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1 – 10 of 146The purpose of this study is to provide an overview of emerging prediction assessment tools for composite-based PLS-SEM, particularly proposed out-of-sample prediction…
Abstract
Purpose
The purpose of this study is to provide an overview of emerging prediction assessment tools for composite-based PLS-SEM, particularly proposed out-of-sample prediction methodologies.
Design/methodology/approach
A review of recently developed out-of-sample prediction assessment tools for composite-based PLS-SEM that will expand the skills of researchers and inform them on new methodologies for improving evaluation of theoretical models. Recently developed and proposed cross-validation approaches for model comparisons and benchmarking are reviewed and evaluated.
Findings
The results summarize next-generation prediction metrics that will substantially improve researchers' ability to assess and report the extent to which their theoretical models provide meaningful predictions. Improved prediction assessment metrics are essential to justify (practical) implications and recommendations developed on the basis of theoretical model estimation results.
Originality/value
The paper provides an overview of recently developed and proposed out-of-sample prediction metrics for composite-based PLS-SEM that will enhance the ability of researchers to demonstrate generalization of their findings from sample data to the population.
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Dana E. Harrison, O.C. Ferrell, Linda Ferrell and Joe F. Hair, Jr
The purpose of this paper is to theoretically develop and empirically validate separate scales that represent a consumer’s expectations of business ethics (BE) and corporate…
Abstract
Purpose
The purpose of this paper is to theoretically develop and empirically validate separate scales that represent a consumer’s expectations of business ethics (BE) and corporate social responsibility (CSR).
Design/methodology/approach
A literature review and qualitative research were conducted to generate items for the scales. Initial item reduction was performed qualitatively based on a panel of experts. A follow-up quantitative assessment using an exploratory factor analysis further reduced the items. The scales were then validated using confirmatory composite analysis with partial least squares-structural equation modeling.
Findings
Separate scales representing consumers’ expectations of BE and CSR behaviors were developed. The scales exhibited reliability, convergent validity, discriminant validity and external validity.
Practical implications
The separation of these scales into two components will facilitate more precise examination of consumer perceptions of these two components of product and brand images, and how they may impact brand attitudes and brand trust.
Originality/value
This is the first effort to develop separate scales for consumer expectations of ethics and CSR, and assess their impact on brand outcomes.
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Joe F. Hair Jr, Marko Sarstedt, Lucas Hopkins and Volker G. Kuppelwieser
The authors aim to present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an…
Abstract
Purpose
The authors aim to present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields.
Design/methodology/approach
In this review article, the authors merge literatures from the marketing, management, and management information systems fields to present the state-of-the art of PLS-SEM research. Furthermore, the authors meta-analyze recent review studies to shed light on popular reasons for PLS-SEM usage.
Findings
PLS-SEM has experienced increasing dissemination in a variety of fields in recent years with nonnormal data, small sample sizes and the use of formative indicators being the most prominent reasons for its application. Recent methodological research has extended PLS-SEM's methodological toolbox to accommodate more complex model structures or handle data inadequacies such as heterogeneity.
Research limitations/implications
While research on the PLS-SEM method has gained momentum during the last decade, there are ample research opportunities on subjects such as mediation or multigroup analysis, which warrant further attention.
Originality/value
This article provides an introduction to PLS-SEM for researchers that have not yet been exposed to the method. The article is the first to meta-analyze reasons for PLS-SEM usage across the marketing, management, and management information systems fields. The cross-disciplinary review of recent research on the PLS-SEM method also makes this article useful for researchers interested in advanced concepts.
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Abstract
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Joe F. Hair, Jr., Marko Sarstedt, Lucy M Matthews and Christian M Ringle
The purpose of this paper is to provide an overview of unobserved heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and…
Abstract
Purpose
The purpose of this paper is to provide an overview of unobserved heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social science researchers. Part II – in the next issue (European Business Review, Vol. 28 No. 2) – presents a case study, which illustrates how to identify and treat unobserved heterogeneity in PLS-SEM using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software.
Design/methodology/approach
The paper merges literatures from various disciplines, such as management information systems, marketing and statistics, to present a state-of-the-art review of FIMIX-PLS. Based on this review, the paper offers guidelines on how to apply the technique to specific research problems.
Findings
FIMIX-PLS offers a means to identify and treat unobserved heterogeneity in PLS-SEM and is particularly useful for determining the number of segments to extract from the data. In the latter respect, prior applications of FIMIX-PLS restricted their focus to a very limited set of criteria, but future studies should broaden the scope by considering information criteria, theory and logic.
Research limitations/implications
Since the introduction of FIMIX-PLS, a range of alternative latent class techniques have emerged to address some of the limitations of the approach relating, for example, to the technique’s inability to handle heterogeneity in the measurement models and its distributional assumptions. The second part of this article (Part II) discusses alternative latent class techniques in greater detail and calls for the joint use of FIMIX-PLS and PLS prediction-oriented segmentation.
Originality/value
This paper is the first to offer researchers who have not been exposed to the method an introduction to FIMIX-PLS. Based on a state-of-the-art review of the technique in Part I, Part II follows up by offering a step-by-step tutorial on how to use FIMIX-PLS in SmartPLS 3.
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The purpose of this paper is to provide an overview of predictive analytics, summarize how it is impacting knowledge creation in marketing, and suggest future developments in…
Abstract
Purpose
The purpose of this paper is to provide an overview of predictive analytics, summarize how it is impacting knowledge creation in marketing, and suggest future developments in marketing and predictive analytics for both organizations and researchers.
