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The fifth RIPCO research day, focused on "well-being/malaise at work," brought together 93 participants and featured 35 presentations from 63 international contributors at the ICN campus in Paris-La Défense on June 6, 2024, and the editorial committee is considering transforming this annual event into a two-day academic congress. SUBMIT
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Appel à contributions : Special Issue Download the call in PDF
 
Artificial intelligence in organisations, how to (better) work with it?
 
Perceptions, attitudes, and behaviours of stakeholders regarding the deployment of AI in the workplace
 
Guest editors :
 

Christelle MARTIN LACROUX - Université Grenoble Alpes

Fabienne PEREZ - ESSCA School of Management

 
Abstract

Organisations are undergoing a significant transformation, described as the fourth industrial revolution or the era of algorithms. Artificial Intelligence (AI), defined as a technology that allows machines to reproduce human-like behaviours, plays a major role in this, with technologies now widely deployed in organisations. Machine Learning is a notable technology that enables cumulative learning from training data to create algorithmic decision support systems. These technologies are reshaping practices and processes at both team and individual levels. AI has transformed how people collaborate with algorithmic systems and how those systems interact. In recruitment, AI is used in various stages, from information extraction to automated interview analysis. "Augmented recruitment" raises questions about perceptions and behaviour with AI-integrated systems. Some managerial tasks are now automated, leading to "algorithmic management," which requires research into its impact on managers' roles and employees' attitudes. Studies on algorithmic decision systems indicate trust is a critical predictor of individuals' choices to use AI-based advice. Introducing algorithms and AI prompts organisational changes that may lead to resistance. The special issue invites contributions to enhance understanding of AI's impact on organisational behaviour. Contributions should address the consequences of AI tools on employee beliefs, perceptions, emotions, stress, and attitudes, and mechanisms influencing trust and behaviour toward AI tools. The issue seeks submissions using a variety of methodologies to explore AI's impact on organisational behaviour.

 

Presentation of the Call for Contributions

Organisations are at the heart of a major shift, known as the fourth industrial revolution (Schwab, 2017) or the age of algorithms (Danaher et al., 2017). Artificial intelligence (AI), defined as a technology enabling a machine to "reproduce behaviours related to humans, such as reasoning, planning, and creativity," plays a significant role here, with the development of technologies now widely deployed within organisations (Haesevoets et al., 2021). Among these technologies is Machine Learning (ML), which encompasses a set of predictive methods based on algorithms that "learn" cumulatively from training data and build algorithmic decision support systems (ADSS).

The applications of these technologies integrating AI are widely distributed within organisations, leading to a transformation of practices and processes, both at the team and individual levels.

  • First, the development of AI has changed how individuals collaborate with algorithmic systems and how these systems interact with each other. For instance, in recruitment, AI is used at every stage, from relatively simple tasks like extracting information from CVs to highly complex and subjective tasks like automated interview analysis or multi-criteria selection of the "best candidate" (Nawaz, 2020). The term "augmented recruitment," defined as a process in which individuals work closely with AI to accomplish a task (Raisch & Krakowski, 2021), raises new questions (Langer et al., 2021), especially about the perceptions and behaviour of recruiters interacting with AI-integrated systems (chatbots, automatic recommendations of candidates, automatic analyses of asynchronous video interviews). Generative AIs are another example of a tool introduced into organisations; they can autonomously create content (texts, images, and videos) and change interactions between humans and machines, prompting contributions related to ethics, intellectual property, organisational efficiency, and employee well-being and engagement (Budhwar et al., 2023). The deployment of virtual agents within teams to coordinate tasks also has unexplored effects on job satisfaction, perceptions of conflict, and trust (Dennis et al., 2023).
  • Secondly, some tasks traditionally assigned to managers are now sometimes automated: automatic performance evaluation, task assignment, and decision-making on pay or even potential sanctions (Gagné et al., 2022). Here again, research is needed to analyse how this "algorithmic management" changes managers' roles (Sutherland et al., 2021) in terms of power, the development of new algorithmic skills, and the effects on their employees in terms of algorithmic aversion attitudes (Dietvorst et al., 2015) or, conversely, machine heuristics (Lee, 2018; Sundar & Kim, 2019). Few studies have been conducted to date, and they tend to conclude that employees perceive algorithmic decisions as less fair and reliable, eliciting more negative emotions than human decisions (Lee, 2018).
  • Furthermore, today there is extensive literature on using algorithmic systems in human-machine interfaces from various scientific fields such as psychology, computer science, and information systems management. In all published work on the use of algorithmic decision-making systems, trust is studied as a crucial predictor of individuals' choices to use or follow the advice provided (Lacroux & Martin-Lacroux, 2022). Several integrative models of organisational trust have been proposed, including concepts like perceived control over the process and risk aversion (Mayer et al., 1995; Solberg et al., 2022).
  • Finally, the introduction of algorithms and AIs entails significant changes in the organisation, in terms of the transformation of expertise, the redefinition of tasks, coordination, and control (Faraj et al., 2018). These changes can potentially lead to individual or collective resistance reactions (Kellogg et al., 2019). The issue of work design becomes essential in a context of transformation (Parker & Grote, 2020), both in how AI can change work and how individuals and organisations can be actors and stakeholders in work design.

