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This study aims to clarify influencing factors of overcapacity of new energy enterprises in China and accurately predict whether these enterprises have overcapacity.

Based on relevant data including the experience and evidence from the capital market in China, the research establishes a generic univariate selection-comparative machine learning model to study relevant factors that affect overcapacity of new energy enterprises from five dimensions. These include the governmental intervention, market demand, corporate finance, corporate governance and corporate decision. Moreover, the bridging approach is used to strengthen findings from quantitative studies via the results from qualitative studies.

The authors' results show that the overcapacity of new energy enterprises in China is brought out by the combined effect of governmental intervention corporate governance and corporate decision. Governmental interventions increase the overcapacity risk of new energy enterprises mainly by distorting investment behaviors of enterprises. Corporate decision and corporate governance factors affect the overcapacity mainly by regulating the degree of overconfidence of the management team and the agency cost. Among the eight comparable integrated models, generic univariate selection-bagging exhibits the optimal comprehensive generalization performance and its area under the receiver operating characteristic curve Area under curve (AUC) accuracy precision and recall are 0.719, 0.960, 0.975 and 0.983, respectively.

The proposed integrated model analyzes causes and predicts presence of overcapacity of new energy enterprises to help governments to formulate appropriate strategies to deal with overcapacity and new energy enterprises to optimize resource allocation. Ten main features which affect the overcapacity of new energy enterprises in China are identified through generic univariate selection model. Through the bridging approach, the impact of the main features on the overcapacity of new energy enterprises and the mechanism of the influence are analyzed.

The effectiveness of rhetorical structure is essential to communicate key messages in research articles (RAs). The interdisciplinary nature of library and information science (LIS) has led to unclear patterns and practice of using rhetorical structures. Understanding how RAs are constructed in LIS to facilitate effective scholarly communication is important. Numerous studies investigated the rhetorical structure of RAs in a range of disciplines, but LIS articles have not been well studied.

In this study, the authors encoded rhetorical structures to 2,216 articles in the Journal of the Association for Information Science and Technology covering a period from 2001 to 2018 with the approaches of co-word analysis and visualization. The results show that the predominant rhetorical structures used by LIS researchers follow the sequence of Introduction-Literature Review-Methodology-Result-Discussion-Conclusion (ILMRDC).

The authors' temporal examination reveals the shifts of evolutionary pattern of rhetorical structure in 2008 and 2014. More importantly, the authors' study demonstrates that rhetorical structures have varied greatly across research areas in LIS community. For example, scholarly communication and scientometrics studies tend to exclude literature review in articles.

The present paper offers a first systematic examination of how rhetorical structures are used in a representative sample of a LIS journal, especially from a temporal perspective.

This article reviews the intercultural adaptability (IA) development models for students in South Korea and China in response to the growing internationalisation of higher education. The article provides significant implications for Confucian heritage culture (CHC) countries and others that wish to enhance in higher education.

This scoping review systematically investigates the literature on different IA development strategies for students in higher education across these two Asian countries.

South Korea has promoted the self-growth model reflected in internationalised programmes such as English as a Medium of Instruction (EMI), an international learning environment with an increasing number of international students and student exchange programmes. Through these initiatives, domestic students’ identity and intercultural learning dynamics are enriched in an intercultural learning environment. Meanwhile, China has adopted the “Outward-oriented” higher education internationalisation model to attract international students to study in Chinese universities and entice Chinese returnees through its International Talent Training Programmes. In this strategy, international students are exposed to Chinese language and cultural knowledge development programmes, and Chinese students are motivated to learn the English language and cultures through interactions with their international peers.

Student IA development models in CHC countries remain underexplored. This article responds to the need of higher education institutions in Asian countries that wish to access and learn from effective IA models from other CHC countries. The study makes a valuable contribution by putting forward significant recommendations for developing students’ IA in CHC countries.

The purpose of the study was to investigate the effects of artificial intelligence (A.I.) awareness, advertisement models and source-message incongruence on consumer evaluations of A.I.-generated advertisements. It explores how these factors interact in shaping consumer perceptions and advertising effectiveness.

