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Purpose – This chapter provides an overview of multilevel modeling with a focus on the application of hierarchical linear modeling (HLM) in international management research.

Findings – The key topics covered include an introduction to hierarchical linear models, how to apply appropriate hierarchical linear models to address different types of international management research questions, and six methodological issues concerning international management research with a multilevel analysis.

Originality/value – The overview of HLM and its relevance for international management research facilitates researchers to apply this powerful analytical strategy in their future research.

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.

Seeks to examine the interaction effect of leadership and follower characteristics on follower self‐leadership, using hierarchical linear modeling.

Longitudinal data were collected using a questionnaire at two points in time, with ten weeks between each collection. These data facilitate the causal inference between leadership and follower need for autonomy (wave 1) and follower self‐leadership behaviors (wave 2). Hierarchical linear modeling (HLM) was used to analyze the hierarchical structure data.

Both empowering and directive leadership (group level) interacted with follower's need for autonomy (individual level) to enhance subsequent follower self‐leadership (individual level). That is, empowering leadership had a stronger positive effect on followers who were high on the need for autonomy, and directive leadership had a stronger negative effect on followers who were high on the need for autonomy. In summary, the influence of leadership on follower self‐leadership was contingent on follower need for autonomy. Overall, the results supported the view that attributes of the follower can be an important element in contingency theories of leadership.

This study does not include other possible individual characteristics, group level characteristics, and organizational level or environmental characteristics. A future research design might include organizational‐level characteristics.

Both the leadership context and the trait of the individual employee work hand in hand to produce true self‐leadership. Therefore, organizations need to develop empowering leaders who will, in turn, develop followers who are effective at self‐leadership.

This research contributes to the literature by testing a contingency model of leadership and follower self‐leadership. This study also demonstrated the usefulness of HLM to test interaction effects between group‐level variables and an individual‐level variable on individual‐level dependent variables.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

Over the last decade, latent growth modeling (LGM) utilizing hierarchical linear models or structural equation models has become a widely applied approach in the analysis of change. By analyzing two or more variables simultaneously, the current method provides a straightforward generalization of this idea. From a theory of change perspective, this chapter demonstrates ways to prescreen the covariance matrix in repeated measurement, which allows for the identification of major trends in the data prior to running the multivariate LGM. A three-step approach is suggested and explained using an empirical study published in the Journal of Applied Psychology.

There is a growing body of comparative research examining country differences in job satisfaction and its determinants. However, existing research cannot explain similarities in job satisfaction levels across very different countries, nor can it explain the differences between seemingly similar countries. Moreover, there has been no significant research conducted to date that has examined the country-level contextual conditions that are poised to impact worker satisfaction and its determinants. The paper aims to discuss these issues.

In this research, the author address this existing gap in the academic literature on job satisfaction by using non-panel longitudinal data from the International Social Survey Program (Work Orientations I, II, and III: 1989, 1997, and 2005) to examine cross-national differences in job satisfaction and its determinants. The author compare and combine previous international political economy theoretical work and hierarchical linear modeling (HLM) to examine global macro-level variables and their impact on worker satisfaction cross-nationally.

Study results demonstrate that both intrinsic and extrinsic work characteristics strongly impact worker job satisfaction. Furthermore, country by country regression and HLM results suggest that there are important country differences in both the perceived importance of various work characteristics and workers’ self-report experiences with both intrinsic and extrinsic work characteristics.

To get a clearer picture in the HLM analysis as to the full impact of these various country-contextual impacts on differences in perceived job characteristics and worker satisfaction, future research needs to examine a greater number and wider variety of countries, while exploring other theoretically relevant country-level variables that may help to explore country-level differences from these various cross-national theoretical frameworks. Additionally, a more diverse and greater number of participating countries would also potentially help in achieving levels of significance in the level-2 covariates in the HLM models.

Due to the fact the worker job satisfaction impacts firm performance and various measures of worker well-being, firms (regardless of economic sector or private/public status) need to be cognizant of these differences and unique challenges and work to tailor management philosophy and policy to create a unique work atmosphere that will benefit the interests of both the employer and the employee, as well as society at large.

While the nature of work has changed dramatically in the post-war era in response to economic shifts and an increasingly global economy, particularly over the past two decades, this paper examines the previously unexamined country-level contextual and global macro-historical variables driving differences in work quality and perceived worker satisfaction.

The objective of this study is to examine how the heterogeneity of the institutional environments within a single country influences International Financial Reporting Standards (IFRS) convergence and earnings quality based on a meso- and multi-level approach.

Using hierarchical linear modeling (HLM) to capture the between-group heteroskedasticity and within-cluster interdependence, this study investigates the simultaneous effect by incorporating institutional factors residing at different hierarchical levels and the interaction effects of factors within the same level on IFRS convergence and earnings quality in the largest IFRS adopter, China.

The results show that after IFRS convergence (i.e. 2007–2015), earnings quality decreases in terms of conservatism. However, the further analysis indicates that the strong institutional environment could mitigate the negative impact of IFRS on conservatism.

Consistent with the emphasis of heterogeneity within a country by Terracciano et al. (Science, 2005, 310 (5745)), this study indicates that the heterogeneity in the institutional environments and the simultaneous effect of the multilevel institutional environments within a single country cannot be ignored. This study also indicates that, equally important, research methodology plays a substantial role in investigating the outcomes of IFRS convergence. Finally, this study, based on an integrated theory, adopts a meso-paradigm linking macro- and micro-level institutions to provide comprehensive insights into IFRS convergence and conservatism.

Although the use of data from different levels is very common in international marketing research, the practice of employing multi‐level analysis techniques is relatively new. The paper aims to provide an application of a specific case of multi‐level modelling – where the dependent variable is dichotomous, which is often the case in marketing research (e.g. whether a consumer buys the brand or not, whether he/she is aware of the brand or not, etc.)

A hierarchical generalized linear model is employed.

Since this is a technical paper, the authors would like to emphasize the process rather than the empirical findings. In summary, the paper: provides a brief theoretical overview of Hierarchical Linear Modeling and Hierarchical Generalized Linear Modeling; illustrates the application of the method using the domains of consumers within countries and a dichotomous dependent variable; focuses on interpretation of log‐odds results; and concludes with practical issues and research implications.

The main value of this research is to demonstrate how to employ multi‐level models when the dependent variable is dichotomous. Multi‐level techniques are quite new in international marketing research, although nested data structures are relatively common in our field. This is a technical paper that guides the researchers as to how to apply and interpret the results when modeling such data with a dichotomous dependent variable.

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.