Skip to main content
Log in

An integrated model predicting customers’ continuance behavioral intention and recommendations of users: a study on mobile payment in emerging markets

  • Original Article
  • Published:
Journal of Financial Services Marketing Aims and scope Submit manuscript

Abstract

The present study combines three models, namely TPB (Theory of planned behavior), Big 5 personality traits, and TAM (technology adoption model), to measure continuance behavioral intention, electronic word of mouth (e-word of mouth), and recommendations of users. Data were collected from 280 respondents using a convenience sampling technique and standardized instruments. Statistical techniques such as reliability, validity, and mediation analysis were carried out through SPSS and Macro Process (Hayes). The study highlighted the importance of TPB, TAM, and Big 5 personality traits models on continuance intention. It identified a few vital dimensions (ease of use, personality, attitude, perceived behavior, subjective norms) that may directly or indirectly affect a user's continuance behavioral intention, satisfaction, personal and electronic recommendations to others. The findings of the study contribute by identifying the relevant determinants of continuance intention; these determinants should be analyzed by marketing firms, payment providers, and policymakers to enhance the continuance of adoption of mobile payment services.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Abhishek, A., and S. Hemchand. 2016. Adoption of sensor-based communication for mobile marketing in India. Journal of Indian Business Research 8 (1): 65–76.

    Article  Google Scholar 

  • Ajzen, I. 1991. The theory of planned behavior. Organizational Behavior and Human Decision Processes. 50: 179–211.

    Article  Google Scholar 

  • Ajzen, I., and M. Fishbein. 1980. Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: PrenticeóHall.

    Google Scholar 

  • Ashraf, R.U., F. Hou, and W. Ahmad. 2019. Understanding continuance intention to use social media in China: The roles of personality drivers, hedonic value, and utilitarian value. International Journal of Human-Computer Interaction 35 (13): 1216–1228.

    Article  Google Scholar 

  • Baabdullah, A.M., A.A. Alalwan, N.P. Rana, H. Kizgin, and P. Patil. 2019. Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model. International Journal of Information Management 44: 38–52.

    Article  Google Scholar 

  • Bandura, A. 1977. Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review 84: 191–215.

    Article  Google Scholar 

  • Bentler, P.M., and C.P. Chou. 1987. Practical issues in structural modeling. Sociological Methods & Research 16 (1): 78–117.

    Article  Google Scholar 

  • Bhattacherjee, A. 2001a. An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support System. 32 (2): 201–214.

    Article  Google Scholar 

  • Bhattacherjee, A. 2001b. Understanding information systems continuance: An expectation confirmation model. MIS Quarterly. 25 (3): 351–370.

    Article  Google Scholar 

  • Bhattacherjee, A., J. Perols, and C. Sanford. 2008. Information technology continuance: A theoretic extension and empirical test. Journal of Computer Information Systems 49 (1): 17–26.

    Article  Google Scholar 

  • Chawla, D., and H. Joshi. 2019. Consumer attitude and intention to adopt mobile wallet in India–an empirical study. International Journal of Bank Marketing. https://doi.org/10.1108/IJBM-09-2018-0256.

    Article  Google Scholar 

  • Chiu, C. M., M.H. Hsu, S.Y. Sun, T.C. Lin, and P.C. Sun. 2005. Usability, quality, value and e-learning continuance decisions. Computers & Education. 45 (4): 399–416.

  • Costa, P.T., R.R. McCrae, A.B. Zonderman, H.E. Barbano, B. Lebowitz, and D.M. Larson. 1986. Cross-sectional studies of personality in a national sample: 2. Stability in neuroticism, extraversion, and openness. Psychology and Aging. 1 (2): 144–149.

    Article  Google Scholar 

  • Davis, F.D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information System Quarterly 13: 319–340.

    Article  Google Scholar 

  • Efron, B., and R.J. Tibshirani. 1993. An introduction to the bootstrap. London: Chapman and Hall.

    Book  Google Scholar 

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

    Article  Google Scholar 

  • Fuller, C.M., M.J. Simmering, G. Atinc, Y. Atinc, and B.J. Babin. 2016. Common methods variance detection in business research. Journal of Business Research 69 (8): 3192–3198.

  • Goldberg, L.R. 1981. Language and individual differences: The search for universals in personality lexicons. In Review of personality and social psychology, vol. 1, ed. L. Wheeler, 141–165. Beverly Hills: Sage.

    Google Scholar 

  • Gupta, K. 2018. Mobile wallet transactions hit record ₹14,170 crore in May. https://www.livemint.com/Industry/T21bhXCN6dTi3MQPkyGNWO/Mobile-wallet-transactions-hit-record-14170-crore-in-May.html/. Accessed 6th July 2018.

  • Gupta, A., A. Yousaf, and A. Mishra. 2020. How pre-adoption expectancies shape post-adoption continuance intentions: An extended expectation-confirmation model. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2020.

    Article  Google Scholar 

  • Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate data analysis. (7th ed.). New Jersey: Prentice-Hall, Inc.

