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Artificial neural networks (ANNs), which represent computational models simulating the biological neural systems, have become a dominant paradigm for solving complex analytical problems. ANN applications have been employed in various disciplines such as psychology, computer science, mathematics, engineering, medicine, manufacturing, and business studies. Academic research on ANNs is witnessing considerable publication activity, and there exists a need to track the intellectual structure of the existing research for a better comprehension of the domain. The current study uses a bibliometric approach to ANN business literature extracted from the Web of Science database. The study also performs a chronological review using science mapping and examines the evolution trajectory to determine research areas relevant to future research. The authors suggest that researchers focus on ANN deep learning models as the bibliometric results predict an expeditious growth of the research topic in the upcoming years. The findings reveal that business research on ANNs is flourishing and suggest further work on domains, such as back-propagation neural networks, support vector machines, and predictive modeling. By providing a systematic and dynamic understanding of ANN business research, the current study enhances the readers' understanding of existing reviews and complements the domain knowledge.

The proliferation of bibliometric review articles is a true reflection of how bibliometrics is gaining popularity and has been widely adopted in various disciplines. The growing interest of scholars has encouraged us to dwell upon the what, why, when, how, and where of bibliometric literature reviews. The study explained the bibliometric review with the standpoint that it can be considered a strong review method for analyzing a large volume of data and scholars can supplement their traditional reviews with bibliometric reviews to strengthen their knowledge. This will help researchers to justify the (a) need for a study on the particular topic; (b) type or method of review chosen; (c) number of articles selected; (d) inclusion and exclusion criterion; (e) method of analysis; and (f) presentation of the findings.

Purpose: This study aims to review the existing literature on human resource management (HRM) as a major theme and sub-theme of human resource (HR) analytics. This chapter has the objective of analysing the trends in HR analytics.

Design/Methodology/Approach: It covered the publications between 2010 and 2021. There was a total of 500 articles sourced through ProQuest. The systematic literature review is applied as a research methodology. The metadata analysis was carried out to understand the trends, challenges, best practices, and scope of HR analytics. The authors have taken the help of keywords, journals, authors, domains, and topics for sourcing, screening, shortlisting, and finalising the article for review purposes.

Findings: It was found that research published in the early period concentrated on HRM’s theoretical and conceptual frameworks. In the middle phase, HR analytics gained momentum while the recent publications have reiterated on adopting various tools and technologies for optimum utilisation of resources and sustainable organisational development.

Practical Implications: This research will add to the literature in the HR analytics domain. It also provides a deeper insight to the researcher to explore and analyse more trends in the area that needs further exploration.

Originality: This study is relevant and comprehensive within the context of digitalisation, people analytics and competition management. It provides valuable insights to analyse the present scenario of the volatility, uncertainty, complexity, and ambiguity environment exploring new horizons for organisational efficiency and human capital.

The improvement of the agri-food supply chain sustainability plays pivotal role in the planet’s survival and in overcoming of climate disasters. Digital technologies that support the collection of Big Data produced along the agri-food supply chain (SC) emerge as powerful tools to accelerate the ecological transition of the sector. Digital technologies can support the implementation of circular business models by sharing data across the SC, monitoring in real time the materials flow, automatizing some agricultural practices and improving the decision-making through the development of decision support systems. Despite the relevance of these arguments, there is a lack of shared frameworks and guidelines for the effective development of a “data-driven circular economy” in the agri-food SC. In this scenario, this chapter examines how scholars investigate data-oriented strategies to accelerate the ecological transition and the adoption of circular economy (CE) models in the agri-food sector (AFS). To this end, a systematic literature review (SLR) was performed. Twenty-nine papers were selected following a rigorous sampling process. Both bibliometric and descriptive results are provided in the first part of this chapter. According to the analytical framework developed, the selected papers were examined in light of the “reduce, reuse and recycle” (3R) paradigm. Moreover, an additional R was retrieved from the systematic review (i.e., redesign), broadening the analytical perspective. The results indicate that scholars have predominantly provided theoretical contributions concerning the role of digital technologies and big data for the agri-food circular transition from a macro-perspective. The findings are useful for policy-makers and managers, who can promote and implement the big data-oriented approach to facilitate the circular transition. Limitations and future research directions are also provided.

This chapter uses multiple research methods, including quantitative science mapping analysis (SciMat) and a qualitative literature review, to provide insight into the academic debate unfolding at the intersection of big data and business processes. SciMat analysis based on keyword co-occurrence enabled identifying 12 of the most productive research themes, as reflected in a poll of 301 articles about big data and business processes. The three most important themes are: firm performance, Industry 4.0, and innovation. The traditional literature review on firm performance indicated that big data analytics (BDA) positively influence business process performance and have a beneficial impact on a firm’s performance, that is, the role of big data is viewed as critical in the context of Industry 4.0 because it enhances productivity and improves business processes. The benefits of BDA can be achieved only if the organizational obstacles related to planning, workforce attitude, and alignment with strategy are overcome. Moreover, big data is perceived as a significant source of innovation in an organization and can be conceptualized with the use of a resource-based view (RBV) of the firm. BDA positively influence business processes, which is strengthened by adequate implementation and openness to innovation.

Bibliometrics is an instrument that allows the analysis of evolution, current state, and future trends in a scientific field. Many disciplines, such as hospitality and tourism, have undertaken bibliometric studies. Based on a review of the leading bibliometric methods used in the main bibliometric documents published in the hospitality and tourism field between 2010 and 2019, this chapter aims to propose a bibliometric guide to help researchers undertake studies based on bibliometric techniques, both evaluative and relational. Any bibliometric research comprises five phases: setting the research questions, selecting the appropriate database, establishing the criteria to follow, filtering the data, applying the proper methods, and analyzing the results. This chapter elaborates on these stages, highlighting the most important evaluative and relational techniques to study the structure of a scientific field from a bibliometric approach. Bibliometric studies provide valuable knowledge for academia, governments, and research centers. It also helps journals' editors evaluate publications and make editorial decisions.