We present an integrative conceptual review that reconciles the organizational support, social exchange, and social support literatures. In particular, we argue that the prevailing, singular conceptualization of organizational support is misaligned with contemporary perspectives on social exchange-which has served as the bedrock for organizational support theory since organizational support theory's inception-and is inconsistent with the social support literature-which has long recognized that support takes several forms. Thus, we draw on both the social exchange and social support literatures to develop four unique forms of organizational support: Teleological, Personalized, Collectivistic, and Monistic Organizational Support. With this enlarged framework for understanding organizational support in hand, we then detail the various research opportunities that the integration of these literatures affords. Specifically, we explain that this framework warrants future research related to the development of new measures, the differential prediction of outcomes, and the discovery of organizational support profiles. We also invoke the social support literature to highlight the potential opportunities in applying optimal matching theory to organizational support, examining relationships between received and perceived organizational support, and identifying the consequences of excessive organizational support.
The advent of wearable sensor technologies has the potential to transform organizational research by offering the unprecedented opportunity to collect continuous, objective, highly granular data over extended time periods. Recent evidence has demonstrated the potential utility of Bluetooth-enabled sensors, specifically, in identifying emergent networks via colocation signals in highly controlled contexts with known distances and groups. Although there is proof of concept that wearable Bluetooth sensors may be able to contribute to organizational research in highly controlled contexts, to date there has been no explicit psychometric construct validation effort dedicated to these sensors in field settings. Thus, the two studies described here represent the first attempt to formally evaluate longitudinal Bluetooth data streams generated in field settings, testing their ability to (a) show convergent validity with respect to traditional self-reports of relational data; (b) display discriminant validity with respect to qualitative differences in the nature of alternative relationships (i.e., advice vs. friendship); (c) document predictive validity with respect to performance; (d) decompose variance in network-related measures into meaningful within- and between-unit variability over time; and (e) complement retrospective self-reports of time spent with different groups where there is a "ground truth" criterion. Our results provide insights into the validity of Bluetooth signals with respect to capturing variables traditionally studied in organizational science and highlight how the continuous data collection capabilities made possible by wearable sensors can advance research far beyond that of the static perspectives imposed by traditional data collection strategies. (PsycINFO Database Record
The empirical study of change has proven to be one of the most vexing challenges in organizational science. Fortunately, contemporary methodologies originating from developmental psychology may provide a potential solution and are consequently working their way into the literature. In particular, organizational researchers are increasingly employing variations of latent change score (LCS) models to address questions regarding change, development, and dynamics. Although these models may indeed be used to reliably study change, development, and dynamics, many studies utilizing these models—and published in premier outlets—are characterized by questionable methodological choices, improper modeling procedures, and suboptimal research designs. Thus, the purpose of the present article is to (a) provide a critical review of LCS models, (b) outline appropriate modeling procedures (with corresponding Mplus and R syntax), (c) compare and contrast LCS modeling with other analytical techniques, and (d) delineate best practices. Ultimately, we endorse the use of LCS models by organizational researchers interested in studying longitudinal phenomena. However, we also heed researchers to do so judiciously because their misuse may lead to their unwarranted rejection by the field.
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