Partial Least Squares Structural Equation Modeling Analysis Research Articles

Antecedents and consequences of wandering scholars' affect: the case of multi-cultural Malaysia in the internationalization era

PurposeThis study, drawing upon affective events theory (AET), examines the relationships between work environment features, work events, affective states and attitudes in the Malaysian academic environment. Specifically, the authors examine the impact of supervisory support and welfare on role conflict, the impact of role conflict on affective states and the impact of affective states on job satisfaction using data collected from the international faculty.Design/methodology/approachThe authors collected data from 152 respondents through an online platform, and given this study's causal predictive-explanatory nature, the authors apply partial least squares structural equation modeling (PLS-SEM) for data analysis. In addition, the authors complement their analysis by examining the nonlinear effects within the model as a robustness check.FindingsFrom an explanatory perspective, the authors find support for all the hypotheses, implying the applicability of AET in academic contexts. Regarding the predictive perspective, the authors’ model exhibits a high out-of-sample predictive power. Importantly, the robustness check provides additional support for their model.Practical implicationsGiven that the international faculty comes from different countries, the authors’ findings suggest that university managers should formulate and implement policies, which encourage and celebrate cultural diversity to increase the international faculty's job satisfaction. Additionally, policies that improve the process of job designs and descriptions need amendments in a way that reduces the role conflict faced by the multicultural faculty which, in turn, leads to negative affect and ultimately to job dissatisfaction.Originality/valueThis article is one of the first research studies focusing on the verification of AET in the context of international higher education. The authors apply the recent evaluative guidelines for a solid and rigorous PLS-SEM analysis and prove that the wandering scholars' emotions matter in achieving desirable organizational outcomes in cross-cultural institutions of higher learning.

Understanding learners’ acceptance of high-immersion virtual reality systems: Insights from confirmatory and exploratory PLS-SEM analyses

This article reports on a study that investigated user acceptance of a high-immersion virtual reality learning environment to learn English paragraph writing structure. 134 undergraduate university students participated in the study by using a high-immersion virtual reality system combined with an in-house developed virtual reality learning environment program for the purpose of learning paragraph structure. A post-session questionnaire was used to collect data. A partial least squares structural equation modelling (PLS-SEM) analysis was then conducted to test an extended technology acceptance model conceptualized from a priori theoretical deduction and previous empirical findings. The extended model featured the constructs of Imagination, Immersion, and Interaction as exogenous variables with Perceived Ease of Use and Perceived Usefulness in mediating roles leading to the endogenous construct of Behavioral Intention to Use. Results of the confirmatory PLS-SEM analysis revealed inherent problems with the model's application to the high-immersion virtual reality system and program used. Considering these findings, a PLS-SEM exploratory model optimization analysis was conducted which saw the variable of Immersion best relocated to be a predicator for both the Interaction and Imagination constructs. This exploratory model showed superior predictive relevance and improved significance of the relevant construct paths. Based on these results, this paper provides new theoretical reasoning that is in line with these statistical findings, namely that the construct of Immersion is best positioned as a singular external exogenous variable impacting Interaction and Imagination for the high-immersion virtual reality technology acceptance model used in this study.

