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Presently, existing electric car sharing platforms are based on a centralized architecture which are faced with inadequate trust and pricing issues as these platforms requires an intermediary to maintain users’ data and handle transactions between participants. Therefore, this article aims to develop a decentralized peer-to-peer electric car sharing prototype framework that offers trustable and cost transparency.

This study employs a systematic review and data were collected from the literature and existing technical report documents after which content analysis is carried out to identify current problems and state-of-the-art electric car sharing. A use case scenario was then presented to preliminarily validate and show how the developed prototype framework addresses the trust-lessness in electric car sharing via distributed ledger technologies (DLTs).

Findings from this study present a use case scenario that depicts how businesses can design and implement a distributed peer-to-peer electric car sharing platforms based on IOTA technology, smart contracts and IOTA eWallet. Main findings from this study unlock the tremendous potential of DLT to foster sustainable road transportation. By employing a token-based approach this study enables electric car sharing that promotes sustainable road transportation.

Practically the developed decentralized prototype framework provides improved cost transparency and fairness guarantees as it is not based on a centralized price management system. The DLT based decentralized prototype framework aids to orchestrate the incentivize monetization and rewarding mechanisms among participants that share their electric cars enabling them to collaborate towards lessening CO₂ emissions.

The findings advocate that electric vehicle sharing has become an essential component of sustainable road transportation by increasing electric car utilization and decreasing the number of vehicles on the road.

The key novelty of the article is introducing a decentralized prototype framework to be employed to develop an electric car sharing solution without a central control or governance, which improves cost transparency. As compared to prior centralized platforms, the prototype framework employs IOTA technology smart contracts and IOTA eWallet to improve mobility related services.

This study aims to explore auto mechanics awareness of repairs and maintenance related to the car’s cybersecurity and provide insights into challenges based on current practice.

This study is based on an empirical study consisting of semistructured interviews with representatives from both branded and independent auto workshops. The data was analyzed using thematic analysis. A version of the capability maturity model was introduced to the respondents as a self-evaluation of their cybersecurity awareness.

Cybersecurity was not found to be part of the current auto workshop work culture, and that there is a gap between independent workshops and branded workshops. Specifically, in how they function, approach problems and the tools and support available to them to resolve (particularly regarding previously unknown) issues.

Only auto workshop managers in Sweden were interviewed for this study. This role was picked because it is the most likely to have come in contact with cybersecurity-related issues. They may also have discussed the topic with mechanics, manufacturers or other auto workshops – thus providing a broader view of potential issues or challenges.

The challenges identified in this study offers actionable advice to car manufacturers, branded workshops and independent workshops. The goal is to further cooperation, improve knowledge sharing and avoid unnecessary safety or security issues.

As cars become smarter, they also become potential targets for cyberattacks, which in turn poses potential threats to human safety. However, research on auto workshops, which has previously ensured that cars are road safe, has received little research attention with regards to the role cybersecurity can play in repairs and maintenance. Insights from auto workshops can therefore shed light upon the unique challenges and issues tied to the cybersecurity of cars, and how they are kept up-to-date and road safe in the digital era.

Unplanned urban mobility causes negative effects on the population and the environment. This study aims to understand how higher education institutions (HEIs) are managing the transportation issues related to their activities and how they are implementing actions towards more sustainable practices in this regard.

A systematic review and bibliometric analysis were performed using the Proknow-C method, and the bibliographic portfolio was evaluated to answer guiding questions about distribution of articles over the years and around the world, the most relevant and frequent topics, the travel behavior of university life and the main methodologies used. The meta-analysis was modeled using a programming language in R to execute Bibliometrix package.

Sharing systems, active transport, public transport, urban planning, car parking management and travel behavior are the most relevant topics related to sustainable mobility in HEIs. Different strategies to reduce car use are adopted, and the geographic location of the university and the availability of housing and shopping services in close proximity directly influence travel patterns. As a result, seven guidelines and strategic actions associated were proposed to promote the engagement of institutions in the development of sustainable transport and guide future studies about new solutions to promote sustainable university commutes.

This paper presents a new perspective by performing a critical literature review based on the experiences reported by several isolated studies on the subject. Initiatives of sustainable transport guidelines can be used by academics, urban planners, higher education administrators and other stakeholders to make universities more environmentally friendly, inclusive and accessible.

The displaced commercial risk is one of the specific risks in the Islamic finance that creates a serious debate among practitioners and researchers about its management. The purpose of this paper is to assess a new approach to manage this risk using machine learning algorithms.

To attempt this purpose, the authors use several machine learning algorithms applied to a set of financial data related to banks from different regions and consider the deposit variation intensity as an indicator.

Results show acceptable prediction accuracy. The model could be used to optimize the prudential reserves for banks and the incomes distributed to depositors.

However, the model uses several variables as proxies since data are not available for some specific indicators, such as the profit equalization reserves and the investment risk reserves.

Previous studies have analyzed the origin and impact of DCR. To the best of authors’ knowledge, none of them has provided an ex ante management tool for this risk. Furthermore, the authors suggest the use of a new approach based on machine learning algorithms.

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.

