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Industry 5.0 is referred to the subsequent industrialization. The ultimate goal of this transformation is to enable manufacturing solutions through collaboration with man and machine which are more user-friendly and increase work quality in comparison to Industry 4.0. This will be accomplished through the consumption of the creative potential of human specialists in the creation of an industry with more efficient, clever, and precise machines. It is predicted that several exciting breakthroughs and apps will help Industry 5.0 in its plan to gain more productivity and supply personalized goods in an open system. On the other hand, Industry 5.0 has had a greater global and international renown from the very beginning of its existence. Machine learning (ML) technology, the Internet of Things (IoT), and big data will create a collaboration with people, robots, and other intelligent devices. Industry 5.0 continues to serve as an attractive driver for our society's workforce skills and young talent in search of purposeful professional lives. There are some challenges as well, such as working with advanced robots requires people to develop skills. People need to gain proper knowledge about collaboration with smart machines and the robot manufacturers industry. However, this ultimate overhaul is necessary for the industry to certify its reason as a solution provider for our society. These things will unquestionably ensure the long-term sustained development (SD) of any nation's economy.

Service robots offering low-contact services are trending in the hospitality industry. Nevertheless, there is limited empirical research on consumer perspectives. Using the stimulus-organism-response (SOR) model, this study aims to investigate consumer acceptance of service robots and explores the relationships among stimuli (coolness and affinity), organisms (utilitarian and hedonic value) and responses (behavioral intention).

This study uses a purposive sampling method and conducts a questionnaire survey in restaurants with service robots in Taiwan. A total of 554 valid responses are analyzed, and confirmatory factor analysis and structural equation modeling are performed.

The findings reveal that coolness and affinity positively influence utilitarian and hedonic values, whereas utilitarian and hedonic values positively affect behavioral intention. Additionally, these values mediate how coolness and affinity impact behavioral intentions, showing that coolness more significantly influences hedonic value, while affinity has a greater effect on utilitarian value.

As a theoretical basis, the SOR model examines the relationships among stimulus, organism and response. This study comprehensively investigates the factors influencing consumer perception toward service robots, testing the applicability of this theory in the food and beverage industry and expanding its practical value. Additionally, the author introduces the “fit” concept, indicating that, for maximum effect, coolness pairs best with hedonic value and affinity with utilitarian value.

提供低接触服务的服务机器人在酒店业中越来越受欢迎。然而, 关于消费者视角的实证研究仍然有限。本研究采用刺激-有机体-反应（SOR）模型, 调查消费者对服务机器人的接受度, 并探讨刺激（酷炫度和亲和力）、有机体（实用价值和享乐价值）和反应（行为意图）之间的关系。

本研究采用目的性抽样方法, 在台湾有服务机器人的餐厅进行问卷调查。共收集到554份有效问卷, 并进行了确认性因子分析和结构方程模型分析。

研究结果表明, 酷炫度和亲和力对实用价值和享乐价值有正向影响, 而实用价值和享乐价值对行为意图有正向影响。此外, 这些价值在酷炫度和亲和力对行为意图的影响中起中介作用, 表明酷炫度对享乐价值的影响更显著, 而亲和力对实用价值的影响更大。

本研究以SOR模型为理论基础, 检验了刺激、有机体和反应之间的关系。我们全面调查了影响消费者对服务机器人感知的因素, 测试了该理论在餐饮业中的适用性, 并扩展了其实际价值。此外, 我们引入了“匹配”概念, 指出为了达到最大效果, 酷炫度最好与享乐价值搭配, 而亲和力最好与实用价值搭配。

When encountering novel technology, customers often use the term “cool” to express their thoughts; therefore, coolness has become crucial for launching service robots. However, research on the impact mechanism of “coolness” is lacking. This study explored the relationship between delight and behavioral intention regarding the coolness of service robots in the food and beverage industry while discussing the mediating roles of utilitarian and hedonic values.

Questionnaires were distributed online with links to the survey posted on restaurant discussion boards on Facebook and online community platforms such as Dcard. In total, 540 responses were deemed valid. The hypotheses were tested using the partial least squares structural equation modeling method.

The results indicate that coolness positively impacted both utilitarian and hedonic values and that both perceived values positively impacted delight. Moreover, coolness does not directly impact delight but must be mediated by perceived value to be effective.

Increasing customer perceptions of the coolness of service robots is recommended. Moreover, regarding customer revisits, utilitarian value services can delight customers more effectively than hedonic value services.

The stimulus-organism-response model was used to identify the relationships among coolness, perceived value, delight and behavioral intention. Moreover, the authors investigated the impact of coolness on utilitarian and hedonic values. These findings are significant for the development of smart restaurants and provide a critical reference for exploring service robots.

