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Extended expectation-confirmation model to predict continued usage of ODR/ride hailing apps: role of perceived value and self-efficacy

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Abstract

The rapid expansion of technologies, especially Internet and mobile technologies has enabled the development of many mobile applications that facilitate travel and tourism. This study proposes and empirically tests the extended expectation-confirmation model (EECM) with two additional constructs—self-efficacy and perceived value—to explain the continued usage of on-demand ride services/ride hailing applications (ODRHA) by riders. Results strongly support the integrated model of TAM and ECM for predicting post adoption behavior of ODRHA customers. They also show that perceived value, self-efficacy and satisfaction contribute significantly to the continued usage of app-based services. The study offers valuable insights for ODRHA service providers in understanding rider behavior and following best practices to achieve higher rate of continued usage of ODRHA services through mobile applications.

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References

  • Agarwal R, Karahanna E (2000) Time flies when you’re having fun: cognitive absorption and beliefs about information technology usage. MIS Q 24(4):665–694

    Google Scholar 

  • Agarwal R, Prasad J (1999) Are individual differences germane to the acceptance of new information technologies? Decision Sci 30(2):361–391

    Google Scholar 

  • Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50(2):179–211

    Article  Google Scholar 

  • Alalwan AA, Dwivedi YK, Rana NP, Algharabat R (2018) Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk. J Retail Consum Serv 40:125–138

    Google Scholar 

  • Alemi FC (2018) On-demand ride services in California: investigating the factors affecting the frequency of use of Uber/Lyft (No. 18-05563)

  • Anderson JC, Gerbing DW (1988) Structural equation modeling in practice: a review and recommended two-step approach. Psychol Bull 103(3):411–423

    Google Scholar 

  • Bagozzi RP, Yi Y, Phillips LW (1991) Assessing construct validity in organizational research. Adm Sci Q 36(3):421–458

    Google Scholar 

  • Bandura A (1977) Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev 84(2):191–215

    Google Scholar 

  • Bhattacherjee A (2000) Acceptance of e-commerce services: the case of electronic brokerages. IEEE Trans Syst Man Cybern Part A: Syst Hum 30(4):411–420

    Google Scholar 

  • Bhattacherjee A (2001) Understanding information systems continuance: an expectation-confirmation model. MIS Q 25(3):351–370

    Google Scholar 

  • Bhattacharya A (2018) As Uber sputters, Ola is really stepping on the gas in India. Quartz India. https://qz.com/india/1200878/with-uber-in-crisis-ola-zooms-ahead-in-indias-taxi-wars/. Accessed 15 Feb 2018

  • Bolton Ruth N (1998) A dynamic model of the duration of the customer’s relationship with a continuous service provider: the role of satisfaction. Mark Sci 17(1):45–65

    Google Scholar 

  • Buhalis D, Law R (2008) Progress in information technology and tourism management: 20 years on and 10 years after the Internet—the state of eTourism research. Tour Manag 29:609–623

    Google Scholar 

  • Chakraborty S (2017) For Ola and Uber, India’s shared taxi market is the next battleground. Live Mint. http://www.livemint.com/Companies/zurwJmatKucNvacjRmwxLK/Shared-rides-the-next-battleground-for-Ola-Uber.html. Accessed 06 June 2017

  • Chang T-Z, Wildt A (1994) Price, product information, and purchase intention: an empirical study. J Acad Mark Sci 22(4):16–27

    Google Scholar 

  • Chau PYK, Hu PJ (2001) Information technology acceptance by individual professionals: a model comparison approach. Decision Sci 32(4):699–719

    Google Scholar 

  • Cheverst K, Davies N, Mitchell K, Friday A, Efstratiou C (2000) Developing a context-aware electronic tourist guide: some issues and experiences. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, pp. 17–24

  • Chi HK, Yeh HR, Jang BF (2008) The effects of service quality, customer perceived value, customer satisfaction on behavioral intentions: a study of mobile value-added services in Taiwan. Bus Rev 10(1):129–135

