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The purpose of this paper is to identify and analyse the interactions among various enablers which are critical to the success of big data initiatives in operations and supply chain management (OSCM).

Fourteen enablers of big data in OSCM have been selected from literature and consequent deliberations with experts from industry. Three different multi criteria decision-making (MCDM) techniques, namely, interpretive structural modeling (ISM), fuzzy total interpretive structural modeling (fuzzy-TISM) and decision-making trial and evaluation laboratory (DEMATEL) have been used to identify driving enablers. Further, common enablers from each technique, their hierarchies and inter-relationships have been established.

The enabler modelings using ISM, Fuzzy-TISM and DEMATEL shows that the top management commitment, financial support for big data initiatives, big data/data science skills, organizational structure and change management program are the most influential/driving enablers. Across all three different techniques, these five different enablers has been identified as the most promising ones to implement big data in OSCM. On the other hand, interpretability of analysis, big data quality management, data capture and storage and data security and privacy have been commonly identified across all three different modeling techniques as the most dependent big data enablers for OSCM.

The MCDM models of big data enablers have been formulated based on the inputs from few domain experts and may not reflect the opinion of whole practitioners community.

The findings enable the decision makers to appropriately choose the desired and drop undesired enablers in implementing the big data initiatives to improve the performance of OSCM. The most common driving big data enablers can be given high priority over others and can significantly enhance the performance of OSCM.

MCDM-based hierarchical models and causal diagram for big data enablers depicting contextual inter-relationships has been proposed which is a new effort for implementation of big data in OSCM.

Several manuscripts are adopting knowledge-based dynamic capabilities (KBDCs) as their main theoretical lens. However, these manuscripts lack consistent conceptualization and systematization of the construct. Consequently, the purpose of this study is to advance the understanding of KBDCs by clarifying the dominant concepts at the junction of knowledge management and dynamic capabilities domains, identifying which emerging themes are gaining traction with KBDCs scholars, demonstrating how the central thesis around KBDCs has evolved and explaining how can KBDCs scholars move towards finding a mutually agreed conceptualization of the field to advance empirical assessment.

The Clarivate Analytics Web of Science Core Collection database was used to extract 225 manuscripts that lie at the confluence of two promising management domains, namely, knowledge management and dynamic capabilities. A scientometric analysis including co-citation analysis, bibliographic coupling, keyword co-occurrence network analysis and text mining was conducted and integrated with a systematic review of results to facilitate an unstructured ontological discovery in the field of KBDCs.

The co-citation analysis produced three clusters of research at the junction of knowledge management and dynamic capabilities, whereas the bibliographic coupling divulged five themes of research that are gaining traction with KBDCs scholars. The systematic literature review helped to clarify each clusters’ content. While scientific mapping analysis explained how the central thesis around KBDCs has evolved, text mining and keyword analysis established how KBDCs emerge from the combination of knowledge management process capabilities and dynamic capabilities.

Minimal attention has been paid to systematizing the literature on KBDCs. Accordingly, KBDCs view has been investigated through complementary scientometric methods involving machine-based algorithms to allow for a more robust, structured, comprehensive and unbiased mapping of this emerging field of research.

The purpose of this paper is to find and classify the most relevant works in the literature on the latest technologies applied in global supply chains. To help future researchers find the most relevant the authors according to the authors’ research interest quickly and to provide insights into the most promising areas.

The authors provide a bibliometric analysis of 292 documents referenced in the Scopus® database clustering by relatedness of works and keywords.

The authors present insights and deduce new perspectives in the potential search for new business models. The authors show that in specific fields, some works and authors have a much greater influence than others.

Some documents published on the web or in paper form may be missing. The analyses largely depend on the choice of keywords. Another selection might have shown different results.

This paper provides the basis for new research in applications of the latest technologies in supply chains and corresponding new business models.

This work is a first effort to help researchers make sense of the mass of published scientific results on new technologies and their impact on new supply chain business models.

This paper aims to advance the international marketing debate by presenting the results of a structured literature review (SLR) focusing on Big Data implementation in customer relationship management (CRM) strategizing. It outlines past and present literature and frames a future research agenda.

