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Generalized structured component analysis (GSCA) and partial least squares path modeling (PLSPM) are two key component-based approaches to structural equation modeling that facilitate the analysis of theoretically established models in terms of both explanation and prediction. This study aims to offer a comparative evaluation of GSCA and PLSPM in a predictive modeling framework.

A simulation study compares the predictive performance of GSCA and PLSPM under various simulation conditions and different prediction types of correctly specified and misspecified models.

The results suggest that GSCA with reflective composite indicators (GSCA_R) is the most versatile approach. For observed prediction, which uses the component scores to generate prediction for the indicators, GSCA_R performs slightly better than PLSPM with mode A. For operative prediction, which considers all parameter estimates to generate predictions, both methods perform equally well. GSCA with formative composite indicators and PLSPM with mode B generally lag behind the other methods.

Future research may further assess the methods’ prediction precision, considering more experimental factors with a wider range of levels, including more extreme ones.

When prediction is the primary study aim, researchers should generally revert to GSCA_R, considering its performance for observed and operative prediction together.

This research is the first to compare the relative efficacy of GSCA and PLSPM in terms of predictive power.

The purpose of this paper is to propose a practical conceptualization of the balanced scorecard (BSC) to describe the mechanism producing creation of monetary value for hospitals in the territorial context of Lombardy region (Italy).

The authors propose a model‐building strategy that assigns key indicators to key performance areas, and identifies causal relationships between key performance areas. Second, the authors utilize a suitable statistical approach to estimate causal relationships among involved latent variables, taking into account the hierarchical structure of data. Utilizing a suitable data decomposition, the causal model is applied separately to the within data (hospitals) and to the between data (local health agencies).

In the measurement model a new latent construct (medical human capital) was found that resumes the amount of formal training and the performance of surgical staff in hospitals. The estimated causal models reflect the usual directional assumptions, supposed in a typical BSC causal scheme, with some differences. For local health agencies, fruits (financial measures) are strongly related to clinical processes (leaves) for which the medical human capital constitutes its unique trunk. However, for hospitals, fruits (financial measures) are directly linked to clinical processes and Patient Satisfaction.

The main limitations of this study are the lack of new independent data to validate the obtained causal structures and the limited number of indicators that reflect the deficiency of available information in regional administrative archives.

The present study may be useful to guide further efforts which attempt to conceptualize BSC in the health sector. As more information can be made available, other performance indicators can prove to be linked with this structure using the same methodology.

Forecasting product returns plays an important role in the operations of reverse logistics (RL). However, their contribution to sustainability performance is yet to be explored. The purpose of this paper is to explore the product returns in Indian electronics industry and examine the relationship of forecasting product returns with triple bottom line performance of RL.

In this study, based on past literature review, four hypotheses, relating to forecasting of product returns and its association with performance, were developed. A questionnaire was sent to 700 respondents from the Indian electronics industry. Overall, 208 received responses were found suitable for the research. The necessary statistical analysis was carried out to ensure the reliability and validity of the questionnaire. In order to test different hypotheses, partial least square path modelling (PLSPM) technique of structural equation modeling was utilized.

Measurement model had shown sufficient data fit for the modeling. PLSPM results reveal that the accuracy in forecasting product returns is positively associated with operational performance of RL. It also plays an important role in the sustainability efforts of an organization.

Managers can utilize results of study for exploring and emphasizing issues of product returns for improving RL performance. One of the limitations is that data are collected only from Indian electronics industry. Another limitation is that only product returns are considered for the operational and TBL performance of RL. In future, study may be carried out considering different factors in other sectors and countries.

The intent of forecasting product returns is considered to be operational efficiency. It can make significant contributions to the sustainability efforts of an organization. Review of the past literature indicates that research in the field of RL is in developing stage, and issues related to forecasting product returns are under-represented. The paper adds value to the few available articles on product returns.

Outsourcing has become a strategic decision for most of the organizations in the present context of globalization and circular economy. The objective of proposed study is to empirically investigate the relationship between the outsourcing benefits and triple bottom line (TBL)-based performance of reverse supply chain (RSC) comprising of economic, environmental and social factors.

In the present study, the partial least square path approach of structural equation modeling has been applied for testing hypotheses. A questionnaire was developed for collecting the information and responses from the experts of electronics industry, India. Measurement models were tested for ensuring the fitness of data for further applying partial least square path approach of SEM.

