As key drivers of competitive advantage for innovative platforms, complementors’ choice of innovation strategies are inevitably influenced by both platform governance and heterogeneous user behavior. Therefore, based on a software platform ecosystem, this paper develops a tripartite evolutionary game model incorporating the innovative platform, complementors, and users. By conducting simulation analyses on the sensitivity of key parameters to the strategic choice of each entity, it reveals the dynamic evolutionary process underlying their strategy choice. The findings demonstrate that the primary factor influencing complementors to pursue original innovation is the expected future return generated by such innovation, while the strong governance by the platform companies serves as a secondary mechanism influencing this choice. Measures such as increasing the cost of strong-governance review, lowering the approval rate of imitative-innovation software, and maintaining a reasonable range for the platform’s cost-sharing ratio for promotion can help innovative platforms guide complementors toward original innovation. Although increasing user subsidies has limited influence on complementors’ original innovation decisions and on the platform’s strong-governance efforts, it can effectively improve users’ purchase probability in the early stages of platform development. These findings offer significant practical implications for complementors’ innovation strategy choice and for the formulation of governance strategies by innovative platform enterprises.

To address the issue of insufficient capital investment in production and preservation in rural cooperatives due to the impact of fresh e-commerce payment period. A supply chain is constructed, consisting of a financially constrained rural cooperative and a financially sufficient fresh e-commerce and the optimal operations and financing strategies of both parties under three modes are analyzed: unconstrained centralized decision-making mode, order financing mode with payment period and immediate payment mode with shared preservation costs. The findings show that in the order financing mode, rising bank loan interest rates prompts the cooperative to reduce preservation technology investment to ease financial pressure, and the optimal pricing varies depending on market size. In the immediate payment mode, fresh e-commerce bearing more preservation costs can incentivize the cooperative to increase preservation technology investment, thus improving profits for both parties. Compared to the order financing mode, the immediate payment mode is more effective in motivating the rural cooperative to increase preservation technology investment. Especially when the preservation costs borne by fresh e-commerce is more than half, the cooperative’s preservation technology investment level can reach or exceed the centralized decision-making. When choosing a mode, setting a lower cost-sharing proportion for preservation helps guide both parties in the supply chain to choose the immediate payment mode, realizing a win-win situation.

Based on a supply chain consisting of a manufacturer and an e-commerce platform, this paper examines the interaction mechanisms between manufacturer encroachment and platform demand information sharing in the context of demand-enhancing investments undertaken by the manufacturer. Based on whether the manufacturer pays a commission to the platform to open an agency channel and enter the platform (i.e., encroachment) and whether the platform shares demand information with the manufacturer, it develops four incomplete-information dynamic game models. The findings reveal that when investment efficiency is either high or low, manufacturer encroachment has no significant effect on platform information sharing. However, when investment efficiency is at a moderate level, sharing becomes complementary: manufacturer encroachment promotes platform information sharing, and platform information sharing further enhances the manufacturer’s incentive to encroach. When investment efficiency is moderately high, increasing the commission rate leads to the equilibrium strategy profile to transition along the path of “encroach+non sharing→encroach+sharing→non encroach+non sharing”. During this transition, the manufacturer’s (platform’s) profit exhibits upward (downward) jumps and shows a non-monotonic relationship with the commission rate. Additionally, when the commission rate is low and investment efficiency is high, encroachment increases the wholesale prices in the resale channel, a result that differs from previous literature.

As consumers increasingly prefer green energy-efficient products, enterprises engage in green research and development (R&D) to improve the environmental performance of their products and strengthen  market competitiveness. However, because green R&D exhibits spillover effects, competitors can easily learn from or imitate these efforts, reducing their won R&D costs. Some competitors may even “leapfrog” and achieve an advantage in product greenness. This creates a dilemma for firms: should they engage in green R&D early to gain a first-mover advantage, or should they wait and benefit from competitors’ spillovers? To address problem, this paper develops a two-period duopoly game model. The results show that the there are two pure-strategy Nash equilibria in the timing of green R&D. When the spillover rate is low, the duopolies choose to conduct green R&D in the first period. However, the equilibrium profit in this scenario is lower than if they conduct R&D in the second period, creating a classic prisoner’s dilemma. When the spillover rate is high, the duopolies choose to conduct green R&D at different periods. In this case, the firm that invests first achieves higher profits and exerts greater R&D effort than the later firm. As the spillover rate increases, the first-mover’s profit and R&D effort gradually decline, while the second-mover’s profit and effort increase. Furthermore, in the scenario where the two firms conduct R&D in different periods, a mixed-strategy Nash equilibrium also exists. In this case, the probability that each firm conducts green R&D in the second period increases with the spillover rate.