    Google Scholar 

  • Compeau DR, Higgins CA (1995) Computer self-efficacy: development of a measure and initial test. MIS Q 19(2):189–211

    Google Scholar 

  • Compeau D, Higgins CA, Huff S (1999) Social cognitive theory and individual reactions to computing technology: a longitudinal study. MIS Q 23(2):145–158

    Google Scholar 

  • Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340

    Google Scholar 

  • Davis FD, Bagozzi RP, Warshaw PR (1989) User acceptance of computer technology: a comparison of two theoretical models. Manage Sci 35(8):982–1003

    Google Scholar 

  • Dodds WB, Monroe KB (1985) The effect of brand and price information on subjective product evaluations. ACR North American Advances

  • Fesenmaier DR, Xiang Z (2014) Tourism marketing from 1990–2010: Two decades and a new paradigm. In: McCabe S (ed) The Routledge handbook of tourism marketing. Routledge, London, pp 549–560

    Google Scholar 

  • Fishbein M, Ajzen I (1975) Belief, attitude, intention and behavior: an introduction to theory and research. Addison-Wesley, Reading

    Google Scholar 

  • Frommer's Unlimited (2011) Digital travel content and user experience survey. Frommers.biz, London

    Google Scholar 

  • Gefen D, Straub DW, Boudreau MC (2000) Structural equation modeling and regression: guidelines for research practice. Commun Assoc Inf Syst 4(7):1–70

    Google Scholar 

  • Guo SC (2018) A simple but quantifiable approach to dynamic price prediction in ride-on-demand services leveraging multi-source urban data. In: Proceedings of the ACM on interactive, mobile wearble and ubiquitous technologies, p 112

    Google Scholar 

  • Hair JF Jr, Black WC, Babin BJ, Anderson RE, Tatham RL (2009) Multivariate data analysis (6th ed.). India: Pearson Education–Dorling Kindersley

  • Harrison AW, Rainer RK Jr (1992) The influence of individual differences on skill in end-user computing. J Manag Inf Syst 9(1):93–111

    Google Scholar 

  • Hasan B (2006) Delineating the effects of general and system-specific computer self-efficacy beliefs on IS acceptance. Inf Manag 43(5):565–571

    Google Scholar 

  • Hong S-J, Tam KY (2006) Understanding the adoption of multipurpose information appliances. Inf Syst Res 17(2):167–179

    Google Scholar 

  • Höpken W, Fuchs M, Zanker M, Beer T (2010) Inf Technol Tour 12:175–195

    Google Scholar 

  • Hsu MH, Chiu CM (2004) Internet self-efficacy and electronic service acceptance. Decision Support Syst 38(3):369–381

    Google Scholar 

  • Hsu CL, Lin JCC (2015) What drives purchase intention for paid mobile apps? An expectation-confirmation model with perceived value. Electron Commer Res Appl 14(1):46–57

    Google Scholar 

  • Hsu CL, Lu HP, Hsu HH (2007) Adoption of the mobile Internet: an empirical study of multimedia message service (MMS). Omega 35(6):715–726

    Google Scholar 

  • Huang YM, Chiu PS (2015) The effectiveness of a meaningful learning-based evaluation model for context-aware mobile learning. Br J Edu Technol 46(2):437–447

    Google Scholar 

  • Igbaria M, Iivari J, Maragahh H (1995) Why do individuals use computer technology? a finish case study. Inf Manag 5(2):7–38

    Google Scholar 

  • Johnson RD, Marakas GM (2000) Research report: the role of behavioral modeling in computer skills acquisition—toward refinement of the model. Inf Syst Res. https://doi.org/10.1287/isre.11.4.402.11869

    Article  Google Scholar 

  • Junglas I, Abraham C, Watson RT (2008) Task-technology fit for mobile locatable information systems. Decision Support Syst 45(4):1046–1057

    Google Scholar 

  • Karahanna E, Straub D, Chervany N (1999) Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Q 23(2):112–121

