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Article
Publication date: 25 June 2019

Galit Shmueli, Marko Sarstedt, Joseph F. Hair, Jun-Hwa Cheah, Hiram Ting, Santha Vaithilingam and Christian M. Ringle

Partial least squares (PLS) has been introduced as a “causal-predictive” approach to structural equation modeling (SEM), designed to overcome the apparent dichotomy…

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Abstract

Purpose

Partial least squares (PLS) has been introduced as a “causal-predictive” approach to structural equation modeling (SEM), designed to overcome the apparent dichotomy between explanation and prediction. However, while researchers using PLS-SEM routinely stress the predictive nature of their analyses, model evaluation assessment relies exclusively on metrics designed to assess the path model’s explanatory power. Recent research has proposed PLSpredict, a holdout sample-based procedure that generates case-level predictions on an item or a construct level. This paper offers guidelines for applying PLSpredict and explains the key choices researchers need to make using the procedure.

Design/methodology/approach

The authors discuss the need for prediction-oriented model evaluations in PLS-SEM and conceptually explain and further advance the PLSpredict method. In addition, they illustrate the PLSpredict procedure’s use with a tourism marketing model and provide recommendations on how the results should be interpreted. While the focus of the paper is on the PLSpredict procedure, the overarching aim is to encourage the routine prediction-oriented assessment in PLS-SEM analyses.

Findings

The paper advances PLSpredict and offers guidance on how to use this prediction-oriented model evaluation approach. Researchers should routinely consider the assessment of the predictive power of their PLS path models. PLSpredict is a useful and straightforward approach to evaluate the out-of-sample predictive capabilities of PLS path models that researchers can apply in their studies.

Research limitations/implications

Future research should seek to extend PLSpredict’s capabilities, for example, by developing more benchmarks for comparing PLS-SEM results and empirically contrasting the earliest antecedent and the direct antecedent approaches to predictive power assessment.

Practical implications

This paper offers clear guidelines for using PLSpredict, which researchers and practitioners should routinely apply as part of their PLS-SEM analyses.

Originality/value

This research substantiates the use of PLSpredict. It provides marketing researchers and practitioners with the knowledge they need to properly assess, report and interpret PLS-SEM results. Thereby, this research contributes to safeguarding the rigor of marketing studies using PLS-SEM.

Details

European Journal of Marketing, vol. 53 no. 11
Type: Research Article
ISSN: 0309-0566

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Article
Publication date: 6 August 2020

Wynne Chin, Jun-Hwa Cheah, Yide Liu, Hiram Ting, Xin-Jean Lim and Tat Huei Cham

Partial least squares structural equation modeling (PLS-SEM) has become popular in the information systems (IS) field for modeling structural relationships between latent…

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1140

Abstract

Purpose

Partial least squares structural equation modeling (PLS-SEM) has become popular in the information systems (IS) field for modeling structural relationships between latent variables as measured by manifest variables. However, while researchers using PLS-SEM routinely stress the causal-predictive nature of their analyses, the model evaluation assessment relies exclusively on criteria designed to assess the path model's explanatory power. To take full advantage of the purpose of causal prediction in PLS-SEM, it is imperative for researchers to comprehend the efficacy of various quality criteria, such as traditional PLS-SEM criteria, model fit, PLSpredict, cross-validated predictive ability test (CVPAT) and model selection criteria.

Design/methodology/approach

A systematic review was conducted to understand empirical studies employing the use of the causal prediction criteria available for PLS-SEM in the database of Industrial Management and Data Systems (IMDS) and Management Information Systems Quarterly (MISQ). Furthermore, this study discusses the details of each of the procedures for the causal prediction criteria available for PLS-SEM, as well as how these criteria should be interpreted. While the focus of the paper is on demystifying the role of causal prediction modeling in PLS-SEM, the overarching aim is to compare the performance of different quality criteria and to select the appropriate causal-predictive model from a cohort of competing models in the IS field.

Findings

The study found that the traditional PLS-SEM criteria (goodness of fit (GoF) by Tenenhaus, R2 and Q2) and model fit have difficulty determining the appropriate causal-predictive model. In contrast, PLSpredict, CVPAT and model selection criteria (i.e. Bayesian information criterion (BIC), BIC weight, Geweke–Meese criterion (GM), GM weight, HQ and HQC) were found to outperform the traditional criteria in determining the appropriate causal-predictive model, because these criteria provided both in-sample and out-of-sample predictions in PLS-SEM.

Originality/value

This research substantiates the use of the PLSpredict, CVPAT and the model selection criteria (i.e. BIC, BIC weight, GM, GM weight, HQ and HQC). It provides IS researchers and practitioners with the knowledge they need to properly assess, report on and interpret PLS-SEM results when the goal is only causal prediction, thereby contributing to safeguarding the goal of using PLS-SEM in IS studies.

