Search results

Partial least squares structural equation modeling (PLS-SEM) is an important statistical technique in the toolbox of methods that researchers in marketing and other social sciences disciplines frequently use in their empirical analyses. The purpose of this paper is to shed light on several misconceptions that have emerged as a result of the proposed “new guidelines” for PLS-SEM. The authors discuss various aspects related to current debates on when or when not to use PLS-SEM, and which model evaluation metrics to apply. In addition, this paper summarizes several important methodological extensions of PLS-SEM researchers can use to improve the quality of their analyses, results and findings.

The paper merges literature from various disciplines, including marketing, strategic management, information systems, accounting and statistics, to present a state-of-the-art review of PLS-SEM. Based on these findings, the paper offers a point of orientation on how to consider and apply these latest developments when executing or assessing PLS-SEM-based research.

This paper offers guidance regarding situations that favor the use of PLS-SEM and discusses the need to consider certain model evaluation metrics. It also summarizes how to deal with endogeneity in PLS-SEM, and critically comments on the recent proposal to adjust PLS-SEM estimates to mimic common factor models that are the foundation of covariance-based SEM. Finally, this paper opposes characterizing common concepts and practices of PLS-SEM as “out-of-date” without providing well-substantiated alternatives and solutions.

The paper paves the way for future discussions and suggests a way forward to reach consensus regarding situations that favor PLS-SEM use and its application.

This paper offers guidance on how to consider the latest methodological developments when executing or assessing PLS-SEM-based research.

This paper complements recently proposed “new guidelines” with the aim of offering a counter perspective on some strong claims made in the latest literature on PLS-SEM. It also clarifies some misconceptions regarding the application of PLS-SEM.

In their paper titled “A Miracle of Measurement or Accidental Constructivism? How PLS Subverts the Realist Search for Truth,” Cadogan and Lee (2022) cast serious doubt on PLS’s suitability for scientific studies. The purpose of this commentary is to discuss the claims of Cadogan and Lee, correct some inaccuracies, and derive recommendations for researchers using structural equation models.

This paper uses scenario analysis to show which estimators are appropriate for reflective measurement models and composite models, and formulates the statistical model that underlies PLS Mode A. It also contrasts two different perspectives: PLS as an estimator for structural equation models vs. PLS-SEM as an overarching framework with a sui generis logic.

There are different variants of PLS, which include PLS, consistent PLS, PLSe1, PLSe2, proposed ordinal PLS and robust PLS, each of which serves a particular purpose. All of these are appropriate for scientific inquiry if applied properly. It is not PLS that subverts the realist search for truth, but some proponents of a framework called “PLS-SEM.” These proponents redefine the term “reflective measurement,” argue against the assessment of model fit and suggest that researchers could obtain “confirmation” for their model.

Researchers should be more conscious, open and respectful regarding different research paradigms.

Researchers should select a statistical model that adequately represents their theory, not necessarily a common factor model, and formulate their model explicitly. Particularly for instrumentalists, pragmatists and constructivists, the composite model appears promising. Researchers should be concerned about their estimator’s properties, not about whether it is called “PLS.” Further, researchers should critically evaluate their model, not seek confirmation or blindly believe in its value.

This paper critically appraises Cadogan and Lee (2022) and reminds researchers who wish to use structural equation modeling, particularly PLS, for their statistical analysis, of some important scientific principles.

The purpose of this paper is to evaluate the efficiency of Algerian banks and examine the effects of explanatory factors on their performance.

In this paper, a methodology of two-stage network data envelopment analysis (DEA) is used to explore the efficiency of a sample of 13 Algerian banks during the 2013–2017 period. In the first stage, the network DEA is used to assess the overall and stages efficiencies. In the second stage, the partial least squares (PLS) regression is conducted to determine the potential effects of explanatory factors on stages efficiency.

The main empirical results indicate that Algerian banks need an efficiency improvement in both stages. The overall efficiency of the Algerian banking system improves over the study period. The deposit producing efficiency is positively affected by bank size and bank age. The revenue earning efficiency is negatively associated with bank size and bank age. The domestic banks are more efficient than foreign banks in the deposit producing stage and the foreign banks are more efficient than domestic banks in the revenue earning stage.

The results might be used as guidelines for both managers and policymakers in order to improve banks and banking system performance.

