Role of artificial intelligence in operations environment: a review and bibliometric analysis

Artificial Intelligence and Finance: A bibliometric review on the Trends, Influences, and Research Directions

Background

This bibliometric study examines the intersection of artificial intelligence (AI) and finance, providing a comprehensive analysis of its evolution, central themes, and avenues for further exploration. The study aims to uncover the theoretical foundations, methodological approaches, and practical implications of AI in financial contexts.

Methods

The research employs bibliometric techniques, using 607 Web of Science (WoS) indexed papers. The Theory-Context-Characteristics-Methodology (TCCM) framework guides the analysis, focusing on thematic mapping to explore key topics. Core areas such as risk management, market efficiency, and innovation are analyzed, alongside emerging themes like ethical AI, finance applications, and factors influencing AI-driven financial decision-making.

Results

The findings reveal critical gaps in interdisciplinary methods, ethical considerations, and methodological advancements necessary to develop robust and transparent AI systems. Thematic mapping highlights the increasing importance of ethical AI practices and the influence of AI on financial decision-making processes. Emerging research areas emphasize the need for innovative frameworks and solutions to address current challenges.

Conclusions

This study provides valuable insights for academics, industry practitioners, and policymakers to harness transformative potential of AI in finance. This research offers a foundation for future studies and practical applications by addressing key gaps and promoting interdisciplinary and ethical approaches in a rapidly evolving field.

Enhancing Environmental and Human Health Management Through the Integration of Advanced Revitalization Technologies Utilizing Artificial Intelligence

Pollution can be broadly defined as the presence of contaminants or energy sources detrimental to ecosystems and human health. The human organism serves as a valuable indicator of ecosystem contamination. However, understanding physiological disorders and correlating specific contaminants with disease development is a complex and arduous task, necessitating extensive scientific research spanning years or even decades. To facilitate a more rapid and precise understanding of the physiological impairments induced by various contaminants, a comprehensive approach is indispensable. This review proposes a model for such an approach, which involves the systematic collection and analysis of data from ecosystem contamination monitoring, integrated with biomedical data on compromised physiological conditions in humans across different temporal and spatial scales. Given the complexity and sheer volume of data, alongside the imperative for strategic decision-making, this model leverages the capabilities of artificial intelligence (AI) tools. Although this paper exemplifies the model by investigating the effects of contaminants on the human organism, the model is adaptable to all ecosystem components, thereby supporting the conservation of plant and animal species.

Revolutionizing energy practices: Unleashing the power of artificial intelligence in corporate energy transition

Corporate energy transition is crucial for long-term sustainable development. The widely discussed Artificial Intelligence (AI), as a disruptive technological innovation, is highly potential for enhancing environment performance. However, the specific impact of AI on the process of corporate energy transition and its underlying mechanisms have not been fully explored. This study focuses on A-share listed corporates in Shanghai and Shenzhen stock markets in China spanning from 2011 to 2021. Based on corporate annual report information and information from over 200,000 patent application texts, we innovatively construct indicators for corporate energy transition and AI technology application. Furthermore, we empirically investigate the impact of AI technology on corporate energy transition and its potential mechanisms through combining information asymmetry theory and institutional theory. The empirical results indicate that: 1) AI can drive corporate energy transition and the promoting effect of AI collaborative innovation on corporate energy transition should not be ignored. 2) AI can help corporates achieve energy transition through pathways such as mitigating information asymmetry, reducing financing constraints, adjusting sustainable development concepts and practices. 3) The driving effect of AI on corporate energy transition varies depending on the characteristics of different types of corporates, industries, and regions. This study provides strategic guidance and decision support for business managers and policymakers, assisting both corporates and governments in better utilizing AI technology during the social energy transition process to achieve a dual optimization of environmental and economic goals.

Analysis of artificial intelligence in thyroid diagnostics and surgery: A scoping review

BACKGROUND

Artificial Intelligence provides numerous applications in the healthcare sector. The main aim of this study is to evaluate the extent of the current application of artificial intelligence in thyroid diagnostics.

METHODS

Our protocol was based on the Scoping Reviews extension of the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA-ScR). Information was gathered from PubMed, Cochrane, and EMBASE databases and Google Scholar. Eligible studies were published between 2017 and 2022.

RESULTS

The search identified 133 records, after which 18 articles were included in the scoping review. All the publications were journal articles and discussed various ways that specialists in thyroid diagnostics and surgery have utilized artificial intelligence in their practice.

CONCLUSIONS

The development and incorporation of Artificial Intelligence applications in thyroid diagnostics and surgery has been moderate yet promising. However, applications are currently inconsistent and further research is needed to delineate the true benefit and limitations in this field.

Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector

Purpose: The aim of this research is to investigate the factors that facilitate the adoption of artificial intelligence (AI) in order to establish effective human resource management (HRM) practices within the Indian pharmaceutical sector. Design/methodology/approach: A model explaining the antecedents of AI adoption for building effective HRM practices in the Indian pharmaceutical sector is proposed in this study. The proposed model is based on task-technology fit theory. To test the model, a two-step procedure, known as partial least squares structural equational modeling (PLS-SEM), was used. To collect data, 160 HRM employees from pharmacy firms from pan India were approached. Only senior and specialized HRM positions were sought. Findings: An examination of the relevant literature reveals factors such as how prepared an organization is, how people perceive the benefits, and how technological readiness influences AI adoption. As a result, HR systems may become more efficient. The PLS-SEM data support all the mediation hypothesized by proving both full and partial mediation, demonstrating the accuracy of the proposed model. Originality: There has been little prior research on the topic; this study adds a great deal to our understanding of what motivates human resource departments to adopt AI in the pharmaceutical companies of India. Furthermore, AI-related recommendations are made available to HRM based on the results of a statistical analysis.

