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The current study examines the effects of race, agency and environment on traffic stops in rural and non-rural spaces.

Using traffic stop data collected in a Midwest US County from January 1, 2020 to December 31, 2021, the current study uses logistic regression to examine racial disparities in traffic stops.

The results indicate that police decision-making in traffic stops may be influenced by other factors besides a driver’s race or ethnicity. In other words, the police officer’s decision making in a traffic stop varies between small and large agencies as well as rural and non-rural places.

This study provides one of the few examinations of racial disparities in traffic stops in rural places.

The attempt to establish a common European framework for core platforms' duties and responsibilities toward other actors in the digital environment is at the core of the recent scholarly debate surrounding the Digital Markets Act (DMA) proposal. In particular, the everlasting juxtaposition between the “data power” – as emerging from recent cases (Section 2) – that dominant tech companies enjoy and the concept of consumer sovereignty (Section 3) lies at the core of the proposal's attempt to identify digital core platforms as market gatekeepers. Accordingly, this chapter critically investigates the divide between power imbalance and consumer sovereignty in light of the architecture designed by the DMA, with a specific focus on its effectiveness in identifying gatekeepers' power drivers (Section 4). After highlighting the main critical aspects of the pertinent rules, opportunities for fruitful developments are then identified through the reframing of some of the notions considered in the proposal, and namely the role of “lock-in” effects and “data accumulation” (Section 5). Lastly, this chapter suggests that the DMA advancements – while desirable – are bound to be fragmentary in the absence of a wider appraisal of the nature of data power imbalance dynamics in the modern digital markets (Section 6).

General Data Protection Regulation (GDPR) of the European Union (EU) was passed to protect data privacy. Though the GDPR intended to address issues related to data privacy in the EU, it created an extra-territorial effect through Articles 3, 45 and 46. Extra-territorial effect refers to the application or the effect of local laws and regulations in another country. Lawmakers around the globe passed or intensified their efforts to pass laws to have personal data privacy covered so that they meet the adequacy requirement under Articles 45–46 of GDPR while providing comprehensive legislation locally. This study aims to analyze the Malaysian and Saudi Arabian legislation on health data privacy and their adequacy in meeting GDPR data privacy protection requirements.

The research used a systematic literature review, legal content analysis and comparative analysis to critically analyze the health data protection in Malaysia and Saudi Arabia in comparison with GDPR and to see the adequacy of health data protection that could meet the requirement of EU data transfer requirement.

The finding suggested that the private sector is better regulated in Malaysia than the public sector. Saudi Arabia has some general laws to cover health data privacy in both public and private sector organizations until the newly passed data protection law is implemented in 2024. The finding also suggested that the Personal Data Protection Act 2010 of Malaysia and the Personal Data Protection Law 2022 of Saudi Arabia could be considered “adequate” under GDPR.

The research would be able to identify the key principles that could identify the adequacy of the laws about health data in Malaysia and Saudi Arabia as there is a dearth of literature in this area. This will help to propose suggestions to improve the laws concerning health data protection so that various stakeholders can benefit from it.

Smart cities require data governance to articulate data sharing and use among relevant stakeholders. Given the lack of a comprehensive examination of this research topic, this study aims to review data governance publications to detect and categorize endeavors backing up data sharing in smart cities.

A systematic literature review was conducted, and 568 academic and professional sources were identified, but finally, only 10 relevant papers were selected.

Results reveal that data governance must be based on well-defined mechanisms, procedures and roles to achieve accountability and responsibility in a multi-actor environment. Moreover, data governance should be adapted to address power imbalances among all interested parties.

The main limitation is the list of sources considered for the literature review. However, this study provides a holistic overview for researchers and professionals willing to know more about smart city data sharing.

This review identifies the data governance approaches supporting data sharing in smart cities, analyzes their data dimension, enhances the state-of-the-art literature on this topic and suggests possible areas for future research.

This study aims to investigate tourists’ attitudes and intentions regarding the usage of Chat Generative Pre-trained Transformer (ChatGPT) for accessing tourism information. Furthermore, by integrating the perceived risks associated with ChatGPT and the theory of planned behavior (TPB), this research examines the impact of three types of perceived risks, such as privacy risk, accuracy risk and overreliance risk, on tourists’ behavioral intention.

Data were gathered for this study by using two online survey platforms, thus resulting in a sample of 536 respondents. The online survey questionnaire assessed tourists’ perceived risks, attitude, subjective norm, perceived behavioral control, behavioral intention and demographic information related to their usage of ChatGPT.

The structural equation modeling analysis revealed that tourists express concerns about the associated risks of using ChatGPT to search for tourism information, specifically privacy risk, accuracy risk and overreliance risk. It was found that perceived risks significantly influence tourists’ attitude and intention toward the usage of ChatGPT, which is consistent with the hypotheses proposed in previous literature regarding tourists’ perceived risks of ChatGPT.

