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A mind model for intelligent machine innovation using future thinking principles
Abstract
Purpose
The purpose of this paper is to address the possible future evolution of innovation from a human-only initiative, to human–machine co-innovation, to autonomous machine innovation and to arrive at a conceptual mind model that outlines the role of innovation regimes and innovation agents.
Design/methodology/approach
This is a concept paper where a theoretical “thought experiment” is done, using future thinking principles and data that originate from the literature.
Findings
A conceptual mind model is developed to facilitate a better understanding of complexity at the edge of innovation where intelligent machines will emerge as innovators of the cyber world. It was found that innovation will gradually evolve from a human-only activity, to human–machine co-innovation, to incidences of autonomous machine innovation, based on the growth of machine intelligence and the adoption of human–machine partnership management models in future.
Research limitations/implications
Very little information is available in the literature on intelligent machines doing innovation. The work is based on a theoretical approach that presents new concepts to be debated, but have not been tested in engineering and technology management practice, except for a conference presentation and academic discussion.
Practical implications
The current world view is that future “smartness” is only possible through the creative abilities that humans have, but as machines are entering the workplace and our daily lives, not only as static robots on a manufacturing line, but as intelligent systems with the potential to replace lawyers and accountants, doctors and teachers, companions and partners, their role in innovation in complex environments needs to be explored.
Social implications
Human–machine interaction is often an emotional social issue of concern in terms of the replacement of human intelligence with machine intelligence. It should be asked whether humans will or should remain in control of innovation? Artificial intelligence (AI) may complement and even substitute human intelligence, but huge value is embedded in the new goods, services and innovations AI will enable, especially in manufacturing, where value embedded in the project becomes complex and dynamic.
Originality/value
The thinking presented in this paper is original and should lead to debate to question the way innovation systems will work in future and inspires thinking about AI and innovation.
... Muito provavelmente, a Indústria 4.0 (I4.0) culminará nas designadas "fábricas do futuro", as quais trarão filosofias de produção inovadoras, englobando a digitalização, a reconfiguração das estruturas organizacionais e operacionais (Botha, 2019). Também acarretará a necessidade de novas competências e talentos em recursos humanos, a integração da produção com a logística, a reestruturação e a agilização da cadeia de valor, além da criação de novos modelos de negócios e fontes de receita. ...
... Também acarretará a necessidade de novas competências e talentos em recursos humanos, a integração da produção com a logística, a reestruturação e a agilização da cadeia de valor, além da criação de novos modelos de negócios e fontes de receita. Além disso, será enfatizada a importância da segurança cibernética e o cumprimento de normativas legais ainda em desenvolvimento (Botha, 2019). As inovações introduzidas pela I4.0 estão promovendo mudanças significativas nos processos produtivos, sendo a tendência para a automação completa uma de suas características mais marcantes. ...
... O futuro da indústria de manufatura será definido e direcionado pela I4.0, sustentada pela digitalização, IoT Industrial, sistemas ciber físicos, hiper conexão, e o uso e análise de grandes quantidades de dados, impulsionando rapidamente novas demandas por inovação no setor (Botha, 2019). A transformação digital é uma megatendência global que é desencadeada pela evolução da tecnologia digital, que tem o potencial para cada organização otimizar o seu negócio por meio de uma inovação ou da disrupção digital (Woitsch, 2020). ...
Objetivo: O objetivo deste estudo é identificar os benefícios esperados com a adoção de tecnologias de robótica na produção. Originalidade/Valor: Este estudo preenche uma lacuna teórica sobre os benefícios da robótica na fabricação, além da substituição de mão de obra, aprofundando a compreensão das vantagens na Indústria 4.0 e contribuindo para o desenvolvimento de futuras tecnologias e práticas industriais. Métodos: Uma Revisão Sistemática da Literatura analisou 35 artigos das bases Scopus e Web of Science , utilizando um protocolo estruturado, resultando em uma análise detalhada dos benefícios em categorias temáticas. Resultados: A adoção da robótica na fabricação oferece benefícios como aumento na eficiência produtiva, melhoria na qualidade, maior competitividade, melhorias ergonômicas e de segurança, e redução de custos operacionais. Esses benefícios foram agrupados em cinco categorias principais. Conclusões: O valor do artigo reside em fornecer uma visão abrangente dos benefícios da robótica na produção, com implicações para a teoria e a prática, destacando a importância de políticas públicas que incentivam a adoção segura dessas tecnologias.
... As the factors are interconnected and influence each other, decision makers should consider them carefully. Users´ trust in AI is an essential factor to enable successful AI adoption and use in production [52,68,78,79,88,90]. From the users´ perspective, AI often exhibits the characteristics of a black box because its inherent processes are not fully understood [50,90] which can lead individuals to develop a fear towards the unknown [71]. ...
... From the users´ perspective, AI often exhibits the characteristics of a black box because its inherent processes are not fully understood [50,90] which can lead individuals to develop a fear towards the unknown [71]. Because of this lack of understanding, successful interaction between humans and AI is not guaranteed [90], as trust is a foundation for decisions that machines are intended to make autonomously [52,91]. To strengthen faith in AI systems [76,80], AI users can be involved in AI design processes in order to understand appropriate tools [54,90]. ...
... Ignorance, as a kind of resistance to change, is a main obstacle to successful digital transformation [51,56,65]. Some employees may resist the change brought about by AI because they fear losing their jobs [52] or have other concerns [78]. Overcoming resistance to technology adoption requires organizational change and is critical for the success of adoption [50, 51, ...
Our paper analyzes the current state of research on artificial intelligence (AI) adoption from a production perspective. We represent a holistic view on the topic which is necessary to get a first understanding of AI in a production-context and to build a comprehensive view on the different dimensions as well as factors influencing its adoption. We review the scientific literature published between 2010 and May 2024 to analyze the current state of research on AI in production. Following a systematic approach to select relevant studies, our literature review is based on a sample of articles that contribute to production-specific AI adoption. Our results reveal that the topic has been emerging within the last years and that AI adoption research in production is to date still in an early stage. We are able to systematize and explain 35 factors with a significant role for AI adoption in production and classify the results in a framework. Based on the factor analysis, we establish a future research agenda that serves as a basis for future research and addresses open questions. Our paper provides an overview of the current state of the research on the adoption of AI in a production-specific context, which forms a basis for further studies as well as a starting point for a better understanding of the implementation of AI in practice.
... The company owns an innovation system to develop new products and services to meet customers' expectations (Afraz et al., 2021). The results of the company's continuous innovation can increase competitiveness by implementing new ways to improve the company's operational efficiency using technology (Botha, 2019). The innovation system formed can improve the company's response to changes in regulations and market trends with rapid changes (Prajogo & Sohal, 2006). ...
... The innovation system is not only related to the products or services formed by the company. Still, it shows the company's ability to manage and organize its system to interact with its environment, increasing competitiveness (Botha, 2019). Management innovation is something that companies must do to develop an organization for the better (Pratono, 2022). ...
