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This paper aims to attempt to provide an overview of the copyright legal framework for audiovisual resources in Europe and Greece, how Audiovisual (AV) content is currently licensed by Greek providers and how licenses or copyright exceptions enable its reuse. The motivation for this work was the development of an aggregation service for audiovisual resources in Greece, the Open AudioVisual Archives (OAVA) platform.

Copyright licenses and exceptions in the European Union and in Greek Legislation have been thoroughly reviewed along with the reuse of content, based on the terms of Fair Use, Rights Statements and Creative Commons. Licensing issues for the most well-known aggregation services, such as Europeana, Digital Public Library of America, Trove, Digital New Zealand and the National Digital Library of India, have also been studied and considered. Audiovisual content providers in Greece have been recorded, and their licensing preferences have been analyzed. Pearson’s chi-square test was applied to test the relationship between the provider’s type, resources’ genre and licenses used.

Despite the abundance of copyright legislation in the European Union and in Greece, audiovisual content providers in Greece seem to ignore it or find it difficult to choose the right license. More than half of them choose to publish their resources on popular audiovisual platforms using the default licensing option provided. Creative Commons licenses are preferred for audiovisual content that falls into the following categories: open courses (almost exclusively) and interviews and digital collection/research projects (about half of the content).

This paper examines audiovisual content aggregation, in the EU and Greece, from a legal point of view. To the best of the authors’ knowledge, it is the first attempt to record and analyze the licensing preferences of Greek AV content providers.

Today, digital piracy remains a growing challenge for legislators and businesses operating in the entertainment industry. For these reasons, policymakers place significant efforts in reducing piracy activities through copyright enforcement policies. Yet, the effectiveness of these frameworks remains questionable since empirical evidence offers countervailing insights. For these reasons, this study adopts a demand-side approach to investigate how users' emotional reactions to the copyright enforcement policy influence their digital piracy-related judgments and intention.

The author used a scenario-based approach to test the hypotheses and surveyed 262 users who actively stream movies and TV shows.

By drawing on the appraisal tendency framework, the author finds that among individuals experiencing fear due to the copyright enforcement policy, there is a decreasing intention to continue using illegal streaming services. On the contrary, individuals experiencing indignation are more likely to have increased intention to use illegal streaming services. On top of this, we reveal indirect effects by mediating individuals' vulnerability to sanctions (fear path) and subjective norms (indignation path) as judgments cues.

The author contributes to digital piracy literature in two ways with the study. First, the author unfolds the affective background that explains the emergence of deterrence or defiance effects when users respond to the copyright enforcement policy. Thus, the author shows that fear is a trigger for decreasing while indignation increases the usage of illegal streaming services. Second, through the appraisal tendency framework, the author enriches the literature by theorizing and providing empirical evidence on how previously established vulnerability to sanctions and subjective norms reinforce affective influences on encouraging or discouraging digital piracy intentions. Overall, the findings also provide policymakers with original insights on designing their copyright enforcement tools to combat digital piracy further.

Artificial intelligence (AI) has sparked interest in various areas, including marketing. However, this exhilaration is being tempered by growing concerns about the moral and legal implications of using AI in marketing. Although previous research has revealed various ethical and legal issues, such as algorithmic discrimination and data privacy, there are no definitive answers. This paper aims to fill this gap by investigating AI’s ethical and legal concerns in marketing and suggesting feasible solutions.

The paper synthesises information from academic articles, industry reports, case studies and legal documents through a thematic literature review. A qualitative analysis approach categorises and interprets ethical and legal challenges and proposes potential solutions.

The findings of this paper raise concerns about ethical and legal challenges related to AI in the marketing area. Ethical concerns related to discrimination, bias, manipulation, job displacement, absence of social interaction, cybersecurity, unintended consequences, environmental impact, privacy and legal issues such as consumer security, responsibility, liability, brand protection, competition law, agreements, data protection, consumer protection and intellectual property rights are discussed in the paper, and their potential solutions are discussed.

Notwithstanding the interesting insights gathered from this investigation of the ethical and legal consequences of AI in marketing, it is important to recognise the limits of this research. Initially, the focus of this study is confined to a review of the most important ethical and legal issues pertaining to AI in marketing. Additional possible repercussions, such as those associated with intellectual property, contracts and licencing, should be investigated more deeply in future studies. Despite the fact that this study gives various answers and best practices for tackling the stated ethical and legal concerns, the viability and efficacy of these solutions may differ depending on the context and industry. Thus, more research and case studies are required to evaluate the applicability and efficacy of these solutions in other circumstances. This research is mostly based on a literature review and may not represent the experiences or opinions of all stakeholders engaged in AI-powered marketing. Further study might involve interviews or surveys with marketing professionals, customers and other key stakeholders to offer a full knowledge of the practical difficulties and solutions. Because of the rapid speed of technical progress, AI’s ethical and regulatory ramifications in marketing are continually increasing. Consequently, this work should be a springboard for more research and continuing conversations on this subject.

