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. We also build prediction models for classifying sentiments of textual reviews by using Support Vector Machines (SVM). The findings can be referenced for O2O market enterprises to carefully response customer’ comments to avoid hurting customers’ trust and improve service quality.

2. RELATED WORKS 2.1 O2O (Online to Offline/ Offline to Online)

Recently, the rapid development of O2O has led many businessmen and researchers to pay attention on the O2O e-commerce model, which can be defined as a seamless shopping experience between online commerce and offline entities. The concept of O2O was first proposed by Alex Rampell, founder of TrialPay in 2011. Rampell believes that many people still consume offline, such as cafes, gyms, bars, barbershops, restaurants, etc., so they proposed O2O (offline to online/online to offline) concept. “Offline to online” refers to consumers can use online services in physical storefronts (offline orders). For example, the famous apparel brand GAP has combined SoLoMo (Social, Local & Mobile) and LBS (Location Based Service). “Online to offline” means that consumers can order products and services online, and then go offline or physical stores to enjoy the service or receive the order. Online to offline model is not limited to physical goods transactions. Restaurant reservations, taxis, etc. are also included in the online to offline model.

Lots of researchers have also studied relevant issues of O2O. Some of them have suggested how vendors can provide more attractive service methods in the O2O model and maintain a competitive advantage (Tsai et al., 2015; Wu, 2015; Zhang & Huang, 2015; He et al., 2016). They have begun to notice that there may be trust problems in the O2O environment (Wu, 2015; Liang et al., 2014), but the current published works on trust in the O2O environment is not sufficient. Therefore, this study attempts to

5 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) study this issues.

At present, almost all available O2O related works use questionnaires as a means of collecting data and using qualitative approaches to do research and analysis (Liang et al., 2014; Gao et al., 2014; Li et al., 2016; Wu, 2015). It may imply some problems, including the inability to determine whether the respondent has actually used the O2O service, and collecting questionnaires requires a lot of manpower and time. Therefore, this study attempts to use customer-generated content, text comments, as research objects and data source.

Among previous published works, it can be found that most of them are aimed at the use intentions of O2O users, and the O2O platform strategy. Relatively few researches studied on O2O trust issues. For example, Nisar and Prabhakar (2017) believed that online transactions and card payment require a lot of procedures and complicated operation methods, etc., but all of which require consumer trust. From available literatures, there are a lot of literatures to confirm the relationship between consumer trust and transactions. However, there is little discussion and research on the issue of trust. Therefore, this study attempts to find out the factors affecting the trust of O2O platform. .

2.2 O2O Trust Factors in Social Media Social media has been changing not only the way people communicate with friends, but also the way providers communicate with consumers (Shen et al., 2018). In social commerce, trust has been one of important factors. Many scholars believe that there must be basic trust before a successful transaction (Liang et al., 2014; Kim, 2005). Thus, it is very important to understand the factors that may affect trust. Wang et al. (2016) made a successful model for the group buying model, and it mentioned that consumer satisfaction and perceived value will affect consumers’ trust in suppliers. In recent years, many consumers have turned online shopping experiences into text comments and published them on social media (Mudambi and Schuff, 2010). Therefore, this study attempts to identify potential factors affecting the semantics of consumer commentators on the O2O platform in terms of restaurant service quality, satisfaction, and reviews.

There are four trust dimensions in the O2O business model. They are “consumers”, “vendor & their product and service”, “O2O website platform”, and “trading environment” (Liang et al., 2014). And the text comments of consumers in the O2O platform are almost all written and compared with the expectations of the vendors’ services. In the text comments, everyone writes the comments differently. It is very important to understand the readability of the text comments. Take the online

6 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) commentary of the restaurant as an example. The text comments are based on the experience of each consumer to write their own consumption or dining experience. Potential consumers will also carefully read the content of the review to understand the restaurant’s dining quality, service content, etc., and then decide whether to book in the restaurant (Fang et al., 2016). Consequently, the readability of text comments will affect the reader’s perceived value, ideas, decisions, etc. (Liu and Park, 2015; Fang et al., 2016). So, understanding the readability of text comments is one of the important factors. The readability contains many features, such as text difficulty, type-token ratio, Flesch-Kincaid grade level, tokens (Klare, 1984). These reviews and rating stars indeed affect sales (Maeyer, 2012), while ratings and reviews are the one of major sources of consumer-related information for consumers and online retailers (Guo et al., 2014). Engler et al. (2015) suggested that online reviews could be considered as customer satisfaction with products. Lovinger et al. (2019) introduced Gist system to automatically summarize large amounts of text into key sentences. Yan et al. (2016) believed consumer satisfaction in online group buying network will affect consumers’ trust in the website. So, comments have become one of the reasons that affect consumer trust. Consequently, this study will try to identify the factors that influence the sentiment of consumer reviews. Take OpenTable, the largest O2O restaurant reservation platform in Europe and America, as an example. In OpenTable, we can find all the comments on each restaurant, and ensure that every commenter has consumed in the store, so fake comments can be reduced. Consequently, this study will use OpenTable as our experimental data.

2.3 Sentiment classification The growth of social media has made customer-generated content gradually be noticed. Online reviews and ratings have become a reference for consumers’ decision-making (Yan et al., 2016; Xie et al., 2019). Companies and suppliers can also find valuable information about their products or services from these comments (Engler et al., 2015; Chen & Cai, 2014; Chen et al., 2015), and then make appropriate improvement. Suppliers can use sentiment of reviews to know which services or products offered by the company are accepted, and to further enhance the quality of services or products. Sentiment classification aims to classify text comments into negative and positive sentiment (Pang et al., 2002; Chen et al., 2011). Lots of works aim to enhance the performance of sentiment classification. For example, Xie et al. (2019) proposed an entropy-PLSA model to increase accuracy for sentiment classification. In fact, there are two common methods in the sentiment classification are semantic orientation (SO) and machine learning (ML) methods. SO is to establish a positive and negative lexicon first (Molina-González et al., 2013), then calculate the score of the relationship with the

7 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) positive or negative lexicon, respectively, finally, sum up the scores to determine the sentiment of a specific text comment. Lots of works have successfully applied SO methods to complete sentiment classification tasks. For example, Murthy and Suresha (2015) used SO method to effectively classify XML pages. Chaovalit and Zhou (2005) utilized SO-based unsupervised and supervised learning methods to classify film reviews (Chaovalit et al., 2005).

