Complex products, which are central to the high-end equipment manufacturing industry, require significantly higher standards of aftermarket service quality compared to standard products. This creates a challenge for the main manufacturer, as it must incentivize aftermarket vendors to enhance collaborative cooperation, improve aftermarket service quality, and further elevate their quality control practices. This paper, based on the collaborative cooperation model of the “main manufacturer-aftermarket vendors” relationship, applies the Stackelberg game theory to construct a collaborative cooperation model where the main manufacturer acts as the leader and the aftermarket vendors act as the followers, from the perspective of incentivizing the aftermarket vendor by the main manufacturer. It examines the influence of quality cost-sharing strategy on the optimal decision-making of the main manufacturer and aftermarket vendors and investigates the impact of the main manufacturer’s effort cost coefficient and profit coefficient on the quality control outcomes. The results show that the main manufacturer, in addition to providing technical support, continues to implement the quality cost-sharing incentive, which facilitates strengthened collaboration between the main manufacturer and aftermarket vendors, improves aftermarket service quality, and achieves Pareto improvement. However, the quality cost-sharing ratio between achieving the optimal profit and the optimal aftermarket service quality for the main manufacturer is inconsistent. Only when the quality cost-sharing ratio remains within a certain threshold range can both “quality control” and “economic interests” be effectively balanced. Additionally, the main manufacturer can optimize its quality control level by improving supervisory control efficiency, reducing unit effort cost, and enhancing unit quality returns.

The ongoing development of the cloud computing technology promotes the maturity and application of SaaS, which makes software vendors who traditionally used retail channels to sell their products begin to consider the introduction of a SaaS channel. This paper explores the optimal channel strategy for software vendors in a supply chain composed of a vendor and a retailer, taking into account consumer disappointment aversion. It compares decentralized and centralized decision-making models, both with and without the SaaS channel. The results show that in low consumer disappointment aversion, if the unfit cost is high, the vendor will not introduce the SaaS channel under centralized decision-making. However, in a decentralized setting, they introduce the SaaS channel as a means of channel. If the unfit cost is low, the vendor will introduce a SaaS channel to compete directly with the retail channel, adopting a competitive strategy. In high consumer disappointment aversion, the vendor will always choose to discontinue the retail channel and sell exclusively through the SaaS channel, adopting a replacement strategy. Interestingly, although consumer disappointment aversion will aggravate the disadvantages of the retail channel and motivate the software vendors to introduce the SaaS channel, when the quality uncertainty is low and the unfit cost is high, the profits of the retailer will actually increase instead of decreasing.

Considering an online retail channel and two types of live streaming channels, this paper constructs a model for e-commerce supply chain to study the optimal decision-making under different scenarios, and further analyzes the influence of the share of online celebrities’ channel, the diversion coefficient of online celebrities, and the commission ratio on the equilibrium solution. The results show that when the cross-price elasticity coefficient reaches a certain threshold, the optimal choice for the brand is to establish both its self-broadcast and online celebrities’ live streaming channels (i.e., a hybrid mode). In the hybrid mode, when the price competition coefficient and commission ratio fall within a feasible range, the price in online celebrities’ live streaming channel initially increases and then decreases as the share of online celebrities’ channel increases. As a link of live streaming sales, high commission rate does not necessarily lead to higher profits for online celebrities. After selecting the hybrid mode, the brand should strive for a higher diversion coefficient, a lower share of the online celebrities’ live streaming channel, and a lower commission rate in order to maximize profitability.

In recent years, advancements in science and technology have accelerated the pace of product innovation within enterprises. However, the launch of new products often creates internal competition with existing products in the market. As a result, enterprises must decide how to select new product seeds to quickly penetrate the market and whether old products should be immediately delisted to make space for new products. This paper addresses the problem of selecting new product seeds and determining the optimal delisting time for old product rollover. Specifically, it explores the launch of new products in social networkalready populated with old products. Both new and old products influence each other through a diffusion process driven by internal competition. The goal is to maximize product diffusion profit by strategically selecting new product seeds and timing the delisting of old products. Therefore, it develops an integer programming model, which describes the influence diffusion process considering internal competition, and proposes an iterative updating hybrid greedy algorithm (IUHGA) to find the optimal solution. Comparative experiments with classic algorithms indicate that IUHGA has a higher solution quality. The results show that the timing of delisting old products is closely related to the profit of newer products, the number of selected seeds, the maximum diffusion period, and the aggregation of network users.

