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The purpose of this paper is twofold: to identify and map contemporary research on advanced technology implementations for problem-solving purposes in the manufacturing industry, and to further understand the organizational learning possibilities of advanced technology problem-solving in the manufacturing industry.

This paper outlines a scoping review of contemporary research on the subject. The findings of the review are discussed in the light of theories of contradicting learning logics.

This paper shows that contemporary research on the subject is characterized by technological determinism and strong solution-focus. A discussion on the manufacturing industries’ contextual reasons for this in relation to contradicting learning logics shows that a Mode-2 problem-solving approach could facilitate further learning and expand knowledge on advanced technology problem-solving in the manufacturing industry. A research agenda with six propositions is provided.

The introduction of advanced technology implies complex effects on the manufacturing industry in general, while previous research shows a clear focus on technological aspects of this transformation. This paper provides value by providing novel knowledge on the relationship between advanced technology, problem-solving and organizational learning in the manufacturing industry.

This paper aims to investigate the characteristics and dynamics of the organizational changes needed to facilitate the management of an Industry 4.0 transformation in manufacturing SMEs and propose an approach to manage them.

This research focuses on a single manufacturing SME in North America, and data were collected using a research intervention method. Data were collected through observation and intervention within the SME over 27 months.

The research has shown that organizational changes are required in manufacturing SMEs to better manage their Industry 4.0 transformation projects.

Using the case study method limits the generalization of the results. The organizational changes observed, and their characteristics might be specific to the studied manufacturing. Although results could vary in different contexts, many manufacturing SMEs have similar characteristics to those observed in this study.

This research provides preliminary evidence of an iterative organizational change management approach that manufacturing SMEs must adopt to facilitate the management of their digital transformation.

This research provides a better understanding of how a manufacturing SME can improve its capabilities to manage its digital transformation by introducing iterative organizational changes. From these results, a link to the organizational learning literature can be drawn and developed upon.

Purpose: Blockchain technology has exploded in popularity in the social economy over the years. Because blockchain technology offers so many advantages, it was first employed for Bitcoin transactions. The interaction between blockchain technology and financial industries will grow more personal and interwoven with the introduction of Bitcoin. As the value of blockchain technology became evident, more financial companies began to test its use in monetary operations. Numerous financial companies have been experimenting with blockchain technology to reduce the transactional costs and to improve the efficiency of operations, specifically in the domain of financial notes, cross-border payments, and asset-backed securitisation. Blockchain will have a broad range of applications in the financial industry shortly.

Methodology: Through financial tools and methods, trust may result in successful businesses. In the long term, blockchain technology is generally a significant source of the finance industry revolution. It allows you to create immutable transaction histories available to everyone on the network. A blockchain system consists of a sequence of blocks connected by a connection to the block before each block. Each block comprises more than one transaction representing changes in the ownership of assets on the ledger. A consensus procedure is employed to add new blocks to the existing chain, in which members of the public blockchain confirm that transactions are accurate.

Findings: Blockchain technology is still in its infancy. The challenges of sustainability, privacy, secrecy, and latency, among others, have surfaced. Financial markets must get a deeper grasp of Blockchain to provide trustworthy alternatives.

Significance: The book chapter highlights the architecture, protocols, and applications of Blockchain Technology. The chapter will also enlighten the readers about the challenges and solutions of Blockchain Technology in the Financial Market. Moreover, the chapter also discusses the future research scope of Blockchain Technology in the Financial Market.

There is increasing interest in the connection between Industry 4.0 (I4.0) and operational excellence approaches; however, studies on the integration between Six Sigma (SS) and I4.0 have been absent from the literature. Integration with I4.0 technologies can maximize the positive effects of SS. The purpose of this study is to understand what types of relationships exist between SS and I4.0 and with I4.0's technologies, as well as the benefits derived from this integration and future directions for this field of study.

A Systematic Literature Review (SLR) was carried out to analyze studies about connections between I4.0 technologies and SS. SLR analyzed 59 articles from 2013 to 2021 extracted from the Web of Science and Scopus databases, including documents from journals and conferences.

The SLR identified relationships between SS and several I4.0 technologies, the most cited and with the greatest possibilities of relationships being Big Data/Big Data Analytics (BDA) and Internet of Things (IoT). Three main types of relationships were identified: (1) support of I4.0 technologies to SS; (2) assistance from the SS to the introduction of I4.0 technologies, and, to a lesser extent; (3) incompatibilities between SS and I4.0 technologies. The benefits are mainly related to availability of large data sets and real-time information, enabling better decision-making in less time.

In addition, the study can help managers to understand the integration relationships, which may encourage companies to adopt SS/Lean Six Sigma (LSS) in conjunction with I4.0 technologies. The results also drew attention to the incompatibilities between SS and I4.0 to anticipate potential barriers to implementation.

The study focuses on three previously unexplored subjects: the connection between SS and I4.0, the existing relationships with different technologies and the benefits resulting from the relationships. In addition, the study compiled and structured different types of relationships for SS and I4.0 and I4.0's technologies, identifying patterns and presenting evidence on how these relationships occur. Finally, exposes current trends and possible research directions.

Virtual and augmented reality solutions in medicine are generally applied in communication, training, simulation and therapy. However, like most new digital developments, these technologies face a large number of institutional barriers that are inherent to the medical sector.

Following Richard Scott's view on institutions and organizations, a multiple case study is used to analyze regulatory, normative and cultural-cognitive institutional pillars in the medical industry.

The results of the study demonstrate that (1) the regulatory pillar inhibits the advancement of new technologies in the approach to treatment, regulation of patient data, educational processes for medical staff, and information and financial flows; (2) the number of barriers increases based on the solution's level of disruption and the number of variable conventional procedures; (3) trust between participants in the medical industry plays an important role in introducing new technologies; (4) new participants need to address certain pillars depending on the area of application.

