Recognizing important factors of influencing trust in O2O models: an example of OpenTable

Consumer Behavior in Online-to-Offline (O2O) Commerce: A Thematic Review

Online-to-offline (O2O) commerce is a popular business model which links offline business activities with online channels. Consumer behavior in O2O commerce is more complex than in other traditional business models as both online and offline channels are involved. Despite the growing number of publications focused on this issue, no review paper has discussed the current research trends and factors influencing consumer behavior in O2O commerce. Therefore, this review aimed to synthesize literature on O2O commerce from 2015 to April 2022, focusing on consumer behavior. A set of inclusion and exclusion criteria was developed for searching and screening articles from two dominant databases (i.e., WOS and Scopus), and 53 eligible articles were included in this review. A thematic review approach using ATLAS.ti 9 software was conducted. Quantitative results presented the research trends of O2O commerce. Qualitative analyses generated eight main clusters of factors which influence consumers’ O2O behavior: (1) service and product quality, (2) technical and utilitarian factors, (3) emotional and hedonic factors, (4) trust and risk, (5) price and cost (6), social factors, (7) online content, and (8) habit. This paper also highlighted promising future research directions. The findings are expected to benefit the sustainable management and the future research of O2O commerce.

Pattern Recognition of Holographic Image Library Based on Deep Learning

The final loss function in the deep learning neural network is composed of other functions in the network. Due to the existence of a large number of non-linear functions such as activation functions in the network, the entire deep learning model presents the nature of a nonconvex function. As optimizing the nonconvex model is more difficult, the solution of the nonconvex function can only represent the local but not the global. The BP algorithm is an algorithm for updating parameters and is mainly applied to deep neural networks. In this article, we will study the volume holographic image library technology, design the basic optical storage path, realize single-point multistorage in the medium, and multiplex technology with simple structure to increase the information storage capacity of volume holography. We have studied a method to read out the holographic image library with the same diffraction efficiency. The test part of the system is to test the entire facial image pattern recognition system. The reliability and stability of the system have been tested for performance and function. Successful testing is the key to the quality and availability of the system. Therefore, this article first analyzes the rules of deep learning, combines the characteristics of image segmentation algorithms and pattern recognition models, designs the overall flow chart of the pattern recognition system, and then conducts a comprehensive inspection of the test mode to ensure that all important connections in the system pass through high-quality testing is guaranteed. Then in the systematic research of this paper, based on the composite threshold segmentation method of histogram polynomial fitting and the deep learning method of the U-NET model, the actual terahertz image is cut, and the two methods are organically combined to form terahertz. The coaxial hologram reconstructs the image for segmentation and finally completes the test of the system. After evaluation, the performance of the system can meet the needs of practical applications.

Determining the factors affecting customer satisfaction using an extraction-based feature selection approach

The coronavirus disease 2019 (COVID-19) causes tremendous damages to the world, including threats to human's health and daily activities. Most industries have been affected by this pandemic, particularly the tourism industry. The online travel agencies (OTAs) have suffered from the global tourism market crisis by air travel lockdown in many countries. How online travel agencies can survive at stake and prepare for the post-COVID-19 future has emerged as an urgent issue. This study aims to examine the critical factors of customers' satisfaction to OTAs during the COVID-19 pandemic. A text mining method for feature selection, namely LASSO, was used to deal with online customer reviews and to extract factors that shape customers' satisfaction to OTAs. Results showed that refunds, promptness, easiness and assurance were ranked as the most competitive factors of customers' satisfaction, followed by bad reviews & cheap and excellent service & comparison. New factors to customers' satisfaction were revealed during the global tourism recession. Findings provide OTAs guidelines to reset services priorities during the pandemic crisis.

The key successful factors of video and mobile game crowdfunding projects using a lexicon-based feature selection approach

The emergence of crowdfunding has given many capital demanders a new fund-raising channel, but the overall project success rate is very low. Many scholars have begun to discover key suscessful factors of crowdfunding projects. Previous studies have used questionnaires survey to identify important project features. In addition to requiring a lot of manpower and time, there may also be sampling bias. Moreover, related studies also reported that the project description will affect the success of the crowdfunding project, but there is no research to tell fundraisers which success factors should be included in the content of the project description. Besides, in recent years, game crowdfunding projects have been attracted lots of attention in terms of total fundraising amount and number of projects. Moreover, in traditional feature selection and text mining approaches, the selected terms are un-organized and hard to be explained. Therefore, this study will focus on real video and mobile game project descriptions to replace conventional questionnaires. To solve these issues, we present a lexicon-based feature selection method. We attempt to define "content features" and build lexicons to determine the attributes' values. Three feature selection methods including decision tree (DT), Least Absolute Shrinkage and Selection Operator (LASSO), and support vector machine-recursive feature elimination (SVM-RFE) will be employed to find organized candidate key successful factors. Then, support vector machines (SVM) will be used to evaluate the performances of candidate feature subsets. Finally, this study has identified the key successful factors for video and mobile games, respectively. Based on the experimental results, we can give fundraisers some useful suggestions to improve the success rate of crowdfunding projects.

Using a Text Mining Approach to Hear Voices of Customers from Social Media toward the Fast-Food Restaurant Industry

Due to the COVID-19 pandemic, the sales of fast-food businesses have dropped sharply. Customer satisfaction has always been one of the key factors for the sustainable development of enterprises. However, in the fast-food restaurant business, gaining the knowledge of customer satisfaction is one of the critical tasks. Moreover, text reviews in social media have become one of important reference sources for customers’ decisions in buying services and products. Therefore, the main purpose of this study is to explore whether customer voices from social media reviews are different during the COVID-19 outbreak and to propose a new method to reduce interpersonal contact when collecting data. A text mining scheme which includes least absolute shrinkage and selection operator (LASSO) and decision trees (DT) are presented to discover the essential factors for customers to increase their satisfaction from unstructured online customer reviews. Finally, three real world review sets were employed to validate the effectiveness of the presented text mining scheme. Experimental results can help companies to properly adapt to similar epidemic situations in the future and facilitate their sustainable development.