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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 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 review and address current approaches and limitations to modeling change over time in social entrepreneurship research.

The article provides a narrative review of different practices used to assess change over time. It also shows how different research questions require different methodologies for assessing changes over time. Finally, it presents worked examples for modeling these changes.

Our review suggests that there is a lack of research in social entrepreneurship that takes into account the many different considerations for addressing how time influences outcomes.

This chapter introduces an analytic technique to social entrepreneurship that effectively models changes in predictors and outcomes even when data are non-normal or nested across time or levels of analysis.

The purpose of this paper is to provide a systematic overview with guidelines how to use partial least squares (PLS) path modeling in longitudinal studies. Practical examples from a study of the acceptance of battery electric vehicles (BEVs) in corporate fleets are used for demonstration purposes.

In this study, data at three points in time were collected: before the initial use of a BEV, after three and after six months of extensive usage of BEVs.

Three different models are identified depending on the research objective and on the data basis. Multigroup analyses are suggested to test the difference between the path coefficients of latent variables at different points in time. Limitations for the use of repeated cross-sectional data have to be observed.

Academics and practitioners will benefit from this paper by receiving an overview of the different PLS path models in longitudinal studies. A decision-tree enables them to make a choice regarding the most appropriate model and suggests a sequence of complementary analyses. So far, there is a lack of a tutorial type paper delivering such guidance.

The purpose of this paper is to test a multilevel, cross-classified model that seeks to illuminate the dynamic nature of relationships among leadership, teaching quality, and student learning in school improvement. The study's primary goal is to shed light on the paths through which leadership influences student learning. At the school level, the model examines the mediating effect of the school's instructional environment on leadership and student learning. At the classroom level, it examines how instructionally focussed leadership can moderate teacher effects on student learning. Then these multiple paths are examined in a single model that seeks to test and highlight the means by which leadership contributes to school improvement.

The current study employed a multilevel longitudinal data set drawn from 60 primary schools in one state in the USA. Using a cross-classification approach to quantitative modeling, the research analyzes the complex cross-level interactions that characterize school-level and classroom level practices that contribute to school improvement and student learning.

The results illustrate the utility of specifying multilevel relationships when examining the “paths” that link school leadership to student learning. First, leadership effects on student learning were fully mediated by the quality of the school's instructional environment. Second, the findings indicated that the classroom-related paths examined in this study directly influenced the measures of student math achievement. Third, the research found that instructionally focussed school leadership moderated the effect of individual teachers on student learning. Fourth, the results suggest that school leaders can enhance student outcomes by creating conditions that lead to greater consistency in levels of effectiveness across teachers.

The study makes substantive contributions to the global knowledge base on school improvement by testing and elaborating on the “paths” that link school leadership and student learning. More specifically, the findings offer insights into strategic targets that instructional leaders can employ to enhance teacher effectiveness and school improvement. Thus, these results both support and extend findings from prior cross-sectional and longitudinal studies of leadership and school improvement.

This is the first study that has tested a conceptualization of leadership for learning in a single “cross-classified longitudinal model” capable of capturing interactions among leadership, classroom teaching processes and growth in student learning. The research illustrates one “state-of-the-art” methodological approach for analyzing longitudinal data collected at both the school and classroom levels when studying school improvement.

The purpose of this study is to extend previous research by using a longitudinal design to examine the differential contribution of brand understanding (BU) drivers at various moments in the early tenure of service employees. Employee BU is a prerequisite of brand promise delivery among service employees. Previous studies, using cross-sectional samples, established that brand-oriented recruitment, training and leadership are significant BU drivers.

A three-wave survey was collected from a 105-member panel of recent hires at a restaurant chain that displayed a strong brand culture and adopted internal brand management (IBM) practices. Structural equation models with carryover effects were estimated to measure the impact of BU drivers on Day 1, as well as at four and seven months of tenure. In addition, a latent growth model of BU was estimated using random coefficients modeling.

Results show a significant positive effect of IBM practices on BU at each point in time; however, despite this, by the seven month milestone, BU is still not fully developed.

As with most organizational longitudinal studies, there was sample attrition because of the high turnover that characterizes the restaurant industry. This attrition is not believed to be correlated with the variables measured in the study.

Managers seeking a differentiated customer experience should not assume new hires attain a good understanding of the service brand even after the first seven months of tenure. Hence, brand training and leadership should extend well beyond this time frame.

This study is the first, as per the authors’ understanding, to use a longitudinal design to model BU as a dynamic variable because it befits the learning trajectories of new employees.

This research aims to present a novel cooperative control architecture designed specifically for roads with variations in height and curvature. The primary objective is to enhance the longitudinal and lateral tracking accuracy of the vehicle.

In addressing the challenges posed by time-varying road information and vehicle dynamics parameters, a combination of model predictive control (MPC) and active disturbance rejection control (ADRC) is employed in this study. A coupled controller based on the authors’ model was developed by utilizing the capabilities of MPC and ADRC. Emphasis is placed on the ramifications of road undulations and changes in curvature concerning control effectiveness. Recognizing these factors as disturbances, measures are taken to offset their influences within the system. Load transfer due to variations in road parameters has been considered and integrated into the design of the authors’ synergistic architecture.

The framework's efficacy is validated through hardware-in-the-loop simulation. Experimental results show that the integrated controller is more robust than conventional MPC and PID controllers. Consequently, the integrated controller improves the vehicle's driving stability and safety.

The proposed coupled control strategy notably enhances vehicle stability and reduces slip concerns. A tailored model is introduced integrating a control strategy based on MPC and ADRC which takes into account vertical and longitudinal force variations and allowing it to effectively cope with complex scenarios and multifaceted constraints problems.

This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used.

The authors estimate three-level models with repeated measures, offering conditions for their correct interpretation.

From the concepts and techniques presented, the authors can propose models, in which it is possible to identify the fixed and random effects on the dependent variable, understand the variance decomposition of multilevel random effects, test alternative covariance structures to account for heteroskedasticity and calculate and interpret the intraclass correlations of each analysis level.

Understanding how nested data structures and data with repeated measures work enables researchers and managers to define several types of constructs from which multilevel models can be used.

Multivariate latent growth modeling (multivariate LGM) provides a flexible data analytic framework for representing and assessing cross-domain (i.e., between-constructs) relationships in intraindividual changes over time, which also allows incorporation of multiple levels of analysis. Using the chapter by Cortina, Pant, and Smith-Darden (this volume) as a point of departure, this chapter discusses important preliminary data analysis and interpretation issues prior to performing multivariate LGM analyses.

This chapter describes how to use popular software programs (Hierarchical Linear Modeling, LISREL) to analyze multiwave panel data. We review prevailing methods for panel data analyzes in strategic management research and identify their limitations. Then, we explain how multilevel and latent growth modeling provide more rigorous methodologies for studying dynamic phenomena. We present an example illustrating how firm performance can initiate temporal change in the human and social capital of members of Board of Directors, using hierarchical linear modeling. With the same data set, we replicate this test with first-order factor latent growth modeling (LGM). Next, we explain how to use second-order factor LGM with panel data on employee cognitions. Finally, we review the relative advantages and disadvantages of these new data-analytical approaches.