... The integration by the company with partners has used adequate information technology inefficient processes (Agostini et al., 2020). Innovations set by companies using technology will expose companies to increase transparency in supply chain processes (Botha, 2019). Companies can quickly find out the ability and constraints of suppliers in supplying goods, and vice versa; suppliers can find out the problems the company faces quickly (Siagian et al., 2019). ...
Integration with external supply chain partners can reduce the risk of process and product development disruptions. Hence, the companies should anticipate and prepare for any risk that could emerge in the supply chain network. This study aims to analyze the role of supply chain integration, information sharing, supply chain quality integration, and innovation systems in improving business performance in the manufacturing industry. The study surveyed manufacturing companies located in East Java, as many as 258 companies. Data was collected using questionnaires designed with a five-point Likert scale and distributed through Google Forms and company visits. 222 questionnaires were distributed through Google Forms, and 36 were distributed during company visits. The smartPLS software version 4.0 was used for descriptive analysis and hypothesis testing. The results showed that supply chain integration positively impacts information sharing, quality integration, and innovation systems. Information sharing significantly supports the implementation of quality integration and innovation systems. However, quality integration does not affect the innovation system. Likewise, innovation systems have no impact on improving business performance. Many manufacturing companies in East Java had not done innovation systems appropriately after the COVID-19 disruption as the company still focused on current processes and products to maintain company sustainability. Furthermore, information sharing, and supply chain quality integration significantly improve business performance. The results of this study could contribute to managers building close partnerships with external parties to maintain the quality of processes and products. Business owners must also consider using the latest technology for process and product innovation. These findings enrich the current supply chain management theory, particularly with quality integration and innovation systems.
... Once a decision has been made to conduct a thought experiment, the next step is planning it. Creating an engaging and convincing "mind model" for the experiment is crucial to facilitate a better understanding of complex abstractions (Botha, 2019 can also help set the scene visually. As an illustration, Botha (2019) used several diagrams to explain the mind model of the experiment. ...
... given the abstract nature of thought experiments, particularly for boundary conditions (Botha, 2019;Caste, 1992;Hong, 2012;Nothhaft & Stensson, 2019). As an illustration, Bozeman and Feeney (2007) explained that "often the concepts presented are suggestive, identifying the attributes of mentoring rather than stipulating the meaning of the concept itself and, in particular, its boundary conditions" Next, to show and not just tell that our recommendations are actionable, practically doable, and useful, and that thought experiments have great potential for making theory advancements, we describe an original thought experiment in the area of allyship. ...
... (p. 721). They went on to cite several researchers who failed to define their focal construct.Hatherly et al. (2020) defined the central concept of the thought experiment to ensure clarity in how they defined stakeholders.To more fully immerse the reader in the experiment and help detail what occurred in the imagination of the researcher, researchers should also consider including a figure or diagram to help ground the thought experiment and bring it to life(Botha, 2019;Brown, 2011;Nothhaft & Stensson, 2019). Figures can help facilitate understanding as well as provide evidence for the explanations for each of the questions raised in the imagined scenario(Botha, 2019;Brown, 2011). ...
Thought experiments have been used as an effective methodological approach to advance theory in numerous scientific fields. However, they are underutilized in organizational behavior (OB) and adjacent fields. Accordingly, we conducted a comprehensive and multidisciplinary literature review of thought experiments that entailed 174 sources in economics, psychology, marketing, medicine, sociology, finance, and other fields. We leveraged insights from this literature review to define and describe the unique nature of thought experiments and offer a taxonomy of four main types based on a theory’s development stage (i.e., early vs. late) and a study’s theoretical goal (i.e., confirmation vs. disconfirmation). We also provide a decision‐making tree useful for evaluating whether conducting a thought experiment is beneficial for a particular research situation and which of the four types is most likely to produce a meaningful contribution. Then, we offer best‐practice recommendations for conducting thought experiments that address how to plan, execute, report results, and discuss implications. In addition, we demonstrate the potential of thought experiments by using the best‐practice recommendations to design and conduct a thought experiment in the domain of workplace allyship. Finally, we offer suggestions for future substantive research that would benefit from thought experiment methodology (e.g., diversity and inclusion, leadership, performance, selection and recruitment, teams, and turnover). Overall, our article offers a comprehensive review and recommendations that we hope will be a catalyst for using thought experiments to advance theory in OB and related fields.
... IT professional experts have a sound understanding of AI and robotics. They have the skillsets and adequate expertise to ensure social robots' success in service operations [95]. has explained different types of innovation, such as incremental, disruptive, radical, ambidextrous, frugal, market, open, sustainable, user, integration, value, social, business model, soft, and technological innovation. ...
... The research propositions are broadly derived from the antecedents, decisions, outcomes, and applications using the extended ADO-TOE framework [41]. observe that innovations play an important role in improving guests' experiences in a high-contact service industry [95]. defines technological innovation as new technologies supporting innovative products and services [26,29]. ...
... The 2018 World Economic Forum included a focus on 'augmented humanity', namely machines and humans working together (Botha, 2019). Wesche and Sonderegger (2019) contend that, increasingly, computers are taking over leadership functions and that a future in which computers are leading humans is probable. ...
... Wesche and Sonderegger (2019) contend that, increasingly, computers are taking over leadership functions and that a future in which computers are leading humans is probable. This concept is supported by Botha (2019), who claims that innovation is evolving from a human-only activity to humanmachine co-innovation, and through to autonomous machine innovation. Botha deduces that this is due to the growth of machine intelligence and the increased adoption of humanmachine partnership management models. ...
This research aimed to assess the critical elements of intelligent automation adoption and identify successful implementation factors, in order to make recommendations to New Zealand organisations, from the platforms of robotic process automation, machine learning, and artificial intelligence. A qualitative study was conducted, collecting responses from 13 semi-structured interviews. The interviews were designed to seek information regarding: (1) the extent and scale to which automation technology is presently and actively used in New Zealand organisations; (2) the impacts of automation for those organisations; and (3) the participant lessons learned in terms of key enablers and barriers to successful implementation, and subsequent adoption of the technologies. Findings suggest that automation adoption produces both positive and negative impacts, for employees, customers, and companies. For employees there are changes in the way they work, their associated skills, their income, and also psychological effects. From an operational efficiency perspective, automation can reduce cost and improves productivity. In addition, COVID-19 has accelerated some automation change, mostly in customer touchpoints. Implications and recommendations are discussed.
... The innovation processes and the new business models are being revolutionised. There are discussions whether robots or intelligent machines will innovate in the future (Botha, 2019). Since digitalisation affects all our life in production, business, education, health system, at home, it is discussed in many different contexts, in conferences, by policy makers also in relation with funding strategies. ...
... The applied methodologies are mainly of explorative character such as case studies, interviews, stakeholder survey and questionnaires. Only one article is a concept paper which deals with the future of innovation and whether machines will do this (Botha, 2019). Find here a short description of each of the eight articles. ...