CORPORATE REPUTATION MODELLING AS VITAL ENTERPRISE VALUE FOR STAKEHOLDERS: STRUCTURAL EQUATION MODELLING PERSPECTIVE

Many research studies suggest that the increasing competitiveness of the global economy emphasizes the importance of identifying the drives of sustainable competitive advantage in the intangible domain. This research aims to, according to resource-based theory of the enterprise, model corporate reputation as an invisible resource of the enterprises that enables the creation of the various advantages such as customer satisfaction and gaining loyal customers that further leads to creating competitive advantage and superior business performance. The research aim is to determine the significance and intensity of the simultaneous relationships between corporate reputation, presented as a construct composed of two elements: competence and likeability, and customer satisfaction and customer loyalty. It seeks to determine the significance of the mediator customer satisfaction in the relationship between corporate reputation and customer loyalty. Research data are collected by a survey questionnaire fulfilled by 116 respondents to participate in the research on enterprises from the telecommunication sector of transition economy Republic of Srpska, Bosnia and Herzegovina. To test the proposed research model partial least squares structural equation modelling (PLS-SEM) is applied. Prior to application of the PLS-SEM certain preliminary data and sample analysis are performed. The presence of outliers and requirements of the sample size for PLS-SEM evaluation are examined. To determine whether final sample size is appropriate to conduct PLS-SEM analysis both rule of thumb and results of the analysis made by G*Power software were used. Construct likeability has nearly two times stronger direct effect on customer satisfaction compared to other element of corporate reputation called competence. Only one element of corporate reputation called likeability has significant direct effect on final target endogenous construct customer loyalty. Customer satisfaction has strong significant direct effect on customer loyalty. Beside direct effects of the latent constructs corporate reputation and customer satisfaction on customer loyalty, mediation analysis is performed to test the significance of the mediator called customer satisfaction. Research results indicate the importance of the mediator customer satisfaction in the relationship between corporate reputation and customer loyalty. According to them, both elements of corporate reputation have significant indirect effect on customer loyalty. Likewise, research results enable ranking of the exogenous factors that create and improve corporate reputation. Directing the management attention towards improving certain exogenous factors ensures the development of certain corporate reputation elements – competence and likeability, and provides efficient and effective corporate reputation management. It is revealed that corporate social responsibility, promoting products’ and services’ quality and better business performances improve competence element of corporate reputation, while corporate social responsibility and investments into raising the attractiveness of the enterprise positively influence sympathy and likeability of the enterprises from a stakeholder perspective.

Technology Readiness and Cryptocurrency Adoption: PLS-SEM and Deep Learning Neural Network Analysis

Today’s world is increasingly dependent on technology directly or indirectly. The rapid technological advancement has impacted people to adopt the technology. As cryptocurrency recently commenced, few studies have attempted to investigate this use of technology. In this study, the technology readiness aspects- Optimism, Innovativeness, Discomfort, and Insecurity are used to understand the people’s adoption of cryptocurrency. A multi-approach of Partial Least Squares- Structural Equation Modeling (PLS-SEM) and Deep learning Artificial Neural Network (ANN) analysis was performed. Deep learning Artificial Neural Network (ANN) analysis was performed to complement PLS-SEM findings and predict higher accuracy. This study shows that technology readiness dimensions - Optimism, Innovativeness, Discomfort, and Insecurity have meaningful relationships with cryptocurrency adoption.

Sensing, seizing, and reconfiguring: Key capabilities and organizational routines for circular economy implementation

Circular economy (CE) has a huge potential to overcome sustainability challenges. The EU and several national governments have been urging firms for CE implementation. However, to date, most firms are failing in translating the CE concept into their business operations. Some scholars point out that firms may attain CE objectives through dynamic capabilities. To empirically investigate whether or not dynamic capabilities facilitate CE implementation, we analyzed a sample of 220 manufacturing firms in Italy through partial least squares structural equation modeling (PLS-SEM). Our PLS-SEM analysis shows that sensing, seizing, and reconfiguring capabilities and their underlying organizational routines significantly facilitate CE implementation. This paper contributes to the literature in the following ways. Firstly, this paper presents the measurement scales of dynamic capabilities and demonstrates how to operationalize a reflective-formative hierarchical component model (HCM) of dynamic capabilities in PLS-SEM. Secondly, this paper highlights the key capabilities and organizational routines through which firms can identify and pursue CE opportunities.

What motivates the accountant’s awareness of their impact on corporate reputation

The study probes the mediating effect of affective commitment (AC) on the linkage between job satisfaction (JS) of accountants and their awareness of how their work impacts their corporate's reputation (AICR). We collected primary data from 110 professional accountants from seven metropolitan cities in India. The data was analysed by using partial least squares-structural equation modelling (PLS-SEM) technique and checked for robustness through fuzzy set qualitative comparative analysis (fsQCA) technique. It was observed that AC has a significant and mediating linear effect on the linkage between JS and the accountant's AICR. No quadratic effects were observed. The study makes three distinct contributions. First, it contributes to a unique behavioural understanding of the antecedents influencing the professional accountant's AICR in the context of an emerging markets. Second, it contributes to the methods literature by testing both the linear effects and the quadratic effects and also by undertaking the robustness check of PLS-SEM analysis by fsQCA. Finally, the study uses the theoretical underpinning of the social exchange theory (SET) to build a deeper understanding of the antecedents of AICR.

