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Disasters and armed conflict often co-occur, but does that imply that disasters trigger or fuel conflict? In the small but growing body of literature attempting to answer this question, divergent findings indicate the complex and contextual nature of a potential answer to this question. The purpose of this study is to contribute a robust cross-country analysis of the co-occurrence of disaster and conflict, with a particular focus on the potential role played by disaster.

Grounded in a theoretical model of disaster–conflict co-occurrence, this study merges data from 163 countries between 1990 and 2017 on armed conflict, disasters and relevant control variables (low human development, weak democratic institutions, natural resource dependence and large population size/density).

The main results of this study show that, despite a sharp increase in the co-occurrence of disasters and armed conflict over time, disasters do not appear to have a direct statistically significant relation with the occurrence of armed conflict. This result contributes to the understanding of disasters and conflicts as indirectly related via co-creation mechanisms and other factors.

This study is a novel contribution, as it provides a fresh analysis with updated data and includes different control variables that allow for a significant contribution to the field.

This paper recommends a method entitled “SMED 4.0” as a development of conventional single minute exchange of die (SMED) to avoid defect occurrence during production and improve sustainability, besides reducing setup time.

The method builds upon an extensive literature review and in-depth explorative research in SMED and zero defect manufacturing (ZDM). SMED 4.0 incorporates an evolutionary stage that employs predict-prevent strategies using Industry 4.0 technologies including the Internet of Things (IoT) and machine learning (ML) algorithms.

It presents the applicability of the proposed approach in (1) identifying the triple bottom line (TBL) criteria, which are affected by defects; (2) predicting the time of defect occurrence if any; (3) preventing defective products by performing online setting on machines during production as needed; (4) maintaining the desired quality of the product during the production and (5) improving TBL sustainability in manufacturing processes.

The extended view of SMED 4.0 in this research, as well as its analytical approach, helps practitioners develop their SMED approaches in a more holistic way. The practical application of SMED 4.0 is illustrated by implementing it in a real-life manufacturing case.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

The purpose of this study is to determine the strategy adopted by small- and medium-scale enterprise (SME) owners in setting up internal control systems, examine the relationship between the numbers of employees’ fraud and strategy adopted in setting up internal control and evaluate the difference between proactive and reactive strategies in employee’s fraud prevention among SMEs.

A survey research approach was adopted to determine whether proactive or reactive strategies were adopted by the SME owners. Specifically, a survey questionnaire was used to gather primary data from selected respondents in Lagos and Oyo States. Descriptive statistics, Spearman correlation and the Mann–Whitney test were used to analyse the primary data.

The study found that most of the SME owners used reactive internal control strategies in setting up their internal control systems after they suffered financial losses occasioned by employees’ fraud. Also, the study found a positive relationship between the number of employees’ fraud and reactive strategy. Finally, the study found a significant difference in the number of employees’ fraud occurrences between proactive and reactive internal control strategies in SMEs.

The study provides further confirmation that where internal control is properly set up and strengthened, a lower number of employee frauds will occur. Thus, giving credence to the fraud theory. The study was, however, conducted in six selected local government areas in two states.

The study provides recommendations on the adoption of a proactive strategy for curbing employees’ fraud at the onset of business operations and not until devastating events of employees’ fraud become a reality.

The study is original, as it focuses on the strategy adopted by SME owners in setting up internal control systems, which is rare in fraud empirical studies, particularly for studies conducted in emerging markets like Nigeria. It provides the need for the sustainability of SMEs as engine of growth and employment through the adoption of appropriate strategies in setting up internal control systems.

The aim of this study is to approach the way in which corporate governance influences the occurrence of financial fraud, as expressed by the M-Beneish score. In order to get further into the topic, we have first computed a corporate governance score based on the comply-explain statement and then selected a few elements that are part of the corporate governance reporting: equilibrium of board members (EQUIL), independence of board members (INDEP), selection of the board members (NOM), remuneration policy (REM), audit committee (AUDIT) and the proportion of female directors on boards (GenF). They were tested, one by one, using the financial fraud score to see the way in which they interact.

