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The purpose of this paper is to scrutinise the effectiveness of four derivative exchanges’ enforcement efforts since 2007. These exchanges include the Commodity Exchange Inc. and ICE Futures US from the United States and ICE Futures Europe and the London Metal Exchange from the UK.

The paper examines 799 enforcement notices published by four exchanges through a behavioural science lens: HUMANS conceived by Hunt (2023) in Humanizing Rules: Bringing Behavioural Science to Ethics and Compliance.

The paper finds the effectiveness of the exchanges’ enforcement efforts to be a mixed picture as financial markets transition from the digital to artificial intelligence era. Humans remain a key cog in the wheel of market participants’ trading operations, albeit their roles have changed. Despite this, some elements of exchanges’ enforcement regimes have not kept pace with the move from floor to remote trading. However, in other respects, their efforts are or should be, effective, at least in behavioural terms.

The paper’s findings are arguably limited to exchanges based in Anglophone jurisdictions. The information published by the exchanges is variable, making “like-for-like” comparisons difficult in some areas.

The paper makes several recommendations that, if adopted, could help exchanges to increase the potency of their enforcement programmes.

A key aim of the paper is to shift the lens through which the debate concerning the efficacy of exchange-level oversight is conducted. Hitherto, a legal lens has been used, whereas this paper uses a behavioural lens.

The cold supply chain industry is still emerging and digital transformation is in the nascent stage in this industry. This paper argues that there are various barriers to implementing blockchain technology in the cold supply chain and aims to develop and validate a model for overcoming key barriers to implementing blockchain technology in the cold supply chain.

The adoption of blockchain technology was proposed through interpretive structural modeling (ISM) and further it is validated using structural equation modeling (SEM).

In this study, ten key barriers to implementing blockchain technology in the cold supply chain were identified, modelled and analysed. Poor leadership style of top management was found to be the most important barriers to implementing blockchain technology in the cold supply chain. The results of SEM indicate that all the paths are supported. The findings showcase the barriers responsible for the lack of blockchain technology infrastructure that ultimately impacts the cold supply chains.

This study highlights the fact that the fate of blockchain technology infrastructure development depends on the leadership style of top management. Demonstrating good leadership style by top management can help overcome the barriers. A good leader pulls the entire team instead of pushing the team. A good leader can guide the entire team to improve IT governance, financial investment, digital footprint, digital readiness, skills and collaboration with service providers to implement blockchain technology. Not only that, a good leader provides mental strength to the team and helps overcome the fear of implementing blockchain in the cold supply chain. A good leader demonstrates good administrative skills and focus on security and privacy policies.

This is a novel contribution towards analysing the key barriers to implementing blockchain technology in the South African cold supply chain using the integrated ISM–MICMAC and SEM approach.

The objectives of the study were to identify the effects of blockchain technology (BT) on the university librarians, the impact of BT on the university library services and to reveal the challenges to adopt BT in the university libraries.

A systematic literature review was applied to address the objectives of the study. Around 25 studies published in peer-reviewed journals were selected to conduct the study.

The findings of the study revealed that blockchain technology (BT) has positive effects on the university librarians as it assists them in digital resources management, provision of integrated library services, effective records management and continued professional development. The study also displayed that BT has a positive impact on the university libraries through effective information management, user privacy, collaboration, technological innovation and access control. Results also revealed that technical issues, financial constraints, security problems, skill issues and sociocultural issues created challenges to adopt BT in the university libraries.

The study has offered theoretical implications for future investigators through the provision of innovative literature on the prospectus and challenges associated with blockchain in the context of librarianship. The study has also provided practical implications for management bodies by offering recommendations for the successful adoption of blockchain in the university libraries. Additionally, a framework has been developed to adopt BT successfully in the university libraries for the delivery of smart library services to library patrons.

This research interrogates how the construction of narratives and accounting forecasts contributes to managing the emotional state of actors involved in reporting meetings by promoting discourses of hope in their organization's future, mitigating their anxiety. This study shows how narratives are built from multiple antenarratives and accounting forecasts, which restore and strengthen organizational actors' commitment to their organizations. This study contributes to a better understanding of the role played by narratives and accounting documents in mitigating organizational members' anxiety.

Over eight months, an interventionist research design method gave one of the authors the opportunity to record discussions held during reporting meetings in a business incubator. These recordings captured the production of narratives and forecasts in these meetings.

This study shows how the production of multiple antenarratives and accounting forecasts helps organizational actors who attend reporting meetings mitigate the anxiety triggered by disappointing performance figures and restore collective discourses full of hope for the organization's future. This case highlights how personal antenarratives and successive versions of accounting forecasts contribute to restoring a collective commitment to a failing organization.

This study refines current understanding of the under-explored links between accounting forecasts, narratives and anxiety management. The study provides insight into how accounting practices contribute to the production of narratives that successfully restore organizational members' commitment to working for a failing organization. The study also exemplifies the original insights gained from interventionist research protocols.