To address the problem of time-dependent electric vehicle routing problem (VRP) with drones, this paper comprehensively considers factors such as no-fly zones, simultaneous delivery and pickup, the ability to serve multiple customers in a single drone trip, and the impact of load and speed on energy consumption for both vehicles and drones. To minimize total delivery costs, it develops an optimization model for the electric vehicle routing problem with drones. Given the characteristics of the problem, the initial paths for both vehicles and drones are generated using multi-layer integer coding, and an adaptive large neighborhood search algorithm is designed to solve the model. The algorithm incorporates an operator scoring mechanism to adaptively select removal and insertion operators, along with an inferior solution acceptance criterion form simulated annealing to avoid local optima. The effectiveness of the model and algorithm is demonstrated through several examples, and the impact of factors such as detouring due to no-fly zones, the number of customers served per drone trip, and the lowest state of charge on the delivery scheme is analyzed. The research not only enriches the field of VRP but also expands its scope, providing a theoretical basis for logistics enterprises in optimizing their delivery schemes.

Under the carbon cap-and-trade mechanism, based on the centralized decision-making model of the supply chain for ordering big data services, a decentralized decision-making model is developed for manufacturers and retailers to order big data services separately. Then, the models are solved and compared to assess the impact of different supply chain partners’ ordering decisions on carbon emission reduction, output, and profit. Finally, a revenue-sharing contract is designed to coordinate the supply chain in cases of dominance under a decentralized supply chain. The results indicate that compared with the retailer ordering big data services, the manufacturer achieves higher carbon emission reduction, product output, and firm profits when ordering big data services. Carbon emission reduction, product output, the effort of the big data service provider, the payment level of the manufacturer, and the firm’s profit all increase with consumers’ low carbon preference or the impact of big data marketing on consumers and decrease with the ratio of the firm’s cost after versus before ordering big data services. If the retailer shares a proportion of his revenue with the manufacturer, it can reduce carbon emissions, increase output, and improve profits for supply chain partners.

Due to the severe destructiveness of natural disaster, societal attention to emergency events has been steadily increasing. Studying efficient and rational delivery routes has become an urgent task in emergency management. To address this issue, this paper proposes using a synchronized truck-drone delivery mode to transport emergency supplies during post-disaster relief efforts, aiming to rapidly deliver critical resources to affected populations. First, it introduces a deprivation cost to evaluate the suffering experienced by victims due to lack of resources and develops a synchronized truck-drone delivery routing model which is non-linear integer programming, with the goal of minimizing both the total logistics cost and the deprivation cost experienced by the disaster victims. Next, it solves the model by employing an improved elitist nondominated sorting genetic algorithm (INSGA-II). Finally, it verifies the proposed model and algorithm through numerical analysis and comparisons with other algorithms. The results indicate that the synchronized truck-drone delivery mode significantly reduces both the total logistics cost and the hardships faced by disaster victims. INSGA-II outperforms the NSGA-II algorithm in terms of effectiveness.

The response time of the recovery of instant delivery disruption under the background of new retail affects the experience of high-quality users and the connectivity of various delivery components. Combining scenario response and data-driven ideas, this paper classifies disruption scenarios and generates and stores historical disruption scenarios in the preparatory stage before any disruption occurs. In the scenario response decision-making stage after the interference occurs, it introduces a hybrid approach that combines traditional scenario response methods with data-driven techniques, and develops a matching and response rule based on the historical scenario storage format, enabling rapid recovery and decision-making. To evaluate the effectiveness of the method, it conducts a simulation analysis. The parameter sensitivity analysis shows how to set the similarity threshold and the number of classifications (k) to reduce costs and improve the speed under different levels of time urgency in interference processing. These findings provide reference and suggestions for the rapid response of instant delivery interference recovery decision-making in the new retail context.

