The study of congruence is central to organizational research. Congruence refers to the fit, match, similarity, or agreement between two constructs and is typically framed as a predictor of outcomes relevant to individuals and organizations. Previous studies often operationalized congruence as the algebraic, absolute, or squared difference between two component variables.
Partial least squares (PLS) path modeling is increasingly being promoted as a technique of choice for various analysis scenarios, despite the serious shortcomings of the method. The current lack of methodological justification for PLS prompted the editors of this journal to declare that research using this technique is likely to be deck-rejected (Guide and Ketokivi, 2015).
In various forms, research on stress and well-being has been a part of the Journal of Applied Psychology (JAP) since its inception. In this review, we examine the history of stress research in JAP by tracking word frequencies from 606 abstracts of published articles in the journal.
Statistical control is widely used in correlational studies with the intent of providing more accurate estimates of relationships among variables, more conservative tests of hypotheses, or ruling out alternative explanations for empirical findings.
Task conflict has been the subject of a long-standing debate in the literature—when does task conflict help or hurt team performance? We propose that this debate can be resolved by taking a more precise view of how task conflicts are perceived in teams.
In organizational psychology research, most theories put forth directional predictions, such as stating that an increase in one construct will result in an increase or decrease in another construct. Such predictions are imprecise, given that they can be confirmed by a wide range of values, and theories that rely on such predictions bear little risk of falsification.
We discuss seven methodological improvements that would stimulate important advancements in management research. We refer to these improvements as ‘wishes’ that we hope will materialize within the next decade.
Economic recoveries can be slow, fast, or involve double dips. This paper provides an explanation based on the dynamic interactions between bank lending standards and firm entry selection. In the model, bank lending standards refer to both how banks screen borrowers with unknown quality and whether well-qualified borrowers are credit rationed, and firm entry selection refers to the mechanism through which financing conditions select firms of different quality to enter the lending market.
We model a dynamic economy with strategic complementarity among investors and study how endogenous government interventions mitigate coordination failures. We establish equilibrium existence and uniqueness, and we show that one intervention can affect another through altering the public information structure.
We disentangle and study the relative importance of different risk preferences in explaining extended warranty purchases and the high premia paid for them. Empirical and behavioral research on insurance is at odds with whether diminishing returns (curvature of the utility function), or loss aversion and nonlinear probability weighting lead to observed consumer behavior. This lack of consensus is primarily due to the inability of standard choice data to separate different risk preferences, and the consequent need to rely on strong parametric assumptions.
We study Granger causality testing for high-dimensional time series using regularized regressions. To perform proper inference, we rely on heteroskedasticity and autocorrelation consistent (HAC) estimation of the asymptotic variance and develop the inferential theory in the high-dimensional setting. To recognize the time series data structures we focus on the sparse-group LASSO estimator, which includes the LASSO and the group LASSO as special cases.
Using a proprietary dataset from 2016 to 2019, we find that order flows from foreign investors, facilitated by regulatory liberalization through several channels, present strong predictive power for future stock returns in the Chinese market.
We study the effect of government-subsidized childcare on women's career outcomes and firm performance using linked tax filing data. Exploiting a universal childcare reform in Quebec in 1997 and the variation in its timing relative to childbirth across cohorts of parents, we show that earlier access to childcare increases employment among new mothers, particularly among those previously unemployed.
It may be possible to offer people a new understanding of their best-self concepts, leading to positive personal and social change. We developed theory about how best-self activation can lead to both immediate and long-term outcomes through recursion, interaction, and subjective construal between the self concept and the social system.
In this paper, we build on research on the microfoundations of strategy and learning processes to study the individual underpinnings of organizational learning. We argue that once an individual has accumulated a certain amount of experience with a task, the benefit of accumulating additional experience is inferior to the benefit of deliberately articulating and codifying the experience accumulated in the past.
In this paper, we examine how connecting to beneficiaries of one’s work increases performance, and argue that beneficiaries internal to an organization (i.e., one’s own colleague) can serve as an important source of motivation, even in jobs that — on the surface — may seem routine and low on potential impact. We suggest that this occurs because words of beneficiaries strengthen one’s sense of belongingness, a key driver of human behavior.
Across the globe, the average commute is 38 minutes each way, and it is well known that lengthy commutes negatively affect employees’ well-being and job-related outcomes leading to decreased job satisfaction and increased turnover. Despite the importance of commuting in employees’ everyday life, little is known about how negative effects of lengthy commutes could be attenuated.
Determining how best to route work is a key element of service system design. Not surprisingly then, many analytical models have identified various optimal routing algorithms for service operations management. However, in many settings, humans make routing decisions dynamically, either because algorithms don't exist, decision support tools have not been implemented, or existing rules are not enforced.
The paper introduces structured machine learning regressions for heavy-tailed dependent panel data potentially sampled at different frequencies. We focus on the sparse-group LASSO regularization. This type of regularization can take advantage of the mixed frequency time series panel data structures and improve the quality of the estimates.
The unprecedented increase in non-bank financial intermediation, particularly open-end mutual funds and ETFs, over the last two decades, accounts for nearly half of external financing flows to emerging markets exceeding cross-border lending by global banks.