The study is conducted on a sample of 65 companies listed on the Bucharest Stock Exchange (BSE) for the 2016–2022 period. The data were processed using three-stage general least square [general least squares (GLS), with iteration, igls and option] with a common first-order panel-specific autocorrelation correction, so as to explain how a poor adoption of the corporate governance score and its elements has a negative implication for the M-Beneish score, controlling for the auditor opinion, type of auditing company and if the company is privately owned.

The results support most of our research hypothesis, revealing that a poor adoption of the corporate governance score and its components – AUDIT, EQUIL, INDEP and GenF – negatively influences the M-Beneish score, i.e. a low corporate governance score will lead to an increase in financial fraud. This is an encouraging aspect, for an improved adoption of the corporate governance principles reduces the occurrence of financial fraud.

This is a study that concerns the relationship between corporate governance and financial fraud for the case study for Romania.

The study highlights the importance of adopting the corporate governance code applied to the Romanian business environment. By measuring the presence of financial fraud appearance through the M-Beneish score, we have managed to outline the negative relationship between the two components. Thus, it is an important aspect of which companies should take account, so they will have long-term benefits and ensure the continuity of the business.

The policy implications of this project are for policymakers, so that they will understand how a good corporate governance mechanism will enhance high-performing businesses. Different aspects regarding corporate governance were validated and are in the process of being validated. Managers can extract and try to understand and apply the good characteristics of corporate governance for the well-being of their companies. At a broader level, the macroeconomic environment will increase its own well-being while encouraging market players to enhance qualitative corporate governance reporting. There is no doubt that corporate governance has a positive impact on businesses.

The study highlights the importance of adopting the corporate governance code as applied to the Romanian business environment. By measuring the occurrence of financial fraud using the M-Beneish score, we have managed to outline the negative relationship between the two components. Therefore, this is an important aspect that companies should take into account in order to have long-term benefits and ensure the continuity of their business.

This paper reviews the literature on the business models of small and medium-sized enterprises (SMEs). It seeks to examine the profile, conceptual and intellectual structure of the literature whilst leveraging the findings to suggest promising future paths to advance our knowledge on business models of SMEs.

The study resorts to a systematic literature review that conducts descriptive, bibliometric (i.e. co-word occurrence analysis and bibliographic coupling of documents analysis) and content analyses to review the literature on business models of SMEs. The research protocol included 301 articles collected in the Web of Science (WoS) database in the descriptive and bibliometric analyses. The bibliometric analysis was performed using the VOSviewer software.

The descriptive analysis portrayed the profile of this research stream. The systematisation of the co-word occurrence analysis describes the four clusters that comprise the conceptual structure of this research field. The content analysis of the bibliographic coupling of documents’ clusters portrays the seven clusters that involve the intellectual structure of this research area.

The integrated and holistic approach adopted in this study provides a detailed overview of the literature on business models of SMEs. We propose an integrative framework for the literature that bridges the main themes that form the conceptual and intellectual structure of this field of research. A comprehensive agenda for future research is suggested and implications for theory, policy and practice are stated.

This paper aims to reveal the interdisciplinarity of information science (IS) from the perspective of the evolution of theory application.

Select eight representative IS journals as data sources, extract the theories mentioned in the full texts of the research papers and then measure annual interdisciplinarity of IS by conducting theory co-occurrence network analysis, diversity measure and evolution analysis.

As a young and vibrant discipline, IS has been continuously absorbing and internalizing external theoretical knowledge and thus formed a high degree of interdisciplinarity. With the continuous application of some kernel theories, the interdisciplinarity of IS appears to be decreasing and gradually converging into a few neighboring disciplines. Influenced by big data and artificial intelligence, the research paradigm of IS is shifting from a theory centered one to a technology centered one.

