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The personality profile of
early generative AI adopters:
abig five perspective
Anna Kovbasiuk
Kozminski University, Warszawa, Poland
Tamilla Triantoro
Quinnipiac University,Hamden, Connecticut, USA
Aleksandra Przegali
nska, Konrad Sowa and Leon Ciechanowski
Kozminski University, Warszawa, Poland, and
Peter Gloor
MIT,Cambridge, Massachusetts, USA
Abstract
Purpose –This pilot study aimed to evaluate the impact of the big five personality traits on user engagement
with chatbots at the early stages of artificial intelligence (AI) adoption.
Design/methodology/approach –The pilot study involved 62 participants segmented into two groups to
measure variables including engagement duration, task performance and future AI usage intentions.
Findings –The findings advocate for the incorporation of psychological principles into technology design to
facilitate more tailored and efficient human–AI collaboration.
Originality/value –This pilot research study highlights the relationship between the big five personality traits
and chatbot usage and provides valuable insights for customizing chatbot development to align with specific
user characteristics. This will serve to enhance both user satisfaction and task productivity.
Keywords Human–AI collaboration, Generative AI, Personality traits, Big five, AI adoption, Chatbot
Paper type Research paper
Introduction
Artificial intelligence has become an integral part of modern workplaces. It revolutionized task
performance across various industries (Paschen, Pitt, &Kietzmann, 2020) (Hart-Davis, 2023).
Despite the widespread adoption, we should remember that employees are not ahomogeneous
group; i.e. each individual brings aunique set of traits and preferences to their interactions with
technology (Przegalinska & Triantoro, 2024). This diversity suggests the need for anuanced
approach to AI implementation, which considers the varied human elements in the workplace
(Ludik, 2021;Cebulla, Szpak, &Knight, 2023).
Prior studies show that AI affects human labor in creative tasks, noting that automation can
either increase or decrease labor demand depending on task complementarity (Brynjolfsson &
Mitchell, 2017). Eloundou, Manning, Mishkin, and Rock (2023) estimate that large language
models (LLMs) could impact at least 10% of tasks for 80% of US workers, with 19%
potentially seeing over 50% of their tasks affected. Eloundou et al. (2023) suggest that LLMs
could significantly accelerate about 15% of US worker tasks. Dell’Acqua et al. (2023)
Central European
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©Anna Kovbasiuk, Tamilla Triantoro, Aleksandra Przegali
nska, Konrad Sowa, Leon Ciechanowski and
Peter Gloor.Published in Central European Management Journal.Published by Emerald Publishing
Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone
may reproduce, distribute, translate and create derivative works of this article (for both commercial and
non-commercial purposes), subject to full attribution to the original publication and authors. The full
terms of this license may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Funding: This research was funded by the Polish National Science Center (NCN) OPUS 21 grant no.
2021/41/B/HS4/03664.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2658-0845.htm
Received 15 January 2024
Revised 25 April 2024
23 July 2024
Accepted 30 September 2024
Central European Management Journal
Emerald Publishing Limited
e-ISSN: 2658-2430
p-ISSN: 2658-0845
DOI 10.1108/CEMJ-02-2024-0067
demonstrated AI’sproductivity benefits in an experiment with 758 consultants, showing GPT-
4’simproved task speed and quality.This supports the “jagged technological frontier” concept,
according to which AI excels in specific tasks and benefits workers across skill levels.
The big five personality traits model is awidely accepted framework for evaluating human
personality.It offers avaluable lens through which to examine the adoption of AI (Triantoro &
Przegali
nska, 2022). This model categorizes personality into five broad dimensions: openness,
conscientiousness, extraversion, agreeableness and neuroticism (McCrae &John, 1992).
Understanding how these traits influence the way individuals engage with generative AI can
provide critical insights into designing more effective AI-based information systems and
interfaces (Yorks, Rotatori, Sung, &Justice, 2020).
In this pilot research, we aimed to explore the relationship between the big five personality
traits (De Raad, 2000)and employee interactions with generative AI technologies.
By examining arange of tasks that vary in complexity and creativity,we sought to identify
how personality differences affected the use and perception of generative AI tools. We based
this investigation on aproposition that personality traits can significantly impact how
employees interact with AI, potentially influencing their efficiency,creativity and overall
satisfaction with AI-enabled processes.
