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This chapter introduces EmoGenPath, an innovative machine learning-based model designed to deliver adaptive therapy to children and youth affected by the trauma of armed conflict. The model synthesizes advanced artificial intelligence (AI) techniques, including convolutional neural networks for emotion recognition and an advantage-actor critic-trained reinforcement learning model for therapeutic content tailoring toward goal achievement, to provide a dynamic and personalized therapeutic experience. Recognizing the importance of empathetic and culturally sensitive interventions, EmoGenPath offers a unique approach by prioritizing the emotional states and individual narratives of its users. In regions where conflict has impeded traditional mental health services, this model aims to bridge the gap, facilitating resilience and recovery through a virtual therapeutic environment that can be accessed via low-bandwidth internet connections, ensuring broader reach and impact. This chapter emphasizes the ethical implementation of AI in sensitive settings. It discusses the imperative of privacy, security, and inclusive design, ensuring that the model is responsive to diverse emotional expressions across different ethnicities and backgrounds. Additionally, it outlines the potential of such a model to scale therapeutic resources effectively, delivering tailored interventions with a compassionate approach.

This study addresses the challenge of generating material waste from support structures in 3D printing manufacturing and aims to explore more cost-effective manufacturing strategies for 3D printing manufacturers by considering two strategies: technology upgrading and material recycling.

This study examines the optimal decisions for manufacturers under each scenario (including a benchmark model and models for the two strategies) and explores the most profitable strategy by comparing the optimal profits of the manufacturer and analyzing the impact of key factors.

This study reveals that the choice of the optimal manufacturing strategy depends on the cost coefficient of technological effort and the fixed cost associated with introducing material recycling. In addition, it finds that material recycling is particularly effective in enhancing consumer surplus.

The analysis provides an important basis for decision-making for 3D printing manufacturers considering technology upgrading and material recycling, which can not only enhance economic benefits but also contribute to the sustainable advancement of 3D printing technology.

To the best of the authors’ knowledge, this study is the first to focus on the adverse effects of support structures in 3D printing manufacturing and systematically explore the economic feasibility of improving this issue through both technology upgrading and material recycling.

Short-form video advertisements have recently gained popularity and are widely used. However, creating attractive short video advertisements remains a challenge for sellers. Based on the visual-audio perspective and signaling theory, this study investigated the impacts of three visual features (number of shots, pixel-level image complexity and vertical versus horizontal formats) and two audio features (speech rate and average spectral centroid) on user engagement behavior.

We conducted a field study on TikTok. To test our various hypotheses, we used regression analysis on 2,511 videos containing product promotion information posted by 60 sellers between January 1, 2020 and November 20, 2021.

For visual variables, the number of shots and pixel-level image complexity were found to have nonlinear (inverted U-shaped) relationships with user engagement behavior. The vertical video form was found to have a positive effect on comments and shares. In the case of audio variables, speech rate was found to have a significant positive effect on shares but not on likes and comments. The average spectral centroid was found to have significant negative influences on likes and comments.

This study provides specific suggestions for sellers who create short-form videos to improve user engagement behavior.

This study contributes to the literature on short-form video advertising by extending the potential drivers of user engagement behavior. Additionally, from a methodological perspective, it contributes to the literature by using computer vision and speech-processing techniques to analyze user behavior in a video-related context, effectively overcoming the limitations of the widely adopted survey method.

Addressing users’ migration is a prerequisite for the sustainable development of the sharing economy. Ethical concerns that may lead to users’ migration are frequent in sharing economy platforms. Therefore, this study explores whether the long-term governance of sharing economy platforms can effectively mitigate users’ migration caused by ethical concerns.

Using a questionnaire survey of 549 participants, this study investigated the mechanism of users’ migration and governance strategies in the platform ecosystem based on trust theory.

The results indicate that users’ ethical concerns regarding the platform ecosystem significantly and positively influence their migration. Furthermore, users’ continued trust played a significant mediating role in the relationship between ethical concerns and users’ migration. The results also showed that future orientation and resilience significantly moderated the impact of users’ ethical concerns on their continued trust, thereby weakening this effect.

The author clarified the relationship between ethical concerns and users’ migration, identified the underlying mechanisms and provided guidance on how to mitigate migration behavior. However, users’ migration is influenced by various factors beyond ethical concerns. In addition to some factors that lead to migration, other factors make users stay on the platform. Future research should integrate multiple factors.

This study reveals the mechanism of action between users’ migration and ethical concerns in the platform ecosystem and sheds light on the output of long-term orientation practices of the platform.

This study aims to explore the extent to which Chinese multinational enterprises (MNEs) rely on supranational institution – the Belt and Road Initiative (BRI) – versus host country institutional quality to navigate their foreign location choice.

This study uses a conditional logit regression model using a sample of 1,302 greenfield investments by Chinese MNEs in 54 BRI participating countries during the period 2011–2018.

The results indicate that as a supranational institution, the BRI serves as a substitution mechanism to address the deficiencies in institutional quality in BRI participating countries, thereby attracting Chinese MNEs to invest in those countries. In addition, the BRI’s substitution effect on host country institutional quality is more pronounced for large MNEs, MNEs in the manufacturing industry and MNEs in inland regions.

This study expands the understanding of the BRI as a supranational institution for MNEs from emerging markets and reveals its substitution effect on the host country institutional quality. Furthermore, it highlights that MNEs with diverse characteristics gain varying degrees of benefits from the BRI.

