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Understanding continuance intention to use online to offline (O2O) apps

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Abstract

O2O commerce is a new business model combining online shopping and offline transactions. While many reports indicate the large potential size of the O2O market, little is known about users’ continuance intention to use. This study applies an expectation confirmation model (ECM) that incorporates perceived hedonic benefits, product information intensity and transaction costs as belief-related constructs to predict users’ continuance intention to use O2O apps. The proposed model was empirically evaluated using survey data collected from 333 users concerning their perceptions of O2O apps. The results indicated that confirmation of O2O app usage experience was positively related to both perceived benefits such as utilitarian and hedonic benefits and satisfaction. Perceived benefits, satisfaction and transaction costs were found to have a direct impact on continuance intention. Specifically, there was a significant difference between task-oriented O2O apps users and entertainment-oriented O2O apps users. The results may provide further insights into O2O app marketing strategies.

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References

  • Atchariyachanvanich, K., Okada, H., & Sonehara, N. (2006). What Keep Online Customers Repurchasing through the Internet? ACM SIGecom Exchanges, 6(2), 47–57.

    Google Scholar 

  • Aubert, B. A., Rivard, S., & Patry, M. (2004). A Transaction Cost Models of IT Outsourcing. Information & Management, 41, 921–932.

    Google Scholar 

  • Bang, Y., Lee, D.-J., Han, K., Hwang, M., & Ahn, J.-H. (2013). Channel Capabilities, Product Characteristics, and Impacts of Mobile Channel Introduction. Journal of Management Information Systems, 30(2), 101–125.

    Google Scholar 

  • Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351–370.

    Google Scholar 

  • Brislin, R. W. (1980). Translation and content analysis of oral and written material. In H. C. Triandis & J. W. Berry (Eds.), Handbook of Cross-cultural Psychology (Vol. 2, pp. 389–444). Boston: Allyn & Bacon.

    Google Scholar 

  • Bucklin, L. P., & Sengupta, S. (1993). Organizing successful co-marketing alliances. Journal of Marketing, 57, 32–46.

    Google Scholar 

  • Che, T., Peng, Z., Lim, K. H., & Hua, Z. (2015). Antecedents of consumer' intention to revisit an online group-buying website: A transaction cost perspective. Information & Management, 52(5), 588–598.

    Google Scholar 

  • Chintagunta, P. K., Chu, J., & Cebollada, J. (2012). Quantifying Transaction Costs in Online/Offline Grocery Channel Choice. Marketing Science, 31(1), 96–114.

    Google Scholar 

  • Chiu, C. M., Wang, E. T., Fang, Y. H., & Huang, H. Y. (2014). Understanding customers' repeat purchase intentions in B2C e-commerce: the roles of utilitarian value, hedonic value and perceived risk. Information Systems Journal, 24(1), 85–114.

    Google Scholar 

  • Chong, A. Y. L. (2013). Understanding mobile commerce continuance intentions: an empirical analysis of Chinese consumers. Journal of Computer Information Systems, 53(4), 22–30.

    Google Scholar 

  • Coase, R. H. (1937). The Nature of the firm. Economic, 4, 386–405.

    Google Scholar 

  • Conci, M., Pianesi, F., & Zancanaro, M. (2009). Useful, social and enjoyable: mobile phone adoption by older people. IFIP International Federation for Information Processing, 2009, 63–76.

    Google Scholar 

  • Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–339.

    Google Scholar 

  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132.

    Google Scholar 

  • Deci, E. L. (1971). Effects of externally mediated rewards on intrinsic motivation. Journal of Personality and Social Psychology, 18, 105–115.

    Google Scholar 

  • Feng, X., Fu, S., & Qin, J. (2016). Determinants of consumers’ attitudes toward mobile advertising: The mediating roles of intrinsic and extrinsic motivations. Computers in Human Behavior, 63(2016), 334–341.

    Google Scholar 

  • Fornell, C. R. (1982). A second generation of multivariate analysis methods. New York: Praeger Special Studies Vols. I and II.

