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This paper aims to consolidate empirical studies between 2013 and 2022 to investigate the impact of artificial intelligence (AI) in higher education. It aims to examine published research characteristics and provide insights into the promises and challenges of AI integration in academia.

A systematic literature review was conducted, encompassing 44 empirical studies published as peer-reviewed journal papers. The review focused on identifying trends, categorizing research types and analysing the evidence-based applications of AI in higher education.

The review indicates a recent surge in publications concerning AI in higher education. However, a significant proportion of these publications primarily propose theoretical and conceptual AI interventions. Areas with empirical evidence supporting AI applications in academia are delineated.

The prevalence of theoretical proposals may limit generalizability. Further research is encouraged to validate and expand upon the identified empirical applications of AI in higher education.

This review outlines imperative implications for future research and the implementation of evidence-based AI interventions in higher education, facilitating informed decision-making for academia and stakeholders.

This paper contributes a comprehensive synthesis of empirical studies, highlighting the evolving landscape of AI integration in higher education and emphasizing the need for evidence-based approaches.

This study aims to develop the alleviating bullwhip effects framework (ABEF) replenishment rules, and bullwhip, inventory fluctuations and customer service fulfilment rates were examined. In addition, automated smoothing and replenishment rules can alleviate supply chain bullwhip effects. This study aims to understand the current artificial intelligence (AI) implementation practice in alleviating bullwhip effects in supply chain management. This study aimed to develop a system for writing reviews using a systematic approach.

The methodology for the present study consists of three parts: Part 1 deals with the systematic review process. In Part 2, the study applies social network analysis (SNA) to the fourth phase of the systematic review process. In Part 3, the author discusses developing research clusters to analyse the research state more granularly. Systematic literature reviews synthesize scientific evidence through repeatable, transparent and rigorous procedures. By using this approach, you can better interpret and understand the data. The author used two databases (EBSCO and World of Science) for unbiased analysis. In addition, systematic reviews follow preferred reporting items for systematic reviews and meta-analyses.

The study uses UCINET6 software to analyse the data. The study found that specific topics received high centrality (more attention) from scholars when it came to the study topic. Contrary to this, others experienced low centrality scores when using NETDRAW visualization graphs and dynamic capability clusters. Comprehensive analyses are used for the study’s comparison of clusters.

This study used a journal publication as the only source of information. Peer-reviewed journal papers were eliminated for their lack of rigorousness in evaluating the state of practice. This paper discusses the bullwhip effect of digital technology on supply chain management. Considering the increasing use of “AI” in their publications, other publications dealing with sensor integration could also have been excluded. To discuss the top five and bottom five topics, the author used magazines and tables.

The study explores the practical implications of smoothing the bullwhip effect through AI systems, collaboration, leadership and digital skills. Artificial intelligence is rapidly becoming a preferred tool in the supply chain, so management must understand the opportunities and challenges associated with its implementation. Furthermore, managers should consider how AI can influence supply chain collaboration concerning trust and forecasting to smooth the bullwhip effect.

Digital leadership and addressing the digital skills gap are also essential for the success of AI systems. According to the framework, it is necessary to balance AI performance and accountability. As a result of the framework and structured management approach, the author can examine the implications of AI along the supply chain.

The study uses a systematic literature review based on SNA to analyse how AI can alleviate the bullwhip effects of supply chain disruption and identify the focused and the most important AI topics related to the bullwhip phenomena. SNA uses qualitative and quantitative methodologies to identify research trends, strengths, gaps and future directions for research. Salient topics for reviewing papers were identified. Centrality metrics were used to analyse the contemporary topic’s importance, including degree, betweenness and eigenvector centrality. ABEF is presented in the study.

This study aims to synthesize existing findings in the gig worker training literature and identify the training rationales adopted by these studies, using a synthesized framework of organizational training rationales. This study seeks to delineate the rationales behind gig worker training and highlight unaddressed training needs within digital platforms, ultimately proposing a research agenda for future studies in this area.

A systematic review methodology is adopted to synthesize and analyze empirical, peer-reviewed studies on gig worker training.

The systematic review reveals that competency and economic rationales are predominantly adopted in gig worker training studies, with the relationship rationale, common in traditional training, notably absent. This study also outlines seven future research directions to highlight identified challenges and unaddressed training needs.

To the best of the author’s knowledge, this study is the first work that systematically reviews existing findings on gig worker training.

This paper aims to provide insight into the evolving relationship between humans and machines, understanding its multifaceted impact on our lifestyle and landscape in the past as well as in the present, with implications for the near future. It uses bibliometric analysis combined with a systematic literature review to identify themes, trace historical developments and offer a direction for future human–machine interactions (HMIs).

To provide thorough coverage of publications from the previous four decades, the first section presents a text-based cluster bibliometric analysis based on 305 articles from 2,293 initial papers in the Scopus and Web of Science databases produced between 1984 and 2022. The authors used VOS viewer software to identify the most prominent themes through cluster identification. This paper presents a systematic literature review of 63 qualified papers using the PRISMA framework.

