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The purpose of this paper is to evaluate the impact of Entrepreneurship Education Programs (EEPs) from students’ and educators’ viewpoint to improve the quality of EEPs.

This research applies a qualitative-quantitative methodology. Its sample is included 291 students were selected randomly and 35 educators were chosen by convenience technique from universities of Applied Science and Technology of Iran.

The results revealed that essence of EEPs had a positive direct effect on objectives and content of EEPs; objectives and content of EEPs had a positive direct effect on methods of EEPs; essence of EEPs had a positive direct effect on impact of EEPs; and essence of EEPs had a positive indirect effect on methods through objectives and content based on students’ and educators’ perspective. Moreover, as opposed to educators’, students believed that methods of EEPs have not a positive direct effect on impact, while educators were opponent to students approach about the positive direct effect of essence of EEPs on methods.

The study was limited to Applied Science and Technology universities were selected by convenience sampling method. Similar studies in other universities are needed to be conducted by simple random sampling to evaluate EEPs.

The study recommends policy-makers to be aware of students’ needs of EEPs’ methods, as well inform educators about effective and initiative methods.

Evaluating impact of EEPs based on demand and supply-side viewpoint is the first study conducted in Applied Science and Technology universities of Iran.

This paper addresses the effectiveness of the processes involved in business administration proprammes for managers in Iran. It aims to understand the foundation of appropriate business administration for managers and to explore their views and perceptions in relation to the skills and knowledge required of them.The methodology employed consists of survey questionnaire and fieldwork involving semi‐structured interviews as the main sources of qualitative and quantitative data. The respondents were current participants in business administration programs at the Payame Noor University. The study tested the validity and relevance of the “integrated model” of managerial skills in order to identify the required categories of skills and knowledge, namely task, people, and self‐related skills. The results showed that a combination of these skills enables the managers to work more effectively. However, there seems to be a gap between what is offered and what managers actually need. Also it was discovered that in Iran business managers tend to place more emphasis on the importance of the task and self‐development categories of knowledge and skills rather than the people‐related ones. These results have numerous implications for the business schools and other universities who deal with managers in Iran.

This paper aims to review 69 studies related to Muslim consumer behavior and determine the relationship between these topics and Islamic rationality. In addition, this paper elaborates on Al-Ghazali’s Islamic rationality model.

A text analytics approach is used to map 69 studies on Muslim consumer behavior. In addition, the historical-critical and inductive approach is used to identify Muslim scholars’ concepts and opinions regarding Islamic rationality, especially Al-Ghazali.

This study confirms that Muslim consumer behavior is in line with the concept of Islamic rationality proposed by Al-Ghazali. This is evidenced by a strong awareness of Islamic morals and values, which fosters a high commitment to halal products.

The findings of this study will provide essential benefits in the development of Islamic rationality theory, which can then be used as an alternative in explaining Muslim consumer behavior and also can be used as a reference for stakeholders in the industry to mainstream halalfication on products offered in the Muslim market.

The value of originality in this study lies in identifying the relation between Islamic rationality and Muslim consumer behavior, and this effort was confirmed through 69 selected studies related to Muslim consumer behavior.

Sharing information security best practices between experts via knowledge management systems is valuable for improving information security practices, exchanging expertise, mitigating security risks, spreading knowledge, reducing costs and saving efforts. The purpose of this paper is developing a conceptual model to enhance the transfer of information security best practices between professionals in virtual communities through a Web-based knowledge management system to exchange their successful experience in handling different information security situations.

The model is validated by surveying 17 experts’ reviews on the correctness of the model’s structure and its related components through applying deep rich peer debriefing to test suitability. Quantitative data has been collected to achieve confirmatory results.

The resulting model incorporates five main components that support the formal mechanism for the acquisition and dissemination of knowledge: identification, classification, storage, validation and sharing. The success of knowledge sharing is highly dependent on the active collaboration of community members and highly influenced by motivation. Validating transferred knowledge is vital for ensuring the credibility of the system.

To the best of the author’s knowledge, this paper is one of the first to highlight the role of integrating knowledge management to enhance the effective share and reuse of information security best practices knowledge. The research results can support researchers investigating the topic and generate trustworthy literature to guide information security virtual community developers.

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.

By combining the notion of prospect theory with advanced machine learning algorithms, this study aims to predict whether financial institutions (FIs) adopt a reactive stance when they perceive climate change as a risk, consequently leading to the adoption of environmental, social and governance (ESG) practices to avoid this risk. Prospect theory assumes that decision-makers react quickly when decisions are framed as a risk or threat rather than as an opportunity.

We used a sample of 168 FIs across 27 countries and seven regions over the period 2003–2020. To conduct our empirical investigation, we compared the prediction accuracy of various machine learning algorithms.

Our findings suggest that out of 12 machine learning algorithms, AdaBoost, Gradient Boosting and XGBoost have the most precision in predicting whether FIs react to climate change risk in adopting ESG practices. This study also tested the overall climate change risk and risks associated with physical, opportunity and regulatory shocks of climate change. We observed that risks associated with physical and regulatory shocks significantly impact the adoption of ESG practices, supporting prospect theory predictions.

The insights of this study provide important implications for policymakers. Specifically, policymakers must take into account the risk posed by climate change in the corporate decision-making process, as it directly influences a firm’s adoption of corporate actions (ESG practices).

To the best of our knowledge, this is the first study to investigate the firm-level climate change risk and adoption of ESG practices from a prospect theory perspective using novel machine learning algorithms.