To address the decline in consumer trust caused by product information opacity, this paper introduces the blockchain technology into a competitive dual-channel direct-selling supply chain, considering both the trust-enhancing effect of blockchain and consumers’ concerns over privacy leakage. By comparing the optimal pricing and profit under four adoption scenarios: neither channel adopts blockchain (NN), only the offline channel adopts (BN), only the online channel adopts (NB), and both channels adopt (BB), it explores in detail equilibrium adoption strategies for blockchain in dual-channel settings and further examines the influence of blockchain adoption on offline channel service levels. The results show that regardless of which channel adopts blockchain, an increase in consumer privacy concern costs lead to a decrease in the channel’s product prices and profits. Moreover, the incentive for either channel to adopt blockchain weakens as the initial product trust level increases. When both consumer privacy concern costs and initial product trust are low, the equilibrium strategy is for both channels to adopt blockchain (BB), When both are high, the equilibrium strategy is for neither to adopt (NN). When both are at moderate levels, the equilibrium strategy is for the offline channel to adopt while the online channel does not (BN). Regarding service levels, under the BN and BB scenarios, offline channel service levels decline as consumer privacy concern costs increase; in contrast, under the NB scenario, the offline service level rises with increasing privacy concern costs.

In response to the high-risk exposure in mega projects and considering the potential role of insurance institutions in onsite risk management, this paper develops a multi-period incentive model for owners and contractor groups under the participation of insurance institutions. Based on a computational experimental approach, it simulates the evolution of risk management behaviors of different stakeholders under various scenarios, exploring the effects of fairness preferences, incentive mechanisms, and insurance participation on the efficiency of risk management incentives. The results indicate that the fairness preference of contractors significantly impacts incentive effectiveness. Dynamic incentives implemented by owners and insurance institutions are significantly more effective than static, constant incentives. Proactive participation by insurance institutions effectively promotes the risk management efforts of mega projects, enhancing the payoffs of all parties involved. These findings offer new theoretical insights and decision-making guidance for risk management in mega projects.

With the deepening of healthcare reform, public hospitals can no longer rely solely on government funding to sustain operations and development, making it imperative to explore new sources of capital and investment methods. Public-private partnership (PPP) projects introduce private capital, providing a feasible funding solution for public healthcare infrastructure. Based on the assumption of bounded rationality, this paper develops an evolutionary phase diagram and game model involving the government, private capital, and public hospitals to analyze the strategy choices of each stakeholder. Using numerical simulations, it further investigates the influence of different initial conditions and variations in key parameters on the the evolution of strategies among the three parties. The findings provide theoretical insights for improving the management of public hospital construction, optimizing the quality of public services, and enhancing investment efficiency.

To address the allocation problem of rural integrated energy systems, this paper proposes a multi-objective, bi-level robust optimization allocation method that considers constraints from external electricity, heat, and gas networks, as well as efficient energy cascade utilization, with flexible heat-to-power ratio combined heat and power (CHP) units as the core equipment. First, it develops a system model incorporating both the external networks and the core CHP units. Then, balancing thermodynamic efficiency and economic performance, it constructs a planning-and-operation integrated two-level optimization model. Finally, it solves the lower-level model using the ideal point method, while transforming and solving the upper-level model using the fully tolerant hierarchical method combined with a comprehensive subjective-objective weighting approach to obtain the optimal configuration scheme. The results indicate that the introduction of flexible heat-to-power ratio CHP units significantly improves multiple energy supply metrics. In particular, during peak heating or cooling seasons, the system can effectively balance economic performance with thermodynamic completeness (exergy efficiency).

