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Upon the realization of the need for guideline in cross-organizational data integration, in an exploratory manner, this study developed a public data governance framework, specifically, the governance for integrated public data (GIPD) framework and identified the influential factors of its successful implementation. This framework was then subjected to an analysis of a real data integration case in the South Korean public sector to test its efficacy.

To develop the GIPD framework, the authors conducted an extensive meta study, focus group interviews and the analytic hierarchy process involving field experts. Further, the authors performed topic modeling on documents from Korean research and development data integration projects, and compared the extracted factors to those of the GIPD to illustrate the latter's usefulness in a real case.

Legislation, policy goals and strategies, operation organization, decision-making council, financial support size and objective, system development and operation, data integration, data generation, system/data standardization and master data management were derived as the 10 important factors in implementing the GIPD framework. The illustrative case of Korea revealed that decision-making council, financial support size and objective, legislation, data generation and data integration were insufficient.

Although this study reveals important findings, it has a few limitations. First, the potential factors for data governance might vary depending on the attribute of the “interviewee” (such as their career or experience period) and the goal and area of GIPD framework building. Second, the inherent limitation of topic modeling in determining topics from groups of extracted keywords means that topics may be interpreted in various ways, depending on the perspective of the expert.

This study is highly significant in that it provides a starting point for discussions on the issue of data integration among public institutions. Therefore, although this study examined public data governance based on R&D data, it will contribute to providing a sufficient guideline for any type of inter-institutional data governance framework, what to discuss and how to discuss between institutions.

The findings are expected to provide a roadmap to formulate practical guidelines on inter-institutional data cooperation and a diagnostic matrix to improve the existing data governance system, especially in the public sector, from the existing practice of empirical analysis using a mixed methodology approach.

This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability.

The real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs.

DT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised “student loan,” “dad occupations,” “mom educational level,” “department,” “mom occupations,” “admission type,” “school fee waiver” and “main sources of living.”

This work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention.

Although first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.

A stroke is a serious, life-threatening condition that occurs when the blood supply to a part of the brain is cut off. The earlier a stroke is treated, the less damage is likely to occur. One of the methods that can lead to faster treatment is timely and accurate prediction and diagnosis. This paper aims to compare the binary integer programming-data envelopment analysis (BIP-DEA) model and the logistic regression (LR) model for diagnosing and predicting the occurrence of stroke in Iran.

In this study, two algorithms of the BIP-DEA and LR methods were introduced and key risk factors leading to stroke were extracted.

The study population consisted of 2,100 samples (patients) divided into six subsamples of different sizes. The classification table of each algorithm showed that the BIP-DEA model had more reliable results than the LR for the small data size. After running each algorithm, the BIP-DEA and LR algorithms identified eight and five factors as more effective risk factors and causes of stroke, respectively. Finally, predictive models using the important risk factors were proposed.

The main objective of this study is to provide the integrated BIP-DEA algorithm as a fast, easy and suitable tool for evaluation and prediction. In fact, the BIP-DEA algorithm can be used as an alternative tool to the LR model when the sample size is small. These algorithms can be used in various fields, including the health-care industry, to predict and prevent various diseases before the patient’s condition becomes more dangerous.

This study aims to differentiate crime-related characteristics (such as the number of cases filed against current convictions and criminal history) based on the criminal thinking prevailing among convicts. However, because of the low reliability of subscales and poor structural validity of indigenous and translated versions of international instruments, a new instrument criminal attitude measure (CAM) was extracted to measure criminal thinking patterns among convicts incarcerated in central prisons of Punjab.

A cross-sectional research design was used. Data was collected from 1,949 male convicts (extracting mutually exclusive data from 649 respondents for EFA and 1,300 respondents for confirmatory factor analysis [CFA]). Both data samples were collected from convicts incarcerated in the nine (all) central jails of Punjab, Pakistan.