A 2 (source-message (in)congruence: incongruent vs. congruent) x 3 (A.I. awareness: unawareness, pre-advertisement, post-advertisement) x 3 (advertisement model: traditional human, virtual human, digital twin) between-subjects design was employed in this study. Using stratified random sampling, a total of 231 undergraduate students were recruited from course groups and randomly assigned to one of nine experimental treatments, each involving the viewing of a specific A.I.-generated advertisement followed by a survey. Data were analyzed using two-way ANCOVA and regression analyses, controlling for participants' involvement in sports and brand.

The results indicated that A.I. awareness timing, advertisement model types and source-message incongruence significantly affected consumer evaluations of advertisements. A.I. awareness generally had a positive impact on evaluations, with the most favorable outcomes when awareness of the A.I.-generated nature occurred after viewing the advertisement. Virtual human models were rated the lowest, while digital twin and traditional human models received similarly positive evaluations. Source-message incongruence negatively influenced evaluations. An interaction effect was observed between A.I. awareness timing and advertisement model types under high source-message incongruence, where virtual human models showed the highest effectiveness when A.I. awareness occurred after viewing.

Given that sports are characterized by the transcendence of human limitations and the emphasis on physical and emotional challenges – elements that A.I. cannot replicate – it is essential to examine how sports consumers perceive A.I., which, despite offering efficiency and personalization advantages, contrasts with the fundamentally human nature of athletic performance. This research contributes to the literature on A.I.-generated advertising by uniquely investigating the interaction between A.I. awareness timing and advertisement model types within the context of source-message incongruence. It offers critical insights for practitioners and researchers on strategically timing A.I.-generated ad disclosures and selecting appropriate advertisement models to optimize their effectiveness. By addressing these underexplored variables, the study enhances understanding of consumer perceptions and provides a foundation for more effective A.I. integration in advertising practices.

This study explores how the high-end equipment manufacturing industry (HEMI) achieves intelligent development through the digital innovation ecosystem. While this industry urgently needs to achieve intelligent development through innovation breakthroughs, existing research lacks a deep analysis in conjunction with the digital innovation ecosystem. Considering the sophisticated nature of HEMI and the unique characteristics of the digital innovation ecosystem, this paper aims to uncover the innovation potential and synergetic development opportunities that arise from their integration.

This study uses Dynamic Qualitative Comparative Analysis (QCA) to explore the evolving relationship between the digital innovation ecosystem and intelligent development in HEMI enterprises. Data from 60 HEMI enterprises were collected from 2015 to 2022, and the study window was divided into two-year intervals for analysis. Compared to traditional QCA methods, this approach overcomes the limitations of cross-sectional analysis, fully accounting for time’s influence on causal relationships for more accurate results.

The study reveals that the digital innovation ecosystem of HEMI drives intelligent development through the coordinated interactions of its elements within each time window. Configuration paths and key driving factors evolve dynamically, reflecting the complexity of the ecosystem’s role in driving intelligent development. The study suggests that enterprises dynamically adjust their strategies to different stages, enhancing the effectiveness of intelligent transformation.

The paper proposes and validates a digital innovation ecosystem framework for HEMI, systematically exploring its role in driving intelligent development. The study fills a research gap and extends innovation ecosystem theory by identifying core driving factors and their evolutionary trends through Dynamic QCA. It offers a new perspective on the dynamic role of digital innovation ecosystems in intelligent transformation.

The purpose of this article is based on the unit patent license fee model in the closed-loop supply chain.

This paper analyzes the impact of the bundling strategy of the retailer selling new products and remanufactured products on the closed-loop supply chain under the condition that the original manufacturer produces new products and the remanufacturer produces remanufacturing products.

The results show that alternative products can be bundled, and in many cases, the bundling of remanufactured products and new products is better than selling alone.

If the retailer chooses bundling, for the remanufacturer, when certain conditions are met, the benefits of bundling are greater than the separate sales at that time; for the original manufacturer, when the recycling price sensitivity coefficient is high, the bundling is better than separate sales.

With the rise in international construction projects, addressing the psychological health concerns of expatriate construction professionals (EXCPs) has become of paramount importance. Therefore, this study aims to identify and evaluate effective intervention measures to promote optimal psychological health and well-being in overseas working environments.

Intervention measures were identified through a comprehensive literature review. Subsequently, using a case study of Chinese international contractors, data were collected via a quantitative survey administered to EXCPs holding managerial positions in international construction projects. Fuzzy synthetic evaluation was employed to analyze the data.

