Search results

Previous studies have shown how the nature of businesses and the strategies pursued by their owners are affected by the personality traits of their owners. These personality traits can be formed in the early stages of life due to experiences and the surrounding context, where religion is a particularly important aspect of this context. This study aims to explore how religion affects entrepreneurial activities through the personality traits created.

This study uses interviews with 43 Muslim entrepreneurs in Scotland to examine the role played by religion. This ensures that the national institutional context is kept consistent but also allows an in-depth examination of relationships, which are likely to be interlinked and recursive.

The traits created influence the nature of the entrepreneurial activities undertaken with the potential to harm and support the entrepreneurial endeavours. It is the combination of personality traits that are formed which have the greatest effect. As such, it is found that Muslim entrepreneurs display less openness and creativity associated with new ideas, but this does not reflect risk aversion rather hard work in itself is valued, and patience combined with an external locus of control mean entrepreneurial behaviours are not altered to boost poorly performing business activities.

For Muslim entrepreneurs in Scotland, their traits explain why growth may not be a foremost consideration of these entrepreneurs rather they may value hard work and meeting the ideals of formal and informal institutions associated with religion. For those seeking to support minority groups through the promotion of entrepreneurship, either they must seek to overcome these ingrained traits or alter support to complement the different objectives held by Muslim entrepreneurs.

This study aims to dissect and understand the latent themes of Islamic work ethic (IWE) and explore the driving factors of IWE research.

Structural topic modeling (STM), a sophisticated machine learning technique, was used to analyze a corpus of 205 articles sourced from the Scopus database. These articles cover the 36 years of research on IWE, from 1988 to 2024. Moreover, negative binomial regression was applied to examine the driving factors of IWE research.

The STM analysis unfolds ten topics in conjunction with IWE including individual success, workplace dynamics, organizational work ethics, knowledge management, employee citizenship behavior, financial ethics, job satisfaction, organizational commitment, performance enhancement and leadership. The further STM outputs included word clouds, prevalence proportions, correlation matrix, heatmap, relationship of topics with metadata, topic prominence in the publishing journals and, finally, illustrating trends and future prospects of research on IWE. The results of negative binomial regression reveal that number of authors, article age, journal indexing, authors from multiple countries and number of references are strong drivers of fostering research in IWE, by having significant positive impacts on total citations.

The insights from this study provide valuable guidance for businesses and organizations looking to integrate IWE principles into their operations. By promoting values such as fairness, hard work and ethical behavior, organizations can foster a more inclusive and morally grounded workplace culture. This, in turn, may lead to enhanced employee satisfaction, greater organizational commitment and improved overall performance. Additionally, the emphasis on ethical practices can contribute to broader societal benefits, such as increased trust in business practices and a stronger alignment with social responsibility initiatives.

This is a unique study that explores the latent themes and characteristics of the IWE literature through STM and provides insights on the future research directions. In addition, this study also examines the driving factors of IWE research.

Blockchain, a groundbreaking technology that recently surfaced, is under thorough scrutiny due to its prospective utility across different sectors. This research aims to delve into and assess the cognitive elements that impact the integration of blockchain technology (BT) within library environments.

Utilizing the Stimulus–Organism–Response (SOR) theory, this research aims to facilitate the implementation of BT within academic institution libraries and provide valuable insights for managerial decision-making. A two-staged deep learning structural equation modelling artificial neural network (ANN) analysis was conducted on 583 computer experts affiliated with academic institutions across various countries to gather relevant information.

The research model can correspondingly expound 71% and 60% of the variance in trust and adoption intention of BT in libraries, where ANN results indicate that perceived possession is the primary predictor, with a technical capability factor that has a normalized significance of 84%. The study successfully identified the relationship of each variable of our conceptual model.

Unlike the SOR theory framework that uses a linear model and theoretically assumes that all relationships are significant, to the best of the authors’ knowledge, it is the first study to validate ANN and SEM in a library context successfully. The results of the two-step PLS–SEM and ANN technique demonstrate that the usage of ANN validates the PLS–SEM analysis. ANN can represent complicated linear and nonlinear connections with higher prediction accuracy than SEM approaches. Also, an importance-performance Map analysis of the PLS–SEM data offers a more detailed insight into each factor's significance and performance.

The presence of heavy metals in milk causes many acute and chronic physiological dysfunctions in human organs. The present study aims to investigate the heavy metals in cow's and buffalo's milk of two major cities, Karachi and Gujranwala, Pakistan to estimate metal intake by humans from this source.

