Design : Capturing the Dynamics of Emergence

Quantitative multilevel research has advanced organizational science, but is limited because the focus is incomplete. Most research examines top-down, contextual, cross-level relationships. Emergent phenomena – those that manifest bottom-up from the psychological characteristics, processes, and interactions among individuals – have been largely neglected. Emergence is theoretically assumed, examined indirectly, and treated as an inference regarding the construct validity of higher level measures. This paper advances more direct, dynamic, and temporally sensitive quantitative research methods designed to unpack emergence as a process. We argue that direct quantitative approaches, largely represented by computational modeling / agent-based simulation, have much to offer with respect to illuminating the mechanisms of emergence as a dynamic process. We illustrate how indirect approaches and direct approaches can be complementary and, appropriately integrated, have the potential to substantially advance theory and research. We conclude with a set of recommendations for advancing multilevel research on emergent phenomena in teams and organizations. Kozlowski et al. Capturing the Dynamics of Emergence 3 SPECIAL FEATURE TOPIC: RESEARCH DESIGN Advancing Multilevel Research Design: Capturing the Dynamics of Emergence The nature of the linkage between lower level entities and higher level collectives in concrete (i.e., physical) and abstract (i.e., social) systems has been theorized by philosophers, psychologists, and sociologists for over a century (Corning, 2002; Katz & Kahn, 1966; Sawyer, 2001). Indeed, in an effort to grapple with this complexity, the disciplines that comprise organizational science have sliced the system into distinct levels – micro, meso, and macro – that have been, at least historically, each associated with different disciplines and research methods (Roberts, Hulin, & Rousseau, 1978). Yet, there is reasonable consensus across disciplines that two fundamental processes span the multiple levels of organizational systems: (a) top down, contextual effects whereby higher level phenomena constrain, shape, and influence different lower level phenomena (i.e., cross-level effects) and (b) bottom up, emergence whereby dynamic interaction processes among lower level entities (i.e., individuals, teams, units) – over time – yield phenomena that manifest at higher, collective levels. Empirical research designed to study these phenomena can be conducted using qualitative or quantitative methods. Arguably, qualitative methods, largely used by sociologists, have been at the forefront of efforts to describe the systemic character of social behavior in organizations and the “behavior” of these collective entities (e.g., Burns & Stalker, 1961; Emery & Trist, 1960). Indeed, until recently, empirical research encompassing the reciprocal complexity of contextual and emergent effects was largely limited to qualitative treatments (e.g., Barley, 1986; Orlikowski, 1992; Orlikowski & Yates, 2002). Quantitative research methods were simply not up to the task. That has changed. The development of a multilevel paradigm – an integration of theoretical principles, research design and measurement, and analytics – for investigating systems phenomena in organizations is an important quantitative research advance. Early multilevel pioneers recognized the shortcomings of an organizational science based on systems conceptualizations that failed to directly study the linkages connecting distinct levels of analysis – micro, meso, and macro (House, Rousseau, & Thomas, 1995; Klein, Dansereau, & Hall, 1994; Roberts et al., 1978; Rousseau, 1985). Multilevel research has been 1 Micro = individual, meso = group or team, and macro = organization and higher levels. 2 We use the term “multilevel” in a generic sense, to encompass cross-level and multilevel research. Kozlowski et al. Capturing the Dynamics of Emergence 4 spurred by the development of meta-theoretical principles to guide theory building (Kozlowski & Klein, 2000), creation of frameworks to guide multilevel construct conceptualization and measurement (Bliese, 2000; Chan, 1998; Chen, Bliese, & Mathieu, 2005; James, 1982; James, Demaree, & Wolf, 1984; Kozlowski & Hattrup, 1992; Kozlowski & Klein, 2000; LeBreton & Senter, 2008; Morgeson & Hofmann, 1999), and evolution of analytics to enable appropriate modeling of multilevel phenomena (Bryk & Raudenbush, 1989, 1992; Burstein, 1980; Hofmann, Griffin, & Gavin, 2000; Muthen, 1994). Although all the conceptual, measurement, and analytical challenges are not resolved, in a relatively short time span, quantitative multilevel research has pushed beyond purely metaphorical treatments of “organizations as systems” (Kozlowski & Klein, 2000) to yield empirical knowledge that bridges multiple levels of the organizational system together (Mathieu & Chen, 2011). This is an important scientific advance. This advance, however, is