Nida Rahman and Krishan Sharma
Regional comprehensive economic partnership (RCEP) is understood as the world's largest trading bloc given its contribution to the world output (30%). The mega trade bloc brings…
Abstract
Purpose
Regional comprehensive economic partnership (RCEP) is understood as the world's largest trading bloc given its contribution to the world output (30%). The mega trade bloc brings together 15 countries of East Asia, Southeast Asia and Oceania to eliminate tariff and non-tariff barriers in goods and services trade. The study suggests the importance of sector specific reforms for Malaysia to strengthen domestic capability.
Design/methodology/approach
The analytical framework constructs upon the partial equilibrium analysis and uses WITS SMART simulations.
Findings
The study finds that Malaysia's elimination of tariffs under the RCEP will cause a surge in imports from developed member countries of RCEP like Australia, South Korea and Japan. The study also finds a trade diversion in countries such as India. The empirical results establishes that RCEP would further strengthen intra-ASEAN trade.
Research limitations/implications
The study explores select sectors of the manufacturing industry in Malaysia.
Practical implications
The implementation of RCEP would impact the manufacturing sector immensely, especially in sectors like electrical machinery and equipment and inorganic chemicals, which are two of the major trading commodities of the Malaysian economy.
Social implications
Any trade agreement has a larger impact on the society. It may raise income, boost the consumer preferences and create or erode consumer welfare. The study reports the consumer welfare effect of the implementation of RCEP in Malaysia.
Originality/value
The study is the first attempt to do a partial equilibrium analysis for the electrical machinery and equipment sector and inorganic chemicals sector of Malaysia using both aggregated and disaggregated data at HS two-digit and HS six-digit level.
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K.K. Sharma and Bhupendra Singh
Describes how sampling distribution of the estimates of the reliability functions for various failure time distributions is either hard to obtain or is quite complicated and, as…
Abstract
Describes how sampling distribution of the estimates of the reliability functions for various failure time distributions is either hard to obtain or is quite complicated and, as such, it becomes difficult to draw inferences about the reliability of a system. To overcome this situation, presents some distribution‐free inference techniques on system reliability and gives Bayesian reliability analysis using conjugate priors.
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Lukman Raimi, Nurudeen Babatunde Bamiro and Syamimi Ariff Lim
Artificial Intelligence (AI)-powered technologies are revolutionising the landscape of education, ushering in a myriad of possibilities and challenges. This article delves into…
Abstract
Artificial Intelligence (AI)-powered technologies are revolutionising the landscape of education, ushering in a myriad of possibilities and challenges. This article delves into the dual nature of AI-driven tools in education, spotlighting pivotal advancements like automated grading, personalised learning algorithms, online monitoring, content filtering, AI-based learning tools, Chat Generative Pre-trained Transformer (ChatGPT) and standardised testing (ST) platforms. Ultimately, the examination reveals a spectrum of advantages, risks and considerations associated with AI-driven educational applications. Employing the PRISMA protocol, this study systematically reviews peer-reviewed literature concerning the implementation and ethical implications of AI in higher education. The analysis incorporates 36 scholarly articles, uncovering the entrepreneurial advantages of AI, such as tailored learning experiences, self-assessment opportunities, heightened efficiency, skill enhancement and reduced educational disparities. Concurrently, the research identifies potential hazards, including user profiling, plagiarism, academic integrity breaches and excessive reliance on technology that may hinder creative learning dynamics. Crucial concerns emerge, encompassing the possible devaluation of educators' roles, privacy issues inherent in personalised learning platforms and the intrusive nature of online surveillance. Additionally, the study highlights biases embedded within AI algorithms and apprehensions regarding job displacement within the academic community. To steer AI integration responsibly within higher education, the investigation explores ethical frameworks and models, offering pragmatic suggestions for institutions. Recommendations advocate for a balanced approach, emphasising judicious AI utilisation and the formulation of institutional policies. This chapter's distinctiveness lies in its innovative stance, striving to reconcile the technical and entrepreneurial benefits of AI applications with the preservation of creativity in higher education contexts.
