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This chapter explores the significance of blockchain technology in protecting data for intelligent applications across various industries. Blockchain is a distributed ledger that ensures the immutability and security of transactions. Given the increasing need for security measures in industries, understanding blockchain technology is crucial for preparing for its future applications.

This chapter aims to examine the use of blockchain technology across industries and presents a compilation of existing and upcoming blockchain technologies for intelligent applications. The methodology involves reviewing research to understand the security needs of different industries and providing an overview of methods used to enhance multi-institutional and multidisciplinary research in areas like the financial system, smart grid, and transportation system.

The findings highlight the benefits of blockchain networks in providing transparency, trust, and security for industries. The Responsible Sourcing Blockchain Network (RSBN) is an example that utilizes blockchain's decentralized ledger to track sustainable sourcing from mine to final product. This information can be shared with auditors, corporate governance organizations, and customers.

The practical implications of this chapter are significant, serving as a valuable resource for industries concerned with identity privacy, traceability, immutability, transparency, auditability, and security. Understanding and implementing blockchain technology can address the growing need for secure and intelligent applications, ensuring data protection and enhancing trust in various sectors.

This study considers transitive service triads, which consist of three dyads formed by three actors: supplier, logistics service provider and customer, who remain directly linked by one or more of the upstream and downstream flows of products, information and finances. This paper aims to explore the link between information governance, decentralized information technologies and supply chain self-organization, and their resulting impact on network performance in the transitive service triads.

Drawing upon the tenets of the theory of complex adaptive systems and supply chain practice view, this paper involves an empirical investigation that uses survey data gathered from transitive service triads in the European countries. The study uses partial least squares structural equation modeling to estimate the formative-reflective hierarchical component model and test the research hypotheses.

Information governance defines how supply chain information flows are controlled, accessed and used by a focal organization and its business partners. As empirically evidenced in this study, it can be depicted as a latent construct consisting of three distinct dimensions of information custody, information ownership and right to data access. Likewise, the study also indicates that supply chain self-organization, as a second-order construct, consists of three interactive self-organization actions undertaken by specific firms participating in the triadic arrangement. Supply chain self-organization is thus produced by firms that are reciprocally interrelated and interacting, having effects on one another. Furthermore, the study also highlights that information governance creates an environment for applying decentralized information technologies, which then positively affects supply chain self-organization. Finally, the research also empirically operationalizes the construct of network performance within the transitive service triads.

Although the results provide several major contributions to theory and implications for practitioners, the study still demonstrates some methodological constraints. Specifically, although the study uses a relatively large research sample of 350 transitive service triads, it still focuses only on a selected group of industries and is limited to investigating solely a particular type of service triads.

Given the increasing interest in investigating triads, this study examines how information governance and decentralized information technologies support supply chain self-organization to yield network performance in transitive service triads.

This study is centered on the identification of the most appropriate Technology Adoption (TA) model for investigating the adoption of Industry 4.0 technologies within pharmaceutical and related enterprises. The aim is to facilitate a smooth transition to advanced technologies while concurrently achieving environmental sustainability.

Selection of a suitable TA theory is carried out using a hybrid multi-criteria decision-making (MCDM) approach incorporating PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) and Fuzzy Measurement of alternatives and ranking according to Compromise solution (F-MARCOS) methods. A group of three experts is formulated for the ranking of criteria and alternatives based on those criteria.

The results indicate that out of all six TA models considered unified theory of acceptance and use of technology (UTAUT) model gets the highest utility function value, followed by the technical adoption model (TAM). Further, sensitivity analysis is conducted to confirm the validity of the MCDM model employed.

Challenging times like COVID-19 pointed out the importance of technology in the pharmaceutical and healthcare sectors. TA studies in this area can help in the identification of critical factors that can assist pharmaceutical firms in their efforts to embrace emerging technologies, enhance their outputs and increase their efficiency.

The novelty of this research lies in the fact that the utilization of a TA theory prior to its implementation has not been witnessed in existing scholarly literature. The utilization of a TA theory, specifically within the pharmaceutical industry, can assist enterprises in directing their attention toward pertinent factors when contemplating the implementation of emerging technologies and achieving sustainable development.

