Michael Howe

Getting Explicit About the Implicit A Taxonomy of Implicit Measures and Guide for Their Use in Organizational Research

Accumulated evidence from social and cognitive psychology suggests that many behaviors are driven by processes operating outside of awareness, and an array of implicit measures to capture such processes have been developed. Despite their potential application, implicit measures have received relatively modest attention within the organizational sciences, due in part to barriers to entry and uncertainty about appropriate use of available measures. The current article is intended to serve as an implicit measurement “toolkit” for organizational scholars, and as such our goals are fourfold. First, we present theory critical to implicit measures, highlighting advantages of capturing implicit processes in organizational research. Second, we present a functional taxonomy of implicit measures (i.e., accessibility-based, association-based, and interpretation-based measures) and explicate assumptions and appropriate use of each. Third, we discuss key criteria to help researchers identify specific implicit measures most appropriate for their own work, including a discussion of principles for the psychometric validation of implicit measures. Fourth, we conclude by identifying avenues for impactful “next-generation” research within the organizational sciences that would benefit from the use of implicit measures.

Small sample mediation testing: misplaced confidence in bootstrapped confidence intervals.

Bootstrapping is an analytical tool commonly used in psychology to test the statistical significance of the indirect effect in mediation models. Bootstrapping proponents have particularly advocated for its use for samples of 20-80 cases. This advocacy has been heeded, especially in the Journal of Applied Psychology, as researchers are increasingly utilizing bootstrapping to test mediation with samples in this range. We discuss reasons to be concerned with this escalation, and in a simulation study focused specifically on this range of sample sizes, we demonstrate not only that bootstrapping has insufficient statistical power to provide a rigorous hypothesis test in most conditions but also that bootstrapping has a tendency to exhibit an inflated Type I error rate. We then extend our simulations to investigate an alternative empirical resampling method as well as a Bayesian approach and demonstrate that they exhibit comparable statistical power to bootstrapping in small samples without the associated inflated Type I error. Implications for researchers testing mediation hypotheses in small samples are presented. For researchers wishing to use these methods in their own research, we have provided R syntax in the online supplemental materials.

The Promise and Perils of Wearable Sensors in Organizational Research

Rapid advances in mobile computing technology have the potential to revolutionize organizational research by facilitating new methods of data collection. The emergence of wearable electronic sensors in particular harbors the promise of making the large-scale collection of high-resolution data related to human interactions and social behavior economically viable. Popular press and practitioner-oriented research outlets have begun to tout the game-changing potential of wearable sensors for both researchers and practitioners. We systematically examine the utility of current wearable sensor technology for capturing behavioral constructs at the individual and team levels. In the process, we provide a model for performing validation work in this new domain of measurement. Our findings highlight the need for organizational researchers to take an active role in the development of wearable sensor systems to ensure that the measures derived from these devices and sensors allow us to leverage and extend the extant knowledge base. We also offer a caution regarding the potential sources of error arising from wearable sensors in behavioral research.

Investigating the Uniqueness and Usefulness of Proactive Personality in Organizational Research: A Meta-Analytic Review

Using meta-analysis (283 effect sizes from 122 studies), we extend prior qualitative and quantitative reviews of research on proactive personality in a number of meaningful ways. First, we examine the discriminant and incremental validity of proactive personality using meta-analytic regression analyses. Our results reveal that more than 50% of variance in proactive personality is unrelated to the Big Five personality traits collectively. Also, proactive personality accounts for unique variance in overall job performance, task performance, and organizational citizenship behaviors, even after controlling for the Big Five personality traits and general mental ability (for overall job performance and task performance). Moreover, we find no subgroup differences in proactive personality, highlighting its potential use in selection contexts. In conclusion, we discuss implications of our findings for research and practice.

The importance of velocity, or why speed may matter more than distance

Theory and research on self-regulation emphasizes the importance of goals for guiding human behavior. Critical phenomena within the self-regulation literature are discrepancies between actual states and goal states. When such discrepancies are detected, they capture attention and effort is mobilized to move actual states closer to goal states (or in some cases align the latter with the former). While discrepancy feedback, or the distance between actual and goal states, is important, so too is velocity feedback, or the rate at which actual–goal discrepancies are decreasing. Unfortunately, research has mostly ignored the role played by velocity in the self-regulation process. To redress this limitation, we review the concept of velocity, the empirical studies that have examined this concept, and how velocity is commonly measured. We then discuss the role of velocity as it pertains to three self-regulatory functions at work: achieving performance goals, satisfying belonging needs, and satisfying esteem needs.

Understanding Affect, Stress, and Well-being within a Self-regulation Framework

Abstract Research on self-regulation has tended to focus on goal-related performance, with limited attention paid to individuals’ affect and the role it plays during the goal-striving process. In this chapter we discuss three mechanisms to integrate affect within a control theory-based self-regulation framework, and how such integrations inform future research concerning employee stress and well-being. Specifically, affect can be viewed as a result of velocity made toward one’s desired states at work. Fast progress results in positive affect, which enhances employee well-being and reduces the detrimental effects associated with exposure to occupational stressors. On the other hand, slow or no progress elicits negative affect, which induces employee distress. Second, affect can also be considered an input of self-regulation, such that employees are required to regulate their emotional displays at work. Employees who perform emotional labor compare their actual emotional display against the desired display prescribed by display rules. Third, affect can function as a situational disturbance, altering employees’ perceptions or assessments of the input, comparator, and output for other self-regulatory processes.

Wearable bluetooth sensors for capturing relational variables and temporal variability in relationships: A construct validation study.

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.