... A theoretical distinction is sometimes made between 'process innovation' and 'product innovation'. A good explanation of the different types of innovation and different innovation processes is given by Botha [3] and Edwards-Schachter [4]. Process innovation concerns production techniques, the organisation of work, and business model innovations. ...
... Botha [3] proposes a mind model for intelligent machine innovation in which, in the 4IR context, three innovation agents will emerge: human innovation as we know it; human-machine coinnovation; and autonomous machine innovation. These three innovation agents will be applied in different innovation regimes, based on the open and proprietary innovation domains, and addressing grassroots innovation and systematic innovation modes. ...
Will the market adoption of innovative products and services in the fourth industrial revolution require an alternative reality? This question is investigated in a concept paper in which new product adoption patterns, alternative innovation regimes that include intelligent machines as innovation partners with humans, disruption of the producer, the fourth industrial revolution consumer, and a fundamental change in business models are considered. Thought models are proposed in which these four entities drive a new concept of ‘life-world’ products through which consumers innovate for their own personalisation and customisation, manufacturing plants for volume products become algorithm factories, and the linear value chain is destroyed and replaced by the value network. This happens because the consumer becomes part of the value chain, and overlaps of producer and consumer functions as we know them merge into a new production ecosystem driven by social commerce. Innovation by the consumer takes away the producer’s concerns about market adoption; they may now servitise innovation support. This paper is meant to stimulate academic debate and to initiate research that will validate the thought models it suggests.
... Od czasu wydania tej książki minęło ponad siedemdziesiąt lat, optyka się zmieniła, świat się rozwinął, a co za tym idzie w przyszłości można oczekiwać kreatywnej współpracy pomiędzy człowiekiem a maszyną (Olszewski, 2023). Jednym z futurologów, który uważa, że autonomiczne maszyny zostaną "kustoszami innowacji" jest Anthon P. Both, który wskazuje na możliwość przyszłej ewolucji innowacji, zaczynając od inicjatywy podejmowanej wyłącznie przez człowieka, poprzez wspólne innowacje człowieka i maszyny, do innowacji autonomicznych maszyn (Botha, 2019). Obecnie tylko człowiekowi przysługują prawa autorskie, ale wraz z rozwojem technologii może się to zmienić, a kreatywność przestanie być tylko domeną ludzi. ...
Technologie określane jako sztuczna inteligencja – AI w istotnym stopniu wpływają na zmiany szeroko rozumianego przemysłu muzycznego w jego wymiarze popkulturowym. Nie inaczej wygląda sytuacja w branży muzycznej w Republice Korei, gdzie K-pop, czyli muzyka popularna, zyskuje coraz większą rozpoznawalność w wymiarze globalnym, a wykorzystanie sztucznej inteligencji odgrywa tutaj kluczową rolę, przyczyniając się do dynamicznego rozwoju tego sektora. Dlatego celem niniejszej pracy jest zbadanie, w jaki sposób rozwój sztucznej inteligencji kształtuje południowokoreański przemysł muzyczny oraz jakie to przyniesie skutki dla przyszłości K-popu. W artykule wykorzystano metodę analizy porównawczej do zestawienia rzeczywistych grup muzycznych z wirtualnymi. Zastosowano także metodę studium przypadku, aby przeanalizować kampanię promocyjną z użyciem aplikacji specjalizujących się w komunikacji między wykonawcami a fanami. Dodatkowo artykuł odnosi się do wyzwań natury społecznej i etycznej związanych z rosnącym wykorzystaniem sztucznej inteligencji.
... The IM can be defined as a machine or device that has the ability to interact with its operating environment autonomously by learning from it and adapting to its changes for making decisions (Botha, 2019). This trait distinguishes IM from normal machines, i.e., a normal machine operates in accordance with a predetermined set of tasks that have been programmed into it, and IM has a purpose to fulfil and a learning mechanism to assist in achieving that purpose (Takamuku & Arkin, 2007). ...
Intelligent machines are the machines or devices that make use of artificial intelligence and robotics technologies. It has the ability to accomplish a specific task in the presence of uncertainty and variability in its operating environment. Certainly, it can be used to support information and communication technology to streamline the creation, collection, processing, transmission, and storage of information for sustainable marketing practices. The flawless application of sustainable marketing practices results in beneficial impacts on business performance. In fact, the issue of unsustainable marketing practices can be effectively managed by intelligent machines. Therefore, this study is undertaken to uncover how intelligent machines can influence sustainable marketing practices for beneficial impacts on retailers’ business performance by proposing a unique conceptual framework. The theoretical contributions discuss two techno-sustainable marketing applications. First, intelligent machines improve incremental innovation. This allows retailers to balance technology risk with sustainable marketing and lower the cost of innovations. Second, intelligent machines increase business efficiency by automating sustainable marketing practices. This allows retailers to efficiently manage the inventory, improve fulfilment efficiency, and optimise stock levels. The managerial implications discuss two goals of sustainable marketing practices. First, it can attract sustainability-minded customers who support the retail business for their own well-being. Second, it builds a strong sustainable brand reputation that can lower the price sensitivity.
... According to research on the implementation of AI-enabled HRM, it increases productivity, lowers costs, improves operational efficiency (such as flexibility, scalability, safety, and dependability), and fosters customer engagement and loyalty (Botha, 2019;Lu et al., 2020;Prentice & Nguyen, 2020;Ransbotham et al., 2017;Tarafdar et al., 2019). Additionally, AI can increase returns on investment by making the company more cost-effective (Torres & Mejia, 2017). ...
Human capital is a crucial requirement in this competitive globe to boost the work places' effective performance. In order to increase the workers' performance and stand out from the competition, firms must work to adopt innovative human resource practices. In very near future, human resource management (HRM) will transition from conventional human resource management practices to most advanced practices like augmented intelligence, automation, robotics, and artificial intelligence (AI). This literature analysis was conducted to examine the potential opportunities and challenges of practicing artificial intelligence in HRM. The proliferation of AI-based HRM practical over the past ten years has provoked a delightful new series of researches under the topics of the consequences of AI adoption on both human and corporate results, and the assessment of AI-based HRM practices. The way we work is organized in businesses as a result of the adoption of these technologies. AI has the power to fundamentally decide how we live or life and work. The AI facilitates the HRM with both an opportunities and challenges. Today's HR experts are more focused on maximizing the interaction between human and automated work to provide a straightforward and understandable working environment which provide an adequate time to improve their performance. The true challenge now is how each HR department will retrain and transform their employees to grasp AI and collaborate and interact with AI and advanced machines in order to optimize their performance. In order to present a conceptual overview of AI and its favourable and unfavourable impacts on human resource, this paper will concentrate on the opportunities and challenges of using AI in Human resource management. This is done by investigating numerous journals and published articles on the emergence of artificial intelligence.