The machine learning method is learning from term-document matrix by using supervised or semi-supervised methods (Chen et al., 2011). In addition, we should first label the sentiment of reviews before training classifiers. ML methods can have better performance than SO, but they need additional learning time and efforts of giving class labels. SO methods can have short response time in determining sentiment. However, users need to frequently extend positive and negative semantic dictionaries when dealing with different kinds of documents. The increasing size of documents will be a big problem for SO methods. Therefore, this study will use SO methods to label classes of reviews, and then utilize machine learning to establish classifiers to determine sentiment.

2.4 Text Mining

Text mining refers to the extraction of important information from the text, that is, the extraction of useful or important information in unstructured data, unlike traditional data mining which deals with structured data (Li et al., 2016; Su et al., 2017). Text mining is often handled articles with different lengths, irregularities, and various terminology or spoken vocabulary. When implementing text mining, it is necessary to perform word segmentation and simplify data (Niemann et al., 2017). There are several methods in word segmentation and word extraction. Commonly, there are SAOs (Subjec Action Objectstructures) and the way of N-gram (Niemann et al., 2017).

The SAOs method mainly extracts the main structural words in English sentences (Choi et al., 2012), where subject refers to the topic to be represented in the sentence; action refers to the action; and object refers to the purpose. The way of SAOs is to clearly express the meaning and purpose that the sentence wants to express. The sentence after the SAOs structure will be presented, such as “Gasoline Start Engine”, Gasoline is the Subject theme; Start is the verb; Engine is the Object purpose. So, if you use the SAOs method, only the main structure words will be left in the English sentence. Another word segmentation method is N-gram (Moehrleand and Gerken, 2012), where N represents the number of words. Lots of literatures used N-grams for word segmentation (Lucini et al., 2017; Al-Daihani & Abrahams, 2016; Niemann et al., 2017; Winkler et al., 2016), so this study uses N-grams for word segmentation.

8 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) Text mining approaches are widely used in various fields. For examples, Shen et al. (2018) employed text mining methods to analyze the trends of O2O commerce from the viewpoints of social media. In the work of Small et al. (2017), they used text mining to predict cardiovascular disease. In addition, Hsiao et al. (2017) applied text mining to the design of logistics services. Therefore, this study will use the techniques of text mining to find out the important factors affecting trust from the text comments in the O2O platform, instead of using questionnaires.

2.5 Feature Selection

Feature selection is usually used in the field of machine learning. It is necessary to select effective and discriminative features from the original attribute set, according to specific performance evaluation indicators to determine the optimal feature subset (Gaudioso et al., 2017). The use of feature selection allows this experiment to screen out the important factors affecting the sentiment of consumer comments in O2O platforms, so that important influencing factors can be extracted through this method. Therefore, this study will use feature selection methods to identify important influencing factors.

2.5.1 SVM-RFE

SVM-RFE is mainly based on SVM algorithm to train the classifier, using the feature coefficients generated during training to sort, and to delete feature with minimal coefficient in each iteration (Witten et al., 2011). SVM re-trains and ranks the remaining features. Finally, the descending ordering of all features is obtained.

SVM-RFE has been successfully used in many areas. For instances, Shao et al. (2017) used SVM-RFE to establish electricity price prediction model. Hidalgo-Munoz et al. (2013) employed SVM-RFE and brain mechanics to build model for analyzing brainwave maps, and the built model can be applied to the treatment of emotional dysfunction, phobia, etc. Therefore, this study uses SVM-RFE to explore the importance of influencing factors in O2O.

2.5.2 LASSO

LASSO is an effective normalization method (Tibshirani, 1996; Connor et al., 2015) and is also widely used in high-dimensional linear regression models (Kwon et al., 2015). The main feature of LASSO is that the compression factor and the variable selection can be automatically completed in the estimation process. It constructs a valve function to obtain a more refined model, so that it compresses some feature coefficients. And at the same time, it sets some feature coefficients to zero. It indicates these features are not recommended to be selected in models. Thus, LASSO preserves

9 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) the advantages of subset shrinkage (Chan et al., 2015; Yan and Yao, 2015).

In the past, LASSO has lots of successful applications for feature selection. For example, Gauthier et al. (2017) used LASSO to predict sound quality. In the work of Lee et al. (2016), LASSO is utilized for radar measurement. Chan et al. (2015) also used LASSO to simplify the model process. Consequently, this study also lists LASSO as one of the methods of feature selection.

2.6 SVM

SVM is a supervised learning algorithm based on statistical learning in classification and regression analysis (Cortes and Vapnik, 1995). SVM is widely used because of its complete theoretical architecture and well-developed tools, and is suitable for small samples, high dimensional models, and nonlinear problems (Paul et al., 2016). For example, Gaudioso et al. (2017) used SVM for feature selection to optimize the Lagrangian Relaxation method in mathematics. Ikram and Cherukuri (2017) applied SVM to build intrusion detection models to reduce training time and test time, and improve the accuracy rate. Barkana et al. (2017) also used ANN and SVM to predict the retinopathy of diabetic patients, and SVM is also found to be more sensitive from experiments. Some literatures also show SVM has a better performance and sensitivity than other algorithms (Barkana et al., 2017; Yoon et al. 2016; Paul et al., 2016). Li et al. (2016) utilized SVM to evaluate the stability of the tunnel, which also mentions the performance and accuracy of the SVM model is superior to other classification models (Li et al., 2016; Zhong et al., 2015). Therefore, this study will use SVM to evaluate the performances of feature selection.

