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This study delineates two Taiwanese TESOL teachers' efforts of combining English writing with entrepreneurship education to cultivate English majors' interdisciplinary competence in academic writing classes. An integrated business-and-writing approach was proposed to foster English majors' academic writing skills and entrepreneurial capacities. In this study, farming metaphors ("The two farmers," "Planting the seed," "Growing the seed," and "Harvest") will be used to portray the instructors' shared journey of guiding students to complete a creative business planning project. Feedback from students reveals that students gained content knowledge about the business world, and they learned how to write a business plan. For the two instructors, their endeavors to transform English writing courses to link academic knowledge with real-world purposes have encouraged them for more cross-campus collaborations to inject new energy into higher education institutions. To conclude the paper, pedagogical recommendations are provided to foster English majors' learning of English beyond disciplinary boundaries.

The heterogeneous land surface spanning the Yellow River irrigated oasis and the adjacent Kubuqi and Ulan Buh Desert (Hetao area) in Inner Mongolia, China, has been noted to frequently generate planetary boundary layer convergence line (BLCL), providing an important source of low-level lifting for convection initiation (CI). As the first field experiment to collect comprehensive observations of vegetation-contrast-resulting thermal circulations that consistently generate BLCLs and lead to CI, the Desert-Oasis Convergence Line and Deep Convection Experiment (DECODE) was conducted from 5 July to 9 August 2022 in the Hetao area. Two oasis and four desert observation sites were set up in the region that exhibits the highest frequency of BLCL and CI occurrences, equipped with a suite of advanced instruments probing land-atmosphere interactions, planetary boundary layer processes, and evolution of BLCLs and their associated CI, including Doppler lidars, microwave radiometers, soil temperature and moisture sensors, eddy covariance systems, portable radiosondes, C-band polarimetric Doppler radar, aircraft, and Geostationary High-speed Imager onboard FY-4B satellite. DECODE captured 29 BLCLs (16 with CI), 66 gust fronts, 12 horizontal convective rolls, and one tornado. The observations unveiled full thermal circulations spanning the desert-oasis boundary characterized by a horizontal width of ~25 km, a convergence height of ~1 km above ground level (AGL), and divergence from 2 to ~3.5 km AGL, with vertical wind speeds of up to 2 m [s.sup.-1]. Future publications stemming from DECODE will delve into a spectrum of scientific inquiries, including but not limited to land surface and boundary layer processes, BLCL dynamics, CI mechanisms, convective organization, predictability, and model evaluation. SIGNIFICANCE STATEMENT: The purpose of Desert-Oasis Convergence Line and Deep Convection Experiment (DECODE) is to collect intensive observations around desert-oasis divide to better understand the physical processes of boundary layer convergence lines (BLCLs), their associated thermal circulations, convection initiation (CI), and organization over heterogeneous land surfaces. This is important because deep convection initiation poses a major challenge for weather forecasting over heterogeneous land surfaces due to limited understanding of land-atmosphere interactions. In addition, the targeting divide between a desert and an irrigated oasis facilitates a unique situation where human activities can change the weather. This campaign captured 29 BLCLs (16 with CI), which provided a rich dataset for studies not only on convection dynamics and microphysics but also on land-atmosphere interactions, boundary layer processes, and even interdisciplinary studies such as wind farm impact and convective dust storms. KEYWORDS: Complex terrain; Convection lines; Convective-scale processes; Boundary layer; Vegetation; Orographic effects, 1. Background In early October 2016, by fortuitously clicking on a radar image folder labeled 'Linhe' on a computer at the Training Center of the China Meteorological Administration, the first [...]

Background: The importance of information technology to current business practices has long drawn the attention of practitioners and academicians. Aim: This paper aims to broaden understanding about service innovation as a critical organizational capability through which information technology adoption influences the competitive advantage of a firm. In the context of financial firms, this study examines how information technology is adopted and managed to enhance service innovation practices and whether and how service innovation practices may influence the competitive advantage of firms. Method: A research framework and the associated hypotheses are proposed. An empirical survey was conducted and questionnaires were mailed to 558 financial firms in Taiwan. Analysis: A total of 124 valid observations was collected and analysed using the partial least squares technique. Conclusions: The results suggest that adopting information technology has positive effects on service innovation practices, which increase the competitive advantage of firms.