Bibliometric analysis of the global scientific production on machine learning applied to different cancer types

Cancer disease is one of the main causes of death in the world, with million annual cases in the last decades. The need to find a cure has stimulated the search for efficient treatments and diagnostic procedures. One of the most promising tools that has emerged against cancer in recent years is machine learning (ML), which has raised a huge number of scientific papers published in a relatively short period of time. The present study analyzes global scientific production on ML applied to the most relevant cancer types through various bibliometric indicators. We find that over 30,000 studies have been published so far and observe that cancers with the highest number of published studies using ML (breast, lung, and colon cancer) are those with the highest incidence, being the USA and China the main scientific producers on the subject. Interestingly, the role of China and Japan in stomach cancer is correlated with the number of cases of this cancer type in Asia (78% of the worldwide cases). Knowing the countries and institutions that most study each area can be of great help for improving international collaborations between research groups and countries. Our analysis shows that medical and computer science journals lead the number of publications on the subject and could be useful for researchers in the field. Finally, keyword co-occurrence analysis suggests that ML-cancer research trends are focused not only on the use of ML as an effective diagnostic method, but also for the improvement of radiotherapy- and chemotherapy-based treatments.

Factors for the implementation of the circular economy in Big Data environments in service companies in post pandemic times of COVID-19: The case of Colombia

In emerging economies, Big Data (BD) analytics has become increasingly popular, particularly regarding the opportunities and expected benefits. Such analyzes have identified that the production and consumption of goods and services, while unavoidable, have proven to be unsustainable and inefficient. For this reason, the concept of the circular economy (CE) has emerged strongly as a sustainable approach that contributes to the eco-efficient use of resources. However, to develop a circular economy in DB environments, it is necessary to understand what factors influence the intention to accept its implementation. The main objective of this research was to assess the influence of attitudes, subjective norms, and perceived behavioral norms on the intention to adopt CE in BD-mediated environments. The methodology is quantitative, cross-sectional with a descriptive correlational approach, based on the theory of planned behavior and a Partial Least Squares Structural Equation Model (PLS-SEM). A total of 413 Colombian service SMEs participated in the study. The results show that managers' attitudes, subjective norms, and perceived norms of behavior positively influence the intentions of organizations to implement CB best practices. Furthermore, most organizations have positive intentions toward CE and that these intentions positively influence the adoption of DB; however, the lack of government support and cultural barriers are perceived as the main limitation for its adoption. The research leads to the conclusion that BD helps business and government develop strategies to move toward CE, and that there is a clear positive will and intent toward a more restorative and sustainable corporate strategy.

Intelligent manufacturing eco-system: A post COVID-19 recovery and growth opportunity for manufacturing industry in Sub-Saharan countries

The lagging behind intelligent technologies and the COVID-19 pandemic together have impacted the emerging economy particularly the manufacturing sector in sub-Saharan countries. This paper systematically discusses intelligent manufacturing technologies with an aim to map out their importance and industrial applicability and to show their significance to contain COVID-19 pandemic. Intelligent Manufacturing Systems (IMS) is then adapted as a post COVID-19 recovery and growth opportunity to ensemble to production processes of manufacturing industry in the sub-Saharan countries. Proposition of a Triple Helix Collaboration Eco-system that delineate a recursive contribution of Government(s), academia, and industry accompanies the IMS adoption. The intention is to shape the existing industrial challenges through networking in the area of intelligence technologies. While proposing the Eco-system, a post COVID-19 recovery and growth opportunity and intra-Africa scientific collaborations are taken into account.

Investigating the Influence of Artificial Intelligence on Business Value in the Digital Era of Strategy: A Literature Review

For organizations, the development of new business models and competitive advantages through the integration of artificial intelligence (AI) in business and IT strategies holds considerable promise. The majority of businesses are finding it difficult to take advantage of the opportunities for value creation while other pioneers are successfully utilizing AI. On the basis of the research methodology of Webster and Watson (2020), 139 peer-reviewed articles were discussed. According to the literature, the performance advantages, success criteria, and difficulties of adopting AI have been emphasized in prior research. The results of this review revealed the open issues and topics that call for further research/examination in order to develop AI capabilities and integrate them into business/IT strategies in order to enhance various business value streams. Organizations will only succeed in the digital transformation alignment of the present era by precisely adopting and implementing these new, cutting-edge technologies. Despite the revolutionary potential advantages that AI capabilities may promote, the resource orchestration, along with governance in this dynamic environment, is still complex enough and in the early stages of research regarding the strategic implementation of AI in organizations, which is the issue this review aims to address and, as a result, assist present and future organizations effectively enhance various business value outcomes.

Considering IT Trends for Modelling Investments in Supply Chains by Prioritising Digital Twins

Supply chain disruptions and challenges have and will always exist, but preparing in advance and improving resilience for the upcoming consequences should be the utmost important goal. This paper explores trends that affect innovation in the technological sphere of supply chain systems. More precisely, the research is focused on Digital Twin technology applicability through other logistics IT trends and aims to research the pressing issue of ensuring the visibility and resilience of future supply chain systems. The paper’s objective is to produce a conceptual model enabling the investment assessment of the necessary IT resources. Initially, a theoretical confirmation of logistics IT trends’ relevance to supply chain systems was established. After, propositions of Digital Twin technology applications to other logistics IT trends were made, which were divided into corresponding constant multitudes of supply chain systems. Lastly, the conceptual model for the investment assessment of the necessary IT resources was derived in the form of a matrix. It considers 16 parameters for investment assessment and applicability to all companies, regardless of their specifics. It also supports the notion of digital IT competencies’ fundamental importance to the continuous operation of supply chain systems.

Revisiting the bullwhip effect: how can AI smoothen the bullwhip phenomenon?

Purpose

Although scholars argue that artificial intelligence (AI) represents a tool to potentially smoothen the bullwhip effect in the supply chain, only little research has examined this phenomenon. In this article, the authors conceptualize a framework that allows for a more structured management approach to examine the bullwhip effect using AI. In addition, the authors conduct a systematic literature review of this current status of how management can use AI to reduce the bullwhip effect and locate opportunities for future research.

Design/methodology/approach

Guided by the systematic literature review approach from Durach et al. (2017), the authors review and analyze key attributes and characteristics of both AI and the bullwhip effect from a management perspective.

Findings

The authors' findings reveal that literature examining how management can use AI to smoothen the bullwhip effect is a rather under-researched area that provides an abundance of research avenues. Based on identified AI capabilities, the authors propose three key management pillars that form the basis of the authors' Bullwhip-Smoothing-Framework (BSF): (1) digital skills, (2) leadership and (3) collaboration. The authors also critically assess current research efforts and offer suggestions for future research.

Originality/value

By providing a structured management approach to examine the link between AI and the bullwhip phenomena, this study offers scholars and managers a foundation for the advancement of theorizing how to smoothen the bullwhip effect along the supply chain.

