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1 – 10 of over 2000
Article
Publication date: 2 February 2021

Yusra Qamar and Taab Ahmad Samad

This paper aims to identify the current research trends and set the future research agenda in the area of human resource (HR) analytics by an extensive review of the existing…

4516

Abstract

Purpose

This paper aims to identify the current research trends and set the future research agenda in the area of human resource (HR) analytics by an extensive review of the existing literature. The paper aims to capture state of the art and develop an exhaustive understanding of the theoretical foundations, concepts and recent developments in the area.

Design/methodology/approach

A portfolio of 125 articles collected from the Scopus database was systematically analyzed using a two-tier method. First, the evolution, current state of the literature and research clusters are identified using bibliometric techniques. Finally, using content analysis, the research clusters are studied to develop the future research agenda.

Findings

Based on the bibliometric analysis, network analysis and content analysis techniques, this study provides a comprehensive review of the existing literature. The study also highlights future research themes by identifying knowledge gaps based on content analysis of research clusters.

Research limitations/implications

The evolution and the current state of the HR analytics literature are presented. Some specific research questions are also provided to help future research.

Originality/value

This study enriches the literature of HR analytics by integrating bibliometric analysis and content analysis to develop a more systematic and exhaustive understanding of the research area. The findings of this study may assist fellow researchers in furthering their research in the identified research clusters.

Details

Personnel Review, vol. 51 no. 1
Type: Research Article
ISSN: 0048-3486

Keywords

Article
Publication date: 22 December 2022

Meenal Arora, Anshika Prakash, Saurav Dixit, Amit Mittal and Swati Singh

This study aims to analyze the existing literature in human resource analytics and highlights the future research agenda and trends in the same context. It deals with evaluating…

Abstract

Purpose

This study aims to analyze the existing literature in human resource analytics and highlights the future research agenda and trends in the same context. It deals with evaluating regional distribution, identifying key authors, publications, journals and keyword occurrences while examining current literature.

Design/methodology/approach

A total of 127 articles exported from the Scopus database were systematically analyzed using bibliometric analysis through VOSviewer, including performance analysis and science mapping of the literature studied.

Findings

This research postulates the inconsistency between the number of publications and citations received by an author. There was an increase in collaborative research over the years. Human Resource Management Review was regarded as the most influential journal with maximum citation. Maximum publications came from Asian countries. The study revealed that the author with maximum citation were mostly the first authors of the most cited documents.

Practical implications

This research may be beneficial for both researchers and human resource (HR) practitioners because it identifies the research gaps and research needs in the HR analytics domain. Besides, this study recognizes the patterns in HR analytics literature that helps researchers better understand the subject area.

Originality/value

This research incorporates bibliometric analysis for analyzing HR analytics literature to establish a more exhaustive and systematic understanding of the research area. This research contributes to the existing body of literature and assists fellow researchers in future studies.

Details

Information Discovery and Delivery, vol. 51 no. 3
Type: Research Article
ISSN: 2398-6247

Keywords

Content available

Abstract

Details

Library Hi Tech, vol. 41 no. 2
Type: Research Article
ISSN: 0737-8831

Article
Publication date: 27 December 2021

Anjali Shishodia, Rohit Sharma, R. Rajesh and Ziaul Haque Munim

The study identifies nine important research areas and critically maps the structural relationships among supply chain resilience (SCRES) dimensions, namely, vulnerabilities…

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Abstract

Purpose

The study identifies nine important research areas and critically maps the structural relationships among supply chain resilience (SCRES) dimensions, namely, vulnerabilities, capabilities, strategies and performance metrics. The analysis also revealed various influential authors, journals, institutions and trending articles, across SCRES literature.

Design/methodology/approach

This study performs a bibliometric analysis of 771 articles published over the 32 years from 1988 to 2020, and network analysis in combination with content analysis of 90 most cited articles published in research fronts of SCRES.

Findings

The results demonstrate the underlying research areas within the SCRES, which are clustered into nine research themes: (1) conceptual development of SCRES, (2) recent developments of designing resilient supply chain (SC) networks, (3) focus on identifying drivers of SC vulnerability and risks, (4) impact of risks on network resilience, (5) risk assessment to avoid breakdowns/disruptions, (6) measuring resilience approaches/drivers to improve SC performance, (7) building resilient capabilities by integrating other SC dimensions, (8) quantification of SC network and (9) emphasis on developing robustness in SC networks.

Practical implications

This research offers implications for classifying the works in literature based on bibliometric information and network analysis techniques. This can help researchers and practitioners to understand the prominent areas in SCRES and provide guidelines for future research in this area.

