Search results

The paper aims to examine the digital economy's influence on China's regional innovation and development. It focuses on direct effects and spatial spillover across regions, and the mediating role of human capital. This analysis is vital for policy and strategic planning in the digital era.

This study uses panel data from 30 Chinese provinces (2004–2019) and uses the entropy method to quantify the digital economy's development. It investigates its impact on regional innovation using a dynamic spatial Durbin model (SDM) and mediation effect model, assessing direct effects, spatial spillover and human capital's mediating role. Various control variables are included for comprehensive analysis.

Findings show the digital economy significantly boosts regional innovation, acting as a growth driver. However, impacts vary regionally, with the central region gaining more than the eastern and western areas. Spatial spillover effects are mixed, showing negative short-term and positive long-term impacts under different weight matrices. Human capital is crucial for fostering innovation through the digital economy.

The paper offers unique insights into the spatial dynamics of the digital economy's impact on regional innovation in China. It advances understanding of the digital economy's role in regional development using innovative methods like the entropy method and dynamic SDM. Highlighting human capital as a key mediating factor enriches discussions on digital economy strategies for regional innovation.

Short-form videos have gradually become important marketing tools for tourist destinations. However, chaotic sources and homogenized content have led to poor user experiences. Taking Kulangsu and Xi'an City Wall in China as examples, this study explored the influence of matching short-form video sources with destination types on user engagement and visit intention.

This study selected three short-form videos from different sources for each destination on TikTok and conducted an empirical research using a 3 × 2 experimental design to examine the proposed research model.

The results showed that the matching effect between short-form video sources and destination types will positively affect user engagement and visit intention. (1) the short-form videos with user-generated content (UGC) or professional user-generated content (PUGC) in hedonic destinations can obtain higher user engagement and visit intention; (2) the short-form videos with professionally generated content (PGC) or PUGC in utilitarian destinations can obtain higher user engagement and visit intention and (3) perceived credibility and perceived usefulness played mediating roles in these interactions.

This study considers short-form video sources as antecedent variables influencing user engagement and visit intention and confirms the matching effect between short-form video sources and tourism destination types. The findings will help researchers and marketers better understand the impact of short-form video on destination marketing.

Driven by the rapid expansion of online retail and the surge in livestream commerce, the impact of different livestream mode on brand and platform performance has become a critical issue. This paper analyzes the impact of artificial intelligence (AI) and key opinion leader (KOL) livestream on the profitability of brands and the platform, incorporating the effects of horizontal interactions to identify the optimal livestream mode.

This paper develops a model of a platform supply chain involving two brands and a platform, where each brand independently decides whether to utilize KOL or AI livestream. Applying Stackelberg game approach, the study derives equilibria for various livestream scenarios, identifying the optimal livestream mode for both parties. Additionally, the model is extended to incorporate asymmetric market potential and network externality to evaluate their impact on a brand’s choice of livestream mode.

Several interesting and important results are derived in this paper. Firstly, it is found that AI livestream enables brands to leverage network externality and mitigate the market disadvantage, thereby gaining a competitive advantage. Secondly, while KOL livestream promotes trust, the medium KOL commission rates could cause brands to be trapped in a prisoner’s dilemma, and excessively high commission rates may render them less profitable. Thirdly, the KOL commission rate, network externality intensity, horizontal interactions and market disadvantage are critical determinants influencing a brand’s choice of livestream mode.

This study is the first to investigate the effects of horizontal interactions, asymmetric market potential and asymmetric network externality on livestream mode selection by brands within a platform supply chain. The research provides valuable insights into optimizing livestream strategies to enhance brand profitability.

The purpose of the study is to investigate strategies for enhancing pollution oversight by local governments while reducing government-enterprise collusion (GEC) levels. Additionally, the factors influencing pollution control efforts at incineration plants are explored. Potential approaches to improving them and for effectively reducing waste incineration pollution are suggested.

The authors examined the most effective methods for mitigating incineration-related pollution and preventing collusion and developed a differential game model involving interactions between local governments and incineration plants. The findings of this work have significant policy implications for central governments worldwide seeking to regulate waste incineration practices.