The research analyzes papers published in journals from 2013 to 2020, deriving significant insights about Big Data applications in CRM. A sample of 48 articles indexed at Scopus was preliminarily submitted for bibliometric analysis. Finally, 46 papers were analyzed with content and a bibliometric analysis to identify areas of thematic specializations.

The paper presents a conceptual multilevel framework demonstrating areas of specialization emerging from the literature. The framework is built around four coordinated sequences of actions relevant to “why,” “what,” “who” and “how” Big Data is implemented in CRM strategies, thus supporting the conception and implementation of an internationalization marketing strategy.

Implications for the development of the future research agenda on international marketing arise from the comprehension of Big Data in CRM strategy.

The paper provides a comprehensive SLR of the articles dealing with models and processes of Big Data for CRM from an international marketing perspective. Despite these issues' relevance and the increasing literature focused on them, research in this area is still fragmented and underexplored, requiring more systematic and holistic studies.

Currently, in the health-care sector, information security and privacy are increasingly important issues. The improvement in information security is highlighted in adopting digital patient records based on regulation, providers’ consolidation, and the growing need to exchange information among patients, providers, and payers.

Big data on health care are likely to improve patient outcomes, predict epidemic outbreaks, gain valuable insights, prevent diseases, reduce health-care costs and improve analysis of the quality of life.

In this paper, the big data analytics-based cybersecurity framework has been proposed for security and privacy across health-care applications. It is vital to identify the limitations of existing solutions for future research to ensure a trustworthy big data environment. Furthermore, electronic health records (EHR) could potentially be shared by various users to increase the quality of health-care services. This leads to significant issues of privacy that need to be addressed to implement the EHR.

This framework combines several technical mechanisms and environmental controls and is shown to be enough to adequately pay attention to common threats to network security.

This study aims to provide an in-depth understanding of big data analytics (BDA) in human resource management (HRM). The emergence of digital technology and the availability of large volume, high velocity and a great variety of data has forced the HRM to adopt the BDA in managing the workforce.

This paper evaluates the past, present and future trends of HRM through the bibliometric analysis of citation, co-citation and co-word analysis.

Findings from the analysis present significant research clusters that imply the knowledge structure and mapping of research streams in HRM. Challenges in BDA application and firm performances appear in all three bibliometric analyses, indicating this subject’s past, current and future trends in HRM.

Implications on the HRM landscape include fostering a data-driven culture in the workplace to reap the potential benefits of BDA. Firms must strategically adapt BDA as a change management initiative to transform the traditional way of managing the workforce toward adapting BDA as analytical tool in HRM decision-making.

This study presents past, present and future trends in BDA knowledge structure in human resources management.

The purpose of this paper is to explore the research status and development trend of the field of event detection in social media (ED in SM) through a bibliometric analysis of academic publications.

First, publication distributions are analyzed including the trends of publications and citations, subject distribution, predominant journals, affiliations, authors, etc. Second, an indicator of collaboration degree is used to measure scientific connective relations from different perspectives. A network analysis method is then applied to reveal scientific collaboration relations. Furthermore, based on keyword co-occurrence analysis, major research themes and their evolutions throughout time span are discovered. Finally, a network analysis method is applied to visualize the analysis results.

The area of ED in SM has received increasing attention and interest in academia with Computer Science and Engineering as two major research subjects. The USA and China contribute the most to the area development. Affiliations and authors tend to collaborate more with those within the same country. Among the 14 identified research themes, newly emerged themes such as Pharmacovigilance event detection are discovered.

This study is the first to comprehensively illustrate the research status of ED in SM by conducting a bibliometric analysis. Up-to-date findings are reported, which can help relevant researchers understand the research trend, seek scientific collaborators and optimize research topic choices.

Human resource analytics (HRA) has developed as a new business trend and challenge, stressing the strategic relevance of human resource management (HRM) to senior management executives. HRA is a process that uses statistical techniques, to link HR practices to organizational performance. The purpose of this study is to carry out recent development in HRA, bibliometric analysis and content analysis to present a comprehensive account of HRA to fill the gap in the evolution and status of its research.