Default positive rating rules automatically record unsubmitted consumer reviews as positive. While this practice significantly increases the overall positive rating rate, the resulting information bias diminishes the reference value of online review systems for building trust between buyers and sellers. To address this issue, this paper develops a latent variable model based on online review systems of Chinese e-commerce platforms. The model effectively utilizes actual transaction data to identify and reveal the relationship between consumers’ true satisfaction and biased online reviews. Combining survey data on consumers’ review habits, it employs maximum likelihood estimation to infer consumers’ true satisfaction and proposes corresponding bias correction methods. The proposed latent variable model quantifies the degree of bias caused by default positive rating rules in current online review systems and reveals the true evaluation tendencies of silent consumers. These findings provide theoretical guidance for optimizing e-commerce platform review systems and supporting consumer purchase decisions.

As competition in online retail intensifies, live-stream promotion has become a critical marketing tool. However, strategic consumers’ intertemporal waiting behavior poses new challenges to retailers’ live-streaming promotion decisions. Based on consumers’ strategic purchasing behavior, this paper develops two-stage pricing decision models for retailers under platform-based promotions and live-streaming promotions. It further analyzes the influence of strategic consumer behavior, the extent to which live streaming affects consumers’ product valuations, and the streamer’s commission rate on the retailer’s optimal pricing and promotional strategies. The results show that both the degree to which live streaming enhances consumers’ product valuations and the streamer’s commission rate can either amplify or mitigate strategic consumer behavior. Optimal pricing and discount rates under live-streaming promotions are not always lower than those under platform promotions. Compared with platform promotions, live-streaming promotions do not always yield higher profits for retailers. Only when the streamer’s commission rate is low and live streaming effectively increases consumers’ product valuations can live-streaming promotions significantly enhance retailer profits. Moreover, the higher the proportion of strategic consumers and the stronger their strategic behavior, the more pronounced the profit advantage of live-streaming promotions over platform promotions. Under certain conditions, live-streaming promotions can also increase consumer surplus and social welfare. This paper identifies key factors influencing firms’ promotional decisions in live-streaming contexts and provides theoretical guidance for online retailers to optimize their promotional strategies.

Digital-green synergistic transformation is a key task in current economic development and an essential levers for accelerating the formation of new-quality productive forces. Drawing on the productivity theory and the resource-based view, and utilizing panel data from A-share listed manufacturing enterprises from Shanghai and Shenzhen Stock Exchanges between 2011 and 2022, this paper empirically investigates the mechanisms and boundary conditions of “dual synergy” in promoting the formation of new-quality productive forces. The findings show that “dual synergy” significantly promotes the development of new-quality productive forces, although its marginal effect diminishes as the level of new-quality productive forces increases. Employee education, financing constraints, and innovation capability play mediating roles in the relationship between “dual synergy” and new-quality productive forces. The effect of “dual synergy” on new productive forces is more pronounced when an enterprise’s growth potential is higher, and when regional digital economy development and environmental regulations are more advanced. These findings not only extend the theoretical research on “dual synergy” and new-quality productive forces but also offer a theoretical support and practical guidance for promoting “dual synergy” and accelerating the cultivation of new-quality productive forces in enterprises, providing substantial academic and practical significance.

Intraorganizational knowledge flows lay the foundation for innovation through which inventors integrate existing knowledge and create new knowledge. As channels for resource transfer, research and development collaboration networks, represented by relational and structural embeddedness such as tie strength and structural holes, shape the efficiency and effectiveness of knowledge flow within organizations. However, existing studies present inconsistent views on the coupling of tie strength and structural holes, and have largely overlooked its impact on knowledge flow as well as the contingent role of inventor types. To address these gaps, this paper draws on U.S. pharmaceutical patent data to examine the influence of the coupling of tie strength and structural holes on knowledge flow within organizations, and further explores the moderating effect of inventor types. The findings indicate that the coupling of tie strength and structural holes positively affects intraorganizational knowledge absorption but negatively influences intraorganizational knowledge transfer. Furthermore, such contradictory effects vary with different inventor types. In particular, the positive impact of tie strength with structural holes coupling on intraorganizational knowledge absorption of key inventors is stronger than that of general inventors. However, such relationship is opposite in intraorganizational knowledge diffusion. These findings validate the interaction between network attributes and individual attributes, providing targeted managerial implications for understanding the coupling mechanisms of network embeddedness and optimizing knowledge flow within organizations.