In total, 48 milk samples from 2 cities were drawn from animals' udder to avoid contamination. Each sample was digested with nitric acid at 105 ^oC (degree Celsius) on a pre-heated electric hot plate to investigate the metals by atomic absorption spectroscopy (flame type). Air-acetylene technique analyzed chromium, cadmium and lead, and the hydride method analyzed arsenic in the milk samples.

The results revealed the highest mean lead concentration (19.65 ± 43.86 ppb) in the milk samples, followed by chromium (2.10 ± 2.33 ppb) and arsenic (0.48 ± 0.73 ppb). Cadmium was not detected in any sample, assuming cadmium's occurrence was below the detection level. The concentrations of all the metals in the samples of the two cities do not differ statistically. Lead concentrations in the buffalo's milk were higher than in cow's milk (p < 0.05). However, the concentrations of arsenic and chromium between buffalo's and cow's milk do not differ statistically. The present study reveals a lower level of metals in the milk than those conducted elsewhere. The mean concentrations of all the metals met the World Health Organization's (WHO) safety guidelines (1993).

Although cadmium causes toxicity in the human body, cadmium could not be measured because cadmium's concentration was below the detection level, which is 1 ppb.

This study will help reduce the toxic metals in our environment, and the sources of heavy metals, particularly from the industrial sector could be identified. The feed and water consumed by the milking animals could be carefully used for feeding them.

This study will help reduce the diseases and malfunction of human organs and organ systems since these heavy metals cause toxicity and carcinogenicity in humans. Arsenic and chromium cause cancer while lead causes encephalopathy (a brain disease).

The study reports heavy metal concentrations in the two attributes of four independent variables of raw milk samples that were scarcely reported from Pakistan.

Grounded in social cognitive theory, this study examines the role of responsible leadership in enhancing the reputation of universities within Bangladeshi higher education. Specifically, it explores the antecedents, outcomes and moderating factors influencing university reputation by connecting it with responsible leadership, teaching satisfaction, and faculty academic performance.

Data were collected from a web-based online survey of 388 faculty members across public and private universities in Bangladesh. Structural equation modeling was used to test the hypothesized relationships.

The study shows that responsible leadership significantly improves university reputation and faculty teaching satisfaction. Teaching satisfaction influences reputation but does not moderate the link between leadership and reputation. Surprisingly, university reputation does not significantly affect faculty’s perceived academic performance, underscoring the complexity of the reputation–performance relationship in the Bangladeshi context.

Universities should prioritize responsible leadership to enhance faculty satisfaction and institutional reputation. Leadership training, faculty support initiatives and resource allocation toward teaching excellence are recommended to maximize impact, as reputation does not directly influence academic performance.

This study applies social cognitive theory to investigate the dynamics between responsible leadership, faculty satisfaction and university reputation in a developing country. It contributes by uncovering the complex and indirect pathways through which leadership impacts academic environments, providing actionable insights for higher education leadership and policymaking in resource-constrained settings.

This study aims to examine the dynamics of the market development of Islamic banking in Pakistan. This study investigates how shocks to the economy in the form of changes in benchmark rate and exchange rate and internal factors such as efficiency, profitability and asset quality affect the development of Islamic banking. The study also evaluates the impact of Islamic banking on the real economy in the macro perspective and society at large in terms of inclusiveness, competitiveness and fairness.

Autoregressive distributed lagged model method is used for analysing the short-run and long-run determinants of market development of Islamic banking and the economic impact of Islamic banking on the real economy.

Profitability and exchange rate have a positive effect on market development of Islamic banking while higher inefficiency and interbank rate have a negative effect. On the other hand, financing intensity and profitability in Islamic banking positively affect the large-scale manufacturing sector.

Stable profits, high asset quality, efficiency and rising import demand with low policy rate environment complement Islamic banking growth. Moreover, the economic assessment shows that Islamic banks have been able to achieve the financial inclusion of those who want to avoid Riba, but they need concerted efforts to improve competitiveness and distinction with regard to distributional impact.

To the best of the authors’ knowledge, this is the first study in Pakistan to evaluate determinants of market development of Islamic banking taking 16-year quarterly data and assessing the economic effects of Islamic banking on inclusiveness, competitiveness and fairness.

The study aims to explore the mechanism to strengthen service recovery performance among public sector employees. Delving on the notion that ethical aspects of public leaders could serve as reliable models, the research investigated the role of ethical leadership, thriving at work and moral self-efficacy.

