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Recognizng Important Factors of Influencing Trust in O2O Models: an Example of Opentable C.-R. Chang, M.-Y. Chen, L.-S. Chen*, W.-T. Chien, 2019, RecognizngImportant Factors of Influencing Trust in O2O Models: An Example of OpenTable, SoftComputing (DOI10.1007/s00500-019-04019-x) Recognizng Important Factors of Influencing Trust in O2O Models: An Example of OpenTable Jing-Rong Chang1, Mu-Yen Chen 2, Long-Sheng Chen3*, Wan-Ting Chien4 1Department of Information Management, Chaoyang University of Technology, No.168, Jifong E. Rd., Wufong Dist., Taichung, 41349, Taiwan. Email: [email protected] 2Department of Information Management, National Taichung University of Science and Technology, No. 129, Sec. 3, Sanmin Rd., North Dist., Taichung, 40401, Taiwan. Email: [email protected] *3Department of Information Management, Chaoyang University of Technology, No.168, Jifong E. Rd., Wufong Dist., Taichung, 41349, Taiwan. Email: [email protected] 4Department of Information Management, Chaoyang University of Technology, No.168, Jifong E. Rd., Wufong Dist., Taichung, 41349, Taiwan. Email: [email protected] *Corresponding Author: Chen, Long-Sheng, Ph.D. Professor Department of Information Management Chaoyang University of Technology No. 168 Jifong E. Rd, Wufong District, Taichung, 41349, Taiwan. TEL: +886-4-2374-2304 FAX: +886-4-2374-2305 E-mail: [email protected] 1 C.-R. Chang, M.-Y. Chen, L.-S. Chen*, W.-T. Chien, 2019, RecognizngImportant Factors of Influencing Trust in O2O Models: An Example of OpenTable, SoftComputing (DOI10.1007/s00500-019-04019-x) ABSTRACT O2O (Online to Offline/ Offline to Online) business models have attracted lots of enterprisers to enter this market. In such a fast-growing competition, some studies indicated that lack of trust will bring a great damage to O2O business. Related works already comnfirm trust is the key factor to the success of O2O. Besides, social media has been changing the the way providers communicate with consumers. Negative comments in social media will decrease the consumers’ trust to O2O companies and platforms. Available O2O literatures are almost always conducted by means of questionnaires or interviews, which can not provide immediate customer response and require a lot of manpower and time. Since online reviews are the main information sourece for consumers. Therefore, this study presented a text mining based scheme which uses text mining technique to find important factors from online electronic word-of-mouth, to replace the traditional questionnaire survey method of collecting data. Two feature selection methods, Support Vector Machines Recursive Feature Elimination (SVM-RFE) and Least Absolute Shrinkage and Selection Operator (LASSO) have employed to select important factors that affect O2O trust. We also evaluate the performance of extracted feature subsets by Support Vector Machines (SVM). The findings can be referenced for O2O market enterprises to carefully response customers’ comments to avoid hurting customers’ trust and improve service quality. Keywords: O2O; Trust; Sentiment classification; Feature selection; SVM-RFE; LASSO. 2 C.-R. Chang, M.-Y. Chen, L.-S. Chen*, W.-T. Chien, 2019, RecognizngImportant Factors of Influencing Trust in O2O Models: An Example of OpenTable, SoftComputing (DOI10.1007/s00500-019-04019-x) 1. Introduction The development of information and communication technologies has led to the rapid development of electronic commerce (e-commerce). According to report of Statista (2019), the total global retail e-commerce sales in 2020 will grow to 4 trillion US dollars. In such growing rapidly environment, business models have changed from B2B (Business to Business), B2C (Business to Consumer), C2B (Consumer to Business) to social and mobile commerce. People can easily connect to the Internet anytime and anywhere through tablet, smartphone, smartwatch, etc. It enables social media, cloud applications, group purchases which provided by online services enterprises to be more quickly integrated into the lives of consumers (Chen et al., 2015, Pan et al., 2019). According to the well-known German data survey company, Statista, the penetration rate of the global mobile network will reach 63.4% of the global total in 2019. The development of mobile commerce has enabled enterprises and manufacturers to launch more online consumption modes, such as service reservations, restaurant reservations, online car calls, and so on. Consumers can book or consume (online) and enjoy the service in the physical storefront (offline). That is so called O2O (Online to Offline or Offline to Online) model (Zhang and Huang, 2015). Many related researches and reports indicated that many companies in China are very concerned about the development of O2O, and many companies are competing to enter the O2O market because O2O is considered as a major trend in e-commerce (Carsten et al., 2016; Zhang and Huang, 2015; Huang et al., 2017; Ji et al., 2017; Pan et al., 2019). According to the report of the famous restaurant reservation website, OpenTable, there have been more than 1 billion meal orders since its establishment