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.

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.

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 × 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.

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.

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.