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This study aims to explore the challenges faced by third-party agencies as quality assurance (QA) bodies for higher education in Yunnan Province, China.

This qualitative case study included 10 respondents – six experts from a higher education evaluation center and four directors from two private undergraduate colleges (PUCs). Semi-structured and in-depth interviews were conducted with ethical considerations, from topic selection to references. The data were analyzed using thematic and inductive methods.

Third-party agencies primarily function as administrative tools affiliated with the government and face deficits in trust and autonomy. They lack formal legislation, legal status, authority and independence and face shortages of experts, expertise, accountability mechanisms and market-oriented service structures.

This study may not be generalizable to the country’s landscape and other higher education institutions. Future research should explore these issues using quantitative or mixed methods in different provinces and institutions.

Insights from experts in evaluation centers and PUC management provide empirical evidence for policymakers on QA reform, advance knowledge on QA governance and extend resource dependence theory. Under a centralized system, China’s context mirrors provincial challenges, offering a foundation for addressing similar issues.

The study fills the gap in current research by providing empirical knowledge on the obstacles faced by third-party agencies as QA bodies. This could lead to policymaking or legislation formulation to empower these agencies as legal and independent evaluation bodies, promoting the development of higher education and economic prosperity.

This study aims to explore the spatio-temporal dynamic characteristics and influencing factors of the coordination degree of the three systems of digital economy, energy and human habitat in Western China and to provide academic research support for promoting coordinated and sustainable development in similar regions of the world.

Based on system theory and sustainable development theory, this study primarily uses the coupled coordination degree model to assess the degree of coordination between the three systems.

The findings of this study indicate that: The three systems’ overall coordination is low. The distribution of the degree of coordination has spatial differences and its coefficient of variation is small. The probability of the coordination type changing for the better is greater than that of the opposite, and neighboring provinces interact with one another. The old-age dependence ratio, the resident population’s urbanization rate and public budget expenditure have the strongest gray association with the degree of coordination.

This study’s findings will be valuable for policymakers in developing policies to promote the coordinated and sustainable growth of the region’s digital economy, energy and human habitat. Additionally, the findings will aid in facilitating regional exchanges and cooperation to enhance the level of sustainable development.

This study’s findings will contribute to increased social interest in coordinating sustainable growth in the digital economy, energy and human habitat.

This study examines the digital economy, energy and human habitat within the same framework and investigates spatial spillover effects using spatial Markov chains.

In large-scale group decision-making (LSGDM) situations, existing TODIM group decision-making methods often fail to account for the influence of social network relationships and the bounded rationality of decision-makers (DMs). To address this issue, a new TODIM-based group decision-making method is proposed that considers the current trust relationships among DMs in a large-scale trust relationship network.

This method consists of two main stages. In the first stage, the large-scale group is partitioned into several sub-clusters based on trust relationships among DMs. The dominance degree matrix of each sub-cluster is then aggregated into the large-scale group dominance degree. In the second stage, after aggregating the large-scale group dominance degree, the consensus index is calculated to identify any inconsistent sub-clusters. Feedback adjustments are made based on trust relationships until a consensus is reached. The TODIM method is then applied to calculate the corresponding ranking results. Finally, an illustrative example is applied to show the feasibility of the proposed model.

The proposed method is practical and effective which is verified by the real case study. By taking into account the trust relationships among DMs in the core process of LSGDM, it indeed has an impact on the decision outcomes. We also specifically address this issue in Chapter Five. The proposed method fully incorporates the bounded rationality of DMs, namely their tendency to accept the opinions of trusted experts, which aligns more with their psychology. The two-stage consensus model proposed in this paper effectively addresses the limitations of traditional assessment-based methods.

This study establishes a two-stage consensus model based on trust relationships among DMs, which can assist DMs in better understanding trust issues in complex decision-making, enhancing the accuracy and efficiency of decisions, and providing more scientific decision support for organizations such as businesses and governments.

This study aims to develop an associative text analyzer (ATA) to support users in quickly grasping and interpreting the content of large amounts of text through text association recommendations, facilitating the identification of the contextual relationships between people, events, organization and locations for digital humanities. Additionally, by providing text summaries, the tool allows users to link between distant and close readings, thereby enabling more efficient exploration of related texts.

To verify the effectiveness of this tool in supporting exploration of historical texts, this study uses a counterbalanced design to compare the use of the digital humanities platform for Mr. Lo Chia-Lun’s Writings (DHP-LCLW) with and without the ATA to assist in exploring different aspects of text. The study investigated whether there were significant differences in effectiveness for exploring textual contexts and technological acceptance as well as used semi-structured in-depth interviews to understand the research participants’ viewpoints and experiences with the ATA.