The results provide insightful information on outsourcing and RSC performance. It reveals that outsourcing decisions are important and have great influence on the economic, environmental and social performance of RSC.

The study will provide useful guidance to the organizations for outsourcing decisions. One of the limitations of the study is that it has considered data collected from the Indian electronics industry. Therefore, results may not be generalized.

The third-party service providers have core competencies, and they are the experts of providing RSC services. It is also apparent from the exploration of past research and theoretical background that there are limited number of studies focusing on the impact of outsourcing decisions in RSC performance specifically from the TBL perspective. Hence, the proposed study is an effort to explore the impact of outsourcing RSC activities on the performance of RSC from a sustainability perspective.

The purpose of this paper is to evaluate the effect of CRM performance and technological innovation on performance of media entrepreneurs considering firm size.

This is an analytical study used to empirically test the hypotheses proposed for SEM techniques using PLS and R packages. It used two steps in this way: the assessment of the outer model and the assessment of the inner model. Moreover, a bootstrapping method was employed to test indirect effects. Data were collected by distributing 127 questionnaires between the managers and deputies of active firms across Rasht, Iran.

The effect of CRM performance on SMEs performance development is partially mediated by media entrepreneurship. Moreover, the effect of technological innovation on SMEs performance development is mediated by media entrepreneurship. Furthermore, permutation test results indicated that there is no significant difference between small- and medium-sized firms.

This study used cross-sectional sampling method that can seriously limit result generalization. Therefore, conducting longitudinal studies is strongly recommended.

The results of IPMA matrix indicated the serious importance of technological innovation, as a variable with the highest importance for SMEs performance development. Nevertheless, this variable has received the lowest importance in the studied population. Therefore, SMEs’ managers should pay sufficient attention to the concepts of “product innovations” and “process innovations.”

This study is of high importance in that it has adopted new and effective indices for statistical analysis. IPMA matrix, permutation test, CTA and FIMIX are examples. In addition, plspm and Matrixpls packages in R were used for the first time in this study.

This research aims to examine the effect of the two dimensions of technology readiness – motivator and inhibitor on behavioural intention to adopt a mobile agricultural finance app called e-AgriFinance app among the farmers in Sarawak, Malaysia.

Data were collected from 337 farmers who cultivated the 4 major crops in Sarawak – oil palm, rubber, cocoa and pepper using a face-to-face questionnaire survey. Collected data were analysed using partial least squares structural equation modelling with R plspm package.

The research found that both motivator and inhibitor dimensions of technology readiness predicted the farmers’ behavioural intention to adopt the e-AgriFinance app, with the former had a relatively stronger positive effect and the latter had a relatively weaker negative effect.

This research was conducted in the context of rural farmers in an emerging economy. As such, modern farmers in developed countries may have different adoption behaviour of mobile agricultural finance app. The data were collected from farmers of the four major crops – oil palm, rubber, cocoa and pepper, and thus it may not be representative of the whole population of farmers in Sarawak.

This research served as one of the few studies that focused on the relationship between technology readiness and mobile app adoption among farmers from the perspective of an emerging economy.

This paper aims to theorise and test a causal model of predominantly lean-driven quality improvement (QI) in the context of health-care clinical microsystems, examining the effects contextual factors in this setting have on improvement activity.

QI practitioners at a New Zealand District Health Board were surveyed on a range of contextual factors hypothesised to influence improvement outcomes. Survey responses were analysed via partial least squares path modelling to test the causal model that was designed to be consistent with the “model for understanding success in quality” (MUSIQ) model (Kaplan et al., 2012) adopted in health-care QI.

Defined variables for teamwork, respect for people, lean actions and negative motivating factors all demonstrated significant effects. These findings support the representation of the microsystem layer within the MUSIQ model. The final model predicted and explained perceived success well (adjusted R² = 0.58).

The sample was a non-probability sample and the sample size was small (n = 105), although power analysis indicated that we exceeded the minimum sample size (97 cases). Even though health-care processes have universality, this study was conducted in only one district in New Zealand.

