Augmented Reality (AR) technology has been increasingly utilised in mobile shopping apps to enhance consumers’ mobile shopping experiences, thereby affecting consumers’ impulse buying behaviour. Drawing upon the Spatial Presence Theory and the Elaboration Likelihood Model, the current study aims to empirically investigate AR-related factors (vividness, spatial accuracy, local presence) and their impacts on impulse buying behaviour related factors (arousal, perceived diagnosticity, and urge to buy impulsively) in the context of AR mobile shopping apps. In this study, an experiment employing a full 2X2 factorial between-subject experimental design was carried out. There were a total of 137 participants actively involved in this experiment. The data analysis results and findings, along with the implications of this study, are provided in the paper.

The purpose of this study is to explore the moderating effect of Big Five personality traits on the relationship between the predictors (need for affiliation, need for popularity, and self-esteem) and self-disclosure on Facebook. The sample of this study is 354 Facebook users from 5 different cultures in East Asia. A partial least square structural equation modeling data analysis technique was performed to analyze the moderator research framework. This study demonstrates that particular Big Five personality traits moderate each relationship. Specifically, Conscientiousness and Emotional Stability negatively moderate the relationship between need for affiliation and self-disclosure. Agreeableness negatively moderates the relationship between self-esteem and self-disclosure. Openness to New Experience negatively moderates the relationship between need for popularity and self-disclosure. The study suggests the importance of the interaction of personality variables to better understand self-disclosure phenomenon on Facebook. The findings and implications of this study are discussed in the article.

Blockchain technology is widely recognized for its security, transparency, and decentralization, yet ensuring the stability of blockchain networks as they scale remains a significant challenge. This study introduces a novel approach by integrating machine learning models to evaluate and predict blockchain stability, offering a proactive solution to maintain network reliability. The primary objective was to identify the key factors influencing stability and assess the effectiveness of different machine learning models in predicting instability events. Using a dataset derived from blockchain transaction data and network metrics, we applied Random Forest, Support Vector Machine (SVM), Long Short-Term Memory (LSTM) neural networks, and K-Means Clustering algorithms. The LSTM model demonstrated the highest accuracy (94.3%) and an AUC-ROC of 0.952, significantly outperforming other models in predicting stability events. The Random Forest model revealed that transaction throughput and network latency are the most critical factors, contributing 35.2% and 28.1% to network stability, respectively. Additionally, K-Means Clustering identified three distinct stability patterns, each representing different risk levels, providing actionable insights for network management. The key contribution of this research lies in the integration of machine learning into blockchain management, presenting a novel approach that enhances the predictability and resilience of blockchain systems. The findings suggest that machine learning can be effectively employed to develop early warning systems, enabling timely interventions to prevent network instability. This study not only advances the understanding of blockchain stability but also offers practical solutions for its enhancement, marking a significant step forward in the field. Future work should focus on the real-time implementation of these models and the exploration of more advanced techniques to further improve predictive capabilities.

The hospitality industry services many types of travellers. Each of them needs to be analysed differently in order to accurately determine their respective service quality attributes and customer delight. The effective and efficient enterprise information systems EISs play a significant role in traveller’s experience and in the competitive advantages that hotels can gain. For this study, self-service kiosks and guest service agents as part of hotel’s EISs are used to investigate the impact each has on the experience of business and leisure travellers. This study provides empirical results that further investigate how to improve the service quality for both types of travellers during the registration process. This process is the first physical interaction between the travellers and the hotel. Any problem in registering would generate a long-lasting negative first impression on the travellers, thus diminishing the customer delight. In the light of our findings, this study will contribute to the effective and efficient design of self-service kiosk systems, hotel’s EISs, and improved front office management to better accommodate the specific needs of both types of travellers.