The results of this study showed poor model fit for both the indigenous criminal thinking scale and the translated version of criminogenic cognition scale. CAM was extracted through principal component analysis and proposed as a 15-item questionnaire with five factors extracted through varimax rotation. Those five factors are power orientation, mollification, entitlement, mistrust toward authorities and short-term orientation. The results of CFA for CAM confirmed the proposed five-factor structure for the construct. Findings based on MANOVA further found that CAM differentiates between the thinking patterns of recidivists, convicts with multiple charges filed against them in current convictions and convicts with a familial criminal record. The findings of this study showed that CAM is a practical, valid and reliable instrument for measuring criminal thinking among convicts.

In this study, using the survey method was inevitable because of the restrictions imposed by the granted permission. However, this time duration was extended because of the courtesy of the Superintendent and Deputy Superintendent of each jail. This study is focused on a male sample only, and the findings cannot be generalized to females. The phenomena proposed (based on large data sets) in this study can further be elaborated using qualitative research designs and methods (using a small sample with an in-depth study). So, it is also suggested to test this new instrument on a comparative study between prisoners and non-prisoners to explore whether scale can differentiate between these two groups.

A short-scale and easy-to-administer instrument was developed for assessing major criminogenic needs among convicts for prison management, i.e. assigning barracks, allocating treatment and also detecting changes in attitude after imprisonment.

To the best of the authors’ knowledge, this study is the first study to explore and validate the construct of criminal attitudes among convicts using both the EFA and CFA. A small and valid instrument facilitates the measurement of criminogenic needs among prisoners. Data was collected from all central jails in Punjab. This study explored comparatively less researched crime characteristics in a relatively large sample.

The study utilized the consumption value theory to explore the motivational factors that define and differentiate the users and nonusers of fashion rental services

A focus group was conducted to generate an initial list of measurement items. These items were refined through a pretest and then used in a self-administered online questionnaire to collect data from a total of 300 users and 300 nonusers. The collected data were analyzed using factor analysis to identify the factors that define users and nonusers. A MANOVA was then conducted to explore the differences in the identified factors between users and nonusers.

Using factor analysis, nine factors were extracted across the five consumption values (functional, social, emotional, conditional and epistemic). MANOVA revealed a significant difference between users and nonusers across all factors. Further analyses suggested that the most differentiating factors are two emotional value factors and one social value factor.

Despite existing studies of fashion rental services, it is debatable whether the phenomenon is fully understood since previous studies primarily focus on consumers who engage in fashion renting services – there is a lack of focus on nonusers. This study provides unique contributions by exploring the phenomenon from both the user's and the nonuser's perspective.

The concept of a creative place has been considered by experts, but a review of the research background shows that the definitions provided for creative place are different, and the factors that make an urban space a creative place are not clear. The main purpose of this study is to investigate the concept of creative place and to extract the indicators that make an urban space considered a creative place.

By extensive library studies and using a specialized panel, 59 items in the form of 12 indicators were extracted as identifiers of a creative place and a researcher-made questionnaire was prepared and tested in a case study. Data analysis of this study was performed in two stages by using the factor analysis method in R software.

The factors of “competitive advantage (economically)”, “freedom”, “attractiveness”, “entrepreneurship and professionalism”, “culture and art”, “vitality”, “diversity”, “distinction”, “participation”, “reconstruction, nobility and infrastructure”, “meaning” and “creative experiences” are important as identifiers of creative place, respectively. The evaluation of the case study showed that the total score of creative places in this urban space is 69.6 (out of 100) and “meaning” gained the most point in this urban space.

The factors of this research can be provided to architects and urban planners as identifiers of a creative place and a case study can be evaluated in terms of the degree of compliance with creative place identifiers.

Despite the wide research and discussion on international performance, innovation and digital resilience dimensions of enterprises, the investigation and understanding of their interrelations seem to be limited. The purpose of this study is to identify the influential factors affecting the mentioned dimensions, determine the causal relationships among these identified factors and finally evaluate their importance in an aggregated framework from the viewpoint of small and medium-sized enterprises (SMEs).

A hybrid methodology is used to achieve the objectives. First, the main factors of international performance, innovation and digital resilience are extracted by an in-depth review of the literature. These factors are then screened by expert opinions to localize them in accordance with the conditions of an emerging economy. Finally, the relationship and the importance of the factors are determined using an uncertain multi-criteria decision-making (MCDM) approach.

