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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 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.

The purpose of this paper is to integrate aspects of the literature on strategic and performance groups and explicitly derive strategic/performance groups which exhibit differences with respect to both strategy and performance, as well as display associations and potential interrelationships between the two sets of variables.

A two‐way clusterwise bilinear spatial model was formulated (e.g. a scalar products or vector multidimensional scaling model (MDS)) for the analysis of two‐way strategic and performance data which simultaneously performs MDS and cluster analysis. An efficient alternating least‐squares procedure was devised that estimates conditionally globally optimum estimates of the model parameters within each iterate in analytic, closed‐form expressions.

This bilinear MDS methodology was deployed in the context of strategic/performance group estimation using archival data for public banks in the NY‐NJ‐PA tri‐state area. For this illustration, four strategic/performance groups and two underlying dimensions were found.

Consideration of both strategy and performance data should be employed in describing the heterogeneity amongst firms competing in the same industry.

The paper provides a new spatial methodology to derive strategic/performance groups in any given industry to more completely summarize intra‐industry heterogeneity.

This study aims to determine the effect of personality on professionalism.

This research was conducted in Makassar Police Office in Makassar City. The reason for conducting the research in the police officer was the low performance of police officers in Makassar Police while the workload was very high. The population in this study were all 1,185 police officers in Makassar Police Office. Using the probability sampling technique, there were 299 police officers selected as respondents. Further, this study employs descriptive statistical analysis and inferential statistical analysis using generalized structure component analysis (GSCA) as data analysis methods.

The result found that there is a significant effect of personality on professionalism and OCB. Different results are obtained on the effect of personality on performance, which has no significant effect. However, there is an indirect effect of personality on performance through professionalism and OCB as mediating variables. The results also found that there is a significant influence between Professionalism on OCB and performance, as well as a significant influence between OCB on Performance.

This study focus to determine the effect of personality on professionalism. It has never been done before, so this study will contribute a new empirical explanation on both relationships. In addition to differences in the use of constructs and measurements, this research is also different in terms of the analysis unit. This study examines the effect of organizational citizenship behavior (OCB) on the performance of members/employees. This research is different from previous researches which generally examine the effect of OCB with group performance such as performed by George and Bettenhausen (1990), Podsakoff et al. (1997), which both found a close association between OCB and group performance. This study examines the performance of individual members because the tasks of members of the police force require professional ability in individuals who are expected to give a good image to the police in general.

Rigorous applications of analytical tools in information systems (IS) research are important for developing new knowledge and innovations in the field. Emerging tools provide building blocks for future inquiry, practice and innovation. This article summarizes the findings of an analysis of the adoption and reporting of partial least squares structural equation modeling (PLS-SEM) analytical tools by Industrial Management & Data Systems authors in the most recent five-year period.

Selected emerging advanced PLS-SEM analytical tools that have experienced limited adoption are highlighted to broaden awareness of their value to IS researchers.

PLS-SEM analytical tools that facilitate understanding increasingly complex theoretical models and deliver improved prediction assessment are now available. IS researchers should explore the opportunities to apply these new tools to more fully describe the contributions of their research.

Findings demonstrate the increasing acceptance of PLS-SEM as a useful alternative research methodology within IS. PLS-SEM is a preferred structural equation modeling (SEM) method in many research settings and will become even more widely applied when IS researchers are aware of and apply the new analytical tools.

Emerging PLS-SEM methodological developments will help IS researchers examine new theoretical concepts and relationships and publish their work. Researchers are encouraged to engage in more complete analyses by applying the applicable emerging tools.

Applications of PLS-SEM for prediction, theory testing and confirmation have increased in recent years. Information system scholars should continue to exercise sound practice by applying these new analytical tools where applicable. Recommended guidelines following Hair et al. (2019; 2022) are included.

The purpose of the study is to examine the following two research objectives. The first was to examine the predictive relationships that consumer characteristics of financial literacy, thinking styles and self-control have with a consumer's financial behaviors. The second goal was to ascertain financial management products' ability to aid those consumers who need it the most by weakening the predictive effects of consumer traits on financial behaviors.

The study employed a web-based survey to gather information. The measurement and structural models were analyzed using generalized structured component analysis (GSCA), a component-based structural equation model. The mediation effect of self-control is assessed using the GSCA. The conditional mediation of demographic variables and use of personal financial management products are evaluated using multi-group analysis (MGA) in GSCA.

Antecedents, financial literacy, thinking styles and self-control consumer characteristics are predictors of financial behaviors. However, self-control plays a more prominent role as a mediator between the other variables, strengthening the overall relationship. Also, financial products can have a beneficial moderation effect assisting those consumers who need them the most.

These insights help in creating target specific financial literacy strategies to influence consumers' financial behaviors. Also, there is a need to develop mechanisms to influence a consumer's self-control and thinking styles to improve financial behavior. In conjunction with other initiatives, the impact of financial literacy has a greater effect on financial behaviors. Further, the insights assist financial institutions and financial technology firms in offering and creating products to help customers make better financial decisions and improve their financial behaviors.

The research addressed a significant global issue – consumer financial health. The Great Recession and the COVID-19 recession highlight the need to focus on the consumer and efforts to improve their financial health.

This research highlighted the mediating role of self-control and suggested that existing and future financial products can positively influence consumer behavior drivers.

It is a great honor to be selected as a marketing legend, and 117 of my refereed journal articles are published in nine volumes by Sage India as part of the Legend series. In this chapter, I discuss my preparation for an academic career and the trajectory my research has followed. I reflect on my research contributions to marketing by selectively summarizing the key contributions in each of the nine volumes and draw out some lessons and principles I have learned in the process.