 

Contributions

Expected contributions can range from studies in real-life situations involving decision support tools (Glikson & Woolley, 2020; Solberg et al., 2022) to the use of conceptual frameworks that integrate variables related to trust in human-machine collaboration (Solberg et al., 2022).

  • Tools that integrate AI also disrupt how work is changing within organisations (Brynjolfsson et al., 2018) and how employees perceive the implementation of these tools regarding the potential loss of qualifications, the redefinition of their work flexibility to adapt to collaboration with intelligent systems, while maintaining a certain form of authority over them. Individuals may have to redefine and rethink their work, especially with job crafting behaviours (Perez et al., 2022; Wrzesniewski & Dutton, 2001) to align it with their expectations and values. Contributions are therefore expected related to work design in a multi-level perspective, including how individuals, teams, organisations, or sectors contribute to the redefinition of work.

With this special issue, we hope to showcase research that provides insights into the effects of AI on organisational behaviour, aimed at both researchers and practitioners. This research will provide theoretical and empirical elements that guide managers' choices regarding adopting and integrating these technologies in contemporary work environments. We hope this special issue will address several of the questions below:

  • What are the consequences of deploying AI tools on employees working in organisations regarding beliefs, perceptions of ethics and justice, emotions, stress, behaviour, and attitudes (algorithmic aversion, machine heuristics)?
  • What mechanisms underlie individuals' trust and behaviour towards tools integrating AI?
  • What can be the effects of introducing these tools on workplace health and well-being?
  • How do individuals redefine their room for manoeuvre regarding skills acquisition and power? How do professional practices evolve with AI?
  • How do individuals view their careers with the advent of AI, particularly regarding skill development, specialisation choices, or career paths?
  • How do they give new meaning to their work when they are "augmented" (or not) by AI?
  • How does teamwork change in contexts where AI tools (virtual assistants, automatic decision support systems, etc.) are introduced?
  • What are the effects of AI on group functioning in terms of power, leadership, and intergroup behaviours?
  • What are the levers for successfully implementing AI-integrated solutions within organisations? How can organisations rethink job content and facilitate individuals' approaches to shaping their work?
  • What individual strategies do actors use to maintain meaning and identity at work?

This special issue calls for all forms of contributions that improve understanding of the links between AI deployment in all its diversity and individuals' behaviour within organisations. This call is open to a wide variety of methodologies: narrative, systematic, meta-analytic, or bibliometric literature reviews; empirical, experimental, cross-sectional, or longitudinal analyses.

 
How to submit ?
 

Submitting articles to the RIPCO is done via the RIPCO manuscript manager website at : https://www.manuscriptmanager.net/ripco

When submitting, authors must choose the special issue "Special Issue : IA dans les Organisations" from the drop-down menu in the field " If the manuscript is destinated to a Special Issue, please make a choice" found in the "DETAILS" page of the submission. Proposals should follow the editorial standards of the journal: ripco-online.com/en/avantSoumission.asp

 
Review process
 

All articles submitted to the journal are reviewed on a double-blind basis and all resubmitted manuscripts go through the same review process, and the previously solicited reviewers give an assessment based on consideration of the changes suggested in the first round of review. The final editorial decision will be made on the basis of the proposed revised manuscript, in the form of either an acceptance for publication or a final rejection, possibly with an invitation to resubmit for a regular issue of the journal.

 
Tentative schedule
 

Manuscript submission: 31 August 2024
Notice to authors: 6 November 2024
Submission of revised manuscripts: 15 December 2024
Additional reviews and final acceptance: 15 March 2025
Submission of the final version of the special issue to be sent to RIPCO: 15 April 2025