Assessing the determinants of intentions and behaviors of organizations towards a circular economy for plastics

The production and consumption of plastics, although inevitable in our modern life, are predominantly unsustainable and inefficient. Hence, the concept of a circular economy for plastics has been proposed as a sustainable approach to thrive both economy and our modern life. To implement a circular economy for plastics, an understanding of both individuals’ and organizations’ behaviors is needed since psychological effects often undermine technical solutions. We particularly focus on organizations’ behaviors since commercial plastic waste has not been thoroughly investigated compared to household plastic waste. Using the Theory of Planned Behavior (TPB) and Partial Least Squares Structural Equation Modeling (PLS-SEM), we assess the determinants of intentions and behaviors of 637 organizations in Belgium towards a circular economy for plastics. Our PLS-SEM analysis support that attitudes, subjective norms, and perceived behavioral control of decision makers positively influence organizations’ intentions to implement best practices of plastic recycling. Furthermore, organizations’ intentions, perceived behavioral control, pressures, and enablers positively, whereas barriers negatively, influence organizations’ behaviors. Our study shows that most organizations have positive intentions, yet they seem to be failing in implementing best practices of plastic recycling due to some critical barriers. To overcome this intention-behavior gap and to attain a circular economy for plastics, our study suggests some measures.

This fast car can move faster: a review of PLS-SEM application in higher education research

The relevance and prominence of the partial least squares structural equation modeling (PLS-SEM) method has recently increased in higher education research, especially in explanatory and predictive studies. We therefore first aim to assess previous PLS-SEM applications by providing a systematic review; second, we aim to highlight and summarize important guidelines for conducting a rigorous PLS-SEM analysis of the current state of results reporting in higher education journals. Specifically, this study focuses on empirical PLS-SEM applications in 14 major higher education journals indexed in the Thomson Reuters Web of Science and in the Elsevier-Scopus databases between 1999 and 2018. We initially identified 49 relevant papers published in 10 higher education journals. Based on these papers’ generally followed guidelines, we thereafter identified various issues related to data screening, model characteristics, measurement model evaluation, structural model evaluation, and the application of state-of-the-art PLS-SEM advanced methods requiring particular attention. Furthermore, we recommend recent guidelines to improve PLS-SEM applications and practices, besides providing specific suggestions regarding utilizing the method’s strength in terms of relevant higher education research questions. Our findings remind researchers, reviewers, and journal editors to remain vigilant, should help them avoid inaccuracies in future publications, and ensure rigor.

Corporate Social Responsibility in Higher Education: A PLS-SEM Neural Network Approach

Corporate social responsibility (CSR) has become vital to the competitiveness of organizations. However, there is very limited research on CSR in the context of higher education. Therefore, this study investigates the impact of CSR factors on the university competitiveness with other universities. The contribution of this study includes applying a multi-analytical methodology by applying Partial Least Squares- Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN). First, PLS-SEM analysis was applied to measure which CSR factor has the most significant influence on the increased competitiveness of Saudi Arabian universities. Then, an ANN model analysis was applied to rank the relative influence of the significant CSR factors attained from the PLS-SEM analysis. The findings show that the CSR factors such as market-oriented CSR activities, society-oriented CSR activities and workforce-oriented CSR activities have a significant positive impact on increased competitiveness of Saudi Arabian universities. However, based on the ANN findings, society-oriented CSR is the most significant predictor of a university's increased competitiveness, followed by workforce-oriented CSR and finally by market-oriented CSR. The practical implications of this study provide Saudi universities to focus on society-oriented CSR to maintain and sustain their competitive advantage over other universities.

