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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.

Competition in the modern, knowledge-based economy is utterly pendant on innovation, rendering it indispensable in virtually every organisation. Knowledge workers, therefore, must remain vigilant, spanning novel ways to innovate. Given the relevance of innovation orientation (IO) in knowledge work, it is imperative to possess an extensive understanding of the concept. Therefore, this study aims to develop and validate a measurement scale to gauge employees’ IO.

Considering that the instruments now in existence exhibit insufficiency for measuring knowledge workers’ IO in its entirety, the mixed-method approach used in this study draws on both qualitative and quantitative findings across various studies, to address this problem. This study has been organised into five stages: item generation, scale purification, scale refinement, nomological validation and generalizability.

This study establishes and verifies a second-order, reflective–reflective IO measure founded on multiple samples, encompassing the dimensions of creative orientation, learning orientation, first-mover orientation, trust orientation and agility orientation. The resultant IO scale serves as a robust and reliable tool that is capable of being leveraged to explain, assess and enhance IO for knowledge workers.

The rigorous methodology used in this scale development procedure serves as a benchmark for prospective scale development methodologists. From a managerial stance, this study serves managers/leaders concerning how to foster an innovation-oriented work environment to uncover employees’ hidden innovators. Organisations can leverage this study to discover, cultivate and capitalise on knowledge workers’ IO.

Although there exists an abundance of research on IO viewed from an institutional standpoint, research centred on the IO of knowledge workers is scarce. To bridge this gap, this study has developed and validated a scale for measuring knowledge workers’ IO.

The research aims to explore the influence mechanism of peer feedback and users' knowledge contribution behavior. This study draws on the social identity theory and considers social identity as a mediating factor into the research framework.

This paper collected users' activity data of 142,191 ideas submitted by 76,647 users from the MIUI community between October 2010 and May 2018 via Python software, and data were processed using Stata 16.0.

The results indicate that knowledge feedback and social feedback positively influence users' knowledge contribution (quantity and quality), respectively. User's cognitive identity positively mediates the relationship between peer feedback and knowledge contribution behavior, affective identity positively mediates the relationship between peer feedback and knowledge contribution behavior, while evaluative identity positively mediates the relationship between peer feedback and knowledge contribution quality, but there is no mediating effect between peer feedback and knowledge contribution quantity.

This study advances knowledge management by highlighting peer feedback on online innovation communities. By demonstrating the significant mediating effect of social identity, this study empirically clarifies the relationships of peer feedback (knowledge feedback and social feedback) to specific dimensions of knowledge contribution, thereby providing managerial guidance to the online innovation community on incentivizing and managing user interaction to foster the innovation development of firms.

Compared to traditional (domestic) e-commerce consumers, cross-border electronic commerce (CBEC) consumers may face greater information asymmetry in the CBEC purchase process. Given this background, however, the literature has paid limited attention to the informational antecedents that influence consumers' perceptions of transaction costs and their CBEC purchase intentions. To fill this gap, this study integrates the elaboration likelihood model (ELM) and transaction cost theory (TCT) to develop a model for exploring how product (website informativeness, product diagnosticity and website interactivity as the central route) and external (country brand, website policy and vendor reputation as the peripheral route) informational antecedents affect consumers’ evaluations of transaction costs in terms of uncertainty and asset specificity and their CBEC purchase intentions.

This study employs a survey approach to validate the model with 766 Generation Z CBEC consumers based on judgment sampling. The partial least squares (PLS) technique is adopted for data analysis.

The results show that all the proposed central and peripheral informational antecedents reduce consumers’ perceptions of uncertainty and asset specificity, which in turn negatively influences their CBEC purchase intentions.

Through this investigation, this study increases our understanding of how product and external informational antecedents affect consumers’ evaluations of transaction costs, which subsequently determine their CBEC purchase decisions. This study offers theoretical contributions to existing CBEC research and has practical implications for CBEC organizations and managers.

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.

With the widespread collection and utilization of user data, privacy security has become a crucial factor influencing online engagement. In response to the growing concern about privacy security issues on social media, this research aims to examine the key causes of social media users' privacy calculus and how the balance between perceived privacy risks and benefits affects users' privacy concerns and their subsequent willingness to disclose personal information.