In the process of building a quality-driven nation, key core technologies play an important role, with their innovation quality determining the development of the entire industry. To improve the innovation quality of key core technologies and accelerate technological breakthroughs, this paper approaches the issue from the perspective of quality management and applies the 4M1E method to analyzes the factors influencing innovation quality such as innovation personnel, innovation facilities, innovation materials, innovation models, and innovation environment, and develops a “rocket model” of key core technology innovation elements. Based on the “B-L” model, it establishes Logistic dynamic equations for innovation elements to explore the mechanisms for improving the innovation quality of key core technologies. A reasonable innovation model can provide the driving force for the development of innovation quality, while effective collaboration of innovation elements can guarantee its stable growth. Accordingly, it distills two mechanisms: the innovation model-driven mechanism and the element-coordination mechanism, aimed at promoting the improvement of key core technology innovation quality and facilitating technological breakthroughs.

Faced with the negative effects of data sharing transfer costs and the consumer surplus extraction caused by “big data-enabled price discrimination” under data monopolies, e-commerce platforms encounter “data sharing decision dilemma” when sharing consumer data to empower supplier product innovation. To address this, the paper develops a dynamic game model involving suppliers, e-commerce platforms, and heterogeneous consumers, analyzes the decision-making of e-commerce platforms on consumer data sharing levels under competitive environments, explores the effects of product innovation levels and inter-platform price competition intensity on data-sharing decision-making, determines the upper and lower bounds of platforms on consumer data-sharing levels, and further explores the optimal data-sharing decision of the system from a supply chain perspective. Based on this framework, it demonstrates the existence of feasible incentive intervals for consumer data-sharing on platforms and specifies the conditions for implementing such incentives in different scenarios. The results show that data sharing effectively promotes product innovation, and product innovation level is the key factor for platforms to decide whether to proactively share consumer data. When inter-platform price competition is relatively weak and product innovation is within a certain range, there is potential to increase the consumer data-sharing level. Under these conditions, designing data-sharing incentives can effectively increase the willingness of competitive e-commerce platforms to share data. The ease of implementation data-sharing incentive mechanisms depends on the product innovation level, and the flexible allocation of the additional profits generated in the supply chain allows for increasing platform data sharing while achieving Pareto improvements for supply chain members.

This paper, from the unique perspective of predictable versus unpredictable announcement timing, examines investor information behaviors in a data-driven context. Using online search indices of listed companies and user behavior data from securities service apps, it empirically constructs and analyzes a novel measure of investor attention. The results show that for financial statement-related announcements, unpredictable announcement timing generates greater investor attention compared to predictable timing. Moreover, when announcement timing is unpredictable, financial statement-related announcements attract more investor attention than non-financial statement announcements. Furthermore, under unpredictable timing, different types of announcements exhibit a complementary effect, jointly influencing investor attention behavior.

This paper, employing a computational experimental approach, constructs an artificial Bitcoin market comprising whale traders, trend-following traders, and noise traders, and investigates the mechanism through which investor attention allocation affects wealth distribution. The findings reveal that the artificial Bitcoin market exhibits stylized features consistent with the real Bitcoin market, such as leptokurtic returns and volatility clustering. Over time, investor wealth distribution evolves from a relatively equal state to a relatively unequal one, ultimately displaying a “rich-get-richer” phenomenon. Exogenous shocks affecting investor attention allocation further exacerbate wealth inequality. Robustness analyses show that these conclusions hold even when varying the proportion of exogenous shocks, the proxies for exogenous shocks, the share of whale traders, and the proportion of noise traders. This paper not only expands the research boundaries on the market impact of investor attention allocation but also provides a new perspective for understanding the dynamics of investor wealth distribution.

