The K-pop fan economy currently holds a significant position through its unique operational model, yet quantitative analysis of its development trajectory and big data application research require further exploration. This research focuses on investigating the K-pop fan economy. Taking K-pop idol groups as the research subjects, this study analyzes relevant data from fan interaction platforms and social media by deriving the effects of strategies such as quantifying return cycles and releasing multiple album versions. This analysis combines big data tools including cluster analysis and time series methods. Findings reveal that high-frequency comebacks and merchandise marketing significantly boost fan enthusiasm and sales. Big data tools enable precise operations, providing robust support for the efficient development of the fan economy.

This article focuses on brand co-marketing, a mainstream market strategy, systematically organizing its core concepts, theoretical foundations, and common forms. Brand co-marketing refers to the activity where independent brands from different fields collaborate closely to launch new products or services in order to expand the user base and enhance value. Its effectiveness is supported by major theories, including symbolic interactionism, synergy theory, which form typical ways such as cross-industry resource complementarity, deep exploration of cultural IP, and penetration into user circles. The study finds that current brand co-marketing practices face issues such as poor brand synergy, delayed market response, and incorrect user targeting. To address these problems, this article suggests reinforcing brand synergy from the perspectives of preliminary research on values and resource matching, establishing cooperation mechanisms in the mid-term, and unifying communication approaches in the later stages, while also emphasizing regular market research to optimize strategies, providing theoretical references and practical guidance for companies to enhance the effectiveness of co-marketing.

Against the backdrop of global warming, carbon emissions have increasingly become a focal point, making research on their various influencing factors a hot topic in academia. To explore the relationship between corporate carbon emissions and employee compensation, this study investigates whether an inverted U-shaped relationship exists between Chinese enterprises' carbon emissions disclosure index and their employees' wage levels. Employing empirical analysis, this study conducts regression analysis on data from selected Chinese enterprises. It utilizes Lind-Mehlum's triplet and slope tests to validate the inverted U-shaped relationship between the two variables. It also analyses industry heterogeneity to examine whether a significant inverted U-shaped relationship exists across different sectors. The findings demonstrate that the impact of employee compensation levels on carbon emissions disclosure is not monotonically increasing but follows an inverted U-shaped pattern—initially promoting disclosure before inhibiting it—with significant heterogeneity across industries. This research offers practical guidance for enterprises seeking to reduce carbon emissions and optimize their carbon disclosure indices. It also provides insights for balancing environmental pollution concerns with employee compensation issues while developing different types of enterprises.

The effects of social media algorithms on consumer behavior have received considerable attention, but the strategies to assuage the negative effects of it remain insufficient. This paper analyzes the dual influences of algorithmic mechanisms, which is on the one hand, enhance efficiency meanwhile inducing information cocoons and polarization and on the other hand, cause the influence on psychological and behavioral outcomes. The study reveals that while algorithms improve information matching and reduce decision costs, the system, in the meantime, concurrently diminishes content diversity and increases political polarization rates, alongside pervasive privacy risks. To address these issues, this paper proposes: First, developing explainable recommendation systems as well as real-time user fatigue monitoring to enhance transparency and responsiveness. Second, cultivating algorithmic literacy education as well as active information-seeking behavior for the empowerment of users. These recommendations seek to strike a balance between personalized services and responsible design for the promotion of an equitable digital ecosystem.

Digital marketing has shifted from campaign-centric persuasion to data-driven, interactive, and omnichannel systems, reshaping how brand meaning forms and endures. Synthesizing research on customer-based brand equity, brand experience, authenticity, and causal measurement, this paper examines whether digital transformation enhances the connotation-based development of brand image, defined as endogenous, self-reinforcing meaning rooted in coherent identity, lived experiences, and community participation. This paper argues that value co-creation platforms, transparent, consented personalization, consistent omnichannel journeys deepen intrinsic meaning by strengthening associations, perceived warmth, and competence, and attachment. However, three hazards erode "inner beauty": the personalization–privacy paradox and opaque data practices; contextual risks such as misinformation adjacency, deepfakes, and influencer fraud; and measurement myopia that favors short-term optimization over memory structures and trust. This paper proposes a managerial agenda: operationalize connotative goals via a scorecard linking brand knowledge, experience quality, and long-horizon outcomes; design value-for-data personalization; invest in governed communities and co-creation; enforce suitability and creator-integrity controls; and pair randomized experiments with marketing-mix modeling to rebalance portfolios. Properly governed, digital marketing shifts growth from exogenous exposure to endogenous, meaning-driven development.

