Tracy A. Jenkin

Problem definition: Large language models (LLMs) are being increasingly leveraged in business and consumer decision-making processes. Because LLMs learn from human data and feedback, which can be biased, determining whether LLMs exhibit human-like behavioral decision biases (e.g., base-rate neglect, risk aversion, confirmation bias, etc.) is crucial prior to implementing LLMs into decision-making contexts and workflows. To understand this, we examine 18 common human biases that are important in operations management (OM) using the dominant LLM, ChatGPT. Methodology/results: We perform experiments where GPT-3.5 and GPT-4 act as participants to test these biases using vignettes adapted from the literature (“standard context”) and variants reframed in inventory and general OM contexts. In almost half of the experiments, Generative Pre-trained Transformer (GPT) mirrors human biases, diverging from prototypical human responses in the remaining experiments. We also observe that GPT models have a notable level of consistency between the standard and OM-specific experiments as well as across temporal versions of the GPT-3.5 model. Our comparative analysis between GPT-3.5 and GPT-4 reveals a dual-edged progression of GPT’s decision making, wherein GPT-4 advances in decision-making accuracy for problems with well-defined mathematical solutions while simultaneously displaying increased behavioral biases for preference-based problems. Managerial implications: First, our results highlight that managers will obtain the greatest benefits from deploying GPT to workflows leveraging established formulas. Second, that GPT displayed a high level of response consistency across the standard, inventory, and non-inventory operational contexts provides optimism that LLMs can offer reliable support even when details of the decision and problem contexts change. Third, although selecting between models, like GPT-3.5 and GPT-4, represents a trade-off in cost and performance, our results suggest that managers should invest in higher-performing models, particularly for solving problems with objective solutions. Funding: This work was supported by the Social Sciences and Humanities Research Council of Canada [Grant SSHRC 430-2019-00505]. The authors also gratefully acknowledge the Smith School of Business at Queen’s University for providing funding to support Y. Chen’s postdoctoral appointment. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0279 .

The COVID-19 pandemic instigated the rapid shift to remote work and virtual interactions, constituting a new normal of professional interaction over information and communication technologies (ICT), such as videoconferencing platforms, email, and mobile devices. While ICT may provide many benefits for remote work, such as flexibility, reductions in travel time, and geographical interaction, ICT may also contribute to increases in job strain, reductions in social interactions, and the decline of mental health. While reliance on ICT for remote work interactions is becoming the new normal of organizational activity, scholarly appreciation of the stages in which employees, particularly educators, enact interactions over ICT is limited. Further, the intensity of the pandemic is unlike anything management scholars have studied before, and previous research into ICT use offers little insight into how ICT behaviors evolve over time. Our research explores how educators enact ICT interactions with students throughout the first two waves of the COVID-19 pandemic. We conducted 24 open-ended interviews with educators to learn about their experiences shifting to ICT for virtual classes. We found that ICT interactions between educators and students are enacted through two sequential, interrelated stages:divergent interaction behaviorsandconvergent interaction behaviors, with each stage corresponding to the first and second waves of the pandemic. We delineate the phases within each enacting stage of ICT interaction and conjecture that future use of ICT may include iterative cycles of divergent and convergent interaction behaviors, particularly if educators explore how to leverage ICT in more creative ways. Our research presents a theorization of ICT interaction as an increasingly prevalent form of IT-enabled educational interactions and communication. We provide insight into how educators successfully shifted to ICT use for remote work, and we offer implications for the facilitation of hybrid work arrangements in educational settings using ICT.

Teams that innovate encounter challenges in divergent and convergent thinking processes related to the need to: 1) leverage diverse internal and external knowledge, and 2) produce something that is both novel and valuable. Integrating the extant literature, we describe these challenges and propose a new approach to solving issues related to divergent and convergent thinking in groups in an innovation context. Specifically, we design group processes to support divergent and convergent thinking, including the use of several information technology (IT) tools to support them: 1) a novel-information discovery tool to foster computer-supported divergent thinking and sensemaking, and 2) a collaborative-filtering tool to foster computer-supported convergent thinking and sensegiving. Findings indicate that the novel-information discovery tool increases efficiency and effectiveness in the divergent thinking process and that the collaborative-filtering tool supports convergent thinking by focusing the group's attention on ideas that might otherwise be neglected. Combining these two IT tools with group processes for divergent and convergent thinking has important implications for both research and practice.