Details

Industrial Management & Data Systems, vol. 120 no. 12
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 14 January 2019

Joseph F. Hair, Jeffrey J. Risher, Marko Sarstedt and Christian M. Ringle

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…

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55227

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Details

European Business Review, vol. 31 no. 1
Type: Research Article
ISSN: 0955-534X

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Article
Publication date: 27 January 2021

Mei Peng Low and Heath Spong

This research aims to examines the impact of micro-level corporate social responsibility (CSR) practices on employee engagement within the public accounting firm setting.

Abstract

Purpose

This research aims to examines the impact of micro-level corporate social responsibility (CSR) practices on employee engagement within the public accounting firm setting.

Design/methodology/approach

This research uses a quantitative approach with a survey instrument as the data collection tool. A total of 269 complete responses were collected from employees working in the public accounting firms. Micro-level CSR practices were analysed with a hierarchical component model (HCM) in partial least square structural equation modelling (PLS-SEM) to examine the influence of such practices on employee engagement. A predictive performance metric was applied to assess the out-of-sample prediction.

Findings

This study uncovers a positive and significant relationship between micro-level CSR practices and employee engagement. Furthermore, the PLSpredict results indicate that the current model possesses high predictive power with all indicators in the PLS-SEM analysis demonstrating lower root mean squared error (RMSE) values compared to the naïve linear regression model benchmark.

Research limitations/implications

While the methods applied in this analysis are at the frontier of CSR research, the present study has not explored the heterogeneity amongst groups of respondents and size of accounting firms. Sampling weight adjustment for the purposes of representativeness was not used in the current research. These could be the subject of future work in this area.

Practical implications

These research findings shed light on the positive manifestation effect of micro-level CSR practices at firm level as well as individual level. Through micro-level CSR practices, firms can reap the benefits of enhanced employee engagement, which leads to productive workforce while also facilitating increased employees’ intrinsic job satisfaction.

Social implications

Micro-level CSR practices address the needs of the millennium workforce, whereby employees are no longer solely focussed on pay checks as their compensation. Employees are seeking out employers whose CSR practices appeal to their social conscience. Micro-level CSR practices meet the needs of the contemporary workforce yet enable companies to attract and retain skilled employees.

Originality/value

The originality of this research is attributed to the vigorous statistical analysis by the use of HCMs and PLSpredict in PLS-SEM context for the assessment of predictive performance. Also, micro-level CSR practices are conceptualised in HCM for parsimonious purpose.

Details

Social Responsibility Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1747-1117

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Article
Publication date: 22 July 2020

Wen-Lung Shiau, Ye Yuan, Xiaodie Pu, Soumya Ray and Charlie C. Chen

The purpose of this study is to clarify theory and identify factors that could explain the level of fintech continuance intentions with an expectation confirmation model…

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1922

Abstract

Purpose

The purpose of this study is to clarify theory and identify factors that could explain the level of fintech continuance intentions with an expectation confirmation model that integrates self-efficacy theory.

Design/methodology/approach

With data collected from 753 fintech users, this study applies partial least square structural equation modeling to compare and select the research model with the most predictive power.

Findings

The results show that financial self-efficacy, technological self-efficacy and confirmation positively affect perceived usefulness. Among these factors, financial self-efficacy and technological self-efficacy have both direct and indirect effects through confirmation on perceived usefulness. Perceived usefulness and confirmation are positively related to satisfaction. Finally, perceived usefulness and satisfaction positively influence fintech continuance intentions.

Originality/value

To the best of our knowledge, this is one of the earliest studies that investigates the effect of domain-specific self-efficacy on fintech continuance intentions, which enriches the existing research on fintech and deepens our understanding of users' fintech continuance intentions. We distinguish between financial self-efficacy and technological self-efficacy and specify the relationship between self-efficacy and continuance intentions. Moreover, this study highlights the importance of assessing a model's predictive power using the PLSpredict technique and provides a reference for model selection.

Details

Industrial Management & Data Systems, vol. 120 no. 9
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 11 February 2019

Jun-Hwa Cheah, Hiram Ting, Tat Huei Cham and Mumtaz Ali Memon

The purpose of this paper is to assess the effect of two promotional methods, namely, celebrity endorsed advertisement and selfie promotion, on customers’ decision-making…

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3464

Abstract

Purpose

The purpose of this paper is to assess the effect of two promotional methods, namely, celebrity endorsed advertisement and selfie promotion, on customers’ decision-making processes using the AISAS model.

Design/methodology/approach

A within-subject experimental design was used to observe how young adults in Malaysia would respond to two promotional methods about a new seafood restaurant. A total of 180 responses were collected using a structured questionnaire. Data were assessed and analysed using partial least squares structural equation modelling.

Findings

The results show that while celebrity endorsed advertisement remains relevant to customer’s decision-making processes, the effect of selfie promotion is comparable to celebrity endorsement. The sequential mediation for both models is found to be significant, but the AISAS model with selfie promotion produces better in-sample prediction (model selection criteria) and out-of-sample prediction (PLSpredict) compared to celebrity endorsed advertisement, thus suggesting its better representation to reality.

