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Supply chain finance (SCF) has become a vital ingredient that fosters growth and provides flexibility to the global supply chain. Thus, it becomes essential to understand the factors that contribute to the success of the supply chain finance ecosystem (SCFE). This study aims to identify the critical success factors (CSFs) for the development of an efficient and effective SCFE. Based on their characteristics, the study intends to classify the factors into constructs and further establish a hierarchical relationship among the CSFs.

The study is based on empirical data collected from 221 respondents based on administered questionnaires. Exploratory factor analysis (EFA) is carried out on 16 selected factors (out of 21 proposed factors) based on the feedback of the experts and the factors were classified into four constructs. The total interpretive structural modeling (TISM) model was developed by identifying and finalizing CSFs of the SCFE. The model developed a hierarchical relationship between the various factors.

The study identified significant CSFs for the efficient and effective SCF ecosystem. Four constructs were developed by analyzing CSFs using the EFA. The finalized 16 CSFs modeled through the TISM and further hierarchical relationship established between the CSFs concludes that governmental policies and sectoral growth are the strongest driving forces and financial attractiveness is the weakest driving force. Based on the CSFs and the constructs identified, it was found that for the success of the SCF ecosystem, the existence of an economic ecosystem provides a facilitating framework for the overall development of the SCFE. Also, the trustworthiness among the partners fosters better relationships and results in financial feasibility and offers business opportunities for all the stakeholders.

This study will help the SCF partners across the globe understand the CSFs that ensure development of mutually beneficial SCF ecosystems and provide flexibility to the supply chain partners. The CSFs would provide insights to the policymakers and the financial intermediaries for providing a conducive environment for the development of a better SCF ecosystem. Also, the buyers and sellers would understand the CSFs that would develop better relationships among them and ultimately help in development of business across the globe.

The study identifies the CSFs for the SCF ecosystem. The study ascertains the significant factors and classifies them into clusters using EFA. Unlike the literature available, the paper develops the hierarchical relationship between the CSFs and develops a model for an efficient and effective SCF ecosystem.

The effectiveness of Six Sigma programs has varied across different industries and organizations, and leadership styles have been identified as a critical success factor for the installation of Six Sigma initiatives. Therefore, this study aims to investigate the specific elements of leadership styles that are linked with the successful deployment of Six Sigma programs in the automobile industry.

To conduct the study, the researchers utilized a Likert scale questionnaire with a rating system of 1–7 and a simple random sampling method. The survey was distributed to 2,325 potential participants, with 573 responses received, mostly from Germany, the United Kingdom and Sweden. Out of those responses, 260 completed questionnaires were received. The study utilized a mixed-methods research design and exploratory research approaches to investigate the implication of leadership style on the success of Six Sigma implementation. The research employed several analysis techniques, including Structural Equation Modeling (SEM), exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and Survey methods.

Through various SEM methods, such as EFA and CFA, the study revealed two vital leadership elements: (1) the long-term success of Six Sigma depends on leadership’s support and recognition of it as an improvement strategy and (2) leadership must commit to the organization’s suppliers to ensure quality and the provision of defect-free products.

By incorporating the identified key elements of leadership into their strategies, organizations and researchers can ensure the sustainable implementation of Six Sigma.

This research presents a distinct contribution to the evaluation of leadership style components within the European automotive sector, utilizing a mixed-methods research design and incorporating a variety of descriptive statistics.

Numerous attempts at installing six sigma (SS) have faced challenges and fallen short of the desired success. Thus, it becomes vital to identify the critical factors and characteristics that play a pivotal role in achieving successful adoption. In this study the research has aimed to highlight that a considerable number of corporate SS initiatives, around 60%, fail primarily due to the improper incorporation of essential elements and flawed assumptions.

To validate the influence of critical success factors (CSFs) on SS accomplishment, the study employed a research design combining exploratory and mixed-methods approaches. A Likert-scale questionnaire was utilized, and a simple random sampling method was employed to gather data. Out of the 2,325 potential participants approached, 573 responses were received, primarily from Germany, the United Kingdom and Sweden. The analysis focused on 260 completed questionnaires and statistical methods including structural equation modeling (SEM), exploratory factor analysis (EFA) and Confirmatory Factor Analysis (CFA) were utilized for data analysis.

The study acknowledged four essential components of CSFs that are imperative for sustaining the success of SS: (1) Competence of belt System employees; (2) Project management skills; (3) Organizational economic capability and (4) Leadership commitment and engagement. These factors were identified as significant contributors to the maintenance of SS’s success.