This study aims to propose a law enforcement strategy for investment fraud through comparative studies in the United States of America (USA), Canada and Indonesia, and to identify the factors that cause weak law enforcement on investment fraud with the object of a binary options case study in Indonesia.

This research is a type of legal research, namely, research based on legal materials (library-based). The legal materials used include primary legal materials and secondary legal materials. The approaches used are the statute approach, the case approach and the comparative approach. The data collection technique used in this research is a literature study. The analysis was carried out qualitatively by using an interactive model.

In 2022, the Indonesian Financial Services Authority (OJK) recorded that the total value of public losses because of investment fraud in Indonesia reached 117.4tn IDR. Weak law enforcement is the reason investment fraud thrives in society. Strategies that can be implemented to prevent investment fraud include early detection of new investment fraud modes through the whistleblower program, mutual legal assistance in criminal matters, criminal restitution and improvement of public financial literacy.

This study examines the problems of law enforcement against investment fraud with a case study of binary options in Indonesia. A law enforcement strategy is built on identifying issues and adopting law enforcement policies against investment fraud in Canada and the USA.

For individuals, the results of this research can be used as reading material to increase their understanding of investment fraud. For the government, the results of this study can be a reference in an effort to eradicate the rise of investment fraud cases more effectively and create a safe digital economic space for investors.

The results of this study are expected to be useful in providing recommendations for strategies to strengthen law enforcement against the problems of investment fraud cases so as to form a conducive investment climate in the sense of being safe, comfortable and profitable.

Legal frameworks to prevent investment fraud are rarely discussed. The rise in binary options cases that occur is an indication of weak law enforcement in the investment sector. Therefore, an in-depth study of law enforcement strategies to prevent investment fraud is needed, with comparative studies in the USA, Canada and Indonesia.

The purpose of this research is to propose and empirically validate a theoretical framework to investigate the willingness of the elderly to disclose personal health information (PHI) to improve the operational efficiency of AI-integrated caregiver robots.

Drawing upon Privacy Calculus Theory (PCT) and the Technology Acceptance Model (TAM), 274 usable responses were collected through an online survey.

Empirical results reveal that trust, privacy concerns, and social isolation have a direct impact on the willingness to disclose PHI. Perceived ease of use (PEOU), perceived usefulness (PU), social isolation, and recognized benefits significantly influence user trust. Conversely, elderly individuals with pronounced privacy concerns are less inclined to disclose PHI when using AI-enabled caregiver robots.

Given the pressing need for AI-enabled caregiver robots due to the aging population and a decrease in professional human caregivers, understanding factors that influence the elderly's disclosure of PHI can guide design considerations and policymaking.

Considering the increased demand for accurate and comprehensive elder services, this is the first time that information disclosure and AI-enabled caregiver robot technologies have been combined in the field of healthcare management. This study bridges the gap between the necessity for technological improvement in caregiver robots and the importance of transparent operational information by disclosing the elderly's willingness to share PHI.

The study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for predicting stock return movement in Indonesia.

The study uses multilayer perceptron (MLP) analysis, a deep learning model subset of the ML method. The model utilizes findings from conventional accounting studies from 2019 to 2021 and samples from 10 firms in the Indonesian stock market from September 2018 to August 2019.

Incorporating RFVs improves predictive accuracy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs.

The study uses Indonesian firms as its sample. We believe our findings apply to other emerging Asian markets and add to the existing ML literature on stock prediction. Nevertheless, expanding to different samples could strengthen the results of this study.

Governments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, practitioners, analysts and investors can be inspired to develop RFV-based AI tools.

Studies in the literature on ML-based stock prediction find limited use for fundamental values and mainly apply technical indicators. However, this study demonstrates that including RFV in the ML model improves investors’ decision-making and minimizes unethical data use and artificial intelligence-based fraud.

Financial institutions actively seek to leverage the capabilities of artificial intelligence (AI) across diverse operations in the field. Especially, the adoption of AI advisors has a significant impact on trading and investing in the stock market. The purpose of this paper is to test whether AI advisors are less preferred compared to human advisors for investing and whether this algorithm aversion diminishes for trading.

The four hypotheses regarding the direct and indirect relationships between variables are tested in five experiments that collect data from Prolific.

The results of the five experiments reveal that, for investing, consumers are less likely to use AI advisors in comparison to human advisors. However, this reluctance to AI advisors decreases for trading. The author identifies the perceived importance of careful decision-making for investing and trading as the psychological mechanism. Specifically, the greater emphasis on careful decision-making in investing, as compared to trading, leads to consumers’ tendency to avoid AI advisors.