3. EMPLOYED METHODOLOGY

This section will introduce the implementation steps of employed method in this study. The implementation procedure, which described in figure 1, could be divided into 8 steps. Concise descriptions of implementing are listed as below. Step 1: Collect Data This study uses the OpenTable website as our data source. We’ll program a crawler tool to retrieve comments on the webpage and other information, including member account, date, label, rating star, etc. Reviews indicate the consumer experience or opinions expressed in text.

10 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)

Step 1 Collect data

Step 2: Pre-process Data

Step 3: Build lexicons

Step 4: Screen Reviews

Step 5: Define Attributes

-Determine class labels -Define candidate trust factors

Step 6: Data Format Transformation

Step 7: Feature Selection

-SVM-RFE -LASSO

Step 8: Performance Evaluation by SVM

Step 9: Make Discussion and Conclusions

Figure 1 The implemental procedure of the employed text mining based scheme for discovering O2O trust factors

Step 2: Pre-process Data Since this experiment uses the comments on the OpenTable of the restaurant reservation website, the collected reviews are mainly written in English. Before building Term-Document Matrix (TDM), we have to implement the following pre-process steps.

11 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) Step 2.1: Remove stop words, conjunction, emoticons, symbols, and other unnecessary labels. This study removes stop words based on the stop word list (available at http://snowball.tartarus.org/algorithms/english/stop.txt). Step 2.2: Segment corpuses by using unigram Step 2.3: Extract stems Step 2.4: Remove some words with the lower frequency Step 2.5: Build Term-Document Matrix (TDM) Step 3: Build lexicons In this step, we need to build two kinds of lexicons. One is “trust” related lexicon. We follow the work of Liang et al. (2014). They indicated four trust dimensions in the O2O business model. They are “consumers”, “vendor & their product and service”, “O2O website platform”, and “trading environment”. For these dimensions, we collect synonyms, similar words, related words, and antisense words from dictionaries or related documents to build “trust” lexicon. The second one is sentiment lexicon which contains positive and negative dictionaries. Based on constructed opinion lexicon such as SentiWord Net, Sentic Net, and General Inquirer, we have to collect additional O2O, tourism, hotels, restraints related comments to build our sentiment lexicon. Step 4: Screen Reviews After building “trust” lexicon, we can filter and remove those comments which are not related to trust. The left comments can be analyzed further. Step 5: Define Attributes

Step 5.1 Determine class labels

Based on constructed sentiment lexicon in step 3, we follow the method of Hu and Lin (2004) to compute sentiment scores, and then we can define class labels. In this study, we employed binary classification for sentiment. If positive terms is larger than negative words, its sentiment will be determined as positive. If negative terms is larger than positive words, its sentiment will be determined as positive.

Step 5.2 Define candidate trust factors

Based on literature review, this work attempts to define candidate attributes that potentially affect the sentiment of the comments. This experiment mainly analyzes customer reviews. In fact, the platform and trading environment are less suitable for analysis by customer comments. Therefore, this study does not incorporate these facet features into the experiment. We define 17 factors. They are “Speed of Service”, “Friendliness of Staff”, “Cleanliness”, “Menu Design/Diversity”, “Restaurant Atmosphere”, “Environment”, “Meal Size”, “Location”, “Price”, “Understanding of

12 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) Customer Needs”, “Text Difficulty”, “Type-Token Ratio”, “Grade Level”, “Tokens”, “Sentimental Features”, “Recommendation”, and “Expertise”. The employed formula and calculation of “Text Difficulty”, “Type-Token Ratio”, and “Grade Level” are described below. (1)Text Difficulty Text difficulty can be defined in equation (1). The higher scores indicate material that is easier to read; lower numbers mark passages that are more difficult to read. The formula for the Flesch Reading Ease Score (FRES) test is as follow.  total words   total syllables 206.8351.015   84.6  (1)  total sentences  total words  This equation developed by Xu and Jia (2009) is based on spelling and grammar functions of Microsoft Office. And, we use this attribute to define “readability” of one review. (2)TTR (Type-Token Ratio) Type-Token Ratio (TTR) is another index to measure the readability of one text comment. The variation of used words will influence the readability of one article. The article that has a simple structure can be easier to be read (Hu et al., 2012). TTR can be defined as equation (2).

Types TTR  (2) Token

,where “Tokens” is the number of individual words in the text and “Types” means the number of types in a word frequency list is the number of unique word forms, rather than the total number of words in a text. This attribute tells you how rich or “lexically varied” the vocabulary in the text is.

Table 1 Flesch-Kincaid grade levels Elementary school Ages Middle school Ages High school Ages 9th Grade Preschool 3-4 6th Grade 11-12 14-15 (Freshman) 10th Grade Pre-kindergarten 4-5 7th Grade 12-13 15-16 (Sophomore) Kindergarten 5-6 8th Grade 13-14 11th Grade (Junior) 16-17 1st Grade 6-7 12th Grade (Senior) 17-18 2nd Grade 7-8 3rd Grade 8-9 4th Grade 9-10 5th Grade 10-11

13 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) (3)Grade Level (Flesch-Kincaid Grade Level) Based on education level, we can judge their reading abilities. Take the famous speech “I have a dream” of Dr. King for example. This article has 69 sentences, a total of 1410 words, and the average sentence length of 20 characters. According to Flesch-Kincaid Grade Level (Kincaid et al., 1975), the article is suitable for nine grades (a high school) students to read. Grade level is calculated mainly through word essay sentences (ASL) and the average number of syllables and the article (ASW). Then, we can map the results to a U.S. grade level listed in table 1. A normal article falls level 7.0 to 8.0. Therefore, a professional writer of product review will take this factor into consideration (Hu et al., 2012).

 total words   total syllables 0.39  11.8  15.59 (3)  total sentences  total words 

