Search results

The use of structured literature review methods like bibliometric analysis is growing in the management fields, but there is limited knowledge on how they can be facilitated by technology. Hence, we conducted a broad overview of software tools, their roles, and limitations in structured (bibliometric) literature reviewing activities. Subsequently, we show that several software tools are freely available to aid in searching the literature, identifying/ extracting relevant publications, screening/assessing quality of the extracted data, and performing analyses to generate insights from the literature. However, their applications may be confronted with several challenges such as limited analytical and functional capabilities, inadequate technological skills of researchers, and the fact that the researcher's insights are still needed to generate compelling conclusions from the results produced by software tools. Consequently, we contribute toward advancing the methodologies for performing structured reviews by providing a comprehensive and updated overview of the knowledge base of key technological software tools and the conduct of structured or bibliometric literature reviews.

This paper systematically evaluates the existing literature of innovations in freight transport, including all modes, to uncover the key research themes and methodologies employed by researchers to study innovations and their implications in this industry. It analyses the role of transport and the impact of innovations during crises, such as COVID-19.

Qualitative and quantitative analysis of the innovations in freight transport unravels the pre-requisites of such endeavours in achieving a resilient and sustainable transport network that effectively and efficiently operates during a crisis. The authors performed keyword co-occurrence network (KCON) analysis and research focus parallelship network (RFPN) analysis using BibExcel and Gephi to determine the major resulting research streams in freight transport.

The RFPN identified five emerging themes: transport operations, technological innovation, transport economics, transport policy and resilience and disaster management. Optimisation and simulation techniques, and more recently, artificial intelligence and machine learning (ML) approaches, have been used to model and solve freight transport problems. Automation innovations have also penetrated freight and supply chains. Information and communication technology (ICT)-based innovations have also been found to be effective in building resilient supply chains.

Given the growth of e-commerce during COVID-19 and the resulting logistics demand, along with the need for transporting food and medical emergency products, the role of automation, optimisation, monitoring systems and risk management in the transport industry has become more salient. Transport companies need to improve their operational efficiency using innovative technologies and data science for informed decision-making.

This paper advises researchers and practitioners involved in freight transport and innovation about main directions and gaps in the field through an integrated approach for evaluating research undertaken in the area. This paper also highlights the role of crisis, e.g. COVID-19, and its impacts on freight transport. Major contributions of this paper are as follows: (1) a qualitative and quantitative, systematic and effective assessment of the literature on freight transport through a network analysis of keywords supplemented by a review of the text of 148 papers; (2) unravelling major research areas; (3) identifying innovations in freight transport and their classification as technological and non-technological and (4) investigating the impact of crises and disruptions in freight transport.

The application of digital twins to optimise operations and supply chain management functions is a bourgeoning practice. Scholars have attempted to keep pace with this development initiating a fast-evolving research agenda. The purpose of this paper is to take stock of the emerging research stream identifying trends and capture the value potential of digital twins to the field of operations and supply chain management.

In this work we employ a bibliometric literature review supported by bibliographic coupling and keyword co-occurrence network analysis to examine current trends in the research field regarding the value-added potential of digital twin in operations and supply chain management.

The main findings of this work are the identification of four value clusters and one enabler cluster. Value clusters are comprised of articles that describe how the application of digital twin can enhance supply chain activities at the level of business processes as well as the level of supply chain capabilities. Value clusters of production flow management and product development operate at the business processes level and are maturing communities. The supply chain resilience and risk management value cluster operates at the capability level, it is just emerging, and is positioned at the periphery of the main network.

This is the first study that attempts to conceptualise digital twin as a dynamic capability and employs bibliometric and network analysis on the research stream of digital twin in operations and supply chain management to capture evolutionary trends, literature communities and value-creation dynamics in a digital-twin-enabled supply chain.

The emergent field of Internet of Things (IoT) has been evolving rapidly with a geometric growth in the number of academic publications in this field. The purpose of this paper is to review the literature of IoT in past 16 years using rigorous bibliometric and network analysis tools, offering at the same time future directions for the IoT research community and implications for managers and decision makers.

The authors adopted the techniques of bibliometric and network analysis. The paper reviewed the articles published on IoT from 2000 to 2015.

This study identifies top contributing authors; key research topics related to the field; the most influential works based on citations and PageRank; and established and emerging research clusters. Scholars are encouraged to further explore this topic.