Research limitations/implications

Despite being limited to young adults in Malaysia and a particular product, the study is essential to understanding the effect of celebrity endorsed advertisement and selfie promotion on decision-making processes.

Practical implications

The study provides insights into how business organisations could exploit the advancement of communication technology to encourage selfie behaviour to promote their products in an innovative and competitive manner.

Originality/value

The assessment of the effect of celebrity endorsed advertisement and selfie promotion on decision-making processes using PLSpredict and model selection criteria articulates the relevance of selfie as a promotional tool. It also provides an alternative technique for conducting model comparison research.

Details

Internet Research, vol. 29 no. 3
Type: Research Article
ISSN: 1066-2243

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Article
Publication date: 26 November 2020

Wen-Lung Shiau, Xiaodie Pu, Soumya Ray and Charlie C. Chen

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111

Abstract

Details

Industrial Management & Data Systems, vol. 120 no. 12
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 27 July 2021

Shahriar Akter, Ruwan J. Bandara and Shahriar Sajib

Analytics thrives in navigating emergency situations. Emergency operations management needs to develop analytics empowerment capability (ANEC) to prepare for uncertainty…

Abstract

Purpose

Analytics thrives in navigating emergency situations. Emergency operations management needs to develop analytics empowerment capability (ANEC) to prepare for uncertainty, support continuity and tackle any disruptions. However, there is limited knowledge on ANEC and its effects on strategic emergency service agility (SESA) and emergency service adaptation (ESAD) in such contexts. Drawing on the dynamic capability (DC) theory, we address this research gap by developing an ANEC model. We also model the effects of ANEC on SESA and ESAD using SESA as a mediator. We also assess the moderating and quadratic effects of ANEC on two higher-order DCs (i.e. SESA and ESAD).

Design/methodology/approach

Drawing on the literature on big data, empowerment and DC, we develop and validate an ANEC model using data from 245 service systems managers in Australia. The study uses the partial least squares-based structural equation modelling (PLS-SEM) to prove the research model. The predictive power of the research model is validated through PLSpredict (k = 10) using a training sample (n = 220) and a holdout sample (n = 25).

Findings

The findings show that analytics climate, technological enablement, information access, knowledge and skills, training and development and decision-making ability are the significant components of ANEC. The findings confirm strategic emergency service agility as a significant partial mediator between ANEC and emergency service adaptation. The findings also discuss the moderating and quadratic effects of ANEC on outcome constructs. We discuss the implications of our findings for emergency situations with limitations and future research directions.

Originality/value

The findings show that building ANEC plays a fundamental role in developing strategic agility and service adaptation in emergency situations to prepare for disruptions, mitigate risks and continue operations.

Details

International Journal of Operations & Production Management, vol. 41 no. 9
Type: Research Article
ISSN: 0144-3577

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Article
Publication date: 9 August 2021

Jaime Romero and Nora Lado

COVID-19 is expected to enhance hospitality robotization because frontline robots facilitate social distancing, lowering contagion risk. Investing in frontline robots…

Abstract

Purpose

COVID-19 is expected to enhance hospitality robotization because frontline robots facilitate social distancing, lowering contagion risk. Investing in frontline robots emerges as a solution to recover customer trust and encourage demand. However, we ignore how customers perceive these initiatives and, therefore, their efficacy. Focusing on robot employment at hotels and on Generation Z customers, this study aims to analyze guests’ perceptions about robots’ COVID-19 prevention efficacy and their impact on booking intentions.

Design/methodology/approach

This study tests its hypotheses combining an experimental design methodology with partial least squares. Survey data from 711 Generation Z individuals in Spain were collected in 2 periods of time.

Findings

Generation Z customers consider that robots reduce contagion risk at hotels. Robot anthropomorphism increases perceived COVID-19 prevention efficacy, regardless of the context where the robots are used. Robots’ COVID-19 prevention efficacy provokes better attitudes and higher booking intentions.

Research limitations/implications

The sampling method used in this research impedes this study’s results generalization. Further research could replicate this study using random sampling methods to ensure representativeness, even for other generational cohorts.

Practical implications

Employing robots as a COVID-19 prevention measure can enhance demand, especially if robots are human-like. Hoteliers need to communicate that robots can reduce contagion risk, particularly in markets more affected by COVID-19. Robots must be employed in low social presence contexts. Governments could encourage robotization by financially supporting hotels and publicly acknowledging its benefits regarding COVID-19 prevention.

Originality/value

This study combines preventive health, robotics and hospitality literature to study robot implementation during the COVID-19 pandemic, focusing on Generation Z guests – potential facilitators of robot diffusion.

Details

International Journal of Contemporary Hospitality Management, vol. 33 no. 11
Type: Research Article
ISSN: 0959-6119

Keywords

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Abstract

Details

Applying Partial Least Squares in Tourism and Hospitality Research
Type: Book
ISBN: 978-1-78756-700-9

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