Investigating the Determinants of Users’ Willingness to Pay for Answers on Q&A Platforms
Charging for answers on Q&A platforms is gaining popularity in Mainland China. For the purpose of making profit, understanding the determinants of users’ willingness to pay for answers is crucial for Q&A platforms, yet remains unclear. To narrow the research gap, this study develops an extended UTAUT framework, which integrates trust and long tail effect. In particular, the impacts of seven antecedents are empirically investigated, including performance expectancy, effort expectancy, social influence, facilitating conditions, trust towards answer providers, trust towards the Q&A platform, and long tail effect. Data was collected from 123 Chinese Q&A platform users (all of them have paid for answers) and analyzed with SPSS 22.0. Findings indicate that users’ willingness to pay is positively influenced by performance expectancy, facilitating conditions, trust towards the Q&A platform, and long tail effect. The potential theoretical and practical contributions are discussed.
KeywordsQ&A platforms Unified Theory of Acceptance and Use of Technology (UTAUT) Trust Long tail effect Willingness to pay
This study was supported by the Fundamental Research Funds for the Central Universities: No. NR2018002 awarded to second author and the Creative Studio of Electronic Commerce in Nanjing University of Aeronautics and Astronautics.
- 2.Cathy: Zhihu.Com, bridging the gap of growing knowledge sharing demand in china. In: Harvard Business School Digital Initiative (2017)Google Scholar
- 4.Cheng, D., Liu, G., Qian, C., Song, Y.F.: Customer acceptance of internet banking: integrating trust and quality with UTAUT model. In: IEEE International Conference on Service Operations and Logistics. IEEE (2008)Google Scholar
- 6.Codyallen, E., Kishore, R.: An extension of the UTAUT model with e-quality, trust, and satisfaction constructs. In: ACM SIGMIS CPR Conference on Computer Personnel Research: Forty Four Years of Computer Personnel Research: Achievements. ACM (2006)Google Scholar
- 7.Dodds, W.B., Monroe, K.B., Grewal, D.: Effects of price, brand, and store information on buyers. J. Mark. Res. 28(3), 307–319 (1991)Google Scholar
- 8.Feng, E.: Chinese tech apps trade knowledge for cash. Financial Times (2017)Google Scholar
- 10.Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E.: Multivariate Data Analysis, 7th edn. Pearson, London (2009)Google Scholar
- 12.Keat, T.K., Mohan, A.: Integration of TAM based electronic commerce models for trust. J. Am. Acad. Bus. 5(1/2), 404–411 (2004)Google Scholar
- 13.Keith, M., Santanam, R., Sinha, R.: Switching costs, satisfaction, loyalty and willingness to pay for office productivity software. In: Proceedings of 43rd Hawaii International Conference on System Sciences (HICSS). IEEE Computer Society (2010)Google Scholar
- 14.Likert, R.: A technique for the measurement of attitudes. J. Arch. Psychol. 22(140), 1–55 (1932)Google Scholar
- 18.Singh, M., Matsui, Y.: How long tail and trust affect online shopping behavior: an extension to UTAUT2 Framework. Pac. Asia J. Assoc. Inform. Syst. 9(4), 1–24 (2017)Google Scholar
- 24.蒋楠, & 王鹏程: 社会化问答服务中用户需求与信息内容的相关性评价研究——以“百度知道”为例. 信息资源管理学报(3), 35–45 (2012)Google Scholar
- 25.赵宇翔, 刘周颖, & 宋士杰. :新一代知识问答平台中提问者付费意愿的影响因素探究. 数据分析与知识发现, 2(8), 16–30 (2018)Google Scholar