Originality/value

This study provides an overview of the evolution of SCRES over time in the domain of supply chain management and also outlines a future research agenda claimed by the trending articles to encourage further investigations in the field of SCRES.

Details

The International Journal of Logistics Management, vol. 34 no. 4
Type: Research Article
ISSN: 0957-4093

Keywords

Article
Publication date: 20 December 2022

Javaid Ahmad Wani and Shabir Ahmad Ganaie

The current study aims to map the scientific output of grey literature (GL) through bibliometric approaches.

Abstract

Purpose

The current study aims to map the scientific output of grey literature (GL) through bibliometric approaches.

Design/methodology/approach

The source for data extraction is a comprehensive “indexing and abstracting” database, “Web of Science” (WOS). A lexical title search was applied to get the corpus of the study – a total of 4,599 articles were extracted for data analysis and visualisation. Further, the data were analysed by using the data analytical tools, R-studio and VOSViewer.

Findings

The findings showed that the “publications” have substantially grown up during the timeline. The most productive phase (2018–2021) resulted in 47% of articles. The prominent sources were PLOS One and NeuroImage. The highest number of papers were contributed by Haddaway and Kumar. The most relevant countries were the USA and UK.

Practical implications

The study is useful for researchers interested in the GL research domain. The study helps to understand the evolution of the GL to provide research support further in this area.

Originality/value

The present study provides a new orientation to the scholarly output of the GL. The study is rigorous and all-inclusive based on analytical operations like the research networks, collaboration and visualisation. To the best of the authors' knowledge, this manuscript is original, and no similar works have been found with the research objectives included here.

Details

Library Hi Tech, vol. 42 no. 1
Type: Research Article
ISSN: 0737-8831

Keywords

Article
Publication date: 19 July 2023

Dieudonné Tchuente and Anass El Haddadi

Using analytics for firms' competitiveness is a vital component of a company's strategic planning and management process. In recent years, organizations have started to capitalize…

Abstract

Purpose

Using analytics for firms' competitiveness is a vital component of a company's strategic planning and management process. In recent years, organizations have started to capitalize on the significant use of big data for analyses to gain valuable insights to improve decision-making processes. In this regard, leveraging and unleashing the potential of big data has become a significant success factor for steering firms' competitiveness, and the related literature is increasing at a very high pace. Thus, the authors propose a bibliometric study to understand the most important insights from these studies and enrich existing conceptual models.

Design/methodology/approach

In this study, the authors use a bibliometric review on articles related to the use of big data for firms' competitiveness. The authors examine the contributions of research constituents (authors, institutions, countries and journals) and their structural and thematic relationships (collaborations, co-citations networks, co-word networks, thematic trends and thematic map). The most important insights are used to enrich a conceptual model.

Findings

Based on the performance analysis results, the authors found that China is by far the most productive country in this research field. However, in terms of influence (by the number of citations per article), the most influential countries are the UK, Australia and the USA, respectively. Based on the science mapping analysis results, the most important findings are projected in the common phases of competitive intelligence processes and include planning and directions concepts, data collection concepts, data analysis concepts, dissemination concepts and feedback concepts. This projection is supplemented by cross-cutting themes such as digital transformation, cloud computing, privacy, data science and competition law. Three main future research directions are identified: the broadening of the scope of application fields, the specific case of managing or anticipating the consequences of pandemics or high disruptive events such as COVID-19 and the improvement of connection between firms' competitiveness and innovation practices in a big data context.

Research limitations/implications

The findings of this study show that the most important research axis in the existing literature on big data and firms' competitiveness are mostly related to common phases of competitive intelligence processes. However, concepts in these phases are strongly related to the most important dimensions intrinsic to big data. The use of a single database (Scopus) or the selected keywords can lead to bias in this study. Therefore, to address these limitations, future studies could combine different databases (i.e. Web of Science and Scopus) or different sets of keywords.

Practical implications

This study can provide to practitioners the most important concepts and future directions to deal with for using big data analytics to improve their competitiveness.

Social implications

This study can help researchers or practitioners to identify potential research collaborators or identify suitable sources of publications in the context of big data for firms' competitiveness.

Originality/value

The authors propose a conceptual model related to big data and firms' competitiveness from the outputs of a bibliometric study.

