Dr Greg Barnes

"This book gives an in-depth overview of the integration of supply chain and blockchain technology, and its potential applications while providing contemporary theoretical frameworks and the latest empirical findings"--

Innovative technologies such as big data and predictive analytics are facilitating a global revolution in operational management. The present study aims to investigate the extent of their adoption among micro-, small and medium enterprises. The context of choice is Malaysia—a growing economy hosting some of the relatively more technologically advanced enterprises in ASEAN union. The research plan of the present paper pursued three objectives. First, we aimed to identify the general rate of adoption of BDPA. Second, we explored the most common uses of this set of innovations in Malaysian micro-, small, and medium enterprises. Third, we strove to abstract the antecedents and consequences of BDPA in micro-, small, and large firms in the manufacturing and service sectors. We discovered that BDPA are almost never used to outsource suppliers or to optimise spending and purchasing. Conversely, the most frequent applications of BDPA are in customer relationship management and in warehouse and operations improvement. Moreover, we smaller firms are more resilient to the challenges of adopting BDPA than their smaller and larger counterparts. Thus, this paper presents a snapshot of the BDPA dissemination in the micro-, small and medium ecosystem in Malaysia in 2021, while exploring opportunities for the future development and dissemination of this set of innovations.

When constructing integrated markets, regulatory coherence is important to ensure consistency in application, and response from market actors. This coherency is difficult when the market develops incrementally, however. In the case of the EU’s Data Market, the speed with which it has been built to facilitate data use by Artificial Intelligence (AI) has led to several types of Smart Regulation (SR) approaches being utilised. This paper considers the regulatory structure of the EU’s Data Governance Act (EU) 2022/868 in the context of the European Green Deal (EGD). To facilitate the goals of the EGD, a dynamic approach to governance of data is required that involves both private and governmental agencies and actors in the implementation of the policy. SR establishes a regulatory pluralism that involves ‘flexible, imaginative and innovative forms of social control’ but as a result sacrifices regulatory coherence. This paper critically analyses the potential for use of SR in the implementation of 2022/868 within the field of EU sustainability policy. A coherent SR approach should lead to a successful response to the pressing climate change targets set in the EDG. The paper draws upon existing examples of SR and proposes additional conceptual considerations for effective implementation of Data Governance for EDG agenda outcomes.

To explore the area, this chapter analyses the use of Artificial Intelligence in the drive towards sustainability adaptation, specifically in relation to the European Green Deal (EGD). The EGD is founded on evidence-based decision making, and the use of digital technologies to facilitate its aims. Throughout this analysis, the use of AI is framed as the ‘connective tissue’ between areas of sustainability transition (Markard et al., 2020) that would otherwise be disparate in nature. No other technological advancement has the realistic opportunity to draw together the systems and social changes that are required to meet the ambitious EU targets. As such, while there are huge costs, there are major benefits to be accrued.

Purpose The paper is a proof of concept (PoC) intervention study aimed for developing performance management (PM) practices in manufacturing small and medium-sized enterprises (SMEs) with the longer-term aim enabling the SMEs to improve their productivity. The intervention was designed and deployed by a collaborative quartet of academics, management consultants, accountancy firm and a commercial bank manager. Design/methodology/approach The paper firstly musters a set of initialising PM practices aligned to productivity improvement. These are utilised to design a knowledge transfer intervention for deployment with a set of manufacturing SMEs incorporating some associated productivity tools. The evaluation of the intervention utilised a case study approach founded on a logic model of the intervention to assess the development of the PM practices. Findings The intervention contributed to a partial development of the mustered practices and the productivity diagnostic based on the multi-factor productivity (MFP) abstraction and a data extraction protocol had the strongest impact. The study revealed the importance of the three interlaced factors: Depth of engagement, feedback opportunities and the intervention gradient (the increase of independent action from the participating SME's and the diminishment of the external intervention effort). Research limitations/implications The case study is based on a limited number of individual SME's, and within just the manufacturing sector. Practical implications SME businesses will require a more sustained programme of interventions than this pilot to develop PM capability, and depth of engagement within the SME is critical. Professional stakeholders can be utilised in recruitment of firms for intervention programmes. Business can start developing PM capability prior to PMS implementation using the tools from this programme. Originality/value The productivity diagnostic tool, based on a synthesis of MFP and the performance pyramid, an array of potential initialising practices for PM capability and discovery of potential mechanisms for PM practice development.