... HRM automation technologies have an influence on both employee and corporate outcomes. According to research on AIenabled HRM delivery, this leads to better productivity, cost savings, and operational efficiency (including flexibility, scalability, security, and reliability), as well as enhanced customer retention and loyalty (Botha, 2019). Wirtz (2019) noted, on the other hand, that while research emphasizes the positive benefits of new technology, several unfavourable features have also been uncovered. ...
The introduction of machines driven by artificial intelligence (AI) and automation technologies has already had a significant impact on the manufacturing, automotive, logistics, retail, and wholesale industries, and the repercussions of their replacement on the human labour has been a hotly disputed subject. AI and automation technical advancements are having a big influence on workforce turnover. The aim of this study was to look at employees' attitudes on Artificial Intelligence and Automation at work, specifically whether they see AI as a threat or not. Secondary data sources from several authors were utilised in this article. This data was gathered from published and peer-reviewed publications, internet sources, and textbooks pertinent to the issue under consideration. Some researchers contend that automation is likely to add to South Africa's high unemployment rate. There is a scarcity of empirical data in the form of published empirical research concentrating on the stress that AI and automation place on employees, hence the study is being conducted. Based on this study, it appeared that employees feel threatened by this rise of Artificial Intelligence and Automation.
... It also raises human-machine co-innovation environments has a gradual evolution depending on the growth of machines and the adoption of management models, with intelligent systems that will allow it to replace various professions in complex environments. Creative capabilities will accelerate the functioning of innovation systems in the future between AI and innovation [5]. This calls for project manager profiles with specializations suited not only to technology with issues such as applications of cyber-physical systems, big data, artificial intelligence, and intelligent robotics in the management of time, cost, and quality of projects, but also to project management, progress tracking, real-time monitoring, and schedule estimation [6]. ...
Introduction: Social robotics is integrated into everyday activities, addressing social interactions with diverse groups of people. Therefore, the development of Autonomous Social Robot for Ecuadorian Universities (ASREU), an evolutionary social robotic platform in hardware and software that allows the exploratory research of social, technological, and energetic variables for the generation of social robotics prototypes. Method: To improve the management of the project, the structures for the development of technological projects are analyzed from the administrative management of the project, as well as the execution and technological implementation of the Results: A diagram is obtained to identify the interaction of technical, engineering and scientific working groups and the interaction with the project management, as well as a roadmap for the development of technological products defining the systems, the type of prototyping and the intellectual property protection, in addition to the circular strategy for the integration from and for society in the area of social robotics and the identification with the evolution of the prototype with the corresponding intellectual property protection for feasible results to Conclusion: Currently the development of technological projects lead mostly software developments in social robotics, when integrated with physical systems (mechanical, electrical and electronic), there is planning from traditional environments and agile environments, which need a coexistence to jump from one to another environment.Finally, guidelines and tools have been developed to facilitate the management and design of social robots, in the case of ASREU, a 6 DoF and autonomous mobile robot (AMR).KeywordsV-ModelSocial RoboticsTRLIntellectual PropertyTechnological Project Management
... The literature on AI-enabled HRM adoption suggests that it leads to productivity gains, cost reduction and operational efficiencies (e.g. flexibility, scalability, safety and reliability), customer engagement and loyalty (Botha, 2019;Lu et al., 2020;Prentice & Nguyen, 2020;Ransbotham et al., 2017;Tarafdar et al., 2019). AI can also yield greater returns on investment by providing cost-effectiveness to the organisation (Torres & Mejia, 2017). ...
Artificial intelligence (AI) and other AI-based applications are being integrated into firms’ human resource management (HRM) approaches for managing people in domestic and international organisations. The last decade has seen a growth in AI-based applications proliferating the HRM function, triggering an exciting new stream of research on topics such as the social presence of AI and robotics, effects of AI adoption on individual and business level outcomes, and evaluating AI-enabled HRM practices. Adopting these technologies has resulted in how work is organised in local and international firms, noting opportunities for employees and firms’ resource utilisation, decision-making, and problem-solving. However, despite a growing interest in scholarship, research on AI-based technologies for HRM is limited and fragmented. Further research is needed that analyses the role of AI-assisted applications in HRM functions and human-AI interactions in large multinational enterprises diffusing such innovations. In response to these combined issues—the fragmented nature of research and limited extant literature, we present a systematic review on the theme of this special issue and offer a nuanced understating of what is known, yet to be known, and future research directions to frame a future research agenda for international HRM. We develop a conceptual framework that integrates research on AI applications in HRM and offers a cohesive base for future research endeavours. We also develop a set of testable propositions that serve as directions for future research.
... In sum, according to some conditions, such as the presence of shared leadership in teams acting in big manufacturing technology firms, heuristics can positively impact organizational processes, escaping the vision that they always lead to poor performance (Kahneman and Tversky, 1972). From this, biases should not be avoided at all costs during employees' creative process, as implicitly advanced by the "mind model for intelligent machine innovation" of Botha (2019). ...
Purpose
This paper aims to empirically investigate how cognitive biases influence employees' product creativity (EPC) and related product performance. In particular, the paper primarily studies (1) the direct effect of employees' implicit creativity – based on five cognitive biases – and explicit creativity on EPC; and (2) the mediating role of coworkers' heuristic transfer between shared leadership and EPC.
Design/methodology/approach
Data have been obtained from big Italian manufacturing technology firms through a series of online questionnaires that resulted in 555 answers from R&D employees and their direct managers, who are, respectively, involved and responsible for the proposal of manufacturing technology products. The developed four theoretical hypotheses have been tested through correlation analysis, hierarchical regression, mediation analysis and structured equation modelling.
Findings
Cognitive biases positively influence EPC in manufacturing technology firms, leading to positive product performance. In particular, implicit creative personality better predicts EPC than explicit creative personality; whilst, shared leadership leads to a cognitive convergence among co-workers through the spread of heuristics that positively influence EPC.
Originality/value
The originality of this work lies in having: (1) investigated the influence of cognitive biases in creativity, (2) hypothesized and proved that co-workers' heuristic transfer mediates the relationship between shared leadership and EPC; (3) conducted the first specific study on employees' creativity in manufacturing technology firms; and (4) first implemented the implicit creative personality measurement, apart from those who conceptualized it.
... Botha [3] addressed the possible future evolution of innovation from a human-only initiative to human-machine coinnovation and then autonomous machine innovation, arriving at a conceptual mind model that outlines the role of innovation regimes and innovation agents. Wauters and Vanhoucke [4] provided a nearest neighbor-based extension for project control forecasting with earned value management and selected an AI method to reduce the training set to predict the real duration of a project. ...
