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This study explores the influence of cultural heritage tourism perception on China's tourism image. It analyzes the role of the spiritual bond established between overseas Chinese youth and the motherland during their visit to the cultural heritage sites in China. This study constructs a theoretical model with 350 overseas Chinese youth as samples based on the identity theory, Stimulus-Organism-Response (S-O-R) theory, and Howard-Sheth model. The results show that cultural heritage tourism perception directly and positively promotes cultural identity among overseas Chinese youth. It is also indirectly and positively associated with their cultural identity through enhancing the tourism image. Cultural intelligence plays a positive moderating role between cultural heritage tourism perception and cultural identity. The results provide significant implications for developing cultural heritage tourism and cultural communication.

Artificial intelligence-based chatbots are frequently used to handle customer complaints in the hospitality and tourism sectors; however, little is known about their recovery strategies. Further, the widespread usage of chatbots is anticipated to affect customers' favorable responses. Therefore, this study aims to examine how chatbots’ symbolic recovery influences customer forgiveness through customer empathy and explore the moderating effect of time pressure on it. Moreover, it investigates the effect of customer forgiveness on customer reconciliation and customer continuous trust.

Structural equation modeling was used to analyze data collected from 994 customers who have experienced chatbot recovery in tourism and hospitality during the past four months.

The results show that chatbots’ symbolic recovery stimulates customer forgiveness, which subsequently positively affects customer reconciliation and customer continuous trust. Moreover, customer empathy partially mediates the effect of chatbots’ symbolic recovery on customer forgiveness, and time pressure plays a moderating role in the relationship between chatbots’ symbolic recovery and customer forgiveness.

The results offer highly persuasive insights that may be used to promote chatbots’ symbolic recovery in tourism organizations. The effectiveness of chatbots’ symbolic recovery in achieving customer forgiveness will motivate tourism organizations to use chatbots efficiently in service recovery.

This study extends the theoretical scope of chatbot research by investigating the symbolic recovery capabilities of chatbots. Moreover, it expands the application of SOR theory in the context of chatbot service recovery and reveals the underlying mechanism behind the impact of chatbots’ symbolic recovery on customer forgiveness, thus building and testing an integrative model of chatbot service recovery.

系统评估阻碍因素作为技术接受模型（TAM）的先驱方面存在一定的空白。本研究调查了三个阻碍因素, 即不适感、不安全感和风险。此外, 本研究提出了调节变量 - 个人能力（PC）, 并测试其对感知有用性（PU）、感知易用性（PEU）和行为意图（BI）之间关系的影响。

使用量化数据分析验证了通过Smart PLS4使用的调整模型。对327名有效受访者的数据进行了分析。

不适感是影响PU和PEU的显著先驱因素。不安全感和风险分别是PEU和PU的抑制因素。本研究在稀缺文献中贡献了个人能力的调节效应, 积极调节PU和BI之间影响的研究。

本研究通过纳入阻碍因素并探索个人能力在AR眼镜方面的调节作用, 为TAM提供了一种新的拓展。此外, 该研究还使创新公司能够通过用户的反馈来增强其产品和服务的设计。

The purpose of this paper is to identify the dimensions and formation mechanisms of brand value on social media platforms within the prosumption logic based on the theory of value co-creation. By adopting a process-oriented mindset, this study deconstructs and applies the prosumption theory and the theory of value co-creation in the field of social media, thereby addressing the insufficiencies of previous research that focused primarily on the subject mindset. It offers reference ideas for social media brand managers to attract prosumers to engage in value co-creation.

The crawler technology and grounded theoretical method were adopted in this research paper.

Based on the logic of prosumption and the theory of value co-creation, the research finds that the brand value of social media platforms is composed of seven dimensions. The dimensions include brand recognition, brand perception quality, brand experience, brand value-in-use, brand relationship quality, brand loyalty and brand co-creation behavior. From the perspective of prosumption logic, the formation of brand value on social media platforms is a gradual accumulation process. This process involves prosumers participating in prosumption activities, progressing through several stages. Initially, it starts with “prosumption conditions”, which include brand recognition, brand perception quality and brand experience. It then moves to “prosumption processes,” characterized by brand value-in-use and brand relationship quality. Finally, it culminates in “prosumption outcomes,” represented by brand loyalty and brand co-creation behavior. From the perspective of value creation, the formation of brand value on social media platforms is a closed-loop process that includes “brand value identification and empowerment—brand value acquisition and transformation—brand value co-creation and relationship upgradation—brand value co-creation and stability—brand value feedback and iteration.”

