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In this chapter, the authors consider the role of time for research in occupational stress and well-being. First, temporal issues in studying occupational health longitudinally, focusing in particular on the role of time lags and their implications for observed results (e.g., effect detectability), analyses (e.g., handling unequal durations between measurement occasions), and interpretation (e.g., result generalizability, theoretical revision) were discussed. Then, time-based assumptions when modeling lagged effects in occupational health research, providing a focused review of how research has handled (or ignored) these assumptions in the past, and the relative benefits and drawbacks of these approaches were discussed. Finally, recommendations for readers, an accessible tutorial (including example data and code), and discussion of a new structural equation modeling technique, continuous time structural equation modeling, that can “handle” time in longitudinal studies of occupational health were provided.

This paper aims to introduce latent growth curve modeling (LGCM) as a statistical technique to analyze repeated measures of longitudinal data to researchers in hospitality management.

First, the basics and extensions of LGCM are explained. Second, this paper reviews three existing empirical hospitality research studies that could have benefitted from LGCM but did not use this methodology. Third, this paper provides an overview of two specific illustrative examples of how the current authors have already used LGCM for hospitality research.

Based on explaining the basics of LGCM, delineating two examples using LGCM method and presenting new research avenues that would use LGCM to advance theoretical knowledge, this paper shows how LGCM represents a leap forward in the promotion of more rigorous research in hospitality management.

This paper is the first in hospitality to call for research based on LGCM and provide hands-on demonstrations and an agenda for this methodology.

Addressing occupational stress and fostering employee wellness helps meet a host of organizational stakeholder expectations including high quality of work life (employees), reasonable return on investment (investors), increased productivity (management), and competitiveness (owners). Despite being dynamic in nature, stress and wellness are often studied using a static perspective. One reason for the scarcity of dynamic empirical research is the limited knowledge and use of the tools available to assess change over time. To address this limitation, four tools used to assess change and dynamics of occupational stress and well-being are described: growth models, latent change score models, spectral analysis, and computational modeling. First, we begin by discussing growth curve models and then transition to latent change score models. We then expand into spectral analysis, a tool used to determine cycles of ups and downs that repeat regularly. Last, computational modeling is discussed, where computers and simulations are used to understand a dynamic process. For each tool, we give examples of how they have been used, make recommendations for future use, and provide readers with suggestions and references for how to complete analyses in software and programs, most of which are freely available (i.e., R, Vensim).

This article reviews the extensive history of dynamic performance research, with the goal of providing a clear picture of where the field has been, where it is now, and where it needs to go. Past research has established that job performance does indeed change, but the implications of this dynamism and the predictability of performance trends remain unresolved. Theories are available to help explain dynamic performance, and although far from providing an unambiguous understanding of the phenomenon, they offer direction for future theoretical development. Dynamic performance research does suffer from a number of methodological difficulties, but new techniques have emerged that present even more opportunities to advance knowledge in this area. From this review, I propose research questions to bridge the theoretical and methodological gaps of this area. Answering these questions can advance both research involving job performance prediction and our understanding of the effects of human resource interventions.

To measure the dynamic features of compassion as an emotional and behavioral construct, the present research used a univariate latent growth modeling (LGM) approach within the structural equation modeling (SEM) framework. The aim was to trace the dynamic development of compassion longitudinally in accounting and business students during a three-credit English course at university.

The suggested method ensures the measurement invariance over time, deals with the first order latent variable, traces its growth and takes into account the measurement errors. This longitudinal analytical method was used to explore the initial state and the growth of compassion in four points of time during a language course. The data were collected from 60 adult accounting and business students in four time phases using Sprecher and Fehr's Compassionate Love Scale and were analyzed in Mplus 8.4 with univariate LGM.

The model fit was accepted and the invariance of the latent factor was confirmed over time. The negative covariance between intercept and slope (second-order latent variables) suggested that lower initial scores in L2 learners' compassion show a faster increase in compassion over time as the mean of slope is larger than that of the intercept. L2 learners who started off at a higher level of compassion showed a slower change in compassion over time. This can be at least partly explained by the teacher's motivating role or learners' compassion but needs to be further explored in complementary qualitative phases for deeper insights.

In the present research, awareness was raised of the developmental nature of compassion as an emotional and behavioral construct essential to the accounting and business profession. The great strength of this research lies in the dynamic approach to the compassion construct and the LGM used to capture the temporal growth of compassion and how it evolved through the L2 course.

Promoting customer–company identification (CCI) has become a crucial relationship marketing strategy for service firms. The purpose of this study is to examine how customers’ perceptions of service quality and corporate social responsibility (CSR) affect CCI over time. More importantly, a comparative analysis is conducted to compare the long-term effectiveness of service quality versus CSR in forming CCI.