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Thabet Albastaki, Allam Hamdan, Yousif Albastaki and Ali Bakir
Consumers frequently use electronic payments (e-payment) as their first step into formal financial services. The advancement of information and communication technology, on the…
Abstract
Purpose
Consumers frequently use electronic payments (e-payment) as their first step into formal financial services. The advancement of information and communication technology, on the other hand, has resulted in several achievements for human civilization, altering people’s lives, behaviors and societal measures. This study’s main aim is to investigate issues and identify the factors that are likely to influence customers’ acceptance of implementing e-payment in the Kingdom of Bahrain.
Design/methodology/approach
A quantitative research approach was adopted to test the influence of e-payment data security, trust, ease of use, usefulness and accessibility on customers’ acceptance of the service. A questionnaire survey was electronically administered to a purposive sample, and 531 responses were returned, achieving the required sample size for the study. Descriptive statistics analysis was used to ascertain data validity and consistency, and regression analysis was used to test the model’s hypotheses.
Findings
The findings of this study demonstrated a high influence of the mentioned factors on the e-payment acceptance of the customers in the Kingdom of Bahrain. The main recommendations are to increase the adoption of e-payment; focus highly on the security factor in e-payment adoption; create a trustworthy e-payment service; strive to make the e-payment services more user-friendly; increase the longevity of the e-payment services by focusing on usefulness; and make e-payment services more accessible.
Originality/value
This study’s potential contribution is to identify the factors that influence e-payment acceptance by customers in Bahrain and draw attention to issues to be considered in adopting new e-payment services.
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Hassnian Ali and Ahmet Faruk Aysan
The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI).
Abstract
Purpose
The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI).
Design/methodology/approach
Leveraging a novel methodological approach, the study curates a corpus of 364 documents from Scopus spanning 2022 to 2024. Using the term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects the thematic essence of the ethical discourse in generative AI across diverse domains, including education, healthcare, businesses and scientific research.
Findings
The results reveal a diverse range of ethical concerns across various sectors impacted by generative AI. In academia, the primary focus is on issues of authenticity and intellectual property, highlighting the challenges of AI-generated content in maintaining academic integrity. In the healthcare sector, the emphasis shifts to the ethical implications of AI in medical decision-making and patient privacy, reflecting concerns about the reliability and security of AI-generated medical advice. The study also uncovers significant ethical discussions in educational and financial settings, demonstrating the broad impact of generative AI on societal and professional practices.
Research limitations/implications
This study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM. It highlights the need for dynamic governance and continual monitoring of AI’s evolving ethical landscape, offering a model for future research and policymaking in diverse fields.
Originality/value
The study introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains.
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Machine learning is an algorithmic-based auto-learning mechanism that improves from its experiences. It makes use of a statistical learning method that trains and develops on its…
Abstract
Machine learning is an algorithmic-based auto-learning mechanism that improves from its experiences. It makes use of a statistical learning method that trains and develops on its own without the assistance of a person. Data, characteristics deduced from the data, and the model make up the three primary parts of a machine learning solution. Machine learning generates an algorithm from subsets of data that can utilise combinations of features and weights different from those obtained from basic principles. In this paper, an analysis of customer behaviour is predicted using different machine learning algorithms. The results of the algorithms are validated using python programming.
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Akash Saharan, Ashutosh Samadhiya, Anil Kumar, Krishan Kumar Pandey, Sunil Luthra and Jose Arturo Garza-Reyes
Circularity has acted as an essential phenomenon for small and medium enterprises (SMEs) in emerging economies, pressuring entrepreneurs to its adoption in their businesses…
Abstract
Purpose
Circularity has acted as an essential phenomenon for small and medium enterprises (SMEs) in emerging economies, pressuring entrepreneurs to its adoption in their businesses. During the adoption and implementation of circularity, entrepreneurs or circular entrepreneurs (to be precise) are facing various challenges to its effective functioning. However, the scholarly literature has offered limited research into this phenomenon. Thus, the purpose of this research is to identify the various barriers and sub-barriers for circular entrepreneurs to adopt circularity in SMEs of emerging economies.
Design/methodology/approach
A combined qualitative and quantitative approach was employed to achieve the objectives of the study. In the first stage, through an extensive literature review, a list of barriers was identified and in the second stage, a deductive approach was employed to finalize the barriers. Finally, Best-Worst Method (BWM), a multi-criteria decision-making (MCDM) method, was used to analyse the significant importance of the barriers.