This study’s findings have several practical implications for marketing professionals. Emphasising openness and explainability: Marketing professionals should prioritise transparency in their use of AI, ensuring that customers are fully informed about data collection and utilisation for targeted advertising. By promoting openness and explainability, marketers can foster customer trust and avoid the negative consequences of a lack of transparency. Establishing ethical guidelines: Marketing professionals need to develop ethical rules for the creation and implementation of AI-powered marketing strategies. Adhering to ethical principles ensures compliance with legal norms and aligns with the organisation’s values and ideals. Investing in bias detection tools and privacy-enhancing technology: To mitigate risks associated with AI in marketing, marketers should allocate resources to develop and implement bias detection tools and privacy-enhancing technology. These tools can identify and address biases in AI algorithms, safeguard consumer privacy and extract valuable insights from consumer data.

This study’s social implications emphasise the need for a comprehensive approach to address the ethical and legal challenges of AI in marketing. This includes adopting a responsible innovation framework, promoting ethical leadership, using ethical decision-making frameworks and conducting multidisciplinary research. By incorporating these approaches, marketers can navigate the complexities of AI in marketing responsibly, foster an ethical organisational culture, make informed ethical decisions and develop effective solutions. Such practices promote public trust, ensure equitable distribution of benefits and risk, and mitigate potential negative social consequences associated with AI in marketing.

To the best of the authors’ knowledge, this paper is among the first to explore potential solutions comprehensively. This paper provides a nuanced understanding of the challenges by using a multidisciplinary framework and synthesising various sources. It contributes valuable insights for academia and industry.

The purpose of this paper is to explore the potential benefits and challenges of using large language models (LLMs) like ChatGPT to edit Wikipedia.

The first portion of this paper provides background about Wikipedia and LLMs, explicating briefly how each works. The paper's second section then explores both the ways that LLMs can be used to make Wikipedia a stronger site and the challenges that these technologies pose to Wikipedia editors. The paper's final section explores the implications for information professionals.

This paper argues that LLMs can be used to proofread Wikipedia articles, outline potential articles and generate usable Wikitext. The pitfalls include the technology's potential to generate text that is plagiarized or violates copyright, its tendency to produce “original research” and its tendency to generate incorrect or biased information.

While there has been limited discussion among Wikipedia editors about the use of LLMs when editing the site, hardly any scholarship has been given to how these models can impact Wikipedia's development and quality. This paper thus aims to fill this gap in knowledge by examining both the potential benefits and pitfalls of using LLMs on Wikipedia.

The study aimed to assess the digital literacy skills of the students of Sacred Heart College, Chalakudy, to know whether they possess the digital literacy skills to perform well in the digital environment. The study also analyzed how digital literacy skills were affected by various factors.

The study used stratified random sampling technique, and data were collected through a self-assessment survey using an online questionnaire designed based on DigComp 2.1: The Digital Competence Framework by the European Commission.

The results indicated that the college students needed training on digital literacy skills, as the majority students had only moderate to low digital literacy skills. It was proven that exposure to technology and the Internet will not necessarily yield skills to perform well in the digital environment. Also, digital literacy skills were not affected by various factors like age, level of study, etc.

The study helped to identify the digital literacy deficiencies in the students of Sacred Heart College, and it can serve as a valuable model for conducting similar investigations in diverse educational institutions. Conducting such studies offers institutions valuable insights, enabling them to create and implement personalized digital literacy training programs that can enhance students' abilities to navigate the digital landscape with proficiency and effectiveness. The study results can be insightful for educators, policymakers and the Kerala Government to reassess the current approaches and design an effective curriculum for integrating technology in education.

Although there are several studies that evaluated college students’ digital literacy in India and other countries, there are very few studies in the context of Kerala. Therefore, this study is distinctive and will serve as an example for all such studies in the future.

This chapter argues that the Americanisation of online policing has questionable impacts in Australian prosecutions involving drugs obtained and distributed through dark web cryptomarkets. The authors describe several Australian prosecutions of mid- and low-level dealers who have accessed drugs through the dark web and contrast these with the United States (US) case against the cryptomarket, AlphaBay. The discussion in this study emphasises how Australian police and courts view the relative weight of dark web activity associated with the domestic and transnational supply of illicit drugs that result in formal prosecutions. The authors suggest that large-scale forms of online and dark web police surveillance undertaken by US enforcement agencies reflect Ethan Nadelmann’s (Cops across borders: the internationalization of US criminal law enforcement, University Park: Pennsylvania State University Press, 1993) thesis on the Americanisation of global policing through transnational communications networks. The authors then explain how key elements of transnational dark web drug supply appear to have a marginal bearing on criminal investigations into low- and mid-level traffickers in Australia, which rely on conventional surveillance tactics to identify clandestine mail pickups, physical distribution methods, and irregular money trails. However, the authors then illustrate how the Americanisation of online policing that targets high-level entrepreneurs and seeks to dismantle or eliminate dark web cryptomarkets has important implications on Australian reforms aimed at enhancing online surveillance powers to target a range of crimes that are often wrongly associated with illicit drug cryptomarkets. The authors conclude by demonstrating how intensive dark web surveillance has limited direct impact on routine drug policing in Australia, with dark web communications simply another medium for facilitating the physical detection of illicit transnational drug transactions.