We expect the findings to contribute to the development of more personalized and user-
friendly AI applications in the workplace. By tailoring AI technologies to better fit users’
psychological profiles, organizations can enhance user engagement, improve task
performance and foster amore productive and harmonious human–AI collaboration.
The big five personality theory
The big five personality theory posits that five broad dimensions capture the most significant
variations in human personality (Roccas, Sagiv,Schwartz, &Knafo, 2002). These dimensions
include openness, conscientiousness, extraversion, agreeableness and neuroticism.
Collectively,we refer to them by the acronym OCEAN. Scholars widely use this
framework in psychological research for predicting behavior,preferences and interactions
in various contexts, including work environments and technology usage (Triantoro, Gopal,
Benbunan-Fich, &Lang, 2019,2020).
Openness means aperson’swillingness to experience avariety of activities and their
intellectual curiosity (Roccas et al.,2002). Typically,individuals high in openness display
imagination, curiosity about both the inner and outer worlds, and willingness to try new things,
including innovative technologies. They may be more inclined to explore and embrace AI
tools, showing apredisposition to experiment with novel AI functionalities.
Conscientiousness reflects aperson’sself-discipline, carefulness and dependability (Roccas
et al.,2002). Highly conscientious individuals are organized, methodical and responsible. They
might prefer AI systems that enhance productivity,offer structured guidance and help in
managing tasks efficiently,reflecting apreference for reliable and practical AI solutions.
Extraversion features outgoingness, sociability and an energetic approach to the social and
material world (Roccas et al.,2002). Typically,extraverts appreciate AI chatbots that we may
refer to as interactive, engaging and mimic human-like communication patterns, facilitating a
more enjoyable and dynamic interaction experience.
Agreeableness indicates aperson’saltruism, trust and cooperativeness (Roccas et al.,
2002). People with high agreeableness levels may favor AI technologies that promote
collaborative work and enhance team dynamics, valuing user-friendly and supportive AI
interactions that foster asense of community and mutual respect.
Neuroticism refers to the tendency to experience negative emotions, such as anxiety,anger
or depression (Roccas et al.,2002). Individuals with high neuroticism might display more
cautiousness or skepticism about AI. They might prefer interfaces that offer clear guidance,
reassurance and stress-free navigation to mitigate their concerns and enhance their confidence
in using AI tools.
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In this study,we investigated whether and how the big five personality traits influence
individuals’ interactions with AI chatbots in the workplace, especially at the early stages of AI
adoption. Specifically,we examined if personality traits affected user engagement with AI,
including preferences for specific types of AI chatbot functionalities, perceived ease of use,
satisfaction and the potential for creative and efficient task completion. By understanding
these dynamics, we aimed to provide insights into designing AI interfaces and systems that are
more closely aligned with the diverse psychological profiles of users, thereby enhancing the
effectiveness and user experience of AI in professional settings.
Methodology
The methodology design aimed to yield an in-depth understanding of the influence of
individual personality traits on user interactions with AI chatbots. We explored the issue
through astructured framework involving participant engagement, variable measurement,
task execution and metric evaluation.
Participants: We collected data in early 2023, shortly after ChatGPT by OpenAI became
available to users. The research engaged atotal of 62 participants, randomly divided into two
groups, Group A(AI-assisted) and Group B(not AI-assisted), comprising 29 and 33
individuals, respectively.The age distribution of the participants was 18–24 (32%), 25–34
(21%), 35–44 (32%) and 45–54 (15%). Noteworthy,60% identified as male, 39% as female
and 1% as other.The majority of participants (65%) worked full time and 32% pursued a
graduate degree. The sample also included 16% of participants who were pursuing executive
MBA studies. The remaining part of the participants was in the last year of undergraduate
studies in management.
Software: The big five personality traits lied at the heart of our investigation (Costa &
McCrae, 1992). We chose these specific traits because of their potential relevance and impact
in the context of technology interaction. We quantitatively measured these traits using the
Happimeter online platform. Happimeter is aproduct by the Center for Collective Intelligence
at MIT.It aims at quantifying and enhancing individual happiness. The online platform
consists of abattery of psychological tests, including the big five. It tracks the mood and offers
nuanced, personalized insights into mood dynamics (Gloor,Colladon, Grippa, Budner, &
Eirich, 2018;Gloor,Ara~
no, &Guerrazzi, 2020;Sun &Gloor,2020;Roessler &Gloor,2021).