Difficulty in using observations to isolate the impacts of aerosols from meteorology on deep convection often stems from the inability to resolve the spatiotemporal variations in the environment serving as the storm's inflow region. During the U.S. Department of Energy (DOE) Tracking Aerosol Convection interactions Experiment (TRACER) in June-September 2022, a Texas A&M University (TAMU) team conducted a mobile field campaign to characterize the meteorological and aerosol variability in air masses that serve as inflow to convection across the ubiquitous mesoscale boundaries associated with the sea and bay breezes in the Houston, Texas, region. These boundaries propagate inland over the fixed DOE Atmospheric Radiation Measurement (ARM) sites. However, convection occurs on either or both the continental or maritime sides or along the boundary. The maritime and continental air masses serving as convection inflow may be quite distinct, with different meteorological and aerosol characteristics that fixed- site measurements cannot simultaneously sample. Thus, a primary objective of TAMU TRACER was to provide mobile measurements similar to those at the fixed sites, but in the opposite air mass across these moving mesoscale boundaries. TAMU TRACER collected radiosonde, lidar, aerosol, cloud condensation nuclei (CCN), and ice nucleating particle (INP) measurements on 29 enhanced operations days covering a variety of maritime, continental, outflow, and prefrontal air masses. This paper summarizes the TAMU TRACER deployment and measurement strategy, instruments, and available datasets and provides sample cases highlighting differences between these mobile measurements and those made at the ARM sites. We also highlight the exceptional TAMU TRACER undergraduate student participation in high-impact learning activities through forecasting and field deployment opportunities. SIGNIFICANCE STATEMENT: The environment influencing storms often varies across scales that are not always adequately captured by measurements collected at fixed locations. This paper describes our strategy for collecting mobile measurements of the aerosols and meteorology that influenced convection initiated by the sea breeze across the Houston, Texas, region. We show several examples of the local variations in aerosols and meteorology influencing storms that were captured by our mobile platform that were different from those sampled at fixed observation sites. We also highlight potential future studies and science questions that could be addressed using our dataset. KEYWORDS: Deep convection; Sea breezes; Aerosols; Cloud microphysics; Soundings; Aerosol-cloud interaction, 1. Motivation and goals Deep convective systems play a significant role in a number of critical components of the climate system through their large contribution to the hydrological cycle, feedback [...]

This study explores the feasibility of micro-enterprise cluster applications in online-to-offline service platforms to better understand the impact of social innovations and information and communications technology (ICT) capabilities in the digital age. We tried to discover the factors that affect micro-enterprises’ social innovations and sustainable development outcomes. The qualitative data analysis method is summarized using exploratory data analysis. This study reveals that the key drivers of social innovation for microbusiness clusters are shared value proposition and actor engagement, as per the multiple cases through interviews and observations. This study emphasizes that online-to-offline (O2O) e-commerce is an important part of service platforms, especially in the application of microbusiness clustering social innovation, because social innovation can impact their sustainable development by enhancing their ICT ability, which can also increase the application level of service platforms. The sustainable development of microbusiness is one type of sustainable transformation. There are four types of social innovations in microbusiness clusters: the Branding model, the customer interaction mode, the collaboration and profit sharing mode, and the sustainable development mode. The driving forces of the social innovations of microbusiness are frugal-driven, socially driven, and sustainability-driven forces.

In an era of unprecedented global challenges, the cultivation, attraction, and retention of original innovation talent have become critical determinants of national competitiveness and sustainable development. This study introduces a novel multi-agent original innovation talent ecosystem model (MOITEM) to systematically analyze the effects of different talent policies on the ecosystems of original innovation talent across various regions in China. The simulations reveal that while direct talent policies drive short-term talent growth, they often fall short of addressing the long-term needs for resource allocation and environmental optimization, thereby limiting sustained competitiveness. In contrast, indirect and combined talent policies prioritize the overall quality of the ecosystem, providing more enduring support for original innovation. Furthermore, this study explores the interactions among agents within the ecosystem and their synergistic relationships with the external environment, highlighting the necessity of targeted policy formulation and multi-agent collaboration. The findings not only elucidate the strengths and limitations of various talent policies but also offer tailored recommendations based on regional characteristics, emphasizing the importance of continuous evaluation and adaptive policy adjustment. These insights hold substantial theoretical and practical implications for the development and optimization of global policies aimed at fostering original innovation talent.

This study analyzes education, training, and the youth labor market in central and eastern Europe.

The advent of social commerce platforms fueled by the growing commercialization of social media and networking sites represents a significant evolution in e-commerce dynamics. This study investigates the pivotal role of key opinion leaders (KOLs), particularly YouTubers, in shaping consumer purchasing behavior. Recognizing the powerful influence exerted by KOLs, we examined their ability to promote product diffusion through credibility, specialized knowledge, and strategic word-of-mouth campaigns. This study employs a robust theoretical framework that foregrounds the influence of KOLs while integrating critical constructs, such as perceived value and risk, into a comprehensive model. Our empirical analysis, based on data from 411 valid responses, yields the following insights: the expertise and renown of KOLs exert a profound effect on consumer purchase intentions; consumer perceptions of value positively correlate with trust, whereas perceived risk negatively affects it; and trust mediates the relationship between KOL characteristics (popularity and professionalism) and consumers’ relationship strength with purchase intentions. The findings advocate leveraging KOLs’ renown and expertise while mitigating perceived risks to amplify consumer purchase intentions, thus providing actionable strategies for marketers in the burgeoning social commerce landscape.