The results indicate that, first, elevating environmental assessment standards can incentivize local governments to improve their oversight efforts. Second, collusion between incineration plants and local governments can be deterred by transferring benefits from the plants to the local government, while increased supervision by the central government and the enforcement of penalties for collusion can also mitigate collusion. Third, both central and local governments can bolster their supervisory and penalty mechanisms for instances of excessive pollution, encouraging incineration plants to invest more in pollution control. Finally, when the central government finds it challenging to detect excessive incineration-related pollution, enhancing rewards and penalties at the local government level can be a viable alternative.

This study stands out by considering the dynamic nature of pollutants. A differential game model is constructed which captures the evolving dynamics between local governments and incineration plants, offering insights regarding the prevention of collusion from a dynamic perspective. The findings may provide a valuable reference for governments as they develop and enforce regulations while motivating incineration plants to actively engage in reducing waste-incineration pollution.

Silicon-containing groups were introduced into fluoroacrylate polymer to further improve the comprehensive performance of pressure-sensitive adhesive (PSA) for expanded polytetrafluoroethylene (ePTFE) bonding.

A series of silicon-containing fluorinated acrylic copolymers were synthesized through free radical solution polymerization with vinyloxy trimethylsilane, allyltrimethylsilane, 3-(trimethoxysilyl)propyl methacrylate or 1,3,5-tris(3,3,3-trifluoropropyl) methylcyclotrisiloxane as silicon monomers, and comprehensive performance of the copolymers was evaluated based on Fourier transform infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), gel permeation chromatography, glass transition temperatures (T_g), differential scanning calorimetry, thermogravimetric analysis, water contact angle, the track, 180° peel strength, and shear holding power.

Based on the FTIR and XPS results, it is confirmed that the silicon monomers were successfully introduced into the fluorinated acrylate copolymer. XPS analysis indicated that the silicon groups had the tendency to enrich on the surface of the film, thereby reducing the F content on the film surface. The glass transition temperatures (T_g) of the PSAs increased when silicon monomers were introduced, while the thermal stability declined. The contact angles of the acrylic PSA films were increased with the introduction of silicon monomers. From the perspective of bonding performance, the track, 180° peel strength and shear holding power decreased to varying degrees compared to silicon-free PSA, except significantly elevated holding power with MPS as the silicon monomer.

Silicon-containing fluorinated acrylic copolymers were synthesized, and the comprehensive performance was evaluated as PSAs of ePTFE for the first time.

This study examines the role of online customer reviews through text mining and sentiment analysis to improve customer satisfaction across various services within the UK banking sector. Additionally, the study analyses sentiment trends over a five-year period.

Using DistilBERT and Support Vector Machine algorithms, customer sentiments were assessed through an analysis of 20,137 Trustpilot reviews of HSBC, Santander, and Tesco Bank from 2018 to 2023. Data pre-processing steps were implemented to ensure data integrity and minimize noise.

Both positive and negative sentiments provide valuable insights. The results indicate a high prevalence of negative sentiments related to customer service and communication, with HSBC and Santander receiving 90.8% and 89.7% negative feedback, respectively, compared to Tesco Bank’s 66.8%. Key areas for improvement include HSBC’s credit card services and call center efficiency, which experienced increased negative feedback during the COVID-19 pandemic. The findings also demonstrate that DistilBERT excelled in categorizing reviews, while the SVM model, when combined with customer ratings, achieved 96% accuracy in sentiment analysis.

This study focuses on UK bank consumers of HSBC, Santander, and Tesco Bank. A multi-country or cross-cultural study may further enhance our understanding of the approaches and findings.

Online customer reviews become more informative when categorised by service sector. To enhance customer satisfaction, bank managers should pay attention to both positive and negative reviews, and track trends over time.

The uniqueness of this study lies in its exploration of the importance of categorisation in text-mining-based sentiment analysis, its focus on the influence of both positive and negative sentiments, and its emphasis on tracking sentiment trends over time.

The purpose of this study is to examine whether service innovation capability can affect firm performance in the architecture, engineering and construction (AEC) context, and, if so, how.

This study developed a theoretical framework illustrating the performance impacts of service innovation capability through the business model in the AEC sector. An empirical study was conducted to test the hypotheses using 374 valid questionnaires using structure equation model (SEM).

The results verify that service innovation capability positively influences firm performance mediated by the business model. As to the direct effect, service innovation capability is positively associated with firm performance.

