Abstract:Artificial intelligence (AI) technologies, particularly AI-generated summaries (AIGS), are reshaping user interactions and decision-making processes on digital content consumption platforms by altering the way information is presented and processed. Grounded in the Cognitive Miser Theory, this study conducted a field experiment on a leading Chinese video social media platform to examine the impact of AIGS on user endorsement behaviors (e.g., likes, coin-tipping, and favorites) and commenting behaviors. The results show that AIGS reduces users’ cognitive load in watching and comprehending videos, thereby positively influencing both endorsement and commenting engagement. Further analysis reveals that AIGS significantly alters the hierarchical structure of video comments by reducing parent comments and increasing child comments. From a cognitive perspective, this study reveals the double-edged sword effects of AIGS as an information-processing tool, contributing to the theoretical understanding of AI applications and platform governance.

Abstract:This research, oriented toward strengthening the social responsibility of mobility platforms, regulating driver workload, and ensuring economic revenue, proposes a sustainable service operation mode for intelligent mobility platforms led by reservation services and supplemented by instant services. A two-stage 0-1 mixed integer programming model is constructed. The stage I customizes service routes for reservation passengers based on driver workload, while stage II achieves on-demand matching considering passenger absenteeism. Given the different complexities of the two models, stage I is solved using a column generation-based heuristic algorithm (CG-BH) and a hybrid meta-heuristic algorithm based on adaptive large neighborhood search and simulated annealing (ALNS-SA), while stage II is solved using Gurobi. Numerical experiments verify the rationality of the two-stage model and highlights the superiority of the ALNS-SA algorithm in balancing solution quality and computational efficiency. Furthermore, the study quantifies the impact of the number of drivers and empty vehicle duration penalties on experimental results, demonstrating that the proposed method can reasonably regulate driver workload and match them with personalized service routes within their workload capacity. It also reveals that the revenue generated by serving high-quality instant passengers is higher than the losses incurred by defaulting passengers. This research provides management insights for the development of sustainable service operation mode for intelligent mobility platforms.

Abstract:Amid the digital economy, data, as a critical factor of production, is profoundly reshaping enterprise value creation patterns and competitive landscapes through its dual value: Significantly enhancing service quality and boosting advertising precision. The rapid emergence of the data brokerage market provides firms with a strategic lever to harness this dual value. By integrating supplementary datasets from data brokers, firms can optimize service experience and achieve precise ad targeting. However, this widespread data utilization simultaneously intensifies market competition. To investigate firms’optimal data acquisition and service pricing decisions, this study constructs a game-theoretic model involving two competing firms. The results reveal that firms with superior data analytics capabilities purchase more data at high data prices, whereas those with weaker capabilities tend to buy more at low data prices. Counter intuitively, when both the unit advertising revenue and data price are low or high, firms with weaker analytics capabilities tend to set higher service prices. Furthermore, firms with stronger analytics capabilities earn higher profits at high data prices, while those with weaker capabilities can achieve profit superiority at low data prices. Under specific market conditions, a “winner-takes-all” outcome emerges, where firms with stronger analytics capabilities dominate the entire market.

Abstract:As AI service ecosystems continue to evolve, competition in end-user markets increasingly shapes both AI service providers’technology licensing strategies and traditional service firms’ AI adoption decisions, with important implications for ecosystem stability and resilience. This study develops a game-theoretic framework to investigate how economies of scale, ecological feedback effects, and AI technology conversion rates jointly influence licensing arrangements, competitive positioning, and social welfare within the ecosystem. The results highlight three key insights. First, economies of scale and ecological feedback mechanisms significantly affect firms’ strategic incentives to license and adopt AI technologies. Second, while higher AI technology conversion rates facilitate licensing equilibria, they may also generate misalignments between private incentives and social efficiency. Third, the entry of Third-party AI providers can alter established patterns of technological collaboration and fundamentally reshape competitive relationships among ecosystem participants. By integrating market competition with technology licensing decisions, this study advances understanding of AI-enabled service ecosystems and offers actionable managerial insights for designing sustainable AI strategies and governance mechanisms.