This study helps to understand the evolution of the interdisciplinarity of IS in the past 21 years. The main limitation is that the data were collected from eight journals indexed by the Social Sciences Citation Index and a small amount of theories might have been omitted.

This study identifies the kernel theories in IS research, measures the interdisciplinarity of IS based on the evolution of the co-occurrence network of theory source disciplines and reveals the paradigm shift being happening in IS.

This paper aims to review corporate fraud, as a concept, and the emerging research trends in corporate fraud research from 1957 to 2022 using bibliometric analysis techniques.

A total of 7,750 publications from the Scopus database were first assessed using performance analysis to explore the descriptive nature of the bibliographic data, and subsequently, citation, co-citation, co-occurrence and bibliographic coupling analyses were conducted using the VOSviewer software.

The results indicate there has been increasing growth in fraud research over the years, especially since the global corporate scandals of 2008. Although fraud is a global issue, the results suggest that most extant studies originate from developed economies, with a high level of collaboration amongst scholars in these countries. In addition, the co-occurrence analysis indicates that research into corporate fraud has largely focused on its determinants and corruption. The determinants identified are further clustered in the paper as individual, organizational and national-level factors.

The findings should inform practitioners and policymakers of the state of knowledge on corporate fraud which could be useful in developing strategies and policies to mitigate its occurrence.

The study points to the need for research collaborations among scholars in developing economies to increase investigations into the occurrences of fraud.

To the best of the authors’ knowledge, this is the first study to holistically assess the intellectual structure of corporate fraud studies from its inception and the trends over time.

The increasing urgency to address climate change in construction has made green construction (GC) and sustainability critical topics for academia and industry professionals. However, the volume of literature in this field has made it impractical to rely solely on traditional systematic evidence mapping methodologies.

This study employs machine learning (ML) techniques to analyze the extensive evidence-base on GC. Using both supervised and unsupervised ML, 5,462 relevant papers were filtered from 10,739 studies published from 2010 to 2022, retrieved from the Scopus and Web of Science databases.

Key themes in GC encompass green building materials, construction techniques, assessment methodologies and management practices. GC assessment and techniques were prominent, while management requires more research. The results from prevalence of topics and heatmaps revealed important patterns and interconnections, emphasizing the prominent role of materials as major contributors to the construction sector. Consistency of the results with VOSviewer analysis further validated the findings, demonstrating the robustness of the review approach.

Unlike other reviews focusing only on specific aspects of GC, use of ML techniques to review a large pool of literature provided a holistic understanding of the research landscape. It sets a precedent by demonstrating the effectiveness of ML techniques in addressing the challenge of analyzing a large body of literature. By showcasing the connections between various facets of GC and identifying research gaps, this research aids in guiding future initiatives in the field.

Neuromarketing is a marketing communication field that applies neuroscience and physiological research tools to study consumer behavior toward stimuli, viz., ads and brands. This study aims to assess research trends in the neuromarketing field on the most influential journals, authorships, countries, citations and co-occurrences. The Scopus database is used to analyze identified articles from 2013 to 2022 and for the eligible research articles, a “systematic methodological review” (SMR) on consumer behavior through neuromarketing approach was done. “Visualization of Science (VOS)” viewer and “Biblioshiny” by R-studio software have been used for mapping the keyword analysis, co-citation analysis and author occurrence analysis. It was further found that of the top 10 academic institutions, the list is dominated by the six Asian institutions. It was further witnessed that journal “Physiology and Behavior” is trending as the most dedicated and emerging journals on neuromarketing and consumer behavior. Asian nations such as Bangladesh, China, India, Indonesia, etc., are turning out to be an emerging collaborators and publishers in this niche area of research, thereby giving tough competition to most developed countries. The findings of the thematic mapping show that neuromarketing is itself a very novel and newest area of study and topics such as “human marketing,” “neuromarketing,” “consumer behavior” and “electroencephalography” are new dimensions that can be looked upon in future.