The authors discuss top-down and bottom-up approaches for overcoming institutional barriers when implementing augmented and virtual reality solutions for companies focusing on the medical market.

The objective of this chapter is to identify the key characteristics of Global Services businesses that will thrive and achieve success in the future. These factors are integrated into three main pillars, which we refer to as the Triple-Win. The first and most obvious pillar is technology as a tool. The second pillar is the design and sustainability of the business model, without which the previous factor would be merely a cost and not an investment. And last but not the least, there is the purpose which gives meaning to the proposal, focusing on the human being and their environment. The DIDPAGA business model sits at the intersection of these three elements.

This chapter considers aspects of ensuring competitive advantages of the banking sector of Ukraine in the context of global digital transformation. It is proved that operations that previously required a large amount of resources and time can now be carried out automatically using software solutions and artificial intelligence (AI). Companies like FinTech and BigTech that combine financial technology with their core services pose a serious threat to traditional banks. The authors noted that digital technologies make it possible to provide faster and more efficient customer service and improve the security of operations and reduce costs. The emphasis is placed on the fact that the use of AI in the banking sector will contribute to improving the efficiency and accuracy of banks, reducing risks and costs, and also contributes to the selection of personalized approaches to their customers. It has been proven that, thanks to AI, banking companies can improve the efficiency of their work, reduce costs, and improve interaction with their customers, thereby gaining competitive advantages in the market.

Amidst ongoing digital transformation, the current paper provides a 360-degree overview of technology-adoption in Tourism and Hospitality. By combining and consolidating a wide range of sources, mainly in the tourism literature, the analysis depicts how the complex technological ecosystem often enhances or hinders the successful adoption, integration and interoperability of different technologies.

The critical review method was used to assess, analyse and synthesise existing literature in the area of digitisation in tourism and hospitality. The critical review process included a thematic analysis of the literature, where recurring themes, patterns and trends were identified towards addressing the study’s research questions.

The analysis identifies current trends, opportunities, challenges and strategies for technology adoption in tourism and hospitality, the implications for theory, practicable executive directions and avenues for further research.

The paper’s main contribution lies in its comprehensive identification, consideration and incorporation of all primary contemporary technological elements, and the ensuing development of a corresponding conceptual charting framework, which illustrates a multifaceted process with practical implications for various stakeholders, including businesses, authorities, consumers and employees.

Despite an enormous body of literature on conflict management, intra-group conflicts vis-à-vis team performance, there is currently no study investigating the conflict prevention approach to handling innovation-induced conflicts that may hinder smooth implementation of big data technology in project teams.

This study uses constructs from conflict theory, and team power relations to develop an explanatory framework. The study proceeded to formulate theoretical hypotheses from task-conflict, process-conflict, relationship and team power conflict. The hypotheses were tested using Partial Least Square Structural Equation Model (PLS-SEM) to understand key preventive measures that can encourage conflict prevention in project teams when implementing big data technology.

Results from the structural model validated six out of seven theoretical hypotheses and identified Relationship Conflict Prevention as the most important factor for promoting smooth implementation of Big Data Analytics technology in project teams. This is followed by power-conflict prevention, prevention of task disputes and prevention of Process conflicts respectively. Results also show that relationship and power conflicts interact on the one hand, while task and relationship conflict prevention also interact on the other hand, thus, suggesting the prevention of one of the conflicts could minimise the outbreak of the other.

The study has been conducted within the context of big data adoption in a project-based work environment and the need to prevent innovation-induced conflicts in teams. Similarly, the research participants examined are stakeholders within UK projected-based organisations.

The study urges organisations wishing to embrace big data innovation to evolve a multipronged approach for facilitating smooth implementation through prevention of conflicts among project frontlines. This study urges organisations to anticipate both subtle and overt frictions that can undermine relationships and team dynamics, effective task performance, derail processes and create unhealthy rivalry that undermines cooperation and collaboration in the team.

The study also addresses the uncertainty and disruption that big data technology presents to employees in teams and explore conflict prevention measure which can be used to mitigate such in project teams.

The study proposes a Structural Model for establishing conflict prevention strategies in project teams through a multidimensional framework that combines constructs like team power conflict, process, relationship and task conflicts; to encourage Big Data implementation.

Drawing upon a systematic literature review in new technology, innovation transfer and diffusion theories, and from interviews with technology leaders in digital transformation programs in the US Oil & Gas (O&G) industry, the authors explore the relationships among O&G industry dynamics, organization's absorptive capacity and resource commitment for new digital technology adoption-implementation process.

The authors employed the empirical survey method to gather the data (a sample size of 172) in the US O&G industry and used structural equation modeling (SEM) to test the measurement model for validity and reliability and the conceptual model for hypothesized structural relationships.

The results provide support for the study’s causal model of adoption and implementation with positive and direct relationships between the initiation and trial stages, between the trial stages and the evaluation of effective outcomes and between the effective outcomes and the effective implementation stages of digital technologies. The results also reveal partial mediating relationships of industry dynamics, absorptive capacity and resource commitment between respective stages.

Based on the current study's findings, managers are recommended to pay attention to the evolving industry dynamics during the initiation stage of new digital technology adoption, to utilize the organization's knowledge-based absorptive capacity during digital technology trial and selection stages and to support the digital technology implementation project when the adoption decision of a particular digital technology has been made.

The empirical research contributes literature on digital technology adoption and implementation by identifying and demonstrating the importance of industry dynamics, absorptive capacity and resource commitment factors as mediating variables at various stages of the adoption-implementation process and empirically validating a process-based causal model of digital technology adoption and a successful implementation project that has been missing in the current body of literature on digital transformation.