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The main purpose of this paper is to examine the adoption of big data analytics (BDA) in the Tanzania banking industry by investigating the influence of technological, environmental and organizational (TOE) factors while exploring the moderating role of perceived risk (PR).

The study employed a qualitative research design, and the research instrument was developed using per-defined measurement items adopted from prior studies; the items were slightly adjusted to fit the current context. The questionnaires were distributed to top and middle managers in selected banks in Tanzania using the snowball sampling technique. Out of 360 received responses, 302 were considered complete and valid for data analysis. The study employed partial least squares structural equation modeling (PLS-SEM) to examine the developed conceptual framework.

Top management support and financial resources emerged as influential organizational factors, as did competition intensity for the environmental factors. Notably, bank size and perceived trends showed no significant impacts on BDA adoption. The study's novelty lies in revealing PR as a moderating factor, weakening the link between technological readiness, perceived usefulness and the intent to adopt BDA.

This study extends literature by extending the TOE model, through examining the moderating roles of PR on technological factors. Furthermore, the study provides useful managerial support for the adoption of BDA in banking in emerging economies.

This study aims to, firstly, develop a red flag checklist for cryptocurrency Ponzi schemes and, secondly, to test this red flag checklist against publicly available marketing material for Mirror Trading International (MTI). The red flag checklist test seeks to establish if MTI’s marketing material posted on YouTube^® (in the form of a live video presentation) exhibits any of the red flags from the checklist.

The study uses a structured literature review and qualitative analysis of red flags for Ponzi and cryptocurrency Ponzi schemes.

A research lacuna was discovered with regard to cryptocurrency Ponzi scheme red flags. By means of a structured literature review, journal papers were identified that listed and discussed Ponzi scheme red flags. The red flags from the identified journal papers were subsequently used in a qualitative analysis. The analyses and syntheses resulted in the development of a red flag checklist for cryptocurrency Ponzi schemes, with five red flag categories, containing 18 associated red flags. The red flag checklist was then tested against MTI’s marketing material (a transcription of a live YouTube presentation). The test resulted in MTI’s marketing material exhibiting 88% of the red flags contained within the checklist.

The inherent limitations in the design of using a structured literature review and the lack of research regarding the cryptocurrency Ponzi scheme red flags.

The study provides a red flag checklist for cryptocurrency Ponzi schemes. The red flag checklist can be applied to a cryptocurrency investment scheme’s marketing material to establish if it exhibits any of these red flags.

The red flag checklist can be applied to a cryptocurrency investment scheme’s marketing material to establish if it exhibits any of these red flags.

The study provides a red flag checklist for cryptocurrency Ponzi schemes.

The promotion of autonomous vehicles introduces privacy and security risks, underscoring the pressing need for responsible innovation implementation. To more effectively address the societal risks posed by autonomous vehicles, considering collaborative engagement of key stakeholders is essential. This study aims to provide insights into the governance of potential privacy and security issues in the innovation of autonomous driving technology by analyzing the micro-level decision-making processes of various stakeholders.

For this study, the authors use a nuanced approach, integrating key stakeholder theory, perceived value theory and prospect theory. The study constructs a model based on evolutionary game for the privacy and security governance mechanism of autonomous vehicles, involving enterprises, governments and consumers.

The governance of privacy and security in autonomous driving technology is influenced by key stakeholders’ decision-making behaviors and pivotal factors such as perceived value factors. The study finds that the governmental is influenced to a lesser extent by the decisions of other stakeholders, and factors such as risk preference coefficient, which contribute to perceived value, have a more significant influence than appearance factors like participation costs.

This study lacks an investigation into the risk sensitivity of various stakeholders in different scenarios.

The study delineates the roles and behaviors of key stakeholders and contributes valuable insights toward addressing pertinent risk concerns within the governance of autonomous vehicles. Through the study, the practical application of Responsible Innovation theory has been enriched, addressing the shortcomings in the analysis of micro-level processes within the framework of evolutionary game.

One of the insidious effects of government and business imposter scams is the potential erosion of trust among defrauded consumers. This study aims to assess the relationship between prior imposter scam victimization and present ability to discriminate between real and fake digital communications from government agencies and retail companies.

This paper tests whether a short, interactive training can help consumers correctly identify imposter scams without mistrusting legitimate communications. Participants were randomized into one of two control groups or to one of two training conditions: written tips on identifying digital imposter scams, or an interactive fraud detection training program. Participants were tested on their ability to correctly label emails, websites and letters as real or a scam.

This paper find that prior imposter scam victimization is not associated with greater mistrust. Compared to the control conditions, both written tips and interactive digital fraud detection training improved identification of real communications and scams; however, after a two- to three-week delay, the effect of training decreases for scam detection.

Results indicate that prior imposter scam victimization is not associated with mistrust, and that one-time fraud detection training improves consumers’ detection of imposter scams but has limited long-term effectiveness.

In a virtual interconnected digital space, the metaverse encompasses various virtual environments where people can interact, including engaging in business activities. Machine learning (ML) is a strategic technology that enables digital transformation to the metaverse, and it is becoming a more prevalent driver of business performance and reporting on performance. However, ML has limitations, and using the technology in business processes, such as accounting, poses a technology governance failure risk. To address this risk, decision makers and those tasked to govern these technologies must understand where the technology fits into the business process and consider its limitations to enable a governed transition to the metaverse. Using selected accounting processes, this study aims to describe the limitations that ML techniques pose to ensure the quality of financial information.

A grounded theory literature review method, consisting of five iterative stages, was used to identify the accounting tasks that ML could perform in the respective accounting processes, describe the ML techniques that could be applied to each accounting task and identify the limitations associated with the individual techniques.