The characteristics of the privacy calculus were extracted through partially structured interviews. A research model derived from privacy calculus theory was constructed, and latent variable modeling was employed to validate the proposed hypotheses.

Information sensitivity, experiences of privacy violations, social influence and the effectiveness of privacy policies influence users' privacy calculus. Privacy risk positively influences privacy concerns. Personal information disclosure willingness is positively influenced by privacy benefits and negatively influenced by privacy concerns, with both paths moderated by social media identification.

This study explores the key antecedents of users' privacy calculus and how these factors influence privacy concerns and subsequent willingness to disclose information on social media. It offers new insights into the privacy paradox observed within social media by validating the moderating role of social media identification on users' information disclosure willingness.

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.

The purpose of this study is to explore the motivational role of feedback information (positive and negative) provided by the firm in the face of participant heterogeneity, in terms of past success experience, under the research setting of crowdsourcing contests.

Taking insights from feedback studies and the dynamics of self-regulation theory, four theoretical hypotheses are proposed. An integrated dataset of 4,880 contest-participant pairs, which is obtained from an online contest platform and a survey, is empirically analyzed.

Empirical analysis shows that both positive feedback and negative feedback are able to stimulate the inner needs of participants. Notably, negative (positive) feedback becomes more (less) effective in intrinsically motivating crowds as they gain more successful experience during contest participation.

This study brings some new knowledge for the intrinsic motivation of crowds by exploring its antecedents, which have been undervalued in extant literature. The motivational role of feedback information is particularly explored.

Endurance time is an important factor limiting the progress of flapping-wing aircraft. In this study, this paper developed a prototype of a double-wing flapping-wing micro air vehicle (FMAV) that mimics insect-scale flapping wing for flight. Besides, novel methods for optimal selection of motor, wing length and battery to achieve prolonged endurance are proposed. The purpose of this study is increasing the flight time of double-wing FMAV by optimizing the flapping mechanism, wings, power sources, and energy sources.

The 20.4 g FMAV prototype with wingspan of 21.5 cm used an incomplete gear flapping wing mechanism. The motor parameters related to the lift-to-power ratio of the prototype were first identified and analyzed, then theoretical analysis was conducted to analyze the impact of wing length and flapping frequency on the lift-to-power ratio, followed by practical testing to validate the theoretical findings. After that, analysis and testing examined the impact of battery energy density and efficiency on endurance. Finally, the prototype’s endurance duration was calculated and tested.

The incomplete gear facilitated 180° symmetric flapping. The motor torque constant showed a positive correlation with the prototype’s lift-to-power ratio. It was also found that the prototype achieved the best lift-to-power ratio when using 100 mm wings.

A gear-driven flapping mechanism was designed, capable of smoothly achieving 180° symmetric flapping. Besides, factors affecting long-duration flight – motor, wings and battery – were identified and a theoretical flight duration analysis method was developed. The experimental result proves that the FMAV could achieve the longest hovering time of 705 s, outperforming other existing research on double-wing FMAV for improving endurance.

This study aims to conduct a comprehensive analysis of the current body of existing literature on chatbots and online shopping. Additionally, this study identifies and emphasize the future research agenda and emerging trends within this domain.

A thorough investigation was conducted on a set of 147 publications sourced from the Scopus database spanning the years 2016 to 2023 by using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis methodology. The analysis included bibliometric techniques through VOSviewer, including science mapping and performance analysis of the literature under investigation.

The findings of the study indicate a systematic impression of prevailing scientific research on integration of Chatbot in online shopping. A majority of publications were contributed by developing countries specifically Asian regions. There has been a notable rise in research collaborations over the course of time. Further, themes were identified through keyword co-occurrence for exploration of future trends in the domain.

This study identifies and analyzes the patterns in the existing literature on chatbot and online shopping, with the objective of enhancing e-retailers comprehension of this particular topic area. The research findings hold significance for both researchers and organizations in their efforts to enhance strategy design.

This study uses bibliometric analysis to examine the literature on chatbots and online shopping, aiming to develop a systematic comprehension of the research field. This study makes a valuable contribution to the current scholarly discourse and provides support for future scholars in their investigations.