The results of the experiment revealed that the effectiveness of text exploration using the DHP-LCLW with and without the ATA varied significantly depending on the topic of the text being explored. The DHP-LCLW with the ATA was found to be more suitable for exploring historical texts, while the DHP-LCLW without the ATA was more suitable for exploring educational texts. The DHP-LCLW with the DHP-LCLW was found to be significantly more useful in terms of perceived usefulness than the DHP-LCLW without the ATA, indicating that the research participants believed the ATA was more effective in helping them efficiently grasp the related texts and topics during text exploration.

The study’s practical implications lie in the development of an ATA for digital humanities, offering a valuable tool for efficiently exploring historical texts. The ATA enhances users’ ability to grasp and interpret large volumes of text, facilitating contextual relationship identification. Its practical utility is evident in the improved effectiveness of text exploration, particularly for historical content, as indicated by users’ perceived usefulness.

This study proposes an ATA for digital humanities, enhancing text exploration by offering association recommendations and efficient linking between distant and close readings. The study contributes by providing a specialized tool and demonstrating its perceived usefulness in facilitating efficient exploration of related texts in digital humanities.

Surface roughness and delamination during the milling of carbon fiber reinforced polymer (CFRP) composite parts in aviation can lead to component rejection. This article aims to optimize cutting conditions to reduce these failures while ensuring compliance with aviation standards. By improving machinability, the goal is to minimize part rejection rates and scrap, optimizing costs and increasing safety.

Full factorial experimental design and response surface methodology (RSM) were used to establish relationships between the cutting parameters and the cutting force, delamination and surface roughness. To validate the model and identify significant parameters, analysis of variance (ANOVA) was performed. The cutting parameters were optimized to reduce cutting force and improve surface quality using ANOVA and RSM.

The lowest response values can be achieved with a cutting speed of 285.35 m/min and a feed of 358.57 mm/min using the Aluminum Chromium Nitride (AlCrN)-coated tool. Accordingly, the optimum cutting force was obtained as 190.97 N, delamination depth as 1.562 mm and surface roughness as 1.431 µm. It has been seen that the obtained surface roughness and delamination values are consistent with aviation literature studies, sectoral data and standards.

This study uniquely examines cutting force, surface roughness and delamination using Titanium Aluminum Nitride (TiAlN)- and AlCrN-coated tools instead of traditional Poly Cyristaline Diamond (PCD) tools. It employs a two-stage experimental framework, starting with a full factorial design followed by RSM. The initial data have been used as inputs for optimization in the second stage to achieve more accurate results.

The high-load operation of the engine crankshaft causes severe wear and fatigue. This study aims to prepare in situ textures with effective density and study their wear mechanism on the surface of ductile cast iron, which optimizes the tribological properties of engine crankshafts and reduces wear.

A new method was proposed based on the hardness difference in graphite removal to form an in situ texture. The friction performance was evaluated using a combination of computational fluid dynamics and tribological testings. The influence of the texture characteristic parameters on the bearing capacity of the oil film was analyzed. The surface wear morphology was studied by scanning electron microscopy.

The texture density significantly affected the oil film bearing capacity. The surface texture can reduce the average friction coefficient (COF) by more than 35% owing to the oil film bearing and storage capacity. Specifically, the 13% texture density exhibited the lowest wear rate and COF under all three experimental conditions. The reduction in abrasive particles in the wear area of the textured surface indicates that the surface texture can improve the lubrication mechanism.

This study systematically explored the influence of the weight of each model parameter on tribological properties. Subsequently, focusing on the critical parameter (texture density), detailed tribological testings were carried out to reveal the specific effect of texture density on the wear mechanism under different working conditions, and the optimal texture density to achieve the optimal tribological performance was determined accordingly.

This research investigates the barriers impeding innovation within small- and medium-sized enterprises (SMEs) in Brazil, exploring 54 innovation-related barriers categorized into six distinct groups to offer substantial insights and analyses pertinent to the decision-makers, researchers and SMEs.

This research employed a mixed quantitative and exploratory approach, utilizing fuzzy Delphi, fuzzy analytic hierarchy process (AHP) and fuzzy decision-making trial and evaluation laboratory (DEMATEL) methods. The fuzzy Delphi method confirmed the categories and barriers through quantitative analysis, the fuzzy AHP ranked the validated obstacles and the fuzzy DEMATEL method identified causal connections among the top-priority barriers.

Out of 54 barriers, 23 significantly impacted SMEs. The “Financing and Financial” category was the most significant barrier, with “Access to Financing” being the most critical impediment. The barrier with the most influence was “Instability of Fiscal Policies,” and the highest causal priority was “Survival of the Priority Business,” identifying the government’s unstable fiscal policy as the principal barrier confronting SMEs in Brazil.