Decentralised autonomous organisations (DAOs) are internet-native self-governing enterprises where individual groups, communities, agencies, consumers and providers work together using blockchain-led smart contracts (SCs). This study aims to examine the role of DAO marketplaces in technology-led autonomous organisation design for enterprise technology sourcing industries, with algorithmic trust and governance.

The authors examined the importance of an enterprise marketplace governance platform for technology sourcing using DAO as a decentralised/democratised business model. A total of 98 DAO products/services are evaluated across 11 industries that envisage DAO as a strategic choice for the governance of decentralised marketplace platforms.

The research findings validate how a DAO-led enterprise marketplace governance platform can create a cohesive collaboration between consumers (enterprises) and providers (solution vendors) in a disintermediated way. The proposed novel layered solution for an autonomous governance-led enterprise marketplace promises algorithmic trust-led, self-governed tactical alternatives to a strategic plan.

The research targets multiple industry outlooks to understand decentralised autonomous marketplace governance and develop the theoretical foundation for research and extensive corporate suitability.

The research underpinnings boost the entrepreneurs’ ability to realise the practical potential of DAO between multiple parties using SCs and tokenise the entire product and service offerings over immutable ledger technologies.

To the best of the authors’ knowledge, this research is unique and the first of its kind to study the multi-industry role of algorithmic trust and governance in enterprise technology sourcing marketplaces driven by 98 decentralised and consensus-based DAO products across 11 industries.

Large optical mirror processing systems (LOMPSs) consist of multiple subrobots, and correlated disturbance terms between these robots often lead to reduced processing accuracy. This abstract introduces a novel approach, the nonlinear subsystem adaptive dispersed fuzzy compensation control (ADFCC) method, aimed at enhancing the precision of LOMPSs.

The ADFCC model for LOMPS is developed through a nonlinear fuzzy adaptive algorithm. This model incorporates control parameters and disturbance terms (such as those arising from the external environment, friction and correlation) between subsystems to facilitate ADFCC. Error analysis is performed using the subsystem output parameters, and the resulting errors are used as feedback for compensation control.

Experimental analysis is conducted, specifically under the commonly used concentric circle processing trajectory in LOMPS. This analysis validates the effectiveness of the control model in enhancing processing accuracy.

The ADFCC strategy is demonstrated to significantly improve the accuracy of LOMPS output, offering a promising solution to the problem of correlated disturbances. This work holds the potential to benefit a wide range of practical applications.

Motivated to know more about the internal means through which accountability for sustainability takes shape within organisations (in what ways and by whom), this paper aims to explore how accountability for sustainability is constructed within an organisation during a process of establishing a control system for sustainability.

This paper adopts a qualitative case study approach of a decentralised industrial group, operating mainly in Scandinavia, between 2017 and 2020. Both primary and secondary data are used (e.g. document analyses, semi-structured interviews, informal conversations and site visits) to inform the findings and analysis.

The findings reveal a multi-faceted path towards accountability for sustainability that involves several concerns and priorities at organisational and individual levels, resulting in a separate sustainability control systems within each subsidiary company. Although hierarchical structures for accountability exist, socialising accountability activities are needed to (further) mobilise sustainable accounts.

Successful sustainable control systems require employees making sense of formalised accountability instruments (e.g. policies and procedures) to establish their roles and responsibilities in organisations.

This paper proposes socialisation processes as important for driving forward sustainability solutions.

This study elaborates on the internal accountability dynamic for the construction of sustainable accounts. Its novelty is built upon the interaction of hierarchical and socialising accountability forms as necessary for establishing a control system for sustainability. It furthermore illustrates the relationship between the external and internal pathways of accountability.

This study aims to critically review the emerging technological developments and digitalization efforts in the hospitality and tourism (HT) industry and discuss the implications of digitalization on various stakeholders (e.g. consumers, employees, companies and operators) with reference to value creation.

This paper is a conceptual, critical reflection paper. Thus, the study reflects the authors’ assessment and reflection of the current digitalization efforts in the HT industry with a particular interest in value creation.

The study suggests that digitalization is still in its infancy state in terms of adoption and value creation in the HT industry. Yet, there are various opportunities for all stakeholders to benefit from existing and emerging digitalization applications.

This study can be used by industry professionals and scholarly researchers as a reflection of past and current digitalization efforts in the HT industry. Moreover, the study offers directions regarding the future digitalization movement in the HT industry and how such a movement might create important value propositions for various stakeholders.

