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1 – 10 of over 13000Fuli Li, Xin Lai and Kwok Leung
Purpose – This chapter provides an overview of multilevel modeling with a focus on the application of hierarchical linear modeling (HLM) in international management…
Abstract
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.
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The purpose of this paper is to find the new analysis method of virtual team effectiveness in team building, as well as various HR tools.
Abstract
Purpose
The purpose of this paper is to find the new analysis method of virtual team effectiveness in team building, as well as various HR tools.
Design/methodology/approach
This paper investigated 62 virtual teams, distinguished between identified mental models and distributed mental models, tested the relation among team characteristics, and shared mental models and virtual team effectiveness using hierarchical linear modeling.
Findings
Results demonstrated that time would enhance the effect of shared mental models on task effectiveness; virtual team size would affect the relation between shared mental model and cooperative effectiveness, team size could enhance the effect of identified mental model on cooperative effectiveness, but weaken the relation between distributed mental model and cooperative effectiveness. A need is found for application of hierarchical linear modeling of shared mental model on virtual team effectiveness.
Research limitations/implications
Accessibility and availability of data are the main limitations which apply.
Originality/value
This paper presents a new approach of optimal choice of virtual team building. The paper is aimed at HR and psychological researches and managers, especially those who dealt with people, and provides very useful advice for team management in enterprises.
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Burcu Tasoluk, Cornelia Dröge and Roger J. Calantone
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…
Abstract
Purpose
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.)
Design/methodology/approach
A hierarchical generalized linear model is employed.
Findings
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.
Originality/value
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.
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Kai S. Cortina, Hans Anand Pant and Joanne Smith-Darden
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…
Abstract
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.
Peter Hom and Katalin Takacs Haynes
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…
Abstract
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.
Joseph F. Hair Jr. and Luiz Paulo Fávero
This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used.
Abstract
Purpose
This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used.
Design/methodology/approach
The authors estimate three-level models with repeated measures, offering conditions for their correct interpretation.
Findings
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.
Originality/value
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.
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Rebecca Slan‐Jerusalim and Peter A. Hausdorf
The purpose of the present study was to describe the high potential identification practices of Canadian organizations and to assess elements of these practices as they relate to…
Abstract
Purpose
The purpose of the present study was to describe the high potential identification practices of Canadian organizations and to assess elements of these practices as they relate to managers' perceptions of organizational justice.
Design/methodology/approach
The study reviewed the literature on high potential identification practices and organizational justice to develop a survey for managers attending a leadership conference. Distributive and procedural justice was regressed against the elements of these programs (e.g. the extent of manager input into the program, the openness of communications) to determine the impact of program elements on justice outcomes.
Findings
The paper reveals that approximately one‐third (38 percent) of companies reported having a high potential identification program. High potential was most often defined in specific organizational terms based on competencies. Typically, information used to identify these individuals was based on: personal experience with the person, performance appraisals and past performance or results. Hierarchical linear modeling analyses (n=123) indicated that high potential identification programs containing manager input, open communication and formal program evaluation significantly predicted procedural justice. None of the predictions for distributive justice were significant.
Originality/value
This study is the first to empirically investigate the impact of high potential identification practices on managers' perceptions of organizational justice in North America. Manager's justice perceptions reflect an important criterion to evaluate high potential identification programs. The current study found that manager's perceptions of procedural justice were higher when they had more input into the development of the program, when the communication strategy was more open, and the program was evaluated. Despite these important elements, many organizations do not incorporate them into their programs, which have implications for their success.
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Seokhwa Yun, Jonathan Cox and Henry P. Sims
Seeks to examine the interaction effect of leadership and follower characteristics on follower self‐leadership, using hierarchical linear modeling.
Abstract
Purpose
Seeks to examine the interaction effect of leadership and follower characteristics on follower self‐leadership, using hierarchical linear modeling.
Design/methodology/approach
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.
Findings
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.
Research limitation/implications
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.
Practical implications
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.
Originality/value
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.
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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…
Abstract
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.