Abstract
Online-to-offline/offline-to-online (O2O) 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 confirm trust is the key factor to the success of O2O. Besides, social media has been changing the way providers communicate with consumers. Negative comments in social media will decrease the consumers’ trust to O2O companies and platforms. Available O2O studies are almost always conducted by means of questionnaires or interviews, which cannot provide immediate customer response and require a lot of manpower and time. Since online reviews are the main information source 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 and Least Absolute Shrinkage and Selection Operator have employed to select important factors that affect O2O trust. We also evaluate the performance of extracted feature subsets by Support Vector Machines. The findings can be referenced for O2O market enterprises to carefully response customers’ comments to avoid hurting customers’ trust and improve service quality.
Similar content being viewed by others
References
Al-Daihani SM, Abrahams A (2016) A text mining analysis of academic libraries’ tweets. J Acad Librariansh 42:135–143
Barkana BD, Saricicek I, Yildirim B (2017) Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier fusion. Knowl-Based Syst 118:165–176
Carsten P (2016) China’s Wanda, Tencent, Baidu to set up $814 million e-commerce company. http://www.reuters.com/article/us-wanda-tencent-baidu-idUSKBN0GT04020140829
Chan NH, Yau CY, Zhang RM (2015) Lasso estimation of threshold autoregressive models. J Econom 189:285–296
Chaovalit P, Zhou L (2005) Movie review mining: a comparison between supervised and unsupervised classification approaches. In: Proceedings of the 38th Hawaii international conference on system sciences, pp 1–9
Chen L-S, Cai S-C (2014) A cost adjusting method for increasing customers’ sentiment classification performance. Int J Inf Electr Eng 4(5):336–339
Chen L-S, Liu C-H, Chiu HJ (2011) A neural network based approach for sentiment classification in the blogosphere. J Inform 5(2):313–322
Chen L-S, Lin ZC, Chang J-R (2015) FIR: an effective scheme for extracting useful metadata from social media. J Med Syst 39(11):139. https://doi.org/10.1007/s10916-015-0333-0
Choi S, Park H, Kang D, Lee JY, Kim K (2012) An SAO-based text mining approach to building a technology tree for technology planning. Expert Syst Appl 39:11443–11455
Connor P, Hollensen P, Krigolson O, Trappenberg T (2015) A biological mechanism for Bayesian feature selection: weight decay and raising the LASSO. Neural Netw 6:121–130
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297
Engler TH, Winter P, Schulz M (2015) Under standing online product ratings: a customer satisfaction model. J Retail Consum Serv 27:113–120
Fang B, Ye Q, Kucukusta D, Law R (2016) Analysis of the perceived value of online tourism reviews: influence of readability and reviewer characteristics. Tour Manag 52:498–506
Gao Y, Wang T, Wang M (2014) The role of switching costs in O2O platforms: antecedents and consequences. In: International conference on management of e-commerce and e-Government, pp 371–378
Gaudioso M, Gorgone E, Labbé M, Rodríguez-Chía AM (2017) Lagrangian relaxation for SVM feature selection. Comput Oper Res 87:137–145
Gauthier PA, Scullion W, Berry A (2017) Sound quality prediction based on systematic metric selection and shrinkage: comparison of stepwise, lasso, and elastic-net algorithms and clustering preprocessing. J Sound Vib 400:134–153
Guo Y, Li Y, Ito N (2014) Exploring the predicted effect of social networking site use on perceived social capital and psychological well-being of Chinese international students in Japan. Cyberpsychol Behav Soc Netw 17(1):52–58. https://doi.org/10.1089/cyber.2012.0537
He Z, Cheng TCE, Cheng J, Wanf S (2016) Evolutionary location and pricing strategies for service merchants in competitive O2O markets. Eur J Oper Res 254(2):595–609
Hidalgo-Muñoz AR, López MM, Santos IM, Pereira AT, Vázquez-Marrufo M, Galvao-Carmona A, Tomé AM (2013) Application of SVM-RFE on EEG signals for detecting the most relevant scalp regions linked to affective valence processing. Expert Syst Appl 40(6):2102–2108
Hsiao YH, Chen MC, Liao WC (2017) Logistics service design for cross-border E-commerce using Kansei engineering with text-mining-based online content analysis. Telematics Inform 34:284–302
Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 168–177
Hu N, Bose I, Koh NS, Liu L (2012) Manipulation of online reviews: an analysis of ratings, readability, and sentiments. Decis Support Syst 52(3):674–684
Huang J, Zhou J, Liao G, Mo F, Wang H (2017) Investigation of Chinese students’ O2O shopping through multiple devices. Comput Hum Behav 75:58–69
Ikram ST, Cherukuri AK (2017) Intrusion detection model using fusion of Chi square feature selection and multi class SVM. J King Saud Univ Comput Inf Sci 29(4):462–472
Ji J, Zhang Z, Yang L (2017) Comparisons of initial carbon allowance allocation rules in an O2O retail supply chain with the cap-and-trade regulation. Int J Prod Econ 187:68–84
Kim DJ (2005) A multidimensional trust formation model in B-to-C E-commerce: a conceptual framework and content analyses of academia. Decis Support Syst 3(8):143–166
Kincaid JP, Fishburne RP, Rogers RL, Chissom BS (1975) Derivation of new readability formulas (automated readability index, fog count, and flesch reading ease formula) for Navy enlisted personnel. Research branch report, pp 8–75. Chief of Naval Technical Training: Naval Air Station Memphis
Klare GR (1984) Readability. In: Pearson PD, Barr R, Kamil ML, Mosenthal PB (eds) Handbook of reading research, vol 1. Longman, New York, pp 681–744
Kwon S, Lee S, Kim Y (2015) Moderately clipped lasso. Comput Stat Data Anal 92:53–67
Lee Z, Zhan Y, Yap HL, Pribi´c R (2016) Fast convolution formulations for radar detection using lasso. In: 2016 IEEE statistical signal processing workshop (SSP)
Li J, Fong S, Zhuang Y et al (2016a) Hierarchical classification in text mining for sentiment analysis of online news. Soft Comput 20(9):3411–3420
Li X, Li X, Su Y (2016b) A hybrid approach combining uniform design and support vector machine to probabilistic tunnel stability assessment. Struct Saf 61:22–42
Liang M, Yang X, Ou H (2014) The measurement of the consumer trust to O2O E-commerce based on fuzzy evaluation. In: Seventh international joint conference on computational sciences and optimization, pp 113–116, Beijing
Liu Z, Park S (2015) What makes a useful online review? Implication for travel product websites. Tour Manag 47:140–151
Long Y, Shi P (2017) Pricing strategies of tour operator and online travel agency based on cooperation to achieve O2O model. Tour Manag 62:302–311
Lovinger J, Valova I, Clough C (2019) Gist: general integrated summarization of text and reviews. Soft Comput 23(5):1589–1601
Lucini FR, Fogliatto FS, da Silverira GJC, Neyeloff JL, Anzanello MJ, Kuchenbecker RD, Schaan BD (2017) Text mining approach to predict hospital admissions using early medical records from the emergency department. Int J Med Inform 100:1–8
Maeyer PD (2012) Impact of online consumer reviews on sales and price strategies: a review and directions for future research. J Prod Brand Manag 21(2):132–139
Moehrle MG, Gerken JM (2012) Measuring textual patent similarity on the basis of combined concepts: design decisions and their consequences. Scientometrics 91:805–826
Molina-González MD, Martínez-Cámara E, Martín-Valdivia M-T, Perea-Ortega JM (2013) Semantic orientation for polarity classification in Spanish reviews. Expert Syst Appl 40:7250–7257
Mudambi SM, Schuff D (2010) What makes a helpful online review? A study of customer reviews on amazon.com. MIS Q 34(1):185–200
Murthy AK, Suresha (2015) XML URL classification based on their semantic structure orientation for web mining applications. Proc Comput Sci 46:143–150
Niemann H, Meohrle MG, Frishkorn J (2017) Use of a new patent text-mining and visualization method for identifying patenting patterns over time: concept, method and test application. Technol Forecast Soc Chang 115:210–220
Nisar TM, Prabhak G (2017) What factors determine e-satisfaction and consumer spending in e-commerce retailing? J Retail Consum Serv 39:135–144
Pan Y, Wu D, Luo C, Dolgui A (2019) User activity measurement in rating-based online-to-offline (O2O) service recommendation. Inf Sci 479:180–196
Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. In: Annual meeting of the ACL proceedings of the ACL-02 conference on empirical methods in natural language processing, vol 10, pp 79–86