The findings contribute to expanding prosumption and co-creation theory and enriching the prosumption logic frame. Meanwhile, it is conducive to encouraging prosumers to participate in the platform’s prosumption activities and jointly creating the brand value of the social media platform. This paper interprets prosumption through the lens of the value co-creation process.

The focus of industrial cluster innovation lies in the cooperation between enterprises and universities/scientific research institutes to make a theoretical breakthrough in the system and mechanism of industrial cluster network. Under the theoretical framework of cluster network, industrial structure can be optimized and upgraded, and enterprise benefit can be improved. Facing the increasing proliferation and multi-structured enterprise data, how to obtain potential and high-quality innovation features will determine the ability of industrial cluster network innovation, as well as the paper aims to discuss these issues.

Based on complex network theory and machine learning method, this paper constructs the structure of “three-layer coupling network” (TLCN), predicts the innovation features of industrial clusters and focuses on the theoretical basis of industrial cluster network innovation model. This paper comprehensively uses intelligent information processing technologies such as network parameters and neural network to predict and analyze the industrial cluster data.

From the analysis of the experimental results, the authors obtain five innovative features (policy strength, cooperation, research and development investment, centrality and geographical position) that help to improve the ability of industrial clusters, and give corresponding optimization strategy suggestions according to the result analysis.

Building a TLCN structure of industrial clusters. Exploring the innovation features of industrial clusters. Establishing the analysis paradigm of machine learning method to predict the innovation features of industrial clusters.

Although the previous generations of the Industrial Revolution have brought many advantages to human life, scientists have been looking for a substantial breakthrough in creating technologies that can improve the quality of human life. Nowadays, we can make our factories smarter using new concepts and tools like real-time self-optimization. This study aims to take a step towards implementing key features of smart manufacturing including  preventive self-maintenance, self-scheduling and real-time decision-making.

A new bi-objective mathematical model based on Industry 4.0 to schedule received customer orders, which minimizes both the total earliness and tardiness of orders and the probability of machine failure in smart manufacturing, was presented. Moreover, four meta-heuristics, namely, the multi-objective Archimedes optimization algorithm (MOAOA), NSGA-III, multi-objective simulated annealing (MOSA) and hybrid multi-objective Archimedes optimization algorithm and non-dominated sorting genetic algorithm-III (HMOAOANSGA-III) were implemented to solve the problem. To compare the performance of meta-heuristics, some examples and metrics were presumed and solved by using the algorithms, and the performance and validation of meta-heuristics were analyzed.

The results of the procedure and a mathematical model based on Industry 4.0 policies showed that a machine performed the self-optimizing process of production scheduling and followed a preventive self-maintenance policy in real-time situations. The results of TOPSIS showed that the performances of the HMOAOANSGA-III were better in most problems. Moreover, the performance of the MOSA outweighed the performance of the MOAOA, NSGA-III and HMOAOANSGA-III if we only considered the computational times of algorithms. However, the convergence of solutions associated with the MOAOA and HMOAOANSGA-III was better than those of the NSGA-III and MOSA.

In this study, a scheduling model considering a kind of Industry 4.0 policy was defined, and a novel approach was presented, thereby performing the preventive self-maintenance and self-scheduling by every single machine. This new approach was introduced to integrate the order scheduling system using a real-time decision-making method. A new multi-objective meta-heuristic algorithm, namely, HMOAOANSGA-III, was proposed. Moreover, the crowding-distance-quality-based approach was presented to identify the best solution from the frontier, and in addition to improving the crowding-distance approach, the quality of the solutions was also considered.

This study aims to explore the influence of academic service quality on student satisfaction and loyalty, based on a study at Higher Education Legal Entities (HELE) in Indonesia. HELE is a policy in higher education that empowers these institutions to become autonomous in management. Academic service quality (ASQ) is an essential aspect of fostering the satisfaction and loyalty of students.

This study uses the descriptive quantitative and regressive survey approach to examine the influence of academic service quality on student satisfaction and loyalty. The population in this study encompassed all students from HELE. Meanwhile, 750 students were purposefully selected from 16 HELE in Indonesia. Data were collected using validated and reliable questionnaires. The garnered data were analyzed using a structural equation model through Smart PLS 3.