Abstract:As a novel service paradigm emerging from the deep integration of digital and intelligent technologies, intelligent digital services have enhanced service efficiency and personalization while simultaneously triggering a trust crisis among users. This study introduces the concept of a trust deficit into the domain of intelligent digital services to characterize situations in which the displacement of human-AI trust leads to persistently lower service trust levels than expected. As a key constraint on the effectiveness of intelligent digital services, trust deficits cannot be adequately explained by single-dimensional frameworks grounded solely in interpersonal trust or technical trust. This research focuses on the multidimensional structure and interrelationships of trust deficits, proposing a four-dimensional trust deficit model encompassing ability, benevolence, integrity, and controllability. It identifies the evolutionary pathways of trust deficits characterized by cross-cycle amplification, multidimensional coupling, and heterogeneous decay, and examines the mechanisms through which service type, contextual risk, and users’ intelligent digital literacy shape the evolution of trust deficits. Scenario simulations reveal that following service failure, tool-based and empathy-based intelligent digital services differ markedly in their dominant deficit dimensions and evolutionary trajectories; Service risk significantly accelerates the growth of trust deficits and reshapes their evolutionary patterns; And higher intelligent digital literacy effectively mitigates the escalation of trust deficits, even enabling their convergence to zero over multiple service interactions. This study broadens the theoretical boundaries of trust research in the context of intelligent digital services and offers a conceptual foundation for developing strategies to manage trust deficits.

Abstract:Emerging strategic industries serve as a pivotal driver for nations to foster new-quality productive forces and spur new engines of economic growth. This paper treats the construction of emerging strategic industrial clusters as an exogenous shock to investigate its causal effect on labor employment. Employing data on Chinese listed firms from 2015 to 2022, the study demonstrates that emerging strategic industrial clusters enhance labor employment by around 4.6%. Enhanced external financial support and knowledge spillover effects constitute the underlying mechanisms driving this employment growth. Furthermore, heterogeneity analysis reveals that the employment-promoting effect of industrial clusters is more pronounced for firms in key industries related to the national economy and for enterprises with substantial financial constraints. After addressing endogeneity concerns through a series of robustness tests, including randomization inference and the Oster test, the baseline findings remain robust. Finally, the paper finds that industrial clusters primarily promote the employment of high-skilled labor. This study clarifies the mechanism through which emerging strategic industrial clusters affect labor employment, providing implications for accelerating the development of new-quality productive forces, safeguarding employment, and enhancing people’s well-being.

Abstract:Digital transformation (DT) is critical for the survival and growth of traditional manufacturing firms, yet sustained digital advancement remains challenging, especially for Chinese private firms navigating dynamic market and institutional contexts. Existing research has largely overlooked the role of entrepreneurial mindset (EM) in driving long-term, iterative digital transformation. Based on a longitudinal case study of Wolong Group’s three waves of DT (1992-2023), this study addresses this gap by exploring how EM interacts with DT over time. This study conceptualizes digital advancement (that is, a long-term and continuous DT) as a “sustained risk-taking” process for incumbent firms and examines the mechanism of its formation through the co-evolution of EM and DT in Chinese private manufacturing firms. This study found that the DT path of traditional manufacturers unfolds in three sequential phases: Management digitalization, manufacturing process digitalization, and product/service digitalization. The EM, defined as entrepreneurs’ cognition, behaviors, and emotions toward digitalization, drives each phase of DT. In the DT process, EM evolves from “partial, poorly coordinated” to “holistic, fully integrated” states, facilitating continuous progress on the digitalization journey for traditional manufacturing firms. In addition, DT enhances firms’ market status, which in turn enriches entrepreneurs’ social networks and strengthens their EM. Taken together, the co-evolution of EM and DT is the key driving force behind digital advancement. Theoretically, these findings highlight the interaction of the three dimensions of EM and their role in promoting digital advancement and reveal the co-evolutionary mechanism between EM and DT, thereby addressing the theoretical gap from insufficient research on the role of entrepreneurs in existing DT studies. Practically, this study offers advice for both managers and government on accelerating DT by cultivating EM through cognitive development, behavioral alignment, and emotional commitment.

Abstract:The characteristics of emerging fields, including blurred boundaries, temporary regulatory absence, and dynamically evolving member roles and interaction patterns, challenge the established value co-creation logic of platform ecosystems, which has been constructed on the premise of well-defined value propositions. The question of how platform firms can facilitate value co-creation among ecosystem participants under emerging field conditions has thus become a pressing concern for both industrial practice and theoretical inquiry. Through a comparative case analysis of two focal firms, Wedoctor and CreditTech, this study identifies two predominant value co-creation dilemmas that emerge within platform ecosystems under emerging field conditions: Ambiguity in co-creation objectives and ambiguity in co-creation modes. Building on this, the study further distills two strategic pathways through which platform firms can activate value co-creation among ecosystem participants. This paper argues that a platform firm’s identity positioning constitutes the core driver of its strategic actions. On this basis, a dynamic mechanism model of value co-creation in platform ecosystems, structured around the triadic logic of co-creation dilemmas, identity positioning, and strategic choice, is constructed. The findings contribute to the growing body of research on value co-creation in platform ecosystems.

