Management Science and Engineering
Display Method:
2023,
53(1):
5.
doi: 10.52396/JUSTC-2021-0282
Abstract:
With the increasing demand for online home-furnishing products, product delivery services, especially installation services, have become increasingly regarded as bottlenecks and key factors for success. Meanwhile, customers have different preferences for a combination of delivery modes because of separated or synchronized logistics delivery from installation services. It is essential for online home-furnishing e-retailers to self-build or outsource installation services. This study investigates the optimal delivery mode selection of home-furnishing e-retailers in a home-furnishing supply chain consisting of a home-furnishing e-retailer, a third-party installation service provider (ISP), and a third-party logistics service provider (LSP). Specifically, we explore three alternative modes: (ⅰ) The home-furnishing e-retailer undertakes the installation service (Mode E); (ⅱ) the ISP undertakes the installation service (Mode I); (ⅲ) the LSP undertakes the installation service (Mode L). The results reveal that the self-build mode does not always generate the highest installation service level, and the integrated delivery mode may generate the highest installation service level when the cost performance of the installation service is relatively low. Moreover, optimal delivery mode selection depends on the installation service’s cost performance. When the installation service’s cost performance is relatively low, the e-retailer and the LSP reach a “win-win” situation from the integrated delivery mode. When the installation service’s cost performance is relatively high and the self-build fixed cost is low, the e-retailer and the LSP reach a win-win situation from the self-build mode. Interestingly, compared with the outsourced integrated service mode, the self-build integrated service mode is not a better choice for the e-retailer if the self-build fixed cost is too high. Our study contributes to the growing literature on home furnishing and guides the implementation of delivery strategies for large-product online retailers.
With the increasing demand for online home-furnishing products, product delivery services, especially installation services, have become increasingly regarded as bottlenecks and key factors for success. Meanwhile, customers have different preferences for a combination of delivery modes because of separated or synchronized logistics delivery from installation services. It is essential for online home-furnishing e-retailers to self-build or outsource installation services. This study investigates the optimal delivery mode selection of home-furnishing e-retailers in a home-furnishing supply chain consisting of a home-furnishing e-retailer, a third-party installation service provider (ISP), and a third-party logistics service provider (LSP). Specifically, we explore three alternative modes: (ⅰ) The home-furnishing e-retailer undertakes the installation service (Mode E); (ⅱ) the ISP undertakes the installation service (Mode I); (ⅲ) the LSP undertakes the installation service (Mode L). The results reveal that the self-build mode does not always generate the highest installation service level, and the integrated delivery mode may generate the highest installation service level when the cost performance of the installation service is relatively low. Moreover, optimal delivery mode selection depends on the installation service’s cost performance. When the installation service’s cost performance is relatively low, the e-retailer and the LSP reach a “win-win” situation from the integrated delivery mode. When the installation service’s cost performance is relatively high and the self-build fixed cost is low, the e-retailer and the LSP reach a win-win situation from the self-build mode. Interestingly, compared with the outsourced integrated service mode, the self-build integrated service mode is not a better choice for the e-retailer if the self-build fixed cost is too high. Our study contributes to the growing literature on home furnishing and guides the implementation of delivery strategies for large-product online retailers.
2022,
52(2):
6.
doi: 10.52396/JUSTC-2021-0219
Abstract:
With its powerful real-time interaction and rich user experience, live streaming shopping has rapidly become consumers' new favorite. However, the frequent "rollover" incidents affecting the reputation of well-known streamers significantly reduce consumers' trust in the streamers. Academic research on trust in live streaming shopping has thus far mainly focused on purchase motivations. Few studies have focused on the factors influencing trust from the streamer's perspective, and they have ignored the moderating role of streamers and product factors, situational factors and individual characteristics of consumers. Therefore, this study introduces three new moderating variables – streamer-product matching, live streaming online reviews, and online shopping experience – to explore their moderating effects on streamers' reputation, popularity, and trust. The results show that streamers' reputation and popularity have a significant positive impact on trust in streamers, and streamer-product matching has a positive moderating effect on the relationship between streamers' reputation, streamers' popularity, and trust in streamers. Online reviews have a positive moderating effect on the relationship between streamers' popularity and trust, while online shopping experience has a positive moderating effect on the relationship between streamers' reputation and trust in streamers.
2021,
51(6):
468-474.
doi: 10.52396/JUST-2021-0097
Abstract:
Measuring the network connectedness of the financial system is of great importance in systemic risk analysis, and has drawn great attention in recent years. In this paper, we apply the transfer entropy method to analyze the volatility spillover network connectedness of the U.S. stock market. Based on the network structure, we apply the network vector autoregression model (NVAM) and are interested in identifying the influential firms in volatility spillover network of the financial system. In addition, by using rolling windows, the dynamics of total volatility spillover network connectedness indices are obtained, which shows a sharp rise at the beginning of the financial crisis, while it only fluctuates within a controllable range in the steady economic period. The results show that transfer entropy has great potential for understanding the correlation and information flow of financial markets.
