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Multilevel approaches are generally well suited to group communication because what people say and do in groups is a function of intra- and trans-individual mechanisms. This chapter first provides a brief overview of group research as a multilevel problem and then describes more modern approaches to modeling nested data using latent variable models, including multilevel structural equation modeling and latent class analysis. The chapter concludes by addressing conceptual opportunities provided by multilevel latent modeling approaches to group communication.

This paper gives a bibliographical review of the finite element and boundary element parallel processing techniques from the theoretical and application points of view. Topics include: theory – domain decomposition/partitioning, load balancing, parallel solvers/algorithms, parallel mesh generation, adaptive methods, and visualization/graphics; applications – structural mechanics problems, dynamic problems, material/geometrical non‐linear problems, contact problems, fracture mechanics, field problems, coupled problems, sensitivity and optimization, and other problems; hardware and software environments – hardware environments, programming techniques, and software development and presentations. The bibliography at the end of this paper contains 850 references to papers, conference proceedings and theses/dissertations dealing with presented subjects that were published between 1996 and 2002.

The purpose of this paper is to address the statistical issues associated with the hierarchically structured data in previous studies that focused on servant leadership. To resolve these issues, multilevel modeling methods were applied to re-visit the construct validity of the servant leadership questionnaire developed by Barbuto and Wheeler (2006) and investigate the relationship between servant leadership and job satisfaction under a multilevel framework.

The survey data was obtained from a sample of 2,089 teachers from 117 primary and secondary schools in Hong Kong. The analyses were conducted using multilevel confirmatory factor analysis (MLCFA) and multilevel structural equation modeling (MLSEM).

The results revealed the significant and non-trivial variances that were explained at the organization level in the items measuring servant leadership, which justified the use of MLCFA and MLSEM. The results of MLCFA provided empirical support for the multidimensional construct as well as the second-order factorial structure of servant leadership measures at both the individual and organization levels. In addition, the positive relationships between servant leadership and the followers’ job satisfaction were found to vary at different levels.

This study reiterates the importance of using appropriate methods to capture a solid definition of the construct of servant leadership and provides new insights into the conceptual framework of servant leadership as well as the effects of servant leadership on individual and organizational outcomes.

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.

The research practice in management research is dominantly based on structural equation modeling (SEM), but almost exclusively, and often misguidedly, on covariance-based SEM. The purpose of this paper is to question the current research myopia in management research, because the paper adumbrates theoretical foundations and guidance for the two SEM streams: covariance-based and variance-based SEM; and improves the conceptual knowledge by comparing the most important procedures and elements in the SEM study, using different theoretical criteria.

The study thoroughly analyzes, reviews and presents two streams using common methodological background. The conceptual framework discusses the two streams by analysis of theory, measurement model specification, sample and goodness-of-fit.

The paper identifies and discusses the use and misuse of covariance-based and variance-based SEM utilizing common topics such as: first, theory (theory background, relation to theory and research orientation); second, measurement model specification (type of latent construct, type of study, reliability measures, etc.); third, sample (sample size and data distribution assumption); and fourth, goodness-of-fit (measurement of the model fit and residual co/variance).

The paper questions the usefulness of Cronbach's α research paradigm and discusses alternatives that are well established in social science, but not well known in the management research community. The author presents short research illustration in which analyzes the four recently published papers using common methodological background. The paper concludes with discussion of some open questions in management research practice that remain under-investigated and unutilized.

Structural equation modelling (SEM) has increasingly been used by hospitality and tourism researchers to examine complex relationships. This paper aims to highlight the benefits and limitations of SEM for hospitality and tourism research and compare its two main approaches, i.e. covariance-based SEM (CB-SEM) and partial least squares-SEM (PLS-SEM).

By using a comparative approach, this study parallels SEM’s two main approaches, i.e. CB-SEM and PLS-SEM, using three different examples from hospitality and tourism industry. Both the approaches are compared side by side in terms of assumptions, validity and reliability of measurement models, item retention and loadings, strength and significance of path relationships and coefficient of determinations.

The findings show that even though both methods analyse measurement theory and structural path models, there are relatively higher advantages for hospitality and tourism researchers in applying PLS-SEM.

Because of the limitations of only using three examples, the results and trends generated in this study may not be generalized to all research in hospitality and tourism discipline. Moreover, the Likert scale has been used to measure the constructs in both the studies, which may have biased the results.

This study is the first to compare the usage of both the SEM approaches in hospitality and tourism research. The findings of this study provide significant implications and directions for hospitality and tourism researchers to apply PLS-SEM in the future.

Structural equation modelling (SEM) has been defined as the combination of latent variables and structural relationships. The partial least squares SEM (PLS-SEM) is used to estimate complex cause-effect relationship models with latent variables as the most salient research methods across a variety of disciplines, including knowledge management (KM). Following the path initiated by different domains in business research, this paper aims to examine how PLS-SEM has been applied in KM research, also providing some new guidelines how to improve PLS-SEM report analysis.

To ensure an objective way to analyse relevant works in the field of KM, this study conducted a systematic literature review of 63 publications in three SSCI-indexed and specific KM journals between 2015 and 2017.

Our results show that over the past three years, a significant amount of KM works has empirically used PLS-SEM. The findings also suggest that in light of recent developments of PLS-SEM reporting, some common misconceptions among KM researchers occurred mainly related to the reasons for using PLS-SEM, the purposes of PLS-SEM analysis, data characteristics, model characteristics and the evaluation of the structural models.

This study contributes to that vast KM literature by documenting the PLS-SEM-related problems and misconceptions. Therefore, it will shed light for better reports in PLS-SEM studies in the KM field.

Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of observed variables. Like SEM, PLS began with an assumption of homogeneity – one population and one model – but has developed techniques for modeling data from heterogeneous populations, consistent with a marketing emphasis on segmentation. Heterogeneity can be expressed through interactions and nonlinear terms. Additionally, researchers can use multiple group analysis and latent class methods. This chapter reviews these techniques for modeling heterogeneous data in PLS, and illustrates key developments in finite mixture modeling in PLS using the SmartPLS 2.0 package.

Few scholars have analysed the corporate reputation (CR) of multinational corporations (MNCs) internationally, but both CR perception and effect are likely to differ across nations. Most studies have compared a few countries, linked differences to selected dimensions of Hofstede’s cultural approach, and reported ambiguous results. The purpose of this paper is to address the important role of all Hofstede’s cultural dimensions that may influence CR perceptions and effects by applying an appropriate method.

By integrating signalling theory and each cultural dimension, hypotheses are proposed and tested using consumer surveys of a German MNC in 37 countries. Multilevel structural equation modelling (SEM) showed whether and how all cultural value dimensions – the predominant approach in marketing – affect CR perceptions and effects.

Individual CR perceptions and effects are strongly attributable to national culture, which explains up to 62 per cent of country-level variance; however, the explanatory powers of the cultural dimensions differ between CR perceptions and effects. Not all dimensions affect both. The results are stable in alternative models.

The results enhance extant research because the relative importance of Hofstede’s dimensions and not only a possible role in country comparisons is shown. Hofstede’s approach explains considerable country-level variances. MNCs learn which cultural dimensions are core antecedents of CR perceptions (individualism and power distance explain >30 per cent variance) and core moderators of CR effects on loyalty (masculinity, individualism and uncertainty avoidance).

This study provides novel insights into the role of national cultural differences on CR perceptions and CR effects using the still novel multilevel SEM.

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.