    Google Scholar 

  • Fornell, C. R., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Google Scholar 

  • Gao, L., & Waechter, K. A. (2017). Examining the role of initial trust in user adoption of mobile payment services: an empirical investigation. Information Systems Frontiers, 19(3), 525-548.

  • Gerow, J. E., Ayyagari, R., Thatcher, J. B., & Roth, P. L. (2013). Can we have fun @ work? the role of intrinsic motivation for utilitarian systems. European Journal of Information Systems, 22(3), 360–380.

    Google Scholar 

  • Grüschow, R. M., Kemper, J., & Brettel, M. (2016). How do different payment methods deliver cost and credit efficiency in electronic commerce?. Electronic Commerce Research and Applications, 18, 27–36.

  • Ha, I., Yoon, Y., & Choi, M. (2007). Determinants of adoption of mobile games under mobile broadband wireless access environment. Information & Management, 44, 276–286.

    Google Scholar 

  • Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1992). Multivariate data analysis with readings. New York: MacMillan.

    Google Scholar 

  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2006). Tatham RL. Multivariate data analysis. New Jersey, USA: Pearson University Press.

  • Habgood, M. P. J., Ainsworth, S. E., & Benford, S. (2005). Endogenous fantasy and learning in digital games. Simulation and Gaming, 36(4), 483–498.

    Google Scholar 

  • Hsu, C. L. (2010). Exploring the player flow experience in e-game playing. International Journal of Technology and Human Interaction, 6(2), 47–64.

    Google Scholar 

  • Hsu, C. L., & Lin, J. C.-C. (2015). What Drives Purchase Intention for Paid Mobile Apps?-- An Expectation Confirmation Model with Perceived Value. Electronic Commerce Research and Applications, 14, 46–57.

    Google Scholar 

  • Hsu, C. L., & Lu, H. P. (2007). Consumer behavior in on-line game communities: a motivational factor perspective. Computers in Human Behavior, 23, 1642–1659.

    Google Scholar 

  • Humbani, M., & Wiese, M. (2019). An integrated framework for the adoption and continuance intention to use mobile payment apps. International Journal of Bank Marketing, 37(2), 646–664.

    Google Scholar 

  • Keil, M., Tan, B. C. Y., Wei, K. K., & Saarinen, T. (2000). A cross-cultural study on escalation of commitment behavior in software projects. MIS Quarterly, 24(2), 299–325.

    Google Scholar 

  • Kim, B. (2010). An empirical investigation of mobile data service continuance: incorporating the theory of planned behavior into the expectation-confirmation model. Expert Systems with Applications, 37, 7033–7039.

    Google Scholar 

  • Kim, H.-W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: an empirical investigation. Decision Support Systems, 43(1), 111–126.

    Google Scholar 

  • Kim, Y. H., Kim, D. J., & Wachter, K. (2013). A study of mobile user engagement (MoEN): Engagement motivations, perceived value, satisfaction, and continued engagement intention. Decision Support Systems, 56, 361–370.

    Google Scholar 

  • Kim, Y. G., & Li, G. (2009). Customer Satisfaction with and Loyalty towards Online Travel Products: A Transaction Cost Economics Perspective. Tourism Economics, 15(4), 825–846.

    Google Scholar 

  • Kline, R. B. (1998). Methodology in the social sciences. Principles and practice of structural equation modeling. New York (NY, USA): Guilford Press.

  • Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: an extension of the expectation–confirmation model. Computers & Education, 54, 506–516.

    Google Scholar 

  • Lee, Y., & Kwon, O. (2011). Intimacy, familiarity and continuance intention: an extended expectation–confirmation model in web-based services. Electronic Commerce Research and Applications, 10(3), 342–357.

    Google Scholar 

  • Liang, T. P., & Huang, J. S. (1998). An empirical study on consumer acceptance of products in electronic markets: a transaction cost model. Decision Support Systems, 24, 29–43.

    Google Scholar 

  • Limayem, M., & Cheung, C. M. K. (2008). Understanding information systems continuance: the case of Internet-based learning technologies. Information & Management, 45(4), 227–232.

