Swinburne University of Technology Sarawak Campus

Abstract Many guiding articles on literature reviews exist, but few have delivered an authoritative protocol that researchers can rely upon with clarity and confidence. To commemorate the inaugural annual special issue on systematic literature reviews in the International Journal of Consumer Studies , the editors have pooled their expertize and experience of authoring, editing, and reviewing literature reviews to develop a rigorous review protocol—that is, the Scientific Procedures and Rationales for Systematic Literature Reviews ( SPAR‐4‐SLR ) protocol—that researchers can rely upon to guide and justify decisions in systematic literature reviews. Using an interrogative approach, the editors begin with a discussion of the “what,” “why,” “when,” “where,” “who,” and “how” of systematic literature reviews. The editors conclude with systematic literature review exemplars in the inaugural special issue.

Generative artificial intelligence (AI) has taken the world by storm, with notable tension transpiring in the field of education. Given that Generative AI is rapidly emerging as a transformative innovation, this article endeavors to offer a seminal rejoinder that aims to (i) reconcile the great debate on Generative AI in order to (ii) lay the foundation for Generative AI to co-exist as a transformative resource in the future of education. Using critical analysis as a method and paradox theory as a theoretical lens (i.e., the “how”), this article (i) defines Generative AI and transformative education (i.e., the “ideas”), (ii) establishes the paradoxes of Generative AI (i.e., the “what”), and (iii) provides implications for the future of education from the perspective of management educators (i.e., the “so what”). Noteworthily, the paradoxes of Generative AI are four-fold: (Paradox #1) Generative AI is a ‘friend’ yet a ‘foe’, (Paradox #2) Generative AI is ‘capable’ yet ‘dependent’, (Paradox #3) Generative AI is ‘accessible’ yet ‘restrictive’, and (Paradox #4) Generative AI gets even ‘popular’ when ‘banned’ (i.e., the “what”). Through a position that seeks to embrace rather than reject Generative AI, the lessons and implications that emerge from the discussion herein represent a seminal contribution from management educators on this trending topic and should be useful for approaching Generative AI as a game-changer for education reformation in management and the field of education at large, and by extension, mitigating a situation where Generative AI develops into a Ragnarök that dooms the future of education of which management education is a part of (i.e., the “so what”).

Abstract Review articles or literature reviews are a critical part of scientific research. While numerous guides on literature reviews exist, these are often limited to the philosophy of review procedures, protocols, and nomenclatures, triggering non-parsimonious reporting and confusion due to overlapping similarities. To address the aforementioned limitations, we adopt a pragmatic approach to demystify and shape the academic practice of conducting literature reviews. We concentrate on the types, focuses, considerations, methods, and contributions of literature reviews as independent, standalone studies. As such, our article serves as an overview that scholars can rely upon to navigate the fundamental elements of literature reviews as standalone and independent studies, without getting entangled in the complexities of review procedures, protocols, and nomenclatures.

In a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).

This guide explains the focus, rigor, and relevance of qualitative research, highlighting its role in dissecting complex social phenomena and providing in-depth, human-centered insights. The guide also examines the rationale for employing qualitative methods, underscoring their critical importance. An exploration of the methodology’s strengths, such as its ability to offer contextual depth and holistic perspectives, is juxtaposed against challenges like constrained generalizability and potential researcher bias, accompanied by practical mitigation strategies. Covering a spectrum of qualitative approaches, including grounded theory, phenomenology, ethnography, action research, and general inquiry, the guide sheds light on diverse data collection and analysis techniques. Noteworthily, the guide underscores the crucial aspect of trustworthiness in qualitative research, detailing methods to establish credibility, dependability, confirmability, and transferability. The integration of technologies like recording and transcribing tools with data analysis software and the growing influence of artificial intelligence, machine learning, and deep learning are also explored. Concluding with a discussion on ethical considerations, from participant recruitment to data stewardship, this guide serves as an essential resource that offers insightful, actionable guidance for conducting effective and impactful qualitative research.

