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In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.

This research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.

The papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.

The research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.

This study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.

1. A comprehensive understanding of Machine Learning techniques is presented.

2. The state of art of adoption of Machine Learning techniques are investigated.

3. The methodology of (SLR) is proposed.

4. An innovative study of Machine Learning techniques in manufacturing supply chain.

A comprehensive understanding of Machine Learning techniques is presented.

The state of art of adoption of Machine Learning techniques are investigated.

The methodology of (SLR) is proposed.

An innovative study of Machine Learning techniques in manufacturing supply chain.

This paper aims to discuss the suitability of topic modeling as a review method, identifies and compares the machine learning (ML) research trends in five primary business organization verticals.

This study presents a review framework of published research about adopting ML techniques in a business organization context. It identifies research trends and issues using topic modeling through the Latent Dirichlet allocation technique in conjunction with other text analysis techniques in five primary business verticals – human resources (HR), marketing, operations, strategy and finance.

The results identify that the ML adoption is maximum in the marketing domain and minimum in the HR domain. The operations domain witnesses the application of ML to the maximum number of distinct research areas. The results also help to identify the potential areas of ML applications in future.

This paper contributes to the existing literature by finding trends of ML applications in the business domain through the review of published research. Although there is a growth of research publications in ML in the business domain, literature review papers are scarce. Therefore, the endeavor of this study is to do a thorough review of the current status of ML applications in business by analyzing research articles published in the past ten years in various journals.

The purpose of this study is to conduct a systematic literature review of the adoption of immersive marketing technology (IMT) in terms of strategic planning of its adoption, resource requirements and its implications and challenges.

This study categorizes and contextualizes qualitative approaches to evaluate the literature, with Scopus databases serving as the primary source of 90 selected articles in the areas of information technology, business and marketing strands. Theme analysis was carried out using thematic techniques and grounded approach principles to facilitate thematic coding and generate theme analysis.

The analysis was supported by the three concepts of business flexibility, agility and adaptability, which were drawn as a strategy for IMT adoption. The findings presented three main themes: proactive flexibility, responsive agility and reactive adaptability that enable business owner–managers to craft a strategy for IMT adoption.

The novel contribution of this study is the inclusion of key implications related to IMT as a starting point of the next level of innovative marketing for all academics, practitioners and business owner–managers.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

Integrating artificial intelligence (AI) into various industries, including the financial sector, has transformed them. This paper aims to examine the influence of integrating AI, including machine learning, process automation, predictive analytics and chatbots, on financial institutions and explores its various aspects and areas. The study aims to determine the impact of AI integration on financial services, products and customer experience.

The research study uses quantitative and qualitative methods, as well as secondary data analysis. It investigates four AI subfields: machine learning, process automation, predictive analytics and chatbots.

The research findings indicate that integrating AI, particularly in machine learning and chatbot subfields, holds promise and high strategic potential for financial institutions. These subfields can contribute significantly to enhancing financial services and customer experience. However, the significance of predictive analytics integration and process automation is relatively lower. Although these subfields retain their usefulness, they might necessitate alternative workflows and tools that incorporate human involvement. Overall, AI integration minimizes human interactions and errors in financial institutions.

The research study contributes original insights by exploring the specific subfields of AI within the financial industry and assessing their strategic significance. It provides recommendations for financial institutions to adopt AI integration partially in multiple phases, measure and evaluate the impact of the transformation and structure internal units and expertise to strategize adoption and change.

Purpose: Nowadays, many terms like computer vision, deep learning, and machine learning have all been made possible by recent artificial intelligence (AI) advances. As new types of employment have risen significantly, there has been significant growth in adopting AI technology in enterprises. Despite the anticipated benefits of AI adoption, many businesses are still struggling to make progress. This research article focuses on the influence of elements affecting the acceptance procedure of AI in organisations.

Design/Methodology/Approach: To achieve this objective, propose a hierarchical paradigm for the same by developing an Interpretive Structural Modelling (ISM). This paper reveals the barriers obstructing AI adoption in organisations and reflects the contextual association and interaction amongst those barriers by emerging a categorised model using the ISM approach. In the next step, cross-impact matrix multiplication is applied for classification analysis to find dependent, independent and linkages.

Findings: As India is now focusing on the implementation of AI adoption, therefore, it is essential to identify these barriers to AI to conceptualise it systematically. These findings can play a significant role in identifying essential points that affect AI adoption in organisations. Results show that low regulations are the most critical factor and functional as the root cause and further lack of IT infrastructure is the barrier. These two factors require the most attention by the government of India to improve AI adoption.

Implications: This study may be utilised by organisations, academic institutions, Universities, and research scholars to fill the academic gap and faster implementation of AI.