The residential sector is a principal contributor to global energy consumption, underscoring the critical importance of promoting green housing initiatives to mitigate energy use and environmental degradation. The prevalence of uncertainty in the natural environment, exemplified by phenomena like extreme weather events, highlights the urgent need for adaptive strategies and sustainable practices to mitigate the impact on human communities and ecosystems. Against this backdrop, this paper presents a theoretical framework examining the influence of natural environmental uncertainty on consumers' willingness to purchase green housing.

Through three experiments, this study modeled the mechanism by which the natural environment uncertainty affects consumers' willingness to purchase green housing, and then verified the mediating effect of the threat of ontological security and the moderating effect of the degree of consumers' natural connectedness.

This paper concludes (1) natural environmental uncertainty exerts a significant positive impact on the willingness to purchase green housing, with the threat to ontological security serving as a pivotal mediating variable; (2) the degree of natural connectedness significantly moderates the effect of ontological security threats on the purchasing intent for green housing.

This research contributes to the marketing literature by offering a novel perspective on the impact of natural environmental uncertainty on consumer behavior, augmenting the body of knowledge concerning the determinants of green housing purchase intentions, and provides new ideas for marketers.

Despite widespread use of virtual streamers, academic research on this subject remains limited. This study aims to explore the mechanisms by which consumer perceptions of virtual streamers influence consumer purchase intentions.

We used partial least squares structural equation modeling to analyze validated online survey data from 414 consumers watching virtual streamers.

Consumer perceptions of virtual streamers (perceived competence, perceived interaction quality and perceived warmth) promote the establishment of transactional psychological contract and relational psychological contract between consumers and virtual streamers, which further affects consumers’ purchase intention.

This study enriches the research on virtual streamers, facilitates their adoption and introduces the psychological contract into a new research context by revealing the formation of the psychological contract from the perspective of virtual streamers. Moreover, this study provides a new understanding of the relationship between disembodied artificial intelligence and consumers.

The Millennials (1981–1995) witnessed conceptualization, adaptation, incorporation, and improvement of numerous technological aspects like the first personal computers by IBM in 1981 as well as the ARPANET adoption of the TCP/IP protocol which is the fundamental basis for the internet. The Generation Z (Gen-Z) (1996–2010) are born in a period that practically amalgamated a wide range of technologies in various realms – cloud computing, machine learning, introduction of e-commerce, big data analysis, mobile technology, automations, etc. Some of the existing deep learning-based tools are ChatGPT, TensorFlow (open-source library developed by Google), PyTorch (deep learning-based digital library), Keras (TensorFlow and allows users to quickly prototype and experiment with deep learning models), and OpenCV (open-source computer vision library that includes a wide range of image and video processing algorithms). In the academic sector, the Millennials (42–28 years of age) are currently the educators, and the Gen-Z (13–27 years of age) can be from any stage of life – students to educators. The study is to statistically evaluate the perceptions of Gen-Z as well as the Millennials in the incorporation of deep learning-based AI tools in education. The research framework used is the unified theory of acceptance and use of technology-3 (UTAUT-3) model. The research methodology is a qualitative analysis based on the data collected in a questionnaire from 200 participants; 100 each from Gen-Zs and Millennials. The study is limited to the understanding of perceptions regarding application of the deep learning-based AI tools in education. The technical aspects and knowledge required to create deep learning tools are not in the scope.

While the proliferation of chatbots allows companies to connect with their customers in a cost- and time-efficient manner, it is not deniable that they quite often fail expectations and may even pose negative impacts on user experience. The purpose of the study is to empirically explore the negative user experience with chatbots and understand how users respond to service failure caused by chatbots.

This study adopts a qualitative research method and conducts thematic analysis of 23 interview transcripts.

It identifies common areas where chatbots fail user expectations and cause service failure. These include their inability to comprehend and provide information, over-enquiry of personal or sensitive information, fake humanity, poor integration with human agents, and their inability to solve complicated user queries. Negative emotions such as anger, frustration, betrayal and passive defeat were experienced by participants when they interacted with chatbots. We also reveal four coping strategies users employ following a chatbots-induced failure: expressive support seeking, active coping, acceptance and withdrawal.

Our study extends our current understanding of human-chatbot interactions and provides significant managerial implications. It highlights the importance for organizations to re-consider the role of their chatbots in user interactions and balance the use of human and chatbots in the service context, particularly in customer service interactions that involve resolving complex issues or handling non-routinized tasks.

The online healthcare platform (OHP) has become an essential element of the healthcare system, representing a technological shift in the job responsibilities of medical professionals. Drawing on a technology-based job demands–resources (JD-R) model, this study aims to examine how the technological characteristics of OHP affect doctors’ OHP use psychology and behavior.

This empirical study was based on a survey conducted among 423 doctors with OHP use experience. The proposed model underwent assessment through partial least squares structural equation modeling (PLS-SEM) to reveal the effects of technology-based job demands (i.e. technology-based work overload and technology-based work monitoring) and resources (i.e. perceived usefulness, facilitating conditions and IT mindfulness) on doctors’ OHP fatigue and continuance use intention.

Results suggest that technology-based work monitoring, perceived usefulness and facilitation conditions have significant impacts on doctors’ psychological and behavioral responses to using OHP, whereas technology-based work overload and IT mindfulness have a single impact on continuance use intention and fatigue of OHP.

It assists doctors, healthcare administrators, policymakers and technology developers in understanding OHPs’ technological characteristics, enabling them to harness its benefits and mitigate potential challenges. Additionally, given the self-reported cross-sectional data from China, future studies can improve generalizability and adopt experimental methods or longitudinal designs with objective data.

It extends the research on OHP by employing a technology-based JD-R model to explore work attributes and dual effects associated with OHP’s technological characteristics. It also enriches existing research by examining the role of OHP’s technological characteristics in doctors’ psychological and behavioral responses.