Beyond Synthesis: Elevating Scholarly Contributions in the Age of AI

"You can raise the bar or you can wait for others to raise it, but it's getting raised regardless." Quote by Seth Godin Since 2020, the Annual Review and Conceptual Development Issue (ARCDI) of the Journal of Organizational Behavior has welcomed submissions of both review papers and conceptual development papers, recognizing their distinct yet complementary roles in advancing knowledge about organizational behavior (Dasborough 2020). During this time, the ARDCI has served as a platform for scholars to contribute to theoretical development and future empirical enrichment of our field. The reviews and conceptual development articles published in the ARDCI have stimulated future research endeavors by building upon existing scholarly conversations and raising new questions to be investigated. Despite the widening scope of the Annual Review issue to be inclusive of conceptual development submissions, most of the manuscripts submitted to the ARCDI continue to be scholarly reviews. These range from qualitative narrative review articles to quantitative meta-analytic articles and every other type of review in between (see Grant and Booth 2009). Purposeful and rigorous review articles provide valuable syntheses of existing research, helping to consolidate what we know (Rousseau 2024). Review articles published in the ARCDI (and other review outlets) are often highly cited in the literature, as many scholars turn to reviews because they summarize a lot of information on a topic and they contain comprehensive reference lists (Lei and Sun 2020; Moussa 2021). Hence, writing review articles has been a very attractive option for scholars wishing to enhance their reputation as scholarly contributors to the field. AI tools can now assist researchers across almost every stage of preparing a review article (Dasborough 2024). AI tools can synthesize large volumes of scholarly articles and automate the production of systematic and comprehensive literature reviews (e.g., Pallath and Zhang 2023). For instance, platforms like Research Rabbit and Connected Papers enable researchers to identify trends, gaps, and influential works by visualizing relationships among studies and uncovering less obvious connections. This can help with selecting specific topic areas to review. Other tools such as Elicit, Paperfetcher, Eppi-Reviewer, Rayyan, and Scite automate data extraction from large datasets, making systematic reviews and meta-analyses faster and more comprehensive (Rousseau 2024). Similarly, it is evident that tools like ChatGPT, Quillbot, and Semantic Scholar are being used to summarize academic articles, potentially reducing the time that scholars spend reading and extracting key insights from lengthy articles—although this can be a risky practice that I do not encourage. Please read every article you cite! (see Rekdal 2014). As mentioned in prior editorials, commentaries, and articles, advances in artificial intelligence (AI) are reshaping the landscape of academic writing (Dasborough 2023; Grimes et al. 2023; Rousseau 2024). Consequently, this is changing the relative value of different types of contributions to the ARCDI. Since AI tools can help to identify patterns, summarize findings, and highlight gaps within the existing literature (Dasborough 2023), this changes the value proposition of review articles because these articles now require much less time1 and cognitive effort to write than they did before. In 2025, the standard for publishing articles that review areas of scholarly literature is being set at a significantly much higher level. Given the rise in the use of various AI tools, review papers, in their traditional form, are becoming less intellectually demanding and therefore less valuable as a scholarly output. As the editor of the ARCDI, I need to adapt to this new reality by shifting focus to encouraging submissions that demonstrate a level of cognitive complexity and creativity that AI cannot replicate. Moving forward, I encourage submissions to the ARCDI that go beyond merely summarizing the literature as some reviews have in the past (Dasborough 2023, 2024). Instead, I urge authors to leverage systematic reviews as a foundation for generating innovative conceptual and theoretical insights or addressing unresolved paradoxes in our field. Moving forward, this is where I see the most valuable unique intellectual contribution being made. Conceptual development and theoretical development are two distinct yet closely related types of scholarly contributions that can advance our understanding of organizational behavior. Conceptual development articles focus on clearly identifying, defining, and framing new constructs (see Podsakoff, MacKenzie, and Podsakoff 2016). In contrast, theoretical development articles involve proposing frameworks or models that explain phenomena, by integrating multiple constructs and specifying causal mechanisms between them (Smith and Hitt 2005). While conceptual development lays the foundation for future scholarly inquiry, theoretical development builds upon it to provide explanations and predictions that may stimulate future empirical studies. Therefore, both types are crucial for scholarly progress. These types of papers can challenge existing paradigms, propose novel frameworks, or integrate insights across disciplines to address complex organizational phenomena (Grimes et al. 2023; Rousseau 2024; Smith and Hitt 2005). They demand not only a mastery of the literature but also the ability to engage in abstract