Exploring User Acceptance and Intention towards App based Cab Aggregators using Integrated TAM and TPB Model

Ms. Pragya Jayaswal; Dr. N. Malati

Articles 33th Edition of DTR Apr 2020 – Sep 2020

Exploring User Acceptance and Intention towards App based Cab Aggregators using Integrated TAM and TPB Model

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Assistant Professor Delhi, Institute of Advanced Studies Rohini, Delhi Professor, Delhi Institute of Advanced Studies Rohini, Delhi

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Published 2020-04-30

Pages 01-16

Abstract

Rapid internet penetration and increasing smartphone usage in India and the world has changed the consumer behaviour and has altered traditional business paradigms. Online services have replaced a number of traditional offline activities today. The acceptance of digital technology has by far changed the needs and expectations of consumers in today's competitive scenario. One of such major change is the birth of the sharing economy, which is defined as “an economic system in which assets or services are shared between private individuals, either free or for a fee, typically by means of the internet” (Oxford dictionaries, 2016). Application Based Cab Aggregators (ABCA) aggregates cabs for the ease of customers by providing cabs at their destination for rates set by the service. Some of the key players in this new emerging market in India are Uber India Technology Private Limited, ANI Technologies Private Limited, Meru Cab Company Put. Ltd., Carzon rent (India) Put. Ltd, Zoom car India Private Ltd, Saavari, Fast Track Call Cab Private Limited.
Given the rising success of the sharing economy in the digital age, this research work studies the key factors which influences the passenger's acceptance and intention of using this new technology of ABCA services by developing an integrated model of Theory of Planned Behaviour and The Technology Acceptance Model.
The outcomes of the study showed that the commuters have a positive attitude towards the ABCA services and the attitude towards ABCA should be a strong predictor for the intention to use the services. The results also indicated that perceived ease of use and perceived usefulness positively influence the attitude towards ABCA services. Perceived behavioural control was found to have a positive influence on the intention to use the services whereas subjective norm had no effect on the intention to use the services,

Keywords

Technology Acceptance Model (TAM) The Theory of Planned Behaviour (TPB) ABCA Sharing Economy

References

i. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
ii. Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In Action Control (pp. 11–39). Springer, Berlin, Heidelberg.
iii. Ajzen, I. (1987). Attitudes, traits, and actions: Dispositional prediction of behavior in personality and social psychology. Advances in Experimental Social Psychology, Vol. 20, pp. 1–63.
iv. Ajzen, I. (2005). Attitudes, personality, and behavior. McGraw-Hill Education (UK).
v. Ajzen, I. (2011). The theory of planned behaviour: reactions and reflections.
vi. Ajzen, I., & Madden, T. (1986). Prediction of goal-directed behaviour: Attitudes, intentions and perceived behavioural control.
vii. Bhattacherjee, A. (2000). Acceptance of e-commerce services: the case of electronic brokerages. IEEE Transactions on Systems, Man, and Cybernetics, 30(4), 411–420.
viii. Cialdini, R. B., & Trost, M. R. (1998). Social influence: Social norms, conformity and compliance. In Handbook of Social Psychology (pp. 151–192). McGraw-Hill.
ix. Chen, C. D., Fan, Y. W., & Farn, C. K. (2007). Predicting electronic toll collection service adoption. Transportation Research Part C, 15(5), 300–311.
x. Chen, H., Rong, W., Ma, X., Qu, Y., & Xiong, Z. (2017). Extended TAM for mobile social gaming. Mobile Information Systems.
xi. Davis, F. D. (1985). Technology acceptance model (Doctoral dissertation). MIT.
xii. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of IT. MIS Quarterly, 319–340.
xiii. Erasmus, E., Rothmann, S., & Van Eeden, C. (2015). A structural model of technology acceptance. SA Journal of Industrial Psychology.
xiv. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior.
xv. Fisher, J. D., & Fisher, W. A. (1992). Changing AIDS-risk behavior. Psychological Bulletin, 111(3), 455.
xvi. Francis, J., et al. (2004). Constructing questionnaires based on TPB.
xvii. Gamal Aboelmaged, M. (2010). Predicting e-procurement adoption. Industrial Management & Data Systems, 110(3), 392–414.
xviii. Godin, G., & Kok, G. (1996). TPB applications to health-related behaviors. American Journal of Health Promotion, 11(2), 87–98.
xix. M., U., Iqbal, A., & Iqbal, Z. (2018). Internet banking adoption in Pakistan. International Journal of Business Information Systems, 28(3), 342–370.
xx. Jaiswal, M. P. Draft Policy Recommendations for ABCA in India.
xxi. Lee, M. C. (2009). Internet banking adoption: TAM + TPB. Electronic Commerce Research and Applications, 8(3), 130–141.
xxii. Lien, C. H., et al. (2019). Self-service technology adoption by air passengers. The Service Industries Journal.
xxiii. Lule, I., Omwansa, T. K., & Waema, T. M. (2012). TAM in m-banking adoption. International Journal of Computing & ICT Research.
xxiv. Rogers, E. (1995). Diffusion of Innovations. Free Press.
xxv. Safeena, R., et al. (2013). Combination of TAM and TPB in online banking. International Journal of Computer Theory and Engineering.
xxvi. Sheppard, B. H., et al. (1988). Theory of reasoned action meta-analysis. Journal of Consumer Research, 15(3), 325–343.
xxvii. Sparks, P., et al. (1995). Perceived behavioural control study. Appetite, 24(3), 243–255.
xxviii. Trafimow, D., & Finlay, K. A. (1996). Subjective norms importance. Personality and Social Psychology Bulletin.
xxix. Van den Putte, B. (1991). Meta-analysis of TRA. University of Amsterdam.
xxx. Venkatesh, V. (2000). Determinants of perceived ease of use. Information Systems Research, 11(4), 342–365.
xxxi. Venkatesh, V., et al. (2003). User acceptance of IT. MIS Quarterly, 425–478.