This paper models the game process of the value cocreation of enterprises based on evolutionary game theory (EGT). The factors influencing value cocreation are found through mathematical analysis. Taking iFLYTEK as an example, a representative enterprise of artificial intelligence (AI) in China, six factors affecting value cocreation are verified, which are the excess return rate, the distribution coefficient of the excess return rate, coordination costs in the system, the cost-sharing coefficient, imitation costs, and penalties. These six factors have a profound impact on value cocreation in the ecosystem. Through the case study of iFLYTEK, it is concluded that innovation ecosystems can enable small- and medium-sized AI enterprises to grow. In order to build a sound ecosystem, we need to establish a mechanism to select partners, reduce the costs of cooperation, and strengthen the protection of intellectual property. At the beginning of the cooperation, it is necessary to establish a mechanism with clear responsibilities, rights, and interests. The conclusion is of great significance to the development of AI enterprises.
... The changing trend of the demand for human resources in the labor market is indispensable, especially in the context of the rapidly changing technology of Industry 4.0. The trend of automation leads to the status that machines replace humans in doing repetitive, "process" jobs (Botha, 2019;Luong, Tran, & Nguyen, 2018;Nguyen, 2020;Pham, Dao, Cho, Nguyen, & Pham-Hang, 2019). Therefore, education should aim to equip students with a positive attitude which motivates a lifelong learning, the core factor which determines each individual's success or failure because no one can master and teach everything (Jocic et al., 2020;Nguyen, Nguyen, Huynh, & Nguyen, 2020). ...
Employability has recently become the first target of the national higher education. Its model has been updated to catch the new trend of Industry 4.0. This paper aims at analyzing and ranking the determinants of undergraduate employability, focusing on business and economics majors in Ho Chi Minh City, Vietnam. In-depth interviews with content analysis have been primarily conducted to reach an agreement on a key group of factors: human capital, social capital, and identity. The Stochastic Fractal Search Algorithm (SFSA) is then applied to rank the sub-factors. Human capital is composed of three major elements: attitude, skill, and knowledge. Social capital is approached at both structural and cognitive aspects with three typical types: bonding, bridging, and linking. The analysis has confirmed the change of priority in employability determinants. Human capital is still a driver but the priority of attitude has been confirmed in the contemporary context. Then, social capital with the important order of linking, bridging, and bonding is emphasized. Skill, knowledge, and identity share the least weight in the model. It is noted that identity is newly proposed in the model but a certain role has been found. The findings are crucial for education strategies to enhance university graduate employability.
Purpose
This study aims to identify and model deterrents to adopt and institutionalize analytics and artificial intelligence in modern human resource (HR) using interpretive structural modelling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) approach.
Design/methodology/approach
A comprehensive investigation of the literature and feedback from experts led to the identification of 16 deterrents in this study. After that, the ISM tool is used to find connections between the identified deterrents in the HR ecosystem and MICMAC which helps in categorising deterrents on the basis of driving and dependence power and provides deeper insights into their roles and significance.
Findings
Employee resistance and HR transformation are highly influenced by other factors but exert minimal driving power. Data availability, leadership support, communication and collaboration, legal, ethical and regulatory compliance, and infrastructure and resources exhibit strong influence and dependence, making them highly sensitive and crucial. Training and development, learning culture and change management, and data privacy and security have strong driving power with minimal dependence, indicating their foundational role in shaping HR transformation.
Research limitations/implications
This study will assist policymakers and owners/managers in the HR ecosystem in recognising and comprehending the importance and applicability of analytics and AI obstacles while developing HR strategies.
Originality/value
This study explicitly focuses on data analytics and AI technology in the current scenario. It also explores the relationship between deterrents and their driving and dependence powers.
This study aims to comprehend the preferences of young retail investors regarding stock selection attributes and to investigate the relative importance of the stock market attributes that these investors take into consideration. This research study has used five attributes of the stock market viz., company type, fundamental, holding period, annual return, and risk. The researchers have strived to understand which of these attributes are the most and least preferred by the potential participants. 270 young retail investors were surveyed to collect the required primary data. The data was gathered by using availability and purposive sampling techniques. Twenty-five conjoint cards were made and given to the participants along with a well-structured questionnaire. further assessed each choice’s relative value in relation to a variety of attributes. The conjoint analysis results indicate that the attributes of the stock market related to investment are significantly different from each other (p<.05, F=4.503).
Current developments and technological advancements force organizations to stay abreast of them and to gain competitiveness and sustainability. Moreover, AI's influence on the workplace and employees cannot be neglected. Therefore, to achieve effective AI integration and enhance the Quality of Work Life (QWL), a clear understanding of AI in the workplace and its impact on employees' QWL is essential. This study employs a literature-based research approach focusing on theoretical analysis of relevant books and articles to explore the intersection of AI and QWL. The investigation reveals the gap that currently no research explicitly explores the intersection of AI and QWL. This study addresses this gap theoretically by examining relevant books and articles.
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Technological advancement has greatly enhanced the global environment, it has improved every facet of the global industry. Currently in Nigeria, the Legal Profession has taken a bold dive by incorporating the use of technology in enhancing the practice of law. However, the current innovation of robotic lawyers in most countries may seem to be consistent with their legal systems. In this regard, it suffices to opine that given the fact that Nigeria is a developing country, there are legal and socio-economic issues that may affect or truncate the adoption of a robotic lawyer in Nigeria. It is in this regard that this study adopted a hybrid method of research in ascertaining the relevance of robotic lawyers, and the legal and socio-economic issues. Questionnaires were distributed to 305 respondent residents in Nigeria. The study found that the current trend of robotic lawyers is quite impressive, however, the nomenclature of law concerning the study and practice of Law in Nigeria does not recognize a robotic lawyer. Furthermore, some socio-economic issues such as internet fraudster, unemployment, insecurity, and poor maintenance culture may pose a challenge to the adoption of a robotic lawyer in Nigeria. In this regard, it was therefore concluded and recommended that for a smooth adoption of robotic lawyers in Nigeria, there is a need for legal approval and streamlining their roles to mere advisory to a client, training of Nigerian lawyers and judges to enhance the legal profession.
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Purpose
This paper aims to provide insight into the evolving relationship between humans and machines, understanding its multifaceted impact on our lifestyle and landscape in the past as well as in the present, with implications for the near future. It uses bibliometric analysis combined with a systematic literature review to identify themes, trace historical developments and offer a direction for future human–machine interactions (HMIs).
Design/methodology/approach
To provide thorough coverage of publications from the previous four decades, the first section presents a text-based cluster bibliometric analysis based on 305 articles from 2,293 initial papers in the Scopus and Web of Science databases produced between 1984 and 2022. The authors used VOS viewer software to identify the most prominent themes through cluster identification. This paper presents a systematic literature review of 63 qualified papers using the PRISMA framework.
Findings
Next, the systematic literature review and bibliometric analysis revealed four major historical themes and future directions. The results highlight four major research themes for the future: from Taylorism to advanced technologies; machine learning and innovation; Industry 4.0, Society 5.0 and cyber–physical system; and psychology and emotions.