A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption

The majority of previous research on new technology acceptance has been conducted with single-step Structural Equation Modeling (SEM) based methods. The primary purpose of the study is to enhance the new technology acceptance based research with the Artificial Neural Network (ANN) method to enable more precise and in-depth research results as compared to the single-step SEM method. This study measures the relation between technology readiness dimension (optimism, innovativeness, discomfort, insecurity) and the technology acceptance (perceived ease of use and perceived usefulness) – and the intention to use cryptocurrency, such as bitcoin. The contribution of this study include the use of a multi-analytical approach by combining Partial Least Squares- Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analysis. First, PLS-SEM was applied to assess which factor has significant influence toward intention to use cryptocurrency. Second, an ANN was employed to rank the relative influence of the significant predictor variables attained from the PLS-SEM. The findings of the two-step PLS-SEM and ANN approach confirm that the use of ANN further verifies the results obtained by the PLS-SEM analysis. Also, ANN is capable of modelling complex linear and non-linear relationships with high predictive accuracy compared to SEM methods. Also, an Importance-Performance Map Analysis (IPMA) of the PLS-SEM results provides a more specific understanding of each factor’s importance-performance.

Predicting Generation Y’s Purchase Intention towards Brands Advertised on Social Media: A PLS-SEM Analysis

<p><em>Social media advertising has become an integral part of product promotion by many companies. This strategy has impacted the sales and revenue departments of many advertisers. Hence, a strategically targeted advertising is needed to maximise return on investment on advertising in the form of sales, revenue, and profit. Although most businesses recognise the value of social media advertising opportunities, not many have figured out how to execute this strategy accordingly. With many </em><em>w</em><em>estern corporations embracing social media advertising, it is high time for Malaysian businesses to delve into this advertising territory. In order to familiarise them to the attitude of customers’ towards social media advertising, this study is timely</em><em> to provide useful insights to guide Malaysian businesses.</em><em></em><em></em></p><p><em>Findings from this study will help advertising managers to ensure efficient utilisation of their budget and development of more effective advertising strategy, especially when formulating strategies to cater to Gen Y in Malaysia. Based on the data collected from 1,087 Gen Y consumers in Malaysia, the effect of belief factors (lifestyle, privacy and security concern, entertainment and credibility) on attitude towards social media advertising was examined. Partial Least Squares-Structural Equation Modelling (PLS-SEM) was employed to assess the hypothetical relationships between the belief factors and attitude towards social media advertising, purchase intention and actual purchase. The results revealed that the belief factors (lifestyle, privacy and security concern, entertainment and credibility) manifested a positive influence on attitude towards social media advertising.</em></p>

Corporate image as a mediator between service quality and customer satisfaction: difference across categorized exhibitors

Impact of service quality and corporate image on satisfaction and loyalty behavioral intention are explored by using the PLS-SEM (Partial Least Squares Structural Equation Modeling) analysis for the exhibition industry. Service quality has a significant enhancing effect on the corporate image of the trade exhibitions, and both have significant positive effects on exhibitor satisfaction. Also, the role of the image as a partial mediating variable between service quality and satisfaction is emphasized. Additionally, the results obtained from multi-group analysis also supported the hypothesis that corporate image and service quality bring different satisfaction responses in exhibitors of the different industry with 4 business sizes in the capital. In light of scale and types of the enterprises for trade shows market, price segmentation strategies should be offered to maintain satisfaction and loyalty from the SMEs. Service quality and image of the service-offering company are more emphasized by big enterprises in choosing the trade organizer. Multiple group analysis also considered categorizing the specific industrial enterprises. The organizer can apply the result to explore workable market strategies to meet the needs of business partners with different capital size. This research not only has avail for trade exhibition organizers but provides necessary theory-based research in the trade exhibition territory.

Tips to use partial least squares structural equation modelling (PLS-SEM) in knowledge management

PurposeStructural 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.Design/methodology/approachTo 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.FindingsOur 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.Originality/valueThis 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.

