Bibliometric analysis and literature review of ecotourism: Toward sustainable development

In recent decades, rising consumer interest in visiting relatively less commercialized natural destinations has facilitated the growth of ecotourism. Yet the research on ecotourism is fragmented, presenting gaps in the current understanding of this topic. This study performs a bibliometric analysis to assimilate the present knowledge from a total of 878 articles published in six reputable outlets between 1990 and 2019. The study analyzed citation chains and coauthorship networks to acknowledge contributions from select authors, organizations, and countries. Next, a cocitation analysis of the prior literature identified four major thematic areas: ecological preservation, residents' interests, the carbon footprint, and tourists' behaviors. Further, a dynamic cocitation analysis technique mapped the development of these thematic areas. Subsequently, a content analysis of the four thematic areas delivered significant insights about prior research in the domain and indicated future avenues of research.publishedVersio

Industrial Revolution and Environmental Sustainability: An Analytical Interpretation of Research Constituents in Industry 4.0

Purpose – Environmental sustainability is quickly becoming one of the most critical issues in industry development. This study aims to conduct a systematic literature review through which the author can provide various research areas to work on for future researchers and provide insight into industry 4.0 and environmental sustainability. Design/methodology/approach – This study accomplishes this by performing a backward analysis using text mining on the Scopus database. Latent Semantic Analysis (LSA) was used to analyze the corpus of 4,364 articles published between 2013 and 2023. The authors generated 10 clusters using keywords in the industrial revolution and environmental sustainability domain, highlighting ten research avenues for further exploration. Findings – In this study, three research questions discuss the role of environmental sustainability with industry 4.0. The author predicted 10 clusters treated as recent trends on which more insight is required from future researchers. The authors provided year-wise analysis, top authors, top countries, top sources, and network analysis related to the topic. Finally, the study provided industrialization's effect on environmental sustainability and the future aspect of automation. Originality/value – This research is the first-ever study in which a natural language processing technique is implemented to predict future research areas based on the keywords-document relationship

Bibliometric analysis and literature review of ecotourism: Toward sustainable development

In recent decades, rising consumer interest in visiting relatively less commercialized natural destinations has facilitated the growth of ecotourism. Yet the research on ecotourism is fragmented, presenting gaps in the current understanding of this topic. This study performs a bibliometric analysis to assimilate the present knowledge from a total of 878 articles published in six reputable outlets between 1990 and 2019. The study analyzed citation chains and coauthorship networks to acknowledge contributions from select authors, organizations, and countries. Next, a cocitation analysis of the prior literature identified four major thematic areas: ecological preservation, residents' interests, the carbon footprint, and tourists' behaviors. Further, a dynamic cocitation analysis technique mapped the development of these thematic areas. Subsequently, a content analysis of the four thematic areas delivered significant insights about prior research in the domain and indicated future avenues of research

Impact of IoT on Manufacturing Industry 4.0: A New Triangular Systematic Review

The Internet of Things (IoT) has realised the fourth industrial revolution concept; however, its applications in the manufacturing industry are relatively sparse and primarily investigated without contextual peculiarities. Our research undertakes an intricate critical review to investigate significant aspects of IoT applications in the manufacturing Industry 4.0 perspective to address this gap. We adopt a systematic literature review approach by Denyer and Tranfield (2009) to carry out critical analyses that help develop future research domains based on empirical studies. We describe key knowledge gaps in the existing literature and empirical studies by exploring the main contribution categories and finding six critical differences between traditional and manufacturing Industry 4.0 and 10 enablers and 11 challenges of IoT applications. Finally, an agenda for future research is proposed with 11 research domains to focus on the recognised gaps

Blockchain applications in supply chains, transport and logistics : a systematic review of the literature

This paper presents current academic and industrial frontiers on blockchain application in supply chain, logistics and transport management. We conduct a systematic review of the literature and find four main clusters in the co-citation analysis, namely Technology, Trust, Trade, and Traceability/Transparency. For each cluster, and based on the pool of articles included in it, we apply an inductive method of reasoning and discuss the emerging themes and applications of blockchains for supply chains, logistics and transport. We conclude by discussing the main themes for future research on blockchain technology and its application in industry and services

Collaborative Networks, Decision Systems, Web Applications and Services for Supporting Engineering and Production Management

This book focused on fundamental and applied research on collaborative and intelligent networks and decision systems and services for supporting engineering and production management, along with other kinds of problems and services. The development and application of innovative collaborative approaches and systems are of primer importance currently, in Industry 4.0. Special attention is given to flexible and cyber-physical systems, and advanced design, manufacturing and management, based on artificial intelligence approaches and practices, among others, including social systems and services

Node importance measure for scientific research collaboration from hypernetwork perspective