This study focusses only on vision and applications of IoT. Scholars may explore various other aspects of this area of research.

To the best of authors’ knowledge, this is the first study to review the literature on IoT by using bibliometric and network analysis techniques. The study is unique as it spans a long time period of 16 years (2000-2015). The study proposes a five-cluster classification of research themes that may inform current and future research in IoT.

The growing demands of work and life have shifted the concept of work-life balance to work-life integration (WLI). The success of integration depends upon the flexibility to perform the duties. This paper aims to explore the factors that affect WLI and the role of flexible work arrangements (FWAs) in the process of WLI.

Systematic literature review was used to explore the concept of WLI and FWAs. A bibliometric analysis was carried out with Bibexcel and VoSviewer.

This paper explained the organizational and personal factors that create the demand for WLI. The FWAs, perceived flexibility, technology and self-efficacy have important roles in WLI. The result of WLI can be enrichment or strain, depends upon how effectively the work-life domains are integrated.

This paper explores the work-life from both personal and organizational views. The findings of this paper will be useful to design the organizational policies and work arrangements that match the requirements of employees and organizations. This paper helps to develop the future research agenda of investigating the relations of WLI to performance, organizational policies and personal factors.

Decision making in logistics is an increasingly complex task for organizations as these involve decisions at strategic, tactical and operational levels coupled with the triple-bottom line of sustainability. Decision support systems (DSS) played a vital role in arguably solving the challenges associated with decision making in sustainable logistics. The purpose of this paper is to explore the current state of the research in the domain of DSS for logistics while considering sustainability aspects.

A systematic review approach using a set of relevant keywords with several exclusion criteria was adopted to identify literature related to DSS for sustainable logistics. A total of 40 papers were found from 1994 to 2015, which were then analyzed along the dimensions of publishing trend, geographic distribution and collaboration, the most influential journals, affiliations and authors as well as the key themes of identified literature. The analysis was conducted by means of bibliometric and text mapping tools, namely BibExcel, gpsvisualizer and VOSviewer.

The bibliometric analysis showed that DSS for sustainable logistics is an emerging field; however, it is still evolving but at a slower pace. Furthermore, most of the contributing affiliations belong to the USA and the UK. The text mining and keyword analysis revealed key themes of identified papers. The inherent key themes were decision models and frameworks to address sustainable logistics issues covering transport, distribution and third-party logistics. The most prominent sustainable logistics issue was carbon footprinting. Social impact has been given less attention in comparison to economic and environmental aspects. The literature has adequate room for proposing more effective solutions by considering various types of multi-criteria decision analysis methods and DSS configurations while simultaneously considering economic, environmental and social aspects of sustainable logistics. Moreover, the field has potential to include logistics from wide application areas including freight transport through road, rail, sea, air as well as inter-modal transport, port operations, material handling and warehousing.

To the best of the authors’ knowledge, this is the first systematic review of DSS for sustainable logistics using bibliometric and text analysis. The key themes and research gaps identified in this paper will provide a reference point that will encourage and guide interested researchers for future study, thus aiding both theoretical and practical advancements in this discipline.

Although many researchers have widely studied additive manufacturing (AM) as one of the most important industrial revolutions, few have presented a bibliometric analysis of the published studies in this area. This paper aims to evaluate AM research trends based on 4607 publications most cited from year 2010 to 2020.

The research methodology is bibliometric indicators and network analysis, including analysis based on keywords, citation analysis, productive journal, related published papers and authors indicators. Two free available software were employed VOSviewer and Bibexcel.

Keywords analysis results indicate that among the AM processes, Selective Laser Melting and Fused Deposition Modeling techniques, are the two processes ranked on top of the techniques employed and studied with 35.76% and 20.09% respectively. The citation analysis by VOSviewer software, reveals that the medical applications field and the fabrication of metal parts are the areas that interest researchers greatly. Different new research niches, as pharmaceutical industry, digital construction and food fabrication are growing topics in AM scientific works. This study reveals that journals “Materials & design”, “Advanced materials”, “Acs applied materials & interfaces”, “Additive manufacturing”, “Advanced functional materials” and “Biofabrication” are the most productive and influential in AM scientific research.

The results and conclusions of this work can be used as indicators of trends in AM research and/or as prospects for future studies in this area.

