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The purpose of this study is to investigate the various enablers of and constraints on employees' information sharing on an enterprise social media platform. It draws on two theoretical perspectives, communication privacy management theory and the technology affordance framework, as well as on empirical data in an attempt to paint a comprehensive picture of the factors shaping employees' decisions to share or not share information on enterprise social media.

This qualitative field study is based on semi-structured interviews and enterprise social media review data from a large Nordic media organization.

On an enterprise social media platform, privacy management principles shape employees' information-sharing decisions in relation to personal privacy boundaries, professional boundaries and assumed risks, online safety concerns and perceived audience. Additionally, the technological affordances of visibility, awareness, persistence and searchability shape employees' information sharing in varying and sometimes even contradictory ways. Finally, organizational factors, such as norms, tasks and media repertoires, are associated with employees' information-sharing decisions. Together, these three dimensions, personal, technological and organizational, form a model of the enablers of and constraints on employees' decisions to share information on enterprise social media.

This study extends the understanding of different factors shaping employees' decisions to share or not share information on enterprise social media. It extends the two applied theories by uniquely combining interpersonal privacy management principles with a technological affordance framework that focuses on the relationship between the user and the technology. This research also furthers the authors' knowledge of what privacy management principles mean in the organizational context. This study shows connections between the two theories and extends the understanding of technology affordances as not only action possibilities but also constraining factors. Additionally, by revealing what kinds of factors encourage and inhibit information sharing on enterprise social media, the results of this study support organizations in their efforts to manage information sharing on enterprise social media systems.

The purpose of this paper is to examine the extent to which HTTPS encryption and Google Analytics services have been implemented on academic library websites, and discuss the privacy implications of free services that introduce web tracking of users.

The home pages of 279 academic libraries were analyzed for the presence of HTTPS, Google Analytics services and privacy-protection features.

Results indicate that HTTPS implementation on library websites is not widespread, and many libraries continue to offer non-secured connections without an automatically enforced redirect to a secure connection. Furthermore, a large majority of library websites included in the study have implemented Google Analytics and/or Google Tag Manager, yet only very few connect securely to Google via HTTPS or have implemented Google Analytics IP anonymization.

Librarians are encouraged to increase awareness of this issue and take concerted and coherent action across five interrelated areas: implementing secure web protocols (HTTPS), user education, privacy policies, informed consent and risk/benefit analyses.

Third-party tracking of users is prevalent across the web, and yet few studies demonstrate its extent and consequences for academic library websites.

The received wisdom underlying many guides to ethical research is that information is private, and research is consequently seen as a trespass on the private sphere. Privacy demands control; control requires consent; consent protects privacy. This is not wrong in every case, but it is over-generalised. The distorted perspective leads to some striking misinterpretations of the rights of research participants, and the duties of researchers. Privacy is not the same thing as data protection; consent is not adequate as a defence of privacy; seeking consent is not always required or appropriate. Beyond that, the misinterpretation can lead to conduct which is unethical, limiting the scope of research activity, obstructing the flow of information in a free society, and failing to recognise what researchers’ real duties are.

This study aims at investigating the extent of SysTrust’s framework (principles and criteria) as an internal control approach for assuring the reliability of accounting information system (AIS) were being implemented in Jordanian business organizations.

The study is based on primary data collected through a structured questionnaire from 239 out of 328 shareholdings companies. The survey units were the shareholding companies in Jordan, and the single key respondents approach was adopted. The extents of SysTrust principles were also measured. Previously validated instruments were used where required. The data were analysed using t-test and ANOVA.

The results indicated that the extent of SysTrust being implemented could be considered to be moderate at this stage. This implies that there are some variations among business organizations in terms of their level of implementing of SysTrust principles and criteria. The results also showed that the extent of SysTrust principles being implemented was varied among business organizations based on their business sector. However, there were not found varied due to their size of business and a length of time in business (experience).

This study is only conducted in Jordan as a developing country. Although Jordan is a valid indicator of prevalent factors in the wider MENA region and developing countries, the lack of external validity of this research means that any generalization of the research findings should be made with caution. Future research can be orientated to other national and cultural settings and compared with the results of this study.

The study provides evidence of the need for management to recognize the importance of the implementation of SysTrust principles and criteria as an internal control for assuring the reliability of AIS within their organizations and be aware which of these principles are appropriate to their size and industry sector.

The findings would be valuable for academic researchers, managers and professional accounting to acquire a better undemanding of the current status of the implementation of the SysTrust principles (i.e., availability, security, integrity processing, confidentiality, and privacy) as an internal control method for assuring the reliability of AIS by testing the phenomenon in Jordan as a developing country.

Artificial intelligence (AI) offers many benefits to improve predictive marketing practice. It raises ethical concerns regarding customer prioritization, market share concentration and consumer manipulation. This paper explores these ethical concerns from a contemporary perspective, drawing on the experiences and perspectives of AI and predictive marketing professionals. This study aims to contribute to the field by providing a modern perspective on the ethical concerns of AI usage in predictive marketing, drawing on the experiences and perspectives of professionals in the area.

The study conducted semistructured interviews for 6 weeks with 14 participants experienced in AI-enabled systems for marketing, using purposive and snowball sampling techniques. Thematic analysis was used to explore themes emerging from the data.

Results reveal that using AI in marketing could lead to unintended consequences, such as perpetuating existing biases, violating customer privacy, limiting competition and manipulating consumer behavior.

The authors identify seven unique themes and benchmark them with Ashok’s model to provide a structured lens for interpreting the results. The framework presented by this research is unique and can be used to support ethical research spanning social, technological and economic aspects within the predictive marketing domain.

