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Traditional rapid prototyping (RP) uses one nozzle, with a single‐diameter, to conduct spraying of a RP part. The process is usually time‐consuming and inefficient. To improve the traditional method, this research aims to develop a new approach by planning an adaptive path and applying it to the spraying process of every slice. Each accurately sprayed slice then adds one upon another to form a RP part. The processing includes four steps, which will help to produce the adaptive path file, which can be input into the RP machine to make a prototype.

Implementing the 17 Sustainable Development Goals (SDGs) unarguably demands huge financial investments. However, the United Nations has acknowledged the huge financial gap militating against the implementation of the SDGs worldwide, leading experts to question the possibility of complete implementation of the goals by their terminal dateline of 2030. While the bulk of the finance currently outlaid on the SDGs comes from traditional sources including foreign direct investments (FDIs), there is the need to focus more attention on developing and exploiting impact investments that are more suitable for financing development programmes and projects. In this chapter, the SDG implementation profiles of the 12 Arab West Asia countries concerning the five most targeted SDGs were evaluated and sustainable finance issues were discussed. Secondary data were retrieved from World Bank's DataBank. The data were descriptively analyzed. Based on the profiles generated, debt relief is put forward as a possible impact investment mechanism suitable for funding the SDGs. Specifically, this chapter recommends that outright cancellation of debts based on the debt-for-SGD swap could serve as some of the impact investments needed to boost the global drive for a developed, peaceful, and just world.

The purpose of this study is to investigate consumers' emotional responses to artificial intelligence (AI) defeating people. Meanwhile, the authors investigate the negative spillover effect of AI defeating people on consumers' attitudes toward AI companies. The authors also try to alleviate this spillover effect.

Using four studies to test the hypotheses. In Study 1, the authors use the fine-tuned Bidirectional Encoder Representations from the Transformers algorithm to run a sentiment analysis to investigate how AI defeating people influences consumers' emotions. In Studies 2 to 4, the authors test the effect of AI defeating people on consumers' attitudes, the mediating effect of negative emotions and the moderating effect of different intentions.

The authors find that AI defeating people increases consumers' negative emotions. In terms of downstream consequences, AI defeating people induces a spillover effect on consumers' unfavorable attitudes toward AI companies. Emphasizing the intention of helping people can effectively mitigate this negative spillover effect.

The authors' findings remind governments, policymakers and AI companies to pay attention to the negative effect of AI defeating people and take reasonable steps to alleviate this negative effect. The authors help consumers rationally understand this phenomenon and correctly control and reduce unnecessary negative emotions in the AI era.

This paper is the first study to examine the adverse effects of AI defeating humans. The authors contribute to research on the dark side of AI, the outcomes of competition matches and the method to analyze emotions in user-generated content (UGC).

In management accounting research, the capabilities of Partial Least Squares Structural Equation Modelling (PLS-SEM) have only partially been utilized. These yet unexploited capabilities of PLS-SEM are a useful tool in the often explorative state of research in management accounting. After reviewing eleven top-ranked management accounting journals through the end of 2013, 37 articles in which PLS-SEM is used are identified. These articles are analysed based on multiple relevant criteria to determine the progress in this research area, including the reasons for using PLS-SEM, the characteristics of the data and the models, and model evaluation and reporting. A special focus is placed on the degree of importance of these analysed criteria for the future development of management accounting research. To ensure continued theoretical development in management accounting, this article also offers recommendations to avoid common pitfalls and provides guidance for the advanced use of PLS-SEM in management accounting research.

The purpose of this paper is to investigate how WeChat addiction influences users’ physical, mental, and social health.

A national survey was conducted in China. A total of 1,058 responses were collected from 31 regions of China.

The regression results show that WeChat addiction is negatively associated with users’ physical, mental, and social health. The negative effects are significant even after adjusting for the effects of the Big Five personality traits, years of using WeChat, and demographic variables such as age, gender, education level, and monthly income. Years of using WeChat is not significantly related to users’ health. It is also found that the influence of WeChat addiction on health outcomes is sensitive to years of WeChat use. The influence is dormant when users have less than three years of WeChat usage, but starts to exhibit itself after three years.

Addictive use of WeChat is associated with declining overall health among Chinese users. Given the cross-sectional nature of this study, definite causal relationship between WeChat addiction and health deterioration cannot be established. Controlled experiments are needed to further examine the causal effects of WeChat addiction.

WeChat is the most popular mobile social network service (SNS) in China, but its comprehensive impact on users’ health is rarely studied. This paper extends the extant research on SNS addiction by providing a deepened understanding of how mobile SNS addiction affects personal health in the unique context of WeChat, which provides an important contribution to the interdisciplinary research in public health, psychology, and information systems.

The potential of generative AI (GenAI) to stimulate employee creativity has received extensive attention from industry and academia. However, there is still limited research on strategically using GenAI to leverage its positive effects on employee creativity. This study aims to clarify the effects of different GenAI use purposes on employee creativity.

Based on self-determination theory, this study explores the effects of work-related and nonwork-related GenAI use on incremental and radical creativity through the mediator role of exploratory and exploitative learning and the boundary role of perceived ease of use. This study constructs a theoretical model and uses structural equation modeling to test the model by analyzing survey data from 330 employees.

