Factors Affecting Consumers’ Purchasing of Suboptimal Foods during the COVID-19 Pandemic

Since the outbreak of the COVID-19 pandemic, global food production and transportation have been largely impacted. Meanwhile, consumers have purchased and stockpiled large quantities of foods due to panic in the early stage of the pandemic, which has resulted in a lot of uneaten, expired foods and has reduced the varieties of foods available in the markets. Due to the lower prices, some consumers have chosen to buy those foods with an earlier production time or inferior quality (suboptimal foods), and the purchase rate of suboptimal foods has increased. Therefore, this study investigated consumer behavior during the pandemic as the research focus, explored the main dimensions that affect consumers’ purchasing of suboptimal foods during the COVID-19 pandemic, tested their correlations, and proposed suggestions for improvement. The results of this study showed that the impacts of Perceived Benefits on Attitude Toward Behavior, Perceived Behavioral Control, and Subject Norm rank 1st, 2nd, and 3rd in importance, respectively, which are all higher than the related impact of Environmental Concerns. For consumers, the most important thing is whether suboptimal foods have consumption motivation for them, which is also the most direct way to make consumers feel the value of suboptimal foods. Furthermore, for consumers, while the environmentally friendly attributes of suboptimal foods are less perceptible than the economic motivations, they still have considerable influence on consumers, and this is even more prominent during the COVID-19 pandemic. Many families have experienced a shock to their income during the pandemic, and consumers are more sensitive and concerned about commodity prices, which also makes lower-priced and more abundant suboptimal foods more popular. However, in the long term, suboptimal foods can have a positive impact on reducing food waste and protecting the environment. When consumers realize this, they will be more motivated to purchase and try suboptimal foods.

Investment Decisions of the Blockchain-Based Anti-Counterfeiting Traceability Service in A High-Quality Fresh Supply Chain

Applications of the blockchain-based anti-counterfeiting traceability system (hereafter, blockchain-based ACTS) present a positive result in helping improve the repeat purchase rate and the product circulation rate. However, using the blockchain-based ACTS needs chain members’ additional expenditure. They want to know investment conditions about the blockchain-based ACTS and how to coordinate the supply chain. To solve these problems, we chosen a supply chain with one fresh producer and one retailer as the study object. Afterwards, considering the changes of the repeat purchase rate and the product circulation rate, we revised the demand function. Then, we constructed the profit functions before and after adopting the blockchain-based ACTS, and then a price discount and revenue-sharing contract was put forward to coordinate the supply chain. Findings: with the growth of the repurchase rate, benefits of chain members in the proposed three situations will increase. Thus, we can know that after using the blockchain-based ACTS.

Cartilage Conduction Hearing Aid Fitting in Clinical Practice

Background Cartilage conduction hearing aids (CCHAs) were newly devised and spread fast in Japan since their launch in 2017. However, little knowledge is available for this new device. Purpose The aim of this study was to establish the knowledge of CCHAs and suggest their indication. Research Design Correlational study. Study Sample A total 256 patients were registered. Data Collection and Analysis The fitting of CCHAs was surveyed in nine institutions. The outcomes were assessed by audiometric tests. The patients were classified into seven groups, depending on the ear conditions. The clinical characteristics, assessment results, and purchase rates were compared among the groups. The assessment results of CCHAs were also compared with those of previously used hearing aids. Results Most patients who used CCHAs were classified into the bilateral closed (aural atresia or severe stenosis) ear (n = 65) or unilateral closed ear (n = 124) groups. The patients in these groups achieved good benefits that resulted in a high purchase rate. The bilateral continuous otorrhea group also supported a high purchase rate, although the benefits of CCHAs were not always excellent. In contrast, the purchase rate was poor in the patients who could use air conduction hearing aids (ACHAs) without absolute problems. As for using a CCHA as a contralateral routing of signals hearing aid, the benefits depended on the patients. Conclusions CCHAs are considered as a great option not only to the patients with closed ears but also to those who had difficulties in ACHAs usage.