This study finds that (1) there is a significant direct influence of academic service quality on student satisfaction in HELE, (2) there is a reasonably direct influence of student satisfaction on student loyalty in HELE. (3) ASQ of HELE has a feeble direct influence on student loyalty and (4) there is a moderate indirect impact of academic service quality on student loyalty, mediated by student satisfaction at HELE in Indonesia.

The research was conducted across 16 HELEs in Indonesia. Many other universities do services and of course impact on satisfaction and loyalty for students, but are not included in this research. Besides that, other aspects are not included in this research either. Based on that condition, this result cannot cover all aspects of others in all universities in Indonesia.

The practical implication of this study’s results for the leaders of HELES is to commit to student-centered, service-oriented tactics and actions for establishing student loyalty. Besides, these institutions must prioritize developing and maintaining profitable customer relationships in this changing world and offer an agenda for the future research.

The social implication will be indicated by higher universities to always improve the service quality of academics, with continuous improvement in the future. It has to do with every university so that student satisfaction improves, and student loyalty improves too. Exiting HELE in the future depends on the variables of service quality for stakeholders in the community around. If the community gets satisfaction, in the future, they will be back to support the HELE in the future.

The results of this study are original and novel, and strengthen the findings of previous research that there is an effect of academic service quality on student satisfaction. It confirms the effects of satisfaction on loyalty, along with the effects of academic service quality on loyalty mediated by satisfaction. These results rejected previous results that there are effects of academic service quality on student loyalty in HELE as organizations.

This paper aims to investigate the impact of gender diversity in the composition of engagement auditors on the disclosure of key audit matters (KAMs) in a dual-signature environment.

The authors used the unique institutional setup of Taiwan, where the law requires that audit reports be signed by two audit partners. The authors examined the effect of gender diversity composition among engagement auditors on KAM disclosure, considering behavioral differences between female and male auditors.

The empirical results indicate that gender diversity composition in the dual-signature environment is associated with the number of disclosed KAM items (KAMIT) and the length of the explanations for each KAMIT. Furthermore, the authors found that gender diversity composition, particularly when led by female audit partners, has a more pronounced impact on the explanation of each KAMIT rather than on the disclosure of KAMIT. The authors also noted that the moderating effect of audit firm specialization does not influence the gender diversity composition of audit partners in disclosing KAMs.

This study’s empirical findings demonstrate that the interaction between different gender compositions in a dual-signature environment influences KAM disclosure.

Cybersecurity competitions can effectively develop skills, but engaging a wide learner spectrum is challenging. This study aims to investigate the perceptions of cybersecurity competitions among Reddit users. These users constitute a substantial demographic of young individuals, often participating in communities oriented towards college students or cybersecurity enthusiasts. The authors specifically focus on novice learners who showed an interest in cybersecurity but have not participated in competitions. By understanding their views and concerns, the authors aim to devise strategies to encourage their continuous involvement in cybersecurity learning. The Reddit platform provides unique access to this significant demographic, contributing to enhancing and diversifying the cybersecurity workforce.

The authors propose to mine Reddit posts for information about learners’ attitudes, interests and experiences with cybersecurity competitions. To mine Reddit posts, the authors developed a text mining approach that integrates computational text mining and qualitative content analysis techniques, and the authors discussed the advantages of the integrated approach.

The authors' text mining approach was successful in extracting the major themes from the collected posts. The authors found that motivated learners would want to form a strategic way to facilitate their learning. In addition, hope and fear collide, which exposes the learners’ interests and challenges.

The authors discussed the findings to provide education and training experts with a thorough understanding of novice learners, allowing them to engage them in the cybersecurity industry.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

Despite a growing body of research focusing on the dark side of sharing economy development, arguments are fragmented and incomplete. This study aims to address the gap by integrating existing viewpoints based on a provider’s perspective.

This study conducted a bibliometric analysis using text mining and clustering algorithm techniques to measure the scope of scientific output on this topic and identify the main research themes.

Through the bibliometric analysis, this study developed an integrative framework based on the platform providers’ internal management issues and external conflicts with consumers, society, government regulations and traditional business. It also identified significant gaps within each research theme and proposed a future research agenda.

Sharing economy development has not yet been fully understood and regulated, leading to unprecedented challenges to existing business systems. The study addresses knowledge gaps and advances the understanding of the dark side of the sharing economy based on the provider’s internal management and interplay with external forces. It offers a roadmap for future research to advance understanding of the “hidden” dark side of the sharing economy.