(1) Work-related and nonwork-related GenAI use positively impacts incremental and radical creativity through exploratory and exploitative learning; (2) work-related GenAI use contributes more to exploitative learning than to exploratory learning, while nonwork-related GenAI use contributes more to exploratory learning than to exploitative learning; (3) exploitative learning has a stronger positive impact on incremental creativity, and exploratory learning has a stronger positive impact on radical creativity; (4) perceived ease of use weakens the positive effects of nonwork-related GenAI use on exploratory and exploitative learning.

First, this study enriches employee creativity research by revealing the relationship between different GenAI use purposes and incremental and radical creativity. Second, this study enriches employee creativity research by revealing the mediating role of exploratory and exploitative learning between GenAI use and incremental and radical creativity. Finally, this study enriches GenAI use research by revealing the moderating role of perceived ease of use between GenAI use and employee learning.

This study aims to explore AI’s potential impact on the broader landscape of higher education in Tanzania. This study contributes to the ongoing discussion of AI’s potential to transform higher education and highlights the ethical considerations and challenges that must be addressed to ensure its successful implementation. This study informs future research and policy decisions in education and technology by providing a detailed understanding of AI’s perceived benefits and challenges in higher education.

A mixed-methods approach was used, which involves collecting and analyzing quantitative and qualitative data to understand the research problem comprehensively. This approach allowed data triangulation and led to a more robust and detailed understanding of this study.

In this study, it was discovered that enhanced assessment, time-saving, personalized learning, improved accessibility and detecting cheating are the perceived benefits of AI as a tool for enhancing learning in higher education, while cost and infrastructure, academic misconduct, data privacy and security, bias and ethical concerns and lack of human interaction are the perceived challenges of AI as a tool for enhancing learning in higher education. Further, it was revealed that students are more accepting of using AI tools in the classroom because they think they are more effective and engaging. On the other hand, faculty were more cautious and skeptical about employing AI tools in the classroom because they worried about how it would affect their teaching methods and job security.

The data collection was not conducted face-to-face. To fully capture respondents’ emotional responses, feelings, facial expressions, reactions, or body language was challenging. However, a sufficient number of individuals who participated were very cooperative, and their knowledge was very beneficial in understanding the topic.

A unique view of this study is a clear understanding of the perceived benefits and challenges of using AI as a tool for enhancing learning in higher education, as well as the variations in these perceptions among students and faculty. By examining the perspectives of both groups, this study provides a comprehensive understanding of the complex role of AI in higher education. Understanding the broader implications of AI in higher education can inform policy decisions and ensure that AI is used responsibly and ethically.

The purpose of this study is to examine the mechanism of benevolent leadership on employee cheating behavior through two paths – employee uncertainty and perceived acceptability of norm violation – and also reveal the possible dark side of benevolent leadership. Meanwhile, the moderating effects of leader behavioral integrity in the cognition dual path process are also discussed.

This study invites 383 employees and their superiors in seven Chinese enterprises as the research objects and conducts a paired survey at three time points, and then Mplus 7.4 software is used to analyze the empirical data.

The results are shown as follows. Benevolent leadership plays a positive role on uncertainty and perceived acceptability of norm violation. Uncertainty and perceived acceptability of norm violation mediate the relationship between benevolent leadership and cheating behavior, respectively. Leader behavioral integrity moderates the positive role of benevolent leadership on uncertainty and perceived acceptability of norm violation. Leader behavioral integrity moderates the indirect effect of benevolent leadership on employees' cheating behavior through uncertainty and perceived acceptability of norm violation.

This study reveals the mechanism behind the negative role of benevolent leadership through the cognition reaction of employees to benevolent leadership and broadens the research scope of benevolent leadership. Meanwhile, it provides some practical inspiration for leaders to effectively use the benevolent leadership style and restrain employees' cheating behavior.

This study examines the serial mediation mechanism between paternalistic leadership and innovative work behavior through the leader–member exchange (LMX) and employee voice behavior. Particularly, this study utilized the social exchange theory to investigate the indirect effect of three distinct dimensions of paternalistic leadership style on innovative work behavior through LMX and employee voice behavior.

Self-reported questionnaires were used to collect data from 397 employees in Pakistan. Hypotheses were tested using structural equation modeling (SEM).

The two dimensions of paternalistic leadership were significantly related to LMX. LMX had a significant effect on employee voice behavior that was further related to innovative work behavior. The findings also support the mediating role of LMX between authoritarian and moral leadership and employee voice. Further, LMX and employee voice boosted the indirect relationship between moral leadership and innovative behavior. However, authoritarian leadership demonstrated a significant but negative indirect effect on innovative behavior through LMX and employee voice.

The organizational members need to encourage a high LMX and voice behavior to enhance the positive effects of benevolent and moral leadership styles on innovative employee behaviors. Contrarily, they need to discourage authoritarian leadership if they want to enhance innovative work behavior through LMX and employee voice. Furthermore, when leaders provide a safe environment to employees at the workplace, then they may feel secure to take risks and exhibit innovative work behavior, which ultimately contributes to increasing employee and organizational performance.

This study extended the existing literature on paternalistic leadership in two important ways. First, this study examined a serial mediation mechanism to test the effect of paternalistic leadership on innovative work behavior through LMX and voice behavior. Second, this is a key study to investigate which dimension of paternalistic leadership is effective to boost employees' innovative work behavior at the individual level in the Pakistani organizational context.