The Role of Perceived Quality and Brand Attitude on The Relationship Between Perceived Ease of Use and Customer Preference: Research on Mobile Taxi Booking Sector
Keywords:
perceived ease of use, perceived quality, brand attitude, customer preference, mobile taxi bookingAbstract
This study examines the effect of perceived ease of use on brand attitude and perceived quality in mobile taxi booking application selection. A total of 206 survey data were collected from individuals living in Baku and analyzed using structural equation modeling. Applications like Uber, Bolt, Ekonom Taxi, and 189 Taxi—which are often used for taxi transportation in Baku—were listed as examples of mobile taxi booking apps within the inquiries. According to the findings, the effect of perceived ease of use by customers ordering from mobile taxi booking applications is significant and positively related to perceived quality. Moreover, the effect of ease of use on attitude towards the brand is also significant and positive. Respectively, the effect of perceived quality, ease of use and brand attitude on consumer preference has a significant and positive relationship. All hypotheses established in the research were accepted based on the results of the analysis tests. Although research has been done in the literature regarding the factors affecting the customers’ choice of taxi company, it has not been found much to examine the ease of use of the application and, accordingly, the brand quality and attitude. In this direction, this study includes findings on how mobile taxi booking companies can change and shape customers’ quality perceptions and attitudes.
References
Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 227-247.
Ajzen, I. (2005). Attitudes, Personality and Behavior (2nd edition). Open University Press.
Anderson, J. R. (2009). The Architecture of Cognition. New York: Psychology Press.
Assael, H., (2003). Consumer Behavior: A Strategic Approach (1st edition). Cengage Learning.
Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation- confirmation model. MIS Quarterly, 25(3), 351–370.
Chan, L. L., & Idris, N. (2017). Validity and reliability of the instrument using exploratory factor analysis and Cronbach’s alpha. International Journal of Academic Research in Business and Social Sciences, 7(10), 400-410.
Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers’ product evaluations. Journal of Marketing Research, 28(3), 307-319.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics.
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: an Integrated Model.
Hossain, M. A., & Quaddus, M. (2013). Does mandatory pressure increases RFID adoption?: A case study of Western Australian livestock industry. In: Y. Dwivedi, H. Z. Henriksen, D. Wastell, R. De (Eds.), Grand Successes and Failures in IT: Private and Public Sectors, pp. 184–202. Boston: Springer.
Inceoğlu, M. (2010). Tutum Algi İletişim (5. Baskı)/ Attitude Perception Communication (5th Edition). Istanbul: Beykent University Press.
Kotler, P., & Armstrong, G. (2018). Principles of Marketing (17th global edition). Pearson Education Limited.
Li, H., & Liu, Y. (2014). Understanding post-adoption behaviors of e-service users in the context of online travel services. Information & Management, 51(8), 1043–1052.
Liu, M. T., Wong, I. A., Rongwei, C., & Tseng, T. H. (2014). Do perceived CSR initiatives enhance customer preference and loyalty in casinos? International Journal of Contemporary Hospitality Management.
Pai, F. Y., & Huang, K. I. (2011). Applying the Technology Acceptance Model to the introduction of healthcare information systems. Technological Forecasting and Social Change, 78(4), 650–660.
Pallant, J. F. (2000). Development and validation of a scale to measure perceived control of internal states. Journal of Personality Assessment, 75(2), 308-337.
Pipitwanichakarn, T., & Wongtada, N. (2020). The role online review on mobile commerce adoption: an inclusive growth context. Journal of Asia Business Studies, 14(5), 759-778.
Sengupta, J., & Johar, G. V. (2002). Effects of Inconsistent Attribute Information on the Predictive Value of Product Attitudes: Toward a Resolution of Opposing Perspectives. Journal Citation Reports, 29(June), 39-56.
Shiau, W. L., & Chau, P. Y. (2016). Understanding behavioral intention to use a cloud computing classroom: a multiple model comparison approach. Information & Management, 53(3), 355–365.
Solomon, M. (2017). Consumer Behavior. Buying, Having and Being (12th edition). Pearson.
Son, H., Park, Y., Kim, C., & Chou, J. S. (2012). Toward an understanding of construction professionals’ acceptance of mobile computing devices in South Korea: an extension of the technology acceptance model. Automation in Construction, 28, 82–90.
Weng, G. S., Zailani, S., Iranmanesh, M., & Hyun, S. S. (2017). Mobile taxi booking application service’s continuance usage intention by users. Transportation Research Part D: Transport and Environment, 57, 207–216.
Yang, K. C. (2005). Exploring factors affecting the adoption of mobile commerce in Singapore. Telematics and Informatics, 22(3), 257–277.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Sabina Musavi, Galandar Mammadli
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The author fully assumes the content originality and the holograph signature makes him responsible in case of trial.
