Impression management is often portrayed as a self-serving strategy for personal gain. We propose that impression management can also operate as a humanistic act—one in which actors generate favorable impressions through non-exploitative efforts to satisfy the psychological needs of audiences. Drawing on self-determination and signaling theories, we theorize that expressions of mindfulness, curiosity, and encouragement function as relational signals that fulfill audiences’ needs for autonomy, relatedness, and competence. When these signals are received as clear, reliable, and need-supportive, they generate experiences of validation, interest, and confidence that foster favorable impressions. We introduce the beacon metaphor to organize this process, illustrating how impression management can shift from instrumental self-presentation to need-supportive signaling, and we conceptualize impression formation as a reciprocal process whereby audience responses operate as countersignals. Our model shifts attention to the audience's experience of signaling and explains impression management as a relational, need-based process that is dynamically co-constructed.
This special issue seeks to enlarge scholars’ understanding of the different ways that organization science uses theory to produce and disseminate knowledge. It brings the discussion about what theory is and does to the level of the profession as opposed to the paper. Understanding why our profession has theory in the first place to should help scholars avoid some of the unproductive debates about the use of theory within papers. More importantly, it should help scholars produce theories that can solidify rather than destabilize our corpus of knowledge - the basis for our professional authority - without limiting conceptual advancement and innovation. Developing theory that fits with how our profession produces and disseminates knowledge should improve its chances of publication, but more importantly this should improve a paper's chances of being cited and its chances of generating knowledge that can truly be useful and usable to scholars and practitioners. The special issue should thus be a valuable resource for scholars as they produce and review theory, and should lead to a more integrated and coherent science of organizational psychology.
This article applies social exchange theory (SET) principles to the context of algorithmic management (AM), where algorithmic systems act as a technological intermediary between workers and organization. We propose a theoretical model that examines how AM shapes worker-organization social exchange relationships. We posit that organizational choices regarding AM shape system materiality, which workers evaluate through heuristic signals of procedural, informational, and distributive justice. These justice evaluations provide cognitive bases for workers’ trust in the AM system, which in turn shapes trust in the organization. This organizational trust reinforces the norm of reciprocity, fostering workers’ reciprocative behaviors toward the organization. Our model suggests that traditional social exchange principles apply to algorithmically mediated worker-organization relationships, yet require some theoretical modification, offering both theoretical insights and practical guidance for AM use.
Incidental emotions—emotions elicited outside a decision context—can affect decisions at work. Extant theory emphasizes that incidental emotions affect decisions through the emotion's associated appraisals or interpretations of the environment. A related but conceptually distinct component of emotions is action tendency, or the drive to execute an action for a particular goal. We address prior theoretical ambiguities and disparate findings, introducing an Integrative Framework for Incidental Emotions and Decision Making in which these complementary but conceptually distinct components (i.e., the informational and the motivational pathways) of emotions can each affect decision making at work. We present contextual moderators for whether an incidental emotion's appraisals or action tendencies influence subsequent workplace decisions, including drive satisfaction, appraisal override, situation feasibility, and task compatibility. Finally, we outline implications for future theory development and methodology, as well as practical implications for managing incidental emotions’ effects on decision making at work.
This paper examines factors that have led to the dissolution of many diversity, equity, and inclusion (DEI) initiatives in public and private organizations across the United States and globally. Although anti-DEI backlash is not new, the current wave of anti-DEI backlash is unprecedented in many ways. In response, we present a conceptual model that suggests DEI may elicit negative affective (i.e., fear, animosity) and negative cognitive responses (i.e., perceived unfairness, biased construals), particularly among individuals who are members of dominant social groups (e.g., White people, men) and who endorse hierarchy-legitimizing ideologies (i.e., system justifying ideologies, social dominance orientation). These affective and cognitive reactions have led to a series of behaviors, which we categorize as subtle, active, and ambivalent resistance. We conclude by introducing propositions that may guide future research examining ways to effectively reduce anti-DEI backlash.
Although interest in neurodivergence in the workplace has grown in recent years, research on its implications for team functioning remains limited. In this article, we develop a conceptual model of neurodiversity in work teams by focusing on two prevalent forms of neurodivergence: autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). Drawing on the concept of "spiky profiles," which capture individuals' distinct patterns of cognitive strengths and challenges, we theorize how within-person variability can translate into team-level processes through emergent patterns of interaction. We advance eight testable propositions linking team neurodiversity to key team processes - action, transition, and interpersonal - and highlight the crucial moderating role of psychologically inclusive team leadership that emphasizes flexibility in team interdependence. By integrating and extending existing frameworks, our model clarifies how neurodivergent traits shape team processes and effectiveness.
This article develops a multilevel theoretical model explaining how teamwork engagement (TWE), a team state grounded in compatible cognitive representations of vigor and dedication, emerges and shapes team performance and viability. Integrating Job Demands-Resources (JD-R) theory, team and multilevel emergence literature, we specify how team structural conditions (e.g., autonomy) influence members' perceptions of job demands-resources optimal balance. These perceptions activate individual work engagement and through intra and inter-personal mechanisms, foster the emergence of TWE. We conceptualize TWE as compilational emergence arising from the functional alignment of members' differentiated engagement levels, which are unevenly distributed within teams. The model further posits TWE as a dynamic team state that moderates how structural conditions are translated into balance perceptions and contributes to team performance and viability by shaping goal setting and striving. Across eight propositions, we articulate key explanatory mechanisms, research directions, and practical implications for designing and sustaining work-engaged teams.