In the current competitive era of industrialization, a significant level of innovation is necessary to meet the growing competition. There are many economic forces that determine the pace of innovation within a country. Among others, this study aims to focus on exploring the relevant role of corruption control (CC) in determining the innovation level.

For empirical analysis, the authors sample the 24 years of data (1996–2019) of Asian economies and use the fully modified ordinary least square (OLS) and dynamic OLS models to check the regression among variables. The selection of both techniques is based upon the empirical suggestions offered by unit root testing and the Johansen cointegration test.

The empirical findings infer the positive and statistically significant role of CC in boosting innovation. Strengthening the corruption-free environment encourages innovation activities within the country. In addition, foreign direct investment has a negative relationship with CC while financial development, economic growth, export volume and government subsidies positively determine the innovation level.

Based on empirical analysis, it is suggested that the policy officials should do more focus on CC to enhance the competitiveness of the country through more innovation.

The empirical analysis robust the findings of existing literature in an alternative data set and offers innovative views regarding the role of other factors in boosting the innovation level.

This study aims to address a systematic literature review (SLR) using bibliometrics on the relationship between academic integrity and artificial intelligence (AI), to bridge the scattering of literature on this topic, given the challenge and opportunity for the educational and academic community.

This review highlights the enormous social influence of COVID-19 by mapping the extensive yet distinct and fragmented literature in AI and academic integrity fields. Based on 163 publications from the Web of Science, this paper offers a framework summarising the balance between AI and academic integrity.

With the rapid advancement of technology, AI tools have exponentially developed that threaten to destroy students' academic integrity in higher education. Despite this significant interest, there is a dearth of academic literature on how AI can help in academic integrity. Therefore, this paper distinguishes two significant thematical patterns: academic integrity and negative predictors of academic integrity.

This study also presents several contributions by showing that tools associated with AI can act as detectors of students who plagiarise. That is, they can be useful in identifying students with fraudulent behaviour. Therefore, it will require a combined effort of public, private academic and educational institutions and the society with affordable policies.

This study proposes a new, innovative framework summarising the balance between AI and academic integrity.

Technological expansion and adoption in university libraries have precipitated cybercrimes and the need to equip library personnel with the required knowledge to combat this menace. Consequently, this study aims to examine cyber security in university libraries and its implication for Library and Information Science education.

The study adopted descriptive research design, while questionnaire and interview were used to elicit data from library personnel and heads of library schools, respectively. A total of 134 responses were elicited through structured questionnaire (administered online due to the closure of universities) while six heads of library schools were interviewed, one from each of the six geopolitical zones in Nigeria.

The data from the questionnaire which were descriptively analysed revealed that the perceived knowledge of cyber security among the librarians was moderately low. Also, the university libraries were exposed to various cyber threats, with cyber security/guideline been one of the critical measures to combat cybercrime. Also, the result showed that librarians displayed high level of adherence to cyber ethics. However, the disposition of library management towards cyber security issues was revealed to be the main challenge to the deployment of cyber security in university libraries, follow by poor password management. Majority of the librarians possess basic knowledge of cyber security, though with serious interest to learn more about it. They were not taught cyber security in library school and they indicated enthusiasm to learn about it. The result of the interview with heads of library schools showed majority of these schools do not offer cyber security course due to dearth in skilled manpower.

The study presents cybercrime as a menace, if not tackled, would affect the university libraries’ sustainability as information institution, compromising their ability to deliver quality services.

Cybercrimes increase day by day in parallel to cyber-attacks and cyber-threats. Due to such an increase, required cybersecurity precautions against all kinds of cyber-attacks and cyber-threats should be taken by both organizations and individuals. This study aims to develop a reliable and valid measurement tool to measure cybercrime awareness levels of individuals.

In this study, a scale named as Cybercrime Awareness Scale (CAS) has been developed and psychometric properties of the scale have been tested by two separate studies.

The first study included a total of 500 respondents (294 female and 206 male). In the first study, factor structure of the proposed scale has been determined through an exploratory factor analysis. The results revealed a three-factor structure (i.e. information systems crimes, personal data crimes, and privacy and security). Cronbach's alpha reliability coefficients for the subdimensions were 0.95, 0.92, and 0.90, respectively. The sample of the second study consisted of 494 respondents (281 female and 213 male). The confirmatory-factor-analysis results revealed that three-factor structure is valid and optimal model. Further, the proposed scale demonstrated moderate concurrent validity results in relation to the Digital Data Security Awareness Scale.

Findings indicated that the CAS is a valid and reliable measurement tool to measure individuals' cybercrime awareness level. This study makes a novel contribution to the existing cybersecurity literature by providing the CAS, which is developed by following rigorous methodological standards.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-01-2022-0023/