not fully realized because extant quantitative research is primarily focused on only one of the two core processes that cut across system levels; thus, it is incomplete. The vast majority of multilevel research is focused on top-down, cross-level effects, whereas emergence as a bottom-up process is largely neglected by quantitative investigators (Cronin, Weingart, & Todorova, 2011; Kozlowski & Chao, 2012b). There are two primary reasons for this state of affairs. First, when emergence is considered in multilevel research, it is primarily treated as part of measurement and construct validation for indicators that transcend levels (i.e., constructs that are measured at a lower level, but are aggregated to represent a higher level). These are, of course, critical concerns. However, this primary focus yields a treatment of emergence in terms of theoretical assumptions that are – at best – indirectly supported by statistical indicators and conceptual arguments. Emergence as a dynamic process is not examined directly in extant quantitative research. Second, there are substantial research ambiguities with respect to assessing and representing emergent phenomena. How do we study them? For emergent constructs, there are conceptually based measurement and construct validation models (Bliese, Chan, & Ployhart, 2007; Chan, 1998; Chen et al., 2005; Chen, Mathieu, & Bliese, 2004; Kozlowski & Klein, 2000) and well accepted “rules of thumb” with associated statistical justifications (e.g., Bliese, 2000; James, 1982; James et al., 1984; Kozlowski & Hattrup, 1992; LeBreton & Senter, 2008) to support data aggregation from lower-level measurement to higher-level representation. For emergence as a substantive phenomenon, however, research guidance is quite limited. Kozlowski et al. Capturing the Dynamics of Emergence 5 Moreover, emergence is complex because it incorporates both process (i.e., dynamic interactions among entities) and structure (i.e., over time it manifests as a collective property; a construct or “emergent state”). This duality of process and structure is fundamental to social psychological and sociological phenomena in organizations (Allport, 1954; Giddens, 1979; Katz & Kahn, 1966), but it makes a substantive focus on emergence a difficult fit to the commonly used quantitative research designs and methods in organizational science. The purpose of this paper is to advance more direct, dynamic, quantitative research methods for investigating emergence. Lest our purpose be misinterpreted, it is important to highlight some caveats. First, we are not proposing that the approach we advance is the only way to study emergence. It is one potentially powerful way, but one among many (e.g., intensive longitudinal designs, experience sampling methods could be adapted to study emergent phenomena). Indeed, as we will make clear, qualitative researchers have been studying emergence directly for decades. We wish to advance direct quantitative research on emergence. Second, our intent is not to supplant research that examines cross-level effects; such research is fundamental and remains important. Rather, our intent is to supplement it by urging quantitative researchers to investigate the other fundamental system process that is currently neglected. Third, our encouragement for direct quantitative research on emergence is not intended to suggest that attention devoted to assumptions of emergence in measurement development is misguided; such attention is essential. Rather, our effort is intended to expand the array of quantitative research design tools, push multilevel research in new directions, and advance direct investigation of organizations as multilevel, dynamical systems. We begin by discussing the nature of emergence. The term has been applied broadly in the organizational literature in ways that are not equivalent. Indeed, some uses of the term are antithetical (Corning, 2002; Epstein, 1999). Explicit attention to conceptualization is critical. Drawing on complexity theory, we define emergence as a dynamic, interactive process and specify three core conceptual foci to capture its essential nature: it is multilevel, process oriented, and temporal. We then describe research design approaches that address emergent phenomena. We broadly characterize these approaches as indirect or direct, implemented by qualitative or quantitative research methods. Indirect approaches rely on retrospective observations and infer the nature and manifestation of emergence. Direct approaches rely on prospective observations that capture the process and manifestation of emergence as it unfolds. Kozlowski et al. Capturing the Dynamics of Emergence 6 Most quantitative research is indirect because it infers emergence, whereas the vast majority of extant efforts to investigate emergence directly are qualitative (Kozlowski & Chao, 2012b). Qualitative research provides a rich descriptive foundation for theorizing about the process mechanisms that undergird emergence. H