In a world of retention and recruitment challenges for both public and private organizations it has become even more important to secure employee well-being and performance. Leadership is particular important to this end (Inceoglu et al., 2018; Montano et al., 2017; Nielsen & Taris, 2019). Many organizations invest intensively in leadership training with the aim of improving leaders’ capabilities to increase organizational performance and psychosocial work environment. From a growing literature on the effects of leadership training programs, we know that such programs can be effective means to provide managers with the tools to address these demands and hence improve organizational outcomes (e.g., Thaler et al., 2017; Kim & Lee, 2023). However, while we know that leadership training may be an effective mean, we lack causal knowledge about what actually motivates leaders to sign up for such leadership training programs and invest time in improving their competencies. This is unfortunate, as causal knowledge about what factors that shape leaders’ motivation and attitudes toward leadership training programs are important for our theoretical understanding of the mechanisms affecting managers’ willingness to invest time in improving their capabilities. In addition, it is of practical importance to organizations that wish to provide their leaders with more education.

In this study, we therefore investigate how different factors affect leaders’ attitudes toward leadership training programs. To examine our hypotheses, we conduct a large-scale pre-registered survey experiment. In this experiment, we provide more than 5000 public and private Danish managers with different vignettes describing a situation at their work and an opportunity to join a leadership program in psychosocial work environment. Importantly, in these vignettes, we manipulate whether 1) the organization face concrete problems with the psychosocial work environment, 2) the length of the leadership training program, 3) whether their organizations offers to finance and 4) provide time for the leader to participate in the program, and 5) whether the program can be attended virtually or requires physical attendance.

The results show that all of these factors are important to the leaders’ willingness to sign up for the leadership training programs and the expected effects of the program. If the organization is experiencing concrete problems with the psychosocial work environment, the managers are more motivated to participate. In addition, we find that managers are less motivated to participate in long leadership programs and programs that require physical attendance. However, the most important factors for managers’ attitudes toward leadership training programs are whether their organization finance the program and provide time for the manager to participate in the program.

When Max Weber suggested that public servants should be guided by the principle of ‘sine ira et studio’ (Weber 1978), he not only envisioned public bureaucracies as non-politicized, but also as almost dehumanized (Albrow 1992). Starting from the NPM reforms and continuing with subsequent public governance reform paradigms (Torfing et al. 2020), emphasis on public executives to deliver ‘emotionlessly and objectively’ on political principals’ preferences has somewhat persisted. However, research from generic management shows that executives’ decisions and behaviors are partly explained by their psychological attributes (Miller et al., 2022).

Within public management research on psychological attributes of public executives, e.g. their personality traits, cognition, values, remains limited (Huang and Villadsen 2023). This represents a critical gap, as executive decision-making primarily concerns non-routine decisions (Miller et al., 2022), often made in ‘weak situations’ characterized by ambiguity, complexity, and uncertainty and afflicted by information overload, not least in the public sector (Boyne 2002; Kelman et al. 2015; Boye et al. 2022). In such situations, executives’ cognitive attributes are vital for the knowledge that is given prominence, processed, and made sense of when decisions are taken (March and Simon 1958). In addition, the use of proxies in empirical generic management research has produced only limited insights on cognitive attributes, with ensuing calls for exploring alternative ‘methodological opportunities’ in studies on also public executives (Huang and Villadsen, 2023: 1629).

The contributions of this study are two-fold. First, we theorize why and how cognition affects public executives’ decision-making. Second, we present and evaluate a novel Large Language Model (LLM)-based approach to detecting executives’ cognitive attributes in text data. LLMs have shown promise across a broad range of text classification tasks (Minaee et al., 2021), achieving human-level performance (Chew et al., 2023) by enabling not only word frequency counts but also generating scores indicative of cognitive complexity (Bantum et al., 2017). Our data consists of written statements and transcribed interviews with public executives in which they express their values, and leadership styles. We compare our approach with a more traditional dictionary-based method (Graf-Vlachy et al., 2020; Malhotra and Harison 2022) and assess differences in their respective performance. We argue that this approach offers a more nuanced understanding of cognitive attributes, addressing a crucial gap in current research methodologies. Finally, we offer practical advice on how other PA scholars can investigate the cognitive attributes of public executives in large N studies and discuss potential research designs, methods, and ethics.

References

Albrow, M. (1992). Sine Ira et Studio — or Do Organizations Have Feelings? Organization Studies, 13(3), 313-329.

Bantum, E.O., Elhadad, N., Owen, J.E., Zhang, S., Golant, M., Buzaglo, J., Stephen, J. and Giese-Davis, J. (2017) Machine Learning for Identifying Emotional Expression in Text: Improving the Accuracy of Established Methods. Journal of Technological Behavioral Science, 2(1): 21-27.

Boye, S. Nørgaard, R. R, Tangsgaard, E. R. Winsløw, M. A. and Østergaard-Nielsen, M. R. (2022) Public and private management: now, is there a difference? A systematic review, International Public Management Journal, DOI: 10.1080/10967494.2022.2109787

Boyne, G. A. (2002) Public and Private Management: What’s the Difference? Journal of Management Studies, 39(1): 97–122.

Chew, R., Bollenbacher, J., Wenger, M., Speer, J., & Kim, A. (2023). LLM-Assisted Content Analysis: Using Large Language Models to Support Deductive Coding.

Gilad, S., & Alon-Barkat, S. (2018). Enhancing democracy via bureaucracy: Senior managers’ social identities and motivation for policy change. Governance, 31(2), 359–380. https://doi.org/10.1111/gove.12300

Graf-Vlancy, L., Bundy, J. N. and Hambrick, D. C. (2020) Effects of an Advancing Tenure on CEO cognitive Complexity, Organization Science, 31(4): 936-959.

