Search results

Given the positive organizational principles associated with total quality management (TQM) – customer focus, continuous improvement, and process management – one would assume TQM's application is universally beneficial across businesses. Generally, research supports that notion. However, given resource limitations and shallow management teams in small businesses, there are multiple challenges in implementing TQM in small and medium-sized enterprises (SMEs). Therefore, small business leaders should benefit from knowledge linking other management practices to TQM’s positive effect on small firm performance, which enhances these leaders' return on TQM investment.

The authors apply partial least squares structural equation modeling (PLS-SEM) to explore TQM’s effect on small business performance and how other management practices enhance that relationship. Specifically, the authors explore how a comprehensive strategic approach (CSA) – a higher-order construct consisting of strategic planning, goal setting, and financial ratio analysis – moderates the relationship between TQM and small business performance. Given the complexity of the authors' model, the application of higher-order constructs, and the exploratory nature of this work, PLS-SEM is well suited for this study.

Consistent with prior research, the authors found that TQM (also a higher-order construct, consisting of seven lower-order constructs) positively impacts small firm performance. In addition, the authors found that CSA positively moderates the relationship between TQM and financial performance.

TQM’s effect on small business performance is enhanced when leaders implement a CSA. In other words, when small business leaders strategically plan, set goals, and analyze financial ratios, TQM's positive effect on firm performance is enhanced. This finding provides business leaders insights for how to maximize the TQM investment return.

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).

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.

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.

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.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

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.

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.

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.

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.

The findings offer several avenues for future research to extend and strengthen prediction-oriented model assessment and comparison in PLS-SEM.

Guidelines are provided for applying CVPAT extensions and reporting the results to help researchers substantiate their models’ predictive capabilities.

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.

This paper aims to present the fundamental aspects for the development and validation (D&V) of attitudes’ measurement scale, as well as its practical aspects that are not deeply explored in books and manuals. These aspects are the results of a long experience of the authors and arduous learning with errors and mistakes.

The nature of this paper is methodological and can be very useful for an initial reading on the theme that it rests. This paper presents four D&V stages: literature review or interviews with experts; theoretical or face validation; semantic validation or validation with possible respondents; and statistical validation.

This is a methodological paper, and its main finding is the usefulness for researchers.

The main implication of this paper is to support researchers on the process of D&V of measurement scales.

Became a step-by-step guide to researchers on the D&V of measurement scales.

Support researchers on their data collection and analysis.

This is a practical guide, with tips from seasoned scholars to help researchers on the D&V of measurement scales.

Design is an important construct in the retail environment literature. Yet, the measures used for design have not followed appropriate scale development procedures. The purpose of this study is to provide a conceptual definition and then develop a scale for retail environment design (RED).

Interviews with both consumers and marketing researchers are used to generate a potential list of items. Using four different studies, these items are refined, and the RED scale is offered.

This study develops and validates the four-dimensional RED scale to measure the design of retail environments. The dimensions are functional, aesthetic, lighting and signage.

The newly developed RED scale will allow retailing researchers to measure lighting and signage qualities as part of retail design, measure design of retail environments more accurately and allow different studies to be compared.

The newly developed RED scale will allow retailers to better understand customers’ perceptions of the four dimensions of design. Retailers spend significant time and money designing and redesigning retail environments. The RED scale will enable managers to ensure these significant investments create competitive advantages and an appropriate return on investment.

A scale to measure retail environment design is developed. The scale includes two dimensions (lighting and signage) that are not typically investigated.

This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used.

The authors estimate three-level models with repeated measures, offering conditions for their correct interpretation.

From the concepts and techniques presented, the authors can propose models, in which it is possible to identify the fixed and random effects on the dependent variable, understand the variance decomposition of multilevel random effects, test alternative covariance structures to account for heteroskedasticity and calculate and interpret the intraclass correlations of each analysis level.

Understanding how nested data structures and data with repeated measures work enables researchers and managers to define several types of constructs from which multilevel models can be used.

The purpose of this paper is to explore the knowledge infrastructure of methodological research on partial least squares structural equation modeling (PLS-SEM) from a network point of view. The analysis involves the structures of authors, institutions, countries and co-citation networks, and discloses trending developments in the field.

Based on bibliometric data downloaded from the Web of Science, the authors apply various social network analysis (SNA) and visualization tools to examine the structure of knowledge networks of the PLS-SEM domain. Specifically, the authors investigate the PLS-SEM knowledge network by analyzing 84 methodological studies published in 39 journals by 145 authors from 106 institutions.

The analysis reveals that specific authors dominate the network, whereas most authors work in isolated groups, loosely connected to the network’s focal authors. Besides presenting the results of a country level analysis, the research also identifies journals that play a key role in disseminating knowledge in the network. Finally, a burst detection analysis indicates that method comparisons and extensions, for example, to estimate common factor model data or to leverage PLS-SEM’s predictive capabilities, feature prominently in recent research.

Addressing the limitations of prior systematic literature reviews on the PLS-SEM method, this is the first study to apply SNA to reveal the interrelated structures and properties of PLS-SEM’s research domain.

The purpose of this study is to examine how humanitarian organisation size affects inter-organisational coordination and further tested the mediating role of organisational innovativeness, self-organisation in the relationship between humanitarian organisation size and inter-organisational coordination among humanitarian organisations in Uganda.

The study is based on cross-sectional survey; data was collected from 101 humanitarian organisations. The analysis of the proposed hypotheses was done with the help of PLS-SEM using SmartPLS version 3.3.0 for professionals.

The results show that humanitarian organisation size significantly relates with inter-organisational coordination. In addition, self-organisation and organisational innovativeness play a complementary role between humanitarian organisation size and inter-organisational coordination.

The findings of this research provide useful insights into the role of humanitarian organisation size in boosting inter-organisational coordination in humanitarian relief delivery. High levels of self-organisation and organisational innovativeness not only improve inter-organisational coordination in humanitarian relief delivery but also enhance the transformation of humanitarian organisation size benefits into inter-organisational coordination.

This research is one of the few studies that investigated the effect of humanitarian organisation size and inter-organisational coordination. It also brings into the limelight the mediating role of self-organisation and organisational innovativeness between humanitarian organisation size and inter-organisational ordination in humanitarian relief delivery.

The purpose of this project was to explore and identify factors that influence a consumer to purchase wine during an afternoon of product sampling (wine tasting). A panel of consumers was recruited for an afternoon of wine tasting at vineyards in Napa, California. Several potential hedonistic, utilitarian and logistical factors (i.e. winery facilities, quality of the wine and order in which the winery was visited) were measured using a journal log that was maintained by participants following the tasting experience for a period of one‐month. The conclusions drawn from this study were that group size, confidence in one's ability to purchase wine and overall assessment of a vineyard's wine portfolio were more important than the hedonistic factors in terms of inducing a sale immediately following a taste.