Tasks and metrics: We designed the tasks so that they simulated areal-world scenario of
using chatbot-based assistants in the workplace. We asked the participants to fill the role of a
marketing manager preparing for the launch of anew campaign. We collected data and
analyzed it to assess the relationship between the big five personality traits and task quality
metrics.
We specifically designed aseries of tasks to emulate various facets of user interaction
with chatbots. The tasks revolved around creating parts of amarketing campaign for aproduct,
specifically creating aproduct name, apersona, acompetitive analysis and atext-based ad. The
following metrics were evaluated in this study:
(1) Quality of tasks: We evaluated the effectiveness of task completion against aset of
predefined criteria, which allowed for an objective assessment of the chatbot’sutility
in facilitating task execution.
(2) Intention to use technology in the future: We assessed the likelihood of future
engagement with chatbots through post-task questionnaires. We used this metric with
the understanding of the long-term adoption potential of chatbot technologies among
users.
Three independent judges evaluated the quality of the output for each task by rating the
participants’ responses for the tasks on ascale from 1to 5on multiple scales. We recruited the
judges from academic institutions and companies in Poland and the USA. In the product name
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task, judges rated the name’soriginality,its shortness and simplicity,and resonance with the
product features, revealing the benefits of the product. For the competitive analysis task, the
judges rated the analysis depth and variety,such as the presence of direct and indirect
competitors, and substitute products. For the persona task, judges assessed the level of detail in
the persona description, the fit of the persona to the product, and the potential of the persona to
be used in communication activities. In the text-based ad task, judges assessed the fit to
persona and the ability to draw attention. We used these four assessments as ameasure of the
quality of each task (Appendix A). We calculated Cronbach’salpha to measure the
assessment’sreliability: the quality of the product name was 0.83, the competitive analysis –
0.98, the text-based ad –0.96, and the persona –0.98. The average inter-judge reliability was
0.464 (Krippendorff Alpha) which indicated amoderate level of agreement among the judges.
Statistical analysis
We performed the initial preparation and dataset cleaning using Python standard libraries such
as pandas,numpy,scipy and datetime.Next, we conducted astatistical analysis using RStudio
version 2023.12.1 (RStudio Team, 2021)and car library (Fox & Weisberg, 2018).
We conducted atwo-way analysis of variance to explore the effect of big five traits and
interaction with the chatbot on the participants’ performance. We used lsr library and eta
squared function to calculate the effect size (η2)and partial effect size (η2
p). We interpreted
effect sizes based on established guidelines: η2
paround 0.01 –small, η2
paround 0.06 –medium,
η2
paround 0.14 and higher –large (Miles &Shevlin, 2001;Cohen, 2013).
The post hoc analyses included simple slopes and pairwise comparisons performed using
interactions and emmeans libraries to investigate specific group differences. In cases when
interaction with the group was insignificant, we conducted linear regression analysis using the
ordinary least squares (OLS) method to predict performance based on traits. Moreover,we also
used such libraries as dplyr and ggplot2 to explore and visualize data. Out of 62 participants,
Group A(AI-assisted, n529) and Group B(not AI-assisted, n533), we excluded 18 missing
cases for the intention to use technology in the future. Thus, we ensured the integrity and
validity of the data analysis.
Descriptive statistics: The sample included participants with varying levels of personality
traits which allowed the investigation of multiple personality profiles in the interaction with
technology (Table 1).
We did not notice significant differences between the means of the groups except for
Neuroticism, which was significantly higher in the AI-assisted group (M50.58, SD 50.1)
compared to the non-AI-assisted group (M50.52, SD 50.1) (t (60) 52.18, p50.03) (See
Table 2).
