Using economics and game theory, two kinds of models have been proposed in this paper under the assumption that foreign and domestic firms behave under the condition of dynamic game of perfect information. One model i...Using economics and game theory, two kinds of models have been proposed in this paper under the assumption that foreign and domestic firms behave under the condition of dynamic game of perfect information. One model is for calculating Anti-dumping rate which is obtained according to current regulations of Anti-dumping, but it is not optimal. The other is an optimal model of Anti-dumping which is obtained according to the maximum principle of domestic social welfare. Then, through the comparison of this two models in detail, several shortages have been revealed about Anti-dumping rate model based on current regulations of Anti-dumping. Finally, a suggestion is indicated that WTO and China should use the optimal model to calculate Anti-dumping rate.展开更多
With the rapid development of Web 2.0, more and more people are sharing their opinions about online products, so there is much product review data. However, it is difficult to compare products directly using ratings b...With the rapid development of Web 2.0, more and more people are sharing their opinions about online products, so there is much product review data. However, it is difficult to compare products directly using ratings because many ratings are based on different scales or ratings are even missing. This paper addresses the following question： given textual reviews, how can we automatically determine the semantic orientations of reviewers and then rank different items？ Due to the absence of ratings in many reviews, it is difficult to collect sufficient rating data for certain specific categories of products （e.g., movies）, but it is easier to find rating data in another different but related category （e.g., books）. We refer to this problem as transfer rating, and try to train a better ranking model for items in the interested category with the help of rating data from another related category. Specifically, we developed a ranking-oriented method called TRate for determining the semantic orientations and for ranking different items and formulated it in a regularized algorithm for rating knowledge transfer by bridging the two related categories via a shared latent semantic space. Tests on the Epinion dataset verified its effectiveness.展开更多
Purpose:The motivation of this study is to identify whether the overall rating of a banking app actually reflects the customer opinion and to find the causes for reduced ratings.Thus,these causes lead to the dissatisf...Purpose:The motivation of this study is to identify whether the overall rating of a banking app actually reflects the customer opinion and to find the causes for reduced ratings.Thus,these causes lead to the dissatisfaction of customers.Additionally,these insights reflect the overall rating of the app and it is a source of information to the executive management to contemplate on their services and take timely and effective decisions to improve their mobile app.Design/methodology/approach:This research was conducted on ten reputed Sri Lankan mobile banking apps to analyze the textual opinions of the customers.Data were collected from the Google Play Store considering the higher Android consumers in Sri Lanka.Each review was automatically classified into a relevant sentiment(positive,negative or neutral).These classified reviews were examined along with its rating to identify any discrepancies.The trends of the positive and negative reviews of each app were observed separately along with time.Topic modeling techniques were used to identify the causes of such behavior.Findings:Although banks expect to perpetuate good customer reviews all the time,there were aberrant negative trends observed during certain time ranges.The results revealed that unstable versions after recent updates,bad customer service,erroneous functional and nonfunctional features are the root causes toward the dissatisfaction of the customers.Originality/value:No previous study has been done on the textual reviews of Sri Lankan mobile banking apps.Most studies had considered analyzing the reviews of the app on the entire period of its usage,whereas this research finds the trends where negative reviews surpass the positive reviews and analyze the causes of such behavior.展开更多
文摘Using economics and game theory, two kinds of models have been proposed in this paper under the assumption that foreign and domestic firms behave under the condition of dynamic game of perfect information. One model is for calculating Anti-dumping rate which is obtained according to current regulations of Anti-dumping, but it is not optimal. The other is an optimal model of Anti-dumping which is obtained according to the maximum principle of domestic social welfare. Then, through the comparison of this two models in detail, several shortages have been revealed about Anti-dumping rate model based on current regulations of Anti-dumping. Finally, a suggestion is indicated that WTO and China should use the optimal model to calculate Anti-dumping rate.
基金supported by the National Natural Science Foundation of China (No. 60773061)the National Natural Science Foundation of Jiangsu Province of China (No. BK2008381)+1 种基金supported by the National High-Tech Research and Development (863) Program of China (No.2009AA01Z138)supported by the National Natural Science Foundation of China (No.70771043)
文摘With the rapid development of Web 2.0, more and more people are sharing their opinions about online products, so there is much product review data. However, it is difficult to compare products directly using ratings because many ratings are based on different scales or ratings are even missing. This paper addresses the following question： given textual reviews, how can we automatically determine the semantic orientations of reviewers and then rank different items？ Due to the absence of ratings in many reviews, it is difficult to collect sufficient rating data for certain specific categories of products （e.g., movies）, but it is easier to find rating data in another different but related category （e.g., books）. We refer to this problem as transfer rating, and try to train a better ranking model for items in the interested category with the help of rating data from another related category. Specifically, we developed a ranking-oriented method called TRate for determining the semantic orientations and for ranking different items and formulated it in a regularized algorithm for rating knowledge transfer by bridging the two related categories via a shared latent semantic space. Tests on the Epinion dataset verified its effectiveness.
文摘Purpose:The motivation of this study is to identify whether the overall rating of a banking app actually reflects the customer opinion and to find the causes for reduced ratings.Thus,these causes lead to the dissatisfaction of customers.Additionally,these insights reflect the overall rating of the app and it is a source of information to the executive management to contemplate on their services and take timely and effective decisions to improve their mobile app.Design/methodology/approach:This research was conducted on ten reputed Sri Lankan mobile banking apps to analyze the textual opinions of the customers.Data were collected from the Google Play Store considering the higher Android consumers in Sri Lanka.Each review was automatically classified into a relevant sentiment(positive,negative or neutral).These classified reviews were examined along with its rating to identify any discrepancies.The trends of the positive and negative reviews of each app were observed separately along with time.Topic modeling techniques were used to identify the causes of such behavior.Findings:Although banks expect to perpetuate good customer reviews all the time,there were aberrant negative trends observed during certain time ranges.The results revealed that unstable versions after recent updates,bad customer service,erroneous functional and nonfunctional features are the root causes toward the dissatisfaction of the customers.Originality/value:No previous study has been done on the textual reviews of Sri Lankan mobile banking apps.Most studies had considered analyzing the reviews of the app on the entire period of its usage,whereas this research finds the trends where negative reviews surpass the positive reviews and analyze the causes of such behavior.