Details

Journal of Enterprise Information Management, vol. 36 no. 6
Type: Research Article
ISSN: 1741-0398

Keywords

Article
Publication date: 29 March 2024

Anil Kumar Goswami, Anamika Sinha, Meghna Goswami and Prashant Kumar

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers…

Abstract

Purpose

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

Design/methodology/approach

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

Findings

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

Research limitations/implications

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

Practical implications

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

Originality/value

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 8 November 2019

Pulkit Tiwari, P. Vigneswara Ilavarasan and Sushil Punia

The purpose of this paper is to provide a systematic literature review on the technological aspects of smart cities and to give insights about current trends, sources of research…

Abstract

Purpose

The purpose of this paper is to provide a systematic literature review on the technological aspects of smart cities and to give insights about current trends, sources of research, contributing authors and countries. It is required to understand technical concepts like information technology, big data analytics, Internet of Things and blockchain needed to implement smart city models successfully.

Design/methodology/approach

The data were collected from the Scopus database, and analysis techniques like bibliometric analysis, network analysis and content analysis were used to obtain research trends, publications growth, top contributing authors and nations in the domain of smart cities. Also, these analytical techniques identified various fields within the literature on smart cities and supported to design a conceptual framework for Industry 4.0 adoption in a smart city.

Findings

The bibliometric analysis shows that research publications have increased significantly over the last couple of years. It has found that developing countries like China is leading the research on smart cities. The network analytics and article classification identified six domains within the literature on smart cities. A conceptual framework for the smart city has proposed for the successful implementation of Industry 4.0 technologies.

Originality/value

This paper explores the role of Industry 4.0 technologies in smart cities. The bibliometric data on publications from the year 2013 to 2018 were used and investigated by using advanced analytical techniques. The paper reviewS key technical concepts for the successful execution of a smart city model. It also gives an idea about various technical considerations required for the implementation of the smart city model through a conceptual framework.

Details

Benchmarking: An International Journal, vol. 28 no. 5
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 31 May 2019

Lulu Qiu, Elsie Zhou, Tiffany Yu and Neil Smyth

Librarians are challenged by technology transformations in research data metrics to re-position in the evolving cycles of research production, communication and evaluation. They…

468

Abstract

Purpose

Librarians are challenged by technology transformations in research data metrics to re-position in the evolving cycles of research production, communication and evaluation. They are also are challenged by the new and emerging strategic needs of universities for data-driven research intelligence that provides a comparative edge in the global world of higher education. This paper aims to discover how libraries can support universities in applying international assessment standards by delivering new reference and information services based on data analytics.

Design/methodology/approach

This paper focuses on a new reference and information service Research Data Analytics in China that was launched in 2017. There is a discussion of how new university needs have provided the strategic drive for librarians to develop skills.

Findings

The Research Data Analytics service development is significant for showcasing a new role for librarians in relation to research data evaluation metrics linked to publication strategies for authors and strategic intelligence for institutions. This has driven the development of the library’s position and influence in strategic research intelligence services.

Originality/value

The future for librarians is about being adventurous. Librarians are experts who will sparkle and shine beyond the bubble of the library walls. They can demonstrate a commitment to supporting university colleagues in professional services and academia to blossom and flourish. Distinctive visual technologies can be adopted for exploring research data which transform research production, communication and evaluation and therefore transform our position and future through technology-enabled innovations.

Article
Publication date: 31 March 2020

Zeeshan Inamdar, Rakesh Raut, Vaibhav S. Narwane, Bhaskar Gardas, Balkrishna Narkhede and Muhittin Sagnak

The volume of data being generated by various sectors in recent years has increased exponentially. Consequently, professionals struggle to process essential data in the current…

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Abstract

Purpose

The volume of data being generated by various sectors in recent years has increased exponentially. Consequently, professionals struggle to process essential data in the current competitive world. The purpose of the study is to explore and provide insights into the Big Data Analytics (BDA) studies in different sectors.

Design/methodology/approach

This study performs a systematic literature review (SLR) with bibliometric analysis of BDA adoption (BDAA) in the supply chain and its applications in various sectors from 2014 to 2018. This paper focuses on BDAA studies have been carried out across different countries and sectors. Also, the paper explores different tools and techniques used in BDAA studies.

Findings

The benefits of adopting BDA, coupled with a lack of adequate research in the field, have motivated this study. This literature review categorizes paper into seven main areas and found that most of the studies were carried out in manufacturing and service.

Practical implications

This research insight and observations can provide practitioners and academia with guidance on implementing BDA in different sustainable supply chain sectors. The article indicates a few remarkable gaps in the future direction and trends regarding the integration of BDA and sustainable supply chain development.

Originality/value

The study derives a new categorization of BDA, which investigates how data is generated, organized, captured, interpreted and evaluated to give valuable insights to manage the sustainable supply chain.

Details

Journal of Enterprise Information Management, vol. 34 no. 1
Type: Research Article
ISSN: 1741-0398

Keywords

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