Global bibliometric mapping of the frontier of knowledge in the field of artificial intelligence for the period 1990-2019

Artificial Intelligence (AI) has emerged as a field of knowledge that is displacing and disrupting technologies, leading to changes in human life. Therefore, the purpose of this study is to scientifically map this topic and its ramifications, in order to analyze its growth. The study was developed under the bibliometric approach and considered the period 1990-2019. The steps followed were (i) Identification and selection of keyword terms in three methodological layers by a panel of experts. (ii) Design and application of an algorithm to identify these selected keywords in titles, abstracts, and keywords using terms in Web of Science to contrast them. (iii) Performing data processing based on the Journals of the Journal Citation Report during 2020. Knowing the evolution of a field of knowledge such as AI from a bibliometric study and subsequently establishing the ramifications of new research streams is in itself a relevant finding. Addressing a broad field of knowledge as AI from a multidisciplinary approach given the convergence it generates with other disciplines and specialties is of high strategic value for decision makers such as governments, academics, scientists, and entrepreneurs.

Unfolding design and technology for superior sales growth under moderating effect of technological environment

Purpose

Firms use design capability across the globe to compete and increase sales, e.g. Apple. However, the payoff from design know-how has been overlooked thus far. Academic research lags in this space despite the intersection of sales, technology and design in practice. This paper provides researchers and managers with implications of the interplay between design capability and technological market conditions to enhance a firm's sales.

Design/methodology/approach

Firms' capability design, and sales impact have been studied in this paper across different technological market conditions. Primary technological conditions of the industry under which firms operate are captured, which are technological intensity (TI), technological competitive intensity (TCI) and technological maturity (TM). Their interplay has been studied using panel data analysis, examining fixed and random effects.

Findings

Design is an important, interesting and non-imitable capacity that yields positive firm execution results. It provides an urgent differentiator and improves deal development. This study found that all four hypotheses are generally supported. The main finding is that, provided underlying technology is good, design significantly improves sales, but design alone cannot substitute for poor technology.

Practical implications

The results of this study link the three technological environment conditions, namely, TI, TCI and TM with sales growth. The authors find that design can and does add to superior performance, provided technological excellence exists prior. But, in the absence of good technology, design alone will hinder performance.

Originality/value

This paper examines the effect of firm design capability on sales growth. The paper finds a positive moderating effect of TCI and TM but a negative moderating effect of TI. The researchers believe these aspects of the design have not been studied before.

Integrating artificial intelligence into a talent management model to increase the work engagement and performance of enterprises

The purpose of the paper is to create a multidimensional talent management model with embedded aspects of artificial intelligence in the human resource processes to increase employees' engagement and performance of the enterprise. The research was implemented on a sample of 317 managers/owners in Slovenian enterprises. Multidimensional constructs of the model include several aspects of artificial intelligence implementation in the organization's activities related to human resource management in the field of talent management, especially in the process of acquiring and retaining talented employees, appropriate training and development of employees, organizational culture, leadership, and reducing the workload of employees, employee engagement and performance of the enterprise. The results show that AI supported acquiring and retaining a talented employees, AI supported appropriate training and development of employees, appropriate teams, AI supported organizational culture, AI supported leadership, reducing the workload of employees with AI have a positive effect on performance of the enterprise and employee engagement. The results will help managers or owners create a successful work environment by implementing artificial intelligence in the enterprise, leading to increased employee engagement and performance of the enterprise. Namely, our results contribute to the efficient implementation of artificial intelligence into an enterprise and give owners or top managers a broad insight into the various aspects that must be taken into account in business management in order to increase employee engagement and enterprise's competitive advantage.

Digital technologies and circular economy practices: vital enablers to support sustainable and resilient supply chain management in the post-COVID-19 era

Purpose

The COVID-19 pandemic has caused major disruptions and revealed the fragilities in supply chains. This crisis has re-opened the debate on supply chain resilience and sustainability. This paper aims to investigate distinct impacts of COVID-19 on supply chains. It identifies both short- and medium-to-long-term measures taken to mitigate the different effects of the pandemic and highlights potential transformations and their impacts on supply chain sustainability and resilience.

Design/methodology/approach

To address the purpose of the study, a qualitative research approach based on case studies and semi-structured interviews with 15 practitioners from various supply chain types and sectors was conducted. Studied organizations included necessary and non-necessary supply chain sectors, which are differently impacted by the COVID-19 pandemic.

Findings

This study reveals five main challenges facing supply chains during COVID-19, including uncertain demand and supply, suppliers' concentration in specific regions, globalized supply chains, reduced visibility in the supply network, and limited supplier capacity. To help mitigate these challenges and develop both sustainability and resilience, this paper identifies some mitigating actions focusing on the promotion of the health and wellbeing of employees and supply chain stabilization. Further, in the post-COVID era, sustainable and resilient supply chains should consider regionalization of the supply chain, diversification of the supply network, agility, collaboration, visibility, and transparency; and should accelerate the use of smart technologies and circular economy practices as dynamic capabilities to improve supply chain resilience and sustainability.

Originality/value

This study contributes to exploring the sustainability- and resilience-related challenges posed by the COVID-19 pandemic. Its findings can be used by researchers and supply chains decision-makers to limit disruptions and improve responsiveness, resilience, sustainability, and restoration of supply chains. The results support benchmarking through sharing of the best practices and organizations can also integrate the different capabilities discussed in this study into the processes of selection and auditing of their suppliers.

Supply Chain Management: A Review and Bibliometric Analysis

Supply chain management (SCM), which generally refers to horizontal integration management, has steadily become the core competitiveness of company rivalry and an essential approach to developing national comprehensive and national strength since the end of the 20th century due to the numerous needs arising from a competitive international economy. Manufacturers develop a community of interest by forming long-term strategic partnerships with suppliers and vendors throughout the supply chain. This paper defines supply chain management by reviewing the existing literature and discusses the current state of supply chain management research, as well as prospective research directions. Specifically, we conducted a bibliometric analysis of the influential studies of SCM in terms of various aspects, such as research areas, journals, countries/regions, institutions, authors and corresponding authors, most cited publications, and author keywords, based on the 8998 reviews and articles collected from the SCI and SSCI database of the Web of Science (WoS) between 2010 and 2020. The results show that the major research areas were Management (3071, 34.13%), Operations Research & Management Science (2680, 29.78%), and Engineering, Industrial (1854, 20.60%) with TP and TPR%. The most productive journal and institution were J. Clean Prod and Hong Kong Polytech Univ with a TP of 554 and 238, respectively. China, USA, and UK were the top three contributing countries. Furthermore, “sustainability”, “green supply chain (management)”, and “sustainable supply chain (management)” were the most popular author keywords in recent three years and since 2010, apart from the author keywords of SCM. When combined with the most cited articles in recent years, the application of block chain and Industry 4.0 in supply chain management increased rapidly and generated great attention.

