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This study draws on social influence and social comparison theories to investigate the effect of social media influencers on intention to visit with the mediating role of upward social comparison. It studies the reasons underlying people's intention to visit a destination from an emotional perspective.

PLS-SEM models were applied to a total of 527 responses.

This study provides empirical evidence supporting the Source Credibility Model's elements within the context of influencer marketing and travel intentions. Furthermore, it expands the current knowledge of upward social comparison emotions by reporting that upward contrastive emotions would partially mediate the relationship between expertise and intention to visit, and that upward assimilative emotions would fully mediate the relationship between attractiveness and intention to visit while partially mediating the relationship between trustworthiness and expertise and intention to visit.

The article adds new insights to tourism marketing as well as helps both destination marketers and travel influencers. For marketers, it is advised to collaborate with credible influencers known for expertise and trustworthiness, leveraging their perspectives to rebuild travel confidence and reassure tourists about safety measures. Travel influencers are recommended to convey their intrinsic passion and enthusiasm through their posts to create an inspiring connection with the audiences.

This paper is the first to address the relationship between travel influencers and intention to visit with the mediation role of both positive and negative emotions.

The main aim of this research is to portray how augmented reality (AR) characteristics (augmentation, interactivity, personalization, spatial presence, novelty, entertainment and informativeness) can enhance online customer experience (OCE).

This study conceptualizes a new framework that proposes various relationships between AR characteristics and OCE.

This study is extending the relationships between AR and OCE by including various AR characteristics that have not been tackled by the previous research.

This research provides an original framework on the relationship between AR characteristics and OCE through highlighting the role of media richness theory. The study is considered the first of its kind to combine these AR characteristics and customer experience in a comprehensive framework.

The purpose of this paper is to investigate what drives people to show a willingness to mitigate the effects of climate change. To accomplish this goal, this research uses the theory of planned behaviour to examine whether attitude towards climate change, subjective norm and perceived behavioural control are potential determinants of a pro-environmental intention and thus a pro-environmental behaviour.

This explanatory paper applies a Fuzzy Set Qualitative Comparative Analysis to identify the key drivers of pro-environmental intention and behaviour. A non-probability convenience sample of 481 Egyptian respondents was collected.

This study finds that awareness combined with a willingness to pay to mitigate climate change are key drivers of pro-environmental intention. Moreover, personal responsibility and confidence in the ability to mitigate climate change also trigger climate-friendly intentions. Finally, it is found that societal engagement and willingness to take action increase the propensity to exhibit pro-environmental behaviour.

The results of our analysis cannot be generalized to the Egyptian population as a whole as our sample only comprises a sample of Egyptian students.

This paper is novel as it is the first that applies Qualitative Comparative Analysis to the Theory of Planned Behaviour. By doing so, the paper sheds light on the understanding of key cognitive, social-psychological and behavioural factors which lead to environmental actions. Hence, it provides policy-makers with a framework to support a more sustainable society.

Artificial intelligence (AI) has transformed the field of hiring, enabling employers to collect and analyse massive amounts of data to understand and predict the suitability of candidates. However, AI can also have subconscious effects on candidates’ and employers needs through biased data, which can stem from human biases, algorithmic errors or external factors. For example, Amazon scrapped an AI-based recruitment programme that favoured male candidates over female candidates due to the historical patterns in the resumes it analysed. This paper examines how AI can shape candidate's needs through biased data from various sources and types, and what are the consequences for candidate's welfare and rights. We review the literature on AI applications in hiring, the origins and kinds of bias in AI systems, and the potential risks and benefits for candidates. We also suggest some guidelines for reducing bias in AI and enabling candidates to make informed and ethical choices online. We argue that AI can be a double-edged sword for candidate's needs and that more research and regulation are required to ensure its fair and accountable use.

The concept of ‘intelligence’ used to differ between human and machines, until the disruption of artificial intelligence (AI). The field of AI is advancing far more rapidly than the establishment of rules and regulations, which is causing certain fear. However, slowing down this progression to avoid economic crisis is not an option because of open-source AI, which facilitates faster development processes and collective contributions to codes and algorithms. Public policies, such as the ‘European Union AI Act (EU AI)’, ‘Whitehouse AI’, and the G7's ‘Hiroshima Artificial Intelligence Process’ (HAP), are already drafted. Regulators need to adopt a dynamic approach given AI's rapid advancement, and they need to eventually strive for international harmonisation in their rules and regulations for better collaborations. The EU's AI Act is the ‘world's first comprehensive law’ and it focuses on five main pillars similar to other countries drafts: ensuring AI usage is safe, transparent, traceable, non-discriminatory and environmentally friendly. They portray four risk categories against which citizens can file complaints: (1) Unacceptable risk (2) High risk (3) Generative AI (4) Limited risk. The US AI policies include ‘The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People’ and the ‘Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence’. This conceptual study extensively reviews the concept of AI and compares pioneering draft laws while providing recommendations on ethics and responsible AI. The contribution of this study is that it sheds light on the evolving evolution of AI and the challenges posed by the rapid advancement of AI technology, emphasising the necessity for flexible and adaptive regulatory frameworks. This is the first paper to explore AI from the academic and political perspective.

This research, titled ‘AI in Digital Marketing: The Ethical Implications of Digital Influence on Markets and Consumers,’ conducts a comprehensive examination of the nuanced role played by artificial intelligence (AI) in shaping consumer behaviour and influencing decision-making processes. While the incorporation of AI in marketing offers commendable advantages, such as providing personalised content and optimising strategies to enhance customer experiences and market efficiency, it concurrently introduces ethical considerations. This study meticulously scrutinises the latent potential for market grooming, wherein AI subtly guides consumers towards decisions that may not align with their best interests. By delving into instances of data privacy breaches, algorithmic biases and the unintended consequences of hyper-personalisation, this research contributes substantively to the ongoing discourse on the responsible utilisation of AI. The study underscores the imperative need for regulatory frameworks aimed at ensuring ethical practices in the dynamically shifting digital landscape. It endeavours to strike an equitable balance between the constructive contributions and potential pitfalls of AI in the realm of marketing. Through this research, we aim to shed light on the ethical dimensions associated with the digital manipulation of markets and consumers, providing insights that can inform industry practices, policymaking and public awareness.