The primary challenges for Brazilian SMEs center on financing, fiscal policies and maintaining ongoing operations. By addressing these barriers and fostering a resilient business environment, SMEs’ innovation capabilities and competitiveness can be enhanced, serving as key drivers for sustainable economic growth in fluctuating economic conditions. This study contributes to the literature by highlighting and validating the main barriers to SME innovation, providing highly relevant information about the innovation process.

The research purpose is to investigate the impact of green entrepreneurial orientation (GEO) on operational performance (OP) in renewable energy companies, with a particular focus on the mediating roles of green innovation strategy (GIS) and green innovation culture (GIC).

Data were collected through interactive surveys with 338 middle and senior managers of renewable energy companies prioritizing sustainability. The relationships between variables were analyzed using SmartPLS and Jamovi software, which facilitates structural equation modeling.

The analysis revealed that GEO had a significant positive impact on both GIS and GIC, followed by OP. It is supported by the hypotheses that mediating variables GIS and GIC positively influence OP, and their important role in transforming entrepreneurial efforts into operational success is confirmed.

The research is limited to renewable energy companies and findings may not be generalizable to other sectors. Future research could expand the scope to include different industries and geographic contexts. Additionally, dimensional research studies can provide deeper insights into the long-term effects of GEO, GIS and GIC on OP.

The findings suggest that renewable energy companies should encourage a strong entrepreneurial orientation towards green practices. Emphasizing innovation strategies and developing a green culture within organizations can lead to improved OP, supporting overall sustainability goals.

The research provides a comprehensive framework for understanding the drivers of OP in the renewable energy sector, providing a new perspective by combining GEO with innovation strategies and cultural elements. The originality of the research lies in the application of these concepts to an industry where sustainability is very important.

This study aims to explore the impact of artificial intelligence-powered personalised recommendations (AI-PPRs) on user engagement, browsing behaviour and purchase intentions on TikTok (Douyin in China), focusing on how these recommendations affect user satisfaction and purchase intention, while also addressing potential privacy concerns. In addition, the research investigates the influence of AI-recommended product presentation, timing and placement, as well as social factors such as key opinion leaders’ (KOLs) influence on consumer decision-making.

Using the expectancy-value theory and the stimulus-organism-response model, this research used a qualitative methodology through interviews with Douyin users to explore their experiences and perceptions of AI-PPRs.

The findings indicate that Douyin’s proactive “push” mechanism of AI-PPRs enhances user engagement by effortlessly integrating product discovery into the entertainment experience. Content-driven AI-PPRs align with user preferences, decrease search time and increase satisfaction and purchase intentions through engaging short videos and live streaming. However, privacy concerns emerge when personalisation is perceived as excessively intrusive, leading to negative emotions and avoidance behaviours. Recommendation timing and cultural context significantly influence receptiveness, with inappropriate timing (e.g. during holidays) causing negative reactions. Technical challenges, such as network issues during live streaming, negatively impact user experience and engagement. Content quality is crucial, and poor or irrelevant content leads to negative perceptions and disengagement. While KOLs face scepticism due to perceived commercialisation, endorsements from trusted figures and authentic influencers are better received. Innovative payment methods, like “Douyin Monthly Payment”, enhance financial flexibility and promote customer loyalty. This study highlights the need to balance personalisation with privacy, emphasising the importance of content quality and authenticity in influencer marketing. For businesses using AI-PPRs, maintaining this balance is essential for preserving trust and sustaining consumer engagement and loyalty.

This study contributes valuable insights to the field by unravelling the intricate dynamics between AI-PPRs, user preferences and social influences. The findings provide practical implications for companies aiming to optimise personalised recommendation algorithms and enhance user engagement, thereby facilitating business growth in the dynamic short video e-commerce market.

This study explores the factors that influence platform-to-platform cooperation (PPC) and designs a theoretical framework for platform research.

This multi-case study includes a combination of exploratory and explanatory case studies.

From the internal factor perspective, channel integration capability, technology-based order matching capability and service innovation capability positively affects the PPC. From the perspective of external factors, the impact of a new platform entry on the PPC depends on market power and complementarities between platforms in the supply and value chains. Diversity of demand also has a positive effect on the PPC, which is moderated by network externalities. It is worth noting that the incumbent platform prefers to diversify its services for collaborating platforms with a higher level of cooperation. In addition, the higher diversity of demand, the stronger the service innovation capability, which indirectly impacts cooperation positively.

The PPC has gained immense popularity in recent years. However, no scholars have investigated the factors influencing the PPC decisions, which warrants further exploration. This study sheds light on the factors and mechanisms that influence the PPC from both internal and external perspectives.