The results support highly functional teamwork as the critical contextual factor in health-care QI outcomes and suggest lean-driven process improvement can be a valid mediating mechanism. The key recommendation for practitioners is to increase focus on human resource capability when initiating and supporting QI.

The originality is testing the robustness of the MUSIQ model specifically in a lean environment, which provides the context for QI. The paper provides a more detailed specification of contextual factors acting as exogenous variables that moderate the cause (lean actions) and the effect (perceived success).

In a knowledge economy where innovation is a way for an organization to gain a competitive advantage, team creativity becomes an important factor of success. This paper aims to look at how a team's creativity is influenced by the degree of the team's knowledge sourcing.

A theoretical model is developed and tested to assess the influence of four main antecedents of team knowledge sourcing and creativity: learning orientation, intellectual demands, risk aversion, and relational capital. The research model is tested using PLSPM.

The findings show the significant influence of all the independent variables, but more particularly the strong influence of learning orientation on team knowledge sourcing and on team creativity.

Team creativity was examined as team member perceptions of the creative processes in the team task. Data were gathered from graduate students working in a team on an e‐commerce development project. The data set was relatively small (148). Despite these limitations, the initial findings show some interesting patterns that will be worth investigating on a larger scale and in various environments.

The results provide considerable support for the idea that knowledge sourcing and the learning orientation of the team members can play an important role in supporting team creativity.

This research adapts the construct of knowledge sourcing from Gray and Meister to the team level of analysis. It conceptualizes the perspective that individually held knowledge influences creativity primarily through the process of knowledge sourcing (internal and external) at the team level.

The purpose of this paper is to examine the determinants of user adoption of smartphone fitness apps in context of an emerging economy.

The present study uses the extended unified theory of acceptance and use of technology (UTAUT2) as the base model along with two additional constructs, i.e. self-efficacy and personal innovativeness. The data collection was done through an online survey, wherein a total of 324 valid responses were obtained for the statistical analysis. All the hypothesized relationships were tested through partial least square structural equation modelling (PLS-SEM) using an open source programming language and software environment, i.e. R Software along with plspm-package.

Significant predictors of smartphone fitness app adoption intention include effort expectancy, social influence, perceived value, habit and personal innovativeness. Further, this study confirms significant relationship between personal innovativeness and habit, self-efficacy and effort expectancy and effort expectancy and performance expectation. This study reveals that personal innovativeness is the strongest predictor of behavioural intention. Contrary to the expectations, factors like performance expectancy, facilitating conditions and hedonic motivation did not influence behavioural intention.

This study gives significant clues to app developers that can drastically influence the adoption of fitness apps. The findings suggest that marketers should focus on users with high personal innovativeness that can further act as role models and significantly influence their social circle. Interestingly, the findings suggest that fitness apps, as compared to other apps, should not emphasize much on the hedonic value of their offerings.

This study is one of the few studies to examine the adoption of smartphone fitness apps in an emerging economy context by using extended version of UTAUT2 model. Further, this study shows how new endogenous and exogenous variables (i.e. self-efficacy and personal innovativeness) contribute to better explanatory power of the UTAUT2 framework.

The purpose of this paper is to present integrated generalized structured component analysis (IGSCA) as a versatile approach for estimating models that contain both components and factors as statistical proxies for the constructs. The paper sets out to discuss the how-tos of using IGSCA by explaining how to specify, estimate, and evaluate different types of models. The paper’s overarching aim is to make business researchers aware of this promising structural equation modeling (SEM) method.

By merging works of literature from various fields of science, the paper provides an overview of the steps that are required to run IGSCA. Findings from conceptual, analytical and empirical articles are combined to derive concrete guidelines for IGSCA use. Finally, an empirical case study is used to illustrate the analysis steps with the GSCA Pro software.

Many of the principles and metrics known from partial least squares path modeling – the most prominent component-based SEM method – are also relevant in the context of IGSCA. However, there are differences in model specification, estimation and evaluation (e.g. assessment of overall model fit).

Methodological developments associated with IGSCA are rapidly emerging. The metrics reported in this paper are useful for current applications, but researchers should follow the latest developments in the field.

To the best of the authors’ knowledge, this is the first paper to offer guidelines for IGSCA use and to illustrate the method's application by means of the GSCA Pro software. The recommendations and illustrations guide researchers who are seeking to conduct IGSCA studies in business research and practice.