Next, the systematic literature review and bibliometric analysis revealed four major historical themes and future directions. The results highlight four major research themes for the future: from Taylorism to advanced technologies; machine learning and innovation; Industry 4.0, Society 5.0 and cyber–physical system; and psychology and emotions.

There is growing anxiety among humankind that in the future, machines will overtake humans to replace them in various roles. The current study investigates the evolution of HMIs from their historical roots to Society 5.0, which is understood to be a human-centred society. It balances economic advancement with the resolution of social problems through a system that radically integrates cyberspace and physical space. This paper contributes to research and current limited knowledge by identifying relevant themes and offering scope for future research directions. A close look at the analysis posits that humans and machines complement each other in various roles. Machines reduce the mechanical work of human beings, bringing the elements of humanism and compassion to mechanical tasks. However, in the future, smart innovations may yield machines with unmatched dexterity and capability unthinkable today.

This paper attempts to explore the ambiguous and dynamic relationships between humans and machines. The present study combines systematic review and bibliometric analysis to identify prominent trends and themes. This provides a more robust and systematic encapsulation of this evolution and interaction, from Taylorism to Society 5.0. The principles of Taylorism are extended and redefined in the context of HMIs, especially advanced technologies.

Sharia-compliant financial technologies (Islamic fintech) are becoming increasingly popular among Muslims and non-Muslims. As the Islamic fintech landscape continues to grow and transform, it is crucial to understand the factors that influence the acceptance or adoption of Islamic fintech services from the past literature. Therefore, this paper aims to conduct a systematic literature review (SLR) to investigate and compile factors that could influence a user to adopt Islamic fintech.

The current study adopted the systematic literature review approach using Preferred Reporting Items for Systematic Reviews and Meta-Analyses technique to examine research on Islamic fintech adoption. A set of inclusion criteria was applied to filter out irrelevant documents and ensure the selection of only the most relevant ones. The criteria were limited to quantitative journals published in English between 2012 and 2022, as identified in Scopus outlets.

Despite the existence of various theoretical frameworks, the technology acceptance model and Unified Theory of Acceptance and Use of Technology 2 frameworks have gained prominence as the most frequently applied frameworks in examining Islamic fintech adoption. In addition, trust is the most important self-developed construct for the adoption of Islamic Fintech.

The current study does not segregate the intention, actual usage and retention, as the author is interested in understanding the overall adoption of Islamic fintech services. To obtain more specific analysis results, future research could potentially separate the three types of adoption exposure, i.e., consumer acceptance, intention and retention. Next, future studies can also expand their analysis and gain a better understanding of the research topic by exploring alternative data sources such as Web of Science, ERA or JSTOR.

To the best of the author’s knowledge, this study makes a significant contribution to the literature by providing the first systematic literature review of factors influencing fintech adoption from an Islamic perspective.

In this systematic review of the literature on women’s entrepreneurship education, this paper aims to examine the current state of the field. The authors analyze publication trends, identify major themes and propose an agenda for future research.

The authors review 363 articles published between 1993 and 2023, to develop a synthesized overview of women’s entrepreneurship education, complete with insights into the journals that have provided the most coverage of this topic, as well as how it has emerged over time.

The authors tracked the evolution of research themes and collaboration networks over a 30-year period. Results show there has been significant growth in research on women’s entrepreneurship education, as evidenced by a surge of publications on the topic and the total number of citations.

The authors categorized and analyzed six thematic clusters within the literature: entrepreneurial intention, ethical perspectives, gender-specific barriers, gender stereotypes, rural entrepreneurship and entrepreneurial self-efficacy. Building on these thematic clusters, this study discusses future research directions to advance the field.

This study aims to objectively synthesize the volume of accounting literature on financial statement fraud (FSF) using a systematic literature review research method (SLRRM). This paper analyzes the vast FSF literature based on inclusion and exclusion criteria. These criteria filter articles that are present in the accounting fraud domain and are published in peer-reviewed quality journals based on Australian Business Deans Council (ABDC) journal ranking. Lastly, a reverse search, analyzing the articles' abstracts, further narrows the search to 88 peer-reviewed articles. After examining these 88 articles, the results imply that the current literature is shifting from traditional statistical approaches towards computational methods, specifically machine learning (ML), for predicting and detecting FSF. This evolution of the literature is influenced by the impact of micro and macro variables on FSF and the inadequacy of audit procedures to detect red flags of fraud. The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper chronicles the cluster of narratives surrounding the inadequacy of current accounting and auditing practices in preventing and detecting Financial Statement Fraud. The primary objective of this study is to objectively synthesize the volume of accounting literature on financial statement fraud. More specifically, this study will conduct a systematic literature review (SLR) to examine the evolution of financial statement fraud research and the emergence of new computational techniques to detect fraud in the accounting and finance literature.