This paper explores the relationship between different online advertising and the dynamic transition in consumers’ advertising responses, which are not directly observable, from a hidden dynamic perspective. Using the data of a large e-commerce company in China, it develops a continuous-time hidden Markov model to analyze the behavioral characteristics and dynamic transfer paths of advertising response of consumer groups across different advertising channels. The findings show that most online advertising significantly influence the transition of consumers’ advertising responses. Specifically, customer-initiated contacts advertising has a more pronounced effect on consumer advertising response compared to firm-initiated contacts advertising. Additionally, the non-transactional behavior of consumers under CICs advertising remains basically consistent, while the non-transactional behavior of consumers under firm-initiated contacts advertising differs significantly. From the perspective of dynamic transition, this paper reveals the underlying reasons behind the different effects of various online advertising, and provides guidance for enterprises to effectively place online advertisements.

Live streaming e-commerce is an emerging and popular mode of online retail, where products are introduced and promoted through live streaming. Based on the signal theory and the non-verbal cue theory, this paper utilizes audio mining techniques to analyze the live streaming and related sales data from Taobao Live and investigates the influence of streamer’s voice characteristics (specifically pitch, loudness, and speech rate) on sales performance. The results show that pitch, loudness, and speech speed have a significant and robust inverted U-shaped effect on live streaming sales. Additionally, the rhythm of background music positively moderates this relationship between speech rate and sales performance. The matching of background music tempo and speech rate for a specific product category enhances sales performance. This paper further explores different influences of voice characteristics during the two-stage live streaming. The results show that the inverted U-shaped effect of speech rate is insignificant in the first stage of product explanation but becomes significant in the subsequent stage focused on convincing purchase. Overall, this paper provides a new direction for improving the sales performance of live streaming e-commerce. The findings may help live streaming e-commerce merchants and streamers to improve their online performance through strategic use of voice and background music.

Based on 52 independent studies and 33424 individual samples, meta-analysis is used to calculate the overall effect of agricultural subsidies on green production and assess the overall effect size while meta-regression is used to investigate the moderating effect of agricultural subsidies on green production across five dimensions: regional, policy, production, period, and method. The results show that in the regional dimension, subsidy policies in non-grain-producing regions have a more promoting effect on green production than those in major grain-producing regions. In the policy dimension, green agricultural subsidy policies are more effective in promoting green production than traditional agricultural subsidy policies. In the production dimension, subsidy policies encourage farmers to adopt green production technologies, making them more likely to pursue green production. In the period dimension, after the establishment of a green ecology-oriented agricultural subsidy policy system in 2020, the effectiveness of agricultural subsidies in promoting green production has improved. In the method dimension, the types of econometric models used in the study have a moderating effect on the relationship between agricultural subsidy policies and farmers green production. These findings can provide a theoretical basis for the transformation and upgrading of China’s agricultural subsidy policy system.

To address the technical challenges in photolithography and achieve autonomous and manageable progress, it is imperative to stimulate the formation of a multi-coupling network in industrial technology through policy interventions. The preliminary construction of China’s photolithography technology multi-coupling theoretical model is enhanced by incorporating the relevant policy texts to delineate the policy-driven mechanisms. Additionally, the utilization of complex network knowledge flow model and the network coupling theory, along with the analysis of relevant patent data, facilitates the establishment of a multi-coupling network emergence model. This model enables the simulation and analysis of various policy-driven modes in the emergence process of China’s photolithography technology multi-coupling network, thereby facilitating a comprehensive understanding of the underlying mechanisms. The results show that in the innovation coupling period of policy development, the core lithography technology research and development entities are closely coupled with core enterprises in a hybrid drive mode, resulting in strong relationships and dense network characteristics with a high coupling degree and high knowledge transfer efficiency. Supporting technology suppliers emerge in a cluster-driven mode with close cooperation network relationships, and their cooperation strength has more synergistic advantages compared to the core technology coupling domain and the business oriented coupling domain. The hybrid driving mode is more conducive to improving network coupling compared to the leader-driven mode, in which the main innovation subsidy and the network coupling have an inverted “U”-shaped relationship. Moreover, under high innovation subsidy and low collaboration incentives, the overall coupling degree of the network decreases significantly. The mechanism summarized is of great significance for optimizing the top-level design of China’s lithography technology breakthrough, and thereby enhancing China’s ability to independently control key core technologies.