What Country, University, or Research Institute, Performed the Best on Covid-19 During the First Wave of the Pandemic?: Bibliometric analysis of scientific literature - analysing a 'snapshot in time' of the first wave of COVID-19

In this article, we conduct data mining and statistical analysis on the most effective countries, universities, and companies, based on their output (e.g., produced or collaborated) on COVID-19 during the first wave of the pandemic. Hence, the focus of this article is on the first wave of the pandemic. While in later stages of the pandemic, US and UK performed best in terms of vaccine production, the focus in this article is on the initial few months of the pandemic. The article presents findings from our analysing of all available records on COVID-19 from the Web of Science Core Collection. The results are compared with all available data records on pandemics and epidemics from 1900 to 2020. This has created interesting findings that are presented in the article with visualisation tools.

Supplementary information

The online version contains supplementary material available at 10.1007/s40745-022-00406-8.

Unleashing frugal innovation in private higher education institutions via intellectual capital and Information technology capability: a systematic literature review

Background: Given the persistent challenges to the higher education business model, private higher education institutions (PHEIs) are exploring myriad ways to increase enrolment and income, while aggressively managing spending. Many PHEIs are facing financial distress and struggling because of decreasing budgets and declining revenue. Thus, carving unique strategies that direct the institution to focus on its core competencies, making additional budget cuts without compromising quality, developing new revenue streams, embracing new technology, and offering affordable programs, will ultimately lead to financial success. Frugal innovation (FI) can shed light on these challenges.

Methods: This paper presents a systematic literature review to investigate and analyse prior research that focused on FI within the sphere of intellectual capital (IC) and information technology capabilities (ITC) research, and their relationships in PHEIs. Transfield’s five phases were employed to extract journal articles published over a thirty-year period (1990 to 2020) from major online databases using keyword searches. Although an initial search generated 76,025 papers, the search for IC and FI yielded 41 papers, and finally only two papers were selected as they clearly related IC with FI.

Results: There was a research gap in the literature published from 1990 to 2020 regarding IC applications to achieve FI. This work revealed that IC and ITC research for FI in PHEI remain insufficiently explored.  

Conclusions: Further research is required on the evaluation model of IC, ITC and FI, methodologies, empirical analysis, and the development of measurement metrics. A limitation to this study is the number of keywords selected.

Artificial intelligence in learning and development: a systematic literature review

Purpose

The presented research explored artificial intelligence (AI) application in the learning and development (L&D) function. Although a few studies reported AI and the people management processes, a systematic and structured study that evaluates the integration of AI with L&D focusing on scope, adoption and affecting factors is mainly absent. This study aims to explore L&D-related AI innovations, AI’s role in L&D processes, advantages of AI adoption and factors leading to effective AI-based learning following the analyse, design, develop, implement and evaluate approach.

Design/methodology/approach

The presented research has adopted a systematic literature review method to critically analyse, synthesise and map the extant research by identifying the broad themes involved. The review approach includes determining a time horizon, database selection, article selection and article classification. Databases from Emerald, Sage, Francis and Taylor, etc. were used, and the 81 research articles published between 1996 and 2022 were identified for analysis.

Findings

The result shows that AI innovations such as natural language processing, artificial neural networks, interactive voice response and text to speech, speech to text, technology-enhanced learning and robots can improve L&D process efficiency. One can achieve this by facilitating the articulation of learning module, identifying learners through face recognition and speech recognition systems, completing course work, etc. Further, the result also shows that AI can be adopted in evaluating learning aptitude, testing learners’ memory, tracking learning progress, measuring learning effectiveness, helping learners identify mistakes and suggesting corrections. Finally, L&D professionals can use AI to facilitate a quicker, more accurate and cheaper learning process, suitable for a large learning audience at a time, flexible, efficient, convenient and less expensive for learners.

Originality/value

In the absence of any systematic research on AI in L&D function, the result of this study may provide useful insights to researchers and practitioners.

Global research productivity in cybersecurity: a scientometric study

Purpose

The purpose of the study is to conduct a scientometric analysis of cybersecurity literature indexed in the core collection of the Web of Science for a period of ten years (2011–2020).

Design/methodology/approach

Cybersecurity is a focused topic of research across the globe. To identify the global research productivity in the field, the terms “cybersecurity, cyber-security, web security, information security, computer security, etc.” were used for retrieving the publications in the advanced search mode of the database “Web of Science”, limiting the time frame for 2011– 2020. The results retrieved were downloaded in the Excel file for further analysis and interpretation. The harvested data was analysed by using scientometric techniques to measure the progress such as growth rate, doubling time and author collaborations. Besides, the Biblioshiny and VOSviewer software were used for mapping networks.

Findings

The research output in the field of cybersecurity has shown an increasing trend during 2011–2020, and the maximum number of scholarly publications was published in 2020 (1,581), i.e. more than 715% of 2011 (221). A good number of countries (93) have contributed globally in cybersecurity research, and the highest share in research publications was reported by the USA (23.55%), followed by China (23.24%), South Korea (5.31%), UK (5.28%) and India (4.25%). The authorship patterns in cybersecurity publications show a collaborative trend, as most articles have been published by multiple authors. Total 5,532 (90.14%) articles have been published in co-authorship, whereas only 605 (9.86%) articles have been published by single authors. Keyword analysis shows that the most common keyword research by the authors is cybersecurity and its variants such as “cyber security” and “cyber-security” (1,698) followed by security (782), computer security (680) and information security (329).

Research limitations/implications

The database studied for the work does not represent the total literary output available on the theme. There are plenty of other databases, such as Scopus, Compendex, INSPEC, IEEE Xplore, arXiv, contributing to the same theme as well.