The study is uniquely positioned as a critical reflection paper on the digitalization effort of the HT industry and offers new practical insights regarding how digitalization could create value for industry stakeholders as it finds more application areas. In this regard, it differs from prior review studies that focused solely on the use of new and emerging technologies in HT operations.

This study aims to study the role of co-production among heterogenous actors to achieve a digital transformation for public service delivery. The importance of building legitimacy and public value creation during the journey of adopting new technology is mainly discussed through analyzing the case of blockchain-based decentralized identity (DID) in South Korea (Korea).

Using a qualitative method, the conceptual framework for this study draws on public value theory that highlights co-production approaches on adoption and diffusion of new information system.

The results of this study provide empirical support for the claim that co-constructive and cross-sector partnership is critical to the adoption of blockchain-based DID and to maximize the value-in-use and increase citizens’ participation.

This study suggests a new perspective, which contributes to the information system literature and the phenomenological analysis by investigating co-constructive partnership among cross-sector organizations through the legitimacy-building process, which has not been much empirically researched.

Motivated by recent research indicating that the operational performance of an enterprise can be enhanced by building a supporting data-driven environment in which to operate, this paper presents a simulation framework that enables an examination of the effects of applying smart manufacturing principles to conventional production systems, intending to transition to digital platforms.

To investigate the extent to which conventional production systems can be transformed into novel data-driven environments, the well-known constant work-in-process (CONWIP) production systems and considered production sequencing assignments in flowshops were studied. As a result, a novel data-driven priority heuristic, Net-CONWIP was designed and studied, based on the ability to collect real-time information about customer demand and work-in-process inventory, which was applied as part of a distributed and decentralised production sequencing analysis. Application of heuristics like the Net-CONWIP is only possible through the ability to collect and use real-time data offered by a data-driven system. A four-stage application framework to assist practitioners in applying the proposed model was created.

To assess the robustness of the Net-CONWIP heuristic under the simultaneous effects of different levels of demand, its different levels of variability and the presence of bottlenecks, the performance of Net-CONWIP with conventional CONWIP systems that use first come, first served priority rule was compared. The results show that the Net-CONWIP priority rule significantly reduced customer wait time in all cases relative to FCFS.

Previous research suggests there is considerable value in creating data-driven environments. This study provides a simulation framework that guides the construction of a digital transformation environment. The suggested framework facilitates the inclusion and analysis of relevant smart manufacturing principles in production systems and enables the design and testing of new heuristics that employ real-time data to improve operational performance. An approach that can guide the structuring of data-driven environments in production systems is currently lacking. This paper bridges this gap by proposing a framework to facilitate the design of digital transformation activities, explore their impact on production systems and improve their operational performance.

This study aims to synthesize the existing literature with insights gained from interviews conducted with regulatory experts. The objective is to analyse the challenges associated with incorporating cryptocurrencies into regulatory frameworks and to explore constraints in the regulatory institutionalization of cryptocurrencies.

The study methodology consists of two steps. The first step is to identify regulatory constraints in the literature review and in the next step, interviews are conducted with officials of the State Bank of Pakistan (SBP). The study used a qualitative case study methodology, in which a single case (regulatory constraint) was selected as a unit of analysis.

The findings show that lack of traceability, legal status, lack of governmental control due to decentralization, difficulty enforcing laws, volatility, lack of skills with regulators and difficulty integrating cryptocurrencies into the current financial system are the main obstacles to the introduction of a regulatory framework. Thus, on a broader conceptual level, the findings can be grouped into opportunism, lack of strategic capability and fragmented global laws.

This study could inform global cryptocurrency regulation discussions, sharing a developing country’s views on balancing the government, central banks, the financial sector and public interests. This could guide countries to consider cryptocurrency adoption in similar situations. This could affect the cryptocurrency market, impacting demand, supply and investor trust in Pakistan.

The study has implications for policy making officials. The research aims to offer valuable insights to the SBP and other regulatory authorities, helping them identify potential risks and create an effective regulatory framework for cryptocurrencies.

The study has implications for society in knowing about the volatile nature of cryptos and anonymity of their issuers, which poses regulatory constraints. This then implies its harmfullness to its traders and the huge losses that may arise from their trading due to its volatile nature.

This study contributes to the literature on the constraints, responsibilities and consultation framework of cryptocurrency regulations.