Abstract:Traditional analyses of industrial and supply chain ecosystems have predominantly focused on supply relationships involving products and services. However, inter-firm connections extend beyond supply relationships to include equity investments and technological collaborations. Ownership penetration network analysis helps assess the interests represented by enterprises, as well as the stability and reliability of their supplies. Meanwhile, an analysis of technological networks allows for a forward-looking evaluation of a firm’s technological potential and substitutability. To enable a more comprehensive analysis of industrial and supply chain ecosystems, this study proposes the SPOT framework, which integrates four types of networks: Supply relationship, product, ownership, and technology networks, to guide the construction and analysis of such ecosystems. While existing research and applications often rely on a single network, the SPOT framework emphasizes the interconnection and integrated analysis of these four networks. This study elaborates on the data sources and construction methods required for building the SPOT framework, along with its applications at both the macro-meso levels and the individual enterprise level. Moreover, it discusses the implications of the SPOT framework for academic research.

Abstract:Manufacturing firms have two main modes of production: Make-to-order (MTO) and make-to-stock (MTS). While competing with each other, firms in different modes also face fluctuations in market demand and changes in customer preferences. In order to explore the influence of these market factors on the competitive outcome of manufacturing enterprises, this paper simulates the production and pricing decision-making processes of MTO and MTS manufacturers, constructs a market competition model containing four types of subjects: Suppliers, manufacturers, demand side, and market, and investigates the evolution of the market under different types of demand (stable, cyclical, impulsive, and mixed) and different demand preferences (time-sensitive, equilibrium, and price-sensitive). After validating the model, this paper, through experimental design and ANOVA, finds that the effect of demand type on the performance of the two types of firms is insignificant, while demand preference produces a significant effect: MTO firms dominate when the demand is sensitive to delivery time, while MTS firms are better suited to the demand with price preference. MTS firms have a higher probability of survival when there are constant entrants and eliminators in the market.

Abstract:Product involvement plays a crucial role in consumer purchasing decisions. Consumers with varying levels of involvement assign different values to products and are influenced by distinct product attributes. This paper investigates how product line design and pricing decisions are influenced by consumer involvement, considering product quality and advertising as key drivers of purchasing behavior. The findings indicate that both single-product and multi-product strategies are viable under certain conditions, contingent on the relative advertising-quality cost coefficients and the distribution of consumers with different levels of involvement. Specifically, firms are likely to adopt a multi-product strategy when: 1) the relative cost coefficient is sufficiently low, and the proportion of low-involvement consumers is sufficiently high; 2) the relative cost coefficient is moderate; or 3) the relative cost coefficient is high, and the proportion of low-involvement consumers is substantial. Otherwise, a single-product strategy will be favored. These results offer valuable insights into optimizing product line design and pricing decisions.

Abstract:With the increasing enrichment of high-involvement products, such as automobiles and home appliances, designing a recommendation system to assist consumers in choosing high-involvement products has become an important research issue. Focusing on the attributes of high-volume and high value products, as well as multistage of consumer consulting, this paper proposes a modular multistage conversational recommendation method for high-involvement products. The proposed method adopts the paradigm of “system query-user answer” to obtain user preferences through questions and generate recommendation results based on user answers. For the issue of multistage consulting, the proposed method introduces the state variable of the stage to the reinforcement learning algorithm. To conquer the contradiction problem between the tasks of preference acquisition and product recommendation, this paper constructs a modular conversational recommendation system.The system includes three components:A dialogue strategy based on reinforcement learning, an attribute selection method based on reinforcement learning, and a product selection method based on knowledge graph and ideal point method. Experiments based on a real purchase dataset on a well-known Chinese auto forum and on simulated user interaction data indicate that, compared with the benchmark method, the proposed method can achieve higher recommendation accuracy with fewer interactions.

Abstract:The interplay between model diversity and predictive accuracy in ensemble forecasting represents a pivotal area of current research focus. Addressing the prevalent challenges of overfitting and suboptimal predictive accuracy in ensemble forecasting, this study proposes a two-stage dynamic selection ensemble strategy predicated on diversity regularization. In the first stage, a novel ensemble forecasting diversity regularization strategy is devised by instituting a loss function that judiciously balances diversity against predictive accuracy. The second stage introduces a dynamic selection ensemble methodology capable of proficiently identifying candidate predictive models that adeptly reconcile diversity with predictive accuracy. Experimental outcomes derived from publicly available datasets attest to the proposed two-stage selection ensemble strategy’s efficacy in notably enhancing predictive accuracy while concurrently bolstering model diversity.This dual advancement substantially elevates the ensemble forecasting model’s generalization capacity and reduces predictive errors.

Abstract:This paper presents a novel Bayesian Kriging model for quality design which tackles both variable uncertainty and model structure uncertainty in the metamodeling process, thus providing a robust foundation for quality improvement. Within the Bayesian hierarchical framework, significant variables in the global trend model of Kriging are effectively identified, and the validity of the candidate models is rigorously assessed through statistical tests. Initially, factor effect principles are integrated into the prior distributions of parameters to clarify their relationships, thus significantly reducing the dimensionality of the candidate space. Subsequently, Markov Chain Monte Carlo simulations are employed to estimate the posterior probabilities of the models, identifying Kriging models with sparse global trend structures. The validity of candidate models is then analyzed through multiple hypothesis testing, with corrections applied to the underestimated prediction variance. The final model is selected based on a comprehensive assessment of both its validity and generalization capability. The simulation results indicate that the proposed method performs satisfactorily across different sample sizes and significance levels. Additionally, the results of the case studies under two industrial scenarios demonstrate that the proposed method effectively identifies significant variables under both differential and non-differential posterior probability conditions.