People often confuse "theory" and "model" in organizational and management research, which slows scientific progress. A scientific theory is a strong, well-supported explanation built on extensive converging evidence. It provides reliable knowledge for evidence-based practice and supports education that helps professionals adapt over time. A conceptual model, by contrast, is a preliminary idea that proposes how things might relate; it must be tested, critiqued, and refined. When a model's mechanisms are clearly specified and backed by substantial evidence, it can be recognized as a theory. But models are sometimes mistaken for theories-either because long-used models (like Maslow's hierarchy or Lewin's change model) gain assumed legitimacy through familiarity, or because advocates re-label a model as a theory to enhance credibility without adequate evidence. Distinguishing between theories and models guides two research paths: strengthening established theories and rigorously developing models so their explanatory and predictive power can eventually be tested.
Extending theories of relational identity for a diverse workforce, we introduce the concept of intersectional relational identity- the unique, shared identity created by partners through integrating and transforming their intersecting roles and social positions. First, we introduce the dyadic-level construct of intersectional relational identity and locate it within a 2 x 2 framework of workplace relational identities. Situating our theorizing within the context of American gender and race labor stratification, we consider how work partners' social identity (dis)similarity and role (mis)alignment interactively shape relational identity development. Second, we theorize how diverse partners co-create intersectional relational identities, identifying key motivators, facilitators, and mechanisms. We outline how the resulting uncertainty can foster relational identity development through the co-creation of work roles. This framework advances relational identity theory by revealing how identity differences and role-prototype misalignment, typically conceptualized as relational barriers, offer flexibility and innovation opportunities.
Organizational science has been dominated by construct theories, which focus on relationships among constructs to predict outcomes. In contrast, process theories emphasize action/event sequences, and the generative mechanisms that drive them, to explain how and why organizational phenomena unfold over time. Although these approaches have been viewed as distinctly different, we integrate them within a Layers of Theoretical Explanation Typology that encompasses construct relationships, underlying action/event process sequences, and foundational generative process mechanisms that drive the phenomena of interest. Theory building that integrates all three layers of theoretical explanation provides an explanation - the primary purpose of theory - that will improve prediction, strengthen causal inference, and provide better targeted interventions. Computational process theorizing provides improved causal inference that better specifies the who, what, where, when, why and how of organizational behavior and, hence, provides more precise specification for the design of interventions to enhance organizational effectiveness.
Most of the quantitative work in management and organizational psychology emphasizes a deductive theory testing approach. In this paper, we focus on data-driven theorizing as an alternative, complementary approach to deduction. The increasing availability of (big) data and sophisticated methods provide opportunities for data-driven theory building and refinement as another way to build knowledge and advance the field. We explain how using data-driven theorizing in responsible and transparent ways can inform knowledge creation and theorizing, and we discuss opportunities and challenges. We also give recommendations for authors, reviewers, editors, and the discipline aimed at increasing transparency and stimulating responsible data-driven theorizing as well as increasing openness to the explorative use of quantitative data in our field.
Research on staffing organizations traditionally focused on hiring the best performing applicants. This paradigm is restricted to serving the employer's interests, and its utility may be limited by skill shortage. It is therefore proposed to complement traditional recruitment and selection based on a paradigm labeled Assessment and Selection in the Service of the Applicant (PASSA). PASSA is meant to support applicants taking on the agentic role in mutual interactions, such that they become recipients of the information gathered, whereas employers become targets of assessment. This idea is elaborated on along the typical stages of the staffing process, from defining goals and decision criteria, to job analysis, to recruitment, to assessment and selection, to validation. At each stage, implications and challenges for implementing PASSA in research and practice are discussed. Finally, theoretical arguments as to why and under which conditions both employers and applicants may benefit from this implementation are offered.
In dynamic environments, the necessity to adapt mental models that no longer serve their purpose is paramount as these models guide individual behaviors. This article aims to elucidate how engaging in Pure Silence can influence and alter mental models with implications for organizational functioning. The formulation and operationalization of mental models have been central to analyzing intersections between industrial and organizational (I-O) psychology and cognitive psychology. The theoretical framework described and the proposed conceptual model can help organizational psychology researchers and professionals to understand how mental models can be altered to foster effective organizational behavior. The model integrates (a) Pure Silence, (b) meditation, (c) mindfulness, (d) mental models which guide individual behaviors and (e) organizational climate. By drawing insights from mindfulness theory and social learning theory, this article explores mechanisms underlying effective organizational behavior.