Purpose: In the business and data analytics community, intuition has not been discussed widely in terms of its application to executive decision-making. However, the purpose of this paper is to focus on new global research that combines intuition, trust and analytics in terms of how well C-level executives trust their intuition. Design/methodology/approach: Our Fulbright research, as described in this paper and performed by colleagues from the United States, Canada, Poland and Italy, examines executives’ as well as other less experienced employees’ preferences for different types of intuition versus data analysis. This study set out to better understand the degree to which executives prefer intuition versus analysis and the relationship between these approaches to decision-making. Our research combines elements of a review, a cross-cultural/cross-company survey study and a biometrics study in interoception. The research team has a multidisciplinary background in business, information technology, strategy, trust management, statistics and neuroscience. Findings: Based on our research, the main findings are as follows. The use of and preference for intuition types change as employees gain more experience. However, there may be intuition styles that are more static and trait-like, which are linked to roles, differentiating managers from leaders. Using “inferential intuition” and “seeing the big picture” go hand in hand. Listening to your body signals can promote improved intuition. Cross-cultural differences may impact executive decision-making. Executives often prefer to use their intuition over analysis/analytics. Research limitations/implications: This research could be expanded to have a larger sample size of C-level executives. We had 172 responses with 65% C-level executives and 12% directors. However, a recent survey by the Economist Intelligence Unit on intuition used by executives had a sample of 174 executives around the world, which is comparable with our sample size. Practical implications: From our research, executives should continue to apply their experiential learning through intuition to complement their use of data in making strategic decisions. We have often discounted the use of intuition in executive decision-making, but our research highlights the importance of making it a critical part of the executive decision-making process. Originality/value: Based on the results of our survey and biometrics research, executives apply their intuition to gain greater confidence in their decision-making. Listening to their body signals can also improve their intuitive executive awareness. This complements their use of data and analytics when making executive decisions.

Although information systems development (ISD) projects are critical to organizations and improving them has been the focus of considerable research, successful projects remain elusive. Focusing on the cognitive aspects of ISD projects, we investigate how and why mutual understanding (MU) among key stakeholder groups (business and information technology managers, users, and developers) changes within and across projects, and how it affects project success. We examine relationships among project planning and control mechanisms; sensegiving and sensemaking activities by, and MU among, these stakeholder groups; and project success. Combining deductive and inductive approaches for theory building, we develop an initial model based on the literature and then modify it based on the results of a longitudinal embedded mixed-methods study of 13 projects at 2 organizations over a 10-year period. The results provide insights into the development of MU within projects, including (1) how MU changes during projects as a result of cognitive activities (sensegiving and sensemaking); (2) how planning and control mechanisms (and the associated artifacts) affect these cognitive activities; (3) how MU, and achieving it early in the project, affects success; and (4) how stakeholder engagement (in terms of depth, scope, and timing) affects the relationships in (1) and (2). The results also indicate that project management mechanisms, stakeholder engagement, and MU may change (either improve or deteriorate) across projects, depending on the disagreements among stakeholders in previous projects, the introduction of new project elements in subsequent projects, and the reflection on previous projects.

The continued importance of organizational learning has recently led to several calls for further developing the theory. This article addresses these calls by extending Crossan, Lane and White’s (1999) 4I model to include a fifth process, information foraging, and a fourth level, the tool. The resulting 5I organizational learning model can be generalized to a number of learning contexts, especially those that involve understanding and making sense of data and information. Given the need for organizations to both innovate and increase productivity, and the volumes of data and information that are available to support both, the 5I model addresses an important organizational issue.

In recent years, environmental sustainability has become an important management consideration. Yet, despite the ability of information technology and systems (IT/S) to lower organizations' negative environmental impacts, research has shown that the inclusion of "green" elements in IT/S projects is still relatively rare. In order to better understand how green considerations affect the formation and outcomes of IT/S projects, we extend the "Garbage Can Model" of decision making. Specifically, we use agent-based modeling to study the complexities associated with making decisions in an IT/S project context, incorporate individual, organizational, and external considerations for the environment, and add an objective function that selects projects "optimally" rather than randomly. By including these criteria in the IT/S project selection process, our findings reveal variations in the types of project decisions, and show when the inclusion of environmental considerations improves the financial and environmental benefits of the resulting project portfolio.

In this study, we examine the extent to which employees recognize the importance of information technologies and systems (IT/S) in developing and implementing environmental initiatives. To address this question, we first review past research on this topic and draw on a framework for examining environmental motivating forces, strategies, and employee environmental orientations. We then analyze qualitative data based on in-depth interviews with employees in financial services organizations. Our aim is to develop a richer understanding of how employees currently view IT/S issues in relation to environmental sustainability and if similarities exist between different types of financial institutions. We also assess the extent to which these employee perceptions align with both actual organizational practices, as captured in interviews with information technology managers, and practices espoused by organizations, as reflected on their corporate websites. Our findings suggest that organizations are still in the infancy stage of awareness and adoption of “Green” IT/S. As a result, we identify four types of gaps: knowledge gaps, practice gaps, opportunity gaps, and knowing—doing gaps. We suggest that future research should draw on absorptive capacity, organizational learning, and social marketing theories to help align employees’ attitudes, cognitions, and behaviors and to drive environmental changes.