This article describes a structural examination of the interaction among different imaginative capacities and the entrepreneurial intention of electrical and computer engineering students. Two studies were combined to confirm the factor structure of survey items and test the hypothesised interaction model. The results indicated that imaginative capacity has 3 dimensions: conceiving, initiating, and transforming, whereas entrepreneurial intention comprises 2 factors: conviction and preparation. In addition, the results indicated that initiating imagination strongly affected entrepreneurial conviction and preparation; conceiving imagination positively predicted entrepreneurial conviction, whereas transforming imagination negatively predicted entrepreneurial preparation. The results also indicated that students possessing high, as well as those possessing low, conceiving and transforming imaginations demonstrated a high level of entrepreneurial preparation. In addition, an increase in transforming imagination increases the preparation of students possessing low initiating imagination and reduces the preparation of students possessing high initiating imagination. For developing a novel approach to conduct research on entrepreneurial intention and for discussing the practical implications of the findings, this study explicates alternative methods of cultivating students, promoting their interests, and enabling entrepreneurial behaviour in them by stimulating their imaginative capacities.

Unstructured data in social media is as part of the "big data" spectrum. Unstructured data in Social media can provide useful insights into social phenomena and citizen opinions, both of which are critical to government policy and businesses decisions. Teachers of business intelligence and analytics commonly use quantitative data from sales, marketing, finance and manufacturing to demonstrate various analytics concepts in a business context. However, researchers have seldom used social media data to analyze social behavior and communication. In this study we aim to demonstrate an assessment structure for teaching social media analytics concepts with the goal of analyzing and interpreting social media content. We base this assessment on forum postings regarding two recent events: the Christchurch earthquake in New Zealand, and the Japanese earthquake and tsunami. The aim of the assessment is to discover social insights. We base the assessment structure on Cooper's Analytics Framework to cover such concepts as term frequency (TF), term frequency-inverse document frequency (TFIDF), data visualization, sentiments and opinions analysis, the Nearest Neighbor K-NN classification algorithm, and Information Diffusion theory. We review how the students performed on the assignment that used this assessment, and we make recommendations for future studies.

Despite the observed clinical benefits of nirmatrelvir/ritonavir (NMV/r), it is uncertain whether Taiwan will continue covering NMV/r for high-risk individuals with mild-to-moderate coronavirus disease 2019 (COVID-19). This analysis assessed the impact of sustained utilization of NMV/r on COVID-19-associated healthcare resource utilization (HCRU) and mortality from the Taiwanese health authority perspective (THAP). A decision tree model estimated the incremental number of clinical events associated with NMV/r utilization over a 30-day period. Model results compared (1) a base case using current rates of NMV/r from the THAP, and (2) a hypothetical scenario assuming the current supply of NMV/r is not extended in Taiwan. NMV/r utilization rates included 80% and 0% in the base case and hypothetical scenario, respectively. Outcomes included the number of hospitalizations involving a general ward (GW) stay, intensive care unit (ICU) stay, and mechanical ventilation (MV) use, as well as the number of bed days, symptom days, and hospitalization deaths. Based on epidemiologic data, 150,255 patients with COVID-19 were eligible for treatment from the THAP. In the hypothetical scenario, HCRU increased by 175% compared to the base case, including increases in hospitalizations involving GW, ICU, and MV use (differences: 2067; 623; 591, respectively), bed days (difference: 51,521), symptom days (difference: 51,714), and deaths (difference: 480). Findings indicate that sustained utilization of NMV/r from the THAP reduces the clinical burden of mild-to-moderate COVID-19 through the reduced incidence of COVID-19-related HCRU and deaths.

This paper explores in depth the mechanism of enterprise digital capability effects in supply chain digital development by reconstructing the supply chain cooperation capabilities (digital diffusion capability and digital collaborative capability). We use the questionnaire data covering 272 Chinese manufacturing enterprises and apply a structural equation model to test the hypothesis. The empirical result demonstrates that enterprise digital capability does not directly have a significant impact on supply chain digitalization, and the supply chain cooperation capabilities play a mediating role between an enterprise’s digital capability and supply chain digitalization. The results are robust as we thoroughly consider the direction of enterprise capabilities and verify the systematic requirements of supply chain digitalization and the high order of enterprise digital capability. These provide a theoretical basis for enterprises to promote the digital transformation of their supply chain through digital technology.