    Google Scholar 

  • Kim SH, Srinivasan V (2009) A conjoint-hard model of the timing of buyers’ upgrading to improved versions of high technology products. J Prod Innov Manag 26:278–290

    Google Scholar 

  • Kim HW, Chan HC, Gupta S (2007) Value-based adoption of mobile Internet: an empirical investigation. Decision Support Syst 43(1):111–126

    Google Scholar 

  • Konu H, Laukkanen T, Komppula R (2011) Using ski destination choice criteria to segment Finnish ski resort customers. Tour Manag 32(5):1096–1105

    Google Scholar 

  • Lamsfus C, Wang D, Alzua-Sorzabal A, Xiang Z (2014) Going mobile defining context for on-the-go travelers. J Travel Res. https://doi.org/10.1177/0047287514538839

    Article  Google Scholar 

  • Laukkanen T, Sinkkonen S, Kivijarvi M, Laukkanen P (2007) Segmenting bank customers by resistance to mobile banking. In: International conference on the management of mobile business (ICMB 2007). IEEE, Piscataway, pp 42–42

  • Lee SH (2018) Decisional factors leading to the reuse of an on-demand ride service. Sustainability 10:872–883

    Google Scholar 

  • Lee KC, Kang I, Kim JS (2007) Exploring the user interface of negotiation support systems from the user acceptance perspective. Comput Hum Behav 23(1):220–239

    Google Scholar 

  • Lee SH, Lee BY, Kim HW (2019) Decisional factors leading to the reuse of an on-demand ride service. Inf Manag 56(4):493–506

    Google Scholar 

  • Levin T, Gordon C (1989) Effect of gender and computer experience on attitudes toward computers. J Educ Comput Res 5(1):69–88

    Google Scholar 

  • Lin TC, Huang CC (2008) Understanding knowledge management system usage antecedents: an integration of social cognitive theory and task technology fit. Inf Manag 45(6):410–417

    Google Scholar 

  • Ling R (2004) The mobile connection: the cell phone’s impact on society. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Liu X, Xu WW (2019) Adoption of ride-sharing apps by Chinese taxi drivers and its implication for the equality and wellbeing in the sharing economy. Chin J Commun 12(1):7–24

    Google Scholar 

  • Liu Y, Han S, Li H (2010) Understanding the factors driving m-learning adoption: a literature review. Campus-Wide Inf Syst 27(4):210–226

    Google Scholar 

  • Mathieson K (1991) Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Inf Syst Res 2(3):173–191

    Google Scholar 

  • Mathieson K, Peacock E, Chin WW (2001) Extending the technology acceptance model: the influence of perceived user resources. ACM SIGMIS Database: Database Adv Inf Syst 32(3):86–112

    Google Scholar 

  • Natarajan T, Balasubramanian SA, Kasilingam DL (2017) Understanding the intention to use mobile shopping applications and its influence on price sensitivity. J Retail Consum Serv 37:8–22

    Google Scholar 

  • Ofcom (2011) Communications Market Report: UK. http://stakeholders.ofcom.org.uk/binaries/research/cmr/cmr11/UK_CMR_2011_FINAL.pdf. Accessed 12 Feb 2012

  • Okumus B, Bilgihan A (2014) Proposing a model to test smartphone users’ intention to use smart applications when ordering food in restaurants. J Hosp Tour Technol 5(1):31–49

    Google Scholar 

  • Oliver RL (1980) A cognitive model of the antecedents and consequences of satisfaction decisions. J Mark Res 17(4):460–469

    Google Scholar 

  • Oliver RL (1993) A conceptual model of service quality and service satisfaction: compatible goals, different concepts. Adv Serv Mark Manag 2(3):65–85

    Google Scholar 

  • Oliver RL (2010) Satisfaction: a behavioral perspective on the consumer, 2nd edn. M.E. Sharpe, Armonk

    Google Scholar 

  • Park CH, Kim YG (2003) Identifying key factors affecting consumer purchase behavior in an online shopping context. Int J Retail Distrib Manag 31(1):16–29