Spearman’scorrelation analysis revealed asignificant positive relationship between the
measures of output quality across all tasks (See Figure 1). We found the strongest relationship
between text-based ad and persona tasks (ρ50.84, p<0.001) as well as text-based ad and
Table 1. Descriptive statistics for personality traits
Descriptive statistics Agreeableness Neuroticism Openness Extraversion Conscientiousness
Mean 0.652 0.549 0.623 0.684 0.671
SD 0.086 0.104 0.062 0.072 0.063
Min 0.483 0.283 0.483 0.500 0.517
25% 0.588 0.483 0.583 0.633 0.633
50% 0.650 0.542 0.617 0.683 0.683
75% 0.717 0.617 0.650 0.733 0.713
Max 0.867 0.733 0.783 0.817 0.817
Source: Authors’ own elaboration
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competitive analysis tasks (ρ50.83, p<0.001). The intention to use technology was
significantly positively correlated with the quality of the product naming task (ρ50.38,
p50.01). Moreover,we found asignificant positive relationship between neuroticism and the
quality of all tasks, except for the product naming task, indicating that individuals with higher
levels of neuroticism performed better across most tasks. The analysis also identified
Table 2. Descriptive statistics and student t-test results for traits in AI-assisted vs not AI-assisted groups
Trait Group AI-assisted Not AI-assisted
Neuroticism Mean 0.58 0.52
SD 0.10 0.10
T-test t(60) 52.18, p50.03
Extraversion Mean 0.69 0.68
SD 0.08 0.07
T-test t(60) 50.76, p50.45
Openness Mean 0.63 0.62
SD 0.06 0.06
T-test t(60) 50.87, p50.39
Agreeableness Mean 0.67 0.64
SD 0.08 0.09
T-test t(60) 51.45, p50.15
Conscientiousness Mean 0.68 0.67
SD 0.06 0.07
T-test t(60) 50.57, p50.57
Figure 1. Correlation between the variables
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correlations between extraversion and conscientiousness (ρ50.43, p<0.001), as well as
openness and agreeableness (ρ50.29, p50.01).
In the next section, we will compare participants with different levels of personality traits
(minimum, average and maximum) in the AI-assisted and not AI-assisted groups.
Agreeableness: We examined the interaction between individual differences in
agreeableness and group in predicting the quality of the tasks. We identified asignificant
interaction effect specifically for the quality of persona building task (F (1, 58) 57.325,
p50.0089, η2
p
50.11). This indicates that the effect of agreeableness on the quality of the
persona task varied depending on whether participants interacted with the chatbot or not.
The effect size was medium. Simple slopes analysis revealed that the relationship between
agreeableness and the quality of the persona task in the group with the chatbot was statistically
significant and negative (estimate 54.13, t(58) 52.14, p50.04) (See Figure 2).
Conversely,the relationship between agreeableness and the quality of the persona task in the
group without the chatbot was not statistically significant.
We conducted pairwise comparisons to further investigate the interaction between the
agreeableness trait and group (AI-assisted vs not AI-assisted) in predicting the quality of tasks.
The results revealed that less agreeable individuals (at the minimum level of the trait) tended to
produce higher quality of persona task when interacting with the chatbot (estimated marginal
mean (EMM) 53.91, SE 50.388) compared to the group without the chatbot’shelp
(EMM 51.67, SE 50.283) (t(58) 54.671, p50.0003) (See Table 3). We observed asimilar
effect for individuals with amoderate level of agreeableness (at the level of mean)
(t(58) 55.221, p<0.0001). However,the effect was insignificant for very agreeable
individuals classified (at the maximum level of trait) (p>0.05). These findings suggest that the
chatbot’spresence had adifferential impact on task quality based on individuals’
agreeableness levels.
Figure 2. Simple slopes for the quality of persona task for agreeableness and groups
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Next, we explored the effect of agreeableness and group on the intention to use technology
in the future. Our analysis revealed asignificant interaction of agreeableness with the group in
predicting the intention to use technology in future (F (1, 40) 56.478, p50.015, η2
p
50.14).
The effect size was large. Simple slopes analysis was significant on the level of trend only for
the group interacting with the chatbot (estimate 54.68, t(40) 51.95, p50.06) and implied a
possible positive relationship between the intention to use technology in future and
agreeableness in this group (Figure 3).
We noted the highest intention to use in the group of agreeable participants interacting with
the chatbot (EMM 54.55, SE 50.49). There were significant differences compared to the
non-AI-assisted group (EMM 52.76, SE 50.49) (estimate 51.79, t(40) 52.564, p50.014)
(See Table 4).
We observed areverse effect for the low level of agreeableness. Participants who did
not use chatbot help had ahigher intention to use technology (EMM 54.11, SE 50.41)
Table 3. Pairwise comparisons for the quality of persona task for agreeableness and groups
Group Agreeableness EMM SE Estimate SE df t
AI-assisted Low 3.91 0.388 2.242 0.48 58 4.67
***
Not AI-assisted Low 1.67 0.283
AI-assisted Moderate 3.22 0.155 1.1 0.21158 5.22
***
Not AI-assisted Moderate 2.110.144
AI-assisted High 2.33 0.4110.357 0.57 58 0.627
Not AI-assisted High 2.68 0.393
Source(s): Authors’ own elaboration
Figure 3. Simple slopes for the intention to use technology for agreeableness and groups
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compared to the ones who conducted the task with the chatbot (EMM 52.71, SE 50.51)
(t (40) 52.09, p50.043).