Information Technology Inhibitors and Information Quality in Supply Chain Management: A PLS-SEM Analysis

Abstract The development of information technology has simplified the exchange of information between different parts of the supply chain. Information quality plays an important role in enhancing supply chain performance. The aim of this research was to explore the relationships between SCM-IT inhibitors, IT enablers, information sharing, and information quality in supply chain management. Data for 183 business units operating in Vlore, Albania during 2017 were analyzed. Direct and mediating or indirect effects were also analyzed. The data were analyzed using partial least squares structural equation modeling (PLS-SEM), an advanced statistical technique with the help of Smart-PLS version 3.2.7. PLS algorithm was used to determine the factor loadings and path coefficients in the theoretical model. The study has concluded that SCM-IT inhibitors have a negative and significant effect on information quality; the mediation effect of information sharing was significant, the mediation effect of IT enablers was significant; however, the multiple mediation effects of IT enablers and information sharing was not significant. These findings are useful for top management and IT specialists of the business units, and also for information technology services and products providers.

Development and validation of a model for the adoption of structured and standardised data recording among healthcare professionals

BackgroundHealthcare professionals provide care to patients and during that process, record large quantities of data in patient records. Data in an Electronic Health Record should ideally be recorded once and be reusable within the care process as well as for secondary purposes. A common approach to realise this is to let healthcare providers record data in a standardised and structured way at the point of care. Currently, it is not clear to what extent this structured and standardised recording has been adopted by healthcare professionals and what barriers to their adoption exist. Therefore, we developed and validated a multivariable model to capture the concepts underlying the adoption of structured and standardised recording among healthcare professionals.MethodsBased on separate models from the literature we developed a new theoretical model describing the underlying concepts of the adoption of structured and standardised recording. Using a questionnaire built upon this model we gathered data to perform a summative validation of our model. Validation was done through partial least squares structural equation modelling (PLS-SEM). The quality of both levels defined in PLS-SEM analysis, i.e., the measurement model and the structural model, were assessed on performance measures defined in literature.ResultsThe theoretical model we developed consists of 29 concepts related to information systems as well as organisational factors and personal beliefs. Based on these concepts, 59 statements with a 5 point Likert-scale (fully disagree to fully agree) were specified in the questionnaire. We received 3584 responses. The validation shows our model is supported to a large extent by the questionnaire data. Intention to record in a structured and standardised way emerged as a significant factor of reported behaviour (β = 0.305, p < 0.001). This intention is influenced most by attitude (β = 0.512, p < 0.001).ConclusionsThis model can be used to measure the perceived level of adoption of structured and standardised recording among healthcare professionals and further improve knowledge on the barriers and facilitators of this adoption.

Preventing Tire Blowout Accidents: A Perspective on Factors Affecting Drivers’ Intention to Adopt Tire Pressure Monitoring System

The aim of this study is to explore whether risk perception or anticipated regret is responsible for intensifying the participants’ intention to adopt a tire pressure monitoring system (TPMS) to prevent a tire-related accident, and whether the optimism bias has a moderator effect between risk perception/anticipated regret and intention. With 274 valid questionnaires and PLS-SEM (partial least squares structural equation modeling) analysis, the results indicate a significant positive relationship between risk perception and intention to adopt TPMS, but not between anticipated regret and intention. The moderator effect of optimism bias on risk perception and anticipated regret is not found in the model. The findings will prove useful for public service advertising campaigns by providing a basis for an understanding of the role of cognitive and emotional factors in tire-blowout accident prevention, thereby increasing the motivation for drivers in Taiwan to take advantage of the protection afforded them by using TPMS.

An in-depth discussion and illustration of partial least squares structural equation modeling in health care.

Partial least squares structural equation modeling (PLS-SEM) has become more popular across many disciplines including health care. However, articles in health care often fail to discuss the choice of PLS-SEM and robustness testing is not undertaken. This article presents the steps to be followed in a thorough PLS-SEM analysis, and includes a conceptual comparison of PLS-SEM with the more traditional covariance-based structural equation modeling (CB-SEM) to enable health care researchers and policy makers make appropriate choices. PLS-SEM allows for critical exploratory research to lay the groundwork for follow-up studies using methods with stricter assumptions. The PLS-SEM analysis is illustrated in the context of residential aged care networks combining low-level and high-level care. Based on the illustrative setting, low-level care does not make a significant contribution to the overall quality of care in residential aged care networks. The article provides key references from outside the health care literature that are often overlooked by health care articles. Choosing between PLS-SEM and CB-SEM should be based on data characteristics, sample size, the types and numbers of latent constructs modelled, and the nature of the underlying theory (exploratory versus advanced). PLS-SEM can become an indispensable tool for managers, policy makers and regulators in the health care sector.