The Effect of Reviewer Profile Photo on Purchase Decision: Evidence from Event-Related Potentials

Although the number of studies on online reviews is growing, the impact of reviewer photo on consumer purchase decision-making has not yet been examined systematically. In particular, the underlying neural mechanisms have remained underexplored. Thus, the present study investigated whether and how reviewer photos affects consumers to make a purchase decision by using event-related potentials (ERPs). At the behavioral level, participants demonstrated a higher purchase rate with a shorter RT in situations with reviewer photos compared to situations without reviewer photos. Meanwhile, at the neural level, compared with situations without reviewer photos, situations with reviewer photos attracted more rapid attention resources at the early automatic processing phase, which induced a greater P2 amplitude, then mobilized more sustained attention allocation at the cognitive monitoring phase due to its evolutionary significance which elicited a more negative N2 amplitude, and finally resulted in a better evaluative categorization with higher motivational and emotional arousal due to its social presence which evoked a larger late positive potential (LPP) amplitude at the late elaborate cognitive processing phase. Those results illuminated the neural pathway of purchase decision-making when consumers were exposed in different conditions of reviewer photo. Moreover, the current study provided evidence for the underlying influence of reviewer photo on purchase decision-making in online shopping.

Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining

With the advent of the era of big data, data mining has become one of the key technologies in the field of research and business. In order to improve the efficiency of data mining, this paper studies data mining based on the intelligent recommendation system. Firstly, this paper makes mathematical modeling of the intelligent recommendation system based on association rules. After analyzing the requirements of the intelligent recommendation system, Java 2 Platform, Enterprise Edition, technology is used to divide the system architecture into the presentation layer, business logic layer, and data layer. Recommendation module is divided into three substages: data representation, model learning, and recommendation engine. Then, the fuzzy clustering algorithm is used to optimize the system. After the system is built, the performance of the system is evaluated, and the evaluation indexes include accuracy, coverage, and response time. Finally, the system is put into a trial operation of an e-commerce platform. The click-through rate and purchase conversion rate of recommended products before and after the operation are compared, and a questionnaire survey is randomly launched to the platform users to analyze the user satisfaction. The experimental data show that the MAE of this system is the lowest, maintained at about 0.73, and its accuracy is the highest; before the recommended threshold exceeds 0.5, the average coverage rate of this system is the highest: 0.75; in Q1–Q5 subsets, the shortest response time of the system is 0.2 s. Before and after the operation of the system, the average click-through rate increased by 11.04%, and the average purchase rate increased by 9.35%. Among the 1216 users, 43% of the users were satisfied with 4 and 9% with 1. This shows that the system algorithm convergence speed is fast; it can recommend products more in line with user needs and interests and promote higher click-through rate and purchase rate, but user satisfaction can be further improved.

The Effect of Reviewers’ Self-Disclosure of Personal Review Record on Consumer Purchase Decisions: An ERPs Investigation

Personal review record, as a form of personally identifiable information, refers to the past review information of a reviewer. The disclosure of reviewers’ personal information on electronic commerce websites has been found to substantially impact consumers’ perception regarding the credibility of online reviews. However, personal review record has received little attention in prior research. The current study investigated whether the disclosure of personal review record influenced consumers’ information processing and decision making by adopting event-related potentials (ERPs) measures, as ERPs allow for a nuanced examination of the neural mechanisms that underlie cognitive processes. At the behavioral level, we found that the purchase rate was higher and that the reaction time was shorter when the review record was disclosed (vs. when it was not), indicating that the disclosed condition was more favorable to the participants. Moreover, ERPs data showed that the disclosed condition induced an attenuated N400 component and an increased LPP component relative to the undisclosed condition, suggesting that the former condition gave rise to less cognitive and emotional conflict and to more positive evaluations. Thus, by elucidating potential cognitive and neural underpinnings, this study demonstrates the positive impact of reviewers’ disclosure of personal review record on consumers’ purchase decisions.