Research limitations/implications
There is growing anxiety among humankind that in the future, machines will overtake humans to replace them in various roles. The current study investigates the evolution of HMIs from their historical roots to Society 5.0, which is understood to be a human-centred society. It balances economic advancement with the resolution of social problems through a system that radically integrates cyberspace and physical space. This paper contributes to research and current limited knowledge by identifying relevant themes and offering scope for future research directions. A close look at the analysis posits that humans and machines complement each other in various roles. Machines reduce the mechanical work of human beings, bringing the elements of humanism and compassion to mechanical tasks. However, in the future, smart innovations may yield machines with unmatched dexterity and capability unthinkable today.
Originality/value
This paper attempts to explore the ambiguous and dynamic relationships between humans and machines. The present study combines systematic review and bibliometric analysis to identify prominent trends and themes. This provides a more robust and systematic encapsulation of this evolution and interaction, from Taylorism to Society 5.0. The principles of Taylorism are extended and redefined in the context of HMIs, especially advanced technologies.
The potential of artificial intelligence (AI) to constitute a general-purpose technology with diverse algorithmic specifications makes it challenging to assess its overall impact on existing socio-economic regimes. Leveraging the multi-level perspective, we seek to depict the trajectory of micro-, meso-, and macro-level forces and their interactions to characterize AI transition pathways in industry. We treat business and information systems literature as a proxy capturing business practices that relate to factors influencing AI transitions on all three different levels. Based on 10,036 publications over 25 years, we map the topic landscape of AI-related research, longitudinal patterns of topics, and structural changes of topic networks. The results indicate a strong and myopic focus on technological capabilities and efficiency rationales. Topic network structures indicate that transition pathways may diverge between a symbiotic and stabilizing transformation process and a more radical pathway of regime substitution. Based on these findings, we argue that sociotechnical transition pathways may not only occur in sequence, but simultaneously and ambiguously. This highlights the need for a nuanced understanding of convergent and divergent transition pathways for emerging digital general-purpose technology that do not tend to settle on one dominant design. We propose to leverage paradox theory to reconcile these tensions.
JEL: M000, O310, O320, 033
This paper undertakes a systematic review of relevant extant literature to consider the potential societal implications of the growth of AI in manufacturing. We analyze the extensive range of AI applications in this domain, such as interfirm logistics coordination, firm procurement management, predictive maintenance, and shop-floor monitoring and control of processes, machinery, and workers. Additionally, we explore the uncertain societal implications of industrial AI, including its impact on the workforce, job upskilling and deskilling, cybersecurity vulnerability, and environmental consequences. After building a typology of AI applications in manufacturing, we highlight the diverse possibilities for AI's implementation at different scales and application types. We discuss the importance of considering AI's implications both for individual firms and for society at large, encompassing economic prosperity, equity, environmental health, and community safety and security. The study finds that there is a predominantly optimistic outlook in prior literature regarding AI's impact on firms, but that there is substantial debate and contention about adverse effects and the nature of AI's societal implications. The paper draws analogies to historical cases and other examples to provide a contextual perspective on potential societal effects of industrial AI. Ultimately, beneficial integration of AI in manufacturing will depend on the choices and priorities of various stakeholders, including firms and their managers and owners, technology developers, civil society organizations, and governments. A broad and balanced awareness of opportunities and risks among stakeholders is vital not only for successful and safe technical implementation but also to construct a socially beneficial and sustainable future for manufacturing in the age of AI.
Despite its high potential for increasing efficiency, productivity and automation, the implementation of Artificial Intelligence (AI) technologies in production is still at a very early stage and lacks practical experience, particularly in the small and medium sized enterprise segment. One of the main reasons for that is the lack of knowledge on how ready the companies are for adopting and using AI effectively. While approaches to assessing readiness and even maturity in terms of digitalization or Industry 4.0 are well established and discussed in the literature and used actively in companies’ practice, approaches that address AI are still in their infancy. Addressing this gap, this paper presents a qualitative approach to an in-depth analysis and monitoring of the readiness of manufacturing firms for working with AI. Drawing on the literature on technology adoption on the one hand, and the implementation of AI on the other, we develop an integrative framework covering different socio-technical dimensions. For their operationalization, we use a large range of technical, organizational and environmental categories. In order to illustrate the implementation of our approach in practical terms, we present the results of assessing the AI readiness of a German manufacturing company.KeywordsArtificial IntelligenceAIreadinessmanufacturing
With the advent of Industry 4.0 (I4.0), companies were induced to invest in technologies to remain competitive and make their processes more efficient. However, the scarcity of structured processes is pointed out in the literature as one of the biggest barriers to the I4.0 transition since companies need implementation guides warning about possible difficulties encountered. Thus, the paper aims to identify the company’s maturity level regarding I4.0 to present the best direction for implementing 4.0 technologies. Therefore, a case study was carried out through a semi-structured interview and an on-site visit to a company that specialized in manufacturing and selling fibre cement artefacts for civil construction. The Acatech model and a theoretical framework of Industry 4.0 technologies were combined to analyze the company’s maturity level. With the completion of the study, it was identified that the company has the initial level of implementation of I4.0 technology. To improve production processes, the company invests in new systems and equipment, training for the qualification of employees, and changes in organizational culture.KeywordsIndustry 4.0digital technologiesmaturity levelcivil construction
After the first restrictions promoted to control the COVID-19 outbreak, strategies for keeping presential activities in Educational environments have been the focus of several studies because they configure spaces with great risk of viral diseases propagation due to the high circulation of people. However, the exhaled virus can accumulate and infect even people who do not have direct contact with an infected person. This makes the rates of disease transmission in closed environments still a point of discussion in the academic literature. Moreover, it is necessary to find out the main contagious areas in Universities to define the mitigation measures that should be adopted. In this context, this article aims to: (i) demonstrate the main contagion areas in a University building; (ii) assess the impact of physical distancing on the campus with different secondary infection rates. For this, the spread of COVID-19 within the Federal University of Santa Catarina campus was simulated using an adaptation of the SIR model in an agent-based simulation model. The results show that corridors, libraries and bathrooms are also important sources of contagion. Regarding the infection rates evaluated in simulation, the building occupancy should be limited to 33% to avoid contamination. However, reducing classroom capacity by 50%, the infection rate decreases to 1% in the worst infection rates scenario. Finally, with a 50% capacity, the infection rate variation do not significantly impact the final number of infected agents, indicating that physical distancing would be effective even for virus variants with other transmission rates.KeywordsSARS-CoV-2 virusagent-based simulationSIR modeluniversity class-roomsindoor environment risksanylogic