    Google Scholar 

  • Lin, T. C., Wu, S., Hsu, J. S. C., & Chou, Y. C. (2012). The integration of value-based adoption and expectation–confirmation models: an example of IPTV continuance intention. Decision Support Systems, 54(1), 63–75.

    Google Scholar 

  • Lin, C. S., Wu, S., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & Management, 42(5), 683–693.

    Google Scholar 

  • Meas, P., Guttman, R. H., & Moukas, A. G. (1999). Agents that buy and sell. Communications of the ACM, 40(3), 81–91.

    Google Scholar 

  • Moon, J., & Kim, Y. (2001). Extending the TAM for a world-wide-web context. Information and Management, 38(4), 217–230.

    Google Scholar 

  • Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222.

    Google Scholar 

  • Mou, J., Shin, D. H., & Cohen, J. (2017). Understanding trust and perceived usefulness in the consumer acceptance of an e-service: A longitudinal investigation. Behaviour & Information Technology, 36(2), 125–139.

    Google Scholar 

  • O2Oplatform. (2018). O2O Commerce: The challenges of an online-to-offline business, https://www.o2oplatform.com/blog/o2o-commerce, accessed on 5/11/2019.

  • Overby, J. W., & Lee, E. (2006). The effects of utilitarian and hedonic online shopping value on consumer preference and intentions. Journal of Business Research, 59(10/11), 1160–1166.

    Google Scholar 

  • Palmer, J. W., & Griffith, D. A. (1998). An emerging model of web site design for marketing. Communications of the ACH, 41(3), 45–51.

    Google Scholar 

  • Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

    Google Scholar 

  • Sabherwal, R., & Vijayasarathy, L. R. (1994). An empirical investigation of the antecedents of telecommunication-based interorganizational systems. European Journal of Information Systems, 3(4), 268–284.

    Google Scholar 

  • Seyal, A., Rahman, M., & Rahim, M. (2002). Determinants of academic use of the Internet: a structural equation model. Behavior and Information Technology, 21(1), 71–86.

  • Schumacker, R. E., & Lomax, R. G. (2004). A Beginner’s guide to structural equation modeling. 2nd ed. Mahwah (NJ): Lawrence Erlbaum 10.1007/s12525-019-00354-x Associates, Inc.

  • Sterling, G. (2017). https://www.lsainsider.com/though-90-of-spending-still-in-stores-a-time-of-peril-for-retailers/archives, accessed on 3/8/2019.

  • Sweeney, J., & Soutar, G. N. (2001). Consumer perceived value: the development of a multiple item scale. Journal of Retailing, 77(2), 203–220.

    Google Scholar 

  • Szabo, N. (1999). Micropayment and mental transaction costs. The 2nd Berlin Internet Economics Workshop. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.23.9779&rep=rep1&type=pdf.

  • Szymczyk, M. (2016). How Brands And Retailers Are Utilizing Pokémon Go, Zugara.com, http://zugara.com/how-brands-and-retailers-are-utilizing-pokemon-go, accessed on 3/8/2019.

  • Thong, J. Y. L., Hong, S. J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation–confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799–810.

    Google Scholar 

  • Teo, T. S. H., & Yu, Y. (2005). Online buying behavior: a transaction cost economics perspective. Omega, 33, 451–465.

    Google Scholar 

  • Teo, T. S. H., Wang, P., & Leong, H. C. (2004). Understanding online shopping behaviour using a transaction cost economics approach. International Journal of Internet Marketing and Advertising, 1(1), 62–84.

    Google Scholar 

  • Trotter, C. (2018). 25 top brands using O2O retail initiatives. Insider Trends, https://www.insider-trends.com/25-brands-using-o2o-retail/, accessed on 3/8/2019.

  • Turel, O., Serenko, A., & Bontis, N. (2007). User acceptance of wireless short messaging services: deconstructing perceived value. Information and Management, 44, 63–73.

    Google Scholar 

  • der Heijden, V., & Hans. (2004). User Acceptance of Hedonic Information Systems. MIS Quarterly, 28(4), 695–704.

    Google Scholar 

  • Wang, K., & Lin, C. L. (2012). The adoption of mobile value-added services: Investigating the influence of IS quality and perceived playfulness. Managing Service Quality: An International Journal, 22(2), 184–208.