Abstract:Through the implementation of full-caliber budget management, the New Budget Law reformed the local government debt management system, aiming to curb the potential threats of local implicit debt expansion to financial stability and economic growth. Leveraging the variation in the implementation timing of this policy shock across prefecture-level cities, this study employs a staggered difference-in-differences approach to examine the impact of local government debt governance on fiscal expenditure efficiency.The results indicate that the reform significantly improves fiscal expenditure efficiency. This finding remains robust after conducting parallel trend analysis, considering modified heterogeneity treatment effects, and controlling for the impact of other contemporaneous policies. Mechanism tests indicate that the reform effectively curbs the growth rate of local implicit debt and strengthens auditing and supervision of fiscal expenditure projects, though it does not affect the structure of fiscal expenditures. Further analysis shows that the policy effect is more pronounced in regionswith lower ex-ante fiscal expenditure efficiency, greater reform intensity, and more intense inter-governmental competition. In addition, the reform enhances residents’ trust and satisfaction with local governments and promotes real economic growth by alleviating corporate financial constraints. Taken together, this papershows that local government debt governance helps reduce fiscal risks while improving expenditure efficiency, thereby achieving the dual objectives of “low risk” in local public finance and “high efficiency” in fund utilization.

Abstract:Since the launch of the pilot registration system of the Science and Technology Innovation Board in 2019, a prominent issue has immerged regarding the consistency of quotes provided by inquiry institutions during the IPO process in the Chinese stock market. This article theoretically analyzes and empirically tests the formation mechanism of price consistency among inquiry institutions from the perspective of social interaction. The research results indicate that social interaction among inquiry institutions has a significant positive impact on quotation consistency. Furthermore, this article categorizes social relationships and finds that weak relationships are an important link that affects quotation consistency. From the perspective of economic consequences, social interaction leading to consistent pricing has increased the IPO underpricing rate and reduced the information content of quotes during the inquiry stage. From the perspective of governance mechanisms, administrative intervention effectively suppresses the impact of social interaction on quotation consistency. The implementation of the new inquiry regulations has reduced the consistency of inquiry institutions’quotes but has not entirely eliminated the consistent quotes driven by social interactions.In summary, this study has, to some extent, opened up the black box of IPO pricing for listed companies under the registration system, providing guidance to regulatory authorities to improve the pricing mechanism for new stock issuances.

Abstract:Blockchain technology (BCT) has changed the operating process of supply chain finance (SCF). The information transparency provided by BCT can mitigate debtors’ moral hazards and further enables the credit of the supply chain’s core enterprise to be transmitted among various supply chain tiers. Meanwhile, BCT’s smart contracts can anchor a debtor’s specific revenue source and automatically use it to repay the loan debts, thereby reducing the creditor’s credit risk. Based on these above features of BCT, this study formulates a model of confirmed warehouse financing under a blockchain-driven buy-back guarantee across supply chain tiers (CWB) and uses traditional trade credits among multiple supply chain tiers (TTC) as the benchmark. Game equilibrium analysis of these two financing modes show that: 1) The equilibrium order quantity under TTC is independent of the credit risk among supply chain tiers, whereas the equilibrium order quantity under CWB is increased by the risk-sharing effect resulting from the buy-back guarantee offered by the core manufacturer. Therefore, CWB can unconditionally increase the whole supply chain performance. 2) When the retailer’s initial working capital is at a medium level, supply chain participants can achieve Pareto improvement via CWB. 3) The BCT platform can make suitable charging and subsidizing strategies to guide supply chain participants to achieve Pareto improvement through CWB. This paper reveals that CWB can improve overall supply chain performance and achieve a Pareto improvement, which provides theoretical and practical guidance for the successful implementation of BCT in the SCF field.

Abstract:Against the backdrop of “Carbon Peak” and “Carbon neutral” targers, this paper extends Tobin Q theory to the context of carbon emission reduction (henceforth, CER) to develop an asset equilibrium pricing model for a firm with agency conflicts. It further demonstrates that, on the one hand, CER mitigates the negative effect of social environment concerns on capital stock, thereby increasing future consumption and decreasing the volatility of future marginal utility. On the other hand, CER results in additional costs. Moreover, CER leads to capital underinvestment, a higher risk-free rate, an increased dividend yield, a lower equilibrium risk premium, a higher Tobin Q and a reduced agency cost for the controlling shareholder. Finally, the volatility of capital stock, risk aversion, and social environment concerns increase CER, while the controlling shareholder’s cash-flow rights, investor protection, and the margin cost of CER reduce it.