The term metaverse is described as the next iteration of the Internet. Metaverse is a virtual platform that uses extended reality technologies, i.e. augmented reality, virtual reality, mixed reality, 3D graphics, and other emerging technologies to allow real-time interactions and experiences in ways that are not possible in the physical world. Companies have begun to notice the impact of the metaverse and how it may help maximize profits. The purpose of this paper is to offer perspectives on several important areas, i.e. marketing, tourism, manufacturing, operations management, education, the retailing industry, banking services, healthcare, and human resource management that are likely to be impacted by the adoption and use of a metaverse. Each includes an overview, opportunities, challenges, and a potential research agenda.

Literature review is part and parcel of scholarly research. Though many literature review guides are available, they remain limited because they do not adequately account for the different types of literature review. Noteworthily, literature reviews can manifest as part of conceptual or empirical studies, or as independent studies, in which the latter may be curated in various ways. Moreover, despite its importance and popularity, literature reviews, particularly as independent studies, continue to attract unfair criticism and remain scarce in service research. To address the aforementioned gaps, this article endeavors to provide an overview and guidelines for writing literature reviews. Specifically, this article explains (1) what a literature review is and is not, (2) why literature reviews are valuable, and (3) how to conduct a literature review, as well as (4) the areas of service research and (5) the innovative ways in which literature reviews can be curated in the future.

Abstract In marketing, personalization is the action of designing and producing in ways that resonate with customer preferences. Content and products that are personalized according to customer preferences can reduce customer fatigue and time in making choices, thereby decreasing their cognitive load. Despite its importance, the literature on personalized marketing remains fragmented due to the absence of a comprehensive review that consolidates the intellectual structure of the field. This study bridges this knowledge gap through a bibliometric review using performance analysis and science mapping. Through a comprehensive review of 383 publications, this study reveals the publication and citation trends, the most prolific authors, journals, and publications, and six major themes (i.e., personalized recommendation, personalized relationship, personalization–privacy paradox, personalized advertising, personalization concept and discourse in marketing, and customer insights in personalized marketing) that characterize the body of knowledge of personalized marketing. The study concludes with future research directions as ways forward for personalized marketing, wherein a focus on new‐age technologies involving artificial intelligence, big data, blockchain, internet of things, and wearables is encouraged to explore new ways to curate personalized experiences across online and offline channels.

Customer engagement (CE) is a marketing concept that emerged after the new millennium. Despite flourishing interest on CE among marketing academics and professionals, no review, to date, has provided a comprehensive overview of the past, present, and future trends of CE. Instead, past reviews on CE are often limited to conceptual (e.g., construct) or contextual (e.g., hospitality and tourism) insights derived using traditional review methods (e.g., descriptive) with a relatively modest review sample (e.g., tens to low hundreds), which provide a limited understanding of the field in its entirety. To address this gap, this review conducted a combination of bibliometric and thematic analyses on 861 CE articles published in 377 Scopus-indexed journals between 2006 and 2020. As a result, this review reveals the major trends in article, author, country, and journal performance, as well as the past, present, and future thematic trends of CE research, and thus, provides a one stop, state-of-the-art overview of CE that marketing scholars can rely on to position and design future CE research.

Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are first used. Then, hybrid methods which use AdaBoost and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used. Then, a real-world credit card data set from a financial institution is analyzed. In addition, noise is added to the data samples to further assess the robustness of the algorithms. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards.

