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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.

This paper aims to discuss recent criticism related to partial least squares structural equation modeling (PLS-SEM).

Using a combination of literature reviews, empirical examples, and simulation evidence, this research demonstrates that critical accounts of PLS-SEM paint an overly negative picture of PLS-SEM’s capabilities.

Criticisms of PLS-SEM often generalize from boundary conditions with little practical relevance to the method’s general performance, and disregard the metrics and analyses (e.g., Type I error assessment) that are important when assessing the method’s efficacy.

We believe the alleged “fallacies” and “untold facts” have already been addressed in prior research and that the discussion should shift toward constructive avenues by exploring future research areas that are relevant to PLS-SEM applications.

All statistical methods, including PLS-SEM, have strengths and weaknesses. Researchers need to consider established guidelines and recent advancements when using the method, especially given the fast pace of developments in the field.

This research addresses criticisms of PLS-SEM and offers researchers, reviewers, and journal editors a more constructive view of its capabilities.

The current study has a two-fold purpose. Firstly, it aims to analyze the extent to which knowledge management (KM) affects the performance of individuals (task and contextual) on the one hand and that of organizations (product or service, perceived and financial) on the other hand. Secondly, it proposes to investigate the mediating effect of motivation and innovation in the relationship between KM and individual and organizational performance.

Partial least squares structural equation modeling (PLS-SEM) was employed in this study, with mediation analysis performed using advanced PLS-SEM techniques. A total of 1,284 respondents from organizations in both the public and private sectors were included in the sample.

The findings emphasize that KM has a more significant direct effect on individual performance compared to organizational performance. Concurrently, in terms of indirect influence, it is found that KM, through motivation and innovation, has a positive and significant effect on both individual and organizational performances, with a higher influence on the organizational one.

The originality of the work can be noted in designing two different structural models to represent the proposed relationships at the individual and organizational levels. These findings could provide organizational decision makers with empirical evidence, helping them (1) internalize the significance of the KM process in organizations as well as its subsequent effects on individual and organizational performance and (2) identify factors that mediate variable relationships.

Although marketing is essential for a firm’s survival and growth, a set of entrepreneurial strategic orientations may help it gain a competitive advantage. Entrepreneurial marketing (EM) is critical for growing businesses to succeed in today's fast-paced industry. The present study aims to measure the role of EM on the business performance (BP) of Indian startups.

For this purpose, a thorough literature review was conducted to develop a scale from the proposed conceptual framework – EMICO by Jones and Rowley (2009b). To assess the reliability and validity of the scale (62 items), the data are analyzed by employing partial least square-structural equation modeling (PLS-SEM).

The scale is empirically tested, and the results lead to discussions. The results show the positive impact of entrepreneurial orientation (EO), marketing orientation (MO), innovation orientation (IO) and customer orientation (CO) on the BP of the startups. The paper concludes by suggesting managerial and practical implications, also providing future research agenda.

Our objective is to address the existing dearth of comprehensive scales for evaluating EM by undertaking a rigorous statistical approach. Notably, our research represents the pioneering effort in adopting the EMICO framework and subsequently formulating and empirically validating a robust scale specifically tailored for nascent or small-scale startups within the Indian context.

This study aims to provide insights into the possible impact of International Public Sector Accounting Standards (IPSAS) on public governance and perceived levels of corruption in developing countries.

Through a multivariate analysis on panel data applied to 36 countries in the Middle East and North Africa (MENA) region and sub-Saharan Africa over the period 2010–2020, the authors test the impact of IPSAS adoption on transparency, accountability and perceptions of less corruption. The authors examine the moderating role of transparency and accountability in the strength of the relationship between IPSAS and perceived corruption.

The main results show that IPSAS adoption promotes an increase in transparency and accountability and leads to the perception of less corruption. Additional tests show that transparency and accountability strengthen the effect of IPSAS adoption and experience on perceived corruption.

The first limitation may be the use of the Transparency International CPI to measure the level of perceived corruption. Probably, the CPI does not reflect the actual levels of corruption in countries while the literature argues that these two measures are related. Also, the lack of data on the status and level of adoption of IPSAS by governments may be one limitation of the sample.

The study may help public authorities in their decision to adopt IPSAS. In light of the findings, standard-setting bodies could be encouraged to strengthen the disclosure requirements of IPSAS that make governments more transparent and accountable to limit perceptions of corruption.

This study may also help citizens understand the benefits of such reforms in protecting public assets and how such standards may help improve social welfare.

To the best of the authors’ knowledge, this is one of the few studies that examines the impact of IPSAS on good governance by combining the dimensions of transparency, accountability and perceptions of corruption in DCs. It also provides insights into the moderating role of public governance pillars. Finally, it includes the IPSAS experience of the country, which has been little tested previously.