Practical implications

The findings of the study may help researchers, information technologists, library professionals and information specialists to identify the research progress, authorship patterns, collaborative networks and hot topics of research in the field of cybersecurity. Besides, it will assess the global response to the cybersecurity issue.

Originality/value

The study is the scientometric analysis of the cybersecurity based on current literature and will highlight the progress and development of global research in the said field.

Enterprise Privacy Resource Optimization and Big Data Intelligent Management Strategy Oriented to the Internet of Things

In the context of the Internet of Things, user privacy leads to the lack of information security and the obscuration of traditional privacy concepts. Therefore, how to ensure information security and protect user privacy is a key issue that enterprises must solve in the process of using the Internet of Things technology. At present, the research on corporate employees' protection of user privacy in the context of the Internet of Things is mostly focused on the technical level, while the legal and management levels are relatively lacking. Based on the definition of the corporate concept in the context of the Internet of Things, this paper uses management and psychology as the research perspective, and based on theory of persuasion, adjustment orientation theory, and reinforcement theory, it discusses the attitudes of corporate employees to protect user privacy in the context of the Internet of Things and behaviour mechanism, constructing a new theoretical model. This experiment uses 0.001 as the step size to change the corresponding threshold size. The interval range is [0.001, 10], and there are a total of 10,000 points in the interval, which is equivalent to 100 million sensor attack tests. According to the above method, 10,000 points of the ROC curve can be obtained by using 10,000 thresholds, and the corresponding ROC curve can be drawn in the coordinate graph, which can intuitively reflect the performance of the VRADS vehicle anomaly real-time detection system. The challenge of data information protection is analyzed, trying to clarify the ideas for the protection of personal data and information in the Internet of Things environment and even lead to employees' rebellious psychology. This article proves that the pertinence and effectiveness of the persuasive content have a positive impact on employees' attitudes towards privacy protection, and it has been further deepened in the context of the Internet of Things. The balance point is to leave enough room for the long-term sustainable development of the Internet of Things industry on the basis of protecting the personal rights and interests of users.

Social network analysis in business and management research: A bibliometric analysis of the research trend and performance from 2001 to 2020

In the past years, research in Social Network Analysis (SNA) has increased. Initially, the research area was limited to sociology and anthropology but has now been used in numerous disciplines. The business and management discipline has many potentials in employing the SNA approach due to enormous relational data, ranging from employees, stakeholders to organisations. The study aims to analyse the research trend, performance, and the utilisation of the SNA approach in business and management research. Bibliometric analysis was conducted by employing 2,158 research data from the Scopus database published from 2001 to 2020. Next, the research quantity and quality were calculated using Harzing's Publish or Perish while VOSviewer visualised research topics and cluster analysis. The study found an upward trend pattern in SNA research since 2005 and reached the peak in 2020. Generally, six subjects under the business and management discipline have used SNA as a methodology tool, including risk management, project management, supply chain management (SCM), tourism, technology and innovation management, and knowledge management. To the best of the authors' knowledge, the study is the first to examine the performance and analysis of SNA in the overall business and management disciplines. The findings provide insight to researchers, academicians, consultants, and other stakeholders on the practical use of SNA in business and management research.

Artificial intelligence in the field of economics

The history of AI in economics is long and winding, much the same as the evolving field of AI itself. Economists have engaged with AI since its beginnings, albeit in varying degrees and with changing focus across time and places. In this study, we have explored the diffusion of AI and different AI methods (e.g., machine learning, deep learning, neural networks, expert systems, knowledge-based systems) through and within economic subfields, taking a scientometrics approach. In particular, we centre our accompanying discussion of AI in economics around the problems of economic calculation and social planning as proposed by Hayek. To map the history of AI within and between economic sub-fields, we construct two datasets containing bibliometrics information of economics papers based on search query results from the Scopus database and the EconPapers (and IDEAs/RePEc) repository. We present descriptive results that map the use and discussion of AI in economics over time, place, and subfield. In doing so, we also characterise the authors and affiliations of those engaging with AI in economics. Additionally, we find positive correlations between quality of institutional affiliation and engagement with or focus on AI in economics and negative correlations between the Human Development Index and share of learning-based AI papers.

Enabler toward successful implementation of Quality 4.0 in digital transformation era: a comprehensive review and future research agenda

Purpose

Quality 4.0 (Q4.0) is related to quality management in the era of Industry 4.0 (I4.0). In particular, it concentrates on digital techniques used to improve organizational capabilities and ensure the delivery of the best quality products and services to its customer. The aim of this research to examine the vital elements for the Q4.0 implementation.

Design/methodology/approach

A review of the literature was carried out to analyze past studies in this emerging research field.

Findings

This research identified ten factors that contribute to the successful implementation of Q4.0. The key factors are (1) data, (2) analytics, (3) connectivity, (4) collaboration, (5) development of APP, (6) scalability, (7) compliance, (8) organization culture, (9) leadership and (10) training for Q4.0.

Originality/value

As a result of the research, a new understanding of factors of successful implementation of Q4.0 in the digital transformation era can assist firms in developing new ways to implement Q4.0.

Applications of artificial intelligence and machine learning within supply chains:systematic review and future research directions

Purpose

The purpose of this study is to investigate how artificial intelligence (AI), as well as machine learning (ML) techniques, are being applied and implemented within supply chains (SC) and to develop future research directions from thereof.

Design/methodology/approach

Using a systematic literature review methodology, this study analyzes the publications available on Web of Science, Scopus and Google Scholar that linked both AI and supply chain from one side and ML and supply chain from another side. A total of 388 research studies have been identified through the before said three database searches which are further screened, sorted and finalized with 50 studies. The research thoroughly reviews and analyzes the final lists of 50 studies that were found relevant and significant to the theme of AI and ML in supply chain management (SCM).

Findings

AI and ML applications are still at the infant stage and the opportunity for them to elevate supply chain performance is very promising. Some researchers developed AI and ML-related models which were tested and proved to be effective in optimizing SC, and therefore, the application of AI and ML in supply chain networks creates competitive advantages for firms. Other researchers claim that AI and ML are both currently adding value while many other researchers believe that they are still not fully exploited and their tools and techniques can leverage the supply chain’s total value. The research found that adoption of AI and ML have the ability to reduce the bullwhip effect, and therefore, further supports the performance of supply chain efficiency and responsiveness.