This study finds that limitations such as data availability and training time may impact the quality of the financial information and that ML techniques and their limitations must be clearly understood when developing and implementing technology governance measures.

The study contributes to the growing literature on enterprise information and technology management and governance. In this study, the authors integrated current ML knowledge into an accounting context. As accounting is a pervasive aspect of business, the insights from this study will benefit decision makers and those tasked to govern these technologies to understand how some processes are more likely to be affected by certain limitations and how this may impact the accounting objectives. It will also benefit those users hoping to exploit the advantages of ML in their accounting processes while understanding the specific technology limitations on an accounting task level.

Given the many activities of Nigerian investors in the crypto ecosystem, this paper investigates the level of their awareness, attitudes, risk tolerance, experience, reasons for investing and level of financial literacy.

The research approach is based on a self-administered questionnaire. The Organization for Economic Cooperation and Development (OECD) permitted the use of its reliable and validated survey instrument, administered in Malaysia, the Philippines and Vietnam in 2019. The results are tabulated and analyzed.

The key results include the participation of respondents, who are generally young males, not fully financially literate but risk-averse. Many held the false view that investing in global markets is a higher risk than in national markets. Their reasons for investing in crypto include the fear of missing out on good opportunities and the desire to have fun. The results also revealed that social media, conversations with non-experts and online articles are among the most used investment information sources, highlighting the role of digital platforms and informal discussions in shaping perceptions and knowledge about cryptocurrencies. Investments in cryptos are financed through savings, regular monthly budgets or borrowed from friends or family. As for specific attitudes to risk, the results suggest that for most respondents, preserving their invested capital is of paramount importance.

The importance of this research also resides in the possibility of comparing the crypto ecosystem in Asia with Nigeria because the same OECD data instrument is employed in data collection. Moreover, this study is the most comprehensive research about Nigerian investors in cryptocurrencies.

This study assesses the factors influencing customers’ intention to adopt e-banking in the context of the technology acceptance model and the moderation role of cybercrime.

The variables in the study are measured using a five-point Likert scale with measures adopted from existing literature. The independent variables are perceived ease of use, perceived usefulness and security and privacy. These are postulated to be moderated by the perceived risk of cybercrime and to influence e-banking adoption intentions. A quantitative approach is used. Primary data are collected from a sample of 209 randomly selected bank customers. The study uses a two-step (measurement model and structural model) approach to data analysis.

The key findings in this study are that perceived risk of cybercrime strengthens the positive relationship between perceived ease of use and e-banking adoption intentions but dampens or weakens the positive relationship between perceived usefulness and customers’ e-banking adoption intentions. The study makes several recommendations to inform scholarship, policy and practice.

Unlike existing literature, the study makes a unique contribution by including perceived risk of cybercrime as a moderating variable of theoretical significance in the relationship between adoption of e-banking and its determinants.

The aim of this study is to examine the behavioral intention (BI) to adopt the Coinbase Wallet by Spanish users.

A survey was administered to individuals residing in Spain between March and April 2021. There were 301 questionnaires analyzed. This research applies a new predictive model based on technology acceptance model (TAM) 2, the unified theory of acceptance and use of technology (UTAUT) model, the theory of perceived risk and the commitment trust theory. A mixed partial least squares structural equation modeling (PLS-SEM)/fuzzy-set qualitative comparative analysis (fsQCA) methodology was employed for the modeling and data analysis.

The results showed that all the variables proposed have a direct and positive influence on the intention to use a Coinbase Wallet. The findings present clear directions for traders, investors and academics focused on improving their understanding of the characteristics of these markets.

First, this study addresses important concerns relating to the adoption of crypto-wallets during the global pandemic. Second, this research contributes to the existing literature by adding electronic word of mouth (e-WOM), trust, web quality and perceived risk as new drivers of the intention to use the Coinbase Wallet, providing unique and innovative insights. Finally, the study offers a solid methodological contribution by integrating linear (PLS) and nonlinear (fsQCA) techniques, showing that both methodologies provide a better understanding of the problem and a more detailed awareness of the patterns of antecedent factors.

This study aims to evaluate the effectiveness of machine learning models to yield profitability over the market benchmark, notably in periods of systemic instability, such as the ongoing war between Russia and Ukraine.

This study made computational experiments using support vector machine (SVM) classifiers to predict stock price movements for three financial markets and construct profitable trading strategies to subsidize investors’ decision-making.

On average, machine learning models outperformed the market benchmarks during the more volatile period of the Russia–Ukraine war, but not during the period before the conflict. Moreover, the hyperparameter combinations for which the profitability is superior were found to be highly sensitive to small variations during the model training process.

Investors should proceed with caution when applying machine learning models for stock price forecasting and trading recommendations, as their superior performance for volatile periods – in terms of generating abnormal gains over the market – was not observed for a period of relative stability in the economy.

This paper’s approach to search for financial strategies that succeed in outperforming the market provides empirical evidence about the effectiveness of state-of-the-art machine learning techniques before and after the conflict deflagration, which is of potential value for researchers in quantitative finance and market professionals who operate in the financial segment.

The paper aims to enhance the understanding of robust food supply chains (FSC) by exploring the capabilities of various digital technologies and examining their interactions.

This study finding shows that digital technology enhances the resilience of the FSC by improving visibility, traceability and adaptability. This resilience provides a competitive advantage, ultimately enhancing the overall business performance.

In developing countries, inadequate infrastructure, poor Internet connectivity and diverse stakeholder systems pose challenges to implementing advanced digital solutions in the FSC.

This paper is among the first to investigate the impact of digital technology on FSC resilience, exploring visibility, flexibility and collaboration.