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The purpose of this paper is to develop new simple logics and translations for hierarchical model checking. Hierarchical model checking is a model-checking paradigm that can appropriately verify systems with hierarchical information and structures.

In this study, logics and translations for hierarchical model checking are developed based on linear-time temporal logic (LTL), computation-tree logic (CTL) and full computation-tree logic (CTL*). A sequential linear-time temporal logic (sLTL), a sequential computation-tree logic (sCTL), and a sequential full computation-tree logic (sCTL*), which can suitably represent hierarchical information and structures, are developed by extending LTL, CTL and CTL*, respectively. Translations from sLTL, sCTL and sCTL* into LTL, CTL and CTL*, respectively, are defined, and theorems for embedding sLTL, sCTL and sCTL* into LTL, CTL and CTL*, respectively, are proved using these translations.

These embedding theorems allow us to reuse the standard LTL-, CTL-, and CTL*-based model-checking algorithms to verify hierarchical systems that are modeled and specified by sLTL, sCTL and sCTL*.

The new logics sLTL, sCTL and sCTL* and their translations are developed, and some illustrative examples of hierarchical model checking are presented based on these logics and translations.

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.

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.

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.

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.

Accessibility and availability of data are the main limitations which apply.

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.

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.

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.

We are interested in modeling the impact of spatial and interindustry dependence on firm-level innovation of Chinese firms The existence of network ties between cities imply that changes taking place in one city could influence innovation by firms in nearby cities (local spatial spillovers), or set in motion a series of spatial diffusion and feedback impacts across multiple cities (global spatial spillovers). We use the term local spatial spillovers to reflect a scenario where only immediately neighboring cities are impacted, whereas the term global spatial spillovers represent a situation where impacts fall on neighboring cities, as well as higher order neighbors (neighbors to the neighboring cities, neighbors to the neighbors of the neighbors, and so on). Global spatial spillovers also involve feedback impacts from neighboring cities, and imply the existence of a wider diffusion of impacts over space (higher order neighbors).

Similarly, the existence of national interindustry input-output ties implies that changes occurring in one industry could influence innovation by firms operating in directly related industries (local interindustry spillovers), or set in motion a series of in interindustry diffusion and feedback impacts across multiple industries (global interindustry spillovers).

Typical linear models of firm-level innovation based on knowledge production functions would rely on city- and industry-specific fixed effects to allow for differences in the level of innovation by firms located in different cities and operating in different industries. This approach however ignores the fact that, spatial dependence between cities and interindustry dependence arising from input-output relationships, may imply interaction, not simply heterogeneity across cities and industries.

We construct a Bayesian hierarchical model that allows for both city- and industry-level interaction (global spillovers) and subsumes other innovation scenarios such as: (1) heterogeneity that implies level differences (fixed effects) and (2) contextual effects that imply local spillovers as special cases.

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.

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.