Measuring the network connectedness of the financial system is of great importance in systemic risk analysis, and has drawn great attention in recent years. In this paper, we apply the transfer entropy method to analyze the volatility spillover network connectedness of the U.S. stock market. Based on the network structure, we apply the network vector autoregression model (NVAM) and are interested in identifying the influential firms in volatility spillover network of the financial system. In addition, by using rolling windows, the dynamics of total volatility spillover network connectedness indices are obtained, which shows a sharp rise at the beginning of the financial crisis, while it only fluctuates within a controllable range in the steady economic period. The results show that transfer entropy has great potential for understanding the correlation and information flow of financial markets.
2021,
51(6):
475-484.
doi: 10.52396/JUST-2021-0140
Abstract:
We propose a new conditional risk measure, conditional generalized value-at-risk (CoGVaR), from the perspective of measuring systemic risk. The new class of risk measures is a natural generalization of the conditional quantiles including the classic CoVaR. Compared with the classic conditional value-at-risk (CoVaR) and conditional expectile (CoExpectile), it has more potential application in reality as it takes the risk attitude of the decision maker into consideration, which has not been the focus of much study to date. Using generalized quantile regression approach with state variables added, some calculation results are presented in the Dow Jones U.S. Financials Index case, and it is shown that it provides a new perspective on systemic risk contribution. In addition, the result shows that our risk measure can capture the tail risk by using more convex disutility function.
We propose a new conditional risk measure, conditional generalized value-at-risk (CoGVaR), from the perspective of measuring systemic risk. The new class of risk measures is a natural generalization of the conditional quantiles including the classic CoVaR. Compared with the classic conditional value-at-risk (CoVaR) and conditional expectile (CoExpectile), it has more potential application in reality as it takes the risk attitude of the decision maker into consideration, which has not been the focus of much study to date. Using generalized quantile regression approach with state variables added, some calculation results are presented in the Dow Jones U.S. Financials Index case, and it is shown that it provides a new perspective on systemic risk contribution. In addition, the result shows that our risk measure can capture the tail risk by using more convex disutility function.
2021,
51(5):
404-418.
doi: 10.52396/JUST-2021-0102
Abstract:
The COVID-19 pandemic has caused severe public health and economic consequences around the world. It is of great importance to evaluate the impact of the COVID-19 pandemic on the economy, especially the stock market. To this end, we proposed to use several state-of-art sparse principal component analysis (PCA) methods for the stock data of the CSI 300 index from February 1, 2019 to February 1, 2021. To show the influence of the outbreak of the COVID-19 pandemic, we divide this period into two periods, i.e., before and after January 1, 2020. Based on this division, we attempted to extract the principal components and construct portfolio accordingly. The results show that the proportion of principal components representing the market declined after the outbreak. For the constitution in the first two principal components, the important stock sets are substantially different after the outbreak. The stocks from the health care sector start to play an important role in the portfolio of the CSI 300 index after the outbreak. Compared with the CSI 300 index, the first two principal components from the sparse PCA methods can obtain higher returns with a much smaller set of stocks in the portfolio. In conclusion, the outbreak of the COVID-19 pandemic led to changes in both proportion and constitution of the principal component of the stocks in the CSI 300 index.
The COVID-19 pandemic has caused severe public health and economic consequences around the world. It is of great importance to evaluate the impact of the COVID-19 pandemic on the economy, especially the stock market. To this end, we proposed to use several state-of-art sparse principal component analysis (PCA) methods for the stock data of the CSI 300 index from February 1, 2019 to February 1, 2021. To show the influence of the outbreak of the COVID-19 pandemic, we divide this period into two periods, i.e., before and after January 1, 2020. Based on this division, we attempted to extract the principal components and construct portfolio accordingly. The results show that the proportion of principal components representing the market declined after the outbreak. For the constitution in the first two principal components, the important stock sets are substantially different after the outbreak. The stocks from the health care sector start to play an important role in the portfolio of the CSI 300 index after the outbreak. Compared with the CSI 300 index, the first two principal components from the sparse PCA methods can obtain higher returns with a much smaller set of stocks in the portfolio. In conclusion, the outbreak of the COVID-19 pandemic led to changes in both proportion and constitution of the principal component of the stocks in the CSI 300 index.