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Ms. Pragya Jayaswal , Dr. N. Malati . (2020). Exploring User Acceptance and Intention towards App based Cab Aggregators using Integrated TAM and TPB Model. DIAS Technology Review, 17(1), 01-16.

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[1] i. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

[2] ii. Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In Action Control (pp. 11–39). Springer, Berlin, Heidelberg.

[3] iii. Ajzen, I. (1987). Attitudes, traits, and actions: Dispositional prediction of behavior in personality and social psychology. Advances in Experimental Social Psychology, Vol. 20, pp. 1–63.

[4] iv. Ajzen, I. (2005). Attitudes, personality, and behavior. McGraw-Hill Education (UK).

[5] v. Ajzen, I. (2011). The theory of planned behaviour: reactions and reflections.

[6] vi. Ajzen, I., & Madden, T. (1986). Prediction of goal-directed behaviour: Attitudes, intentions and perceived behavioural control.

[7] vii. Bhattacherjee, A. (2000). Acceptance of e-commerce services: the case of electronic brokerages. IEEE Transactions on Systems, Man, and Cybernetics, 30(4), 411–420.

[8] viii. Cialdini, R. B., & Trost, M. R. (1998). Social influence: Social norms, conformity and compliance. In Handbook of Social Psychology (pp. 151–192). McGraw-Hill.

[9] ix. Chen, C. D., Fan, Y. W., & Farn, C. K. (2007). Predicting electronic toll collection service adoption. Transportation Research Part C, 15(5), 300–311.

[10] x. Chen, H., Rong, W., Ma, X., Qu, Y., & Xiong, Z. (2017). Extended TAM for mobile social gaming. Mobile Information Systems.

[11] xi. Davis, F. D. (1985). Technology acceptance model (Doctoral dissertation). MIT.

[12] xii. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of IT. MIS Quarterly, 319–340.

[13] xiii. Erasmus, E., Rothmann, S., & Van Eeden, C. (2015). A structural model of technology acceptance. SA Journal of Industrial Psychology.

[14] xiv. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior.

[15] xv. Fisher, J. D., & Fisher, W. A. (1992). Changing AIDS-risk behavior. Psychological Bulletin, 111(3), 455.

[16] xvi. Francis, J., et al. (2004). Constructing questionnaires based on TPB.

[17] xvii. Gamal Aboelmaged, M. (2010). Predicting e-procurement adoption. Industrial Management & Data Systems, 110(3), 392–414.

[18] xviii. Godin, G., & Kok, G. (1996). TPB applications to health-related behaviors. American Journal of Health Promotion, 11(2), 87–98.

[19] xix. M., U., Iqbal, A., & Iqbal, Z. (2018). Internet banking adoption in Pakistan. International Journal of Business Information Systems, 28(3), 342–370.

[20] xx. Jaiswal, M. P. Draft Policy Recommendations for ABCA in India.

[21] xxi. Lee, M. C. (2009). Internet banking adoption: TAM + TPB. Electronic Commerce Research and Applications, 8(3), 130–141.

[22] xxii. Lien, C. H., et al. (2019). Self-service technology adoption by air passengers. The Service Industries Journal.

[23] xxiii. Lule, I., Omwansa, T. K., & Waema, T. M. (2012). TAM in m-banking adoption. International Journal of Computing & ICT Research.

[24] xxiv. Rogers, E. (1995). Diffusion of Innovations. Free Press.

[25] xxv. Safeena, R., et al. (2013). Combination of TAM and TPB in online banking. International Journal of Computer Theory and Engineering.

[26] xxvi. Sheppard, B. H., et al. (1988). Theory of reasoned action meta-analysis. Journal of Consumer Research, 15(3), 325–343.

[27] xxvii. Sparks, P., et al. (1995). Perceived behavioural control study. Appetite, 24(3), 243–255.

[28] xxviii. Trafimow, D., & Finlay, K. A. (1996). Subjective norms importance. Personality and Social Psychology Bulletin.

[29] xxix. Van den Putte, B. (1991). Meta-analysis of TRA. University of Amsterdam.

[30] xxx. Venkatesh, V. (2000). Determinants of perceived ease of use. Information Systems Research, 11(4), 342–365.

[31] xxxi. Venkatesh, V., et al. (2003). User acceptance of IT. MIS Quarterly, 425–478.