Effective customer journey design (ECJD) is considered a key variable in customer experience management and an essential source of brand meaning and pro-brand behavior. Although previous research has confirmed its importance for driving brand attitudes and loyalty, the role of consumer-brand identification as a social identity-based influence in this relationship has not yet been discussed. Drawing on construal level and social identity theories, this paper aims to investigate whether effective journeys and the resulting overall journey experience are equally powerful in driving brand loyalty among customers with different levels of consumer-brand identification.

The present article develops and tests a research model using data from the European and US service sectors (N = 1,454) to investigate how and when ECJD affects service brand loyalty.

Across two cultural contexts, four service industries and 33 service brands, the results reveal that ECJD is a crucial driver of service brand loyalty for customers with low consumer-brand identification. Moreover, the findings show that different aspects of journey effectiveness positively impact the valence of customers’ experience related to those journeys – a process that is ultimately decisive for their brand loyalty.

This study is unique because it generates theoretical and practical knowledge by combining the literature streams of customer journey design, customer experience and branding. Furthermore, this work demonstrates that consumer-brand identification is a critical boundary condition to be considered in the relationship between ECJD and brand loyalty in services.

The article aims to present the results of adapting the team boosting behaviors (TBB) scale to Polish cultural conditions and validating it.

The research methodology consisted of three steps. In the first step, I translated the TBB scale into Polish using a rigorous back-translation method. Next, to assess content validity, nine domain experts reviewed the initial version of the instrument for clarity and relevance. Finally, I applied the scale to a sample of 532 team members and underwent thorough psychometric testing to assess construct validity. I employed structural equation modeling (SEM) with the partial least squares (PLS) factor-based algorithm technique for confirmatory factor analysis to assess the scale’s reliability and validity.

After development, the Polish version of the TBB scale kept its three sub-scale structures. However, the validation process led to a slight reduction in the number of test items compared to the original scale.

The findings imply that the Polish version of the scale is a valid and reliable tool for assessing TBB. However, I recommend additional studies to confirm this instrument’s structure.

The results confirmed the reliability and relevance of the tool for measuring TBBs in Polish cultural conditions. The tool provides the basis for implementing further research with the TBB construct in Poland and internationally.

The purpose of this paper is to research the impact of digitalization on employees' future competencies and the conditional role of human resource development (HRD) in the relationship between independent and dependent variables.

Empirical research covered 1209 enterprises from all of Slovakia, Poland and the Czech Republic. The research was conducted from 2019 to 2021. Using structural equation modeling (SEM), a theoretical model was tested and verified.

Confirmatory factor analysis has shown a good fit for the tested model. The purpose and character of our data showed a good alignment with the SEM partial least squares method, as the goal is to predict a construct. The model showed that employee-oriented digitalization positively affected the employees' future competencies, with no impact of customer-oriented digitalization treated as a control variable. Also, the moderating role of HRD has not been shown to be significant for the “digitalization – competencies” relationship.

Previous studies on the development of personnel competencies treated these competencies as antecedents of digital transformation and examined the formal role of HRD in building the competencies. The novelty of this study lies in exploring the pattern of interactions among the impact of an environment built by innovative technologies and HRD on the competencies of the future. Also, the research embedded in the environment of Poland, the Czech Republic and Slovakia has contributed to the complex understanding of the transition to digitalization, as this region has often been omitted in the field of human resource management (HRM) research focused on exploring digital transformation.

The study investigates the linkage between personality traits and firm performance. It examines the role of the pursuit of excellence, perseverance, a proactive mindset and formal education in determining the entrepreneurial success of MSMEs.

Data were collected from 432 MSME entrepreneurs using a structured questionnaire from India's two major industrial towns to analyze the impact of personality traits on firm performance. Structural equation modeling (SEM) was employed to assess the direct and indirect relationships with the help of mediation analysis.

The findings assert that personality traits improve firm performance and determine the success of MSMEs. The results reveal that the need for achievement, a proactive mindset and the pursuit of excellence are crucial to firm performance. In addition, formal education mediates between perseverance and the pursuit of excellence personality attributes on the one side and firm performance on the other.

The research has various theoretical and practical implications for entrepreneurs, financial institutions and policymakers. The results could be productively used to nurture the entrepreneurial ecosystem in India.

Although research on personality traits as a driver of firm performance is growing, the pursuit of excellence, perseverance and proactive mindset attributes as enablers of firm performance have not garnered much attention. The study presents a precise conceptual model by integrating the aforementioned dimensions in the backdrop of an emerging market.

This paper investigates one of the potential costs of rising segregation in American cities by evaluating empirically the extent at which ethnic-based segregation contributes to the onset and the speed of propagation of the COVID-19 pandemic.

Regression analysis based on matched data on early incidence of COVID-19 cases, segregation and covariates. Identification resorts on variations in segregation across MSAs and heterogeneity in the geography and timing of stay-at-home orders.

One cross-MSA standard deviation increase in segregation leads to a significant and robust rise of COVID-19 cases of 8.7 per 100,000 residents across urban counties.

Combines spatial data on COVID-19 cases and segregation; use of a new segregation measure; focus on early incidence of the pandemic and its drivers.