Paul S, Magdon-Ismail M, Drineas P (2016) Feature selection for linear SVM with provable guarantees. Pattern Recogn 60:205–214
Saumya S, Singh JP, Dwivedi YK (2019) Predicting the helpfulness score of online reviews using convolutional neural network. Soft Comput. https://doi.org/10.1007/s00500-019-03851-5
Schuckert M, Liu X, Law R (2015) A segmentation of online ere views by language groups: how English and non-English speakers rate hotels differently. Int J Hosp Manag 48:143–149
Shao Z, Yang SL, Gao F, Zhou K, Lin P (2017) A new electricity price prediction strategy using mutual information-based SVM-RFE classification. Renew Sustain Energy Rev 70:330–341
Shen CW, Chen M, Wang CC (2018) Analyzing the trend of O2O commerce by bilingual text mining on social media. Comput Hum Behav. https://doi.org/10.1016/j.chb.2018.09.031
Small AM, Kiss DH, Zlatsin Y, Birtwell DL, Williams H, Guerraty MA, Han Y, Anwaruddin S, Holmes JH, Chirinos JA, Wilensky RL, Giri J, Rader DJ (2017) Text mining applied to electronic cardiovascular procedure reports to identify patients with trileaflet aortic stenosis and coronary artery disease. J Biomed Inform 72:77–84
Statista (2019) Retail e-commerce sales worldwide from 2014 to 2021. https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/
Su Y, Luarn P, Lee Y-S, Yen S-J (2017) Creating an invalid defect classification model using text mining on server development. J Syst Softw 125:197–206
Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B 58:267–288
Tsai T-M, Wang W-N, Lin Y-T (2015) An O2O commerce service framework and its effectiveness analysis with application to proximity commerce. In: 6th international conference on applied human factors and ergonomics (AHFE 2015), and the affiliated conferences, vol 3, pp 3498–3505
Wang S, Chang X, Li X, Sheng QZ, Chen W (2016a) Multi-task support vector machines for feature selection with shared knowledge discovery. Sig Process 120:746–753
Wang W-T, Wang YS, Liu E-R (2016b) The stickiness intention of group-buying websites: the integration of the commitment-trust theory and e-commerce success model. Inf Manag 53(5):625–642
Winkler M, Arahams AS, Gruss R, Ehsani JP (2016) Toy safety surveillance from online reviews. Decis Support Syst 90:23–32
Witten H, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Burlington
Wu Z (2015) Service recommendation method on multiple dimension O2O. In: International conference on intelligent transportation, pp 713–716
Xie X, Ge S, Hu F et al (2019) An improved algorithm for sentiment analysis based on maximum entropy. Soft Comput 23(2):599–611
Xu J, Jia Y (2009) Readability analyzer 1.0: a text difficulty analyzing tool, Beijing. In: The National research centre for foreign language education, Beijing Foreign Studies University
Yan Z, Yao Y (2015) Variable selection method for fault isolation using least absolute shrinkage and selection operator (lasso). Chemometr Intell Lab Syst 146:136–146
Yan Q, Wu S, Wang L (2016) E-WOM from e-commerce websites and social media: which will consumers adopt? Electron Commer Res Appl 17:62–73
Yoon H, Hyun Y, Ha K, Lee K-K, Kim G-B (2016) A method to improve the stability and accuracy of ANN- and SVM-based time series models for long-term groundwater level predictions. Comput Geosci 90:144–155
Zhang B, Huang L (2015) The research status of O2O industry analysis Meituan, for example. In: International conference on logistics, informatics and service sciences (LISS)
Zhong J, Tse PW, Wang D (2015) Novel Bayesian inference on optimal parameters of support vector machines and its application to industrial survey data classification. J Neurocomputing 211:159–171
Funding
This study was partially sponsored by the Ministry of Science and Technology, Taiwan (Contract No. MOST 107-2410-H-324-004). Authors express our thanks for financial supports.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
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.
Additional information
Communicated by Long-Sheng Chen.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Chang, JR., Chen, MY., Chen, LS. et al. Recognizing important factors of influencing trust in O2O models: an example of OpenTable. Soft Comput 24, 7907–7923 (2020). https://doi.org/10.1007/s00500-019-04019-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-019-04019-x