The findings identified 23 intervention measures and 4 constructs, among them, measures focused on enhancing organizational justice and training practices offer the greatest potential for enhancing the psychological health and well-being during international assignments. Furthermore, the analysis showed that the overall criticality of the intervention measures to the international contractors is significant, suggesting that if put into practice, the psychological health and well-being of EXCPs can be significantly enhanced.

While prior research has explored intervention measures suitable for construction professionals working on domestic projects, little attention has been paid to EXCPs in the context of international construction projects. This study offers an initial exploration of the most crucial intervention measures and provides a valuable resource for policymakers and practitioners seeking to establish a psychologically healthy working environment for international construction projects.

Considering two types of subsidies for producers (supplier and manufacturer) and one for consumers based on product greenness and sales quantity, this study aims to formulate optimal supply chain green innovation and subsidy strategies, and to achieve this goal with the support of information systems.

This study introduces a composite green-product supply chain where suppliers focus on green innovation for component greenness and manufacturers focus on green innovation for manufacturing process greenness. Game theory modeling is applied to investigate the differences of product greenness, supply chain members’ profit and social welfare under different government subsidy strategies.

Increasing the unit greenness subsidy coefficient can boost product greenness and supply chain members’ profits, but does not always raise social welfare. When the government exclusively offers subsidies to producers, subsidies should be allocated to suppliers when there is a significant disparity in supply chain green innovation costs. Conversely, it is more beneficial to subsidize manufacturers. Consumer subsidies have the potential to enhance both environmental and economic performance in the supply chain compared with producer-exclusive subsidies, but may not always maximize social welfare when supply chain members have low unit costs associated with green innovation.

This study examines the optimal decisions for green supply chain innovation and government subsidy strategies. Supply chain members and the government can use the information system to collect and evaluate the cost of upstream and downstream green innovation, and then develop reasonable collaborative green innovation and subsidy strategies.

This study adopts the elaboration likelihood model and configuration perspectives to explore the internal mechanisms underlying the influence of live streaming on consumer trust building and purchase intention.

This study invited 757 experienced live streaming e-commerce users from Chinese platforms such as TikTok and RED, who participated in survey by filling questionnaires collected online. The research employed a mixed-method approach using SEM and fsQCA. SEM was utilized to analyze quantitative data to determine the direct and mediated relationships within product trust, while fsQCA served as a complement to identify the combinations of conditions that enhance product trust.

The findings reveal three important insights. Firstly, in the context of live streaming e-commerce, both product characteristics and streamer characteristics significantly influence consumers' trust in products. The para-social interaction plays a partial mediating role in the relationship between streamer characteristics and product trust. Secondly, four distinct paths are identified that contribute to enhancing product trust in live streaming e-commerce. Thirdly, PSI emerging as a core condition across all four paths, underscores the importance for merchants to foster positive social interactions with consumers beyond the live streaming environment.

This study enhances understanding of the dynamic live streaming e-commerce industry, offering insights into consumer behavior and practical guidance for merchants seeking to build engaged, trustworthy customer relationships.

This study aims to empirically test how sponsored video customization (i.e. the degree to which a sponsored video is customized for a sponsoring brand) affects video shares differently depending on influencer characteristics (i.e. mega influencer and expert influencer) and brand characteristics (i.e. brand establishment and product involvement).

This study uses a unique real-world data set that combines coded variables (e.g. customization) and objective video performance (e.g. sharing) of 365 sponsored videos to test the hypotheses. A negative binomial model is used to analyze the data set.

This study finds that the effect of video customization on video shares varies across contexts. Video customization positively affects shares if they are made for well-established brands and high-involvement products but negatively influences shares if they are produced by mega and expert influencers.

This study extends the influencer marketing literature by focusing on a new media modality – sponsored video. Drawing on the multiple inference model and the persuasion knowledge theory, this study teases out different conditions under which video customization is more or less likely to foster audience engagement, which both influencers and brands care about. The chosen research setting may limit the generalizability of the findings of this study.

The findings suggest that mega and expert influencers need to consider if their endorsement would backfire on a highly customized video. Brands that aim to engage customers with highly-customized videos should gauge their decision by taking into consideration their years of establishment and product involvement. For video-sharing platforms, especially those that are planning to expand their businesses to include “matching-making services” for brands and influencers, the findings provide theory-based guidance on optimizing such matches.

This paper fulfills an urgent research need to study how brands and influencers should produce sponsored videos to achieve optimal outcomes.