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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.

Purpose – Partial least squares (PLS) path modeling has become a pivotal empirical research method in international marketing. Owing to group comparisons' important role in research on international marketing, we provide researchers with recommendations on how to conduct multigroup analyses in PLS path modeling.

Methodology/approach – We review available multigroup analysis methods in PLS path modeling and introduce a novel confidence set approach. A characterization of each method's strengths and limitations and a comparison of their outcomes by means of an empirical example extend the existing knowledge of multigroup analysis methods. Moreover, we provide an omnibus test of group differences (OTG), which allows testing the differences across more than two groups.

Findings – The empirical comparison results suggest that Keil et al.'s (2000) parametric approach can generally be considered more liberal in terms of rendering a certain difference significant. Conversely, the novel confidence set approach and Henseler's (2007) approach are more conservative.

Originality/value of paper – This study is the first to deliver an in-depth analysis and a comparison of the available procedures with which to statistically assess differences between group-specific parameters in PLS path modeling. Moreover, we offer two important methodological extensions of existing research (i.e., the confidence set approach and OTG). This contribution is particularly valuable for international marketing researchers, as it offers recommendations regarding empirical applications and paves the way for future research studies aimed at comparing the approaches' properties on the basis of simulated data.

Tourism research contains a large share of consumer behavior-orientated studies using multidimensional constructs (exogenous/endogenous). Accordingly, scholars have mainly made use of a two-step approach that can be referred to as PCA-MLR (principal component analysis and then ordinary least squares multiple linear regression analysis) to examine the relationships among exogenous and endogenous constructs in a statistical model. Although this two-step approach has contributed to the advancement of tourism research, it still suffers from a number of drawbacks which can readily be overcome by a so-called second-generation statistical tool, namely, partial least squares approach to structural equation modeling (PLS-SEM). The current chapter explains and illustrates (with an application to tourism data) the advantages (e.g., several layers of estimations, suiting small sample sizes, robustness to multicollinearity, model-based clustering, etc.) of PLS-SEM both from a statistical and practical point of view. Finally, an elucidation is also provided for suggesting PLS-SEM as an alternative to PCA-MLR instead of COV-SEM (covariance-based structural equation modeling). The chapter concludes by proposing that PLS-SEM is a reliable and flexible statistical approach that is of high value, in particular, for applied research.

In recent years, the use of structural equation modeling (SEM) has become widespread in tourism and hospitality research. Because there are two different approaches to SEM (i.e., covariance-based SEM and variance-based, partial least squares SEM), this brings challenges for researchers about which SEM to use and what to report in each SEM approach. Therefore, the purpose of this chapter is to discuss the differences between CB-SEM and PLS-SEM and to provide comprehensive guidelines for researchers on how to apply each SEM. Within this context, the authors first briefly summarize the fundamentals and advantages of using SEM. Then, the authors explain in detail the major issues that should be considered when selecting between CB-SEM and PLS-SEM. Finally, to ensure rigorous research practices, the authors provide step-by-step guidelines for the application of both CB-SEM and PLS-SEM.

In order to determine the status quo of PLS path modeling in international marketing research, we conducted an exhaustive literature review. An evaluation of double-blind reviewed journals through important academic publishing databases (e.g., ABI/Inform, Elsevier ScienceDirect, Emerald Insight, Google Scholar, PsycINFO, Swetswise) revealed that more than 30 academic articles in the domain of international marketing (in a broad sense) used PLS path modeling as means of statistical analysis. We assessed what the main motivation for the use of PLS was in respect of each article. Moreover, we checked for applications of PLS in combination with one or more additional methods, and whether the main reason for conducting any additional method(s) was mentioned.

Citizens are substantial stakeholders in every e-government system, thus their willingness to use and ability to access the system are critical. Unequal access and information and communication technology usage, which is known as digital divide, however has been identified as one of the major obstacles to the implementation of e-government system. As digital divide inhibits citizen’s acceptance to e-government, it should be overcome despite the lack of deep theoretical understanding on this issue. This research aimed to investigate the digital divide and its direct impact on e-government system success of local governments in Indonesia as well as indirect impact through the mediation role of trust. In order to get a comprehensive understanding of digital divide, this study introduced a new type of digital divide, the innovativeness divide.

The research problems were approached by applying two-stage sequential mixed method research approach comprising of both qualitative and quantitative studies. In the first phase, an initial research model was proposed based on a literature review. Semi-structured interview with 12 users of e-government systems was then conducted to explore and enhance this initial research model. Data collected in this phase were analyzed with a two-stage content analysis approach and the initial model was then amended based on the findings. As a result, a comprehensive research model with 16 hypotheses was proposed for examination in the second phase.