reasoning, make bold intellectual leaps, and construct compelling arguments that advance the boundaries of what we know. In this regard, conceptual and theoretical development papers represent the kind of output that AI, for all its strengths, is unlikely to produce autonomously. They require human ingenuity, intuition, and a nuanced understanding of the organizational context—qualities that remain uniquely human. This is not to say that systematic reviews have no place in the ARCDI. On the contrary, systematic reviews that are purposefully crafted to inform conceptual and theoretical development can be incredibly impactful. For example, a systematic review could identify an emergent but underexplored phenomenon, serving as the foundation for a novel theoretical model (Grant and Booth 2009). Alternatively, it could synthesize insights from adjacent fields, paving the way for cross-disciplinary innovation. In these ways, AI can augment (not substitute) scholar's endeavors to produce impactful work (see Bankins et al. 2024; Von Krogh 2018). The key is for systematic reviews—which may or may not be AI-assisted—to go beyond simple description and summation, by contributing to the generative aspects of theory building. By doing so, these reviews not only remain relevant but also elevate their contribution to broader academic conversations. AI tools should be used to empower scholars to focus on higher-order intellectual tasks, such as critical analysis, conceptual development, and theoretical development while minimizing repetitive and time-consuming processes (Grimes et al. 2023). As we look to the future of the ARCDI, I envision it being an outlet where an abundance of scholarly information provided by AI tools (in a transparent manner) meets passion, creativity, critical thinking, and intellectual rigor. I encourage authors to embrace the opportunities for the efficiencies presented by AI, while simultaneously striving to produce scholarly work that challenges, provokes, inspires, and excites. This issue of the ARCDI presents eight articles that cover a broad range of OB topics. These articles include both reviews and conceptual development papers, with each article offering a unique value proposition as identified by our review teams. The articles are presented in the most logical order that I could think of, starting with within-individual-level phenomena and individual responses, moving on to interpersonal dynamics, and then finishing with group and organizational-level issues. The first two articles focus on how daily fluctuations, timing of work, and recovery processes shape the work experience. First, Sonnentag, Völker, and Wehrt offer a comprehensive descriptive review of experience-sampling studies, analyzing what makes for a "good" versus "bad" day at work. Their review identifies prework factors (e.g., sleep), at-work factors (e.g., work breaks), and their contributions to daily outcomes. By linking their findings to a homeostatic human sustainability perspective, this article provides valuable insights into how daily experiences shape work life. The second article by Calderwood et al. examines the role of contextual factors—specifically work scheduling dynamics—in influencing recovery processes, an essential element of sustaining well-being across workdays. While Sonnentag et al. emphasize the interplay of events in shaping positive and negative workdays, Calderwood et al. extend this perspective by addressing how scheduling structures can constrain or enable resource replenishment. Together, these first two articles offer complementary insights into how organizations can promote employee well-being through intentional scheduling and support of positive day-level experiences. Given that we all (should) want to have good days at work and value well-being, these articles will be of interest to our entire JOB audience. Building on the insights into daily experiences, timing, and recovery, the next pair of articles explore how interpersonal dynamics, such as risk, rank, and trust, influence decision-making in the workplace, highlighting how individual behaviors intersect with relational factors. In their article, Hamstra and Higgins develop a theoretical framework to understand how rank positions in organizations influence decision-making, particularly in choosing between risky and less risky options. By examining the motivational effects of rank through a multilevel analysis—spanning broad motives, intermediate goals, and tactical choices—they highlight the dynamic interplay of rank goals, changes relative to those goals, and competing priorities. Their propositions provide a nuanced understanding of how hierarchical dynamics shape individual behaviors, including risk-taking tendencies. Building on this focus on hierarchical and interpersonal dynamics, in the following article, Lee et al. turn their attention to felt trust, a trustee's perception of being trusted by a trustor, which is (again) often shaped by organizational hierarchies. Their critical review reveals key conceptual and methodological challenges in the field, while their meta-analytic findings establish the distinctiveness and incremental validity of felt trust. Both articles underscore the importance of understanding how individuals navigate hierarchical contexts—whether through risk-related decision-making or trust-related perceptions—and together offer insights into how rank and trust interact to influence workplace behavior and outcomes. Interpersonal dynamics like trust and risk-taking often operate within the broader