The storyline of this study illustrates how the literature has evolved from conventional fraud detection mechanisms to computational techniques such as artificial intelligence (AI) and machine learning (ML). The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper contributes to the literature by providing insights to researchers about why the evolution of accounting fraud literature from traditional statistical methods to machine learning algorithms in fraud detection and prediction.

Scholarly interest in the business effects of organised crime (OC) has recently increased. This study aims to conduct a systematic literature review (SLR) on the conditions under which OC could pose a threat to or take control of firms within a particular context.

We use narrative synthesis and thematic analysis, with a sample of 46 theoretical and empirical studies published over the past 30 years on the relationship between OC and firms within the disciplines of Business, Management and Accounting (BMA).

SLR and thematic analysis show that scholarly interest has focused on four key domains: OC as a firm, the impact of OC on firms, firms’ efforts to counter OC’s influence and governmental interventions. Using medical metaphors, we also develop a diagram depicting the interplay between OC and firms within the BMA literature.

This study contributes to the literature shaping an agenda to steer future research towards these four key themes. The effectiveness of anti-OC tools and measures depends on a thorough understanding of local norms, behaviours and business practices. In addition to measurement and methodological challenges, several grey areas remain, including the distinction between criminal enterprises and legitimate businesses. Ambiguities also surround the circumstances under which the OC preys upon firms or employs them to establish dominance over a territory.

Perceived benefits of building information modelling (BIM) have been discussed for some time, but cost–benefit benchmarking has been inconsistent. The purpose of this paper is to investigate BIM feasibility and evaluate investment worth to elucidate and develop the current understanding of BIM merit. The aim of the study is to propose a research agenda towards a more holistic perspective of BIM use incorporating quantifying investment return.

An in-depth examination of research patterns has been conducted to identify challenges in the assessment of the investment value and return on investment (ROI) for BIM in the construction industry. A total of 75 research articles were considered for the final literature review. An evaluation of the literature is conducted using a combination of bibliometric analysis and systematic reviews.

This study, which analysed 75 articles, unveils key findings in quantifying BIM benefits, primarily through ROI calculation. Two major research gaps are identified: the absence of a standardized BIM ROI method and insufficient exploration of intangible benefits. Research focus varies across phases, emphasizing design and construction integration and exploring post-construction phases. The study categorizes quantifiable factors, including productivity, changes and rework reduction, requests for information reduction, schedule efficiency, safety, environmental sustainability and operations and facility management. These findings offer vital insights for researchers and practitioners, enhancing understanding of ’BIM’s financial benefits and signalling areas for further exploration in construction.

The ’study’s outcomes offer the latest insights for researchers and practitioners to create effective approaches for quantifying ’BIM’s financial benefits. Additionally, the proposed research agenda aims to improve the current limited understanding of BIM feasibility and investment worth evaluation. Results of the study could assist practitioners in overcoming limitations associated with BIM investment and economic evaluations in the construction industry.

Construction projects commonly encounter complicated delay problems. Over the past few decades, numerous delay analysis methods (DAMs) have been developed. There is no consensus on whether existing DAMs effectively resolve delays, particularly in the case of complex concurrent delays. Thus, the primary objective of this study is to undertake a comprehensive and systematic literature review on concurrent delays, aiming to answer the following research question: Do existing delay analysis techniques deal with concurrent delays well?

This study conducts a comprehensive review of concurrent delays by both bibliometric and systematic analysis of research publications published between 1982 and 2022 in the Web of Science (WoS) and Scopus databases. For quantitative analysis, a bibliometric mapping tool, the VOSviewer, was employed to analyze 68 selected publications to explore the co-occurrence of keywords, co-authorship and direct citation. Additionally, we conducted a qualitative analysis to answer the targeted research question, identify academic knowledge gaps and explore potential research directions for solving the theoretical and practical problems of concurrent delays.

Concurrent delays are a critical aspect of delay claims. Despite DAMs developed by a limited number of research teams to tackle issues like concurrence, float consumption and the critical path in concurrent delay resolution, practitioners continue to face significant challenges. This study has successfully identified knowledge gaps in defining, identifying, analyzing and allocating liability for concurrent delays while offering promising directions for further research. These findings reveal the incompleteness of available DAMs for solving concurrent delays.

The outcomes of this study are highly beneficial for practitioners and researchers. For practitioners, the discussions on the resolution process of concurrent delays in terms of identification, analysis and apportionment enable them to proactively address concurrent delays and lay the groundwork for preventing and resolving such issues in their construction projects. For researchers, five research directions, including advanced DAMs capable of solving concurrent delays, are proposed for reference.

Existing research on DAMs lacks comprehensive coverage of concurrent delays. Through a scientometric review, it is evident that current DAMs do not deal with concurrent delays well. This review identifies critical knowledge gaps and offers insights into potential directions for future research.