessing the benefits and

costs of these alternatives, a decision is made. Various strategies can be employed to make

this choice (Willman-Iivarinen, 2017). However, many consumers decide on buying products

based on different types of rational or intuitive decision-making (Launer & Svenson, 2020).

According to the research of Stanovich & West (2000), there are two primary reasoning modes

that interact: intuitive and analytical (Stanovich & West, 2000). These are also referred to as

the tacit and deliberate systems (Hogarth, 2005) or experiential and rational systems (Epstein

et al., 1996). The tacit system operates quickly, automatically, and with minimal effort, while

the deliberate system functions more slowly, with controlled and rule-based processes.

Intuitive judgments made by the tacit system are often challenging to express clearly, whereas

the deliberate system is used selectively for more complex decision-making tasks (Stanovich

& West, 2000; Hogarth, 2005).

Kelly (2013) suggests that predictive modeling is essential for future decisions, despite the

high costs of developing these models, but this approach may not apply to consumer decision-

making. McAfee (2010) advocates for evidence-based decision-making and criticizes intuition,

yet such critiques may not alter consumer decision-making practices. His research aims to

explore the variables and changes in consumer decision-making and contribute to the scientific

discussion (Willman-Iivarinen, 2017).

Haws et al. (2017) explores intuition at the intersection of healthiness and price, revealing that

consumers often perceive healthier foods as more expensive than less healthy options.

Although this may be true in some instances, consumers tend to overapply this belief to

situations where it isn't accurate. Consequently, the intuition that "healthy equals expensive"

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affects consumer decisions by influencing perceptions of missing information and choices

among alternatives (Haws et al., 2017).

Anderson and Engelberg (2006) make a distinction between two distinct shopping approaches:

one driven by instinct, which requires less cognitive effort, and another focused on finding the

best price and quality, consistent with rational decision-making. They classify consumer

shopping behaviors into two main categories: affective and rational (Adam & Engelberg, 2006).

The activation of different systems of consumer can be influenced by an individual’s emotional

state and the context of the decision (Mellers et al., 2002; Kahneman, 2003). Positive emotions

typically result in faster decisions with the use of simplified heuristics (Isen & Labroo, 2003),

whereas negative emotions lead to more systematic processing (Schwarz, 2000). When

decisions are influenced by emotional reactions, the tacit system is often engaged, whereas

cognitive responses tend to activate the deliberate system (Shiv & Fedorikhin, 1999).

Additionally, focusing on either hedonic or utilitarian aspects can shift reasoning modes (Dhar

& Wertenbroch, 2000). The affect heuristic, which suggests that feelings direct judgments,

aligns with intuitive reasoning (Slovic et al., 2002), and various elements of the purchasing

environment can impact emotional states and reasoning modes (Bakamitsos & Siomkos, 2004;

Anderson & Engelberg, 2006).

Money also plays a crucial role in consumer decision-making, influencing both economic

transactions and purchase limits. Attitudes towards money, shaped by personal beliefs and

social influences (Furnham & Argyle, 1998; Furnham & Okamura, 1999), affect how individuals

manage their finances and make purchasing decisions (Mitchell & Mickel, 1999). Despite its

importance, the impact of money on decision-making has not been extensively studied

(Anderson & Engelberg, 2006).

In summary, there is no comprehensive model on rational and intuitive decision-making by

consumers.

Online Buying

In online shopping, both cognitive and affective attitudes are influenced by various functional

attributes such as product information, cost savings, convenience, and perceived ease of use,

as well as the hedonic aspects of online shopping sites, which ultimately affect the decision to

make a purchase (Moon et al., 2017). Research by Chen, Lu, and Wang (2017) indicates that

social commerce components (SCC) impact online purchasing decisions through both

cognitive and affective dimensions, with these attitudes playing a crucial role in determining

customer purchase intentions. Cognitive evaluations tend to be more predictive of purchase

intentions than practical assessments (Sari, 2022). However, product reviews on social media

do not always influence cognitive or affective evaluations significantly; understanding the full

picture of how purchasing decisions are made is essential (Sari, 2022).