Step 6: Data Format Transformation In this step, the study will use feature selection methods. Before implementing these methods, data should be re-formatted. Moreover, the original data will be normalized using equation (4). The values of attributes will be transformed into the interval of [-1, 1].

v  mina v  (4) maxaa min ,where v is the original data, maxa and mina represent the upper bound and low bound of value in ath attribute. Besides, the 5-fold cross-validation experiment will be employed. It means we have to divide the collected data into five equal parts, and take one of them as testing test in turn. Therefore, for each collected data set, we will do five times experiments. It can make the experimental results more reliable. Step 7: Feature Selection Two feature selection methods, SVM-RFE and LASSO, are used to discover the important attributes that affect the sentiment of the comments. If one factor is considered as important at least 3 times in 5-fold experiments, it will be viewed as important factor. The algorithms of each method are as follows. Step 7.1: SVM-RFE The detailed SVM-RFE algorithm has been shown as bellow. Inputs: Training examples T X0  x1,x2,...... xk ,...xl  Class labels T y  y1, y2,...... yk ,...yl  Initialize:

14 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) Subset of surviving features s1,2,.....n Feature rank list r  Repeat until s    Restrict training examples to good feature indices

X  X0(:,s) Train the classifier SVMtrain(X,y) Compute the weight vector of dimension length(s)

wkykxk k Compute the ranking criteria 2 ci  (wi ) , forall i Find the feature with smallest ranking criterion f argmin(c) Update feature rank list r s( f),r Eliminate the feature with smallest ranking criterion s  s(1: f 1, f 1:length(s)) Output: Feature ranked list r .

Step 7.2: LASSO

LASSO is a feature selection method that simultaneously performs feature selection and normalization. LASSO mainly considers the following selection criteria in Equation (5), where 휆 is the adjustment system, T is the number of data, and x is the explanatory variable.

푇 2 푘 min ∑푡=1(푦푡 − 훽0 − 훽1푥1,푡 − ⋯ − 훽푘푥푘,푡) ，푠. 푡. ∑푗=1|훽푗| ≤ 휆 (5)

According to Equation 2, it can be found that the regression parameter value 훽푖 is limited by a specific penalty selection criterion, and then the appropriate variables are selected. Given a k-explained transformation, the value of λ will affect the parameter estimate 훽̂. The one exception is that when the λ value approaches infinity, the parameter estimate 훽̂ will not be limited by Equation 2, and the estimate will be the value calculated by the least squares method. However, the opposite situation is that when λ is adjusted to 0, all parameter estimates will be 0. Therefore, we can establish a feature subset by using whether the coefficient is 0 or not to be a criterion for selecting factors.

15 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) Step 8: Performance Evaluation by SVM In this step, LibSVM is used to verify the experimental results of SVM-RFE and LASSO. The RBF (radial basis function) kernel is used to obtain the optimal parameters. The steps are as follows: Step 8.1: Normalize the input data. Step 8.2: Transform data format. Step 8.3: Use the RBF kernel function listed in equation (6)

2 퐾(푥, 푦) = 푒−훾||푥−푦|| (6) Step 8.4: Use cross-validation to select the best parameters C and γ. Step 8.5: Obtain the best parameters C and γ, and train SVM. Step 8.6: Test with the constructed model.

Step 9: Make Discussion and Conclusions Experiments are performed from the above process, and finally, the experimental results are obtained by the SVM classifier, and the total accuracy (OA), G-mean (GM), Positive Accuracy (PA), Negative Accuracy (NA), and training time are used. The average of the indicators and the standard deviation were used to analyze the results. Based on these results, we can make discussions and conclusions.

4. Experimental Results

4.1 Metric

Regarding to performance evaluation, in tradition, the easiest way to evaluate the classification performance is based on the confusion matrix shown as Table 2. Table 2 Confusion matrix for binary class problem Predicted Positive Predicted Negative Actual TP (the number of True Positive) FN (the number of False Positive Negative) Actual FP (the number of False Positive) TN (the number of True Negative Negative) We use overall accuracy (OA), positive accuracy (PA), negative accuracy (NA), and geometric mean of PA and NA (G-Mean), F1 to evaluate classifiers. The definition of overall accuracy is as equation (7). TP  TN Overall Accuracy  (7) TP  FP  TN  FN In this work, PA and NA represent the ability of detecting the positive and negative

16 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) comments, respectively. They are defined as

PA  TP TP  FN (8)

NA  TN FP  TN (9)

We use an integrated index, G-mean which is defined as equation (10) to measure the performance of imbalance classification. This measure is to maximize the accuracy on each of two classes while keeping these accuracies balanced. For instance, a high PA by a low NA will result in a poor G-mean.

G  mean  PA NA (10)

4.2 Defined Factors After reviewing related works, we can define 17 potential factors that might affect trust in O2O. Because we only can screen the trust related reviews, and cannot measure the levels of trust. Therefore, we use sentiments of those trust reviews. Negative comments can be viewed as harmful to O2O trust, while positive reviews can be considered to enhance O2O trust. Table 3 lists all candidate factors and their definitions.

Table 3 Defined factors that might affect O2O trust

No. Notation Factors Definition The speed of service of the staff. Related words include fast, 1 SP Speed of Service rapid, quick, slow, etc. The attitude of employee service is always friendly, making 2 F Friendliness of Staff consumers feel comfortable. Related adjectives that describe the friendliness of service such as Smile, Sincerity, etc. Cleanliness of environments, meals, counters, seating areas, 3 C Cleanliness etc.; Related adjectives describing environmental sanitation might include dirty, nasty, foul, unclean, etc. Menu Design The diversity and design of the menu meals; Related words 4 MD /Diversity include menu, diversity, multiplex, multiple, etc. Employees can make customers feel at ease; dining Restaurant 5 RA atmosphere related adjectives such as calm, congenial, Atmosphere convivial, cozy, family, etc. The feelings of customers about dining environment such as 6 E Environment bright, airy, noisy, etc. Related words in comments include cozy, warm, cold, dark, voiceless, whispering, etc. The size of the meal. Related adjectives are light, filling, 7 MS Meal Size enough, and so on. Restaurant locations allow consumers to feel far, near or 8 L Location convenient. Related words are place, site, locale, etc. The price level of the restaurant. Related words include 9 P Price cheap, cost, expensive, etc. Understanding of Employees clearly understand customer needs and help; 10 UC Customer Needs Related adjectives might be consideration, constructive,