This work is a preliminary empirical study that assesses tourists’ behavioral intention toward the use of ChatGPT in the field of tourism. Previous research has remained at the hypothetical level, speculating about the impact of ChatGPT on the tourism industry. This study investigates the behavioral intention of tourists who have used ChatGPT to search for travel information. Furthermore, this study provides evidence based on the outcome of this research and offers theoretical foundations for the sustainable development of generative AI in the tourism domain. This study has limitations in that it primarily focused on exploring the risks associated with ChatGPT and did not extensively investigate its range of benefits.

First, to address privacy concerns that pose significant challenges for chatbots various measures, such as data encryption, secure storage and obtaining user consent, are crucial. Second, despite concerns and uncertainties, the introduction of ChatGPT holds promising prospects for the tourism industry. By offering personalized recommendations and enhancing operational efficiency, ChatGPT has the potential to revolutionize travel experiences. Finally, recognizing the potential of ChatGPT in enhancing customer service and operational efficiency is crucial for tourism enterprises.

Recognizing the potential of ChatGPT in enhancing customer service and operational efficiency is crucial for tourism enterprises. As their interest in adopting ChatGPT grows, increased investments and resources will be dedicated to developing and implementing ChatGPT solutions. This enhancement may involve creating customized ChatGPT solutions and actively engaging in training and development programs to empower employees in effectively using ChatGPT’s capabilities. Such initiatives can contribute to improved customer service and overall operations within the tourism industry.

This study integrates TPB with perceived risks in ChatGPT, thus providing empirical evidence. It highlights the importance of considering perceived risks in tourists’ intentions and contributes to the sustainable development of generative AI in tourism. As such, it provides valuable insights for practitioners and policymakers.

本研究旨在调查游客对使用ChatGPT获取旅游信息的态度和意向。此外, 通过将与ChatGPT相关的感知风险与计划行为理论（TPB）相结合, 本研究探讨了三种感知风险（隐私风险、准确性风险和过度依赖风险）对游客行为意向的影响。

本研究通过两个在线调查平台收集了536名受访者的数据。在线调查问卷评估了游客对ChatGPT使用的感知风险、态度、主观规范、感知行为控制、行为意向以及与其使用ChatGPT相关的人口统计信息。

结构方程建模分析显示, 游客对使用ChatGPT搜索旅游信息的相关风险表示关切, 特别是隐私风险、准确性风险和过度依赖风险。发现感知风险显著影响游客对使用ChatGPT的态度和意向, 与先前有关游客对ChatGPT感知风险的文献中提出的假设一致。

本研究将TPB与ChatGPT中的感知风险相结合, 提供了实证证据。它强调了在考虑游客意向时考虑感知风险的重要性, 并为旅游中生成AI的可持续发展提供了贡献。因此, 它为从业者和政策制定者提供了宝贵的见解。

Research ethics and integrity stipulates that research must be conducted with responsibility towards the research community and should benefit the intended population. This chapter will share insights from an ongoing research programme to reduce family conflict in the context of dementia care while discussing the accompanying ethical considerations. Research into dementia care has primarily focused on improving outcomes for the care dyad, leaving the influence and input of the wider family unit under investigated. Family conflict can detrimentally impact the quality of care provided and leave caregivers vulnerable to psychosocial difficulties. Family conflict occurs in the context of dementia care but there is little research on how to reduce, or prevent, such conflict occurring. In this research programme, a systematic review investigated the effectiveness of interventions that include the wider family unit to reduce family conflict; only one study was included which evidenced the lack of interventions in this area. A qualitative scoping review was then conducted to explore the lived experiences of caregiving families with experience of family conflict and reported solutions. It was found that conflict occurred due to factors including care decisions and role transitions which impacted relationships and affected care provision. Solutions to conflict were less often reported, indicating an important gap in the literature. Interviews with Alzheimer's Society staff and volunteers revealed that stigma and denial surrounding dementia were prevalent, and families were often reluctant to seek external help. This research programme is currently establishing public patient involvement (PPI) to develop the research methodology and interview questions for people with dementia (PWD) and their family caregivers to explore their lived experiences and potential solutions to family conflict. To conclude, this research programme will propose a family-focused intervention aimed at systemic family conflict for those caring for someone with dementia.

Recent studies on search-as-learning (SAL) have recognized the significance of identifying users' learning needs as they evolve for acquiring knowledge during the search process. In this study, the authors clarify the extent to which search behaviors reflect the learning outcome and foster the users' knowledge of Chinese art.

The authors conducted an exploratory-sequential mixed-methods approach using simulated work task situations to collect empirical data. The authors used two types of simulated learning tasks for topics related to painting and antique knowledge. A lot of 25 users participated in this evaluation of digital archives (DAs) at the National Palace Museum (NPM) in Taiwan. For each set of topics, a close-ended task related to lower-level learning goals and an open-ended task related to higher-level learning goals.