2021,
51(5):
419-430.
doi: 10.52396/JUST-2021-0083
Abstract:
With the advancement of Internet information technology,the laborer-sharing platform plays a crucial role in promoting the full use of human resources across the whole society. At present, multiple trading modes with different pricing strategies are adopted by the laborer-sharing platform. Considering the heterogeneity of laborers’ abilities, we construct the laborer-sharing platform’s profit functions under the buyer pricing strategy and laborer pricing strategy, and analyze its optimal pricing strategy in the bidding mode.First, our analysis shows that when the mismatch degree between the service of the low-type laborer and the buyer’s task is close to that of the high-type laborer, the laborer pricing strategy is beneficial to the platform. Second,when the task mismatch degree of the low-type laborer is much lower than that of the high-type laborer, the platform’s pricing strategy depends on the buyer’s satisfaction with the completing task. Finally, we compare two transaction models: the bidding mode and the piece mode, and find that under the laborer pricing strategy, the platform’s profit in the bidding mode is not always higher than that in the piece mode.
With the advancement of Internet information technology,the laborer-sharing platform plays a crucial role in promoting the full use of human resources across the whole society. At present, multiple trading modes with different pricing strategies are adopted by the laborer-sharing platform. Considering the heterogeneity of laborers’ abilities, we construct the laborer-sharing platform’s profit functions under the buyer pricing strategy and laborer pricing strategy, and analyze its optimal pricing strategy in the bidding mode.First, our analysis shows that when the mismatch degree between the service of the low-type laborer and the buyer’s task is close to that of the high-type laborer, the laborer pricing strategy is beneficial to the platform. Second,when the task mismatch degree of the low-type laborer is much lower than that of the high-type laborer, the platform’s pricing strategy depends on the buyer’s satisfaction with the completing task. Finally, we compare two transaction models: the bidding mode and the piece mode, and find that under the laborer pricing strategy, the platform’s profit in the bidding mode is not always higher than that in the piece mode.
2021,
51(4):
259-274.
doi: 10.52396/JUST-2021-0081
Abstract:
In the e-commerce market, the success of the hybrid online platform is well proven. The platform is not only an e-retailer but also provides online logistics services for other e-retailers in the platform. Logistics service is an indispensable link in e-commerce, and it also plays a vital role in promoting the online shopping. In our research, we analyze the impacts of logistics service sharing between the platform and the e-retailer and investigate the optimal strategy in two models. The study found that when the third-party logistics provider's logistics service level coordinates with logistics service fees and both are in the middle range, the platform and the e-retailer can achieve a logistics service sharing agreement, forming a win-win scenario. When the logistics service fee charged by the third-party logistics provider is too low, or the third-party logistics provider's logistics service is too high, both the platform and the e-retailer will choose the strategic mode of not sharing logistics service. Simultaneously, the third-party logistics provider's logistics service level promotes the logistics service level of the platform. Finally, numerical analysis is carried out to verify the equilibrium model and analyze the impacts of the equilibrium model's main parameters. Our study contributes to the growing body of research on the platform operation and provides management insights on firms' logistic service strategy choices.
In the e-commerce market, the success of the hybrid online platform is well proven. The platform is not only an e-retailer but also provides online logistics services for other e-retailers in the platform. Logistics service is an indispensable link in e-commerce, and it also plays a vital role in promoting the online shopping. In our research, we analyze the impacts of logistics service sharing between the platform and the e-retailer and investigate the optimal strategy in two models. The study found that when the third-party logistics provider's logistics service level coordinates with logistics service fees and both are in the middle range, the platform and the e-retailer can achieve a logistics service sharing agreement, forming a win-win scenario. When the logistics service fee charged by the third-party logistics provider is too low, or the third-party logistics provider's logistics service is too high, both the platform and the e-retailer will choose the strategic mode of not sharing logistics service. Simultaneously, the third-party logistics provider's logistics service level promotes the logistics service level of the platform. Finally, numerical analysis is carried out to verify the equilibrium model and analyze the impacts of the equilibrium model's main parameters. Our study contributes to the growing body of research on the platform operation and provides management insights on firms' logistic service strategy choices.
2021,
51(4):
275-287.
doi: 10.52396/JUST-2020-0030
Abstract:
We used the social cognitive career theory to investigate how vocational interests affect informal learning behavior in the workplace by building a model with three different dimensions of vocational interests as independent variables and different goal orientation dimensions as mediating variables. Using a sample of 211 Chinese employees from different industries, results showed that investigative and enterprising vocational interests had positive effects, whereas realistic interests had a negative effect, on informal learning. Results also indicated that learning and performance-prove goal orientations positively affected informal learning, whereas performance-avoid goal orientations negatively affected informal learning. Further, each dimension of the goal orientation played a mediating role in the influence of vocational interests on informal learning. Findings of this study enhanced our understanding of the mechanism surrounding of how vocational interests influence informal learning and provided important new evidence for the theoretical and practical development of the relationships among interests, goals, and behaviors in the social cognitive career theory.
