Month: September 2024

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Artificial intelligence in a leadership context – acceptance of the use of robots in leadership positions

The development of artificial intelligence (AI) is changing the working environment. For example, artificial intelligence systems are increasingly applied in the field of employee leadership. Hereby AI can take over routine tasks or provide data-based decision support in employee-related processes. Advancements in robotics, coupled with AI technologies, also enable the construction of social robots. These kinds of robots can interact with their environment and thus theoretically offer the possibility of taking over leadership tasks completely. The successful use of social robots depends on employees’ acceptance of robot supervisors. Therefore, Lilly Mühlbauer, a student of our business psychology program, has conducted a quantitative online survey to gain insights into the acceptance of robots in leadership positions, as part of her bachelors’ thesis. In this context, the influence of the leadership entity (human vs. robot) as well as the impact of the leadership style (transactional vs. transformational) was examined.

Research Overview

The data collection was conducted in November 2023 through a quantitative online experiment using the platform Unipark. Participants were asked to imagine applying for a new job. Therefore, only individuals for whom this scenario realistically reflected their life stage were included. Accordingly, students and retirees were excluded by a filter question at the beginning of the survey.
Participants were initially assigned to the following four groups: G1 (human x transactional leadership style), G2 (human x transformational leadership style), G3 (robot x transactional leadership style), G4 (robot x transformational leadership style).
The central element of the questionnaire was a fictional scenario. For this purpose, images depicting the manager in conversation with an employee were generated in Midjourney. Except for the manager, the pictures were created identically, with no variations in body posture for the different leadership styles. To operationalize the two leadership styles, an excerpt of the conversation between the manager and the employee was presented, consisting of a short monologue by the manager.
The analysis included a total number of 162 participants, with an average age of 23 years.

Main findings of the survey

    • Results indicated a significant difference in the acceptance between the two leadership entities (human vs. robot). The human manager received significantly higher acceptance ratings compared to the robot manager.
    • Results indicated a significant difference in the acceptance between the two leadership styles (transactional vs. transformational). The transformational leadership style led to a greater acceptance of managers.
    • The acceptance of the human manager and the robot manager is not dependent on the presented leadership style.
    • No significant correlation was found between prior knowledge about AI programs or AI-assisted robots and the acceptance of a robot manager. It should be noted that this analysis was based solely on the participants’ self-assessment of their own knowledge.
    • A positive correlation between the experience with AI programs or AI-assisted robots and the acceptance of a robot manager was found. It’s important to consider this result with the caveat that the responses are based on consciously perceived experiences of the participants, and individuals may encounter AI programs in their daily lives more frequently than they are actually aware of.
    • Additionally, results indicated that leadership entity and leadership style not only influence acceptance but also impact the organizational attractiveness of a company.

Conclusion

Overall, the data suggest that a human manager is significantly more accepted compared to a robot manager. Therefore, it can be stated, that employees currently demonstrate no clear willingness to work under the leadership of a robot. In addition, the expected difference in the acceptance rating in relation to the leadership style presented could be measured. Thus, the transformational leadership style scored higher than the transactional leadership style, consequently leading to greater acceptance of the manager. No interaction between the two factors, leadership entity and leadership style, could be observed. This implies that they independently influence the acceptance evaluation. As a result of high practical relevance, it can be stated that the robot manager achieved significantly lower values regarding organizational attractiveness compared to the human manager.
Accordingly, the deployment of a robot leader appears to have a negative impact on the organizational attractiveness of a company. This study contributes to the research field of human-robot interaction and provides statistically significant findings on the acceptance of robots in management positions.

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Why Emotions matter in Technology Acceptance: Insights from Extended Reality

When reviewing previous studies of technology acceptance, it becomes apparent that one crucial aspect has often been overlooked, namely the role of emotions (Kulviwat, Bruner II, Kumar, Nasco & Clark, 2007; Valor, Antonetti & Crisafulli, 2022). Most models used in acceptance research primarily emphasize rational or cognitive factors. But humans don’t make decisions purely based on rational considerations (Bechara & Damasio, 2005; Damasio, 1994). Therefore, when discussing technology acceptance, shouldn’t emotional factors, such as the joy of using a technology or the fear associated with it, be considered as well? Cognitive models alone do not represent the entirety of the components that have an influence on acceptance (Beaudry & Pinsonneault, 2010). Recognizing the role of emotions is vital, especially in times of digital transformation, which entails numerous changes (Kuckelkorn, 2019).

This is especially true for immersive technologies like Extended Reality (XR), as they have a particularly high potential for emotional impact. Through visual, acoustic and haptic stimuli and, especially, real-time feedback to user actions, XR creates a sense of presence in a virtual world (Riva et al., 2007). By increasingly merging virtuality and reality, the way we live, work and interact has changed fundamentally (Singh, Singh, Verma & Prabha, 2023). Understanding the emotional dynamics in the acceptance process offers the opportunity to increase the acceptance of XR by addressing emotions appropriately through marketing and the development of XR technologies.