The learning criteria reflect changes in the users' knowledge structure, revealing the SAL process. Furthermore, users achieved better task performance on the higher-level creative-learning task, which suggests that they met more learning criteria, exhibited a greater variety of search patterns when exploring the topics via interaction with various sources. Finally, there is a close relationship between creative-learning tasks, prior knowledge, keyword search actions and learning outcomes.

The authors discuss implications with respect to the design of DAs in practice and contributions to the body of SAL knowledge in DAs of online museums. For future reference, the authors provide implications for the development of learning measures from the perspective of user search behavior with associated learning outcomes in the context of DAs.

There are two theoretical concepts that can be taught in this case.The new approach to teaching entrepreneurship is termed “lean start-up” and “hypothesis-driven entrepreneurship.” The business model canvas is a core tool of this approach. This framework defines nine key components of a successful business strategy. These components include defining value propositions; identifying customer segments; identifying channels; maintaining customer relationships; defining key activities, key resources and key partners; understanding the revenue model of the business; and the organization’s cost structure. This is considered to be a rigorous approach to learning about and developing a new venture.The other theoretical approach that can be discussed through this case is the link between uncertainty and entrepreneurial growth. These theories associate the willingness of entrepreneurs to bear the perceived uncertainty associated with entrepreneurial acts as representative of the belief-desire model. There is a need for entrepreneurs to experiment and search for alternative paths forward in order to counter this uncertainty. Systematic search processes to discover relevant information will strengthen this process.

This case is based on primary data collected through interviews with company personnel. The company consented freely to the use of their data in the case. The authors have no connection with the company. The four student coauthors had previously pursued an internship with the company and had worked on the machine learning analysis part.The two faculty coauthors in the case contacted the company after the internship and discussed the opportunity to write the case on the company. One of the faculty then interviewed key personnel in the company, including one of the co-founders.

Xoxoday is a technology company that provides employee rewards and corporate gifting to its customers. The company was started by Sumit Khandelwal, Manoj Agarwal, Abhishek Kumar and Kushal Agarwal. In 2018, the company reinvented itself as an experiential gifting company.The company faced some challenges during the lockdowns imposed due to COVID-19. Khandelwal knew that they had to try something new to achieve higher growth in the future. He wondered if higher usage of technology was the solution. It was necessary for them to carve a new path in these times.

This case study can be used at the undergraduate level in courses relating to entrepreneurship strategy and business models for entrepreneurs.The case can be used to highlight the dilemmas faced by entrepreneurs due to unforeseen crises. This case is relevant for classes that will discuss growth crises and out-of-the-box solutions for unprecedented crisis situations.

This study aims to conceptualize the relationship of perceived artificial intelligence (AI) credibility with consumer-AI experiences. With the widespread deployment of AI in marketing and services, consumer-AI experiences are common and an emerging research area in marketing. Various factors affecting consumer-AI experiences have been studied, but one crucial factor – perceived AI credibility is relatively underexplored which the authors aim to envision and conceptualize.

This study employs a conceptual development approach to propose relationships among constructs, supported by 34 semi-structured consumer interviews.

This study defines AI credibility using source credibility theory (SCT). The conceptual framework of this study shows how perceived AI credibility positively affects four consumer-AI experiences: (1) data capture, (2) classification, (3) delegation, and (4) social interaction. Perceived justice is proposed to mediate this effect. Improved consumer-AI experiences can elicit favorable consumer outcomes toward AI-enabled offerings, such as the intention to share data, follow recommendations, delegate tasks, and interact more. Individual and contextual moderators limit the positive effect of perceived AI credibility on consumer-AI experiences.

This study contributes to the emerging research on AI credibility and consumer-AI experiences that may improve consumer-AI experiences. This study offers a comprehensive model with consequences, mechanism, and moderators to guide future research.

The authors guide marketers with ways to improve the four consumer-AI experiences by enhancing consumers' perceived AI credibility.

This study uses SCT to define AI credibility and takes a justice theory perspective to develop the conceptual framework.

This study explores the implementation of personalised information access, driven by machine learning, in Nigerian public libraries. The purpose of this paper is to address existing challenges, enhance the user experience and bridge the digital divide by leveraging advanced technologies.

This study assesses the current state of Nigerian public libraries, emphasising challenges such as underfunding and lack of technology adoption. It proposes the integration of machine learning to provide personalised recommendations, predictive analytics for collection development and improved information retrieval processes.

The findings underscore the transformative potential of machine learning in Nigerian public libraries, offering tailored services, optimising resource allocation and fostering inclusivity. Challenges, including financial constraints and ethical considerations, are acknowledged.

This study contributes to the literature by outlining strategies for responsible implementation and emphasising transparency, user consent and diversity. The research highlights future directions, anticipating advancements in recommendation systems and collaborative efforts for impactful solutions.