17 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) dependable, diplomatic, help, etc. 11 TD Text Difficulty It’s defined in equation (1). 12 TTR Type-Token Ratio It’s defined in equation (2). 13 GL Grade Level The US grade level 14 T Token The length of comments 15 S Sentimental Features Emotional words to express an impression of the restaurant Positive recommendations to express a good impression of 16 RC Recommendation the restaurant. Professional terms/nouns that make others to trust the 17 EX Expertise comment. Possible words include Signiant, Aspic, Canape, etc. 18 Y Sentiment for trust Sentiment scores

4.3 Data Collection This study uses the import.io crawler tool to crawl reviews on OpenTable. We collected four data sets. Data set 1 is selected comments of London’s TheFiveFields and Tramshed restaurants and from June 2013 to September 2016. In data set 2, a total of 13 restaurants were selected in the UK and Australia. The review time was from August 2008 to July 2017. The third data set comes from 10 Australian restaurants, from August 2014 to July 2017. The fourth set contains reviews of 3 British restaurants from August 2008 to July 2017. The summary of collected data sets has been shown in Table 4. Table 4 Summary of collected data Data set 1 Data set 2 Data set 3 Data set 4 Number of 831 14,092 3,967 10,125 valid reviews Period 2013-2017 2008-2017 2014-2017 2008-2017 Area London/UK UK & Australia Australia UK Number of 2 13 10 3 restaurant Class Positive 76.03% Positive 91.86% Positive 94.23% Positive 90.61% distribution Negative 14.49% Negative 8.14% Negative 5.77% Negative 9.38%

4.4 Results of Feature Selection 4.4.1 SVM-RFE First, the experiment uses SVM-RFE to rank of the 17 potential factors. For each fold experiment, we select top 8 factors (more than the half of 17). Then, we select the important factors according to occurrence frequency. Table 5 shows the feature selection results of 5 folds in data set 1. From this table, we can build three feature subsets. They are SVM- RFE #1 (appears 5 times, including

18 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) E, P, SP, GL, T), SVM-RFE #2 (appears more than 3 times, including E, P, SP, GL, T, S, MD, L) and SVM-RFE #3 (appears more than 2 times, including E, P, SP, GL, T, S, MD, L, F). We implement the same process for other data sets. Finally, table 6 summarizes the whole feature selection results of data sets 1~4. Then, we will evaluate the performances of these selected feature sets by SVM.

Table 5 The selected top 8 factors by SVM-RFE (Data set 1) Rank Fold1 Fold2 Fold3 Fold4 Fold5 1 SP SP T T P 2 E E GL SP SP 3 GL T E E T 4 T UC P GL E 5 P GL SP S S 6 L P S P GL 7 F RC MD L L 8 RA F EX MD MD

Table 6 Summary of SVM-RFE results in data sets 1~4 Data set Data set 1 Data set 2 Data set 3 Data set 4 Feature set SVM-RFE #1 E, P, SP, GL, T P E, P, F GL E, P, SP, GL, T, RC, S, MD, SVM-RFE #2 P, RA, TTR GL, L S, MD, L TTR, E, P, F E, P, SP, GL, T, P, RA, TTR, C, SVM-RFE #3 - GL, L, S, P, T S, MD, L, F S, GL

Table 7 shows evaluation results of SVM. In results of data sets 2~4, their NA and GM are equal to 0. It means they have no any ability for classifying negative reviews. There are class imbalance problems which mean the classifiers cannot detect the minor class in the data sets 2, 3, and 4. Therefore, we’re going to make some adjustments on class distribution and re-evaluate in sub-section 4.4.3.

19 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)

Table 7 Evaluations of SVM-RFE Data set 1 Feature set Original SVM-RFE#1 SVM-RFE#2 SVM-RFE#3

(17 factors) (5 factors) (8 factors) (9 factors) Metrics Mean (St.Dev.) Mean (St.Dev.) Mean (St.Dev.) Mean (St.Dev.) PA (%) 96.43(5.2) 98.57(1.822) 98.71 (1.778) 99 (1.862) NA (%) 10.77 (13.71) 3.82 (6.662) 5.3 (6.42) 3.82 (6.662) GM (%) 22.91 (24.20) 11.48 (17.14) 16.94 (16.90) 11.52 (17.16) OA (%) 82.65 (0.0277) 83.37 (0.007) 83.73 (0.008) 83.73 (0.006) Time (s) 24.9 (1.92) 26.4 (8.95) 57.6 (10.49) 51.9 (6.79) Data set 2 Feature set Original SVM-RFE#1 SVM-RFE#2 SVM-RFE#3

(17 factors) (1 factors) (3 factors) (6 factors) Metrics Mean (St.Dev.) Mean (St.Dev.) Mean (St.Dev.) Mean (St.Dev.) PA (%) 94.2 (0.033) 100 (0) 100 (0) 100 (0) NA (%) 6.3 (0.035) 0 (0) 0 (0) 0 (0) GM (%) 23.29 (0.071) 0 (0) 0 (0) 0 (0) OA (%) 53.28 (0.009) 91.86 (0.004) 91.86 (0.003) 91.86 (0.004) Time (s) 23831 (7785) 399.4 (186) 2359.4 (473) 13555 (778) Data set 3 Feature set Original SVM-RFE#1 SVM-RFE#2

(17 factors) (3 factors) (7 factors) Metrics Mean (St.Dev.) Mean (St.Dev.) Mean (St.Dev.) PA (%) 41.1 (0.150) 100 (0) 100 (0)