Research limitations/implications

This research was limited in terms of scope as it covered AI and ML applications in the supply chain while there are other dimensions that could be investigated such as big data and robotics but it was found too lengthy to include these additional dimensions, and therefore, left for future research studies that other researchers could explore and pursue.

Practical implications

This study opens the door wide for other researchers to explore how AI and ML can be adopted in SCM and what are the models that are already tested and proven to be viable. In addition, the paper also identified a group of research studies that confirmed the unexploited avenues of AI and ML which could be of high interest to other researchers to explore.

Originality/value

Although few earlier research studies touch based on the AI applications within manufacturing and transportation, this study is different and makes a unique contribution by offering a holistic view on the AI and ML implications within SC as a whole. The research carefully reviews a number of highly cited papers classifying them into three main themes and recommends future direction.

Future perspectives on progressive farming with adoption of virtual reality technology for sustainable quality in agriculture

Purpose

The paper aims to evaluate how progressive stakeholders view the adoption of contemporary techniques such as virtual technology in driving sustainable quality in an emerging economy context.

Design/methodology/approach

The authors adopted a systematic literature review to develop the theoretical framework for virtual reality (VR) technology adoption in sustaining quality in agriculture production. The framework was refined after discussion with a panel of academic experts. The refined theoretical framework was further empirically validated using Partial Least Square Structure Equation Modelling.

Findings

The study focuses on the future perspective of the perception for progressive farming with the adoption of VR technology in an emerging economy. The data were collected from the stakeholders (farmers, collectives, cooperative, etc.), for their future perspectives for the adoption of VR technology and sustainable quality agriculture production. The study may help build up VR technology in emerging economies which may take years to be established.

Research limitations/implications

The perception of the future perspective of VR technology study conducted has limitations. The findings are well established on technology adoption; however, the technology used will take many extra years to find its application in the agriculture sector. The study offers insightful theoretical, managerial and policy implications for sustainable quality in agriculture production through the adoption of virtual reality (VR) technology. The authors found very few works that focused on VR technology adoption.

Originality/value

The study discusses VR, which has an impact on sustaining the quality of agriculture production. The study has notable managerial and policy implications that suggest the future perspective for VR technology in agriculture production. The study is an unexplored area that needs research to capture future perspectives.

Technology and leadership styles: a review of trends between 2003 and 2021

Purpose

Leadership style denotes the behavioural pattern of a leader, which bears on employee's attitude, perception about organization, manager and job satisfaction. The existence of different styles has presented leadership from diverse perspectives related to individuals' personality and behaviour. The main objective of this article is to explore the association between leadership styles and technology, major themes in this area and what can be the future research directions of this work.

Design/methodology/approach

Leadership style denotes the behavioural pattern of leader, which bears on employee's attitude, perception about organization, manager and job satisfaction. The existence of different styles has presented leadership from diverse perspectives related to individuals' personality and behaviour. The present article aims to review significant work by eminent researchers towards technology and leadership styles in the form trends, annual scientific production; popular affiliations and sources, a three-field plot of countries, scholars and themes, most cited references, trending keywords, thematic analysis of leadership styles and technology research by taking insights from situational leadership theory.

Findings

The findings indicate connections between various keywords and provide interesting themes like transformational leadership style is connected to knowledge management, transactional leadership, empowering leadership, psychological capital and e-leadership. Similarly, leadership is connected to leadership development, gender stereotypes, emotional exhaustion, innovative leadership and organizational performance.

Originality/value

This review analysis of leadership styles and technology is in itself a novice contribution and first of its nature. The identified themes are presenting good knowledge and food for thought for future researches.

Data-driven quality improvement approach to reducing waste in manufacturing

Purpose

Data-driven quality management systems, brought about by the implementation of digitisation and digital technologies, is an integral part of improving supply chain management performance. The purpose of this study is to determine a methodology to aid the implementation of digital technologies and digitisation of the supply chain to enable data-driven quality management and the reduction of waste from manufacturing processes.

Design/methodology/approach

Methodologies from both the quality management and data science disciplines were implemented together to test their effectiveness in digitalising a manufacturing process to improve supply chain management performance. The hybrid digitisation approach to process improvement (HyDAPI) methodology was developed using findings from the industrial use case.

Findings

Upon assessment of the existing methodologies, Six Sigma and CRISP-DM were found to be the most suitable process improvement and data mining methodologies, respectively. The case study revealed gaps in the implementation of both the Six Sigma and CRISP-DM methodologies in relation to digitisation of the manufacturing process.

Practical implications

Valuable practical learnings borne out of the implementation of these methodologies were used to develop the HyDAPI methodology. This methodology offers a pragmatic step by step approach for industrial practitioners to digitally transform their traditional manufacturing processes to enable data-driven quality management and improved supply chain management performance.

Originality/value

This study proposes the HyDAPI methodology that utilises key elements of the Six Sigma DMAIC and the CRISP-DM methodologies along with additions proposed by the author, to aid with the digitisation of manufacturing processes leading to data-driven quality management of operations within the supply chain.

A systematic and network-based analysis of data-driven quality management in supply chains and proposed future research directions

Purpose

This work aims to review past and present articles about data-driven quality management (DDQM) in supply chains (SCs). The motive behind the review is to identify associated literature gaps and to provide a future research direction in the field of DDQM in SCs.

Design/methodology/approach

A systematic literature review was done in the field of DDQM in SCs. SCOPUS database was chosen to collect articles in the selected field and then an SLR methodology has been followed to review the selected articles. The bibliometric and network analysis has also been conducted to analyze the contributions of various authors, countries and institutions in the field of DDQM in SCs. Network analysis was done by using VOS viewer package to analyze collaboration among researchers.

Findings

The findings of the study reveal that the adoption of data-driven technologies and quality management tools can help in strategic decision making. The usage of data-driven technologies such as artificial intelligence and machine learning can significantly enhance the performance of SC operations and network.

Originality/value

The paper discusses the importance of data-driven techniques enabling quality in SC management systems. The linkage between the data-driven techniques and quality management for improving the SC performance was also elaborated in the presented study.

Air quality management using genetic algorithm based heuristic fuzzy time series model

Purpose

The purpose of this paper is to provide a better method for quality management to maintain an essential level of quality in different fields like product quality, service quality, air quality, etc.