La Inteligencia Artificial (IA) ofrece muchos beneficios para mejorar la práctica del marketing predictivo. Sin embargo, plantea preocupaciones éticas relacionadas con la priorización de clientes, la concentración de cuota de mercado y la manipulación del consumidor. Este artículo explora estas preocupaciones éticas desde una perspectiva contemporánea, basándose en las experiencias y perspectivas de profesionales en IA y marketing predictivo. El estudio tiene como objetivo contribuir a la literatura de este ámbito al proporcionar una perspectiva moderna sobre las preocupaciones éticas del uso de la IA en el marketing predictivo, basándose en las experiencias y perspectivas de profesionales en el área.

Para realizar el estudio se realizaron entrevistas semiestructuradas durante seis semanas con 14 participantes con experiencia en sistemas habilitados para IA en marketing, utilizando técnicas de muestreo intencional y de bola de nieve. Se utilizó un análisis temático para explorar los temas que surgieron de los datos.

Los resultados revelan que el uso de la IA en marketing podría tener consecuencias no deseadas, como perpetuar sesgos existentes, violar la privacidad del cliente, limitar la competencia y manipular el comportamiento del consumidor.

El estudio identifica siete temas y los comparan con el modelo de Ashok para proporcionar una perspectiva estructurada para interpretar los resultados. El marco presentado por esta investigación es único y puede utilizarse para respaldar investigaciones éticas que abarquen aspectos sociales, tecnológicos y económicos dentro del ámbito del marketing predictivo.

人工智能（AI）为改进预测营销实践带来了诸多益处。然而, 这也引发了与客户优先级、市场份额集中和消费者操纵等伦理问题相关的观点。本文从当代角度深入探讨了这些伦理观点, 充分借鉴了人工智能和预测营销领域专业人士的经验和观点。旨在通过现代视角提供关于在预测营销中应用人工智能时所涉及的伦理观点, 为该领域做出有益贡献。

本研究采用了目的性和雪球抽样技术, 与14位在人工智能营销系统领域具有丰富经验的参与者进行为期六周的半结构化访谈。研究采用主题分析方法, 旨在深入挖掘数据中显现的主要主题。

研究结果表明, 在营销领域使用人工智能可能引发一系列意外后果, 包括但不限于加强现有偏见、侵犯客户隐私、限制竞争以及操纵消费者行为。

本研究通过明确定义七个独特的主题, 并采用阿肖克模型进行基准比较, 为读者提供了一个结构化的视角, 以解释研究结果。所提出的框架具有独特之处, 可有效支持在跨足社会、技术和经济领域的预测营销中展开的伦理研究。

This paper introduces a new set of equity and rights-based principles for health data governance (HDG) and makes the case for their adoption into global, regional and national policy and practice.

This paper discusses the need for a unified approach to HDG that maximises the value of data for whole populations. It describes the unique process employed to develop a set of HDG principles. The paper highlights lessons learned from the principle development process and proposes steps to incorporate them into data governance policies and practice.

More than 200 individuals from 130 organisations contributed to the development of the HDG principles, which are clustered around three interconnected objectives of protecting people, promoting health value and prioritising equity. The principles build on existing norms and guidelines by bringing a human rights and equity lens to HDG.

The principles offer a strong vision for HDG that reaps the public good benefits of health data whilst safeguarding individual rights. They can be used by governments and other actors as a guide for the equitable collection and use of health data. The inclusive model used to develop the principles can be replicated to strengthen future data governance approaches.

The article describes the first bottom-up effort to develop a set of principles for HDG.

Regulatory initiatives and related technological shifts have been imposing restrictions on data-driven marketing (DDM) practices. This paper aims to find the main restrictions for DDM and the key management theories applied to investigate the consequences of these restrictions.

The authors conducted a unified bibliometric analysis with 104 publications retrieved from both Scopus and Web of Science, followed by a qualitative, in-depth systematic literature review to identify the management theories in literature and inform a research agenda.

The fragmentation of the research outcomes was overcome by the identification of 3 main clusters and 11 management theories that structured 18 questions for future research.

To the best of the authors’ knowledge, this paper sets for the first time a frontier between almost three decades where DDM evolved with no significative restrictions, grounded on innovations and market autoregulation, and an era where data privacy, anti-trust and competition and data sovereignty regulations converge to impose structural changes, requiring scholars and practitioners to rethink the roles of data at the strategic level of the firm.

Coronavirus Disease 2019 (COVID-19) necessitated the need for “Hospital-at-home” improvisations that involve wearable technology to classify patients within households before visiting health institutions. Do-It-Yourself wearable devices allow for the collection of health data leading to the detection and/or prediction of the prevalence of the disease. The sensitive nature of health data requires safeguards to ensure patients’ privacy is not violated. The previous work utilized Hyperledger Fabric to verify transmitted data within Smart Homes, allowing for the possible implementation of legal restrictions through smart contracts in the future. This study aims to explore privacy-enhancing authentication schemes that are operated by multiple credential issuers and capable of integration into the Hyperledger ecosystem.

Design Science Research is the methodology that was used in this study. An architecture for ABC-privacy was developed and evaluated.

While the privacy-by-design architecture enhances data privacy through edge and fog computing architecture, there is a need to provide an additional privacy layer that limits the amount of data that patients disclose. Selective disclosure of credentials limits the number of information patients or devices divulge.

The evaluation of this study identified Coconut as the most suitable attribute-based credentials scheme for the Smart Homes Patients and Health Wearables use case Coconut user-centric architecture Hyperledger integration multi-party threshold authorities public and private attributes re-randomization and unlinkable revelation of selective attribute revelations.

The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.

Drawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

This classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.

This study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.