Gain more insight from your PLS-SEM results

Purpose The purpose of this paper is to introduce the importance-performance map analysis (IPMA) and explain how to use it in the context of partial least squares structural equation modeling (PLS-SEM). A case study, drawing on the IPMA module implemented in the SmartPLS 3 software, illustrates the results generation and interpretation. Design/methodology/approach The explications first address the principles of the IPMA and introduce a systematic procedure for its use, followed by a detailed discussion of each step. Finally, a case study on the use of technology shows how to apply the IPMA in empirical PLS-SEM studies. Findings The IPMA gives researchers the opportunity to enrich their PLS-SEM analysis and, thereby, gain additional results and findings. More specifically, instead of only analyzing the path coefficients (i.e. the importance dimension), the IPMA also considers the average value of the latent variables and their indicators (i.e. performance dimension). Research limitations/implications An IPMA is tied to certain requirements, which relate to the measurement scales, variable coding, and indicator weights estimates. Moreover, the IPMA presumes linear relationships. This research does not address the computation and interpretation of non-linear dependencies. Practical implications The IPMA is particularly useful for generating additional findings and conclusions by combining the analysis of the importance and performance dimensions in practical PLS-SEM applications. Thereby, the IPMA allows for prioritizing constructs to improve a certain target construct. Expanding the analysis to the indicator level facilitates identifying the most important areas of specific actions. These results are, for example, particularly important in practical studies identifying the differing impacts that certain construct dimensions have on phenomena such as technology acceptance, corporate reputation, or customer satisfaction. Originality/value This paper is the first to offer researchers a tutorial and annotated example of an IPMA. Based on a state-of-the-art review of the technique and a detailed explanation of the method, this paper introduces a systematic procedure for running an IPMA. A case study illustrates the analysis, using the SmartPLS 3 software.

Q-TAM: a quality technology acceptance model for technology operations managers

This paper contributes to research on the quality factors that affect customer acceptance and intention to continue purchasing disposable technology. We have developed a model (Q-TAM) that integrates the Technology Acceptance Model and a quality framework. We conducted a survey of 322 smartphones users and analyzed the results using Partial Least Squares – Structural Equation Modeling (PLS-SEM) to determine how our integrated Q-TAM model affected consumers’ intention-to-continue-purchasing disposable technology. The PLS-SEM analysis of the Q-TAM model established that consumers assessed the product quality based on two distinct dimensions: attribute quality and reliability quality. Further, we found support for the positive and significant effects of product quality and perceived ease of use as predictors of a customer’s intention to purchase a technological device. Of interest is our finding that perceived usefulness, in the presence of product quality, is not significant. The finding also suggests that customers perceive the usefulness of a product as a component/part of a product’s quality dimension, confirming Joseph Juran’s quality philosophy of “fitness of use.” This suggests that in order to satisfy consumer demands, technology operations managers must ensure their products are of good attribute and reliability quality. The findings of this study make a case for a quality technology acceptance model (Q-TAM) that encourages technology operations managers to focus their efforts on quality to ensure that their products satisfy customer needs and are perceived as useful.

Development of a polar extreme method for use in partial least squares SEM

The use of polar extremes as a data segmentation approach is a common practice to maximize variance within multivariate analysis. In this research, we propose the first application of the polar extremes methodology within partial least squares structural equation modeling (PLS-SEM). Discussed is the impact of unequal weights of indicators among latent constructs within PLS-SEM and the importance of this methodology to the current established PLS-SEM analysis approach. Using a case study approach, we demonstrate the method. Developing two different models within the case allows comparison of the path coefficients and percentage of variance explained between the current PLS-SEM application and the proposed polar extremes methodology.