Design/methodology/approach

In this paper, a hybrid adaptive time-variant fuzzy time series (FTS) model with genetic algorithm (GA) has been applied to predict the air pollution index. Fuzzification of data is optimized by GAs. Heuristic value selection algorithm is used for selecting the window size. Two algorithms are proposed for forecasting. First algorithm is used in training phase to compute forecasted values according to the heuristic value selection algorithm. Thus, obtained sequence of heuristics is used for second algorithm in which forecasted values are selected with the help of defined rules.

Findings

The proposed model is able to predict AQI more accurately when an appropriate heuristic value is chosen for the FTS model. It is tested and evaluated on real time air pollution data of two popular tourism cities of India. In the experimental results, it is observed that the proposed model performs better than the existing models.

Practical implications

The management and prediction of air quality have become essential in our day-to-day life because air quality affects not only the health of human beings but also the health of monuments. This research predicts the air quality index (AQI) of a place.

Originality/value

The proposed method is an improved version of the adaptive time-variant FTS model. Further, a nature-inspired algorithm has been integrated for the selection and optimization of fuzzy intervals.

Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation

Supply chain resilience (SCRes) and performance have become increasingly important in the wake of the recent supply chain disruptions caused by subsequent pandemics and crisis. Besides, the context of digitalization, integration, and globalization of the supply chain has raised an increasing awareness of advanced information processing techniques such as Artificial Intelligence (AI) in building SCRes and improving supply chain performance (SCP). The present study investigates the direct and indirect effects of AI, SCRes, and SCP under a context of dynamism and uncertainty of the supply chain. In doing so, we have conceptualized the use of AI in the supply chain on the organizational information processing theory (OIPT). The developed framework was evaluated using a structural equation modeling (SEM) approach. Survey data was collected from 279 firms representing different sizes, operating in various sectors, and countries. Our findings suggest that while AI has a direct impact on SCP in the short-term, it is recommended to exploit its information processing capabilities to build SCRes for long-lasting SCP. This study is among the first to provide empirical evidence on maximizing the benefits of AI capabilities to generate sustained SCP. The study could be further extended using a longitudinal investigation to explore more facets of the phenomenon.

Evaluation of machine tool substitute under data-driven quality management system: a hybrid decision-making approach

Purpose

Today, industrial revolutions demands advanced technologies, means, mediums, tactics and so forth for optimizing their operating behavior and opportunities. It is probed that the effectual results can be seized into system by not only developing advance means and technologies, but also capably adapting these developed technologies, their user interface and their utilization at optimum levels. Today, industrial resources need perfect synchronization and optimization for getting elevated results. Accordingly, present study is furnished with the purpose to expose quality-driven insights to march toward excellence by optimizing existing resources by the industrial organizations. The present study evaluates quality attributes of mechanical machineries for seizing performance opportunities and maintaining competitiveness via synchronizing and reconfiguring firm's resources under quality management system.

Design/methodology/approach

In the present study, Kano’s integrated approach is implemented for supporting decision rational concerning industrial assets. The integrative Kano–analytic hierarchy process (AHP) approach is used to reflect the relative importance of quality attributes. Kano and AHP tactics are integrated to define global relative weight and their computational medium is adapted along with ratio analysis, reference point theory and TOPSIS technique for understanding robust decision. The study described an interesting idea for underpinning quality attributes for benchmarking system substitutes. A machine tool selection case is discussed to disclose the significant aspect of decision-making and its virtual qualities.

Findings

The decision executives can realize massive benefits by streaming quality data, advanced information, technological advancements, optimum analysis and by identifying quality measures and disruptions for gaining performance deeds. The study determined quality measures for benchmarking machine tool substitute for industrial applications. Momentous machine alternatives are evaluated by means of technical structure, dominance theory and comparative analysis for supporting decision-making of industrial assets based on optimization and synchronization.

Research limitations/implications

The study linked financial, managerial and production resources under sole platform to present a technical structure that may assist in improving the performance of the manufacturing firms. The study provides a decision support mechanism to assist in reviewing the momentous resources to imitate a higher level of productive strength toward the manufacturing firms. The study endeavors its importance toward optimizing resources, which is an evident requirement in industries as the same not only saves money, escalates production, improves profit margins and so forth, but also gratifies the consumption of scarce natural resources.

Originality/value

The study stressed that advance information can be sought from system characteristics in the form of quality measures and attributes, which can be molded for gaining elevated outcomes from existing system characteristics. The same demands decision supports tools and frameworks to utilize data-driven information for benchmarking operations and supply chain activities. The study portrayed an approach for ease of utilizing data-driven information by the decision-makers for demonstrating superior outcomes. The study originally conceptualized multi-attributes appraisement framework associated with subjective cum objective quality measures to evaluate the most significant machine tool choice amongst preferred alternatives.

Machine learning and optimization-based modeling for asset management: a case study

Purpose

This research is inspired by a real case study from a pump rental business company across the US. The company was looking to increase the utilization of its rental assets while, at the same time, keeping the cost of fleet mobilization as efficient as possible. However, decisions for asset movement between branches were largely arranged between individual branch managers on an as-needed basis.

Design/methodology/approach

The authors propose an improvement for the company's asset management practice by modeling an integrated decision tool which involves evaluation of several machine learning algorithms for demand prediction and mathematical optimization for a centrally-planned asset allocation.

Findings

The authors found that a feed-forward neural network (FNN) model with single hidden layer is the best performing predictor for the company's intermittent product demand and the optimization model is proven to prescribe the most efficient asset allocation given the demand prediction from FNN model.

Practical implications

The implementation of this new tool will close the gap between the company's current and desired future level of operational performance and consequently increase its competitiveness

Originality/value

The results show a superior prediction performance by a feed-forward neural network model and an efficient allocation decision prescribed by the optimization model.

Digital supplier selection reinforcing supply chain quality management systems to enhance firm's performance

Purpose

The geographical scattering of physical facilities in conventional supply chains enforces firms to shift toward digital supply chains (DSCs). While switching to DSCs, the decision-making becomes more complex with an upsurge in the size of the manufacturing firms. The manufacturing firms need to develop supply chain quality management (SCQM) systems to improvise their processes for delivering advance products and services. For developing SCQM, the role of the digital supplier is significant, as they may recuperate the quality management systems (QMS) for enhancing the firm's performance. The purpose of this paper is to explore the factors that affect the selection of digital suppliers. The other purpose is to evaluate the alternatives for identifying the best supplier that enhances the QMS for DSCs.