Abstract:Faced with the challenges of a complex and severe external environment and the critical deepening stage of domestic structural adjustment, a proactive fiscal policy that is moderately intensified, of higher quality, and more efficient is not only crucial for stabilizing expectations but also serves as a significant safeguard for promoting both qualitative improvement and quantitative rational growth of the economy.Production networks are incorporated into a general equilibrium model to theoretically analyze the transmission mechanism of fiscal policy shocks on sectoral output fluctuations.The theoretical analysis reveals that within the production network, the transmission of government spending shocks to sectoral output follows a bottom-up direction.An empirical analysis is then conducted using input-output tables from the World Input-Output Database (WIOD), verifying the significance of the upstream network effect of fiscal policy.Heterogeneity analysis shows that the upstream network effect is significantly negative in sectors with low sensitivity and low upstreamness, influenced by factors such as the price elasticity of supply and demand and the distance to final demand. This stands in sharp contrast to the positive effect observed in sectors with high sensitivity and high upstreamness. In addition, sectoral structure factors significantly moderate the effects of government spending shock.It is argued that while leveraging the demand expansion function of government spending, policymakers should fully prioritize sectoral spillover effects.By amplifying the fiscal multiplier through production networks, fiscal policy can achieve higher quality and efficiency.

Abstract:When suppliers (SMEs) face emergencies that cause demand disruptions, they usually default on credit due to cash flow problems, and how the government incentivizes banks to extend credit to aid SME recovery remains an urgent issue. In this paper, a three-stage government-bank-supplier game model is constructed to examine whether banks offer credit extensions and optimal interest rate decisions, as well as the optimal level of suppliers’demand recovery effort under two different government subsidy policies, namely fiscal interest subsidies and tax preferences. Findings indicate that banks providing credit extensions should comprehensively consider the coefficient of the supplier’s recovery effort costs and fixed expenditure cost per cycle.Both policies encourage banks to extend credit, but only if the government budget meets the “subsidy threshold”.Government subsidies tend to prompt banks to raise interest rates, which in turn reduces the effort of suppliers and causes a decrease in social welfare.Consequently, the optimal government subsidy ratio is that which encourage banks to shift from not extending credit to extending credit. If the government budget is sufficient, it should opt for tax preference policy;otherwise, a fiscal interest subsidy should be chosen. Finally, considering information asymmetry in banks and the government separately, changes in equilibrium strategies are analyzed.

Abstract:Based on data from the China Household Finance Survey (CHFS),this study measures and analyzes the status, trends, and group heterogeneity of household economic risks along three dimensions: Financial vulnerability, poverty vulnerability, and insolvency risk.The results show that although the proportion of households experiencing poverty vulnerability has declined significantly, the share of households facing financial vulnerability and insolvency risk has risen. Moreover, rural households, northern households, and elderly households are exposed to higher economic risks.Applying micro-survey data from other countries, this paper finds that while China’s household economic risks are not particularly high in cross-country comparison, their increasing trend over time calls for attention.Furthermore, this study empirically reveals that exogenous shocks significantly increase financial vulnerability, poverty vulnerability, and insolvency risk, while social networks, social security, financial services, and human capital play important roles in alleviating the economic risks.Our study provides valuable insights for mitigating household economic risks and promoting stable and healthy development of household economies.

Abstract:This paper proposes a new discrete-time option-pricing model, the Bisected Affine Realized Volatility and Jump (BARVJ) model, by jointly modeling overnight volatility, intraday volatility, and jump variation of the underlying asset, and derives a closed-form pricing formula for European options using the Fourier inversion. The pricing performance of the BARVJ model and the benchmarks are examined empirically. By considering overnight high-frequency information and jump variation, the BARVJ model achieves an improvement of 19.99% in pricing accuracy. The BARVJ model performs even better for options with longer maturity and during highly volatile times.

Abstract:Motivating employee performance improvement through negative feedback is a persistent challenge in performance management. As digital and intelligent technologies advance, some tech firms have begun leveraging cutting-edge tools to optimize performance management processes and enhance employee experience. Adopting a motives attribution perspective, this study compares the differentiated performance incentive mechanisms of artificial intelligence (AI) and human leaders when delivering negative feedback. Empirical findings show that, relative to human leaders, AI-provided negative feedback elicits stronger attributed performance-promotion motives and weaker attributed injury-initiation motives, which in turn lead to higher employee performance. Further grounding the inquiry in the indigenous Chinese context reveals the moderating role of leadership style: AI exhibits a greater advantage over authoritarian leadership in transmitting the indirect effects of negative feedback on performance through these two motives; however, compared with benevolent leadership, this differentiated indirect effect is attenuated. The study uncovers the performance-incentive effects of negative feedback in human-AI interaction, broadens the context, perspectives, and approaches for research on AI and negative feedback, and offers insights for the digital and intelligent transformation of performance management practices in Chinese enterprises.

Abstract:Constructing academic discourse power in management research with Chinese characteristics represents a critical challenge within the realm of Chinese management research.This research posits that academic achievements are fundamental components of academic discourse power and thus emphasizes academic journal literature as the primary medium for presenting these accomplishments. Employing the structural topic model methodology, a comparative analysis is conducted on 9 560 organizational-level research articles published in leading Chinese and English journals from 2010 to 2021.This study conducts a comparative analysis of the overarching characteristics of the literature’s topics, encompassing their theoretical and practical dimensions, inter-topic correlations, relevant professional knowledge domains, and their inherent complexity. This paper analyzes the current state and overarching characteristics of academic research in Chinese management, subsequently proposing targeted recommendations for enhancing academic discourse power within this field.