A conceptual framework is developed and then empirically examined using latent growth curve modeling. The study data were collected from restaurant customers in Taiwan in four waves of 213 repeated measures.

The results of this study show that customers’ perceptions of both service quality and CSR affect CCI. Particularly, the results of this study indicate that compared with service quality, customers’ perceived CSR has a stronger effect in the promotion of CCI over time.

This study offers a new insight for service marketing practitioners who are planning and implementing strategies for enhancing CCI. The findings suggest that relationship investments are more effective over the long term when service firms shift their investment priority over time from achieving high service quality to increasing consumers’ belief in the firm’s commitment to CSR.

Though previous research has explored the various drivers of CCI, longitudinal examinations are surprisingly scarce in this context. Using latent growth curve modeling, this study examines how CCI antecedents influence changes in CCI over time. More importantly, this study reveals that CSR has a stronger long-term impact on CCI than service quality.

This study seeks to examine the effect of work-related boredom and a perceived lack of external stimulation on benevolent and entitled employees' perceived inequity and discretionary workplace behaviors.

A total of 668 useable three-wave panel data were obtained via Amazon Mechanical Turk during a three-month period. The hypothesized model was tested using a latent growth curve modeling via EQS 6.4 for Windows.

This study finds the following results. First, benevolent employees who experience higher initial work-related boredom report positive inequity. Second, entitled employees who experience higher initial work-related boredom and a perceived lack of external stimulation report negative inequity. Third, increases in work-related boredom and a perceived lack of external stimulation result in a faster increase in entitled employees' perceived negative inequity. Fourth, entitled employees who perceive higher negative inequity at the initial measurement period report higher interpersonal deviance. Finally, increases in entitled employees' perceived negative inequity result in a faster increase in interpersonal deviance.

This study highlights how employees may assess their effort and rewards when experiencing boredom. This study also offers some practical recommendations that help human resource managers manage boredom in the organization effectively.

In order to deepen understanding of team processes in dynamic organizational contexts, we suggest that analyses employing techniques to identify and analyze team member interaction patterns and trajectories are necessary. After presenting a brief review of interaction data coding and reliability requirements, we first review examples of two approaches used in the identification and analysis of interaction patterns in teams: lag sequential analysis and T-pattern analysis. We then describe and discuss three statistical techniques used to analyze team interaction trajectories: random coefficient modeling, latent growth modeling, and discontinuous growth analysis. We close by suggesting several ways in which these techniques could be applied to data analysis in order to expand our knowledge of team interaction, processes, and outcomes in complex and dynamic settings.

This paper aims to explore the influence of collective orientation (CO) on coordination and team performance for interdependently working teams while controlling for person-related and team variables.

A total of 58 two-person-teams participated in a simulation-based firefighting task. The laboratory study took 2 h for each team. The effects of CO in tasks of increasing complexity were investigated under the consideration of control variables, and the relations between CO, coordination and team performance were assessed using a multivariate latent growth curve modeling approach and by estimating indirect effects in simple mediation models.

Team members high on CO performed significantly better than low-scoring members. The effect of CO on team performance was independent from an increasing task complexity, whereas the effect of CO on coordination was not. The effect of CO on team performance was mediated by coordination within the team, and the positive relation between CO and performance persists when including group efficacy into the model.

As CO is a modifiable person-related variable and important for effective team processes, additional research on factors influencing this attitude during work is assumed to be valuable.

CO is especially important for highly interdependently working teams in high-risk-organizations such as the fire service or nuclear power plants, where errors lead to severe consequences for human beings or the environment.

No other studies showed the importance of CO for coordination and team performance while considering teamwork-relevant variables and the interdependence of work.

This study aims to contribute to entrepreneurial intention research by examining the theory of planned behavior model in a longitudinal follow-up of the same individuals from a point at which they were studying until six to eight years after graduation and the link between entrepreneurial intention and actual behavior. The objectives of the paper are as follows: to examine the development and temporal stability of entrepreneurial intention and to examine the link between entrepreneurial intention and actual start-up behavior in a longitudinal setting.

The data for this research originate in Finland and consist three data collection waves between years 2008 and 2018. In the second wave, 282 respondents were reached, and in the third wave, 89 respondents were reached. For examining the stability of entrepreneurial intention, latent growth curve modeling was used. In addition, a logistic regression analysis was conducted to examine the link between intention and behavior.

The results suggest that entrepreneurial intention is a stable construct over time. High and low levels of entrepreneurial intention remain quite stable. Entrepreneurial intention measured during study time significantly explains entrepreneurial behavior both after one to three years (Exp (B) 2,069***) and after six to eight years (Exp (B) 1,830*). Gender and role models are significant factors in predicting entrepreneurial behavior.

This study provides new information on the stability of entrepreneurial intention in a rare longitudinal setting. The study verifies the value of intention measures in predicting entrepreneurial behavior in the long term.