The practical implications of this research imply that institutions, practitioners, and researchers can utilize the four identified factors to foster the sustainable deployment of SS initiatives. By incorporating these factors, organizations can enhance the effectiveness and longevity of their SS practices.

The investigation's originality lies in its contribution to assessing CSFs in SS deployment within the European automobile industry, utilizing a mixed-methods research design supplemented by descriptive statistics.

This study assesses performance measurement (PM) system implementation efforts across various organizational contexts and investigates which factors are critical to achieving implementation success (IS).

An empirical field study was conducted to refine a framework of PM system IS that consists of 5 dimensions of success and 29 factors. A survey questionnaire was used to investigate actual organizational practice and exploratory factor analysis was conducted to refine constructs corresponding to potential factors and dimensions of IS. The resulting variables were then investigated using multiple regression analysis to identify critical success factors for implementing PM systems.

The survey was completed by representatives from 124 organizations and the exploratory factor analysis results indicated that there are three underlying dimensions of IS (i.e. Use of the System, PM System Performance, and Improved Results and Processes) and 12 factors. Of the factors, nine can be considered critical success factors having a significant relationship with at least one dimension of IS: Leader Support, Design and Implementation Approach, Reward System Alignment, Organizational Acceptance, Organizational Culture and Climate, Easy to Define Environment, IT Infrastructure Capabilities, PM System Design Quality, and PM Participation and Training.

The results show that there are distinct dimensions of IS and, although some factors are associated with all dimensions, most are more closely related to only one dimension. This suggests that different strategies should be utilized based on the types of challenges experienced during implementation.

This study aims to identify causes of (un)ethical behaviour in research and how they influence adherence to research ethics.

The authors developed and tested a conceptual model that includes mediation and helps to understand the mechanism of adherence to ethical standards of research based on the “social judgment theory” (SJT). In Study 1, the authors conducted an exploratory study using the exploratory factor analysis technique to identify factors responsible for adherence to research ethics. In Study 2, the authors used SJT to provide support for establishing a relationship between key variables.

Two factors, “Proclivity to Egoism” and “Proclivity to Emotivism”, were identified based on the personal beliefs of researchers. These factors were found to play an important role in determining the tendency towards adherence to standards of research ethics (Belmont Report and COPE). SJT successfully explains the mechanism of adoption of ethical standards. Adherence to Belmont principles was seen to mediate relationship between factors identified and tendency to adhere to COPE.

Majorly, this study is unique as it establishes and guides to incorporate researchers’ point of view in formulating ethical standards and guidelines, apart, from various other important theoretical and societal implications.

The use of three-dimensional virtual worlds (3DVWs) is increasingly becoming a common practice in language education to provide digital learning environments for second-language (L2) communicative classes. This study aimed to identify the key factors underlying communication in 3DVWs that can improve the communication skills of L2 learners.

To achieve this, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted to validate the identified factors affecting communication in 3DVWs. A self-reported questionnaire with 47 items on a five-point Likert scale was administered to 513 pre-service teachers, teachers and lecturers in the field of language education.

The results of the EFA revealed four factors that contribute to communication in 3DVWs, namely learner motivation, interaction pattern, language development and learner autonomy. CFA results provided support for the updated model, with statistically significant Chi-square results (χ² (df = 83) = 181.049, p < 0.001) indicating a good fit between the model and the data.

The findings suggest that the four EFA-derived parameters are valid and can assist instructional designers and L2 instructors in creating 3DVWs that enhance L2 learners' communication abilities. This study provides valuable insights for educators, instructional designers and researchers in the field of language education and technology-enhanced learning.

This study aims to establish an assessment model to measure big data readiness in the public sector, specifically targeting local governments at the provincial and city/regency levels. Additionally, the study aims to gain valuable insights into the readiness of selected local governments in Indonesia by using the established assessment model.

This study uses a mixed-method approach, using focus group discussions (FGDs), surveys and exploratory factor analysis (EFA) to establish the assessment model. The FGDs involve gathering perspectives on readiness variables from experts in academia, government and practice, whereas the survey collects data from a sample of selected local governments using a questionnaire developed based on the variables obtained in FGDs. The EFA is used on survey data to condense the variables into a smaller set of dimensions or factors. Ultimately, the assessment model is applied to evaluate the level of big data readiness among the selected Indonesian local governments.

