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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.

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.

Corporate social responsibility (CSR) has several dimensions that are inherently unobservable or measured with errors. Due to measurement errors of CSR proxies, regression analysis seems inappropriate for investigating the relationship between CSR and firm value. Accounting for CSR measurement errors, the purpose of this paper is to use a latent variable analysis to examine whether CSR affects firm value.

This study applies a latent variable model that directly takes into account the measurement errors of CSR proxies. Moreover, the inclusion of firm-fixed effects in the model controls for time-invariant unobservable firm-specific characteristics that may drive both CSR and firm value. CSR is measured by environmental, social, and corporate governance activities.

Based on data of US firms between 2002 and 2014, this study finds conflicting evidence of a direct association between each CSR proxy and firm value. When all CSR proxies are incorporated into a latent variable model, CSR significantly positively impacts firm value. Therefore, CSR strategies based on a single measure of CSR or the equal weighting of CSR measures tend to underestimate the influence of CSR on firm value.

Corporate managers should enhance firm value by simultaneously engaging in environmental, social, and corporate governance activities because there is a synergistic effect with firm value. Furthermore, investors who downplay CSR factors in firm valuation can lead to significant errors in making equity investment choices.

This study presents a novel examination of the price-earnings ratio in the CSR valuation by using the latent variable model with firm-fixed effects.

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.

Dynamic market segmentation is a very important topic in many businesses where it is interesting to gain knowledge on the reference market and on its evolution over time. Various papers in the reference literature are devoted to the topic and different statistical models are proposed. The purpose of this paper is to compare two statistical approaches to model categorical longitudinal data to perform dynamic market segmentation.

The latent class Markov model identifies a latent variable whose states represent market segments at an initial point in time, customers can switch to one segment to another between consecutive measurement occasions and a regression structure models the effects of covariates, describing customers’ characteristics, on segments belonging and transition probabilities. The latent class growth approach models individual trajectories, describing a behaviour over time. Customers’ characteristics may be inserted in the model to affect trajectories that may vary across latent groups, in the author’s case, market segments.

The two approaches revealed both suitable for dynamic market segmentation. The advice to marketer analysts is to explore both solutions to dynamically segment the reference market. The best approach will be then judged in terms of fit, substantial results and assumptions on the reference market.

The proposed statistical models are new in the field of financial markets.

An evaluation of libraries and their overall quality should consider the quality of the services they provide. Satisfaction in terms of the service provided is indicative of the quality of reference services and since these services are expensive, evaluation is therefore essential. This paper aims to outline the development of a structural equations model to evaluate service quality and user satisfaction with regard to the electronic reference service provided by Francisco Xavier Clavigero Library belongs to the Iberoamericana University, located in Mexico City.

This model suggests that service quality can be explained by way of the five dimensions of the SERVQUAL methodology, (reliability, assurance, tangibles, empathy and responsiveness) and in turn, quality explains both user satisfaction and the value of the service to its patrons. Finally, this model suggests that a positive increase in user satisfaction leads to a lineal and positive increase in user loyalty. The evaluation considered 297 users who made at least one electronic reference request during 2014.

The adjustment of the structural model reveals that the latent variables that explain quality are reliability and responsiveness, and that quality explains satisfaction, which in turn explains user loyalty.

The generation of an indicator to evaluate the reference services enables identification of its strengths and weaknesses to offer a more efficient service, considering that it represents a significant percentage of the library’s financial and human resources.

Coronavirus disease 2019 (COVID-19) has made telecommuting widely valued, but different individuals have different degrees of acceptance of telecommuting. This article aims to identify suitable individuals for telework and to clarify which types of workers are suitable for what level of telework, set scientific, reasonable hybrid work ratios and processes and measure their suitability.

First, two working scenarios of different risk levels were established, and the theory of planned behavior (TPB) was used to introduce latent variables, constructing a multi-indicator multi-causal model (MIMIC) to identify suitable individuals, and second, constructing an integrated choice and latent variable (ICLV) model of the working method to determine the suitability of different types of people for telework by calculating their selection probabilities.

It is possible to clearly distinguish between two types of suitable individuals for telework or traditional work. Their behavior is significantly influenced by the work environment, which is influenced by variables such as age, income, attitude, perceived behavioral control, work–family balance and personnel exposure level. In low-risk scenarios, the influencing factors of the behavioral model for both types of people are relatively consistent, while in high-risk scenarios, significant differences arise. Furthermore, the suitability of telework for the telework-suitable group is less affected by the pandemic, while the suitability for the non-suitable group is greatly affected.

This study contributes to previous literature by: (1) determining the suitability of different population types for telework by calculating the probability of selection, (2) dividing telework and traditional populations into two categories, identifying the differences in factors that affect telework under different epidemic risks and (3) considering the impact of changes in the work scenario on the suitability of telework for employees and classifying the population based on the suitability of telework in order to avoid the potential negative impact of telework.

The purpose of this paper is to identify and analyze crucial variables of customer satisfaction towards residential facility management (FM) service, and to enable FM companies to deliver high quality services.

The research is based on a survey of customer satisfaction of one residential property in Hong Kong. FM service is divided into two interrelated clusters which are denoted by two latent variables, and then a specific structural equation model is developed for identifying and quantifying the influence of service and management quality on customer satisfaction and clarifying the causal relationships between these latent and observed variables.

The research reveals that: both service and management quality have significant positive effect on customer satisfaction, and the effect of service quality is larger than that of management quality when the indirect effect is taken into account; service quality is a crucial latent variable influencing customer satisfaction and it has a significant direct effect on management quality; how the individual observed variables work together to characterize the corresponding latent variables from an empirical point of view, and some key variables that should be focused on by facility managers in the housing sector are also identified.

Structural equation models are advocated for evaluating customer satisfaction in the housing property sector of facility management service. It can also be used in other sectors of facility management, such as office, retail property or some public property management like hospitals and schools. It has implications for facility management managers in how to improve residential customers' satisfaction level.

This paper presents a quantitative model of characterizing the degree of customer satisfaction.

The purpose of this paper is to use SEM to explore the effects of enabling school structure (ESS) and academic optimism (AO) on school achievement (SA).

A sample of 58 urban schools, including 42 elementary schools and 16 middle schools in a southeastern district in the USA were included in this study. Structural equation modeling was used to test the effects of three exogenous predictor variables (ESS, elementary status, and socio-economic status (SES)) on a latent mediating variable (AO) and a latent dependent variable (academic achievement).

Findings confirm that three factors; collective efficacy, faculty trust in clients, and academic emphasis come together to create the general latent construct referred to as AO by Hoy et al. (2006). Findings also support the importance of ESS in establishing a culture of AO. Together ESS, AO, elementary school level, and SES explained 77 percent of the variance in SA, with AO having the most significant effect above and beyond the effects of SES.

This study was based on a sample of schools in the Southern portion of the USA. Findings may not be generalizable to other areas. The lack of availability of individual student achievement data prevented the use of hierarchical linear modeling.

Findings from this study point to the importance of administrators establishing flexible rules and regulations and engaging in a leadership style that is collaborative. It appears that ESS not only promotes the establishment of AO but contributes to increased SA and is likely to be critical for upper levels of schooling.

Reform efforts need to involve parents and community members. AO may provide an appropriate lens to further explore parent and community perceptions of reform efforts and relationships with administrators and teachers. ESS may assist in creating the structures necessary for increased parent and community involvement as well as increased perceptions of AO.

This study is one of only three studies known to explore the effects of ESS on AO and is one of the first known studies to explore these effects in a middle school setting.