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This research addressed online customer-to-customer (C2C) incivility during digital service recovery.

To examine the effectiveness of managerial responses to online C2C incivility post a restaurant service failure, a 2 (Managerial response: general vs specific) x 2 (Failure severity: high vs low) quasi-experimental design was employed. A pretest was conducted with 123 restaurant consumers via Amazon Mechanical Turk, followed by a main study with 174 restaurant consumers. Taking a mixed-method approach, this research first asked open-ended questions to explore how participants perceived the restaurant’s motivation for providing a generic versus a specific response. Hayes’ (2013) PROCESS procedure was then performed for hypotheses testing.

The results revealed significant interaction effects of managerial responses and failure severity on perceived online service climate and revisit intention, mediated by trust with managerial responses.

This research yielded unique insight into C2C incivility management literature and industry practices in the context of digital customer service recovery.

The objective of this study is to utilize empirical research and analysis to examine the coupling coordination relationship between new quality productivity and higher vocational education sustainable development.

To this end, an evaluation index system for the new quality productivity and higher vocational education sustainable development was constructed. The panel data of 30 Chinese provinces from 2016 to 2022 were then analyzed using the entropy method, the coupling coordination degree model, the Tobit regression model and Dagum’s Gini coefficient.

The findings indicate that the coupling coordination degree of new quality productivity and higher vocational education sustainable development exhibited an upward trend, though significant regional disparities were observed, with the highest coupling coordination degree recorded in the eastern region and the lowest in the northeastern region.

The study’s findings further suggest that the three factors of technological innovation level, rationalization of industrial structure and advanced industrial structure have a significant positive influence on the coupling coordination degree, while the level of government intervention has a significant negative influence on the Coupling Coordination Degree. The study posits that augmenting policy support, optimizing the government’s role, reinforcing the drive for technological innovation, and enhancing regional cooperation and exchange are imperative to foster high-quality development of the integration of industry and education between new quality productivity and higher vocational education.

The primary purpose of this study is to analyze the effects of customer incivility on employees' negative emotions (i.e. anger, fear and sadness) considering the moderating role of organizational power distance.

A survey sample comprising 312 service employees was collected from 51 Taiwanese and Vietnamese companies spanning different industries. Given the multilevel characteristics of the data structure, hierarchical linear modeling was used to rigorously test the proposed hypotheses.

The results indicate a significant contribution of customer incivility to employees' negative emotions. Notably, this impact is more pronounced among employees in organizations characterized by low power distance compared to those in organizations with high power distance.

This research significantly advances our understanding of the emotional repercussions of customer incivility on employees by integrating cognitive–motivational–relational theory and organizational culture perspectives. The findings not only provide valuable theoretical insights but also offer practical implications for effectively managing employee well-being in culturally diverse contexts. The study recognizes certain limitations and puts forth suggestions for future research directions.

Preservice teacher (PST) professional development programs are crucial for cultivating high-quality STEAM teachers of the future, significantly impacting the quality of regional STEAM education. The Guangdong-Hong Kong-Macao Greater Bay Area, as a region of cross-border cooperation, integrates the resources and advantages of Guangdong, Hong Kong, and Macao, possessing rich cultural heritage and innovative capabilities. Transdisciplinary Education for Cultural Inheritance (C-STEAM) is an effective approach to promoting educational collaboration within the Greater Bay Area, facilitating the integration of both technological and humanities education. This study aims to develop a Technology-Enabled University-School-Enterprise (T-USE) collaborative education model and implement it in the Greater Bay Area, to explore its role as a support mechanism in professional development and its impact on C-STEAM PSTs' professional capital.

Adopting a qualitative methodology, the study interviewed PSTs who participated in a C-STEAM teacher education course under the T-USE model. Thematic coding is used to analyze their knowledge acquisition, interaction benefits with community members, and autonomous thinking and decision-making in theoretical learning and teaching practice.

The findings show that the T-USE model significantly enhanced the PSTs' human capital, including teaching beliefs, knowledge, and skills. In terms of social capital, PSTs benefited from collaboration with PST groups, university teaching teams, in-service teachers, and enterprises, though challenges such as varying levels of expertise among in-service teachers and occasional technical instability emerged. For decisional capital, the T-USE model provided opportunities for autonomous thinking and promoted teaching judgment skills through real teaching challenges and scenarios. Reflective practice activities also supported PSTs' professional growth.

This study reveals the effectiveness and internal mechanism of the T-USE model in C-STEAM PST training, offering significant theoretical and practical references for future PST education.