NA (%) 58.41 (0.155) 0 (0) 0 (0)

GM (%) 46.91 (0.042) 0 (0) 0 (0)

OA (%) 51.83 (0.023) 94.23(0.011) 94.23(0.011)

Time (s) 1058 (58) 68 (19) 1598.5 (155) Data set 4 Feature set Original SVM-RFE#1 SVM-RFE#2 SVM-RFE#3

(17 factors) (5 factors) (8 factors) (9 factors) Metrics Mean (St.Dev.) Mean (St.Dev.) Mean (St.Dev.) Mean (St.Dev.) PA (%) 97.4 (0.025) 100 (0) 100 (0) 100 (0) NA (%) 2.40 (0.023) 0 (0) 0 (0) 0 (0) GM (%) 11.63 (0.108) 0 (0) 0 (0) 0 (0) OA (%) 56.01 (0.012) 90.67 (0.0041) 90.62 (0.0057) 90.62 (0.0057) Time (s) 8791 (3112) 222 (6) 345 (77) 10332 (839)

20 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) 4.4.2 LASSO In this section, we use another feature selection method, LASSO, to find factors that affect the sentiment of comments in the O2O platform. After computing, if one factors’ coefficient is equal to 0, this factor will be considered as un-important and then will be removed. Table 8 shows results of LASSO. Due to class imbalance problems, data sets 2~4 cannot find any important factors. Only data set 1 can discover some factors. According to frequency, we determine three feature subsets. They are LASSO #1 (appears 5 times, including RA, RC, SP, TD), LASSO #2 (appears more than 3 times, including RA, RC, SP, TD, S) and LASSO #3 (appears more than 2 times, including RA, RC, SP, TD, S, E, GL).

Table 8 Results of LASSO (Data set 1) Experiment Fold1 Fold2 Fold3 Fold4 Fold5 Frequency Factors RA 0.122 0.173 0.104 0.089 0.095 5 RC 0.036 0.113 0.124 0.106 0.140 5 SP 0.168 0.146 0.102 0.007 0.232 5 TD 0.157 0.186 0.078 0.034 0.046 5 S 0.067 0.057 0.046 0 0.060 4 E 0.051 0.032 0 0 0 2 GL -0.068 -0.069 0 0 0 2 F 0 0.031 0 0 0 1 T 0 0.021 0 0 0 1 C 0 0 0 0 0 0 UC 0 0 0 0 0 0 MD 0 0 0 0 0 0 L 0 0 0 0 0 0 PS 0 0 0 0 0 0 P 0 0 0 0 0 0 EX 0 0 0 0 0 0 TTR 0 0 0 0 0 0

Table 9 Evaluations of LASSO (Data set 1) Feature set Original LASSO#1 LASSO#2 LASSO#3 (17 factors) (4 factors) (5 factors) (7 factors) Metrics Mean (St.Dev.) Mean (St.Dev.) Mean (St.Dev.) Mean (St.Dev.) PA (%) 96.43 (5.2) 99.57 (0.95) 97.8 (3.23) 97.85 (1.75) NA (%) 10.77 (13.7) 6.04 (6.91) 9.89 (7.98) 18.04 (8.32) GM (%) 22.91 (24.20) 18.28 (18.14) 26.74 (17.44) 40.96 (10.08) OA (%) 82.65 (0.027) 84.58 (0.0068) 83.73 (0.023) 84.94 (0.014) Time (s) 24.9 (1.92) 10.6 (0.34) 16.9 (2.42) 22.0 (4.33) Next, we performed SVM to evaluate the three feature subsets established in data set 1. We compare the performance of extracted feature sets and the original feature set.

21 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) Considering OA, it can be found that the performance of LASSO#3 is slightly better than LASSO #1, #2, and original feature set. In addition, the performance of LASSO#3 outperforms other feature sets, when taking GM into consideration. But, the results only come from single data set. The important factors needed to be determined further.

5 Discussions

5.1 Redefine Class and Normalize Data

The experimental results show that, in most cases (data sets 2~4), LASSO is unable to select critical factors from imbalanced data sets. And SVM-RFE has poor classification accuracies for detecting minor examples (negative reviews). Therefore, we’ll discuss class imbalance problems in this subsection.

First, we attempt to adjust the class distribution by redefining class labels. The original class label is positive and negative reviews. But, the distribution of negative reviews is far fewer than positive reviews. Consequently, we redefine the class labels as “negative (harmful) to trust in reviews” and “not negative (not harmful) to trust in reviews” The class distribution of redefined class labels could be found in table 10. Next, we implement LASSO for data sets 2~4. But, we still cannot find any important factors.

Table 10 Class distribution of redefined class labels Class Data set 2 Data set 3 Data set 4

Positive 91.86% Positive 94.23% Positive 90.61% Original Negative 8.14% Negative 5.77% Negative 9.38%

Not negative 53.6% Not negative 48.8% Not negative 55.4% Modified Negative 46.4% Negative 51.7% Negative 44.5%

We use SVM-RFE then. In SVM-RFE results, data set 2 establishes three feature subsets, which are feature set 1 (appears 5 times, including UC), feature set 2 (appears more than 4 times, including UC, F, TTR), and feature set 3 (appears more than 3 times, including UC, F, TTR, RC, P, EX, TD, T). Data set 3 can establish two feature subsets, which are feature set 1 (appears more than 4 times, including PS) and feature set 2 (appears more than 3 times, including PS, C, RC, UC, EX, SP, TD, TTR). Data set 4 can establish three feature subsets, which are feature set 1 (appears 5 times, including S, L, PS, P), feature set 2 (appears more than 4 times, including S, L, PS, P, RA, GL), and feature set 3 (appears more than 3 times, including S, L, PS, P, RA, GL, E, TTR, T). Next, the SVM is then used to perform performance evaluation of established feature

22 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) sets. Table 11 shows results. From this table, it can be found that GM and NA are slightly increased, but the effect is not good.