This research explores how the partitioned pricing strategy of premium subscriptions affects user willingness to purchase with two studies. Study 1 finds that all-inclusive pricing is more effective than traditional partitioned pricing (i.e., partitioned pricing in Single) and the two new formats of partitioned pricing (i.e., partitioned pricing in Combination and partitioned pricing in Blind Box) in terms of increasing user willingness to purchase due to the induced higher perceived value and perceived price fairness. Study 2 further finds that partitioned pricing in Combination or partitioned pricing in Blind Box (vs. partitioned pricing in Single) can affect perceived value positively via perceived playfulness and negatively via user-perceived price complexity simultaneously. These indirect effects hold across hedonic and utilitarian digital content products. These findings contribute to the literature on the pricing strategies of subscription-based digital content platforms. This research also provides suggestions for digital content platforms to adopt that will help them design their partitioned pricing strategies effectively.

As international capitals keep flowing into Asia, in particular China, it experienced rapid development on both economy and financial market during the last three decades. While more and more financial products are introduced, the government also loosens the leash on investment, so that the approaches to investment in the Chinese market are diversified enough to attract more and more investors to invest in it. This study will thus explore in depth what have affected Chinese ETF performance. For empirical analysis, the daily data on total ETF transactions and related economic indices during a period from February 24, 2005 to December 31, 2015 were used with multiple regression models. Our findings indicated that the amount of international capital inflow to China, the stock market, and interest rates affected the investment performance of Chinese ETFs.

The blind box market is fast-growing, from toys, baseball cards, and vibrant second-hand products to the recent growing tourism market, becoming the latest trend sweeping through China, not only in offline retailing but also online businesses and e-commerce. For young consumers, the element of mystery is a big part of the fun that not everyone can obtain the most special and desirable products. The present research aims to elucidate how travel blind boxes attract young consumers in terms of their psychological connections to travel destinations. In particular, building upon psychological distance theory, this study examines the relationship between perceived similarities between travel destinations and hometowns and the attraction of travel blind boxes. Contributing to the existing literature that mainly describes the phenomenon of the blind box craze but often fails to identify its underlying mechanisms, the present study advances our understanding of such trendy marketing practices by investigating the mediating role of the “aha moment” and the boundary conditions of electronic word-of-mouth recommendations and price sensitivity.

Abstract Data assets serve functions such as value preservation and appreciation, financing, and value creation. They contain extensive information about enterprises’ operational status and future development prospects, providing significant references for external investors’ decision-making and greatly impacting the information efficiency in capital market. This study examines the correlation between data assetization and capital market information efficiency, along with the mechanisms and influencing factors involved. We select a sample of Chinese Hidden Champion small and medium-sized enterprises (SMEs) from 2011 to 2021, utilize text analysis methods to build a data assets dictionary, and extract keywords from corporate annual reports to depict the level of data assetization. The research results indicate that data assetization significantly enhances the information efficiency of capital market, specifically reducing stock price synchronicity and stock price delay. By categorizing data assets into own-use data assets and transactional data assets, we find that both types can improve the information efficiency of the capital market. Mechanism analysis reveals that enterprise data assetization can enhance the information efficiency of capital market by alleviating corporate financing constraints and improving forward-looking information disclosure. Factors analysis demonstrates that digital infrastructure and data circulation transactions can strengthen the enhancement effect of data assetization on the information efficiency of capital market. This study enriches the research outcomes in the micro-research field of data assets, providing valuable insights for promoting the digital transformation of Hidden Champion SMEs and the high-quality development of capital market.

Purpose – This study aims to assess the technological capability of Chinese internet platforms (BAT: Baidu, Alibaba, Tencent) compared to US ones (GAFA: Google, Amazon, Facebook, Apple). More specifically, this study explores Baidu’s technological catching-up process with Google by analyzing their patent textual information. Design/methodology/approach – The authors retrieved 26,383 Google patents and 6,695 Baidu patents from PATSTAT 2019 Spring version. The collected patent documents were vectorized using the Word2Vec model first, and then K-means clustering was applied to visualize the technological space of two firms. Finally, novel indicators were proposed to capture the technological catching-up process between Baidu and Google. Findings – The results show that Baidu follows a trend of US rather than Chinese technology which suggests Baidu is aggressively seeking to catch up with US players in the process of its technological development. At the same time, the impact index of Baidu patents increases over time, reflecting its upgrading of technological competitiveness. Originality/value – This study proposed a new method to analyze technology mapping and evolution based on patent text information. As both US and China are crucial players in the internet industry, it is vital for policymakers in third countries to understand the technological capacity and competitiveness of both countries to develop strategic partnerships effectively.