A new Chatbot system is implemented to provide both voice-based and textual-based communication to address student queries without any delay. Initially, the input texts are gathered from the chat and then the gathered text is fed to pre-processing techniques like tokenization, stemming of words and removal of stop words. Then, the pre-processed data are given to the Natural Learning Process (NLP) for extracting the features, where the XLnet and Bidirectional Encoder Representations from Transformers (BERT) are utilized to extract the features. From these extracted features, the target-based fused feature pools are obtained. Then, the intent detection is carried out to extract the answers related to the user queries via Enhanced 1D-Convolutional Neural Networks with Long Short Term Memory (E1DCNN-LSTM) where the parameters are optimized using Position Averaging of Binary Emperor Penguin Optimizer with Colony Predation Algorithm (PA-BEPOCPA). Finally, the answers are extracted based on the intent of a particular student’s teaching materials like video, image or text. The implementation results are analyzed through different recently developed Chatbot detection models to validate the effectiveness of the newly developed model.

A smart model for the NLP is developed to help education-related institutions for an easy way of interaction between students and teachers with high prediction of accurate data for the given query. This research work aims to design a new educational Chatbot to assist the teaching-learning process with the NLP. The input data are gathered from the user through chats and given to the pre-processing stage, where tokenization, steaming of words and removal of stop words are used. The output data from the pre-processing stage is given to the feature extraction phase where XLnet and BERT are used. In this feature extraction, the optimal features are extracted using hybrid PA-BEPOCPA to maximize the correlation coefficient. The features from XLnet and features from BERT were given to target-based features fused pool to produce optimal features. Here, the best features are optimally selected using developed PA-BEPOCPA for maximizing the correlation among coefficients. The output of selected features is given to E1DCNN-LSTM for implementation of educational Chatbot with high accuracy and precision.

The investigation result shows that the implemented model achieves maximum accuracy of 57% more than Bidirectional long short-term memory (BiLSTM), 58% more than One Dimansional Convolutional Neural Network (1DCNN), 59% more than LSTM and 62% more than Ensemble for the given dataset.

The prediction accuracy was high in this proposed deep learning-based educational Chatbot system when compared with various baseline works.

This study aims to examine how big data adoption (BA) helps to improve innovation capability, supply chain integration, resilience and organizational performance through direct and mediating mechanisms.

This study uses a combination of meta-analytic approaches (meta-structural equation modeling and meta-regression) using 205 effect sizes from 76 prior empirical studies. It leverages the organization information processing theory as a theoretical lens to analyze the proposed relationships. This study estimates heterogeneity in the relationship between BA and innovation capability based on the meta-regression by considering different types of moderators: digital competitiveness score (DCS), national culture, type of economies and gross domestic product (GDP) per capita.

The findings indicate that BA improves the innovation capability of the organization, supply chain integration and resilience, which consequently drives organizational performance. The results show that the innovation capability mediating effect is higher between BA and supply chain integration than between BA and supply chain resilience link. However, supply chain resilience and integration are equally effective in translating innovation capability influence to organizational performance. The authors find that developing countries reap more benefits from BA in driving innovation, and country culture plays a vital role in driving innovations.

This study offers multiple theoretical implications. First, deriving from organization information processing theory, the authors recognized that BA and innovation capability complement each other, which improves the information processing capacity of the organizations, enabling supply chain integration, resilience and organizational performance (Bahrami et al., 2022; Gupta et al., 2020; Chatterjee et al., 2022). This study is one of few that analyzed how BA and innovation capability work together to drive supply chain integration, resilience and organizational performance, which was not collectively studied in existing studies, meta-analyses or reviews to ascertain the direct and mediating mechanisms (Aryal et al., 2020; Oesterreich et al., 2022; Ansari and Ghasemaghaei, 2023; Bag and Rahman, 2023; Alvarenga et al., 2023). Second, our study offers integrated and more definitive results regarding identified relationships. More precisely, the study provides statistically significant direct effects with the help of meta-analysis and meta-structural equation modeling to remove the ambiguity in the literature. Third, apart from the above definitive relationships, mediation analysis contributes to academia in identifying significant mediating mechanisms related to innovation capability, supply chain integration and resilience. Innovation capability partially and significantly mediates between BA and supply chain integration/resilience. Fourth, meta-regression provides valuable insights related to DCS, national culture and type of economies in the supply chain context. In fact, this study is the first one to examine the effects of DCS and all dimensions of national culture on the BA−INV relationship and overcome certain limitations that exist in the literature (Oesterreich et al., 2022; Ansari and Ghasemaghaei, 2023; Nakandala et al., 2023).

Big data is captured through evolving digital technologies such as intelligent sensors, radio frequency identification tags, global positioning system (GPS) locations and social media, which generate large data sets. Thus, managers must extract value from such a large data set and transition from big data to BA. This transition encompasses retrieving unknown patterns and insights from big data, its interpretations and extracting meaningful actions (Gupta et al., 2020; Hallikas et al., 2021). This study confirms that organizational capabilities in terms of BA and innovation enable supply chain integration and resilience. Managers must concentrate on BA and innovation capability simultaneously rather than making a trade-off between capabilities (Morita and Machuca, 2018) to drive supply chain integration, resilience and performance. For example, Morita and Machuca (2018) study revealed that many companies are doing trade-offs between capabilities and innovation. Hence, the findings clarified confusion among practitioners and confirmed that BA improves innovation capability, consequently enabling higher supply chain integration and resilience. Thus, managers investing in innovation capability will be more confident about integration, resilience and performance outcomes.