This systematic literature review aims to explore the adoption, global acceptance and implementation of human resources (HR) analytics (HRA) by reviewing literature on the subject. HRA adoption can assist HR professionals in managing complex procedures and making strategic human resource management (SHRM) decisions more effectively. The study also aims to identify the applications of analytics in various disciplines of management.

The review is conducted using a domain-based structured literature review (SLR), emphasizing the diffusion of innovative thinking and the adoption process of HRA among early adopters. The philosophical stances are analyzed with the combination of research onion model and PRISMA protocol. Secondary data are gathered from published journals, books, case studies, conference proceedings, web pages and media stories as the primary source of information.

The study finds that skilled professionals and management assistance can significantly impact adoption intentions, enabling professionals to deal with analytics. The examples and analytical models provided by early adopters allow managers to manage complex processes and make SHRM decisions.

The study suggests that the lack of use of quantitative techniques is a key limitation and should be considered in future studies. Despite the rise in the number of research papers on HRA, its application in the workplace remains limited.

This research can assist managers in implementing HRA and help resolve complex and inefficient processes, making SHRM decisions.

This study adds to the existing body of knowledge on how HRA can aid a company's efficacy and performance and can be considered one of the first to link adoption and HRA.

The importance of learning in development in increasingly dynamic contexts can help individuals and organizations adapt to disruption. Artificial intelligence (AI) is emerging as a disruptive technology, with increasing adoption by various human resource management (HRM) functions. However, learning and development (L&D) adoption of AI is lagging, and there is a need to understand of this low adoption based on the internal/external contexts and organization types. Building on open system theory and adopting a technology-in-practice lens, the authors examine the various L&D approaches and the roles of human and technology agencies, enabled by differing structures, different types of organizations and the use of AI in L&D.

Through a qualitative interview design, data were collected from 27 key stakeholders and L&D professionals of MSMEs, NGOs and MNEs organizations. The authors used Gioia's qualitative research approach for the thematic analysis of the collected data.

The authors argue that human and technology agencies develop organizational protocols and structures consistent with their internal/external contexts, resource availability and technology adoptions. While the reasons for lagging AI adoption in L&D were determined, the future potential of AI to support L&D also emerges. The authors theorize about the socialization of human and technology-mediated interactions to develop three emerging structures for L&D in organizations of various sizes, industries, sectors and internal/external contexts.

The study hinges on open system theory (OST) and technology-in-practice to demonstrate the interdependence and inseparability of human activity, technological advancement and capability, and structured contexts. The authors examine the reasons for lagging AI adoption in L&D and how agentic focus shifts contingent on the organization's internal/external contexts.

While AI-HRM scholarship has primarily relied on psychological theories to examine impact and outcomes, the authors adopt the OST and technology in practice lens to explain how organizational contexts, resources and technology adoption may influence L&D. This study investigates the use of AI-based technology and its enabling factors for L&D, which has been under-researched.

This paper aims to study the economic effects of Cloud Computing for a sample of Israeli firms. The authors propose a framework that considers how this technology affects firm performance also introducing the indirect economic effects that take place through cloud-complementary technologies such as Big Data and Machine Learning.

The model is estimated through structural equation modeling. The data set consists of the microdata of the survey of information and communication technologies uses and cyber protection in business conducted in Israel by the Central Bureau of Statistics.

The results point to Cloud Computing as a crucial technology to increase firm performance, presenting significant direct and indirect effects as the use of complementary technologies maximizes its impact. Firms that enjoy most direct economic gains from Cloud Computing appear to be the smaller ones, although larger enterprises seem more capable to assimilate complementary technologies, such as Big Data and Machine Learning. The total effects of cloud on firm performance are quite similar among manufacturing and service firms, although the composition of the different effects involved is different.

This paper is one of the very few analyses estimating the impact of Cloud Computing on firm performance based on country microdata and, to the best of the authors’ knowledge, the first one that contemplates the indirect economic effects that take place through cloud-complementary technologies such as Big Data and Machine Learning.

This paper uses dialectical inquiry to explore tensions that arise when adopting Industry 4.0 technologies in a lean production system and their reconciliation mechanisms.

We conducted an in-depth qualitative case study over a 3-year period on an Italian division of an international electrotechnical organisation that produces electrical switches. This organisation successfully adopted Industry 4.0 technologies in a lean production system. The study is based on primary data such as observations and semi-structured interviews, along with secondary data.

We identify four empirically validated dialectic tensions arising across different Industry 4.0 adoption stages due to managers’ and workers’ contrasting interpretations of technologies. Consequently, we define the related reconciliation mechanisms that allow the effective adoption of various Industry 4.0 technologies to support a lean production system.

This is the first empirical investigation of tensions in the adoption of Industry 4.0 technologies in a lean production system. Furthermore, the paper presents four theoretical propositions and a conceptual model describing which tensions arise during the adoption of Industry 4.0 technologies in a lean production system and the reconciliation mechanisms that prevent lean production system deterioration.