The rapid popularity of ChatGPT has brought generative AI into broad focus. The content generation model represented by AI-generated content (AIGC) has reshaped the advertising industry. This study explores the mechanisms by which the characteristics of AI-generated advertisements affect consumers’ willingness to accept these advertisements from the perspectives of perceived eeriness and perceived intelligence. It found that the verisimilitude and imagination of AI-generated advertisements negatively affect the degree of perceived eeriness by consumers, while synthesis positively affects it. Conversely, verisimilitude, vitality, and imagination positively affect the perceived intelligence, while synthesis negatively affects it. Meanwhile, consumers’ perceived eeriness negatively affects their acceptance of AI-generated advertisements, while perceived intelligence positively affects their willingness to accept AI-generated advertisements. This study helps explain consumers’ attitudes toward AI-generated advertisements and offers strategies for brands and advertisers for how to use AI technology more scientifically to optimize advertisements. Advertisers should cautiously assess the possible impact of AI-generated advertisements according to their characteristics, allowing generative AI to play a more valuable role in advertising.

A consumption-driven economy refers to an economic growth model primarily driven by domestic consumption and is a common goal for the economic growth of various countries. To explore the impact of industrial digitalization on a consumption-driven economy, this paper conducts an empirical study based on data from 31 provinces in China from 2013 to 2021. The empirical test results indicate: first, industrial digitalization significantly promotes the development of a consumption-driven economy, mainly reflected in the improvement of economic foundation and consumption levels, but shows no significant effect on improving the consumption structure. Second, mechanism analysis results show that industrial digitalization can promote the development of a consumption-driven economy by fostering innovation and advancing the industrial structure. Third, heterogeneity analysis results reveal that the promotion effect of industrial digitalization on the consumption-driven economy exists only in eastern and central China but not in western China, indicating that industrial digitalization in underdeveloped areas cannot exert a positive effect on the consumption-driven economy.

Persona, a special means of image management by social media influencers (SMIs), has become a trending phenomenon and it is expected to substantially affect SMIs’ persuasiveness and ad effect. This study aims to explore the impact of SMIs’ personas constructed through personal values on the effectiveness of social media video ads. Adopting the Stimulus-Organism-Response (S-O-R) framework, this study validates how SMIs’ personas constructed through self-enhancement values and self-transcendence values affect consumer cognitive processes and responses. Consumer behavioral intentions are used to verify the effectiveness of ads derived by SMIs. The findings reveal that influencers’ personas constructed by values can promote consumer purchase intention and forwarding intention. Consumer perceptions of self (i.e., ideal self and actual self) have a mediating role. Results also show that SMIs’ personas with self-enhancement values are more effective in stimulating female consumers’ forwarding intention, whereas personas with self-transcendence values are more influential in female consumers’ purchase intention. This study enriches the influencer marketing literature by providing a more tailored understanding of the impact of SMIs’ personas and extending the applicability of value theory. It also provides insights for stakeholders involved in influencer marketing on how to significantly capitalize on the benefits and deploy marketing campaigns.

In the rapidly evolving platform economy, the competition between platform-owned products and third-party offerings is intensifying. This study examines the entry and pricing strategies of dominant e-commerce platforms such as Amazon and JD Mall which sell both platform-owned and third-party products. We use a complete information game model to analyze the strategic interactions between these platforms and third-party sellers, focusing on cost discrepancies and limited entry capabilities, areas previously underexplored. Our key findings include the following: (1) Platforms with dominant power can restrict third-party product pricing. (2) Increased consumer influence by the platform can reduce competition between platform-owned and third-party products. (3) Platforms prioritize high-value-product markets when entry capabilities are limited. (4) Commission-based revenue models are generally more efficient than entry fees. (5) Regulatory bans on hybrid models do not necessarily enhance social welfare; differentiated taxation on various revenue sources may be more effective. This study contributes by developing a comprehensive game-theoretic model to simulate strategic interactions, analyze pricing competition and entry strategies under cost asymmetry and capacity constraints, and provide theoretical guidance for regulatory policies.

Este artigo propõe um método para aprimorar a produção agrícola sob a perspectiva da produção agrícola inteligente para melhorar a eficiência dessa área em indústrias emergentes em propriedade intelectual. Em primeiro lugar, este artigo analisa os principais indicadores de avaliação da Internet das Coisas na agricultura. O modelo de sustentabilidade e o sistema de indicadores de avaliação são projetados com a estrutura de resposta ao estado do condutor. Em seguida, o indicador é atribuído a um valor usando um algoritmo de análise de componentes principais. Por fim, são calculadas as pontuações do nível de sustentabilidade do modelo da Internet da Agricultura (IoA) de indústrias emergentes em propriedade intelectual em cada município da província. Os resultados da pesquisa fornecem suporte de dados específicos para o desenvolvimento sustentável de indústrias emergentes em propriedade intelectual sob a globalização econômica.