    Google Scholar 

  • Wang, Y. T., Lin, K. Y., & Huang, T. K. (2019). Exploring the antecedents of mobile application usage in the context of english learning, Proceedings of the 52nd Hawaii International Conference on System Sciences, 5691–5700.

  • Williamson, O. E. (1975). Markets and hierarchies: Analysis and anti-trust implications. New York: The Free Press.

    Google Scholar 

  • Wu, L. Y., Chen, K. Y., Chen, P. Y., & Cheng, S. L. (2014). Perceived value, transaction cost, and repurchase-intention in online shopping: A relational exchange perspective. Journal of Business Research, 6(1), 2768–2776.

    Google Scholar 

  • Zhou, T. (2011). An empirical examination of users' post-adoption behaviour of mobile services. Behaviour & Information Technology, 30(2), 241–250.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information Management, National Taipei University of Business, No.321, Sec. 1, Jinan Rd., Zhongzheng District, Taipei, 100, Taiwan, Republic of China

    Chin-Lung Hsu

  2. Department of Computer Science and Information Management, Soochow University, 56 Kueiyang Street, Section 1, Taipei, 100, Taiwan, Republic of China

    Judy Chuan-Chuan Lin

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  1. Chin-Lung Hsu
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Correspondence to Judy Chuan-Chuan Lin.

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Appendix 1

Appendix 1

List of items by construct

Product information intensity (PI)

  1. 1.

    Product descriptions*

  2. 2.

    Customer review*

  3. 3.

    Price information*

  4. 4.

    Discount information

  5. 5.

    Business hours*

  6. 6.

    GPS navigation

  7. 7.

    Order processing information*

  8. 8.

    Payment methods*

Searching cost (SC)

  1. 1.

    I spend a lot of time looking for information.*

  2. 2.

    I spend a lot of effort getting information.*

  3. 3.

    I usually find myself pressed for time.*

  4. 4.

    I wish I had more time to look for information

Monitoring cost (MC)

  1. 1.

    I spend a lot of time contacting online stores.*

  2. 2.

    I spend a lot of effort contacting online stores.*

  3. 3.

    I spend a lot of time monitoring whether products I ordered are being processed.*

  4. 4.

    I spend a lot of effort monitoring whether products I ordered are being processed.

Adapting cost (AC)

  1. 1.

    Making changes to online orders.*

  2. 2.

    Arranging alternative delivery times.

  3. 3.

    Dealing with unexpected changes.*

Perceived utilitarian benefits (PU)

  1. 1.

    O2O apps enable me to increase my work or shopping performance.*

  2. 2.

    O2O apps enable me to work or shop more productively.*

  3. 3.

    O2O apps enable me to work or shop more effectively.*

  4. 4.

    Overall, O2O apps are useful to me.

Perceived hedonic benefits (PH)

  1. 1.

    Using O2O apps make me feel relaxed.*

  2. 2.

    The use of O2O apps make me want to use them again.

  3. 3.

    Using O2O apps makes me feel good.*

  4. 4.

    Using O2O apps gives me pleasure.*

Confirmation (CO)

  1. 1.

    My experience with using O2O apps was better than I expected.*

  2. 2.

    The service level or function provided by O2O apps was better than I expected.*

  3. 3.

    Overall, most of my expectations from using O2O apps were confirmed.*

Satisfaction (SA)

  1. 1.

    Using O2O apps makes me feel very satisfied.*

  2. 2.

    Using O2O apps makes me feel very contented.

  3. 3.

    Using O2O apps makes me feel very delighted.*

Continuance intention (CI)

  1. 1.

    I intend to continue using O2O apps.*

  2. 2.

    I find using O2O apps to be worthwhile.

  3. 3.

    I will frequently reuse O2O apps in the future.*

*Denotes items retained for data analysis.

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Hsu, CL., Lin, J.CC. Understanding continuance intention to use online to offline (O2O) apps. Electron Markets 30, 883–897 (2020). https://doi.org/10.1007/s12525-019-00354-x

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