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This study aims to investigate the impact of virtual tours on the engagement and travel intentions of older adults, emphasizing the role of emotional and informative content. It aims to enhance travel confidence and reduce stress among older travelers, fostering inclusive tourism through advanced avatar technology.

Using two between-subjects experiments, this research compares the effects of emotion-driven and knowledge-centric virtual tours on older adults. It explores the mediating role of travel confidence and stress reduction, along with the moderating influence of positive psychological cues on engagement and travel intentions.

The results highlight the potential of technology in promoting inclusive tourism. Emotionally engaging virtual tours significantly increase travel intentions among older adults by boosting confidence and alleviating stress, with positive psychological cues enhancing these effects.

This study offers valuable insights for tourism industry stakeholders by suggesting the development of avatar-based virtual tours tailored to the emotional and cognitive needs of older travelers. This approach could create more accessible and satisfying tourism experiences for older travelers.

This study extends the socioemotional selectivity theory to the realm of metaverse travel, providing a novel perspective on the emotional and cognitive engagement of older adults in the metaverse. This underscores the importance of inclusive technology in addressing the needs of older travelers.

This study explores the influence of textual social cues on virtual influencers' perceived attractiveness, homophily and credibility, and their impact on consumers' purchase intentions. The moderating role of perceived anthropomorphism is also assessed.

A randomized between-subjects experiment with 265 participants (134 low social cue/131 high social cue) was conducted. Participants viewed a fictional virtual influencer’s social media profile and post, then completed a survey. Partial least squares structural equation modeling (PLS-SEM) analysis was used to examine the effects of textual social cues on attractiveness, attitude homophily, credibility and purchase intention as well as the moderating role of perceived anthropomorphism.

The study found that textual social cues directly influence attractiveness and attitude homophily, which significantly impact virtual influencer credibility. Credibility, in turn, strongly predicted purchase intention.

Incorporating textual social cues into a virtual influencer’s profile to create a likable persona can help overcome the novelty effect and build lasting relationships with followers. Marketers should use textual cues, like emojis and self-disclosure, to enhance marketing effectiveness and select virtual influencers aligned with their target audience.

This study is among the first to explore the role of textual social cues in virtual influencers, extending the source credibility model and social information processing theory to the influencer marketing context.

This study examines various artificial intelligence (AI) models for predicting financially distressed firms with poor profitability (“Zombie firms”). In particular, we adopt the Explainable AI (“XAI”) approach to overcome the limitations of the previous AI models, which is well-known as the black-box problem, by utilizing the Local Interpretable Model-agnostic Explanations (LIME) and the Shapley Additive Explanations (SHAP). This XAI approach thus enables us to interpret the prediction results of the AI models. This study focuses on the Korean sample from 2019 to 2023, as it is expected that the COVID-19 pandemic increases the number of zombie firms. We find that the XGBoost model based on a boosting technique has the best predictive performance among several AI models, including the traditional ones (e.g. the logistic regression). In addition, by using the XAI approach, we provide visualized interpretations for the prediction results from the XGBoost model. The analysis further reveals that the return on sales and the selling, general and administrative costs are the most impactful variables for predicting zombie firms. Overall, this study focusing on several AI models not only shows the improvement for the prediction of zombie firms (relative to the traditional models) but also increases the reliability of the prediction results by adopting the XAI approach, providing several implications for market participants, such as financial institutions and investors.

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.