This research aims to investigate the relationships between Business to Business (B2B) social media marketing, customer relationship management effectiveness (CRME), word-of-mouth (WOM), and loyalty, while also examining the mediating role of relationship marketing. A total of 256 respondents were recruited from a machinery manufacturing company’s customer base, encompassing regions as Taiwan and other Asia countries to assess their perspectives on B2B marketing. The following results were derived: (1) B2B social media marketing significantly impacts customer relationship, further enhancing the firm’s performance; (2) Customer relationship management effectiveness and customer loyalty notably influence word-of-mouth (WOM); (3) Social media marketing influences on CRME, WOM, and customer loyalty can be mediated through relationship marketing. This study’s findings provide crucial references for future academic validations. Moreover, professionals aiming to craft effective B2B customer relationship strategies can leverage these results for considerable potential.

With the worsening environmental crisis and the rapid advancement of Industry 4.0, more enterprises are recognizing the importance of sustainable development, yet practical challenges remain. To better understand how internal knowledge sources and knowledge heterogeneity impact firms’ sustainable performance through frugal innovation, this study investigates the moderating role of internal and external knowledge heterogeneity and the mediating role of frugal innovation, drawing on organizational learning theory and contingency theory. The findings reveal that (1) internal and external knowledge sources positively influence sustainable performance; (2) frugal innovation mediates the relationship between these knowledge sources and sustainable performance; and (3) the moderating effect of internal and external knowledge heterogeneity is not significant. The study explores the reasons behind this non-significant finding and offers theoretical and empirical insights into how companies can effectively leverage knowledge resources and innovation strategies in the context of a green economy.

AbstractThis study reviews the literature to determine how cognitive factors affect consumers’ value perception and video streaming platform subscription intentions. This study analyses 20 Scopus and Web of Science peer-reviewed articles to examine the complex relationship between cognitive factors, perceived value and users’ decision-making. The cognitive factors perceived quality, content relevance, variety, enjoyment, cost-benefit ratio, and personalization help explain how users value video streaming platforms. Analyzing these factors can reveal the complex mechanisms that influence users’ platform subscriptions. This has practical implications for industry professionals. The research shows that technical dependability, tailored content suggestions, a variety of content, emotionally engaging content, clear benefit communication, and adaptable experiences shape users’ value perceptions. The above implications are especially relevant in dynamic digital environments influenced by the global pandemic and technological advances. This study also advances consumer behavior theory. This study applies existing models and frameworks to video streaming platforms, adding to the literature. The findings of this study enhance perceived value theory, technology adoption models, personalization theories, and engagement frameworks. These findings improve our understanding of digital user behavior. The study’s narrow focus on cognitive factors and self-reported data are limitations, but it provides valuable insights. These limitations allow future research to explore unexplored areas and use new methods.

Social media platforms have been major channels for consumers to search for product-related information, compare market prices, and consult other experienced buyers. Particularly, social media influencers play a crucial role in consumers’ decision-making process. Scholars have confirmed that information-seeking behavior enhances the efficiency of decision-making. However, a fundamental question arises about what other variables influence the relationship between information-seeking behavior and consumer efficiency. By combining the theory of consumer shopping productivity and para-social interaction, this study proposed a model that explains how information-seeking behavior enhances consumer efficiency through social media influencers and consumer knowledge. The study further extends the theory of consumer shopping productivity to a social commerce setting. This study used representative data from a national survey through face-to-face interviews in Taiwan. The results identified consumer knowledge as the strongest variable in the consumer decision-making process. Furthermore, social media influencer exposure moderately helps consumers to make efficient decisions. Finally, consumer knowledge moderates the relationship between information-seeking behavior and perceived consumer efficiency.

The main purpose of this study is to investigate the impact of service quality and corporate social responsibility (CSR) on customer satisfaction, and customer satisfaction toward post-purchase intentions from sheltered employment institutions. Work experience plays an important role in career development for those people with intellectual disabilities. When they are not yet capable of obtaining a job in the open market, they must receive job training and daily care in sheltered employment institutions. If the sheltered employment institutions cannot operate properly, they will greatly affect intellectual disabilities. From the study of "Children Are Us Bakeries and Restaurants" sheltered employment institutions are one kind of food service business that has been found to request and improve service quality and execution of CSR. These are two main factors which can enhance brand value and create a good reputation for sheltered employment institutions. The questionnaire results indicate that perceived service quality has a positive relationship with customer satisfaction and the reliability dimension is the most important factor for customers to assess service quality. Meanwhile, correlation analysis shows that customer satisfaction regarding service quality influences post-purchase intentions, indicating that friendly and helpful employees can please customers and enhance their satisfaction level and also induce positive post-purchase intentions of consumers. Regarding the CSR of "Children Are Us Bakeries and Restaurants" sheltered employment institutions, the analysis reveals a statistical significance: the greater customer satisfaction of CSR, the higher the post-purchase intention. In addition, in the work, paired-sample t test analysis reveals there is a significant difference (p less than 0.05) in service quality and CSR in terms of "perceived" and "expected" responses. In summary, since those with intellectual disabilities usually are enthusiastic at work and do their best to provide good service and execute CSR well, the value of sheltered employment institutions establishments should be recognized by all should receive continued support and there should be a willingness to hire these intellectually disabled citizens. (Contains 6 tables.)

