Handling consumer vulnerability in e-commerce product images using machine learning

NEED: In recent years, secondhand products have received widespread attention, which has raised interest in them. The susceptibility issues that consumers encounter while buying online products in reference to the display images of the products are also not well researched. MOTIVATION: Retailers employ clever tactics such as ratings, product reviews, etc., to establish a strong position thereby boosting their sales and profits which may have an indirect impact on the consumer purchase that was not aware of that retailer's behavior. This has led to the novel method that has been suggested in this work to address these issues. PROPOSED METHODOLOGY: In this study, a handling method for reused product images based on user vulnerability in e-commerce websites has been developed. This method is called product image-based vulnerability detection (PIVD). The convolutional neural network is employed in three steps to identify the fraudulent dealer, enabling buyers to purchase goods with greater assurance and fewer damages. SUMMARY: This work is suggested to boost consumers' confidence in order to address the issues they encounter when buying secondhand goods. Both image processing and machine learning approaches are utilized to find vulnerabilities. On evaluation, the proposed method attains an F1 score of 2.3% higher than CNN for different filter sizes, 4% higher than CNN-LSTM when the learning rate is set to 0.008, and 6% higher than CNN when dropout is 0.5

Rapport with a chatbot? The underlying role of anthropomorphism in socio-cognitive perceptions of rapport and e-word of mouth

This study examines the impact of rapport with chatbots on electronic word of mouth (e-WOM), in the first phase, by considering several antecedents including anthropomorphism. In the second phase, deeper insights are provided into the moderated mediation role of rapport and the moderated moderation effect of value creation and hedonic motivation on e-WOM engagement. With tourism services as the research context, a survey was conducted among 257 visitors from three countries (China, India and New Zealand), selected due to their diverse cultural backgrounds and higher number of inbound visitors to Australia. The partial least squares method was used for data analysis along with multi-group analysis. Findings report the positive role of anthropomorphism in developing rapport with chatbots in digital interactions. Interestingly, rapport had the highest moderated mediation impact in the data from China followed by the data from India. The moderated moderation impact of hedonic motivation was only significant in the data from China, whereas value creation was a significant moderator in the data from both China and New Zealand. The study extends social exchange theory in a human–chatbot or artificial intelligence (AI) interaction context with cultural implications. The findings are useful for organizations relying on customer rapport with AI-based chatbots to ensure long-term customer service through digital interactions

Exploring Dynamic Nexus between Economic Growth, Environmental Degradation, and Public Health in Pakistan: A Moderated Mediation Approach

Since the start of this century, much attention has been given to economic growth and environmental changes and their effects on human beings. The present study has developed a comprehensive model to discuss the nexus between economic growth, environmental degradation, and public health. Furthermore, renewable energy consumption and public health spending are used as moderators to make the model more inclusive. The time series data from 1972 to 2020 has been used, and a regression path modeling tool SPPS-PROCESS Model 29, has been applied to carry out the results. The results showed a positive and significant effect of economic growth on environmental degradation, while renewable energy consumption reduces environmental degradation. Furthermore, environmental degradation is negatively affecting the health status in Pakistan. The results of the total effects showed that economic growth positively contributes to public health with a low coefficient. The indirect conditional impact of economic growth on human health through the mediating role of environmental degradation becomes positive from negative in the long run due to renewable energy and public spending on health. Based on the result, some policies are suggested in the last section of this study

AI Adoption in Jordanian SMEs: The Influence of Technological and Organizational Orientations

This study examines the factors influencing the adoption of artificial intelligence (AI) in small and medium-sized enterprises (SMEs) in Jordan, a key player in the growing Middle Eastern economy. Rooted in the Technology–Organization–Environment framework, we specifically focus on the role of technological capabilities and organizational dynamics in shaping AI adoption within Jordanian SMEs. A comprehensive survey involving 364 SME owner-managers in Jordan serves as the empirical foundation. Findings reveal the significant impact of employee IT knowledge, IT infrastructure, managerial commitment, training initiatives and well-designed reward systems in shaping SME owners’ or managers’ attitudes to AI. These findings provide valuable insights for SME leaders and stakeholders, guiding them in developing strategies to smoothly integrate AI technologies in line with Jordan’s societal needs. The article concludes by emphasizing the study’s contributions, implications and limitations while suggesting potential directions for future research in this field