因子分析法在县域经济发展水平评价中的应用

县域经济是国民经济的重要组成部分。为了全面、客观、系统、科学地评价和分析县域经济发展水平,一般要采用多指标综合评价法,这种传统方法具有主观随意性与工作复杂性的缺点。采用因子分析法对县域经济发展水平进行评价,可以有效地克服传统多指标综合评价方法中的不足与缺陷。     

高职英语教学的多模态话语分析

随着网络技术以及多媒体技术的不断发展,语言的阐释已经从传统的单一文本模式转变为当今的多种模态表达模式。本文从多模态话语分析理论视角,对高职英语教学过程中的多模态符号进行微观层面探析,提出要通过协调多模态话语因素,最大化教学效率,促进高职英语教学的发展。     

Strategic product showcasing mode of E-commerce live streaming

E-commerce live streaming can considerably help brands improve sales dynamics, but it is multiple dilemmas in choosing suitable e-commerce live streaming product showcasing modes. To address the multiple dilemmas, we first employ the Stackelberg game to depict three e-commerce live streaming product showcasing modes: brand self-live streaming, influencer-led live streaming mixture, and influencer-led special live streaming. Second, we analyze the relationship between choices and game parameters, including live streaming stimulus sensitivity, unit service quality improvement cost, and service sensitivity. Meanwhile, through the comparative analysis of the three modes, we get the optimal threshold of each mode. Last, we conduct a case study comparing Chinese apparel and accessories brands Hodo and RUANS to verify the above relations. The results suggest that: (i) the more sensitive consumers are to live streaming stimulus, the more beneficial it is for the brand manufacturer to employ special live streaming, while the increase in unit service quality improvement cost will harm the brand manufacturer; (ii) the fixed participation fee determines the choice of e-commerce live streaming product showcasing modes; (iii) e-commerce live streaming product showcasing modes can help brands to optimize their decision-making.     

Mitigating panic buying behavior in the epidemic: An evolutionary game perspective

The panic buying behavior under public health emergencies will lead to many adverse consequences, such as material waste, price fluctuation and uneven distribution of epidemic prevention materials, which will pose a threat to the social stability and economic development. In this paper, we construct a tripartite game model to explore the strategic choices of the public, merchants and the government in order to effectively respond to the panic buying behavior in the epidemic. The results demonstrate that: (1) Eight evolutionary stable strategies emerge in the panic buying events. The worst scenario can be improved by adjusting some relevant parameters. (2) The probability of the public choosing the strategy of “not involving in panic buying” depends on the potential benefits and losses of snapping up, rather than the extent of price rising. (3) The probability of merchants choosing the strategy of “not bid up price” depends on the intangible benefits. (4) The probability of the government choosing the strategy of “active supervision” depends on the supervision costs and government credibility, rather than the amount of fines. In addition, strategic suggestions to mitigate panic buying behavior are put forward from the perspective of each stakeholder.     

Taking two to tango: A comparative nationalism view of cross-border acquisitions

Nationalism is an important yet underexplored issue in the international business strategy literature. As a new reality, the resurgence and augmentation of nationalism in recent decades has brought disruption effects that challenge traditional arguments on the international expansion of multinational enterprises (MNEs). We suggest that nationalism is not merely about one’s own national interest but should be extended to consider the mutual forces from home and host countries in international business (IB) research. This study proposes a comparative nationalism view to investigate the impacts of various types of intercountry nationalism on MNEs’ expansion through cross-border acquisitions. Through scenario analysis, the MNEs’ expansion trajectories are characterized by different degrees of comparative nationalism, including incremental expansion, deceleration, leapfrogging, and divestment strategies. Our findings and propositions create a new vision for cross-border research considering the interacting effect of national sentiments. We also discuss promising future research agendas enabling further exploration of this topic.     