Faced with the changes that happen all the time in the industry and the growing need for consumption, it is necessary to think of approaches that meet these demands without harming the environment. Within this context, the main objective of this study is to explore how digital technologies can be used in the waste reduction, reuse, and recycling of end-of-life products and what contributions they bring to the context of the development of the Circular Economy. For the development of the article, we chose to use the systematic literature review method, as it is a method of easy replication and high reliability. Thus, 27 articles were analyzed to meet the research objective. With the analysis of the articles, it was possible to identify the main technologies adopted within the practices of the 3Rs (reduce, reuse and recycle) of the CE and how these technologies are used to leverage the CE. Integrating digital technologies with CE makes it possible to develop more sustainable production, ensuring greater efficiency in the consumption of resources and reducing the entry of new raw materials due to greater reliability and intelligence in the reuse and recycling processes.KeywordsCircular EconomyIndustry 4.0Digital TechnologiesReuseReduceRecycling
Hospitals consist of service systems that need to be managed for the efficient use of their resources. Beds are one of the most critical features of these systems, and they are generally scarce due to the costs attributed to their operation. Thus, decisions related to the planning and scheduling of beds must be made using appropriate methods, promoting greater use of these. Due to the uncertainties and complexity inherent to hospital systems, hybrid approaches offer opportunities for application in this context. Based on this, we developed a systematic literature review, aiming to investigate the use of hybrid approaches, using simulation and optimization, applied to the planning and scheduling of beds, conducted using the PRISMA method. As a result, the study presents (i) a content analysis, which shows the techniques used and the predominance of research at the tactical decision level; (ii) the perspectives for future researches, which indicate opportunities for enriching simulation models, conducting research at the operational level, developing structures for the supply and analysis of data and the integration with Artificial Intelligence (AI) techniques.KeywordsBed PlanningSimulationOptimization
The Front End of Innovation (FEI) is considered a critical point in the innovation process, as the choices made in the FEI will determine which innovation options should be considered for new product development and commercialization. Studies indicate that Artificial Intelligence (AI) can be used in the FEI and, although the literature suggests that AI may not be ready to fully take on highly creative tasks within the innovation process, it appears much promising as a support for managers and can play a key role in the innovation process. This research seeks to present these potentialities by systematically collecting and analyzing available studies in the literature with the aim to (I) gain a comprehensive understanding of the interconnections between Artificial Intelligence and Front End of Innovation, (II) provide an overview of the current state of the research in this field, and (III) identify important gaps in existing approaches as well as promising research trends. To achieve these goals, a systematic mapping was performed covering articles published in journals from three relevant databases. Initially, 494 primary studies were selected and subjected to a screening and review process, which resulted in the election of 53 articles whose models and solutions for using AI in FEI were classified and summarized. The results of the research point to the increasing use of AI in FEI. The Identification of Opportunities stands out for having the highest concentration of articles with use of AI, followed by areas of Analysis of Opportunities and Generation and Enrichment of Ideas.KeywordsFront End of InnovationArtificial IntelligenceSystematic Literature Mapping
Background: Given the persistent challenges to the higher education business model, private higher education institutions (PHEIs) are exploring myriad ways to increase enrolment and income, while aggressively managing spending. Many PHEIs are facing financial distress and struggling because of decreasing budgets and declining revenue. Thus, carving unique strategies that direct the institution to focus on its core competencies, making additional budget cuts without compromising quality, developing new revenue streams, embracing new technology, and offering affordable programs, will ultimately lead to financial success. Frugal innovation (FI) can shed light on these challenges.
Methods: This paper presents a systematic literature review to investigate and analyse prior research that focused on FI within the sphere of intellectual capital (IC) and information technology capabilities (ITC) research, and their relationships in PHEIs. Transfield’s five phases were employed to extract journal articles published over a thirty-year period (1990 to 2020) from major online databases using keyword searches. Although an initial search generated 76,025 papers, the search for IC and FI yielded 41 papers, and finally only two papers were selected as they clearly related IC with FI.
Results : There was a research gap in the literature published from 1990 to 2020 regarding IC applications to achieve FI. This work revealed that IC and ITC research for FI in PHEI remain insufficiently explored.
Conclusions: Further research is required on the evaluation model of IC, ITC and FI, methodologies, empirical analysis, and the development of measurement metrics. A limitation to this study is the number of keywords selected.
The rapid growth of artificial intelligence (AI) robots has brought new opportunities and challenges. The linkage between AI robots and humans has also gained extensive attention from the legal profession. This study focuses on the extended AI Robot Lawyer Technology Acceptance Model (RLTAM). A total of 385 valid questionnaires are collected through quantitative research, and the relationships among the five variables in the model are reanalyzed and revalidated. Results show that the “legal use” variable in the original extended model is not a direct key variable for consumers to accept AI robot lawyers, but it has a direct effect on “perceived ease of use” and “perceived usefulness” variables. AI robots still need to respond actively to attain legitimacy. AI robot lawyers with national legal certification and good user interface design provide humans a sense of trust. AI robot lawyers based on the development of extended intelligence theory can form a closely coordinated working model with humans. In addition, consumers indicate that the normalized use of AI robots could be a trend in the legal industry in the future, and the types of legal profession that robots can replace will not be affected by gender differences. Practitioners using AI robot lawyers need to establish a complete liability risk control system. This study further optimizes the integrity of RLTAM and provides a reference for developers in designing AI robots in the future.
Background: Given the persistent challenges to the higher education business model, private higher education institutions (PHEIs) are exploring myriad ways to increase enrolment and income, while aggressively managing spending. Many PHEIs are facing financial distress and struggling because of decreasing budgets and declining revenue. Thus, carving unique strategies that direct the institution to focus on its core competencies, making additional budget cuts without compromising quality, developing new revenue streams, embracing new technology, and offering affordable programs, will ultimately lead to financial success. Frugal innovation (FI) can shed light on these challenges.
Methods: This paper presents a systematic literature review to investigate and analyse prior research that focused on FI within the sphere of intellectual capital (IC) and information technology capabilities (ITC) research, and their relationships in PHEIs. Transfield’s five phases were employed to extract journal articles published over a thirty-year period (1990 to 2020) from major online databases using keyword searches. Although an initial search generated 76,025 papers, the search for IC and FI yielded 41 papers, and finally only two papers were selected as they clearly related IC with FI.
Results : There was a research gap in the literature published from 1990 to 2020 regarding IC applications to achieve FI. This work revealed that IC and ITC research for FI in PHEI remain insufficiently explored.
Conclusions: Further research is required on the evaluation model of IC, ITC and FI, methodologies, empirical analysis, and the development of measurement metrics. A limitation to this study is the number of keywords selected.
Purpose
This study aims to suggest that firms and stock market investors are more sensitive about inventory leanness when industry information technology (IT) usage is high. First, when industry IT usage is high, a firm's inventory leanness is more responsive to information inputs (cash holding and sales efficiency). Second, when industry IT usage is high, the price-to-earnings ratio (indicative of stock market investors' willingness to pay a premium) is more sensitive to the firm's inventory leanness.
Design/methodology/approach
This study highlights the contextual role of industry IT usage during the 1998–2009 lost decade (wherein the steepest falls in manufacturing jobs happened in the USA).