Research goal

The objective of the study, conducted by Jana Baudler, was to determine the influence of emotions on the acceptance of new technologies. As previous acceptance research lacks consideration of emotional factors the study was carried out to address this crucial gap. As the basis for the study, the frequently replicated UTAUT2 model was used. The research question raised was whether the addition of emotional factors into the UTAUT2 model improves the prediction of the behavioral intention to use the technology. The study was applied to the new technology XR. By focusing on XR, this study aims to provide insights into how emotional factors influence user acceptance of new technologies.

Research overview

A quantitative online study was conducted to investigate the research question. The final sample consisted of 118 participants, ranging in age from 15 to 61 years (mean = 23.63 years). In the online survey, participants were presented with a scenario they had to envision. The scenario involved taking a city trip that included the use of various XR technologies (including Augmented, Mixed, and Virtual Reality). The participants then had to rate the rational factors (effort expectancy, performance expectancy, social influence and price value) as well as emotional factors (hedonic motivation, affection and anxiety).

The participants’ experience with XR was as follows:

  • Only 19% of the participants had tried XR before.
  • Those who reported having experience with XR had either used the technology once or twice, or in a few cases, occasionally.
  • Experiences were primarily in the education and gaming & entertainment sectors.

Main findings

  • Incorporating emotional factors in addition to the rational factors of the UTAUT2 model significantly improves its predictive power and variance explanation.
  • Among the emotional factors evaluated, only affection emerges as significant. Hedonic motivation and anxiety did not show a significant impact in this study. Unlike previous studies (e.g., Rauschnabel, Rossmann & tom Dieck, 2017; Chuah, 2018) where hedonic motivation consistently showed significance, this study found it to be insignificant when combined with the other two emotional factors.
  • The comparison with the model consisting only of inexperienced participants shows that there are hardly any differences when emotional factors are included. It can be assumed that this is a stable model in terms of this aspect.

As the study shows, the addition of emotional factors leads to a significant improvement of the model as well as to a greater variance explanation of the behavioral intention to use the technology. The results of the study argue for an extension of the UTAUT2 model. Neglecting emotional aspects ignores an important part. 

Conclusion

The study emphasizes the crucial role of emotions in technology acceptance, demonstrating that the acceptance of technologies is not only influenced by rational factors but also significantly by emotional factors. It stresses the substantial impact of emotions on behavioral intention. By focusing on the emotional aspect, the study provides an initial overview of XR acceptance. Given the rapid advances in realizing the human dream of escaping into artificial worlds, further research is essential to address various aspects of the often-overlooked role of emotions in technology acceptance.

References

Beaudry & Pinsonneault. (2010). The Other Side of Acceptance: Studying the Direct and Indirect Effects of Emotions on Information Technology Use. MIS Quarterly, 34(4), 689–710. https://doi.org/10.2307/25750701

Bechara, A. & Damasio, A. R. (2005). The somatic marker hypothesis: A neu ral theory of economic decision. Games and Economic Behavior, 52(2), 336–372. https://doi.org/10.1016/j.geb.2004.06.010

Chuah, S. H.‑W. (2018). Why and Who Will Adopt Extended Reality Technol ogy? Literature Review, Synthesis, and Future Research Agenda. SSRN Electronic Journal, 1–55. https://doi.org/10.2139/ssrn.3300469

Damasio, A. (1994). Descartes’ Error. Emotion, Reason, and the Human Brain. East Rutherford: Penguin Publishing Group. Verfügbar unter: https://ahandfulofleaves.wordpress.com/wp-content/uploads/2013/07/descartes-error_antonio-damasio.pdf

Kuckelkorn, T. (2019, 29. November). Digitale Transformation. Der Mensch zwischen Technologie und Emotion. connect professional. Verfügbar unter: https://www.connect-professional.de/markt/der-mensch-zwischen-techno logie-und-emotion.171683.html

Kulviwat, S., Bruner II, G. C., Kumar, A., Nasco, S. A. & Clark, T. (2007). To ward a unified theory of consumer acceptance technology. Psychology & Marketing, 24(12), 1059–1084. https://doi.org/10.1002/mar.20196

Rauschnabel, P. A., Rossmann, A. & tom Dieck, M. C. (2017). An adoption framework for mobile augmented reality games: The case of Pokémon Go. Computers in Human Behavior, https://doi.org/10.1016/j.chb.2017.07.030

Riva, G., Mantovani, F., Capideville, C. S., Preziosa, A., Morganti, F., Vil lani, D. et al. (2007). Affective interactions using virtual reality: the link be tween presence and emotions. Cyberpsychology & Behavior : the Impact of the Internet, Multimedia and Virtual Reality on Behavior and Society, 10(1), 45–56. https://doi.org/10.1089/cpb.2006.9993

Singh, J., Singh, G., Verma, R. & Prabha, C. (2023). Exploring the Evolving Landscape of Extended Reality (XR) Technology. In 2023 3rd International Conference on Smart Generation Computing, Communication and Net working (SMART GENCON) (S. 1–6). IEEE.

Valor, C., Antonetti, P. & Crisafulli, B. (2022). Emotions and consumers’ adop tion of innovations: An integrative review and research agenda. Technolog ical Forecasting and Social Change, https://doi.org/10.1016/j.techfore.2022.121609