Analysing customers' reviews and ratings for online food deliveries: A text mining approach

The purpose of this study was to explore the relationship between online reviews and ratings through text mining and empirical techniques. An Indian food delivery portal ( Zomato.com ) was used, where 50 restaurants on Presence Across Nation (PAN) basis were selected through stratified random sampling. A total of 2530 reviews were collected, scrutinized, and analysed. Using the NVivo software for qualitative analysis, seven themes were identified from collected reviews, out of which, the ‘delivery’ theme was explored further for identifying sub-themes. Linear regression modelling was used to identify the variables affecting delivery ratings and sentiment analysis was also performed on the identified sub-themes. Regression results revealed that hygiene and pricing (delivery subthemes) demonstrated lower delivery ratings. These variables can be established as indicators for restaurants and related online food delivery services to build their business model around them. Similarly, negative sentiments were observed in pricing and hygiene sub-themes. Restaurants and online food services can enhance hygiene levels of their food delivery process in order to receive higher delivery ratings. Similarly, pricing of food items can be modified such that customers are not deterred from ordering the items—food and ordering service do not become cost-prohibitive. This study devised a standardized methodology for analysing vast amounts of online user-generated content (UGC). Findings from this study can be extrapolated to other sectors and service industries such as, tourism, cleaning, transportation, hospitals and engineering especially during the pandemic.     

E-commerce sellers' ratings: Is user feedback adequate?

The literature on the theory of public procurement points out two well-known informational problems arising out of information asymmetry: (i) adverse selection and (ii) moral hazard. To reduce these issues and foster credibility and trust in the procurement process while maintaining quality and efficiency in public procurement, e-procurement platforms have turned to reputation or rating systems. Therefore, the research and design of such rating systems are crucial. In this study, we discuss the theoretical underpinnings of procurement and employ the information-theoretic, regression analysis, artificial neural network and principal component analysis (PCA) approaches to estimate the weights of the variables entering the rating system. Using real data from Government e-Marketplace, a business-to-business public e-commerce portal, we empirically determine the weights of the rating variables derived from the transaction-level and user feedback data for sellers. The weights obtained from the PCA are the most applicable compared with the other three methods. We compare the old rating system with the newly proposed design using the Wilcoxon signed-rank test. This results in a statistically significant difference between the two ratings. The canonical correlation and Wilks' trial reveal that the ratings derived from transaction-level data and user feedback are uncorrelated to a great extent. Hence, considering only transaction-level data or user feedback is unlikely to divulge sellers' intrinsic worth. E-commerce platforms can use this approach to quickly implement methods to obtain rating scores on a real-time basis for sellers on online platforms.     

Health shocks and mortgage debt payoff among American homeowners over age 50: A survival analysis

Mortgage debt is financially burdensome for many older homeowners in the United States. As people age, declining health can bring about increased healthcare costs. Focusing on homeowners aged 50 and older in the U.S., we investigate two research questions: (1) To what extent does a heath shock affect the likelihood of paying off a mortgage voluntarily or involuntarily? and (2) how long does a health shock delay the time it takes to pay off a mortgage? We used eight biannual waves (2004–2018) of the Health and Retirement Study containing 11,772 borrowers to build survival regression models. Results showed that a health shock reduced the likelihood of voluntary payoff by 12%, while it increased the probability of involuntary payoff by 18%. A health shock delayed voluntary and involuntary payoffs for 30 and 21 months, respectively. We discuss tax deduction and HELOCs as strategies to reduce older homeowners' mortgage strain.     

Learning equilibrium mean-variance strategy

We study a dynamic mean-variance portfolio optimization problem under the reinforcement learning framework, where an entropy regularizer is introduced to induce exploration. Due to the time–inconsistency involved in a mean-variance criterion, we aim to learn an equilibrium policy. Under an incomplete market setting, we obtain a semi-analytical, exploratory, equilibrium mean-variance policy that turns out to follow a Gaussian distribution. We then focus on a Gaussian mean return model and propose a reinforcement learning algorithm to find the equilibrium policy. Thanks to a thoroughly designed policy iteration procedure in our algorithm, we prove the convergence of our algorithm under mild conditions, despite that dynamic programming principle and the usual policy improvement theorem failing to hold for an equilibrium policy. Numerical experiments are given to demonstrate our algorithm. The design and implementation of our reinforcement learning algorithm apply to a general market setup.