I examined the validity and reliability of the Utrecht Homesickness Scale (UHS) in the cultural background of China by translating the original UHS into Chinese. The formal scale was formed through exploratory factor analysis, confirmatory factor analysis, and checks of internal consistency reliability and test???retest reliability. The pretest and formal test samples comprised, respectively, 436 and 687 freshmen from universities in China. The final Chinese version of the UHS had 20 items separated into five dimensions: missing family, loneliness, missing friends, adjustment difficulties, and missing home. According to the statistical results, the Chinese version of the UHS has adequate psychometric properties and can be used to assess the homesickness of college freshmen in China.
Product defects concern various groups of people, such as customers, manufacturers, government officials, etc. Thus, defect-related knowledge and information are essential. In keeping with the growth of social media, online forums, and Internet commerce, people post a vast amount of feedback on products, which forms a good source for the automatic acquisition of knowledge about defects. However, considering the vast volume of online reviews, how to automatically identify critical product defects and summarize the related information from the huge number of user reviews is challenging, even when we target only the negative reviews. As a kind of opinion mining research, existing defect discovery methods mainly focus on how to classify the type of product issues, which is not enough for users. People expect to see defect information in multiple facets, such as product model, component, and symptom, which are necessary to understand the defects and quantify their influence. In addition, people are eager to seek problem resolutions once they spot defects. These challenges cannot be solved by existing aspect-oriented opinion mining models, which seldom consider the defect entities mentioned above. Furthermore, users also want to better capture the semantics of review text, and to summarize product defects more accurately in the form of natural language sentences. However, existing text summarization models including neural networks can hardly generalize to user review summarization due to the lack of labeled data. In this research, we explore topic models and neural network models for product defect discovery and summarization from user reviews. Firstly, a generative Probabilistic Defect Model (PDM) is proposed, which models the generation process of user reviews from key defect entities including product Model, Component, Symptom, and Incident Date. Using the joint topics in these aspects, which are produced by PDM, people can discover defects which are represented by those entities. Secondly, we devise a Product Defect Latent Dirichlet Allocation (PDLDA) model, which describes how negative reviews are generated from defect elements like Component, Symptom, and Resolution. The interdependency between these entities is modeled by PDLDA as well. PDLDA answers not only what the defects look like, but also how to address them using the crowd wisdom hidden in user reviews. Finally, the problem of how to summarize user reviews more accurately, and better capture the semantics in them, is studied using deep neural networks, especially Hierarchical Encoder-Decoder Models. For each of the research topics, comprehensive evaluations are conducted to justify the effectiveness and accuracy of the proposed models, on heterogeneous datasets. Further, on the theoretical side, this research contributes to the research stream on product defect discovery, opinion mining, probabilistic graphical models, and deep neural network models. Regarding impact, these techniques will benefit related users such as customers, manufacturers, and government officials. ; Ph. D. ; Product defects concern various groups of people, such as customers, manufacturers, and government officials. Thus, defect-related knowledge and information are essential. In keeping with the growth of social media, online forums, and Internet commerce, people post a vast amount of feedback on products, which forms a good source for the automatic acquisition of knowledge about defects. However, considering the vast volume of online reviews, how to automatically identify critical product defects and summarize the related information from the huge number of user reviews is challenging, even when we target only the negative reviews. People expect to see defect information in multiple facets, such as product model, component, and symptom, which are necessary to understand the defects and quantify their influence. In addition, people are eager to seek problem resolutions once they spot defects. Furthermore, users also want to better summarize product defects more accurately in the form of natural language sentences. These requirements cannot be satisfied by existing methods, which seldom consider the defect entities mentioned above, or hardly generalize to user review summarization. In this research, we develop novel Machine Learning (ML) algorithms for product defect discovery and summarization. Firstly, we study how to identify product defects and their related attributes, such as Product Model, Component, Symptom, and Incident Date. Secondly, we devise a novel algorithm, which can discover product defects and the related Component, Symptom, and Resolution, from online user reviews. This method tells not only what the defects look like, but also how to address them using the crowd wisdom hidden in user reviews. Finally, we address the problem of how to summarize user reviews in the form of natural language sentences using a paraphrase-style method. On the theoretical side, this research contributes to multiple research areas in Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning. Regarding impact, these techniques will benefit related users such as customers, manufacturers, and government officials.
This study investigates the role of retail pharmacy ownership in the opioid epidemic. Using data of prescription opioid orders, we show that compared with chain pharmacies, independent pharmacies dispense 39.1 percent more opioids and 60.5 percent more OxyContin. After an independent pharmacy becomes a chain pharmacy, opioid dispensing decreases. Using the OxyContin reformulation, which reduced nonmedical demand but not the legitimate medical demand, we show that at least one-third of the difference in the amount of OxyContin dispensed can be attributed to nonmedical demand. We show that differences in competitive pressure and whether pharmacists own the pharmacy drive our estimates. (JEL G32, I12, L22, L81)
Developing countries are characterized by low levels of pharmaceutical innovation. A likely reason is their small market size, which is not because of the population size but because of low levels of income and lack of health insurance coverage. This study exploits a natural experiment from the implementation of a public health insurance program for rural residents in China (New Cooperative Medical Scheme [NCMS]) to examine whether the pharmaceutical industry increases innovation regarding diseases covered by the NCMS that are prevalent in rural areas. We examine the 1993–2009 patent data to gauge pharmaceutical innovation in China. Diseases with a 10% higher rural patient share saw a 12.4% increase in relevant domestic pharmaceutical patent applications and a modest increase in patent quality after the NCMS implementation. By providing public health insurance to low-income individuals in developing countries, governments can create incentives for pharmaceutical firms to develop new medical technologies.
AbstractAs products are constantly updated, brands launch increasingly different versions thereof, and consumers frequently face upgrade choices. However, when and why consumers choose to upgrade has received limited attention. This article thus sheds new light on consumer upgrade intention by distinguishing a novel antecedent, product type (material vs. experiential). Three online studies with 642 participants from the US were conducted; we used t‐tests and bootstrapped mediation analysis via PROCESS Model 4 to analyze the data. The results of these studies reveal that consumers are more likely to upgrade experiential products than material products. Moreover, this effect is mediated by a heightened sense of anticipated regret rather than upgrade degree or perceived product closeness. Specifically, consumers feel greater anticipated regret if they do not upgrade experiential products (vs. material products), which leads to their higher upgrade intentions toward experiential products. This research therefore significantly extends regret regulation theory, provides important insights into the relationship between product type and upgrade intention, and offers valuable knowledge for brands seeking to optimize their marketing strategies.