    Google Scholar 

  • Pedrotti M, Nistor N (2016) User motivation and technology acceptance in online learning environments. In: European conference on technology enhanced learning. Springer, Cham, pp. 472–477

    Google Scholar 

  • Podsakoff PM, MacKenzie SB, Lee J-Y, Podsakoff NP (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 88(5):879–903

    Google Scholar 

  • Schwanen T, Kwan MP (2008) The Internet, mobile phone and space-time constraints. Geoforum 39:1362–1377

    Google Scholar 

  • Shin YM, Lee SC, Shin B, Lee HG (2010) Examining influencing factors of post-adoption usage of mobile internet: focus on the user perception of supplier-side attributes. Inf Syst Front 12(5):595–606

    Google Scholar 

  • Siau K, Shen Z (2003) Building trust in mobile commerce. Commun ACM 46(4):91–94

    Google Scholar 

  • So KKF, Oh H, Min S (2018) Motivations and constraints of Airbnb consumers: findings from a mixed-methods approach. Tour Manag 67:224–236

    Google Scholar 

  • Sweeney JC, Soutar GN (2001) Consumer perceived value: the development of a multiple item scale. J Retail 77:203–220

    Google Scholar 

  • Taylor S, Todd PA (1995) Understanding information technology usage: a test of competing models. Inf Syst Res 6(4):144–176

    Google Scholar 

  • Tseng FM, Chiang HY (2013) Exploring consumers to buy innovative products: mobile phone upgrading intention. J High Technol Manag Res 24(2):77–87

    Google Scholar 

  • Urbach N, Ahlemann F (2010) Structural equation modeling in information systems research using partial least squares. J Inf Technol Theory Appl 11(2):5–40

    Google Scholar 

  • Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci 46(2):186–204

    Google Scholar 

  • Venkatesh V, Morris MG (2000) Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Q 24(1):115–139

    Google Scholar 

  • Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478

    Google Scholar 

  • Venkatesh V, Thong JY, Chan FK, Hu PJH, Brown SA (2011) Extending the two-stage information systems continuance model: incorporating UTAUT predictors and the role of context. Inf Syst J 21(6):527–555

    Google Scholar 

  • Wang HY, Wu SY (2014) Factors influencing behavioural intention to patronise restaurants using iPad as a menu card. Behav Inf Technol 33(4):395–409

    Google Scholar 

  • Wang D, Park S, Fesenmaier DR (2011) An examination of information services and smartphone applications. In: 16th graduate students research conference 2011, Houston, Texas. http://scholarworks.unmass.edu/cgi/viewcontent.cgi?article=1179&content=gradconf_hospitality. Accessed 6–8 Jan 2011

  • Woodruff RB (1997) Customer value: the next source of competitive advantage. J Acad Mark Sci 25(3):139–153

    Google Scholar 

  • Wu J-H, Wang S-C (2005) What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Inf Manag 42(5):719–729

    Google Scholar 

  • Yoo CW, Goo J, Huang CD (2017) Improving travel decision support satisfaction with smart tourism technologies: a framework of tourist elaboration likelihood and self-efficacy. Inf Manag 54(6):757–770

    Google Scholar 

  • Zeithaml VA (1988) Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. J Mark 52(42):2–22

    Google Scholar 

  • Zhang Y, Lee W, Huang YA (2003) Intrusion detection techniques for mobile wireless networks. Wirel Netw 9(5):545–556

    Google Scholar 

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Authors and Affiliations

  1. Xavier School of Management, XLRI Jamshedpur, Jamshedpur, India

    Garima Malik

  2. Accurate Institute of Advance Management, Noida, India

    Garima Malik & A. Sajeevan Rao

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  1. Garima Malik
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  2. A. Sajeevan Rao
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Correspondence to A. Sajeevan Rao.

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Malik, G., Rao, A.S. Extended expectation-confirmation model to predict continued usage of ODR/ride hailing apps: role of perceived value and self-efficacy. Inf Technol Tourism 21, 461–482 (2019). https://doi.org/10.1007/s40558-019-00152-3

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