 
References
 
  • Brynjolfsson, E., Mitchell, T., & Rock, D. (2018). What Can Machines Learn, and What Does It Mean for Occupations and the Economy? AEA Papers and Proceedings, 108, 43-47. https://doi.org/10.1257/pandp.20181019
  • Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., Boselie, P., Lee Cooke, F., Decker, S., DeNisi, A., Dey, P. K., Guest, D., Knoblich, A. J., Malik, A., Paauwe, J., Papagiannidis, S., Patel, C., Pereira, V., Ren, S., … Varma, A. (2023). Human resource management in the age of generative artificial intelligence : Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606-659. https://doi.org/10.1111/1748-8583.12524
  • Danaher, J., Hogan, M. J., Noone, C., Kennedy, R., Behan, A., De Paor, A., Felzmann, H., Haklay, M., Khoo, S.-M., Morison, J., Murphy, M. H., O’Brolchain, N., Schafer, B., & Shankar, K. (2017). Algorithmic governance : Developing a research agenda through the power of collective intelligence. Big Data & Society, 4(2), 205395171772655. https://doi.org/10.1177/2053951717726554
  • Dennis, A. R., Lakhiwal, A., & Sachdeva, A. (2023). AI Agents as Team Members : Effects on Satisfaction, Conflict, Trustworthiness, and Willingness to Work With: Journal of Management Information Systems. Journal of Management Information Systems, 40(2), 307-337. https://doi.org/10.1080/07421222.2023.2196773
  • Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion : People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114-126. https://doi.org/10.1037/xge0000033
  • Gagné, M., Parent-Rocheleau, X., Bujold, A., Gaudet, M.-C., & Lirio, P. (2022). How algorithmic management influences worker motivation : A self-determination theory perspective. Canadian Psychology / Psychologie Canadienne, 63(2), 247-260. https://doi.org/10.1037/cap0000324
  • Glikson, E., & Woolley, A. W. (2020). Human Trust in Artificial Intelligence : Review of Empirical Research. Academy of Management Annals, 14(2), 627-660. https://doi.org/10.5465/annals.2018.0057
  • Haesevoets, T., De Cremer, D., Dierckx, K., & Van Hiel, A. (2021). Human-machine collaboration in managerial decision making. Computers in Human Behavior, 119, 106730. https://doi.org/10.1016/j.chb.2021.106730
  • Kellogg, K., Valentine, M., & Christin, A. (2019). Algorithms at work : The new contested terrain of control. Academy of Management Annals.
  • Lacroux, A., & Martin-Lacroux, C. (2022). Should I Trust the Artificial Intelligence to Recruit? Recruiters’ Perceptions and Behavior When Faced With Algorithm-Based Recommendation Systems During Resume Screening. Frontiers in psychology, 13.
    Langer, M., König, C. J., & Busch, V. (2021). Changing the means of managerial work : Effects of automated decision support systems on personnel selection tasks. Journal of Business and Psychology, 751-769. Scopus. https://doi.org/10.1007/s10869-020-09711-6
  • Lee, M. K. (2018). Understanding perception of algorithmic decisions : Fairness, trust, and emotion in response to algorithmic management, Big Data & Society, 5(1), 205395171875668. https://doi.org/10.1177/2053951718756684
  • Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An Integrative Model Of Organizational Trust. Academy of Management Review, 20(3), 709-734. https://doi.org/10.5465/amr.1995.9508080335
  • Nawaz, N. (2020). Artificial Intelligence Applications for Face Recognition in Recruitment Process. Journal of Management Information & Decision Sciences, 23, 499-509.
  • Parker, S. K., & Grote, G. (2020). Automation, Algorithms, and Beyond : Why Work Design Matters More Than Ever in a Digital World. Applied Psychology. Applied Psychology, 71(4), 1171-1204.
  • Perez, F., Conway, N. & Roques, O. (2022). The Autonomy Tussle: AI Technology and Employee Job Crafting. Relations industrielles/Industrial Relations, 77(3).
  • Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management : The automation–augmentation paradox. Academy of Management Review, 46(1), 192-210. https://doi.org/10.5465/amr.2018.0072
  • Schwab, K. (2017). The Fourth Industrial Revolution. Crown.
  • Solberg, E., Kaarstad, M., Eitrheim, M. H. R., Bisio, R., Reegård, K., & Bloch, M. (2022). A Conceptual Model of Trust, Perceived Risk, and Reliance on AI Decision Aids. Group & Organization Management, 47(2), 187‑222. https://doi.org/10.1177/10596011221081238
  • Sundar, S. S., & Kim, J. (2019). Machine heuristic : When we trust computers more than humans with our personal information. Proceedings of the 2019 CHI Conference on human factors in computing systems, 1-9.
  • Sutherland, W., Kinder, E., Wolf, C. T., Lee, M. K., Newlands, G., & Jarrahi, M. H. (2021). Algorithmic Management in a Work Context. Big Data and Society, 8(2). https://doi.org/10.1177/20539517211020332
  • Wrzesniewski, A., & Dutton, J. E. (2001). Crafting a job: Revisioning employees as active crafters of their work. Academy of Management Review, 26(2), 179-201.
 
Contact
 

contact@ripco-online.com

 
 
Appels à communications
Special Issue: Vol.XXXI, Num. CFP_SI_IAORGA ( 2025)
L’intelligence artificielle dans les organisations, comment (mieux) travailler avec ?
Coordinateurs: Christelle MARTIN LACROUX and Fabienne PEREZ
Date limite : 31/08/2024
Les organisations connaissent une transformation majeure, qualifiée de quatrième révolution industrielle ou d'ère des algorithmes. L'intelligence artificielle (IA), définie comme une technologie permettant aux machines de reproduire des comportements humains, joue un rôle majeur dans ce processus, avec des technologies désormais largement déployées dans les organisations. Le Machine Learning est une technologie notable qui permet un apprentissage cumulatif à partir de données d'entraînement pour créer des systèmes d'aide à la décision algorithmiques. Ces technologies transforment les pratiques et les processus tant au niveau des équipes que des individus. L'IA a transformé la manière dont les individus collaborent avec les systèmes algorithmiques et la façon dont ces systèmes inte ...
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