Deep learning exhibits powerful capabilities for handling long-sequence information and modeling intricate relationships. This paper, utilizing a many to many long short-term memory (M2M-LSTM) network, investigates portfolio strategies under an end-to-end framework. First, within the end-to-end deep learning framework, it constructs a portfolio strategy by integrating a M2M-LSTM neural network with a sliding window technique. Then, using a fixed historical window uniform constant rebalancing strategy as a benchmark, it assesses and adjusts the recent performance of the neural network-based strategy online to mitigate concept drift. Finally, it aggregates adjusted strategies from multiple historical windows to a robust portfolio strategy. Numerical analysis based on domestic and international market data indicate that the proposed strategy outperforms comparison strategies in terms of robustness, profitability, and sensitivity to transaction costs.

Organizational resilience is not only crucial to a firm’s own survival and development, but also to the overall security and stability of the broader socio-economic system. Based on the sample of Chinese A-share listed companies from 2010 to 2019, this paper empirically examines the impact of corporate innovation investment on organizational resilience. The results show that innovation investment significantly enhance organizational resilience. Specifically, firms with higher levels of innovation investment exhibit lower volatility and stronger growth. This conclusion remains robust after the endogeneity treatments, robustness test, and the exclusion of alternative explanations. Mechanism nalyses indicate that promoting strategic transformation, particularly digital strategic transformation and green strategic transformation, is an important pathway through which innovation investment affects organizational resilience. Heterogeneity analyses further reveal that the positive effect of innovation investment on organizational resilience is more pronounced in firms whose executives have research and development backgrounds, a higher innovation-to-output efficiency, a more positive managerial tone, a greater analyst coverage, and a stronger strategic flexibility. Additional analysis shows that even under shocks from extreme events such as COVID-19, corporate innovation investment continues to help strengthen organizational resilience. Overall, this paper demonstrates that firms can “adapt to change with change” through innovation activities, dynamically adapting to environmental shifts and achieving stable and favorable development. The findings offer empirical evidence and managerial insights on how firms can enhance organizational resilience when confronted with environmental changes and shocks.

The corporate sustained charitable donation plays an increasingly important role in China’s progress toward common prosperity, yet the specific factors that shape such sustained donation remain insufficiently understood. Based on the upper echelons theory and imprinting theory, this paper uses a sample of Chinese listed companies from 2006 to 2021 and employs a fixed-effects model to examine the impact of chairpersons’ early-life poverty experience on corporate sustained charitable donations. Incorporating the attribution theory, it further investigates the moderating effect of the chairpersons’ elite education experience and diversified career experience in this relationship. The results show that chairpersons’ early-life poverty experiences have a significant positive impact on corporate sustained charitable donation. However, both elite education experience and diversified career experiences weaken the positive impact of early poverty experiences. The findings suggest that early-life poverty experience has a “prosocial imprinting” effect on chairpersons, but this effect may gradually fade due to the self-interest tendencies embedded in elite education and diversified career paths. Further analysis shows that the chairpersons’ early-life poverty experiences promote sustained charitable donation by increasing corporate altruistic orientation, thereby confirming the transmission mechanism of “early-life poverty experience→corporate altruism→corporate sustained charitable donation”. These findings broaden the research perspectives on managerial experience and corporate social responsibility, extend the application of attribution theory in the corporate social responsibility domain, and provide managerial insights and policy implications for encouraging enterprises to actively engage in sustained charitable donation and promoting common prosperity for all people.

In a VUCA environment, the importance of corporate resilience for firm survival and development has risen an unprecedented height. CEOs, as the core leaders of enterprises, play a key role in enhancing corporate resilience. This paper examines the effect of CEO openness on corporate resilience using a sample of Chinese non-financial listed companies from 2008 to 2022. The results show that CEO openness has a significant positive effect on corporate resilience. This core conclusion remains robust after a series of endogeneity and robustness tests. Moderation analyses indicate that both CEO shareholding and industry competition intensity strengthen the positive effect of CEO openness on corporate resilience. Further analyses reveal that the resilience-enhancing effect of CEO openness is more pronounced in non-state-owned enterprises, high-tech enterprises, and enterprises with higher-quality internal controls. These findings enrich the relevant literature on the determinants of corporate resilience and the economic consequences of CEO openness, while also providing theoretical foundations and empirical evidence to guide CEO selection and enhance corporate resilience.