The findings reveal that there is a correlation between digital resilience and innovation, and both factors have an impact on the international performance of SMEs. The cause-or-effect nature of the factors belonging to each dimension is also determined. Among the effect factors, business model innovation (BMI), agility, product and organizational innovation are known as the most important factors. International knowledge, personal drivers and digital transformation are also determined to be the most important cause factors.

This study extends the literature both in methodological and practical directions. Practically, the study aggregates the factors in the mentioned dimensions and provides insights into their cause-and-effect interrelations. Methodologically, the study proposes an uncertain MCDM approach that has been rarely used in previous studies in this field.

The main purpose of this paper is to identify, screen and prioritize customer requirements (CRs) for men’s denim jeans. Moreover, the effect of demographic factors on the primary evaluation criteria has been examined.

The study was initiated by the growing complaints about denim jeans products of a local manufacturing company. First, 24 CRs were identified from the literature and customer complaints. Then, a survey was conducted to rate the identified CRs and solicit more CRs through closed-ended and open-ended questions, respectively. From the survey, 368 usable responses were collected while the participants were shopping in 14 local retail shops. After analyzing the data using factor analysis, univariate and multivariate analysis of variance (MANOVA), and content analysis, the resulting 15 criteria were prioritized by experts’ pairwise comparisons employing the fuzzy analytic hierarchy process (AHP).

Factor analysis extracted six components (primary criteria) including design cues, pocket design, comfort, size and fit, fashionability, and extrinsic cues from the CRs included in the closed-ended questions. MANOVA showed that age and frequency of purchasing denim jeans significantly affected the primary criteria, while educational level and frequency of wearing denim jeans did not. The weights from the fuzzy AHP revealed that colour fastness, price, durability, fabric weight, workmanship, side pocket design and fit as the most important CRs. Moreover, consumers preferred regular fit, stitched round side pockets, patch back pockets and stretchable denim fabric.

The limitations of the study are discussed in the body of the paper in Section 7.

The paper presents exploratory findings on denim jeans evaluation criteria in a developing country’s context. Moreover, the application of fuzzy AHP for prioritizing denim jeans’ CRs is unique.

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.

In recent times, both academics and industrialists have undertaken research into various areas of circular business models (CBM) in a bid to promote a green economy. Yet despite numerous studies conducted, the ensuing discourse contains scant information regarding the contributions of CBM towards the transition of green economy in the construction industry. This present study therefore aims to explore the contributions of CBM in the transition towards a green economy in the Ghanaian construction industry.

A comprehensive literature review was first conducted to identify the contributions of CBM towards the transition towards a green economy. A quantitative research strategy was then adopted to collect primary questionnaire data from professionals with knowledge of CBM and the green economy from 104 participants for the study. The data gathered was analyzed using descriptive statistics and exploratory factor analysis viz. Principal component analysis.

The contributions of CBM towards the transition towards a green economy were found to be: value contributions (i.e. lower carbon footprint, lower emission of waste by the industry, value creation for clients, innovation in construction materials and methods, reduced maintenance cost, creation of energy efficient infrastructures, improved value proposition for firms, improved sustainability of the industry and reduced pressure on finite resource.); green contributions (i.e. recycling and reuse of construction waste, promotion of green building technology, increased potential for economic growth, increased resource efficiency and creation of green building market) and longevity contribution (i.e. increased life span of buildings). It was evident that CBM make significant contributions in the transition towards green economy and as such, policymakers and other stakeholders within the construction industry must adopt these models to maximize their green credentials and accrue inherent benefits associated with transitioning towards a green economy.

This paper presents a novel and comprehensive study that explores the contributions of CBM towards engendering a green economy. The study’s results provide construction industry stakeholders and policymakers with clear insight into the contributions of CBM towards the transition into a green economy. In practice, this study provides much needed guidance to support construction practitioners to transition towards a green economy in alignment with the United Nations' Sustainable Development Goals (SDGs).