Basics of qualitative research: Grounded theory procedures and techniques

The purpose of this paper is to provide a comprehensive, yet concise, overview of the considerations and metrics required for partial least squares structural equation modeling (PLS-SEM) analysis and result reporting. Preliminary considerations are summarized first, including reasons for choosing PLS-SEM, recommended sample size in selected contexts, distributional assumptions, use of secondary data, statistical power and the need for goodness-of-fit testing. Next, the metrics as well as the rules of thumb that should be applied to assess the PLS-SEM results are covered. Besides presenting established PLS-SEM evaluation criteria, the overview includes the following new guidelines: PLSpredict (i.e., a novel approach for assessing a model’s out-of-sample prediction), metrics for model comparisons, and several complementary methods for checking the results’ robustness.,This paper provides an overview of previously and recently proposed metrics as well as rules of thumb for evaluating the research results based on the application of PLS-SEM.,Most of the previously applied metrics for evaluating PLS-SEM results are still relevant. Nevertheless, scholars need to be knowledgeable about recently proposed metrics (e.g. model comparison criteria) and methods (e.g. endogeneity assessment, latent class analysis and PLSpredict), and when and how to apply them to extend their analyses.,Methodological developments associated with PLS-SEM are rapidly emerging. The metrics reported in this paper are useful for current applications, but must always be up to date with the latest developments in the PLS-SEM method.,In light of more recent research and methodological developments in the PLS-SEM domain, guidelines for the method’s use need to be continuously extended and updated. This paper is the most current and comprehensive summary of the PLS-SEM method and the metrics applied to assess its solutions.

https://www.emerald.com/insight/content/doi/10.1108/EBR-11-2018-0203/full/pdf

When to use and how to report the results of PLS-SEM

Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments

Structural equation modeling (SEM) is a multivariate statistical framework that is used to model complex relationships between directly observed and indirectly observed (latent) variables. SEM is a general framework that involves simultaneously solving systems of linear equations and encompasses other techniques such as regression, factor analysis, path analysis, and latent growth curve modeling. Recently, SEM has gained popularity in the analysis of complex genetic traits because it can be used to better analyze the relationships between correlated variables (traits), to model genes as latent variables as a function of multiple observed genetic variants, and to assess the association between multiple genetic variants and multiple correlated phenotypes of interest. Though the general SEM framework only allows for the analysis of independent observations, recent work has extended SEM for the analysis of data on general pedigrees. Here, we review the theory of SEM for both unrelated and family data, describe the available software for SEM, and provide examples of SEM analysis.

Structural Equation Modeling

Purpose – Partial least squares (PLS) path modeling is a variance-based structural equation modeling (SEM) technique that is widely applied in business and social sciences. Its ability to model composites and factors makes it a formidable statistical tool for new technology research. Recent reviews, discussions, and developments have led to substantial changes in the understanding and use of PLS. The paper aims to discuss these issues. Design/methodology/approach – This paper aggregates new insights and offers a fresh look at PLS path modeling. It presents new developments, such as consistent PLS, confirmatory composite analysis, and the heterotrait-monotrait ratio of correlations. Findings – PLS path modeling is the method of choice if a SEM contains both factors and composites. Novel tests of exact fit make a confirmatory use of PLS path modeling possible. Originality/value – This paper provides updated guidelines of how to use PLS and how to report and interpret its results.

/pdf/using-pls-path-modeling-in-new-technology-research-updated-4mwzhyqaye.pdf

Using PLS path modeling in new technology research: updated guidelines

This research is the first to examine service sweethearting, an illicit behavior that costs firms billions of dollars annually in lost revenues. Sweethearting occurs when frontline workers give unauthorized free or discounted goods and services to customer conspirators. The authors gather dyadic data from 171 service employees and 610 of their customers. The results from the employee data reveal that a variety of job, social, and remuneration factors motivate sweethearting behavior and several measurable employee traits suppress its frequency. The results from the customer data indicate that although sweethearting inflates a firm's satisfaction, loyalty, and positive word-of-mouth scores by as much as 9%, satisfaction with the confederate employee fully mediates these effects. Thus, any benefits for customer satisfaction or loyalty initiatives are tied to a frontline worker that the firm would rather not employ. Marketing managers can use this study to recognize job applicants or company settings...

Service Sweethearting: Its Antecedents and Customer Consequences

Despite the importance of customer value, considerable divergence of opinion exists on how to adequately conceptualize and measure this construct In this study, four commonly used methods for measuring customer value (ie, the methods proposed by Dodds et al (1991), Gale (1994), Holbrook (1999) and Woodruff and Gardial (1996)) are compared First and foremost, the psychometric properties of the different methods are evaluated Next, the authors compare the predictive ability of the different methods with respect to key outcome variables (ie, satisfaction, word of mouth, repurchase intention) Finally, the methods are compared based on their relative practicality and actionability The authors’ findings show that each method has its own benefits and costs and should be used based on its suitability for a particular application The paper culminates with a prescriptive flowchart that summarizes the main findings and provides direction for choosing the optimal value measurement method

/pdf/assessing-the-value-of-commonly-used-methods-for-measuring-3ydkllf0fw.pdf

Assessing the value of commonly used methods for measuring customer value: a multi-setting empirical study

Conveying Trustworthiness to Online Consumers: Reactions to Consensus, Physical Store Presence, Brand Familiarity, and Generalized Suspicion

This study assesses the effects of service evaluations in a failed service experience on complaining intentions in a future encounter. A model is developed and tested that investigates the relation...

A service perspective on the drivers of complaint intentions

In recent years, the significance of offering value to service customers rivals the provision of satisfaction and service quality. The growing importance of the construct is especially apparent in the fast food industry as evidenced by the international popularity of value menus offered by many fast food franchises. The service value trend is particularly relevant in Latin America as more multinational service franchises compete for newfound market share. Zeithaml, in her comprehensive analysis of the service value construct, suggests that service value perceptions emerge when consumers weigh their perceptions of service quality against the necessary sacrifices made to acquire the service. In the current study, this conceptual model is tested using samples derived from fast food consumers in both Ecuador and the USA. The objective is to perform an empirical test of Zeithaml’s model and to compare the relative importance attached to the components of her model across the two samples. An additional consideration is an analysis of the effect of gender on the purchase behaviour of these consumers. The research and managerial implications of the study are considered, as are the research limitations.

Michael K. Brady

Papers

Service Sweethearting: Its Antecedents and Customer Consequences

Assessing the value of commonly used methods for measuring customer value: a multi-setting empirical study

Conveying Trustworthiness to Online Consumers: Reactions to Consensus, Physical Store Presence, Brand Familiarity, and Generalized Suspicion

A service perspective on the drivers of complaint intentions

An exploratory study of service value in the USA and Ecuador