To make scientific inferences about business phenomena, it may not be sufficient to consider the real-world context of the business environment as statistically symmetrical (e.g., linearly, regular frequencies). This is why recently the use of asymmetrical techniques which draw on the reasoning of complexity theory – such as fuzzy-set qualitative comparative analysis (fsQCA) – to better predict and explain real-world business phenomena using a configurational approach is being increasingly promoted. This article aims to identify the key contributors and knowledge structure of business and management research involving the application of complexity theory and fsQCA. Using bibliographic data of 1,155 articles extracted from Scopus, our review conducts (1) a performance analysis to shed light on the field's key contributors based on the criteria of journal, article, author, institution, and country, and (2) a scientific mapping using keyword cooccurrence and PageRank to reveal three knowledge clusters and the prominent articles in each cluster. Taken collectively, this review is a useful resource to gain a comprehensive understanding of the state-of-the-art and promising avenues for future research involving the prediction of business phenomena using complexity theory and fsQCA.

Stakeholder theory (ST) is a profound theory in business ethics and organizational management. Though several ST reviews are available, they remain restricted to functional or niche areas. Specifically, no study, to date, has attempted to review ST research in its entirety. To address this noteworthy gap, this study endeavors to (i) offer a comprehensive definition of ST and (ii) retrospect ST research and prospect its promising avenues for future growth. Given the large corpus (n = 988) and long history (1969–2021) of ST research, this study adopts and performs a systematic literature review using bibliometric analysis (performance analysis and science mapping). Consequently, this study reveals (i) the productivity (publication) and impact (citation) of ST research and its contributors (journals, authors, countries), (ii) the major thematic clusters of ST research (ST and sustainability, ST and organizational performance, ST and strategic management, and ST and stakeholder management), and (iii) the promising avenues to advance ST research across all its major thematic clusters.

Global energy challenges have driven the adoption of renewable energy sources. Usually, an intelligent energy and battery management system is deployed to harness the renewable energy sources efficiently, whilst maintaining the reliability and robustness of the power system. In recent years, the battery‐supercapacitor based hybrid energy storage system (HESS) has been proposed to mitigate the impact of dynamic power exchanges on battery's lifespan. This study reviews and discusses the technological advancements and developments of battery‐supercapacitor based HESS in standalone micro‐grid system. The system topology and the energy management and control strategies are compared. The study also discusses the technical complexity and economic sustainability of a standalone micro‐grid system. A case study of a standalone photovoltaic‐based micro‐grid with HESS is presented.

Solid-oxide fuel cells (SOFCs) are electricity generators that can convert the chemical energy in various fuels directly to the electric power with high efficiency. Recent advances in materials and related key components for SOFCs operating at ≈500 °C are summarized here, with a focus on the materials, structures, and techniques development for low-temperature SOFCs, including the analysis of most of the critical parameters affecting the electrochemical performance of the electrolyte, anode, and cathode. New strategies, such as thin-film deposition, exsolution of nanoparticles from perovskites, microwave plasma heating, and finger-like channeled electrodes, are discussed. These recent developments highlight the need for electrodes with higher activity and electrolytes with greater conductivity to generate a high electrochemical performance at lower temperatures.

Customer engagement (CE) is a marketing concept of great importance and the rise of social media has further amplified the importance of this concept. Yet, our understanding of the progress of CE research remains limited due to the absence of a one-stop state-of-the-art overview of the concept that considers its manifestation on social media. To address this gap, we review CE research on social media since the beginning of the present millennium using the PRISMA protocol for systematic reviews. The outcome of our review reveals the antecedents, decisions, and outcomes; the theories, contexts, and methods; and the ways forward for advancing knowledge, improving representation, and enhancing rigor with respect to future research on CE and social media.

Abstract While bibliometric analysis is inherently quantitative and objective, it necessitates interpretation, thereby introducing an element of subjectivity. This article proposes a sensemaking approach that transitions researchers from mere description to proactive interpretation of bibliometric results, transforming raw information into informed insights. We discuss the applicability of sensemaking in bibliometric analysis and offer practical guidelines for its integration into systematic literature reviews. These guidelines revolve around a three‐stage sensemaking process—that is, scanning, sensing, and substantiating—each crucial to deriving meaningful interpretations. As such, this article serves as a valuable guide for researchers seeking to utilize bibliometric analysis as a potent analytical tool in their review studies.