This is one of the early studies that examine the underlying mechanisms of innovation capability, supply chain integration and resilience between BA and organizational performance. Moderation analysis with a DCS, national culture, type of economies and GDP per capita explains the heterogeneity between the BA and innovation capability relationship.

The purpose of this study is to develop an intelligent tutoring system (ITS) for programming learning based on information tutoring feedback (ITF) to provide real-time guidance and feedback to self-directed learners during programming problem-solving and to improve learners’ computational thinking.

By analyzing the mechanism of action of ITF on the development of computational thinking, an ITF strategy and corresponding ITS acting on the whole process of programming problem-solving were developed to realize the evaluation of programming problem-solving ideas based on program logic. On the one hand, a lexical and syntactic analysis of the programming problem solutions input by the learners is performed and presented with a tree-like structure. On the other hand, by comparing multiple algorithms, it is implemented to compare the programming problem solutions entered by the learners with the answers and analyze the gaps to give them back to the learners to promote the improvement of their computational thinking.

This study clarifies the mechanism of the role of ITF-based ITS in the computational thinking development process. Results indicated that the ITS designed in this study is effective in promoting students’ computational thinking, especially for low-level learners. It also helped to improve students’ learning motivation, and reducing cognitive load, while there’s no significant difference among learners of different levels.

This study developed an ITS based on ITF to address the problem of learners’ difficulty in obtaining real-time guidance in the current programming problem-solving-based computational thinking development, providing a good aid for college students’ independent programming learning.

This paper examines how a firm's hierarchical complexity, which is determined by the way it organizes its subsidiaries across the hierarchical levels, can impact its stock price crash risk.

The authors employ a measure of hierarchical complexity that captures the depth and breadth of how subsidiaries are organized within a firm. This measure is calculated using information about firms' subsidiaries extracted from the Bureau van Dijk (BvD) database that allows the authors to construct each firm's hierarchical structure. The data sample includes 2,461 USA firms for the period from 2012 to 2017 (11,006 firm-year observations). Univariate tests and panel regression are used for the main analysis. Two-stage-least-squares (2SLS) instrumental variable regression and various other tests are employed for robustness check.

The results show a positive relationship between hierarchical complexity and stock price crash risk. This relationship is amplified in firms with a greater number of subsidiaries that are hierarchically distanced from the parent company as well as in firms with a greater number of foreign subsidiaries in countries with weaker rule of law.

This paper is the first to investigate the impact hierarchical complexity has on crash risk. The results highlight the role that a firm's organizational structure can have on asset pricing behavior.

Pores are the primary cause of fatigue failure in laser-directed energy deposition (L-DED) titanium alloys, which are largely determined by their location, size and shape. It is crucial for promoting the application of L-DED titanium alloys and ensuring their safety that establishing a fatigue life prediction method induced by pores, resulting in a proposed fatigue life prediction framework for L-DED Ti-6Al-4V based on a physics-informed neural network (PINN) algorithm.

In this study, a novel fatigue life prediction framework for L-DED Ti-6Al-4V based on a PINN algorithm was proposed. The influence patterns of various fatigue-sensitive parameters were revealed. The paper also included validation and analysis of the method, such as hyperparameter analysis of the PINN, efficacy analysis driven by physical information and comparative analysis of different methods.

The proposed method demonstrated high accuracy, with a correlation coefficient of 0.99 with experimental life. The coefficient of determination was 0.95 and the mean squared error was 0.06.

The results indicate that the proposed fatigue life prediction framework was of strong generalization capability and robustness.

By incorporating the defect feature information, an ML-based linkage between defects and fatigue life unaffected by the time scale is developed, the primary focus is to quantitatively assess and elucidate the impact of different defect features on fatigue life.

A machine learning (ML) framework is proposed to predict the fatigue life of LPBF-built Hastelloy X utilizing microstructural defects identified through nondestructive detection prior to fatigue testing. The proposed method combines nondestructive micro-computerized tomography (micro-CT) technique to comprehensively analyze the size, location, morphology and distribution of the defects.

In the test set, SVM-based fatigue life prediction exhibits the highest accuracy. Regarding the defect information, the defect size significantly affects fatigue life, and the diameter of the circumscribed sphere of the largest defect has a critical effect on fatigue life.

This comprehensive approach provides valuable insights into the fatigue mechanism of structural materials in defective states, offering a novel perspective for better understanding the influence of defects on fatigue performance.