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On the other hand, Fu et al. (2019) highlight that misleading information about products can

significantly reduce consumer purchase intentions over time. To attract and retain customers,

online sellers must adhere to legal and regulatory standards. This is further supported by Park

and Hill (2018), who found that cognitive efforts related to regret from incorrect product

information can lead consumers to justify a poor investment. Additionally, consumer

purchasing decisions are affected by the focus of information available on online forums,

including aspects like price, discounts, and product quality (Fu et al., 2019). Research suggests

that understanding societal consumption patterns and the importance of price is crucial, as it

drives consumer thoughts towards making favorable purchases (Sari, 2022).

As a result, there is also no comprehensive approach on how consumers decide on product

purchases based on rational or intuitive mechanisms.

Different types of decision-making in Marketing and Consumers

Based on various measurement instruments rational and intuitive decision-making can be

described in detail. Thew following table summarizes the decision-making styles by the well-

known studies CEST, GDMS, REI, PMPI, CoSI, PID, TIntS, and USID. These dimensions

might be adaptable for decion-making for marketing managers and consumers (Launer &

Svenson, 2020).

An overview on measurement studies on Rationality and Intuition (Launer & Cetin, 2023)

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The following results by literature are structured according to these dimensions.

Rational or cognitive decision-making

Cognitive limitations impact all product purchasing decisions, often leading to irrational choices

(Wattanacharoensil & La-Ornual, 2019). Consumers' evaluations of product information rely

heavily on their cognitive reflection abilities (Andor, Frondel & Sommer, 2019). According to

Sari (2022), the sequence of product searches is primarily influenced by the incentives or

rewards offered and the cost of the search itself. An individual's cognitive capability determines

when they stop searching for a product and make a purchase decision, potentially without fully

recalling their choices. Technological advancements, particularly those related to digital

purchases, are enhancing decision-making speed and quality. Superior cognitive intelligence

and preferences, along with long-term memory, contribute to better decision-making,

potentially avoiding negative outcomes. Consumers with strong numeracy skills create clearer

mental representations, aiding in more informed decisions (Sari, 2022). Risk aversion,

psychological effort costs, and decision-making errors are crucial factors leading to atypical

purchasing behaviors (Bhatia et al., 2021).

Wattanacharoensil and La-ornual (2019) provide a comprehensive review of cognitive biases

in tourist decisions, noting that these decisions are more intricate than initially perceived. They

highlight that tourists' limited memory can hinder their decision-making processes. Their

framework includes pre-trip experience (destination choice, tourism product rating, and

selection), on-site experience, post-trip experience, and cognitive bias (Wattanacharoensil &

Laornual, 2019).

Kowalczuk et al. (2021) explore consumer responses to augmented reality (AR) in e-

commerce, comparing the IKEA Place app with the IKEA mobile website. Their study assesses

AR characteristics (interactivity, system quality, product informativeness), affective responses

(immersion, enjoyment), cognitive responses (media usefulness, choice confidence), and

behavioral responses (reuse intention, purchase intention) (Kowalczuk et al., 2021). Bhatia et

al. (2021) examines cognitive models of optimal sequential search with recall, focusing on

computational (optimal) and algorithmic (satisficing) approaches, sequential search, risk

aversion, psychological effort cost, and decision errors (Bhatia et al., 2021).

The impact of cognitive reflection on consumers' valuation of energy efficiency and its

interaction with responses to the EU energy label was studied by Andor et al. (2019). They

found that consumers with low cognitive reflection levels react strongly to grade-like energy

efficiency labels and tend to ignore detailed information, whereas those with high cognitive

reflection are more attentive to comprehensive data (Andor et al., 2019). Moon et al. (2017)

investigated how cognitive and affective attitudes towards utilitarian and hedonic attributes of

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websites influence online purchase intentions. Their study focuses on cognitive and affective

attitudes, purchase intentions, utilitarian attributes, and hedonic attributes (Moon et al., 2017).

Chen et al. (2017) describes a social learning perspective, examining how social commerce

factors influence customers' purchase decisions. Their model explores external interactions

(learning from forums and reviews, social recommendations), internal psychological processes

(cognitive and affective appraisals), and decision-making (purchase intention) (Chen et al.,

2017). Fu et al. (2019) studied the effect of price deception on consumer decision-making

when consumers have adequate price information. Their research includes price deception,

deceptive conditions, and truthful conditions (Fu et al., 2019). Park and Hill (2018) explored

the role of cognitive effort and justification in relation to regret in online shopping contexts.