We used the social cognitive career theory to investigate how vocational interests affect informal learning behavior in the workplace by building a model with three different dimensions of vocational interests as independent variables and different goal orientation dimensions as mediating variables. Using a sample of 211 Chinese employees from different industries, results showed that investigative and enterprising vocational interests had positive effects, whereas realistic interests had a negative effect, on informal learning. Results also indicated that learning and performance-prove goal orientations positively affected informal learning, whereas performance-avoid goal orientations negatively affected informal learning. Further, each dimension of the goal orientation played a mediating role in the influence of vocational interests on informal learning. Findings of this study enhanced our understanding of the mechanism surrounding of how vocational interests influence informal learning and provided important new evidence for the theoretical and practical development of the relationships among interests, goals, and behaviors in the social cognitive career theory.
2021,
51(4):
288-298.
doi: 10.52396/JUST-2021-0070
Abstract:
Based on the quarterly data of A-share listed firms between 2019 and 2020, we find that firms with lower leverage and higher cash holdings before perform better under the COVID-19 pandemic, suggesting that strong liquidity helps firms resist risks. In particular, cash holding affects firm performance through the channel of production. Secondly, we calculate firms' position in the global value chain based on a world input-output table and find that downstream firms perform better under the pandemic. Thirdly, to cope with future uncertainty, cash-holding willingness of firms increases significantly after the pandemic. All findings imply that firms need to improve their financial health to be more resilient toward negative shocks and policy makers need to improve the financial environment to satisfy firms' external financing need.
Based on the quarterly data of A-share listed firms between 2019 and 2020, we find that firms with lower leverage and higher cash holdings before perform better under the COVID-19 pandemic, suggesting that strong liquidity helps firms resist risks. In particular, cash holding affects firm performance through the channel of production. Secondly, we calculate firms' position in the global value chain based on a world input-output table and find that downstream firms perform better under the pandemic. Thirdly, to cope with future uncertainty, cash-holding willingness of firms increases significantly after the pandemic. All findings imply that firms need to improve their financial health to be more resilient toward negative shocks and policy makers need to improve the financial environment to satisfy firms' external financing need.
2021,
51(3):
228-245.
doi: 10.52396/JUST-2021-0056
Abstract:
In this paper, we examine firms’ quality and inventory decisions with consumers who behave heterogeneously not only on the product’s valuation (horizontal) but also on the reference price setting (vertical). Through a three-stage Stackelberg leader-follower model, we derive cost-effective solutions for channel members in two distribution scenarios. Counter-intuitively, the analytical result illustrates that profit-maximizing inventory and quality decisions can be higher when the uncertainty of the market increases because the two-dimensional impacts of market uncertainty on demand are diametrically opposite to each other. Specifically, the vertical uncertainty (difference in reference effects) has a buffering effect on the aggregate market demand, which is further amplified by loss-aversion behaviors. However, the horizontal uncertainty (heterogeneity of consumer valuation) has a promoting effect on the market demand and induces firms to order more. The numerical result further shows that market demand may not inherit the behavioral bias of individual consumers, leading to an inconsistent relationship between the sensitivity of market demand to gain/loss and consumers’ loss-aversion behaviors. Our findings have implications not only for understanding the stochastic market demand with behaviorally biased consumers but also for determining the channel members’ optimal inventory and quality decisions.
In this paper, we examine firms’ quality and inventory decisions with consumers who behave heterogeneously not only on the product’s valuation (horizontal) but also on the reference price setting (vertical). Through a three-stage Stackelberg leader-follower model, we derive cost-effective solutions for channel members in two distribution scenarios. Counter-intuitively, the analytical result illustrates that profit-maximizing inventory and quality decisions can be higher when the uncertainty of the market increases because the two-dimensional impacts of market uncertainty on demand are diametrically opposite to each other. Specifically, the vertical uncertainty (difference in reference effects) has a buffering effect on the aggregate market demand, which is further amplified by loss-aversion behaviors. However, the horizontal uncertainty (heterogeneity of consumer valuation) has a promoting effect on the market demand and induces firms to order more. The numerical result further shows that market demand may not inherit the behavioral bias of individual consumers, leading to an inconsistent relationship between the sensitivity of market demand to gain/loss and consumers’ loss-aversion behaviors. Our findings have implications not only for understanding the stochastic market demand with behaviorally biased consumers but also for determining the channel members’ optimal inventory and quality decisions.
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