In the second phase, quantitative method was applied. A questionnaire was developed based on findings in the first phase. A pilot study was conducted to refine the questionnaire, which was then distributed in a national survey resulting in 237 useable responses. Data collected in this phase were analyzed using Partial Least Square based Structural Equation Modeling.

The results of quantitative analysis confirmed 13 hypotheses. All direct influences of the variables of digital divide on e-government system success were supported. The mediating effects of trust in e-government in the relationship between capability divide and e-government system success as well as in the relationship between innovativeness divide and e-government system success were supported, but was rejected in the relationship between access divide and e-government system success. Furthermore, the results supported the moderating effects of demographic variables of age, residential place, and education.

This research has both theoretical and practical contributions. The study contributes to the developments of literature on digital divide and e-government by providing a more comprehensive framework, and also to the implementation of e-government by local governments and the improvement of e-government Readiness Index of Indonesia.

Purpose – Revisiting Fornell et al.'s (1996) seminal study, this chapter looks at the evidence for observed and unobserved heterogeneity within data underlying the American customer satisfaction index (ACSI) model. Examining data for two specific industries (utilities and hotels) reveals only modest differences. However, we suppose that unobserved heterogeneity critically affects the results. These insights provide the basis for shaping further differentiated ACSI model analyses and more precise interpretations.

Methodology/approach – This study applies the partial least squares (PLS) path modeling method and uses empirical data to estimate and compare the ACSI model results on the aggregate and industry-specific data levels. In addition, the finite mixture PLS path modeling (FIMIX-PLS) method is employed to further examine across industry similarities and within industry differences.

Findings – This research uncovers unobserved heterogeneity that guides forming three segments of customers within each industry. The major segment in each industry represents customers that are fairly loyal (i.e., neither disloyal nor extremely loyal) while the other two smaller segments are not as similar across the two industries. Our study identifies substantial differences across these segments within each industry. An importance-performance map analysis illustrates these differences and provides the basis for managerial implications.

Originality/value of the chapter – The unobserved heterogeneity revealed within industries in a given country (i.e., the United States of America) underlines the need to be open to differences within populations, beyond the observed heterogeneity across distinct groups or cultures, and the need to reconsider reporting requirements in academic research.

Although the proliferation of research in emotional intelligence (EI) in the last 25 years has largely focused on the individual level, some researchers have proposed theories and measurement models for EI at the organizational level. Drawing from earlier work which conceptualizes organizational emotional intelligence (OEI) as a climate-level construct involving shared norms and practices this chapter sets out to investigate the relationship between perceptions of organizational emotional intelligence (OEI) and turnover intentions amongst employees. Since turnover intentions are a reliable indicator of actual turnover they are deemed to be a critical indicator for organizational performance. This chapter also builds on previous research which found that the relationship between OEI as a climate-level construct and intention to leave was mediated by organizational emotional appeal (i.e., overall reputation) and trust in senior management to explore the mediating role of other employee attitudes which have been traditionally linked to climate and individual-level outcomes in organizations, namely job satisfaction and affective commitment. By surveying employees in a UK-based charity organization (n = 173), the study finds that both job satisfaction and affective commitment mediate the impact of OEI on intention to leave and explain a moderate amount of variance in the focal construct. However, the majority of the mediation occurs through job satisfaction with a reduced mediation effect for affective commitment. Potential reasons for these results in the charity context are discussed. The chapter contributes to a wider understanding of the way in which perceptions of OEI impact on employee attitudes toward the organization and the job; and, in turn, how these attitudes impact on turnover intentions.

Introduction: In this era, financial literacy is conceptualised diversely in the literature. However, as a general term, ‘Financial Literacy’ can be said to mean acting in line with one’s material realities whilst using or managing money. Increasing the financial literacy rate amongst students in Kosovo is paramount since it will prepare and support them in making informed investment decisions and in managing their financial situations.

Aim: The study aims to determine the financial literacy levels of students in Kosovo and to determine the relationship between investment decisions and the Covid-19 pandemic.

Method: The dataset examines four factors (i.e. financial literacy, self-control, peer influence and COVID-19) that influence investment behaviour amongst young students in Kosovo. A total of 228 students from Kosovo participated in the study, and the survey measured their financial literacy, investment knowledge, and the COVID-19 impact on their investment decisions using the random sampling technique. The reflective measurement was made using the Partial Least Squares Structural Equation Model (PLS-SEM) on the data scanned from the survey (N = 228). The measurement includes the evaluation of external loading, internal reliability, and convergent reliability. The final equation model was evaluated and estimated using the Smart-PLS v.3.3.3 program.

Findings: The research findings are useful for companies seeking potential investors from generation Y. In addition to that, the results of the research show that students from Kosovo who are more financially literate are more skilled and make more well-informed decisions when investing. In this study, it was determined that the COVID-19 pandemic did not have a negative effect on the investment decisions of students from Kosovo.