Their research focuses on cognitive effort and justification (Park & Hill, 2018).

Brooks and Johnson (2012) analyzed how the unique information environment of online forums

influences consumers' information acquisition and subsequent purchase behavior. Their study

examined focal product page browsing, online forum browsing, and focal product purchases

(Brooks & Johnson, 2012). Sobkow et al. (2020) investigated different cognitive abilities and

preferences related to superior decision-making, focusing on cognitive abilities and decision-

making (Sobkow et al., 2020). Guo et al. (2020) explored the effect of review valence on

purchase decisions, noting mixed findings. They investigated perceived credibility, pleasant

vs. unpleasant reviews, perceived diagnosticity, and purchase decisions, finding that positive

reviews increase the likelihood of purchase (Guo et al., 2020).

Sohn and Ko (2021) examined how the justification heuristic and different payment methods

(individual vs. bundle payment) moderate purchase decisions. Their study focused on

precedent purchase type, justification heuristic, payment methods, and willingness to pay,

finding no significant relation between payment method and planned purchasing (Sohn & Ko,

2021). Medina et al. (2020) investigated how price processing differs between consumers with

sustainable habits (prosocial) and those without (non-prosocial), using neuroimaging tools to

explore neural mechanisms. They found that prosocial consumers place higher value on

collective costs and benefits during purchase decisions compared to non-prosocial consumers

(Medina et al., 2020).

Shugan (1980) analyzed various decision-making strategies by examining their costs and

benefits for the decision-maker. In his model, the costs included the effort required and the

number of mistakes made. Shugan discovered that reducing thinking costs often leads to fewer

benefits due to an increase in mistakes. Subsequently, Payne et al. (1996) and Bettman et al.

(1998) expanded this analysis through the accuracy versus effort framework. This framework

posits that each decision strategy can be assessed based on its accuracy (i.e., the level of

mistakes) and the effort it demands. The effort and accuracy model of strategy selection has

been supported by multiple studies (Creyer et al., 1990; Stone & Schkade, 1994). Making

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decisions with high accuracy, or minimizing mistakes, typically requires considerable cognitive

effort. Bettman et al. (1998) noted that decision-making goals can vary; individuals might

prioritize accuracy at times, while at other times they might value ease, speed, or justifiability

(Willman-Iivarinen, 2017).

Many consumers are inclined to seek out products of the highest quality, aim to get the best

value for their money, make impulsive or hedonistic purchases, and regularly shop for specific

brands (Sproles & Kendall, 1986; Bates, 1998; Walsh et al., 2001). Accuracy in decision-

making, characterized by rational and analytical processes, has been described by Epstein

(1994), Scott and Bruce (1995), Pacini and Epstein (1999), Burns and D’Zurilla (1999), and

deliberation (Betsch, 2004). Launer and Cetin (2021, 2023) further distinguish analytical

decision-making from rational planning and the knowing style.

As a interim result, there is a good understanding of rational or cognitive decision making in

marketing.

Intuitive decision-making in marketing

According to Launer and Svenson (2020), there are different types of intuitive decision-making

styles. This might apply for marketing and consumers as well. Many researchers see intuition

as one dimension and do not differentiate intuitive-decision-making in different styles

(Gladwell, 2006).

Heuristically experienced-based decision-making

Willman-Iivarinen (2017) posits that due to limited cognitive capacities and the desire to

minimize decision-making costs, individuals often rely on heuristics. While some people

engage in rational decision-making by considering all possible options and features, others

may rely on intuitive or nearly automatic decisions (Willman-Iivarinen, 2017). Heuristics are

strategies used to simplify decision-making by filtering out and ignoring some information while

focusing on specific aspects of alternatives. Some heuristics are applied intentionally and

deliberately, while others may be used automatically, often without conscious awareness

(Willman-Iivarinen, 2017). In various studies on intuition, heuristics are described as follows:

Experiential heuristics include Associative and Automatic Learning (Epstein, 1994), Automatic

Processing Based on Experience (Burns & D'Zurilla, 1999), Life Experience and Human

Understanding (Betsch, 2004), Inferential and Experience-Based heuristics (Pretz et al., 2014),

and Affective-Based heuristics (Pachur & Spaar, 2015). Launer and Svenson (2020b) further

refine these concepts and enhance research items related to intuition. Launer and Cetin (2021)

confirm that experienced-based intuition, derived from previous training and experiences, is a

significant factor in decision-making.