This paper proposes an enhanced adaptive perturb and observe (EA-P&O) maximum power point tracking (MPPT) algorithm for the photovoltaic system. The objective is to mitigate the limitations of the conventional P&O namely, the steady-state oscillation, diverged tracking direction, and inability to detect the global peak during partial shading. A smart oscillation detection scheme and a dynamic boundary condition resolve the first two problems, respectively. Meanwhile, an intelligent prediction method is designed to ensure that the global peak is always correctly tracked. Another feature is the open-circuit voltage is determined without using sensors. The proposed idea is verified using MATLAB simulations by imposing stringent dynamic irradiance and partial shading tests. Moreover, an experimental validation is carried out using a buck-boost converter in conjunction with dSpace DS1104 DSP board. The performance of the algorithm is compared with four prominent MPPT techniques: first, the artificial bee colony; second, modified incremental conduction; third, cuckoo search; and fourth, the hybrid ant colony optimization-P&O. The results show that the proposed method tracks the global peak successfully under distinctive patterns of partial shading, when other algorithms fail occasionally. On top of that, it improves the tracking speed by two to three times, while efficiency is maintained over 99%.

Artificial intelligence (AI) and blockchain are the two disruptive technologies emerging from the Fourth Industrial Revolution (IR4.0) that have introduced radical shifts in the industry. The amalgamation of AI and blockchain holds tremendous potential to create new business models enabled through digitalization. Although research on the application and convergence of AI and blockchain exists, our understanding of the utility of its integration for business remains fragmented. To address this gap, this study aims to characterize the applications and benefits of integrated AI and blockchain platforms across different verticals of business. Using bibliometric analysis, this study reveals the most influential articles on the subject based on their publications, citations, and importance in the intellectual network. Using content analysis, this study sheds light on the subject's intellectual structure, which is underpinned by four major thematic clusters focusing on supply chains, healthcare, secure transactions, and finance and accounting. The study concludes with 10 application areas in business that can benefit from these technologies.

The gradual depletion of oil resources and the necessity to reduce greenhouse gas emissions portray a concerning image of our contemporary security of liquid transportation fuels in the event of a global crisis. Despite a vast amount of natural gas resources that we have and the huge economic incentive, the conversion of gas to liquid fuels or chemicals is still very limited because of the high technological complexity and capital cost for facilities. However, with the anticipated depletion of liquid petroleum and the soaring price of crude oil, the conversion of natural gas to liquid feedstock or fuels will become more and more important. Higher alcohols are important feedstocks for the chemical and pharmaceutical industries and have wide applications as potential fuel additives or hydrogen carriers for fuel cells for clean energy delivery. There is a long-standing interest in the synthesis of higher alcohols from syngas, an important Fischer–Tropsch technology for natural gas conversion. The purpose of this review is to provide readers with an extensive account of catalytic synthesis of higher alcohols from syngas using various catalysts reviewed from a unique perspective: clarification of the active centers and reaction pathways. In light of the different sources providing the active centers, three major classes of catalysts in terms of monometallic, bimetallic, and trimetallic/multimetallic catalysts are systematically reviewed, and their respective performances are carefully compared. Finally, future works proposed to improve the catalyst design are described.

Leadership is a process influence between leaders and subordinates where a leader attempts to influence the behaviour of
\nsubordinates to achieve the organizational goals. Organizational success in achieving its goals and objectives depends on the
\nleaders of the organization and their leadership styles. By adopting the appropriate leadership styles, leaders can affect employee
\njob satisfaction, commitment and productivity. Two hundred Malaysian executives working in public sectors voluntarily
\nparticipated in this study. Two types of leadership styles, namely, transactional and transformational were found to have direct
\nrelationships with employees’ job satisfaction. The results showed that transformational leadership style has a stronger relationship
\nwith job satisfaction. This implies that transformational leadership is deemed suitable for managing government organizations.
\nImplications of the findings were discussed further.