in 2016, and more than 40,000 restaurants around the world have cooperated with OpenTable. In addition, Taiwan’s famous restaurant reservation website, EzTable, from 2008 to 2016, the number of restaurants that started to cooperate has reached 10,000, including various middle and high-end restaurants. And the famous group phase website, Groupon, has more than 950,000 featured stores from 2008 to 2015. These numbers shows that the O2O industry has matured and is quite large in many countries. It can be seen that both in domestic and abroad, O2O is attracting more and more companies to enter this rapid developed market. It make more and more difficult for enterprises to attract customers in the O2O market. In mainland China, the O2O platform has also developed very fast, and there are also many problems (Huang et al., 2017). According to the work of Kim (2005), before the Internet transaction is done, consumers will first browse transaction platform information including authenticity, 3 C.-R. Chang, M.-Y. Chen, L.-S. Chen*, W.-T. Chien, 2019, RecognizngImportant Factors of Influencing Trust in O2O Models: An Example of OpenTable, SoftComputing (DOI10.1007/s00500-019-04019-x) integrity and reputation of the platform (Long and Shi, 2017). With these initial trusts, the transaction will go smoothly, so consumer trust will be one of the important factors for a successful transaction (Liang et al., 2014). Moreover, from available literatures, trust will be an important issue in the development of O2O. For examples, in 2014, Liang et al. (2014) believed that consumer trust will affect the development of O2O, while Zhang et al. (2015) think that confusion and lack of trust will bring great problems to O2O. From the above viewpoints, trust will be one of the important issues of the O2O platform. Wang et al. (2016) pointed out that consumer satisfaction on group buying websites will affect consumers’ trust and adhesion to group buying websites (Wang et al., 2016). Electronic word-of-mouth (eWoM) trust is one of the important factors (Chen et al., 2011; Saumya et al., 2019). Recently, the influence of social media on e-commerce consumers has increased. Customer-generated contents such text comments and reviews have also become an important source for other consumers and vendors (Guo et al., 2014). Saumya et al. (2019) attempted to predict the best helpful online product review. Yan et al. (2016) suggested that the higher the number of comments, the more product sales. And high volume of comments can attract more consumers (Yan et al., 2016). In today’s life, many consumers will search for the information they want on the social networking sites or e-commerce platforms before traveling, shopping, or eating and lodging, and as a reference for consumption. Related works have suggested that online reviews and ratings can provide consumers, potential consumers, retailers and manufacturers with information about products, thereby reducing the uncertainty of products for potential consumers. It also allows retailers and manufacturers to understand the customer's situation with the product (Engler et al., 2015; Guo et al., 2014). Currently, O2O related literatures (Liang et al., 2014; Gao et al., 2014; Li et al., 2016; Wu et al., 2015) are almost always conducted by means of questionnaires or interviews. But such survey methods do not provide immediate customer comments and require a lot of manpower and time. Schuckert et al. (2015) also mentioned that online reviews are usually spontaneous postings and messages by consumers. The source is usually considered to be objective and informative, and there is no sampling bias problem. Therefore, this study will use the technique of text mining to utilize online electronic word-of-mouth as experimental data to replace the traditional method of collecting data. With the development of the O2O platform, consumer trust has become an issue that needs to be solved and discussed in depth. It is also an important issue to find the factors that affect the trust of O2O consumers. In some published works, feature selection approaches have been employed to discover important factors (Gaudioso et 4 C.-R. Chang, M.-Y. Chen, L.-S. Chen*, W.-T. Chien, 2019, RecognizngImportant Factors of Influencing Trust in O2O Models: An Example of OpenTable, SoftComputing (DOI10.1007/s00500-019-04019-x) al., 2017; Shao et al., 2017; Gauthier et al., 2017; Ikram and Cherukuri, 2017). The common feature selection methods are Support Vector Machines- Recursive Feature Elimination (SVM-RFE) and Least Absolute Shrinkage and Selection Operator (LASSO). Therefore, this study will use SVM-RFE and LASSO to select important factors of affecting customers’ trust in O2O. In addition, due to SVM has better performance and sensitivity (Barkana et al., 2017; Yoon et al. 2016; Paul et al., 2016), this study will use SVM to evaluate the effectiveness of feature selection. To sum up, this study aims to define the potential factors of influencing consumers’ sentiment of textual reviews. SVM-RFE and LASSO have employed to select important factors that affect O2O trust.
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