Conscientiousness: We examined the interaction between individual differences in
conscientiousness and group in predicting the quality of the tasks. The findings revealed a
significant interaction between conscientiousness and group specifically in the prediction of
the quality of product name task (F (1, 58) 54.8343, p50.032, η2
p
50.08). The effect size was
medium. Simple slope analysis was positive and significant on the level of trend only for the
group interacting with the chatbot (estimate 53.43, t51.82, p50.07) (See Figure 4). This
implies that higher conscientiousness can potentially relate to better performance but only in
the AI-assisted group.
Pairwise comparisons revealed significant effects for the moderately (at the mean level of
the trait) and highly (at the maximum level of the trait) conscientious individuals and no
difference for the low level (at the minimum level) of the trait. Individuals with high
Table 4. Pairwise comparisons for the intention to use technology for agreeableness and groups
Group Agreeableness EMM SE Estimate SE Df t
AI-Assisted Low 2.71 0.505 1.36 0.649 40 2.09
*
Not AI-assisted Low 4.110.409
AI-Assisted Moderate 3.54 0.197 0.0261 0.272 40 0.096
Not AI-assisted Moderate 3.52 0.187
AI-Assisted High 4.55 0.491 1.79 0.697 40 2.564
*
Not AI-assisted High 2.76 0.494
Source(s): Authors’ own elaboration
Figure 4. Simple slopes for the quality of product name task for conscientiousness and groups
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conscientiousness tended to produce higher quality of product name tasks when interacting
with the chatbot (EMM 53.91, SE 50.388) compared to the group without the chatbot
(EMM 52.47, SE 50.273) (t(58) 53.79, p50.0004) (See Table 5). We observed asimilar
effect for individuals with amoderate level of conscientiousness (t(58) 54.812, p<0.0001).
We found no significant effects of individual differences in conscientiousness and group on
intention to use technology in future (See Figure 8in Appendix B).
Neuroticism: There was no interaction with group and neuroticism in the prediction of
quality of tasks. However,individual differences in Neuroticism significantly predicted quality
of persona (F(1,60) 55.618, p50.021, η250.09), Facebook ad (F(1,60) 59.48, p50.03,
η250.05) and competitors (F(1,60) 510.4, p50.02, η250.14) tasks. Neuroticism trait
predicted results of product name task only on the level of trend (F(1,60) 53.36, p50.07).
More neurotic participants had better quality of persona task (β52.815, SE 51.18), Facebook
ad (β53.24, SE 51.05) and competitors (β54.04, SE 51.25) tasks (See Figure 5).
We found no significant interaction effect of neuroticism and group in prediction of
intention to use technology in the future (See Figure 9in Appendix B).
Openness and extraversion: We found no significant effects of individual differences in
openness and extraversion and group on the quality of task or intention to use technology in
future (See Figures 6–13 in Appendix B).
For adetailed summary of the results, see Table 7in Appendix C.
Discussion
Our study focused on the effect of personality on AI adoption attempting to fill the gap in AI
research particularly during the phase of early adoption. We conducted this pilot study in early
2023, shortly after ChatGPT by OpenAI became available to users. Therefore, this study
evaluated the attitudes of early adopters when technologies like ChatGPT were not yet widely
used. Consequently,the study provides insights into how individuals interacted with AI
chatbots at atime when they were still relatively new and unfamiliar.