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Payne, Bettman, and Johnson (1993) suggest that people adapt their decision-making

strategies based on the nature of the decision task. They argue that heuristics are deliberately

employed to reduce effort when the decision is of low importance. In contrast, Kahneman and

Tversky (1973, 1974) focused on demonstrating that while people use heuristics, these

heuristics can lead to systematic biases, resulting in errors compared to "rational decision-

making." Gigerenzer and Todd (1999) largely agree with Kahneman and Tversky on the use

of heuristics but hold a different perspective on their efficacy.

Willman-Iivarinen (2017) details various heuristic decision-making approaches used by

consumers, including the Satisficing heuristic, Lexicographic heuristic (take the best heuristic),

Eliminating by Aspects heuristic, Frequency of Good and Bad Features heuristic, and Equal

Weight heuristic (Bettman et al., 1991). Bettman et al. (1991) argue that as decision tasks

become more complex, people simplify their decision-making process by employing simpler

heuristic rules (Willman-Iivarinen, 2017).

Spontaneous decision-making under time-pressure

Time pressure influences various aspects of decision-making, including the amount of

information gathered, the number of alternatives and attributes considered, and the overall

choice process (Willman-Iivarinen, 2017). Under time constraints, information search and

processing are notably affected, leading to quicker and more spontaneous decisions (Edland

& Svenson, 1993; Zur & Breznitz, 1981). This phenomenon is characterized by swift, automatic

decision-making processes (Burns & D’Zurilla, 1999) and spontaneous judgments (Pachur &

Spaar, 2015). When faced with highly complex tasks, individuals may choose to avoid making

a decision altogether (Luce, 1998). In the context of intuition measurement, this tendency is

referred to as an avoidant intuition style, as described in the GDMS study by Scott and Bruce

(1995).

As an interim result, spontaneous, intuitive decision-making in marketing is not well researched

yet.

Emotional decision-making

Emotions significantly impact cognitive processes and decision-making (Isen & Shalker, 1982;

Isen & Patrick, 1983; Pfister & Böhm, 1992). Negative emotions often arise during challenging

decision tasks or under time pressure (Luce, 1998). Emotions provide immediate, automatic

evaluations of the "goodness" or "badness" of a feature or outcome (Slovic et al., 2007). People

particularly rely on their emotions when making difficult decisions, when information is limited,

or when they believe emotions are relevant (Schwarz, 2002). Brands evoke emotional

connections due to their symbolic features and personalities (Belk, 1988; Fournier, 1998),

adding unique attributes to products and enhancing their appeal. According to Sam Gosling

(2008), this use of items as emotional regulators is notable.

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Shiv and Fedorikhin (1999) explored the interaction between affect and cognition in decision-

making, highlighting that when processing resources are limited, affective reactions often

outweigh cognitive evaluations, leading consumers to choose options that are affectively

pleasing but cognitively inferior (e.g., choosing chocolate cake). Conversely, when processing

resources are ample, cognitive considerations about the consequences of choices have a

greater influence on decisions (Shiv & Fedorikhin, 1999).

Research by Guo, Wang, and Wu (2020) on emotional content in online reviews reveals that

positive reviews tend to increase purchase likelihood, demonstrating an emotional bias that

holds significant practical implications for both sellers and consumers (Sari, 2022).

The role of emotions in intuition has been examined by various researchers, including Scott

and Bruce (1995), Pacini and Epstein (1999), Burns and D'Zurilla (1999), Betsch (2004), Pretz

et al. (2014), and Pachur and Spaar (2015). However, Launer and Svenson et al. (2021) argue

that existing studies do not delve deeply enough into the emotional aspects, such as

physiological responses like skin arousal, heartbeat, and gut feelings, which are crucial for a

comprehensive understanding of emotions in decision-making (Launer & Cetin, 2021, 2023).

Decision-making based on mood

The mood marketing mangares or consumers are in might influence their decision-making.

