Journal of Management Science and Engineering最新文献

Comparison effects have been studied extensively in many fields. In particular, existing operations management articles have discussed the impact of comparison effects on enterprises' production and pricing decisions. Research has also shown that consumers' purchasing decisions are primarily determined by three factors: product quality, selling price, and comparison effects. The current study introduces the concepts of social and temporal comparison effects to examine how comparison effects influence a monopolist’s production quality and pricing strategy for substitutable products. Results reveal the following: (1) Setting different prices for even two types of substitutable products with negligible quality differences can divide customers into three groups under the influence of social comparison effects in a single-stage model. (2) The monopolist should avoid using a price discrimination strategy in which products with a short market life cycle have the same quality but different prices. (3) When the market life cycle of products is sufficiently long in the single-product market and the market with two substitutable products, the monopolist’s optimal choice in the second stage is to keep production quality constant and increase the selling price. Consequently, the number of buyers does not decrease because of temporal comparison effects. Therefore, the firm increases its revenue. (4) For the market with two substitutable products with quality differences, one approximate optimal strategy for the enterprise in the second stage is to keep the selling price constant with the assumption that product quality cannot be adjusted after the first period. At this point, the consumption situation in the market is the same as that in the first stage. Therefore, when no external constraints exist, the monopolist firm can obtain more benefits in the second stage than in the first stage by exploiting the temporal comparison effects of consumers in the second stage. (5) When consumer identity information can be confirmed in the market, social comparison effects, similar to temporal comparison effects, could help the enterprise increase its price and profit while maintaining product quality. These social and temporal comparison effects constrain consumers. Thus, the number of people who continue to buy products does not decrease.

Sulfur dioxide (SO₂) is a typical pollutant that affects human health, climate, and environmental and ecological conditions. China has been experiencing high concentrations of SO₂, particularly in urban areas, since the 1990s. In 2010, a “joint prevention and control” (JPC) policy was issued to address air pollution problems and strengthen the regulation of SO₂ emissions. This study aimed to describe the mitigation effects of this policy on SO₂ emissions in 116 Chinese cities from 2003 to 2017. We applied global and local Moran's I indices to confirm the presence of significant spatial autocorrelation in SO₂ emissions and constructed four spatial models to assess the effect of the JPC policy in reducing SO₂ emissions and its transmission mechanism. We found that the policy decreased SO₂ emissions by 1.89 × 10⁴ tons, SO₂ intensity by 1.70 tons per km², and SO₂ per capita of 158.49 tons per 10,000 people in each city, on average, all of which are significant changes. The empirical results also show that population growth, economic structure, and environmental protection significantly decrease SO₂ emissions. Finally, we recommended policies to encourage regional cooperation under the JPC policy, with the aim of promoting further reductions in SO₂ emissions.

We forecast realized volatilities by developing a time-varying heterogeneous autoregressive (HAR) latent factor model with dynamic model average (DMA) and dynamic model selection (DMS) approaches. The number of latent factors is determined using Chan and Grant's (2016) deviation information criteria. The predictors in our model include lagged daily, weekly, and monthly volatility variables, the corresponding volatility factors, and a speculation variable. In addition, the time-varying properties of the best-performing DMA(DMS)-HAR-2FX models, including size, inclusion probabilities, and coefficients, are examined. We find that the proposed DMA(DMS)-HAR-2FX model outperforms the competing models for both in-sample and out-of-sample forecasts. Furthermore, the speculation variable displays strong predictability for forecasting the realized volatility of financial futures in China.

Supply chain (SC) resilience is an increasingly important topic for practitioners and academics because it is a competitive weapon for firms to cope with SC disruptive risks. This study examines the impact of high-involvement human resource management practices on SC resilience from the ability-motivation-opportunity perspective. It also examines the relationship between the dimensions of SC resilience and operational performance. Based on data collected from 206 Chinese manufacturers, the proposed hypotheses were tested using structural equation modeling. The results indicated that employee participation played the most powerful role in improving supplier, customer, and internal resilience. Moreover, employee skills only facilitate internal and customer resilience but have no significant impact on supplier resilience. By contrast, employee incentives do not influence the dimension of SC resilience. It was also found that both internal and customer resilience have positive effects on operational performance, while supplier resilience has no significant effect. The findings contribute to literature and practice.

In this study, we show that changes in profitability predict a firm's stock returns and future profitability. We construct three horizon-based profitability changes, including short-, medium-, and long-term changes. We find that the predictive information for short-term changes in profitability is not subsumed by the profitability level in the Chinese stock market. We also find that short-term profitability changes generate an asymmetrical premium across different market states. Furthermore, we find that the beta anomaly is embedded in the premium generated by a short-term change in profitability. In addition, we explore the underlying mechanisms of the profitability premium and propose a heterogeneous investor belief channel to explain the profitability premium. We find that risk-based q-theory also helps explain the profitability premium. Therefore, the profitability premium comes from a mixed source and cannot be entirely explained by a single theory.