U interdisciplinarnim područjima sve se više pažnje posvećuje međusobnoj suradnji. U organizacijama koje se bave istraživanjem suradnje, od bitne je važnosti evaluirati doprinos istraživača svojoj organizaciji te identificirati glavne istraživače. Mreža suradnje u znanstvenom istraživanju predstavlja osnovni model, ali u kontekstu sve složenijeg suradničkog ponašanja javljaju se problemi oko njegovog semantičkog predstavljanja. U ovom radu, uvođenjem hiper mreže, moćnijeg alata za modeliranje od tradicionalne mreže i uzimajući koautorstvo u znanstvenom radu kao cilj za stvaranje hiper mreže suradnje u znanstvenom istraživanju (scientific research collaboration hypernetwork - SRCH), mjerimo važnost istraživača u dva smjera, kao strukturu suradničkog odnosa i vrijednost postizanja suradnje sa stajališta hiper mreže. Upotrebljena je dodatna metoda mjerenja s prilagodljivim parametrima u svrhu integracije indikatora evaluacije dvaju aspekata te je dobivena procjena sintetičke važnosti istraživača. Analiza rabljenih podataka potvrdila je da je naša mjera važnosti čvora u znanstveno istraživačkoj suradnji sa stajališta hiper mreže razumna i učinkovita.Collaboration has become main stream and trend in interdisciplinary fields. In research collaboration organizations, to evaluate the contributions of researchers to the organization and then to identify core researchers is an important issue to carry out performance appraisal and crisis management of brain drain. Scientific research collaboration network is a basic model to investigate this question, but under the context of increasingly complex collaborative behaviour, it shows its limitations for semantic representations. In this paper, by introducing hypernetwork, a more powerful modelling tool than traditional network, and taking scientific paper co-authorship as object to construct scientific research collaboration hypernetwork (SRCH), we measure the importance of researchers in two aspects, as collaborative relationship structure and collaborative achievement value from a hypernetwork perspective. An additive weighting method with adjustable parameters is utilized to integrate the evaluation indicators of the two aspects, and then the synthetical importance evaluation of researchers is obtained. Analysis of data instance verifies that our node importance measure for scientific research collaboration from hypernetwork perspective is reasonable and effective

A study assessing the characteristics of big data environments that predict high research impact: application of qualitative and quantitative methods

BACKGROUND: Big data offers new opportunities to enhance healthcare practice. While researchers have shown increasing interest to use them, little is known about what drives research impact. We explored predictors of research impact, across three major sources of healthcare big data derived from the government and the private sector. METHODS: This study was based on a mixed methods approach. Using quantitative analysis, we first clustered peer-reviewed original research that used data from government sources derived through the Veterans Health Administration (VHA), and private sources of data from IBM MarketScan and Optum, using social network analysis. We analyzed a battery of research impact measures as a function of the data sources. Other main predictors were topic clusters and authors’ social influence. Additionally, we conducted key informant interviews (KII) with a purposive sample of high impact researchers who have knowledge of the data. We then compiled findings of KIIs into two case studies to provide a rich understanding of drivers of research impact. RESULTS: Analysis of 1,907 peer-reviewed publications using VHA, IBM MarketScan and Optum found that the overall research enterprise was highly dynamic and growing over time. With less than 4 years of observation, research productivity, use of machine learning (ML), natural language processing (NLP), and the Journal Impact Factor showed substantial growth. Studies that used ML and NLP, however, showed limited visibility. After adjustments, VHA studies had generally higher impact (10% and 27% higher annualized Google citation rates) compared to MarketScan and Optum (p<0.001 for both). Analysis of co-authorship networks showed that no single social actor, either a community of scientists or institutions, was dominating. Other key opportunities to achieve high impact based on KIIs include methodological innovations, under-studied populations and predictive modeling based on rich clinical data. CONCLUSIONS: Big data for purposes of research analytics has grown within the three data sources studied between 2013 and 2016. Despite important challenges, the research community is reacting favorably to the opportunities offered both by big data and advanced analytic methods. Big data may be a logical and cost-efficient choice to emulate research initiatives where RCTs are not possible

Information Systems Research Themes: A Seventeen-year Data-driven Temporal Analysis

Extending the research on our discipline’s identity, we examine how the major research themes have evolved in four top IS journals: Management Information Systems Quarterly (MISQ), Information Systems Research (ISR), Journal of the Association for Information Systems (JAIS), and Journal of Management Information Systems (JMIS). By doing so, we answer Palvia, Daneshvar Kakhki, Ghoshal, Uppala, and Wang’s (2015) call to provide continuous updates to the research trends in IS due to the discipline’s dynamism. Second, building on Sidorov, Evangelopoulos, Valacich, and Ramakrishnan (2008) we examine temporal trends in prominent research streams over the last 17 years. We show that, as IS research evolves over time, certain themes appear to endure the test of time, while others peak and trough. More importantly, our analysis identifies new emergent themes that have begun to gain prominence in IS research community. Further, we break down our findings by journal and show the type of content that they may desire most. Our findings also allow the IS research community to discern the specific contributions and roles of our premier journals in the evolution of research themes over time