Zillman’s (2000) mood management theory posits that individuals aim to maintain a good mood

or alter a bad mood by engaging in specific actions. This theory suggests that people use

deliberate activities to enhance their mood, and these actions are generally effective (Willman-

Iivarinen, 2017).

Mood management through media consumption has been studied, particularly in television

program selection (Bryant & Zillman, 1984; Zillman & Medoff, 1980; Helregel & Weaver, 1989;

Meadowcroft & Zillman, 1987), music choices (Knobloch & Zillman, 2002; Knobloch, 2003),

and video rentals (Strizhakova & Krcmar, 2007). However, mood management extends

beyond media consumption. Individuals also manage their mood through activities such as

walking, exercising, spending time with children or pets, and shopping (Thyer et al., 1994). The

effectiveness of these methods depends on the initial mood and the desired change or

maintenance of that mood. Mood management strategies are highly individualized and

context-dependent (Luomala, 2002).

The influence of mood on decision-making in general, in marketing in particular, is very limited.

In the context of intuition measurement, mood has not been thoroughly explored. Launer and

Cetin (2021, 2023) have developed tools to measure mood based on the mood wheel,

providing a new approach to understanding mood in decision-making contexts.

In general, there is a research gap on investigating rational and intuitive decision-making in

marketing. While for some above mentioned decision-making styles results could be found, for

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holistic intuition, unconscious thougths, anticipation, support by others and intuition based on

modern technology no information could be found. Therefore, a comprehensive model on

ratiknal and intuitive decision-making will be introduced as a basis for future research.

Methodology

Launer and Cetin (2023) provide a comprehensive model which is useful to measure rational

and intuitive decision-making of marketing manager company internal as well as the consumer

externally. Their measurement instrument proposees based on the above mentioned studies,

twelve different types of styles:

 Rational decision-making: Analytic, Planning, and Knowing

 Intuitive decision-making: Holistic Unconscious, Spontaneous, Heuristic, Slow

Unconscious, Emotions, Body Impulses, Moods, Anticipation, and Support by Others

The different decision-making styles are briefly introduced and described, where the

dimensions come from:

 Analytic is a rational style with logical evaluation (GDMS), analytical and logical manner

(REI), problem solving (PMPI), deliberative thinking on facts and details (PID).

 Planning is a rational style associated with sequential, structured, conventional,

planned confirmative, and systematic routines (CoSI, PID, USID).

 Knowing is a rational style with understanding facts and details without the reasoning

behind (REI, CoSI, USID).

 Holistic Unconscious is an intuition style based on experiential ability in abstract terms

or holistically in a Gestalt-like, non-analytical manner (CES, TIntS).

 Spontaneous is an intuition style with a speed and efficient automated information

processing (GDMS, PMPI, TIntS, USID).

 Heuristic is an intuition style with an experience-based automated information

processing (CEST, PMPI, TIntS, PID, USID).

 Slow Unconscious is an intuition style with an unconscious reflection and activation

develops in a period of time with distractions (Dijksterhuis, 2004).

 Emotions is an intuition style relying on feelings (GDMS, REI, PMPI, TIntS, PID, USID).

 Body Impulses is an intuition style based on feelings such as gut, heart, skin arousal,

etc. (REI, PMPI, TIntS, PID, USID).

 Moods is an intuition style based on negative and positive versus active and activated

and deactivated states according to the Affective Infusion Model (Forgas, 2001). This

indicates a different information processing mode (Bolte et al, 2003).

 Anticipation is an intuition style based on hunches and vibes (GDMS, REI, PMPI, TIntS,

USID).

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 Support from others is an intuitive style involving seeking advice and direction from

others while experiencing a sense of whether the person is right or wrong (GDMS, REI).

This model might be extended by the intuitive decision style Support by Technologies or the

creating style.

Conclusion

The marketing literature shows, the research on rational and intuitive decision-making by

consumers can be based on the Launer & Svenson et al. (2020b, RHIBA study), Launer &

Svenson, (2022, RIEHUA study) and Launer and Cetin (2021 and 2023): Analytic, Planning,

Knowing, Holistic Unconscious, Spontaneous, Heuristic, Slow Unconscious, Emotions, Body

Impulses, Moods, Anticipation, and Support by Others. This can be extended by the intuition

styles Support by Technology. The items needed to be adapted to consumer research.

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