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This study addresses the need for a theory-guided, rich, descriptive account of research data repositories' (RDRs) understanding of data quality and the structures of their data quality assurance (DQA) activities. Its findings can help develop operational DQA models and best practice guides and identify opportunities for innovation in the DQA activities.

The study analyzed 122 data repositories' applications for the Core Trustworthy Data Repositories, interview transcripts of 32 curators and repository managers and data curation-related webpages of their repository websites. The combined dataset represented 146 unique RDRs. The study was guided by a theoretical framework comprising activity theory and an information quality evaluation framework.

The study provided a theory-based examination of the DQA practices of RDRs summarized as a conceptual model. The authors identified three DQA activities: evaluation, intervention and communication and their structures, including activity motivations, roles played and mediating tools and rules and standards. When defining data quality, study participants went beyond the traditional definition of data quality and referenced seven facets of ethical and effective information systems in addition to data quality. Furthermore, the participants and RDRs referenced 13 dimensions in their DQA models. The study revealed that DQA activities were prioritized by data value, level of quality, available expertise, cost and funding incentives.

The study's findings can inform the design and construction of digital research data curation infrastructure components on university campuses that aim to provide access not just to big data but trustworthy data. Communities of practice focused on repositories and archives could consider adding FAIR operationalizations, extensions and metrics focused on data quality. The availability of such metrics and associated measurements can help reusers determine whether they can trust and reuse a particular dataset. The findings of this study can help to develop such data quality assessment metrics and intervention strategies in a sound and systematic way.

To the best of the authors' knowledge, this paper is the first data quality theory guided examination of DQA practices in RDRs.

This study aims to examine the role of different forms of corporate social advocacy (CSA) in shaping individuals’ attitudinal and behavioral intentions towards companies taking their public stand on controversial socio-political issues. With an online experiment as the research method, this study tests whether depicting nonpolitical or political behaviors in CSA messages increases individuals’ positive behavioral intentions.

This study uses a single factor between subject online experiment. A total of 135 US young adults were recruited through a Qualtrics online panel. Three social media mockups were created to manipulate three levels of actions in CSA messages (no action, nonpolitical action and political action). Participants viewed one of those social media posts depicting presented actions to counter anti-LGBTQ + legislation in the USA and answered questions about values-driven motives behind CSA, brand preference and positive word-of-mouth (WOM) intention.

Participants displayed higher levels of brand preference when they viewed CSA messages depicting the company’s political action intended to repel anti-LGBTQ + legislation. Participants showed more positive WOM intentions towards the company when they perceived its political actions as more values-driven.

The findings of this study offer practical insights to companies when designing CSA messages and strategies. The results of this study indicate that the presence of political actions in CSA communication increases individuals’ positive behaviors towards companies. The results also suggest that depicting altruistic motives behind CSA leads individuals to talk about companies more in positive ways.

This study is one of the early studies investigating the impact of various forms of CSA on individuals’ attitudinal and behavioral intentions to companies practicing CSA. This study provides practical implications on how to effectively appeal individuals’ favorable attitudes and behaviors towards CSA. In particular, this research presents the importance of action aspects in individuals’ attitudes toward corporations’ CSA messages.

This study aims to understand the reasons for individuals switching from traditional banks to Internet-only banks and examine how switching intentions differ between Generation X and Generation Z. Notably, Generation Z, being digital natives, exhibits distinct characteristics compared to Generation X, who often referred to as digital immigrants. Given the technology-driven nature of Internet-only banks, a multi-group analysis between these two generations was conducted.

This study utilizes Bansal’s push–pull–mooring model as a framework to analyze switching intention. The study collected survey data from 383 Korean participants, consisting of 198 participants from Generation Z and 185 participants from Generation X.

The findings indicate that low satisfaction and discomfort are factors that push people to leave traditional banks. Specifically, Generation Z shows a significantly higher inclination to leave traditional banks due to discomfort. On the other hand, relative advantage, compatibility, observability and trialability are factors that pull people to switch to Internet-only banks. Generation X is more likely to consider adopting Internet-only banks when compatibility is high and complexity is low.

This study is the first to explore unique motivators for Generation Z, such as their discomfort with interpersonal interactions in the retail banking sector. These findings challenge earlier research emphasizing human interaction’s importance in technology adoption, offering insights into their future adoption of contactless services.

This study examined how people assess health information from AI and improve their diagnostic ability to identify health misinformation. The proposed model was designed to test a cognitive heuristic theory in misinformation discernment.

We proposed the heuristic-systematic model to assess health misinformation processing in the algorithmic context. Using the Analysis of Moment Structure (AMOS) 26 software, we tested fairness/transparency/accountability (FAccT) as constructs that influence the heuristic evaluation and systematic discernment of misinformation by users. To test moderating and mediating effects, PROCESS Macro Model 4 was used.