To the best of our knowledge, this study is the first that uses the DEA in investigating the efficiency of Algerian banks by dividing the overall efficiency into deposit producing and revenue earning efficiencies. Unlike most studies that have usually used OLS regression, Tobit regression and bootstrapped truncated regression, this study is the first in the bank efficiency literature that uses PLS regression to investigate the potential effect of explanatory variables on deposit producing and revenue earning efficiencies.

The present investigation aimed to evaluate the influence of envy on the predisposition to innovative behavior, starting from a conceptual model that considers not only the direct influence of envy but its indirect influence through ostracism and alignment with the negative behaviors of superiors.

Using a survey applied to 168 individuals, a conceptual model was developed based on the relationship ignored in the literature between envy and innovative behavior. The model was validated using the multivariate statistical technique of structural equation modeling with partial least squares estimation (Partial least squares structural equation modeling [PLS-SEM]).

The results of the study suggest that envy not only has a direct positive influence on alignment with negative boss behaviors and ostracism, but also an indirect influence on ostracism mediated by alignment with negative boss behaviors. Another important result of the present investigation refers to the negative effect of envy on the predisposition to innovative behavior. The results suggest that the greater the envy, the lower the innovative behavior.

This research provides evidence that envy can act as a barrier to innovation by triggering counterproductive behaviors such as ostracism and a decrease in predisposition to innovative behaviors, either due to innovative individuals prematurely exiting the organization or due to them lessening/dampening their innovativeness to avoid the negative consequences. Given this scenario, it becomes necessary to increase managerial awareness on the subject to manage negative emotions to promote the conditions for organizational innovation.

The present research contributes in both practical and theoretical ways to understanding the effects of envy on the predisposition to innovative behavior. Adding to this, this research represents a conceptual advance by linking envy to innovative behavior, providing a promising avenue for extending the psychological relevance of the envy construct to organizational and management studies, which are generally positive, normative and outcome-oriented.

There is a scarcity of empirical evidence in the literature on the chain of causality involving tax incentives for Research and Development (R&D) activities and their subsequent transformation into innovation. This study aims to assess the influence of R&D tax incentives on the organizational attributes of Portuguese firms to identify how they are converted into innovation.

A structural research model consisting of 339 companies that benefited from the Fiscal Incentive System supporting R&D in Enterprises, during the period from 2013 to 2016, was developed. This was done to assess the role of R&D tax incentives on the organizational attributes that form the innovation capacity. The model was validated using the multivariate statistical technique of structural equation modeling with partial least squares estimation (partial least squares structural equation modeling – PLS-SEM).

The results found suggest that although it is not possible to unequivocally identify the mechanisms used to convert tax incentives into innovation, it is possible to conclude that they play an important spillover effect for the construction and strengthening of organizational attributes. These form the basis of innovation capacity, to the extent that they positively influence the firms’ total assets, equity, liabilities, number of employees and sales. Hence, contributions are brought to both the literature on tax incentives and the general literature on innovation.

For policymakers, the evidence points to the fact that in addition to the incentives provided, novel mechanisms need to be established to help firms develop their absorptive capacity. The objective is to effectively convert the incentives received into innovation through the organizational attributes analyzed. From the firms’ point of view, the results found suggest that tax incentives act as a catalyst for making R&D investments. Additionally, there is an influence on employability, which effectively enhances the chances of innovation in the long run. Tax incentives received by Portuguese firms also have the effect of promoting economic dynamism – by enhancing the following: investments in infrastructure, the hiring of employees and the increasing of sales, generating positive externalities for both firms and society.

In management accounting research, the capabilities of Partial Least Squares Structural Equation Modelling (PLS-SEM) have only partially been utilized. These yet unexploited capabilities of PLS-SEM are a useful tool in the often explorative state of research in management accounting. After reviewing eleven top-ranked management accounting journals through the end of 2013, 37 articles in which PLS-SEM is used are identified. These articles are analysed based on multiple relevant criteria to determine the progress in this research area, including the reasons for using PLS-SEM, the characteristics of the data and the models, and model evaluation and reporting. A special focus is placed on the degree of importance of these analysed criteria for the future development of management accounting research. To ensure continued theoretical development in management accounting, this article also offers recommendations to avoid common pitfalls and provides guidance for the advanced use of PLS-SEM in management accounting research.

The purpose of this paper is to develop a scale for measuring consumer doubt toward new products.