The study is based on the recent advances in HRA in terms of it evolution and advancement by analyzing and drawing conclusions 480 articles retrieved from the Web of Science (WoS) database from 2003 to March 2022. The methodology is divided into four steps: data collection, analysis, visualization and interpretation. The study performed a rigorous bibliometric assessment of HRA using the bibliometric R-package and VOS viewer.

The findings based on the literature survey, and bibliometric analysis, reveal the path-breaking articles, the prominent authors, most contributing institutions and countries that have contributed to the HRA scholarship. The results show that the number of publications has significantly increased from 2015 onwards, reaching a maximum of 101 journals in 2021. The USA, China, India, Canada and the United Kingdom were the most productive countries in terms of the total number of publications. Human Resource Management Journal, Human Resource Management, International Journal of Manpower, and Journal of Organizational Effectiveness-People and Performance are the top four academic outlets in the field of HRA. Additionally, the study identifies four clusters of HRA research and the knowledge gaps in HRA scholarship.

The present study is based on the articles retrieved from the WoS. The study underpins HRA research to understand the trends and presents a structured account. However, the study is not free from limitations. It is recommended that future research could be undertaken by combining WoS and Scopus databases to have a more detailed and comprehensive view. This study indicates that the field is still in its infancy stage. Hence, there is a need for more arduous research on the topic to help develop a better understanding of this field.

The findings of knowledge clusters will drive future researchers to augment the field. The evolution of the four clusters and their subsequent development will fill the gaps in the literature. This study enriches the HRA literature and the findings of this study may assist academicians, researchers and managers in furthering their research in the identified research clusters

Today, the rapid development and expansion of advanced technologies have created many changes in society and industry and motivate businesses to use digital transformation strategy (DTS) to create significant changes in the business environment. Therefore, it is necessary to define a roadmap and a vision that will determine the steps forward in this direction. In line with this, the purpose of this study is a comprehensive review of past and present studies in this field to identify future research guidelines and gaps related to the implementation of this concept.

This study is a bibliometric analysis using VOSviewer software for all documents published in the Scopus database in the field of DTS from 2011 (the emergence of Industry 4.0) to 2021. It should also be noted that the data for this study have been collected and analyzed in September 2021.

The current study presents the basic bibliometric results for DTS, and it focuses on DTS performance analysis and its science mapping during the past 10 years. This study first shows the publication process, types and languages of published documents, and the most influential authors, institutions, sources and countries in terms of publishing documents and receiving citations in the field of DTS. Then, by using the VOSviewer software, it shows the bibliographic coupling of top authors, institutions, sources and countries. Finally, it reports the co-occurrence of authors’ frequently occurring keywords and the timeline of their publications.

The study presents the results of the first attempt to conduct a comprehensive bibliometric analysis of DTS-related documents. Its contribution lies in the fact that it has categorized the most frequently co-occurring keywords into specific clusters so that researchers will know which keywords have co-occurred with each other the most. Also, the most influential keywords in each cluster in terms of having total link strength and the number of its co-occurrence with others were identified. Finally, it became clear that the process of publishing documents over time has been concentrated on topics such as acceptance of digital culture, strategic renewal and digital transformation of business models, as well as presentation of a research agenda on the applications and barriers of DTS in critical situations such as COVID-19, which leads researchers to some awareness and insights for conducting new research.

The purpose of this paper was to develop the first standard apparel sizing system for Saudi adult female population originating from anthropometric study using three-dimensional (3D) body scanner.

An anthropometric survey was conducted in four regions of the country where 1,074 participants between the ages of 18 and 63 were scanned using white light 3D body scanner. K-means cluster analysis using stature and hip girth as control variables produced the proposed sizing system, whereas regression equations were used to determine the parameters between measurements of different sizes.

Three sizing groups with 12 size designations in each totalling 36 size designations were identified. The sizing charts developed in this study show that key girth measurement ranges of chest, waist and hips are comparable to that of ISO standard and (ASTM D5585-11), while the Saudi female population falls into shorter height brackets than ISO and ASTM standards.

In this study, the first anthropometric database for Saudi female population was established using 3D body scanning technology, and a sizing system for this target population was developed.