The effect of AI-generated misinformation on people’s perceptions of the veracity of health information may differ according to whether they process misinformation heuristically or systematically. Heuristic processing is significantly associated with the diagnosticity of misinformation. There is a greater chance that misinformation will be correctly diagnosed and checked, if misinformation aligns with users’ heuristics or is validated by the diagnosticity they perceive.

When exposed to misinformation through algorithmic recommendations, users’ perceived diagnosticity of misinformation can be predicted accurately from their understanding of normative values. This perceived diagnosticity would then positively influence the accuracy and credibility of the misinformation.

Perceived diagnosticity exerts a key role in fostering misinformation literacy, implying that improving people’s perceptions of misinformation and AI features is an efficient way to change their misinformation behavior.

Although there is broad agreement on the need to control and combat health misinformation, the magnitude of this problem remains unknown. It is essential to understand both users’ cognitive processes when it comes to identifying health misinformation and the diffusion mechanism from which such misinformation is framed and subsequently spread.

The mechanisms through which users process and spread misinformation have remained open-ended questions. This study provides theoretical insights and relevant recommendations that can make users and firms/institutions alike more resilient in protecting themselves from the detrimental impact of misinformation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2023-0167

The purpose of this paper is to examine the resistance behavior of smart factories in small and medium-sized enterprises (SMEs). Drawing upon dual factor perspective, this study examines how two types of quality and perceived usefulness impact user resistance as enabling factors and how switching cost, skepticism, habit and inertia contribute to user resistance as inhibiting factors. Additionally, multi-group analysis is employed to compare small and medium enterprises.

Purposive sampling technique was employed to collect 460 Korean SMEs employees, consisting of 235 small enterprises and 225 medium enterprises. Partial least squares structural equation modeling was used for data analysis.

The results reveal that all three inhibiting factors, switching cost, skepticism and habit, are key antecedents of inertia. In small enterprises, skepticism has a greater impact on inertia, which in turn strongly affects resistance. Additionally, system quality is more crucial for small enterprises, whereas information quality holds more importance for medium enterprises in mitigating resistance. Moreover, when the implementation level of a smart factory is high, the effect of perceived usefulness on user resistance diminishes.

This study has revealed the importance of considering both enabling and inhibiting factors for the adoption of smart factory systems in the context of SMEs. Additionally, it has provided evidence that as the level of the smart factory system increases, the effect of perceived usefulness on user resistance decreases, thus making the transition to smart factory systems more challenging.

This study aims to investigate the key factors that influence the behavioural intention of doctors to adopt the knowledge sharing driven blockchain technology in government hospitals. The study is based on the Unified Theory of Acceptance and Use of Technology 2, with the addition of trust as an independent variable and knowledge sharing as a mediating variable between trust and behavioural intention.

The data for the study was collected through a correlation and cross-sectional study using a survey, with a sample of 322 responses being used for the final analysis. The initial analysis of the data was conducted using SPSS v.26, followed by a partial least squares structural equation modelling (PLS-SEM) analysis using SmartPLS v.3.9 to test the validity and reliability of the measures and to examine the hypothesized relationships.

The results supported the proposed framework. The results of PLS-SEM indicate that all proposed pathways support the model. In particular, the results of the study reveal that performance expectation, effort expectation, social influence, facilitation conditions and trust are drivers of blockchain adoption and have a significant impact on the behavioural intention of clinicians in hospitals. Furthermore, the study found that knowledge sharing mediated the relationship between trust and behavioural intention.

The present study sheds light on the challenges facing blockchain technology, such as privacy and trust concerns and proposes a more sustainable approach based on knowledge management to enhance the effectiveness of blockchain technology and overcome these challenges.

The significance of this paper lies in the limited literature examining the relationships between blockchain technology and knowledge management processes. Furthermore, a hypothetical framework that includes the knowledge sharing process as a mediating variable between trust and behavioural intention to adopt blockchain technology has not been presented or developed in any previous studies, particularly in the context of Iraq. Thus, this work is novel and unique in its approach.

The purpose of this paper is to explore and evaluate the current legal position on the admissibility and exclusion of illegally obtained evidence in money laundering cases.

A thorough exploratory analytical analysis signifies that such illegally obtained evidence from money laundering offences is admissible, provided it does not undermine the administration of justice or the right to a fair trial.

By virtue of the lack of written or codified rules governing the admissibility and exclusion of illegally obtained evidence in cases involving money laundering, the rule of admissibility remains the primary foundational principle for the governance of the admissibility and exclusion of illegally obtained evidence in money laundering cases.

The Malaysian Criminal Justice System has historically relied on the long-standing admissibility principles to admit and exclude illegally obtained evidence. For decades, courts have used their discretion to admit illegally obtained evidence based on the relevancy test, and they have further demonstrated to use the same discretion to exclude gravely prejudicial evidence. Evidence obtained illegally but if relevant to the matter in issue is deemed admissible. Evidence derived from an act associated with unlawful activities or a predicate offence in money laundering may be obtained illegally, which may influence the prosecution case and conversely, defend the accused’s rights to a fair trial.