Taiwan government has initiated a five-year program since 2002: the National Science and Technology Program for e-Learning. The national program started from 2003 and was completed at the end of year 2007, involving thirteen government agencies. This paper describes the results that the national program has accomplished at its first phase, 2003 to 2007. The results include how the national program has helped enhance competitiveness of commerce; improved public welfare in Taiwan; and how the national program has stimulated the research outputs in both industry and academia. (Contains 14 tables and 1 figure.)

This empirical study is concerned with university-industry collaboration from a knowledge management perspective. The authors introduce the concepts of "enterprise-level core elements" to define the principle status of an enterprise during university-industry collaboration, and "network embeddedness" as an indication of the closeness of the collaboration. Furthermore, they examine the method of knowledge management. Finally, they use both learning performance and product innovation performance to measure an enterprise's innovation performance. (Contains 11 tables and 3 figures.)

This paper examines how businesses in Taiwan’s servicescapes are adapting to the growing trend of mobile payments and innovation ecosystems. Through the analysis of four case studies, we uncover the strategies these firms employ to address the challenges posed by changing consumer payment habits. Our research reveals that these companies are establishing efficient mechanisms within their ecosystems, supported by well-structured organizational frameworks. By leveraging innovation ecosystems, they are reshaping financial services and promoting collaborative growth among participants through technology, platforms, resource sharing, and knowledge exchange. This collaborative approach is driving significant changes in the sector, helping these businesses navigate through various challenges while fostering innovation and growth. Additionally, the scarcity of comprehensive observations of the digital payment ecosystem highlights the necessity for further exploration of actor interactions, regulatory mechanisms, and ecosystem management strategies. Such research efforts are crucial for enhancing our understanding of the evolving landscape of digital payments and innovation ecosystems, facilitating informed decision-making and promoting sustainable development in this dynamic industry.

This paper investigates manufacturer encroachment on a sustainable supply chain, where the manufacturer holds exclusive information on product greenness and is responsible for both corporate social responsibility (CSR) and greening. The manufacturer and the retailer play a signaling game whereby CSR effort and wholesale price serve as joint green signals. Findings reveal that, firstly, encroachment induces higher CSR efforts from manufacturers. When customers exhibit a strong CSR preference, the resulting CSR increment leads to increased offline demand and drives up both wholesale and retail prices in a mutually beneficial manner. This phenomenon is referred to as the CSR effect, yielding a win-win encroachment. Secondly, when signaling product greenness to highly CSR-sensitive customers, the high-greenness manufacturer principally distorts her CSR effort downward to an extent unprofitable for the low-greenness manufacturer to mimic and subordinately distorts the wholesale price downward to counter CSR-induced demand decrement and mitigate CSR cost pass-through downstream. Finally, the win-win encroachment pattern is characterized by encroachment profit and signal expense sharing, with encroachment strengthening downward-distorted signaling while signaling weakens the CSR effect. These insights contribute valuable guidance for green manufacturers in CSR decision-making, which functions as a component of green signaling and facilitates transitioning to dual-channel sustainable supply chains.

The emergence of Chinese companies as key players in the global economy underscores China’s transformation into a global powerhouse. Through large sample analyses, case studies, and literature surveys, this special issue aims to enhance our understanding of this phenomenon, focusing on various aspects of the internationalization of Chinese firms over the past two decades and exploring their success as well as the challenges they face moving forward.

This study investigated central and peripheral route factors to assess the impact of Google Advertisements on how these factors contribute to users' call-to-action (CTA) and electronic word-of-mouth (e-WOM) behaviors. We explored the persuasive effects of Google Advertisements on consumers by using a dataset of 483 valid empirical samples from Taiwan. We employed structural equation modeling (SEM) to examine the hypotheses. The empirical results of this study indicate that both peripheral (image appeal) and central (information completeness) routes positively lead to the persuasion effect. This finding confirms that the peripheral and central routes increase the persuasion effect. The empirical results indicate that the most effective pathway was image appeal to the persuasion effect and, ultimately, to call to action. Image appeal was also determined to be a secondary pathway that enhances the persuasion effect, ultimately leading to e-WOM. These findings have valuable implications for companies seeking to attract customers to purchase electronic products through the Google search engine. The novelty of our study is that it includes the peripheral route. Our study findings were derived from the symbolic value lens, rather than the central route based solely on the utilitarian and hedonistic value perspective.