Theories in organizational science often suffer from fuzzy boundaries, overlapping constructs, and limited falsifiability, raising concerns about coherence and cumulative progress. In response, we explore the potential of artificial intelligence (AI) as a tool for advancing theory-building in organizational science. We conceptualize AI as a theoretical workshop, arguing that when embedded within a human-in-the-loop framework, AI can assist with generating theoretical arguments, evaluating the logical coherence and completeness of theories, and formalizing them through computational modeling. These advances improve the quality of new theories while pruning inadequate theoretical frameworks. AI also lowers barriers that have historically limited progress by democratizing access to computational tools and enabling more dynamic, iterative, and interdisciplinary theorizing. Although integration of AI demands careful attention to ethical considerations and cannot replace the judgment, creativity, and contextual expertise of researchers, its potential to transform organizational research is substantial.
Conditional reasoning is a useful way to assess the implicit aspects of personality. Unfortunately, a limited number of conditional reasoning tests have been developed to measure different personality dimensions. While work exists that attempts to explain the measurement system of conditional reasoning, fewer works attempt to explain the theory that underlies conditional reasoning. The lack of a theoretical description of conditional reasoning may be why researchers have not developed many new conditional reasoning tests. We reviewed conditional reasoning research and contacted authors who have successfully developed conditional reasoning measures to understand better the theory that undergirds conditional reasoning. We identify and present eight foundational assumptions to explain the theory that underlies conditional reasoning. By sharing these assumptions and associated implications, we aim to spur further development of conditional reasoning tests.
Despite the proliferation of artificially intelligent systems capable of social interaction, how and why social interaction influences users over time remains poorly understood. We draw on theories of technology adoption and research in affective computing, social psychology, and management to introduce the concept of human-AI relationships involving interdependence, temporality, and intensity. We develop the Relational Tradeoff Model, extending current theorizing on technology adoption by accounting for a critical third factor in addition to cognitive acceptance and behavioral use: human subjective well-being. The model reveals an important unexplored tradeoff in relationships with socially interactive AI: short-term acceptance and use gains but long-term subjective well-being costs for trust, psychological safety, and emotional labor, depending on AI social function and exacerbating and mitigating individual and relational factors. We discuss implications and suggestions for future exploration, including intrapersonal, interpersonal, and team relational dynamics and evolving expectations of AI in organizations.
Emotional labor involves managing emotions as part of the work role and is often accomplished by using surface acting (i.e., faking, suppressing) and deep acting (i.e., changing felt emotions) emotion regulation strategies. Extant theorizing and established measures characterize the use of these two types of regulation strategies as requiring high effort, which consumes resources and contributes to worse employee well-being and performance outcomes. The purposes of this paper are to make the theoretical case that these emotion regulation strategies can vary in the extent to which they are automatic and effortless as opposed to controlled and effortful, and to consider how automaticity in emotional labor might develop in individuals and be exhibited on the job. In pursuing these ideas, we also develop propositions regarding the conceptualization, antecedents, and consequences of emotional labor automaticity and make the case that a new measurement approach is needed to capture the emotion regulation strategy automaticity continuum. Finally, we discuss the practical implications of these ideas and future research directions.
Although some individuals prefer to separate work and nonwork, others prefer to integrate these domains, and organizations, families, and other contextual factors uniquely impact boundary management. Boundary management fit, which integrates boundary management theories with person-environment fit, examines the interplay between these individual and contextual factors. In this systematic review, we take stock of boundary management fit and its implications for worker well-being. Our results conceptualize boundary management fit and outline its nomological network. Results suggest that, although boundary management fit results in some positive well-being-related outcomes, the consequences are somewhat equivocal. Rather, boundary management misfit (i.e., a lack of fit between person and environment) may be more impactful on well-being. From there, we present a conceptual framework that builds on and expands prior research, comprehensively depicting the body of literature to date. We discuss our review's theoretical, empirical, and practical implications and outline future directions for research and practice.
Scholarly interest in the areas of innovation and harmful workplace behaviors has grown rapidly in recent years. Despite parallel growth in interest, research has largely failed to address the intersection of these phenomena. Moreover, although malevolent innovation is conceptually distinct in the fields of crime, terrorism, and extremism, there is limited clarity for how it differs from established workplace constructs (e.g., workplace incivility, counterproductive work behavior). In this review, we consider related workplace constructs and examine how they overlap with and, more centrally, differ from malevolent innovation. We distinguish deviance and other forms of norm-violating behaviors from the primary elements of malevolent innovation: novelty and intentional harm, as well as secondary elements: intensity, perpatrator-target relationship, and planning. Together, we provide a review to distinguish malevolent innovation as a unique workplace construct. Areas for future research are also discussed.
Over the past twenty years, the construct of leader identity-the internalization of "leader" into one's self-concept-has experienced an exponential growth in attention among researchers and practitioners alike. This boom in research has yielded broad support for leader identity as an important predictor of leadership outcomes; however, this rapid growth has also created a disjointed construct space with theoretical and methodological challenges. In a systematic review of nearly 200 papers, we apply the existing theoretical framework of multi-dimensional leader identity ( Hammond et al., 2017; strength, meaning, level, integration) to organize factors affecting the leader identity construction process as well as three functions leader identity serves-motivation, interpretive lens, and a relational foundation. Finally, we offer guidance to create a road map for the rigorous study and advancement of leader identity research.