context of organizational inequities. The next two articles in this issue focus on topics specifically related to systemic inequalities, namely, gender dynamics and workplace exclusion. First, Bear, Trevino, and Aguinis investigate the gender gap in star emergence, presenting a longitudinal process model that uncovers systemic barriers preventing women from achieving star status in organizations. Then, they propose potential mitigators, such as strategic diversity goals and influential sponsors, that could foster greater gender equity in performance recognition. Related to this focus on systemic inequities in organizations, the next article by Stamper, Metz, and Shore critically examines the construct of workplace exclusion. By developing the construct of perceived workplace exclusion, which centers on an individual's belief that they are being excluded, they establish a unified conceptual foundation that differentiates this construct from related concepts. Together, these articles highlight various systemic barriers and also offer valuable insights into creating more equitable and inclusive workplaces. Addressing systemic challenges such as exclusion and inequity provides a foundation for understanding how peer-level and organizational mechanisms can be designed to foster inclusivity and drive collective development and change. The next article by Terekhin and Aurora explores the dynamics of peer development groups (PDGs). Through a systematic review, they address the inconsistencies in PDG conceptualization and propose a new conceptual framework that identifies key constructs that impact PDG effectiveness. Complementing this focus on peer support, Gagné, Kamarova, Holtrop, and Dunlop tackle the broader challenge of implementing successful organizational change. Using self-determination theory as a foundation, they develop a comprehensive model that links practices and psychological mechanisms to inform organizational change implementation. While Terekhin and Aurora emphasize the dynamics within smaller, peer-driven contexts, Gagné et al. focus on the psychological underpinnings of large-scale change. Together, these final two articles in this issue of ARCDI highlight the interconnectedness of group-level processes and individual behaviors in fostering organizational transformation, providing actionable insights for aligning peer support and organizational practices to achieve sustained improvement. In last year's ARCDI editorial, I emphasized the importance of expressing gratitude to the people in our "behind-the-scenes academic teams" (Dasborough 2024). In that same spirit, I would like to extend my heartfelt thanks to JOB Editor-in-Chief Christian Resick, whose guidance and support were invaluable as we developed this issue. I am also deeply appreciative of Tony Kong, who served as associate editor for one of the articles in this issue, providing thoughtful oversight and expertise. Finally, I want to thank all the reviewers who helped guide these manuscripts through the review process. Although it took me quite a while to get two to three reviewers per submission (apparently people are busy doing other things during late December–early January!), once on board, our reviewers provided highly developmental and constructive advice to improve the quality of the papers we received. JOB has the best editorial board members and ad hoc reviewers! I also wish to highlight special gratitude to three dedicated emergency reviewers—Luke Hedden, Darryl Rice, and Juan Madera—for their timely and exemplary contributions to JOB. Their generous efforts exemplify the collaborative spirit that drives our academic community forward. Generative AI (ChatGPT 4.0) was used to identify the most popular AI tools that can be used to help write literature reviews. This tool was also used to develop a suitable title for the editorial. I also asked ChatGPT to provide references about the use of AI in scholarly research that I could draw from. However, some of the provided references were nonexistent. Example 1: Bishop, T. R., & Mayer, D. M. (2023). The role of artificial intelligence in organizational research: Opportunities and challenges. Journal of Organizational Behavior, 44(1), 1–15. https://doi.org/10.xxxx/jobe.2023.xxxx I was excited about this suggested article that apparently was published in JOB. I was surprised I had not come across it yet—so I looked up Journal of Organizational Behavior: Volume 44, Issue 1. Here is the real article that opens this issue: Vongswasdi, P., Leroy, H., Shemla, M., Hoever, I., & Khattab, J. (2023). Influencing diversity beliefs through a personal testimonial, promotion-focused approach. Journal of Organizational Behavior, 44(1), 1–18. Example 2: Larivière, V., Haustein, S., & Mongeon, P. (2020). The impact of artificial intelligence on scholarly research. PLOS ONE, 15(3), e0231210. https://doi.org/10.1371/journal.pone.0231210 I could not find this in Google Scholar, so I clicked on the link provided and it took me to this article instead: Brumfield, K. D., Hasan, N. A., Leddy, M. B., Cotruvo, J. A., Rashed, S. M., Colwell, R. R., & Huq, A. (2020). A comparative analysis of drinking water employing metagenomics. PLoS One, 15(4), e0231210. My advice to all scholars—please double-check every scholarly reference provided by AI tools! As Huff (2024) explains, human oversight—including checking all AI outputs—is essential. The author declares no conflicts of interest. The data that support the findings of this study are available from the corresponding author upon reasonable request.