Findings
The results highlight the significant contextual role of industry IT usage. In manufacturing industry sectors with high IT usage, (1) inventory levels of firms are more responsive to information inputs and (2) stock market investors have greater appreciation for inventory leanness.
Originality/value
The lost decade, 1998–2009, was a difficult period for the manufacturing industry. Nonetheless, there was variation in stock market valuations of manufacturing firms, with many firms outperforming others. Stock market investors were sensitive to inventory leanness. Firms that positively impressed stock market investors were strategically positioned in high IT usage industry sectors and prioritized inventory leanness. Further, their inventories were sensitive to information inputs – their inventories were leaner in response to improved sales-efficiency and/or shortage in cash.
Today, we live in a dynamic and turbulent global community. The wave of mega-trends, including the velocity of change in globalization and technological advances, is creating new market forces. To survive and prosper in such an environment, innovation is imperative for any organization. However, innovation is no longer just for creating value for the benefit of individuals, organizations, or societies. The ultimate purpose of innovation should be much more far-reaching, helping create a smart future where people can enjoy the best quality of life possible. Thus, innovation must search for intelligent solutions to tackle major social ills, more proactive approaches to predict the uncertain future, and pursue strategies to disrupt the barriers to the smart future. This study explores the detailed requirements of a smart future including both hardware types and soft social/cultural components.
The focus of the following article is on the use of new robotic systems in the manufacturing industry with respect to the social dimension. Since “intuitive” human–machine interaction (HMI) in robotic systems becomes a significant objective of technical progress, new models of work organization are needed. This hypothesis will be investigated through the following two aims: The first aim is to identify relevant research questions related to the potential use of robotic systems in different systems of work organization at the manufacturing shop-floor level. The second aim is to discuss the conceptualization of (old) organizational problems of human–robot interaction (HRI). In this context, the article reflects on the limits of cognitive and perceptual workload for robot operators in complex working systems. This will be particularly relevant whenever more robots with different “roles” are to be increasingly used in the manufacturing industry. The integration of such complex socio-technical systems needs further empirical and conceptual research with regard to “social” aspects of the technical dimension. Future research should, therefore, also integrate economic and societal issues to understand the full dimensions of new human–robot interaction in industry today.
While new organisational models of innovation were intensively discussed in the last decade, there is little systematic exploration concerning their potential for different sectors and areas and their wider implications for economy and society. We present findings from an international foresight project which analyses and discusses the emergence and diffusion of new innovation patterns and their consequences for innovation policy. Based on a collection of international practice examples from industry and society, innovation visions have been generated and assessed involving different experts from all over Europe. A generic trend identified can be best described as open, distributed and networked innovation process which is to a certain extent already addressed in current innovation policy making. However, other trends such as the increased use of information technologies or new spatial shifts related to organising and doing innovation have rather been underestimated so far and will require new policy responses in the future.
Mind design is the endeavor to understand mind (thinking, intellect) in terms of its design (how it is built, how it works). Unlike traditional empirical psychology, it is more oriented toward the "how" than the "what." An experiment in mind design is more likely to be an attempt to build something and make it work—as in artificial intelligence—than to observe or analyze what already exists. Mind design is psychology by reverse engineering.
When Mind Design was first published in 1981, it became a classic in the then-nascent fields of cognitive science and AI. This second edition retains four landmark essays from the first, adding to them one earlier milestone (Turing's "Computing Machinery and Intelligence") and eleven more recent articles about connectionism, dynamical systems, and symbolic versus nonsymbolic models. The contributors are divided about evenly between philosophers and scientists. Yet all are "philosophical" in that they address fundamental issues and concepts; and all are "scientific" in that they are technically sophisticated and concerned with concrete empirical research.
Contributors Rodney A. Brooks, Paul M. Churchland, Andy Clark, Daniel C. Dennett, Hubert L. Dreyfus, Jerry A. Fodor, Joseph Garon, John Haugeland, Marvin Minsky, Allen Newell, Zenon W. Pylyshyn, William Ramsey, Jay F. Rosenberg, David E. Rumelhart, John R. Searle, Herbert A. Simon, Paul Smolensky, Stephen Stich, A.M. Turing, Timothy van Gelder
Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to implement sophisticated forms of human intelligence in machines. This research attempts to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable once we overcome short-term engineering challenges. We believe, however, that phenomenal consciousness cannot be implemented in machines. This becomes clear when considering emotions and examining the dissociation between consciousness and attention in humans. While we may be able to program ethical behavior based on rules and machine learning, we will never be able to reproduce emotions or empathy by programming such control systems—these will be merely simulations. Arguments in favor of this claim include considerations about evolution, the neuropsychological aspects of emotions, and the dissociation between attention and consciousness found in humans. Ultimately, we are far from achieving artificial consciousness.
Technology plays a prominent role in configuring the way we live and work. In this paper we go further and think that it is a first level driver in the configuration of our deepest perceptions and has a paramount influence on shaping our worldviews and metaphors, though this aspect goes unnoticed for most of the population.
In this paper we analyze how metaphors take action in the characterization of technologies, mainly emerging technologies, and in their evolution, and furthermore the impact of technologies and metaphors on the way we perceive our daily life. We analyze metaphors underlying brain nature and artificial intelligence, raising the connections between them and showing how metaphors in one of these fields impact on the way we understand the other. This fact has important consequences, for instance it conditions the evolution of computational systems, and we propose two scenarios for this evolution.
This paper relies on the conceptual model and classification of metaphors proposed by Lakoff and Johnson in “Metaphors we live by”, from the orientational metaphors that show values and mantras, to the deepest structural metaphors that are reconfiguring how life is conceived. It also relies on CLA (Causal Layered Analysis) and to its reference book “CLA 2.0” in order to insert this analysis in a wider and future oriented framework and to analyze scenarios.
The purpose of this chapter is twofold. The primary purpose is to revisit Ned Block's distinction between phenomenal consciousness and access consciousness. The secondary purpose is to examine key case studies from consciousness research in the cognitive sciences. Block has argued that what he calls phenomenal consciousness and access consciousness are completely independent phenomena, and that cognitive scientists have been unduly focused on studying the latter. As against that, I argue that there is an intimate connection between phenomenal and access consciousness: a component of the former is the categorical basis of the latter. I also argue that this vindicates scientific practice in consciousness studies, in that the study of a dispositional property is often a scientific gateway for learning about its categorical basis. Having set out this conceptual framework for understanding the relation between phenomenal and access consciousness, I then consider the presuppositions behind studies of subliminal perception, perception of habituated stimuli, and blindsight, and argue that they are in line with said framework. The overarching goal of the chapter is to contribute to the elucidation of the conceptual foundations of consciousness studies in the cognitive sciences.
I propose to consider the question, “Can machines think?”♣ This should begin with definitions of the meaning of the terms “machine” and “think”. The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous. If the meaning of the words “machine” and “think” are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, “Can machines think?” is to be sought in a statistical survey such as a Gallup poll.
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