This study highlights how service innovation capability affects performance and reveals the underlying mechanism.

The purpose of this paper is to explore mechanisms of the overseas marketing assets needed for marketing dynamic capability in Chinese multinational enterprises (MNEs) settings. Marketing assets of foreign subsidiaries contribute to the dynamic capability of MNEs, which are crucial for their sustained competitiveness. This kind of mechanism attracts much attention in academia and industry. However, there are few studies on how dynamic capabilities are developed in MNEs considering the organizational structure of geographically dispersed assets in multiple locations. This paper aims to examine the effect of knowledge-based and relational-based marketing assets on dynamic marketing capabilities and the mediating effect of customer orientation on Chinese MNEs.

Integrating the dynamic capability approach and the international marketing literature, this study examines the impact of two types of marketing assets of foreign subsidiaries, focusing on knowledge-based and relationship-based marketing assets, on the dynamic marketing capabilities of Chinese MNEs. A large-scale empirical study of Chinese MNEs operating in overseas markets was performed, and the questionnaires were distributed and collected.

The results suggest a positive impact of knowledge-based and relationship-based marketing assets on marketing dynamic capability. We find that customer orientation has a positive mediating effect on the relationship between marketing assets and marketing dynamic capability. We also find that the competitive strength of the overseas market negatively moderates this relationship.

This study aims to contribute to the existing literature with a more fine-grained understanding of marketing assets and marketing dynamic capability, then provides theoretical guidance and management suggestions for the formulation and implementation of internationalization strategies of Chinese MNEs.

The findings outline several important implications for MNEs seeking into expand to overseas markets.

This paper contributes a novel, combined perspective on marketing assets and marketing dynamic capability.

We aim to empirically investigate the effect of affiliated banker directors (ABDs) on corporate tax avoidance. Furthermore, we conduct cross-sectional analyses on the impact of ABDs and explore the underlying mechanisms through which ABDs might influence corporate tax avoidance.

Using a large sample between 1999 and 2016, we empirically examine the impact of ABDs on corporate tax avoidance. We address the endogeneity concerns through an instrumental variable approach and robustness tests with alternative measures of ABDs and corporate tax avoidance.

Our results demonstrate that firms with ABDs exhibit lower levels of corporate tax avoidance. This negative association persists after controlling for potential endogeneity issues and is robust to alternative measures. We further document that the negative effect is stronger when firms are more bank-dependent and financially constrained. Our results indicate that ABDs limit corporate tax avoidance by strengthening corporate governance, mitigating information risks and protecting their reputational capital.

This research extends the existing literature by exploring the influence of ABDs on corporate accounting policies, particularly tax avoidance. These findings enhance our understanding of how directors’ banking experience bolsters corporate governance, information transparency and reputation, ultimately safeguarding stakeholder interests. This paper offers valuable implications for both financial practitioners and policymakers.

Current reinforcement learning (RL) algorithms are facing issues such as low learning efficiency and poor generalization performance, which significantly limit their practical application in real robots. This paper aims to adopt a hybrid model-based and model-free policy search method with multi-timescale value function tuning, aiming to allow robots to learn complex motion planning skills in multi-goal and multi-constraint environments with a few interactions.

A goal-conditioned model-based and model-free search method with multi-timescale value function tuning is proposed in this paper. First, the authors construct a multi-goal, multi-constrained policy optimization approach that fuses model-based policy optimization with goal-conditioned, model-free learning. Soft constraints on states and controls are applied to ensure fast and stable policy iteration. Second, an uncertainty-aware multi-timescale value function learning method is proposed, which constructs a multi-timescale value function network and adaptively chooses the value function planning timescales according to the value prediction uncertainty. It implicitly reduces the value representation complexity and improves the generalization performance of the policy.

The algorithm enables physical robots to learn generalized skills in real-world environments through a handful of trials. The simulation and experimental results show that the algorithm outperforms other relevant model-based and model-free RL algorithms.

This paper combines goal-conditioned RL and the model predictive path integral method into a unified model-based policy search framework, which improves the learning efficiency and policy optimality of motor skill learning in multi-goal and multi-constrained environments. An uncertainty-aware multi-timescale value function learning and selection method is proposed to overcome long horizon problems, improve optimal policy resolution and therefore enhance the generalization ability of goal-conditioned RL.