Design/methodology/approach
Survival in a knowledge‐based economy is derived from the ability to convert information to knowledge. To do so, researchers and managers increasingly are relying on the field of predictive analytics. Data mining identifies and confirms relationships between explanatory and criterion variables. Predictive analytics uses confirmed relationships between variables to predict future outcomes. The predictions are most often values suggesting the likelihood a particular behavior or event will take place in the future.
Findings
Data mining and predictive analytics are increasingly popular because of the substantial contributions they can make in converting information to knowledge. Marketing is among the most frequent applications of the techniques, and whether you think about product development, advertising, distribution and retailing, or marketing research and business intelligence, data mining and predictive analytics increasingly are being applied.
Originality/value
In the future, we can expect predictive analytics to increasingly be applied to databases in all fields and revolutionize the ability to identify, understand and predict future developments, data analysts will increasingly rely on mixed‐data models that examine both structured (numbers)and unstructured (text and images) data, statistical tools will be more powerful and easier to use, future applications will be global and real time, demand for data analysts will increase as will the need for students to learn data analysis methods, and scholarly researchers will need to improve their quantitative skills so the large amounts of information available can be used to create knowledge instead of information overload.
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Tamoor Azam, Wang Songjiang, Khalid Jamil, Sobia Naseem and Muhammad Mohsin
In the modern business world, the main focus of the organizations is to improve the quality of the products and minimize the wastage of raw material. Keeping in view the green…
Abstract
Purpose
In the modern business world, the main focus of the organizations is to improve the quality of the products and minimize the wastage of raw material. Keeping in view the green theory and improve the efficiency of the organization, the focus of the current study is to investigate the relationship between total quality management (TQM) and green innovation (GI), and examine how TQM practices can facilitate firms to achieve GI objectives. Corporate social responsibility (CSR) is also an important factor for organizations, and this study also focuses on the mediating role of CSR between the relationship of TQM and GI.
Design/methodology/approach
This is an empirical study. Data were gathered from the top management of 355 SMEs working in Pakistan through a questionnaire survey; the PLS-SEM approach was used to analyse the data.
Findings
Results of the study show that TQM has significant impacts on two aspects of GI namely green product innovation and green process innovation. Moreover, results also reveal that CSR partially mediates the relationship between TQM and GI.
Research limitations/implications
This study is limited to manufacturing SMEs and future research should test this model on non-manufacturing sector too. The findings of the study provide significant roadmap to the management of small and medium-sized manufacturing firms that how they can reduce wastage and improve the product and process innovation in their organizations through TQM and CSR.
Originality/value
This study contributes to bridging research gaps in the literature and advances how TQM, directly and indirectly, help firms improve green innovation via mediating roles of CSR.
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Ralph I. Williams Jr, Torsten Pieper, Franz Kellermanns and Joe Astrachan
Current approaches to measuring family business performance have limitations: failing to acknowledge the entire family business holistically, and lacking recognition of the…
Abstract
Purpose
Current approaches to measuring family business performance have limitations: failing to acknowledge the entire family business holistically, and lacking recognition of the idiosyncratic nature of family business goals. By applying organizational effectiveness and the achievement of desired organizational outcomes, the purpose of this paper is to develop a scale to measure performance based on a family business’ idiosyncratic goals.
Design/methodology/approach
This study applies mixed methods, including qualitative research, two surveys and structural equation modeling.
Findings
The authors develop a scale employing 21 items, representing six goal dimensions, to measure the family business performance.
Originality/value
The family business performance measurement scale from this study responds to multiple calls for a scale gauging family business performance in a manner including both financial and non-financial outcomes.
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Joseph F. Hair, Pratyush N. Sharma, Marko Sarstedt, Christian M. Ringle and Benjamin D. Liengaard
The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis…
Abstract
Purpose
The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).
Design/methodology/approach
The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.
Findings
The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.
Research limitations/implications
In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.
Practical implications
The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.
Originality/value
To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.
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Pratyush N. Sharma, Benjamin D. Liengaard, Joseph F. Hair, Marko Sarstedt and Christian M. Ringle
Researchers often stress the predictive goals of their partial least squares structural equation modeling (PLS-SEM) analyses. However, the method has long lacked a statistical…
Abstract
Purpose
Researchers often stress the predictive goals of their partial least squares structural equation modeling (PLS-SEM) analyses. However, the method has long lacked a statistical test to compare different models in terms of their predictive accuracy and to establish whether a proposed model offers a significantly better out-of-sample predictive accuracy than a naïve benchmark. This paper aims to address this methodological research gap in predictive model assessment and selection in composite-based modeling.
Design/methodology/approach
Recent research has proposed the cross-validated predictive ability test (CVPAT) to compare theoretically established models. This paper proposes several extensions that broaden the scope of CVPAT and explains the key choices researchers must make when using them. A popular marketing model is used to illustrate the CVPAT extensions’ use and to make recommendations for the interpretation and benchmarking of the results.
Findings
This research asserts that prediction-oriented model assessments and comparisons are essential for theory development and validation. It recommends that researchers routinely consider the application of CVPAT and its extensions when analyzing their theoretical models.
Research limitations/implications
The findings offer several avenues for future research to extend and strengthen prediction-oriented model assessment and comparison in PLS-SEM.
Practical implications
Guidelines are provided for applying CVPAT extensions and reporting the results to help researchers substantiate their models’ predictive capabilities.
Originality/value
This research contributes to strengthening the predictive model validation practice in PLS-SEM, which is essential to derive managerial implications that are typically predictive in nature.
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