Table 11 Evaluations of SVM-RFE (redefined class) Feature Data set 2 set Original SVM-RFE#1 SVM-RFE#2 SVM-RFE#3 (17 factors) (1 factors) (3 factors) (8 factors) Metrics Mean (St.Dev.) Mean (St.Dev.) Mean (St.Dev.) Mean (St.Dev.) PA (%) 100 (0) 100 (0) 100 (0) 99.96 (0.0009) NA (%) 0 (0) 0 (0) 0 (0) 0.03 (0.0006) GM (%) 0 (0) 0 (0) 0 (0) 0.74 (0.0166) OA (%) 56.81(0.005) 56.81(0.005) 56.81(0.005) 56.80 (0.006) Time (s) 29908.58(4884.27) 1563.22 (155.82) 2617.24 (245.67) 22427.52 (1721.25) Feature Data set 3 set Original SVM-RFE#1 SVM-RFE#2

(17 factors) (1 factors) (8 factors) Metrics Mean (St.Dev.) Mean (St.Dev.) Mean (St.Dev.) PA (%) 85.53 (0.0669) 58.76 (0.5364) 96.98 (0.0676)

NA (%) 17.05 (0.0612) 1.39 (0.014) 2.88 (0.0645)

GM (%) 37.49 (0.0510) 8.93 (0.08362) 7 (0.1564)

OA (%) 53.05 (0.0153) 52.46 (0.0131) 52.18 (0.0144)

Time (s) 2497.25 (609.3) 232.11 (11.21) 838.73 (39.97) Feature Data set 4 set Original SVM-RFE#1 SVM-RFE#2 SVM-RFE#3 (17 factors) (1 factors) (3 factors) (8 factors) Metrics Mean (St.Dev.) Mean (St.Dev.) Mean (St.Dev.) Mean (St.Dev.) PA (%) 100 (0) 100 (0) 99.80 (0.004) 100 (0) NA (%) 0 (0) 0 (0) 0.23 (0.003) 0 (0) GM (%) 0 (0) 0 (0) 2.96 (0.042) 0 (0) OA (%) 59.12 (0.012) 59.12 (0.012) 59.05 (0.01) 59.12 (0.01) Time (s) 15420.81(1141.38) 10175.56 (624) 12048.6 (1689.62) 16257.27 (731.53)

Next, we normalize all input data using equation (4). In LASSO, we still cannot find any important factors, even after normalizing input data. Therefore, we implement SVM-RFE again for data sets 2~4. For normalized data, data set 2 can establish two feature subsets, which are feature set SVM-RFE#1 (appears 4 times, including RC, E, P), and feature set SVM-RFE#2 (appears more than 3 times, including RC, E, P, RA, C, EX, F, T). Data set 3 can establish two feature subsets, which are feature set SVM-RFE#1 (appears 4 times, including EX), and feature set SVM-RFE#2 (appears

23 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) more than 3 times, including RC, RA, EX, UC, L, F, TD, TTR). Data set 4 can establish two feature subsets, which are feature set SVM-RFE#1 (appears 4 times, including E and SP), and feature set SVM-RFE#2 (appears more than 3 times, including RC, RA, P, T, E, SP).

Table 12 Evaluations of SVM-RFE (redefined class and with normalization) Data set 2 Feature set Original SVM-RFE#1 SVM-RFE#2

(17 factors) (3 factors) (8 factors) Metrics Mean (St.Dev.) Mean (St.Dev.) Mean (St.Dev.) PA (%) 94.20 (0.033) 99.61 (0.003) 97.08 (0.049) NA (%) 6.3 (0.035) 0.32 (0.002) 2.96 (0.051) GM (%) 23.29 (0.071) 4.9 (0.031) 12.28 (0.120) OA (%) 53.28 (0.009) 53.20 (0.004) 53.36 (0.008) Time (s) 23831 (7785) 7114 (1215) 13910 (1009) Data set 3 Feature set Original SVM-RFE#1 SVM-RFE#2

(17 factors) (1 factors) (8 factors) Metrics Mean (St.Dev.) Mean (St.Dev.) Mean (St.Dev.) PA (%) 41.1 (0.150) 0.05 (0.036) 21.4 (0.185) NA (%) 58.41 (0.155) 100 (0) 78.24 (0.177) GM (%) 46.91 (0.042) 0.99 (0.022) 33.16 (0.204) OA (%) 51.83 (0.023) 51.15 (0.02) 51.4 (0.011) Time (s) 1058 (58) 120 (3) 489 (18) Data set 4 Feature set Original SVM-RFE#1 SVM-RFE#2

(17 factors) (2 factors) (6 factors) Metrics Mean (St.Dev.) Mean (St.Dev.) Mean (St.Dev.) PA (%) 97.40 (0.025) 99.91 (0.002) 98.14 (0.028) NA (%) 2.4 (0.023) 0.009 (0.002) 1.99 (0.325) GM (%) 11.63 (0.108) 4.9 (0.029) 8.37 (0.121) OA (%) 56.01 (0.012) 55.47 (0.01) 55.32 (0.013) Time (s) 8791 (3112) 849 (66) 8477 (789)

Table 12 summarizes results of evaluations for constructed feature sets. From this table, it could be found that the perofrmances have some improvement especially in GM. Therefore, we will use this results to select important factors. In data set 2, SVM-RFE #2 can achieve 12.28% of GM using only 8 factors, and the training time is also about half less than the original feature. Therefore, SVM-RFE #2 is selected as the preferred feature set. In data set 3, it can be found that the selected factors have better abilities of detecting negative reviews. Both considering OA, GM and training time, we select SVM-RFE #2 as feature set which includes 8 factors, RC, RA, EX, UC, L, F,

24 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) TD, and TTR. Finally, in data set 4, it can be found that feature set SVM-RFE #2 can achieve 8.37% of GM using only 6 factors, and the training time is also better than the original feature set. SVM-RFE #2 is selected as the best feature subset of data set 4. Talking about data set 1 is only one data that LASSO can discover important features. We compare SVM-RFE#2 and LASSO #3 to original feature set. The comparisons have been provided in Table 13. From this table, results indicated that LASSO #3 outperforms SVM-RFE#2 and original feature set, no matter in GM, OA and training time. Therefore, for data set 1, we can find 7 important factors, RA, RC, SP, TD, S, E, and GL, based on results of LASSO #3.