This research is the first to investigate whether algorithm aversion varies based on whether one’s approach to the stock market is investing or trading. Furthermore, it contributes to the literature on carefulness by exploring the interaction between a stock market approach and the lay belief that algorithms lack the capability to deliberate carefully.

In today's advanced economy, there is a broader presence of information revolution, such as artificial intelligence (AI). AI primarily drives modern banking, leading to innovative banking channels, services and solutions disruptions. Thus, this chapter intends to determine AI's place in contemporary banking and stock market trading.

Stock market forecasting is hampered by the inherently noisy environments and significant volatility surrounding market trends. There needs to be more research on the mantle of AI in revolutionising banking and stock market trading. Attempting to bridge this gap, the present research study looks at the function of AI in banking and stock market trading.

The researchers have synthesised the literature pool. They undertook a systematic review and meta-synthesis method by identifying the major themes and a systematic literature review aided in the critical analysis, synthesis and mapping of the body of existing material.

The study's conclusions demonstrated the efficacy of AI, which has played a robust role in banking and finance by reducing risk and operational costs, enabling better customer experience, improving regulatory complaints and fraud detection and improving credit and loan decisions. AI has revolutionised stock market trading by forecasting future prices or trends in financial assets, optimising financial portfolios and analysing news or social media comments on the assets or firms.

AI's debut in banking and finance has brought sea changes in banking and stock market trading. AI in the banking industry and capital market can provide timely and apt information to its customers and customise the products as per their requirements.

Robots are widely used in industrial manufacturing and service industries around the world. However, most of the previous studies on industrial robots use data at the national or industry level in the context of developed countries. This study examines the impact of imported industrial robots on firm innovation at the firm level in China.

Drawing on a large dataset of more than three million records in China, including non-publicly traded small and medium firms, the authors adopt a difference-in-differences method to investigate the impact and channels of industrial robots on firm innovation.

The authors find that the application of industrial robots increases firm innovation. Two possible channels are identified through which robots promote innovation: alleviation of financial constraints and the improvement of human capital. Further analysis shows that the effect of robots on innovation is more pronounced for firms that are highly dependent on external financing, belong to high-tech industries, import high-end robots, have insufficient supply of skilled labor and private firms (non-SOEs). The authors also find that industrial robots increase the firms' innovation quality and the marginal contribution of innovation to firms' total factor productivity.

This study provides big data evidence of the unintended positive consequences of industrial robots on firm innovation. The results are helpful to clarify the controversy of industrial robots. It also has important implications for government industrial policy making, firm innovation and human resource management.

A new round of technological revolution is impacting various aspects of society. However, the importance of technology adoption in fostering firm innovation is underexplored. Therefore, this study aims to investigate whether robot adoption affects technological innovation and how human capital plays a role in this relationship in the era of circular economy.

Based on the robot adoption data from the International Federation of Robotics (IFR) and panel data of China's listed manufacturing firms from 2011 to 2020, this study uses regression models to test the impact of industrial robots on firm innovation and the mediating role of human capital.

The results demonstrate that the adoption of industrial robots can significantly promote high-quality innovation. Specifically, a one-unit increase in the number of robots per 100 employees is associated with a 13.52% increase in the number of invention patent applications in the following year. The mechanism tests show that industrial robots drive firm innovation by accumulating more highly educated workers and allocating more workers to R&D jobs. The findings are more significant for firms in industries with low market concentration, in labor-intensive industries and in regions with a shortage of high-end talent.

Due to data limitations, the sample of this study is limited to listed manufacturing firms, so the impact of industrial robots on promoting innovation may be underestimated. In addition, this study cannot observe the dynamic process of human capital management by firms after adopting robots.

The Chinese government should continue to promote the intelligent upgrading of the manufacturing industry and facilitate the promotion of robots in innovation. This implication can also be applied to developing countries that hope to learn from China's experience. In addition, this study emphasizes the role of human capital in the innovation-promoting process of robots. This highlights the importance of firms to strengthen employee education and training.

The adoption of industrial robots has profoundly influenced the production and lifestyle of human society. This study finds that the adoption of robots contributes to firm innovation, which helps people gain a deeper understanding of the positive impacts brought about by industrial intelligence.

By exploring the impact of industrial robots on firm innovation, this study offers crucial evidence at the firm level to comprehend the economic implications of robot adoption based on circular economy and human perspectives. Moreover, this study reveals that human capital is an important factor in how industrial robots affect firm innovation, providing an important complement to previous studies.