Online shopping has developed rapidly, but recently, the sales of some online stores have suffered due to the decrease in people’s income caused by the epidemic. How to grasp the psychology and behavior of consumers and formulate effective marketing strategies is important for increasing sales. This paper puts forward a research model and eight hypotheses based on the research on the promotion situation and the types of products promoted on consumers’ impulse shopping, and uses regression analysis, t-test, stepwise regression and analysis of variance to conduct data analysis. The results show that online promotion has a significant impact on consumers’ willingness, and the anticipated regrets in different directions have totally different effect on willingness; the type of product promoted, and the impulsive characteristics of consumers play a moderating role; online promotion affects consumers’ impulsive online shopping intentions through the intermediary effect of expected regret. The influence of anticipated regrets on impulsive online shopping intention is proposed creatively, and the results also provide e-commerce merchants and customers with new insights in managing and treating online promotions. Managerial implications like controlling the duration of promotions and the number of preferential goods are put forward based on our analysis.

This work attempts to develop a novel framework to reveal the preferences of Chinese car users from online user-generated content (UGC) and guides automotive companies to allocate resources reasonably for sustainable design and improve existing product or service attributes. Specifically, a novel unsupervised word-boundary-identified algorithm for the Chinese language is used to extract domain professional feature words, and a set of sentiment scoring rules is constructed. By matching feature-sentiment word pairs, we calculate car users’ satisfaction with different attributes based on the rules and weigh the importance of attributes using the TF-IDF method, thus constructing an importance-satisfaction gap analysis (ISGA) model. Finally, a case study is used to realize the framework evaluation and analysis of the twenty top-mentioned attributes of a small-sized sedan, and the dynamic ISGA-time model is constructed to analyze the changing trend of the importance of user demand and satisfaction. The results show the priority of resource allocation/adjustment. Fuel consumption and driving experience urgently need resource input and management.

In the context of e-commerce, online travel agencies often derive useful information from online reviews to improve transactions. Based on the dispute on the usefulness of different types of reviews and social exchange theory, this study investigates how the characteristics of pictures and text influence review reading and review posting behaviors and thus influencing the efficiency of online review systems. By analyzing crawled data of online hotels and conducting experiments, we first find that picture reviews are more useful than text reviews, and high-quality pictures in reviews have a significant impact on review usefulness. Second, posting pictures requires review posters to pay more perceived costs. Third, negative review posters have higher perceived costs, so they are more unwilling to post pictures, especially high-quality pictures. Our results indicate that review platforms need to add incentives to encourage consumers to post high-quality picture reviews and design workable interfaces to reduce the burden of negative reviewers to speed up the purchase decision process for review readers. This study provides theoretical implications by demonstrating how the adoption of the picture in review systems influences both review readers’ and review posters’ behaviors. Additionally, our findings also provide useful managerial insights for online travel suppliers in terms of building an effective review system to promote sales.

User-generated content explodes in popularity daily on e-commerce platforms. It is crucial for platform manipulators to sort out online reviews with repeatedly expressed opinions and a large number of irrelevant topics in order to reduce the information processing burden on review readers. This study proposes a framework named TipScreener that generates a set of useful sentences that cover all of the information of features of a business. Called tips in this work, the sentences are selected from the reviews in their original, unaltered form. Firstly, we identify information tokens of the business. Second, we filter review sentences that contain no tokens and remove duplicates. We then use a convolutional neural network to filter uninformative sentences. Next, we find the tip set with the smallest cardinality that contains all off the tokens, taking opinion words into account. The sentences of the tip set contain a full range of information and have a very low repetition rate. Our work contributes to the work of online review organizing. Review operators of e-commerce platforms can adopt tips generated by TipScreener to facilitate decision makings of review readers. The convolutional neural network that classifies sentences into two classes also enriches deep learning studies on text classification.

With the view toward enlivening the Chinese automobile market, this work seeks to develop a framework to reveal the tastes of Chinese car users from online user-generated content (UGC) and identify future product improvement orientation. We construct an importance-satisfaction gap analysis (ISGA) model based on sentiment analysis technique. Specifically, a novel unsupervised word-boundary-identified algorithm for the Chinese language is used to extract domain professional feature words and a set of sentiment scoring rules is constructed. By matching feature-sentiment word pairs, we calculate car users' satisfaction with different attributes based on the rules, and weigh the importance of attributes using TF-IDF method, which lay the statistic foundation for our ISGA model. We also take time into account and further analyze the ISGA model, aiming to provide new insight into the changing tastes of Chinese car users so as to suggest correct design positioning and resource allocation. The final evaluation results show that the method proposed in this paper, which integrates Chinese word-boundary-identified algorithm and sentiment lexicon matching rules, is well supported.

With the view toward improving the racial diversity in organizations, this work seeks to uncover the reasons why larger groups have an advantage in terms of job opportunities. Based on people’s preference for diversity in commodity selection, we propose a potential feature that may exist in human resource management and call it the isolated choice effect, which unconsciously affects the racial diversity of organizations. Specifically, when making selections in isolation (i.e., when they are responsible for selecting a single person at a time), people are less likely to choose the one whose race would increase group diversity than when making selections in collections (i.e., when they are responsible for selecting several people at a time). We set up eight experiments (n = 2,792) in which participants make hiring or firing decisions among choices that are more white people than black people. We find that participants in the isolated choice group are less likely to choose black people, the smaller group, than those in the collective choice group. Our results show a potentially important contributing factor to the underrepresentation of black people in many organizations because hires are often made in isolation while layoffs are often made in collections, which provides a starting point for improving racial diversity in organizations by avoiding the isolated choice effect.