“Technological intelligence” is the capacity to appreciate and adapt technological advancements, and “artificial intelligence” is the key to achieve persuasive operational transformations in majority of contemporary organizational set-ups. Implicitly, artificial intelligence (the philosophies of machines to think, behave and perform either same or similar to humans) has knocked the doors of business organizations as an imperative activity. Artificial intelligence, as a discipline, initiated by scientist John McCarthy and formally publicized at Dartmouth Conference in 1956, now occupies a central stage for many organizations. Implementation of artificial intelligence provides competitive edge to an organization with a definite augmentation in its social and corporate status. Mere application of a concept will not furnish real output until and unless its performance is reviewed systematically. Technological changes are dynamic and advancing at a rapid rate. Subsequently, it becomes highly crucial to understand that where have the people reached with respect to artificial intelligence research. The present article aims to review significant work by eminent researchers towards artificial intelligence in the form of top contributing universities, authors, keywords, funding sources, journals and citation statistics.

As rightly remarked by past researchers that reviewing is learning from experience, research team has reviewed (by applying systematic literature review through bibliometric analysis) the concept of artificial intelligence in this article. A sum of 1,854 articles extracted from Scopus database for the year 2018–2019 (31st of May) with selected keywords (artificial intelligence, genetic algorithms, agent-based systems, expert systems, big data analytics and operations management) along with certain filters (subject–business, management and accounting; language-English; document–article, article in press, review articles and source-journals).

Results obtained from cluster analysis focus on predominant themes for present as well as future researchers in the area of artificial intelligence. Emerged clusters include Cluster 1: Artificial Intelligence and Optimization; Cluster 2: Industrial Engineering/Research and Automation; Cluster 3: Operational Performance and Machine Learning; Cluster 4: Sustainable Supply Chains and Sustainable Development; Cluster 5: Technology Adoption and Green Supply Chain Management and Cluster 6: Internet of Things and Reverse Logistics.

The result of review of selected studies is in itself a unique contribution and a food for thought for operations managers and policy makers.

This research conducts bibliometric analyses and network mapping on smart libraries worldwide. It examines publication profiles, identifies the most cited publications and preferred sources and considers the cooperation of the authors, organizations and countries worldwide. The research also highlights keyword trends and clusters and finds new developments and emerging trends from the co-cited references network.

A total of 264 records with 1,200 citations were extracted from the Web of Science database from 2003 to 2021. The trends in the smart library were analyzed and visualized using BibExcel, VOSviewer, Biblioshiny and CiteSpace.

The People’s Republic of China had the most publications (119), the most citations (374), the highest H-index (12) and the highest total link strength (TLS = 25). Wuhan University had the highest H-index (6). Chiu, Dickson K. W. (H-index = 4, TLS = 22) and Lo, Patrick (H-index = 4, TLS = 21) from the University of Hong Kong had the highest H-indices and were the most cooperative authors. Library Hi Tech was the most preferred journal. “Mobile library” was the most frequently used keyword. “Mobile context” was the largest cluster on the research front.

This study helps librarians, scientists and funders understand smart library trends.

There are several studies and solid background research on smart libraries. However, to the best of the author’s knowledge, this study is the first to conduct bibliometric analyses and network mapping on smart libraries around the globe.

The purpose of this paper is to examine the evolution of collaboration among researchers in Library Hi Tech based on the co-authorship network analysis.

The Library Hi Tech publications were retrieved from Web of Science database between 2006 and 2017. Social network analysis based on co-authorship was analyzed by using BibExcel software and a visual knowledge map was generated by Pajek. Three important social capital indicators: degree centrality, closeness centrality and betweenness centrality were calculated to indicate the co-authorship. Cohesive subgroup analysis which includes components and k-core was then applied to show the connectivity of co-authorship network of Library Hi Tech.

The results indicated that around 42 percent of the articles were written by single author, while an increasing trend of multi-authored articles suggesting the collaboration among researchers in librarian research field becomes popular. Furthermore, the social network analysis identified authorship network with three core authors – Markey, K., Fourie, I. and Li, X. Finally, six core subgroups each included six or seven tightly connected researchers were also identified.

This study contributed to the existing literature by revealing the co-authorship network in librarian research field. Key researchers in the major subgroup were identified. This is one of the limited studies that describe the collaboration network among authors from different perspectives showing a more comprehensive co-authorship network.