It is well-documented that the relational embeddedness of inventor networks plays a vital role in innovation performance. However, most existing literature focuses on how relational resource allocation influences innovation from a static perspective. The understanding of network dynamics, especially relationship between relational dynamics and innovation remains limited. Additionally, little attention has been paid to the further unpacking of different innovation dimensions. To address these gaps, this paper draws on the social resource theory and the innovative combination perspective, further distinguishing between innovation novelty and innovation performance as separate dimensions of innovation performance. It explores how network dynamics, specifically ties added, ties lost, and relational embeddedness dynamics, influence both innovation novelty and impact. Utilizing U.S. pharmaceutical patents data, it employs negative binomial regression to test the hypotheses. The findings indicate that ties added are positively associated with both innovation novelty and impact. The ties lost are negatively related to both innovation novelty and impact. The relational embeddedness dynamics could enhance novelty while impeding impact. In summary, this paper not only scrutinizes the dual effects of relational embeddedness dynamics on individual innovation, but also provides specific suggestions for managers to enhance the novelty or impact of research and development output.

This paper defines short-term extreme upward moves as “right tail risk” and explores its impact on systemic risk in China’s stock market. During periods of risk eruption and spillover, the level of risk spillover is highly sensitive to tail shocks. The growth of long-leveraged funds and increased financial integration tend to amplify spillover risk, while short-leveraged funds and greater economic integration help mitigate it. The policy implications of this paper is that, first, regulators should consider market expectation deviations in policies, as significant deviations can trigger the procyclical accumulation and eruption of right tail risk. Next, specialized monitoring of right tail risk is essential to manage financial risk after accumulation. Finally, in a unified national market, it is crucial to further emphasize risk-stratified management in multi-tiered capital markets.

To meet the needs of financiers with different repayment abilities and to enrich the market structure of patent securitization products which could provide investors with more options, a fixed proportion partial prepayment clause is introduced. This clause stipulates that the amount of prepayment for each period is a fixed proportion of the amount payable for each period of the remaining term. To effectively measure the risks, a pricing model for patent securitization products is developed. Based on the discounted cash flow method, the model uses a  Markov process to describe the prepayment risk and the Creditrisk+ model to account for default risk. This allows for the determination of the issuing cost of patent-backed securities. The pricing model is then applied to a sample patent-backed security. The results show that the pricing model developed in this paper is more sufficient, and the fixed proportion partial prepayment clause designed in this paper can effectively limit the early repayment behavior, helping to manage the risk relatively effectively under the premise of meeting the issuance conditions.

Digital transformation is an inevitable imperative for firms seeking to sustain and reshape competitive advantages in the digital economy. However, the rhythm of digital transformation remains a critical yet underexplored scientific issue in current research. Adopting an organizational change perspective rooted in the absorptive capacity theory, this paper investigates the influence of digital transformation rhythm on firm performance. Additionally, it examines the moderating impact of knowledge stock and digital transformation experience. The empirical analysis reveals that the digital transformation rhythm exhibits a U-shaped effect on firm performance. Specifically, knowledge stock and digital transformation experience act as moderators, influencing the curvilinear relationship in negative and positive manners, respectively. This paper makes a significant contribution to the literature by introducing a dual-logic-driven perspective on organizational change rhythm and exploring the boundary conditions of digital transformation rhythm through the lens of the absorptive capacity theory.