Market sentiment has shown to influence housing prices in the global north, but in emerging economies, the nexus is rare to chance on in the current state of science for policy direction. More importantly in the recent decade where policymakers are yet to conclude on the myriad of factors confronting the housing market in sub-Saharan Africa inhibiting affordability. This paper therefore examines the impact of market sentiment on house prices in South Africa.

The study used the Autoregressive Distributed Lag (ARDL) approach with quarterly data spanning from 2005Q1 to 2020Q4.

In all, it was established that market sentiment plays a minimal role in the property market in South Africa. But there was enough evidence of cointegration from the bound test between sentiment and house prices. Nevertheless, the lag values of sentiment pointed to a rise in house prices. Exchange rate volatilities and inflation had a statistically significant effect on prices in both the long and short term, respectively.

Policymakers could still monitor market sentiment in the housing market due to the strong chemistry between house prices and sentiment, as evidenced from the bound test, but focus on economic fundamentals as the main policy tool for house price reduction.

The findings and the creation of the sentiment index make an invaluable contribution to the paper and add to the paucity of literature on the study of market sentiment in the housing market.

The purpose of the study is to compare methodologies for mapping a historic building, with image capture by smartphones and drones, using photogrammetric techniques for three-dimensional (3D) modeling of the structure. Processes and products are also analyzed, as well as possibilities for storing and visualizing data for structuring a cadastre of historical and artistic heritage are studied.

For mapping with smartphones, the overlapping of photographs was guaranteed, with data acquisition using three different cameras, on the same date as the aerial survey. The models were made from different combinations of camera use. For storage, a conceptual model based on ISO 19.152:2012 is proposed, which was implemented in the MongoDB, resulting in a database for storage. The visualization was carried out on the Cesium ion platform.

The results indicate that the terrestrial 3D reconstruction using smartphones is an efficient alternative to the historical and artistic cadastre, presenting texture quality superior to the aerial survey in a shorter production time. When dealing with the conceptual model, the LADM (Land Administration Domain Model) standardization guarantees interoperability and facilitates data exchange. In addition, it proved to be flexible for the creation of thematic profiles, supporting their effective storage. The insertion of data in the visualization platform was simple and effective, and it even generated sharing links for visualization of the models.

The study analyses a low-cost method with the use of easily accessible devices, with a combination of methodologies and applied techniques. The data storage and visualization method is also simple and flexible, suitable for application in the cadastre of historical heritage.

The aim of this paper is to explore the possibility of using the Define-Measure-Analyze-Improve-Control (DMAIC) cycle, process mining (PM) and multi-criteria decision methods in an integrated way so that these three elements combined result in a methodology called the Agile DMAIC cycle, which brings more agility and reliability in the execution of the Six Sigma process.

The approach taken by the authors in this study was to analyze the studies arising from this union of concepts and to focus on using PM tools where appropriate to accelerate the DMAIC cycle by improving the first two steps, and to test using the AHP as a decision-making process, to bring more excellent reliability in the definition of indicators.

It was indicated that there was a gain with acquiring indicators and process maps generated by PM. And through the AHP, there was a greater accuracy in determining the importance of the indicators.

Through the results and findings of this study, more organizations can understand the potential of integrating Six Sigma and PM. It was just developed for the first two steps of the DMAIC cycle, and it is also a replicable method for any Six Sigma project where data acquisition through mining is possible.

The authors develop a fully applicable and understandable methodology which can be replicated in other settings and expanded in future research.

The case describes a hypothetical hedge fund manager who is examining whether to invest in bitcoin. The case discusses potential risks and rewards of investing in bitcoin, the role of bitcoin and digital currencies more broadly, and financial innovation in the space, such as ICOs. It can be taught as part of a second-year MBA elective course in investments, financial institutions/capital markets, or fintech.

The purpose of this paper is to design a consortium-blockchain based framework for cross-organizational business process mining complying with privacy requirements.

Business process modeling in a cross-organizational setting is complicated due to privacy concerns. The process mining in this situation occurs through trusted third parties (TTPs). It uses a special class of Petri-nets called workflow nets (WF-nets) to represent the formal specifications of event logs in a blockchain-enabled cross-organization.

Using a smart contract algorithm, the proposed framework discovers the organization-specific business process models (BPM) without a TTP. The discovered BPMs are formally represented using WF-nets with a message factor to support the authors’ claim. Finally, the applicability and suitability of the proposed framework is demonstrated using a case study of multimodal transportation.

The proposed framework complies with privacy requirements. It shows how to represent the formal specifications of event logs in a blockchain using a special class of Petri-nets called WF-nets. It also presents a smart contract algorithm to discover organization-specific business process models (BPM) without a TTP.

Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.

The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.

By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.

There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.