Design/methodology/approach

The decision-making is complex for digital supplier selection (DSS) and thus, the study has utilized integrated SWARA-WASPAS methods for their critical evaluation. The stepwise weight assessment ratio analysis (SWARA) method has been utilized for identifying the weightage of factors and weighted aggregated sum product assessment (WASPAS) for assessing the digital suppliers to explore the best alternative. The integrated SWARA-WASPAS method is the most advance approach in multi-criteria decision-making (MCDM) for the evaluation of the factors.

Findings

The study reveals that supplier competency is the most significant factor in selecting digital supplier in DSC that may improve the product and service quality. The study also explores that manufacturing firms needs an efficient system for developing value for the internal and external partners that help them to cope up with the dynamic world. On the basis of the WASPAS results, supplier S8 has been ranked as the best supplier who has highest competency in the form of responsiveness, resilience, sustainable practices and digital innovation.

Research limitations/implications

The factors are assessed on the decision team of experts that may be biased and thus, the research may further be validated through empirical studies. The research has to be extended in other nations for exploring how organizations and customers are responding to the DSCs.

Practical implications

The study has given insights to the manufacturing firms to consider the crucial factors for DSS, as it affects the overall performance of the organizations. The decision makers of manufacturing organizations should consider the factors such as supplier competency, digital innovation and information sharing for value creation that may provide them better opportunities for developing their DSCs along with their digital suppliers to connect with stakeholders appropriately.

Social implications

The improved SCQM aligned with DSS will offer quality products that are sustainable and provide social and economic benefits to the society. The DSS will be able to provide improvisation of the existing products and services for developing a sustainable value chains for the manufacturing organizations. This process will bring more transparency, viability and sustainability in the product and services. As a result, the DSC partners will be more transparent, viable and resilient.

Originality/value

The research on DSS and its importance in enhancing QMS is limited. This research is the novel approach to understand the criteria behind the selection of the digital suppliers’ role and their presence in enhancing the quality of products and services.

Influence of data-driven supply chain quality management on organizational performance: evidences from retail industry

Purpose

The objective of the study is to test a conceptual model based on the interrelation between data-driven supply chain quality management practices (DDSCQMP) and the performance of organized retailing firms in India.

Design/methodology/approach

Based on a comprehensive review of literature, the dimensions of DDSCQMP concerning the Indian organized retail sector have been extracted. Considering the research objectives, the research data has been collected using a structured questionnaire from Indian retailers. Overall 133 questionnaires were responded successfully from retailers. The model was tested using structured equation modeling (SEM) through PLS 3.0.

Findings

The research findings confirm hypotheses and reveal the statistically significant relationship between DDSCQMP and retailers' performance at an aggregate level. However, the results of the individual-level analysis of DDSCQMP appear to vary from practice to practice. Among various DDSCQMP, “customer focus” with the highest beta (ß) value was found to have the greatest impact on performance followed by “employee relations”.

Originality/value

The study provides empirical justification for a structural model that identifies a positive and significant relationship between DDSCQMP and organizational performance within the context of organized retail sector of India.

Examining the role of dialogic communication and trust in donation-based crowdfunding tasks using information quality perspective

Purpose

The study explores the role of dialogic public communication and information quality (IQ) in evaluating the operational performance of donation-based crowdfunding (DBC) tasks. These tasks are primarily used to support disaster relief operations. The authors also test the influence of cognitive trust and swift trust as moderating variables in explaining the relationship between both IQ and dialogic communication with operational performance.

Design/methodology/approach

The authors used a primary survey to test the hypotheses. A total of 203 responses were collected from multiple crowdfunding platforms. The authors used archival data from task creators on donation-based crowdfunding platforms, and a structured questionnaire is also used to collect responses. Data are analyzed using Warp PLS 6.0. Warp PLS 6.0 works on the principle of partial least square (PLS) structured equation modeling (SEM) and has been used widely to test path analytical models.

Findings

The authors found out that the operational performance is explained significantly by the quality of information and its association with dialogic public communication. The results support the arguments offered by dialogic public communication theory and trust transfer theory in assessing the operational success of DBC. The study also confirms that cognitive trust positively moderates the relationship between IQ and organizational public dialogic communication and operational performance. It is also revealed that the duration of the DBC task has no significant control over dialogic public communication.

Practical implications

The study lays practical foundations for task creators on DBC platforms and website designers as it sets the importance of both IQ and dialogic communication channels. The communication made by the task creator and/or the DBC platforms with the donors and potential donors in the form of timely and appropriate information forms the key to the success of any DBC task. The study also helps task creators choose a suitable platform to improve performance.

Originality/value

The authors propose a unique framework by integrating two theoretical perspectives: dialogic public relation theory and trust transfer theory in understanding the operational performance of donation-based crowdfunding tasks. The authors address DBC tasks catering to disaster relief operations by collecting responses from task creators on DBC platforms. The study uniquely positions itself in the area of information and communication.

Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations

Purpose

Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition.

Design/methodology/approach

This study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM.

Findings

This research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition.

Practical implications

This paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition.

Originality/value

This research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.

Industry 4.0, quality management and TQM world. A systematic literature review and a proposed agenda for further research

Purpose

The main purpose of this paper is to analyse the current literature situation in terms of relationships between Industry 4.0 and quality management and TQM. The author wanted to understand what topics and issues can be considered the most relevant referring to the so-called Quality 4.0, what the literature is missing opening avenues for further research.

Design/methodology/approach

This research employed a systematic literature review. In total, 75 papers from different sources were reviewed using specific inclusion and exclusion criteria.

Findings

Four categories of topics emerged, namely: creating value within the company through quality (big) data, analytics and artificial intelligence; developing Quality 4.0 skills and culture for quality people; customer value co-creation; cyber–physical systems and ERP for quality assurance and control. This paper also tried to understand if there is a definition of Quality 4.0 based on determined methods.

Research limitations/implications

Systematic literature review could have introduced some limitations in terms of the number and reliability of reviewed papers. Probably some interesting papers had been not intentionally missed.

Practical implications

Consultants and managers in developing and implementing their own Quality 4.0 models could use many practical and discussed implications concerning I4.0 technologies and quality management.

Originality/value

This is one of the first papers which employed the systematic literature review for researching Industry 4.0, quality management and TQM relationships.