As livestream e-commerce evolves from the “traffic dividend” stage into an era of “stock competition,” consumers increasingly display a form of “immunity” to repetitive technological stimuli. Building on a systematic review of the relevant literature, this paper constructs a dynamic symbiotic analytical framework of “technology–demand–consumer behavior.” The framework integrates the Stimulus–Organism–Response (SOR) model, parasocial interaction theory, and the technology acceptance and diffusion theories, and further proposes an evolutionary logic of “stimulus–adaptation–restimulus.” Within this framework, technological dimensions such as interactivity, immersion, and algorithmic recommendation are paired with consumer demand dimensions including learning-driven, social-driven, and impulse-driven motivations. Together, these dimensions explain the cyclical feedback mechanism between technological evolution and consumer behavioral responses on livestream platforms. To empirically validate the framework, this study employs a structured questionnaire survey and applies structural equation modeling (SEM) to test the hypothesized pathways. The results demonstrate the presence of multi-level mediating effects, showing that technological stimuli significantly shape consumer motivations, which in turn influence behavioral responses. Partial evidence is also found in support of the proposed “stimulus–adaptation–restimulus” logic. These findings not only provide theoretical support for understanding the dynamic evolution of consumer decision-making in livestream e-commerce, but also offer practical implications for platforms aiming to optimize technological strategies and design more adaptive marketing approaches.

Consumerism, a core value or lifestyle centered on the pursuit of material abundance, often links personal satisfaction with consumption behavior. With the development of the global economy and society, these values are becoming increasingly common and accepted, particularly among young people. This paper focuses on the similarities and differences in consumer attitudes, behavioral habits, shopping preferences, and social influencing factors among adolescents aged 13-19 in China (the world's second-largest economy) and Thailand (a renowned tourist destination). Using a combination of qualitative and quantitative research methods, including data analysis and literature review, the paper analyzes these differences by focusing on four specific consumer-related concepts and areas: consumption platforms, consumption motivations, payment methods, and consumption sectors. This paper finds that while the consumption attitudes of adolescents in the two countries share many similarities, they also differ significantly, stemming from their different cultural backgrounds and perspectives. Thai adolescents are relatively more inclined to spend their income immediately, while Chinese adolescents are more inclined to save their income. Furthermore, due to the development of infrastructure and logistics, Chinese adolescents are more accustomed to shopping online. On the contrary, since Thailand's logistics industry still has relatively room for improvement, and one-fifth of the country's population and nearly half of its GDP are concentrated in the Bangkok metropolitan area, offline stores are more popular among Thai teenagers because of their convenience.

In the context of the ongoing implementation of the “Digital Village” strategy, digital marketing—including short video promotions and live commerce—has exerted a notable influence on rural areas. However, this development does not necessarily correspond to a broad enhancement in educational quality. Instead, it has given rise to a dichotomy characterised as “marketing prosperity versus educational stagnation,” observable across multiple dimensions. To elucidate this paradox, this study develops a township-level indicator of “digital marketing intensity.” Using a fixed-effects panel regression model and incorporating mediating variables—such as the degree of social attention and educational coverage—along with heterogeneity and robustness tests, this research draws on panel data from 372 counties spanning the period 2011 to 2022. It systematically investigates the impact of digital marketing on the urban–rural education gap and its underlying mechanisms. By empirically evaluating the effects of digital marketing on rural education in a comprehensive manner, this study offers a new paradigm for addressing the phenomenon of “digital suspension,” fostering the deeper integration of digital marketing and rural education, and advancing the “soft infrastructure” essential for rural revitalisation.

In recent years, the rapid development of generative artificial intelligence technology has driven global industrial transformation and emerged as a central domain of strategic competition among major nations. By implementing multi-stage and multi-level chip export control policies against China, the United States has endeavored to restrict the development of China's artificial intelligence technology. This study systematically reviews the three-stage evolution of U.S. chip control policies toward China since 2018, analyzes its two main characteristics, and evaluates the actual effects of these policies in terms of short-term suppression and long-term backlash. It also points out that although U.S. controls have hindered China's high-end chip imports and AI R&D progress in the short term, they have objectively stimulated China’s efforts to achieve breakthroughs in various areas, including independent chip innovation, computing power efficiency optimization, supply chain restructuring, and the construction of international rule-making power. Through pathways such as software-hardware collaborative innovation, policy-industry synergy, and international ecosystem co-construction, China has gradually weakened the impact of U.S. technological blockade. This study not only reveals the dynamic game mechanism between technological control and independent innovation, but also provides theoretical frameworks and practical insights for China’s strategic approach to artificial intelligence.

This study investigates how framing sports betting odds as AI-predicted win rates influences wagering behavior, compared to identical predictions labeled as originating from human experts. Drawing on behavioral economics concepts such as the Illusion of Control, belief updating biases, and trust in algorithmic decision-making, we conducted an online survey experiment with 230 participants from 26 provinces in mainland China. Respondents were exposed to hypothetical football match scenarios in which betting advice was attributed either to AI or to human experts, with detailed demographic, AI trust, and Illusion of Control measures collected. Probit and OLS regressions reveal that expert framing significantly increases both the likelihood of placing a bet and the selection of the advised team, whereas AI framing has no significant effect on market entry or choice direction. However, both expert and AI labels increase intended stake sizes among participants who choose to bet, suggesting that algorithmic advice can heighten financial commitment without attracting new bettors. The results indicate a persistent trust advantage for human experts in the Chinese sports betting context, consistent with global evidence on algorithm aversion. Policy implications include the need for transparent AI performance disclosure, targeted responsible gambling measures, and public education to mitigate cognitive biases. This research contributes to the literature by empirically linking AI framing effects, cognitive bias, and demographic moderators in a high-stakes decision-making environment, offering insights for both regulators and platform designers seeking to balance innovation with consumer protection.