Impact of Consumer Satisfaction on Fresh E-commerce Repeat Purchase Behavior

Fresh e-commerce appeals to consumers with its fast speed, easy operation, low price and various types in the field of fresh. However, the fresh e-commerce market is facing unprecedented competitive pressure. The repeat purchase behavior of consumers has become the focus of fresh e-commerce enterprises. Based on the literature research of consumer satisfaction and fresh e-commerce repeat purchase behavior, through the investigation of online shopping experience of consumers using fresh e-commerce and empirical research on the relationship between consumer satisfaction and fresh e-commerce repeat purchase, this paper puts forward some suggestions to improve consumer satisfaction and increase fresh e-commerce repeat purchase rate.

A Study on the Influence of E-Commerce Live Streaming on Consumer Repurchase Intentions

The year 2019 witnessed an exponential growth of the e-commerce live streaming industry. Notably, competitions among live streamers have become increasingly fierce as more newcomers are marching in. To survive and thrive in the cut-throat market competitions, it is key for them to increase consumers’ repeat purchase rate and win customer loyalty. This study uses empirical research methods to probe into the influence of e-commerce live streaming on consumer repurchase intentions. According to this study, perceived entertainment and perceived similarity have a positive impact on consumer repurchase intentions, and this relationship is partially mediated by consumer satisfaction. In addition, perceived product quality, perceived interactivity, and perceived professionalism have a positive and indirect effect on consumer repurchase intentions, and this relationship is fully mediated by consumer satisfaction.

A Study on Optimistic need of Artificial Intelligence in Fashion Retail Industry

The fashion e-commerce market has been growing steadily in the past few years accounting for USD 371 billion or 21% retail sales of apparel and footwear globally in 2019. But as most of the worlds are experiencing self-isolation and lockdown measures, the corona virus crisis is pushing brands to digitalize even faster to survive, engage with customers, designers, manufactures and redesign their supply chain operations. Many sectors are reeling from the fallout of the COVID-19 pandemic as they stare into the abyss of the impending recession and fashion has not been immune. But aside from economic factors, the industry is also facing lasting structural change. Artificial Intelligence optimizes conversion, Average Order Value (AOV) and repeat purchase rate by understanding a customer’s preferences and suggesting the right products and outfits for them. Recommendations are tailored to the physical stores with latest technologies by implementing virtual trail room, regional trends, as well as the customers’ body type, color, desired occasions and personal style.

Fake Comments Detection with Sentiment Anatomy using Iterative Sequential Minimal Optimization Algorithm

It is significant to create electronicon stream markets,on stream communication networks, peer-to-peer functions, social media providerson stream and convenience customers. In reality, web based amenities are specially designed to overcome the risk of uncertainties & distrust inherent in the main concern of ecommerce applications & to increase the robustness of the system& resistance against fake clients & unbelievers. The aim of the Ecommerce platform is, moreover, to embrace one of the most efficient methods for understanding and evaluating user attempts to expose fraudsters. Or else, the fundamental objective of ecommerce amenities to exploit the profit & purchase rate, will be endangered & deteriorated through fake and ill-intentioned users. Individuals and organizations need to detect fake Comments. With disappointing and hidden features, it is difficult to identify counterfeit Comments simply by looking at a single Comments text. It is also why it is a difficult task to identify falsified Comments.This paper uses the sentiment anatomy (SA) tool for the identification of fake Comments to analyzeon stream film Comments. The texts and the SA system are used for a specific dataset of film Comments. We particularly compared the supervised SVM & SMO machine-learning process with the feeling classification methods of the analyzes in two different cases, without stopping phrases. Measured outcomes display that SMO process compared to the SVM process for both methodes, &it arrives at the maximum precision not only in the classification of text but also for finding duplicate analyses.