At these early stages, our results suggest that personality traits influence human–AI
interaction. Particularly agreeableness and conscientiousness interact with task quality when
using AI chatbots. The observed effects were medium and large. This indicates asubstantial
relationship between these traits and the outcomes. We linked higher levels of
conscientiousness to enhanced task performance, particularly in AI-assisted settings,
aligning with previous research in human–computer interaction (HCI) literature (Cruz-
Maya & Tapus, 2016). Conversely,less agreeable individuals demonstrated higher task quality
when assisted by AI technology compared to the non-AI-assisted group. Individuals low in
agreeableness often exhibit tendencies such as assertiveness and independence (Kammrath,
McCarthy,Cortes, &Friesen, 2015), thus, when provided with AI assistance, these individuals
may thrive, leveraging the autonomy,objectivity and efficiency provided by AI technology to
overcome challenges typically associated with teamwork. Furthermore, our investigation
unveiled asignificant interaction between agreeableness and technology engagement in
predicting individuals’ future technology adoption intentions. Agreeable participants
Table 5. Pairwise comparisons for the quality of product name task for conscientiousness and groups
Group Conscientiousness EMM SE Estimate SE Df t
AI-assisted Low 2.95 0.321 0.153 0.421 58 0.364
Not AI-assisted Low 3.110.272
AI-assisted Moderate 3.52 0.115 0.767 0.159 58 4.812
***
Not AI-assisted Moderate 2.76 0.11
AI-assisted High 3.98 0.29 1.51 0.398 58 3.791
***
Not AI-assisted High 2.47 0.273
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displayed astronger inclination toward technology adoption, especially in the presence of AI
assistance, consistent with recent findings (Stein, Messingschlager,Gnambs, Hutmacher, &
Appel, 2024). This underscores the influential role of personality traits in shaping attitudes
toward technological advancements. Neuroticism emerged as adeterminant of quality across
various tasks, regardless of chatbot usage. Contrary to conventional beliefs that associate
neuroticism with unfavorable workplace outcomes, our findings suggest adifferent
perspective. Similarly to Beckmann, Birney,Minbashian, and Beckmann (2021),we found
that moderate levels of neuroticism can benefit cognitive task performance.
While we observed no significant effects for openness and extraversion and task quality or
intention to use technology,their roles warrant further exploration in future studies.
Understanding the influence of personality traits on chatbot interaction can guide designers in
creating more personalized and effective systems.
Understanding the effect of personality in early adoption stages helped us formulate
suggestions for designing and implementing chatbot and other generative AI-powered
technologies. Based on our findings, generative AI-powered technologies should consider
Figure 5. Relationship between quality of tasks and neuroticism
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personality traits as important factors. For example, agreeable users may be more likely to
interact with chatbots that are friendly and approachable, while conscientious users may be
more likely to interact with chatbots that are accurate and reliable. By taking it into account,
chatbots and generative AI systems can be designed to be more effective and user-friendly,
which can lead to improved task performance and user satisfaction.
Implications for research and practice
The findings of this pilot study extend our understanding of user interaction with generative AI
technologies, particularly from the point of view of the user’s personality traits. This research
highlights how personality traits influence user engagement with generative AI, providing a
basis for developing more personalized and effective AI-based systems. For researchers, this
model opens new avenues for exploring the psychological dimensions of AI adoption and its
impact on productivity and user satisfaction. Practitioners can leverage these insights to design
AI tools that cater to diverse user profiles, thereby enhancing the efficiency and satisfaction of
AI-assisted tasks. Societally,the study underscores the importance of integrating
psychological principles into AI development. This promotes amore human-centric
approach to technology adoption, leading to greater acceptance and optimal utilization of
AI innovations.
Limitations and future research
While this study offers new understanding of the relationship between personality traits and
interactions with generative AI, we must address certain limitations. One limitation was the
relatively small sample size of 62 participants. This limitation raises concerns about the
sample’srepresentativeness, as it may not capture the full spectrum of personality diversity of
abroader population. For example, the effect of neuroticism on performance could have
happened due to the higher levels of neuroticism in the AI-assisted group, which occurred by
chance despite the random assignment of participants. Future research directions should
consider expanding the participant base to include alarger and more diverse group.
Conclusions
The pilot study investigated the impact of personality traits on user engagement with chatbots.
Drawing on the big five personality traits framework, we explored how individual differences
in openness, conscientiousness, extraversion, agreeableness and neuroticism influence the
way people interact with AI chatbots. The findings indicated that personality traits play a
significant role in shaping users’ interactions. This study contributes to the growing body of
literature on human–AI interaction and has implications for designing and developing more
effective and user-friendly AI applications. The findings suggest that tailoring AI technologies
to better fit the users’ psychological profiles can enhance user engagement and improve task
performance.
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Supplementary material
The supplementary material for this article can be found online.
Corresponding author
Aleksandra Przegali
nska can be contacted at: aprzegalinska@kozminski.edu.pl
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