FGDs identify 32 essential variables for evaluating the readiness of local governments to adopt big data. Subsequently, EFA reduces this number by five and organizes the remaining variables into four factors: big data strategy, policy and collaboration, infrastructure and human resources and data collection and utilization. The application of the assessment model reveals that the overall readiness for big data in the selected local governments is primarily moderate, with those in the Java cluster displaying higher readiness. In addition, the data collection and utilization factor achieves the highest score among the four factors.

This study offers an assessment model for evaluating big data readiness within local governments by combining perspectives from big data experts in academia, government and practice.

The purpose of this study is to develop a financial performance measurement model for real estate business.

The study uses balanced scorecard (BSC) proposed by Kaplan and Norton (1996) as a theoretical support. The study, being exploratory in nature, uses survey method to collect data on several dimensions of BSC as well as on other performance measures used by real estate businesses in India. The survey data collected is analyzed using exploratory factor analysis (EFA) to explore the model constructs. This is followed by building an integrated conceptual model for measuring the financial performance of a real estate business. The model is tested using partial least squares structural equation modeling (PLS-SEM).

The study finds that the financial performance of the real estate business revolves around customer satisfaction, employee satisfaction and external networks. The right alignment of these components lead to superior financial performance. It also provides a competitive advantage to the real estate business. These three components (customer satisfaction, employee satisfaction and external networks) have direct and indirect influences on the financial performance of real estate business.

A small sample size (78 respondents), as well as the respondent’s geographical concentration in India, are the limitations of the study. Hence, generalization of findings may be difficult until the findings are validated across the globe.

The conceptual performance measurement model suggested in this research provides an effective tool to plan and strategize to achieve superior financial performance, particularly for stakeholders in the real estate business.

To the best of the authors’ knowledge and belief, this is the first attempt to develop a comprehensive financial performance measurement model for real estate business and test it using EFA and PLS-SEM.

The purpose of this study is to frame the dimensions of the external institutional environment, namely, cultural-cognitive, normative and regulative dimensions as the main actors in the organisational field. More precisely, Libyan commercial banks have been identified as empirical evidence, to identify constraints of the institutional environment governing the behaviour and decision-making of commercial banks, when adopting Islamic financial transactions.

A questionnaire has been designed for 14 Libyan commercial banks which is distributed to the Board of Directors, managers, directors of departments, and personnel. The exploratory factor analysis (EFA) and the measurement model by using the first-order and second-order confirmatory factor analysis (CFA) have been applied as essential steps to embody the conceptual framework and test the research hypotheses.

The results of the EFA indicated sufficient correlation among the dimensions of the external environment. The CFA supported this study’s hypotheses. The modelling showed that the cultural-cognitive, normative and regulative dimensions are institutional constraints impeding Libyan commercial banks’ adoption of Islamic financial transactions. Interestingly, the findings of the CFA align with the EFA findings in supporting the conceptual framework of the research. They portrayed that the cultural-cognitive dimension has been identified by explicit and implicit cognition.

This study systematically embodies the dimensions of the external institutional environment, namely, cultural-cognitive, normative and regulative dimensions, as the main factors in the organisational field to be conceptually rich lenses to investigate social considerations to reinforce institutional thought broadly. The results of this study were consistent with extant Islamic financial literature, reflecting symmetry and similarity across commercial banks, particularly at the first stage of adopting Islamic financial transactions.

This study aims to develop and validate a multidimensional scale to measure the motivating factors that lead to opportunity recognition in social entrepreneurship among higher education institute (HEI) students.

The scale was developed through two phases; in phase 1, semi-structured interviews with social entrepreneurs and aspiring students were conducted to explore themes for item generation. Phase 2 included developing and validating the scale using exploratory (EFA) and confirmatory factor analysis (CFA). The sample included HEI students (n = 300 for EFA, n = 300 for CFA) with either academic background or volunteering experiences in social entrepreneurship.

A 24-item scale is developed in the study, with six factors measuring the motivating factors influencing opportunity recognition in social entrepreneurship: life experiences, social awareness, social inclination, community development, institutional voids and natural option for a meaningful career.

The scale facilitates the development of theories and models in social entrepreneurship. The scale also enables policymakers and social entrepreneurship educators to understand the motivating factors that lead to opportunity recognition among students. It would help them to provide target-specific support to students.

To the best of the authors’ knowledge, this study is the first attempt to develop a scale that measures opportunity recognition in social entrepreneurship based on specific motivating factors. The study used the model by Yitshaki and Kropp (2016) as the conceptual framework. This study is the first attempt to triangulate the model’s findings using a quantitative methodology and through the development of a measurement scale. Besides, the scale adds value to social entrepreneurship research, which lacks empirical research on HEI students.