This paper aims to provide a research agenda for knowledge management (KM) scholars interested in the sharing economy.

A total of 342 articles published between 2012 and 2021 are analysed to identify the topics examined, how the sharing economy was conceptualized, the academic disciplines of authors and the methods used.

Based on the findings, research gaps and conceptualization issues relevant to KM scholars are discussed. Collaboration opportunities and methodological challenges are also identified.

By facilitating KM scholars’ engagement with the sharing economy, this paper contributes to a better understanding of this emerging phenomenon and helps uncover potential KM trends.

Trust plays a crucial role in overcoming uncertainty and reducing risks. Uncovering the trust mechanism in the sharing economy may enable sharing platforms to design more effective marketing strategies. However, existing studies have inconsistent conclusions on the trust mechanism in the sharing economy. Therefore, this study aims to investigate the antecedents and consequences of different dimensions of trust (trust in platform and trust in peers) in the sharing economy.

First, we conducted a meta-analysis of 57 related articles. We tested 13 antecedents of trust in platform (e.g. economic benefits, enjoyment, and information quality) and eight antecedents of trust in peers (e.g. offline service quality and providers’ reputation), as well as their consequences. Then, we conducted subgroup analyses to test the moderating effects of economic development level (Developed vs Developing), gender (Female-dominant vs Male-dominant), platform type (Accommodation vs Transportation), role type (Obtainers vs Providers), and uncertainty avoidance (Strong vs Weak).

The results confirm that all antecedents and consequences significantly affect trust in platform or peers to varying degrees. Moreover, trust in platform greatly enhances trust in peers. Besides, the results of the moderating effect analyses demonstrate the variability of antecedents and consequences of trust under different subgroups.

This paper provides a clear and holistic view of the trust mechanism in the sharing economy from an object-based trust perspective. The findings may offer insights into trust-building in the sharing economy.

This study aims to analyze the factors that drive or prevent interorganizational data sharing in the context of digital transformation (DT). Data sharing appears as a precondition for companies to capture emerging opportunities in supply chain management and for product-related servitization; however, there are ongoing concerns, and data are often perceived as the “new oil.” It is thus important to gain a better understanding of the determinants of firms’ decisions.

The authors develop an embedded case study analysis involving 16 firms within an extended supply network in the automotive industry. The authors focus on the peculiarities of the new context, as opposed to elements highlighted by research prior to the advent of the latest technologies. Abductive reasoning is applied to the theoretical foundations of the resource-based view, resource dependence theory and the complex adaptive systems perspective.

Data sharing is largely underpinned by factors identified prior to DT, such as data specificity, dependence dynamics and protection mechanisms and the dynamism of the business context. DT, however, can influence the extent of data sharing. New factors concern complementarities whenever data are pooled from different sources and digital platforms, as well as different forms of data ownership protection.

This study stresses that data sharing in the context of DT can be explained through established theoretical lenses, providing the integration of elements accounting for new technological opportunities.

Ethereum-based blockchain technology (EBT) affords members of the Enterprise Ethereum Alliance (EEA) a market advantage in deploying blockchain within their organizations, including cybersecurity and operational benefits, that leads firms to strategically invest in this nascent technology. However, the impact of such strategic investments in EBT has yet to be explored in the context of its relationship to firm value. Therefore, this study explores EBT-specific firm-level characteristics that result in a stock market reaction to announcements of strategic investments.

The authors use the event study methodology, strategic investment literature and signaling theory as contextualizing frameworks for their study. Additionally, the authors explore a new method for examining technology investments as a strategic counter to cybersecurity threats.

Firms that signal to the market their strong commitment to their strategic investment by developing an EBT proof of concept see significantly higher market returns. Firms that have had prior cybersecurity incidents are rewarded by the market for strategically investing in EBT, and when firms with large undistributed free cash flows utilize this cash for strategic EBT investment, the market is more likely to reward these firms, indicating the market views EBT investment positively in these circumstances.

The results of this study provide new evidence of the value impact of EBT for firms that suffered cybersecurity events in the past. The authors provide empirical evidence of firm-level characteristics that investors use to discern whether a strategic investment in EBT will drive organizational value. Likewise, the authors demonstrate how signaling affects investor perceptions of strategic information technology (IT) investments in EBT.

This research explores the differential effects of pixel-level and object-level visual complexity in firm-generated content (FGC) on consumer engagement in terms of the number of likes and shares, and further investigates the moderating role of image brightness.

Drawing on a deep learning analysis of 85,975 images on a social media platform in China, this study investigates visual complexity in FGC.

The results indicate that pixel-level complexity increases both the number of likes and shares. Object-level complexity has a U-shaped relationship with the number of likes, while it has an inverted U-shaped relationship with the number of shares. Moreover, image brightness mitigates the effect of pixel-level complexity on likes but amplifies the effect on shares; contrarily, it amplifies the effect of object-level complexity on likes, while mitigating its effect on shares.

Although images play a critical role in FGC, visual data analytics has rarely been used in social media research. This study identified two types of visual complexity and investigated their differential effects. We discuss how the processing of information embedded in visual content influences consumer engagement. The findings enrich the literature on social media and visual marketing.