A quantitative approach with two waves of data collection from both supervisors' and subordinates’ perspectives in public organizations was conducted. The final sample size was 331.

Ethical leadership has a positive effect on public sector employees' thriving at work. In turn, the state of thriving at work mediates the association between ethical leadership and service recovery performance. Public servants’ moral self-efficacy moderates both the direct relationship between ethical leadership and thriving at work, as well as the indirect effect of ethical leadership on service recovery performance through thriving at work.

The research findings suggest an understanding of how ethical leadership could cultivate public service recovery performance through employees’ thriving. The common values between subordinates and leaders were found to facilitate a better translation of the leaders’ role model into employees’ psychology and behaviors.

This research focuses on a very important research question of determining the appropriate feature selection methods for software defect prediction. The study is centered on the creation of a new method that would enable the identification of both positive and negative selection criteria and the handling of ambiguous information in the decision-making process.

To do so, we develop an improved method by extending the WASPAS assessment in the context of bipolar complex fuzzy sets, which leads to the bipolar complex fuzzy WASPAS method. The approach also uses Einstein operators to increase the accuracy of aggregation and manage complicated decision-making parameters. The methodology is designed for the processing of multi-criteria decision-making problems where criteria have positive and negative polarities as well as other ambiguous information.

It is also shown that the proposed methodology outperforms the traditional weighted sum or product models when assessing feature selection methods. The incorporation of bipolar complex fuzzy sets with WASPAS improves the assessment of selection criteria by taking into account both positive and negative aspects of the criteria, which contributes to more accurate feature selection for software defect prediction. We investigate a case study related to the identification of feature selection techniques for software defect prediction by using the bipolar complex fuzzy WASPAS methodology. We compare the proposed methodology with certain prevailing ones to reveal the supremacy and the requirements of the proposed theory.

This research offers the first integrated framework for handling bipolarity and uncertainty in feature selection for software defect prediction. The combination of Einstein operators with bipolar complex fuzzy sets improves the DM process, which will be useful for software engineers and help them select the best feature selection techniques. This work also helps to enhance the overall performance of software defect prediction systems.

This study aims to review the content of academic libraries’ websites with specific reference to the general information, services, information resources, features and functionalities, instructional tools to support teaching, learning and research for the patrons and users, and the visibility or activeness of the navigational tools on the websites.

The study focused on six categories of tertiary institutions: public universities, technical universities, chartered private universities, university colleges, colleges of education and nursing and midwifery schools. From each category, the researchers purposively selected five different institutions with library websites. Thus, 30 library websites were reviewed and analyzed for this study.

The review disclosed a generally low content and information on the websites in terms of the availability of information resources and collections; the types of services available at the libraries via the websites; the features and functionalities of the websites; the instructional tools to support teaching, learning and research for the patrons and users; and the visibility or activeness of the navigational tools on the websites. Despite this, the public universities displayed more and above average information, services, information resources, instructional tools and navigational tools on their library websites, followed by the technical universities, chartered private universities and university colleges. Conversely, the colleges of education and nursing and midwifery schools were to a large extent unable to provide information relating to electronic resources, instructional tools and navigational tools.

The study was limited to 30 library websites from all six categories of tertiary institutions in Ghana. From the study, the management of the universities and the libraries must appreciate the significant role of websites in providing information and resources in support of teaching, research and learning at the institutions. In this regard, they need to provide the necessary support and resources for the effective deployment and operationalization of websites.

To the best of the author’s knowledge, this study is the first of its kind as no study seeks to provide a comprehensive analysis of the content of academic libraries’ websites with specific reference to the general information, services, information resources, features, functionalities, instructional tools to support teaching, learning and research for the patrons and users, and the visibility or activeness of the navigational tools on the websites.

(1) developing a dynamic and progressive software defect prediction model to successfully manage novel and huge amounts of software defect data and lessen the computational time. (2) to avoid the great diminish of static batch learning algorithms efficiency once the amount of data achieves a certain level.

This study explores the proficiency of the incremental classification based approach to elaborate anincremental software defect prediction system which helps recognizing and treating real-time software data streams.

The proposed method, as demonstrated by experimental results, is clearly competitive with the relevant two-class classifiers currently in use for software defect diagnosis. Detailed experimental findings clearly demonstrated the performance and efficiency of the suggested software defect detection approach: Incremental Discriminant-based Support Vector Machine (IDSVM) to differentiate between defective and non-defective objects.

To the best of our knowledge, this is the first a real-time prediction method that investigates incremental classification in software defect prediction research