Although the carbon pricing policy is a critical driving factor that will help China achieve economic growth, energy transition, and dual climate change mitigation goals, the kind of carbon pricing policy that will complement the country's current development situation remains controversial. We apply the World Induced Technical Change Hybrid (WITCH) model to explore the heterogeneity and synergy of different carbon pricing policies, and the results indicate that it will be challenging to achieve carbon neutrality before 2060. The study find that the combined policy —a mix of carbon tax and carbon market policies — has the optimal emission reduction effect but comes with the highest economic cost, proving to be unsuitable in the long run. The carbon tax policy is an important transitional means to assist in emission reduction, which can serve as an important supplement to carbon market policy and be phased out after the market mechanism matures.

E-commerce is a typical form of retail digitalization that introduces online uncertainty and product returns. To decrease the negative influence of online uncertainty, the largest Chinese e-commerce company, the Alibaba Group, invited an insurance company to develop return-freight insurance (RFI), a new kind of insurance, to compensate for consumers' losses in the event of online product returns. Complimentary RFI can increase consumer confidence in the retailer and attract more demand. Retailers who offer complimentary RFI demonstrate to consumers that their products and services are too good to incur excessive product returns. However, some low-quality online retailers can mimic competitors’ behavior by offering complimentary RFI to consumers. This study aims to introduce an innovative online return policy based on RFI and to explore whether low-quality online retailers would use complimentary RFI as their return strategy to mislead consumers. Using signaling theory, we built a conceptual economic model that includes three exogenous pricing variables: RFI, insurance premium, and compensation. These variables play different roles in the model because consumers cannot observe the insurance premium, but the compensation can be. The main finding of this study is that innovative complimentary RFI could be abused by low-type retailers when the premium and compensation are appropriate. Interestingly, compensation plays different roles for retailers with different product values: low-type retailers use complimentary RFI as a noise tool. When the product works for the consumer and the insurance profit is not too high, the compensation for the low-quality product should be larger than that for the high-quality product, which is different from conventional wisdom. Although high-type online retailers may use complimentary RFI as a product quality signal, there is still a significant risk that nefarious elements will use it to create product quality noise.

Additive manufacturing (AM) has attracted significant attention in recent years based on its wide range of applications and growing demand. AM offers the advantages of production flexibility and design freedom. In this study, we considered a practical variant of the batch-processing-machine (BPM) scheduling problem that arises in AM industries, where an AM machine can process multiple parts simultaneously, as long as the two-dimensional rectangular packing constraint is not violated. Based on the set-partitioning formulation of our mixed-integer programming (MIP) model, a branch-and-price (B&P) algorithm was developed by embedding a column-generation technique into a branch-and-bound framework. Additionally, a novel labelling algorithm was developed to accelerate the column-generation process. Ours is the first study to provide a B&P algorithm to solve the BPM scheduling problem in the AM industry. We tested the performance of our algorithm using a modern MIP solver (Gurobi) and real data from a 3D printing factory. The results demonstrate that for most instances tested, our algorithm produces results similar or identical to those of Gurobi with reasonable computation time and outperforms Gurobi in terms of solution quality and running time on some large instances.

This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility. Considering day-of-the-week effects, structural breaks, or both, we propose three classes of HAR models to forecast electricity volatility based on existing HAR models. The estimation results of the models showed that day-of-the-week effects only improve the fitting ability of HAR models for electricity volatility forecasting at the daily horizon, whereas structural breaks can improve the in-sample performance of HAR models when forecasting electricity volatility at daily, weekly, and monthly horizons. The out-of-sample analysis indicated that both day-of-the-week effects and structural breaks contain additional ex ante information for predicting electricity volatility, and in most cases, dummy variables used to measure structural breaks contain more out-of-sample predictive information than those used to measure day-of-the-week effects. The out-of-sample results were robust across three different methods. More importantly, we argue that adding dummy variables to measure day-of-the-week effects and structural breaks can improve the performance of most other existing HAR models for volatility forecasting in the electricity market.

With increasing public environmental awareness, green activities in retail and distribution processes have become crucial tools for retailers to boost demand and enhance competitiveness. This study develops an analytical model to study the green investment choices of two differentiated retailers dealing with a common green manufacturer. It also explores the impacts of these investment choices on the manufacturer's operational decisions, channel efficiency, consumer welfare, and the environment. We derive three main results. First, the powerful retailer always favors green investments, whereas the less powerful (inferior) retailer may either prefer or avoid green investments. The fiercer the inter-retailer competition, the lower the willingness of the inferior retailer to introduce green investments. Second, although all supply chain parties may disagree on their preferences for retailers' green investments, a bilateral green investment (i.e., both retailers make green investments) can reach an incentive alignment for all firms if the differentiation between retailers is low enough and the competition between them is not substantially fierce. Moreover, a bilateral green investment improves consumer welfare and channel efficiency because of the great demand expansion and double marginalization reduction. Third, the retailers' green investments can motivate the manufacturer to produce greener products, but they do not necessarily benefit the environment. We show that the supply chain's economic sustainability aligns with its environmental sustainability only if the environmental improvement efficiency of green investments is substantially high. We further examine the impact of retailers with differentiated green investment abilities and the manufacturer's green investment efficiency to verify the robustness of the main results.