The scale was developed in several steps. A large pool of items to represent consumer doubt was generated. Experts reviewed the scale items for conciseness and clarity. An exploratory factor analysis to examine the unidimensionality, convergent validity, and discriminant validity of each construct was conducted. The model was then validated using partial least squares modeling. Finally, the scale and its form were validated, and potential response biases assessed. Data from three studies were used.

The results show that by focusing on reasons for deference, rather than acceptance, the scale yields new insight into innovation success and failure. The CDNP scale is a reliable and valid measurement instrument to assess consumer doubt toward new products.

For researchers, the results show that only considering positive aspects on innovation adoption can lead to only a partial understanding of how innovation diffuses in the market.

By overcoming consumer doubt at early stages of innovation launch, companies could overcome problems related to innovation failure.

The literature on innovation adoption has almost exclusively focused on why innovations succeed by examining consumer acceptance of innovations. Yet, a potentially more serious issue that would need to be tackled is why innovations fail. This paper focuses on consumer doubt toward new products, i.e. a lack of conviction that a new product will fulfill its promises. Three studies show that the scale of consumer doubt is valid, and it provides new insights into innovation adoption.

The set of compositional approaches to product space development is expanded to include confirmatory methods. Specifically, describes and compares product space development (perceptual mapping) via confirmatory factor analysis and partial least squares with the aid of an empirical example. Both of these procedures are widely used in causal or structural equation modelling. Since they tend to be confirmatory extensions to factor analysis and principal components analysis, the approaches are also well suited to the development of product spaces. Confirmatory approaches have several advantages over exploratory approaches including the incorporation of prior knowledge, the elimination of rotational indeterminacy, and the use of a wide variety of measurement tools to assess the reliability and validity of model results.

While mobile TV (m-TV) is beneficial and is currently an emerging area within mobile commerce, the adoption rate is at the infancy stage. The purpose of this paper is therefore to adapt the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model to explore on the factors in predicting users’ behavioral intention (BI) of m-TV.

Partial least square structural equation modeling approach was employed using 193 respondents to test the findings.

The findings revealed that effort expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM) and habit (HT) have significant effects on BI. In addition, gender was found to have moderating effect between FC and BI.

Findings from this study provide valuable insights and contribute to the marketing strategy for mobile marketers, wireless communications vendors and application developers in the m-TV industry.

Since the emergence of m-TV is regarded as a new concept, what factors that may motivate users to watch TV using m-devices remains unexplored and therefore is currently a neglected research area. This paper provides clues and valuable insights for mobile marketers, wireless communications vendors and application developers in the m-TV industry.

This study aims to formulate a new framework for identifying deception in consumer reviews through the lens of interpersonal deception theory (IDT) and the persuasion knowledge model (PKM). It evaluates variables contributing to consumer intentions to purchase after reading deceptive reviews and proposes deception identification cues to be incorporated into the interpersonal communication theoretical framework.

The first study is qualitative and quantitative, based on sentiment and lexical analysis of 1,000 consumer reviews. The second study uses the US national consumer survey with a partial least squares partial least squares-structural equation modeling and a process-based mediation–moderation analysis.

This study shows deceptive characteristics that cannot be dissimulated by reviewing consumers that represent review legitimacy based on review valence, authenticity, formalism and analytical writing. The results also support the central role of consumer suspicion of an ulterior motive, with a direct and mediation effect regarding consumer emotions and intentions, including brand trust and purchase intentions.

This paper presents a new framework for identifying deception in consumer reviews based on IDT and PKM, adding new theoretical elements that help adapt these theories to written digital communication specificities. This study clarifies the role of suspicion in a deceptive communication context and shows the variables contributing to consumers’ purchase intention after reading deceptive reviews. The results also emphasize the benefits of lexical analysis in identifying deceptive characteristics of reviews.

Companies can consider the vulnerability of certain generations based on lower levels of suspicions and different linguistic cues to detect deception in reviews. Long-term, marketers can also implement deception identification practices as potential new business models and opportunities.

Policymakers and regulators need to consider critical deception cues and the differences in suspicion levels among segments of consumers in the formulation of preventative and deception management measures.

This study contributes to the literature by formulating a new framework for identifying deception in consumer reviews, adapted to the characteristics of written digital communication. This study emphasizes deception cues in electronic word-of-mouth and provides additional opportunities for theorizing deception in electronic communication.