Hambrick, D. C., & Mason, P. A. (1984). Upper Echelons: The Organization as a Reflection of Its Top Managers. Academy of Management Review, 9(2), 193–206. https://doi.org/10.5465/amr.1984.4277628

Huang, T. and Villadsen, A. R. (2023) Top managers in public organizations: A systematic literature review and future research directions, Public Administration Review doi.org/10.1111/puar.13628

Kelman, S., Sanders, R. and Pandit, G. (2015) “I Won’t Back Down?” Complexity and Courage in Government Executive Decision Making, Public Administration Review, 76(3):465-471.

Malhotra, S. and Harison, J. S. (2022) A blessing and a curse: How chief executive officer cognitive complexity influences firm performance under varying industry conditions, Strategic Management Journal, 43(13): 2809-2828.

March, J. C. and Simon, H. A. (1958) Organizations. New York: Wiley.

Miller, C. C., Chiu, S., Vera, D. and Avery, D. R. (2022) Cognitive Diversity at the Strategic Apex: Assessing Evidence on the Value of Different Perspectives and Ideas Among Senior Leaders, Academy of Management Annals, 16(2): 806-852.

Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. (2021). Deep Learning–based Text Classification: A Comprehensive Review. ACM Comput. Surv., 54(3).

Speer, A. B., Perrotta, J., Tenbrink, A. P., Wegmeyer, L. J., Delacruz, A. Y., & Bowker, J. (2023). Turning words into numbers: Assessing work attitudes using natural language processing. Journal of Applied Psychology, 108(6), 1027–1045. https://doi.org/10.1037/apl0001061

Weber, M. (1978) Economy and society, translated by G. Roth and C. Wittich. Berkeley: University of California Press.

Weick, K. (1995) Sensemaking in Organizations Thousand Oaks, CA: Sage Publications

The rise and immense popularity of ChatGTP for human resource management (HRM) purposes reflects the relevance of global digitalization and the significance of artificial intelligence (AI) for societies, citizens, organizations and employees (Budhwar et al., 2023). Generative AI is expected to affect employment relations, employee well-being, diversity and inclusion, organizational performance and sustainable HRM for the greater ‘good, bad and ugly’. In other words, further digitalization and use of AI are surrounded by insecurity for societies, organizations and employees. Some of the impact will be positive and some of the impact will be negative as suggested by Budhwar et al. (2023). AI can affect jobs in both the private and the public sector. In addition it can also affect knowledge workers and highly educated employees, for example tech jobs, graphic designers, customer service agents, market research analysts, finance jobs, accountants, teachers, legal industry jobs, traders, and media jobs. The Harvard model presented by Beer et al. (1984) presents the corner stones of contemporary HRM theory and concepts with an emphasis on situational factors (context), multiple stakeholders and a multidimensional performance construct. Beer et al. (2015) acknowledge the strategic tensions between organizational effectiveness, employee well-being and societal well-being. In this conceptual paper we focus an open resource based view of the firm to strategic human resource management:

• Organizational effectiveness can be achieved through efficiency improvements and more focus on HRM services delivery given ChatGPT's ability to take over all kinds of HRM practices and activities of HRM professionals and line managers.

• Employee well-being in multiple ways including (a) increased information availability, (b) direct knowledge sharing and (c) instant employee involvement through ChatGPT – human interface.

• Societal well-being (public value creation) is achieved through open and broad sharing of information through ChatGPT.

An open resource based view (Paauwe and Boselie in: Budhwar et al., 2023) using AI and ChatGPT can contribute to HRM knowledge sharing and knowledge circulation between organizations and between individuals. Open resources can be part of collaborative innovation and even co-opetition (cooperation in a competitive environment), because collaboration and knowledge exchange can be beneficial to the population in an era that individual organizations are not always capable of solving big societal and organizational challenges (Van den Broek et al, 2018). Our alternative open resource based view to SHRM is aimed at the sustained benefits for organizations, individuals (employees and citizens) and societies (Farndale et al, 2022). A specific public sector example for applying an open resource based approach is the open science movement in academia (Miedema, 2022). Open science is aimed at science for a better world through open access, FAIR data & software, public engagement, open education and an alternative recognition & rewards for those working in academia. The central research question in this paper is: To what extent can AI and ChatGPT contribute to open resource practices in HRM with an impact on employee well-being, organizational effectiveness and societal well-being?

References

Beer, M., Spector, B., Lawrence, P., Mills, D. Q. and Walton, R. (1984). Human resource management: A general manager’s perspective. New York, NY: Free Press.

Beer, M., Boselie, P., and Brewster, C. (2015). Back to the Future: Implications for the Field of HRM of the Multistakeholder Perspective Proposed 30 Years Ago. Human Resource Management, 54(3), 427-438.

Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., Boselie, P., Cooke, F. L., Decker, S., DeNisi, A., Kumar Dey, P., 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, 606–659.

Farndale, E., Beamond, M., Corbett-Etchevers, I., and Xu, S. (2022). Accessing host country national talent in emerging economies: A resource perspective review and future research agenda. Journal of World Business, 57(1), 101256.

Miedema, F. (2022). Open Science: the Very Idea. Springer, open access.

Van den Broek, J., Boselie, P. and Paauwe, J. (2018). Cooperative innovation through a talent management pool: a qualitative study on coopetition in healthcare, European Management Journal, 36(1): 135-144.