Findings
The findings of the study suggested the “financial barrier” as the first-ranked barrier in the adoption of Circular Business Models (CBMs), followed by the “regulatory and operational barrier” as the top second and third barriers. In terms of sub-barriers, “lack of access to funding and capital” has been identified as the top sub-barrier in the adoption of CBM, followed by “excessive regulations and red tape” and “challenges due to ambiguity of the concept”.
Practical implications
To transition from a circular to a linear business approach considerably quicker and smoother, entrepreneurs may utilize the findings of this study as a blueprint for the steps to overcome the barriers in a linear to a circular transition.
Originality/value
This research differentiates from other studies due to solicited input directly from the people who are most familiar with the challenges of making the transition from linear to CBM, i.e. the entrepreneurs themselves.
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Anamika Saharan, Akash Saharan, Krishan Kumar Pandey and T. Joji Rao
The low level of financial literacy among young adults is a pressing concern at both individual and country levels. Therefore, there is a dire need to understand the best-worst…
Abstract
Purpose
The low level of financial literacy among young adults is a pressing concern at both individual and country levels. Therefore, there is a dire need to understand the best-worst antecedents of financial literacy and how they influence each other.
Design/methodology/approach
A two-phased multicriteria decision-making (MCDM) technique consisting of best-worst method and interpretive structural modeling (BWM-ISM) was employed for pair-wise comparison, assigning weights, ranking and establishing the relationship among antecedents of financial literacy.
Findings
Results suggest that use of Internet (SF1), role of financial advisors (SF3) and education level of individuals (DS7) are top ranked antecedents, whereas masculinity/feminity, language and power distance in society are the least ranked antecedents of financial literacy. Findings will help both academicians and practitioners focus on the key factors and make efforts to increase financial literacy by minimizing resource usage.
Originality/value
The current study provides clarity among antecedents of financial literacy by following BWM-ISM approach for the first time in the financial literacy context.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0746
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Rajesh Kumar, Ashutosh Samadhiya, Anil Kumar, Sunil Luthra, Krishan Kumar Pandey and Asmae El jaouhari
The paper aims to enhance the understanding of robust food supply chains (FSC) by exploring the capabilities of various digital technologies and examining their interactions.
Abstract
Purpose
The paper aims to enhance the understanding of robust food supply chains (FSC) by exploring the capabilities of various digital technologies and examining their interactions.
Findings
This study finding shows that digital technology enhances the resilience of the FSC by improving visibility, traceability and adaptability. This resilience provides a competitive advantage, ultimately enhancing the overall business performance.
Research limitations/implications
In developing countries, inadequate infrastructure, poor Internet connectivity and diverse stakeholder systems pose challenges to implementing advanced digital solutions in the FSC.
Originality/value
This paper is among the first to investigate the impact of digital technology on FSC resilience, exploring visibility, flexibility and collaboration.
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Irina Zlotnikova, Hlomani Hlomani, Tshepiso Mokgetse and Kelebonye Bagai
The increasing adoption of generative artificial intelligence (GenAI) tools in university education has raised significant ethical concerns regarding academic integrity and…
Abstract
Purpose
The increasing adoption of generative artificial intelligence (GenAI) tools in university education has raised significant ethical concerns regarding academic integrity and fairness. This study aims to address these concerns by reviewing existing models and frameworks for ethical GenAI use and proposing a preliminary roadmap to establish ethical standards for GenAI use in higher education.
Design/methodology/approach
This study reviews current models and frameworks for ethical GenAI use, identifying their strengths and limitations. Based on this literature review and an approach combining interpretative phenomenological analysis and a hybrid phenomenological qualitative method, a six-phase roadmap is proposed, consisting of awareness and understanding, policy development, curriculum integration, technology and infrastructure, continuous evaluation and adaptation and collaboration and outreach.
Findings
This paper emphasizes the need for clear policies, interdisciplinary curriculum integration, robust technological infrastructure and ongoing stakeholder collaboration. Practical recommendations are provided for each phase of the roadmap, offering strategic guidance for universities to navigate the ethical complexities of GenAI implementation.
Originality/value
The proposed roadmap serves as a foundational step for developing policies and guidelines that ensure GenAI supports academic integrity and fosters innovative learning environments. Future research will focus on empirical validation and refinement of the roadmap to enhance its applicability and effectiveness in diverse educational contexts.