Based on the difference in consumer perceived quality of products sold in different channels, this paper examines dual-channel retailing composed of one online retailer and multiple offline retailers, and develops game models of the online retailer without and with the live streaming to analyze the conditions for the introduction of live streaming by the online retailer and explore the impact of the offline travel cost as well as the hassle cost of watching live streaming on pricing and the profits of the online and offline retailers. The results show that in dual-channel retailing, whether the online retailer introduces the live streaming depends on the number of offline retailers. The decision interval for the online retailer to introduce live streaming increases as consumers’ perceived quality of offline channel products or offline travel cost increases. As product value increases, the decision interval for introducing live streaming first increases and then decreases. When online retailer adopts live streaming, as the number of offline retailers increases, the price of both live streaming and offline retailing, as well as the demand and profits for both the online and offline retailers decrease.

Managers play a key role in financial decision-making of enterprises, and to a certain extent, can transform their personal preferences into corporate behavior. This paper, using a sample of A-share listed companies from 2015 to 2021, explores the intermediary pathways and governance mechanism through which managerial risk preferences on the formation of financial distress in enterprises. The results show that higher managerial risk preference increases the likelihood of financial distress, mainly through the intermediary effect of debt expansion and leverage manipulation. These findings remain robust after controlling for endogeneity and conducting robustness tests. The heterogeneity analysis shows that in non-state-owned enterprises, enterprises with low capacity utilization, those in poor financing environments, and those operating in highly uncertain environments, managerial risk preference has a more pronounced positive effect on financial distress. Further research indicates that both salary incentive and equity incentive in the incentive mechanism as well as internal controls in the constraint mechanism can effectively mitigate   financial distress caused by managerial risk preference. However, the effect of external audits in reducing financial distress is not significant, suggesting that the governance effect of homogenized external audit is limited. The research conclusion helps to clarify the generation mechanism of enterprise financial distress from the perspective of managerial risk preference, and has a positive reference value for alleviating agency conflict and resolving enterprise financial distress.

Culture plays a crucial role as both a guiding and supportive force in digital transformation. This paper, focusing on non-financial listed companies in the Shanghai and Shenzhen stock markets from 2009 to 2021, analyzes the intricate influence and underlying mechanisms of Confucian culture on corporate digital transformation through a resource-based perspective. The empirical results demonstrate a positive promotion effect of Confucian culture on corporate digital transformation, a conclusion remains valid even after conducting some endogeneity analysis, including two-stage least squares, propensity score matching, and various robustness tests such as changing core variables, excluding alternative explanations, and sample adjustments. The mechanism analysis reveals that Confucian culture influences digital transformation through its impact on human, credit, and technological resources as channels through which Confucian culture bolsters digital transformation efforts, underscoring its prowess in facilitating resource mobilization for this transformation. Further heterogeneity analysis reveals that the promotion effect of Confucian culture on digital transformation is more pronounced in non-state-owned enterprises, those with abundant redundant resources, those operating in high uncertainty environments, and those less influenced by foreign cultures. Finally, this paper explores the interaction between “resources” and “capabilities” in digital transformation and finds that the combination of Confucian culture and executives with information technology background further enhances corporate digital transformation. This paper broadens the scope of studies on Confucian culture in corporate digital transformation, providing valuable insights into promoting digital transformation, fostering cultural self-confidence, and empowering enterprises.

A Chinese style of innovation requires firms to integrate corporate social responsibility (CSR), the construction of innovation ecosystems, and the improvement of innovation performance. Based on the resource dependency theory and the stakeholder theory, and using a longitudinal case study approach, this paper investigates the relationship between CSR, innovation ecosystems, and open innovation performance in high-tech enterprises, following an in-depth analysis of social responsibility fulfillment and innovation ecosystem building in the iFLYTEK’s 24 years journey. The findings show that the social responsibility fulfillment of high-tech enterprises generally follows three stages: responsibility ideal, policy response, and strategy matching. The fundamental pattern of social responsibility fulfillment for innovation ecosystem construction is social responsibility→innovation species symbiosis→innovation community interaction→innovation ecosystem evolution”. Innovation ecosystem contributes to open innovation strategic performance by facilitating innovation resource acquisition and innovation synergies. Innovation ecosystem serves as a bridge between social responsibility fulfillment and open innovation strategic performance enhancement. This paper provides theoretical support and practical insights for high-tech firms seeking to build a self-sustaining and self-supporting innovation ecosystem.