We aimed to provide a realistic portrayal of customer sentiment and product competitiveness, as well as to inspire businesses to optimise their products and enhance their services. This paper uses 119,190 pairs of real composite review data as a corpus to examine customer sentiment analysis and product competitiveness. The research is conducted by combining TF-IDF text mining with a time-phase division through the k-means clustering method. The study identified ‘quality’, ‘taste’, ‘appearance packaging’, ‘logistics’, ‘prices’, ‘service’, ‘evaluations’, and ‘customer loyalty’ as the commodity dimensions that customers are most concerned about. These dimensions should therefore serve as the primary entry point for improving the commodity and understanding customers. A review of customer feedback reveals that the composite reviews can be divided into three time stages. Furthermore, the sentiment expressed by customers can become increasingly negative over time. The product competitiveness based on the composite review can be characterised by four regional quadrants, such as ‘Advantage Area’, ‘Struggle Area’, ‘Opportunity Area’, and ‘Waiting Area’, and merchants can target these areas to improve product competitiveness according to the dimensional distribution. In the future, it will also be possible to take customer demographics into account in order to gain a deeper understanding of the customer base.

Environmental, social, and governance (ESG) performance, along with the novelty of innovation, are significant factors in promoting sustainable development for corporations. This study employs the Heckman two-stage model to examine the impact of ESG performance on innovation novelty using a dataset comprising A-share listed companies in the Shanghai and Shenzhen Stock Exchanges from 2011 to 2020. The results indicate that enhanced ESG performance significantly fosters innovation by expanding innovation resources and mitigating agency problems. Further analysis reveals that this relationship is more pronounced under high economic policy uncertainty, a challenging information environment, elevated financing constraints, and in the context of digital transformation. This study not only expands the research on the economic implications of ESG performance for enterprise innovation behavior but also places a distinct emphasis on innovation quality rather than quantity. Additionally, this study enriches the existing literature on the factors influencing innovation novelty and extends research into the link between environmental protection and firm innovation, particularly from the perspective of stakeholder theory. Furthermore, this study holds practical significance for governments aiming to enhance ESG evaluation systems and establish guiding policies that encourage novel innovations, ultimately contributing to sustainable and high-quality development. Simultaneously, this study provides guidance to enterprises by encouraging them to embrace green development, improve social responsibility, and elevate their governance standards to promote innovation quality.

Blockchain technology enables innovative financing models in supply-chain finance. This research constructs a tripartite evolutionary game model that includes core enterprises as employers, small- and medium-sized enterprises (SMEs) as contractors, and banks as financial institutions, where they have been simulated for their impact on blockchain technology, especially on the strategic choices of supply-chain financing behavior and the system’s evolutionary path under core enterprises’ guarantee mechanism. The findings show the application of blockchain technology can effectively reduce the regulatory and review costs for financial institutions, thereby enhancing the efficiency of supply-chain financing. Particularly, blockchain technology provides a more reliable credit endorsement platform for SMEs in reducing their tendency to default. The guarantee mechanism of core enterprises is more effective with the support of blockchain technology, which helps to build more solid supply-chain financial cooperation relationships. The research contributes to the theoretical research on the integration of blockchain technology into supply-chain finance, especially for improving the operational efficiency of financial services. It also highlights the need for blockchain-backed guarantees from core enterprises in optimizing supply-chain financial services.

In the burgeoning realm of cryptocurrency, social media platforms like Twitter have become pivotal in influencing market trends and investor sentiments. In our study, we leverage GPT-4 and a fine-tuned transformer-based BERT model for a multimodal sentiment analysis, focusing on the impact of emoji sentiment on cryptocurrency markets. By translating emojis into quantifiable sentiment data, we correlate these insights with key market indicators such as BTC Price and the VCRIX index. Our architecture’s analysis of emoji sentiment demonstrated a distinct advantage over FinBERT’s pure text sentiment analysis in such predicting power. This approach may be fed into the development of trading strategies aimed at utilizing social media elements to identify and forecast market trends. Crucially, our findings suggest that strategies based on emoji sentiment can facilitate the avoidance of significant market downturns and contribute to the stabilization of returns. This research underscores the practical benefits of integrating advanced AI-driven analyzes into financial strategies, offering a nuanced perspective on the interaction between digital communication and market dynamics in an academic context.