Table 13 The comparisons of original feature set, SVM-RFE#2, and LASSO #3 (Data set 1) Feature set Original SVM-RFE#2 LASSO#3 (17 factors) (8 factors) (7 factors) Metrics Mean (St.Dev.) Mean (St.Dev.) Mean (St.Dev.) PA (%) 96.43 (5.2) 98.71 (1.778) 97.85(1.75) NA (%) 10.77 (13.71) 5.3 (6.42) 18.04 (8.326) GM (%) 22.91 (24.203) 16.94 (16.909) 40.96 (10.085) OA (%) 82.65 (0.0277) 83.73 (0.0085) 84.94% (0.01413) Time (s) 24.9 (1.92) 57.6 (10.49) 22.0 (4.33)

6. CONCLUSIONS

This study aims to identify the key factors affecting O2O trust by using the sentiment of online reviews. We presented a text mining based scheme to find important factors from the text comments, instead of traditional questionnaire survey. By using the presented scheme, we not only can save manpower and time for collecting data, but also to find objective opinions from the huge amount of reviews and comments in social media.

We defined 17 potential factors based on O2O related literatures. SVM-RFE and LASSO have been employed to select important factors. Table 14 lists the extracted factors for 4 data sets. From this table, we can find that the “Restaurant Atmosphere (RA)” and “Recommendation (RC)” are valued by reviewer readers and consumers in the UK and Australia. In all collected review sets, these two factors are ranked top 2 factors for O2O trust. Dining environment is the 3rd important factor. The bright, airy, light of the restaurant also affect customers’ trust of the restaurant in reviews. In addition, “Price (P)”, “Token (T)”, “Text Difficulty (TD)”, and “Expertise (EX)” have been considered as important in 2 of 4 data sets. It’s no surprise for which “Price (P)”

25 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) is the one of crucial factors for customers’ trust. “Token (T)” and “Text Difficulty (TD)” mean that one powerful review should have appropriate length and are easy to be read. “Expertise (EX)” indicates that one review should contain professional terms to make others to trust this comment.

Table 14 The selected important factors for all data sets UK& London/UK UK Australia Areas Australia (Data set 1) (Data set 4) (Data set 3) (Data set 2) RA RA RA RA RC RC RC RC E E E UC Selected SP SP F TTR important S P P L factors GL T T F TD C TD EX EX

Table 15 The important factors in the UK UK Australia Areas London/UK UK (Data set 3) (Data set 1) (Data set 4) RA (Restaurant RA (Restaurant RA (Restaurant Atmosphere) Atmosphere) Atmosphere) RC RC (Recommendation) RC (Recommendation) (Recommendation) UC (Understanding of E (Environment) E (Environment) Selected Customer Needs) important TTR (Type-Token SP (Speed of Service) SP (Speed of Service) factors Ratio) S (Sentimental P (Price) L (Location) Features) GL (Grade Level) T (Token) F (Friendliness of Staff) TD (Text Difficulty) TD (Text Difficulty) EX (Expertise)

Next, we want to compare the differences in the UK and Australia. First, we talk about England restaurant reviews. It can be seen from Table 15 that the extracted factors by the different data sets are not exactly the same; even they all come from England restaurants. But it still discovers four factors for both data sets. In the UK, customers view restaurant atmosphere (RA), recommendation (RC), dining

26 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) environment (E) and service speed (SP) as important. Therefore, for these four factors, O2O platform suppliers can make operational adjustments based on the above factors, such as improve dining atmosphere, environment, and service speed, while the restaurant management staff can enhance the atmosphere of the restaurant (romantic, warm, etc.) according to the restaurant target customers. Moreover, the recommendation can build more powerful electronic word of mouth (eWoM). Inviting foodies, gourmet bloggers, YouTubers to write recommended reviews might be useful for increase eWoM and positive impact to trust of customers.

Then, we talk about trust factors in Australian restaurant reviews. There are 10 Australian restaurants are included in this experiment and the number of valid comments is between 3,000 and 4,000. The included areas in Australia are wide, but the data size is less than reviews in the UK. According to the feature selection results, there are 8 influencing factors, as shown in Table 14. Restaurant atmosphere (RA) and recommendation (RC) are also the most important trust factors. So, the Australian restaurants also have to enhance these two factors. Like England customers, Australian consumers also think “readability” including “TTR (Type-Token Ratio)” and “TD (text difficulty)” are important in reviews.

Moreover, compare to England restaurants, Australian customers pay attention on services, such as “understanding of customer needs (UC)” and “friendliness of staff (F)”. And, the results also show “location (L)” and “expertise (EX)” are crucial for customers’ trust in reviews.

In addition to the adjustment of the restaurants in their operations, for the O2O platform developers, it can be used as a reference basis. For example, the O2O platform developers can select a large number of comments to include the above-mentioned influencing factors, for O2O service providers to carefully respond and make appropriate treatments. So, we might reduce consumers’ negative images, and increase consumer positive impact on trust.

Some potential directions of future works are also presented. First, further researches can try to solve class imbalance problems. Second, trust factors in different types of O2O industries such as tourism can be another research topic. Finally, a wide range of comparisons and discussions for Asia, Europe and the United States could be considered.

Compliance with Ethical Standards

Funding: This study was partially sponsored by the Ministry of Science and Technology,

27 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)

Taiwan (Contract No. MOST 107-2410-H-324-004). Authors express our thanks for financial supports.

Conflict of Interest: All authors declare that they have no conflict of interest.

Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.

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