  • Hayes, A.F., and N.J. Rockwood. 2017. Regression-based statistical mediation and moderation analysis in clinical research: Observations, recommendations, and implementation. Behaviour Research and Therapy 98: 39–57.

    Article  Google Scholar 

  • Hays, J. 2015. Indian character and personality. Retrieved from facts and Details.com: http://factsanddetails.com/india/People_and_Life/sub7_3c/entry-4166.html.

  • He, P., and M. Veronesi. 2017. Personality traits and renewable energy technology adoption: A policy case study from China. Energy Policy 107: 472–479.

    Article  Google Scholar 

  • Hossain, Md Alamgir, Md Shakhawat Hossain, and N. Jahan. 2018. Predicting continuance usage intention of mobile payment: an experimental study of Bangladeshi customers. Asian Economic and Financial Review 8. https://doi.org/10.18488/journal.aefr.2018.84.487.498.

    Article  Google Scholar 

  • Hsu, C.L., and H.P. Lu. 2004. Why do people play online games? An extended TAM with social influences and flow experience. Information & Management 41 (7): 853–868.

    Article  Google Scholar 

  • Hsu, C.L., M. Chen, K.C. Chang, and C.M. Chao. 2010. Applying loss aversion to investigate service quality in logistics: A moderating effect of service convenience. International Journal of Operations & Production Management 30 (5): 508–525.

    Article  Google Scholar 

  • Jamwal, M. 2017. How big is mobile payment in India? Retrieved from yourstory.com: https://yourstory.com/mystory/8098f4c7e8-how-big-is-mobile-paym/. Accessed on Jan 11th, 2018.

  • Joo, T.M., and C.E. Teng. 2017. Impacts of social media (Facebook) on human communication and relationships: A view on behavioral change and social unity. International Journal of Knowledge Content Development & Technology 7 (4): 27–50.

  • June, L. 2014. Are personal innovativeness and social influence critical to continue with mobile commerce? Internet Research 24 (2): 134–159. https://doi.org/10.1108/IntR-05-2012-0100.

    Article  Google Scholar 

  • Kamble, S., A. Gunasekaran, and H. Arha. 2019. Understanding the Blockchain technology adoption in supply chains-Indian context. International Journal of Production Research 57 (7): 2009–2033.

    Article  Google Scholar 

  • Khalifa, M., and V. Liu. 2007. Online consumer retention: Contingent effects of online shopping habit and online shopping experience. European Journal of Information Systems 16: 780–792.

    Article  Google Scholar 

  • Kim, K.K., H.K. Shin, and B. Kim. 2011. The role of psychological traits and social factors in using new mobile communication services. Electronic Commerce Research & Applications 10 (4): 408–417.

    Article  Google Scholar 

  • Kim, M.K., S.F. Wong, Y. Chang, and J.H. Park. 2016. Determinants of customer loyalty in the Korean smartphone market: Moderating effects of usage characteristics. Telematics and Informatics 33 (4): 936–949.

  • KimMirusmonov, C.M., and I. Lee. 2009. An empirical examination of factors influencing the intention to use mobile payment. Journal of Computers in Human Behavior 26 (3): 310–322.

    Google Scholar 

  • Kizgin, H., A. Jamal, B.L. Dey, and N. Rana. 2018. The impact of social media on consumers’ acculturation and purchase intentions. Information Systems Frontiers 20: 503–514.

    Article  Google Scholar 

  • Lăzăroiu, G., Popescu, G.H., and Alexandru, B. 2021. The adoption of mobile payment technologies, social interactive consumer-oriented applications, and online purchasers’ decision-making process. In SHS Web of Conferences, vol. 92, EDP Sciences.

  • Lee, K.E., S.H. Kim, T.Y. Ha, Y.M. Yoo, J.J. Han, J.H. Jung, and J.Y. Jang. 2016. Dependency on smartphone use and its association with anxiety in Korea. Public Health Reports 131 (3): 411–419.

  • Liébana-Cabanillas, F., J. Sánchez-Fernández, and F. Muñoz-Leiva. 2014. The moderating effect of experience in the adoption of mobile payment tools in Virtual Social Networks: The m-Payment Acceptance Model in Virtual Social Networks (MPAM-VSN). International Journal of Information Management 34 (2): 151–166.

    Article  Google Scholar 

  • Liébana-Cabanillas, F., F. Muñoz-Leiva, and J. Sánchez-Fernández. 2017a. A global approach to the analysis of user behavior in mobile payment systems in the new electronic environment. Service Business. https://doi.org/10.1007/s11628-017-0336-7.

    Article  Google Scholar 

  • Liébana-Cabanillas, F., V. Marinković, and Z. Kalinić. 2017b. A SEM-neural network approach for predicting antecedents of m-commerce acceptance. International Journal of Information Management 37: 14–24.

    Article  Google Scholar 

  • Liébana-Cabanillas, F.L., V. Marinkovic, I.R. Luna, and Z. Kalinic. 2018. Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach. Technological Forecasting & Social Change 129: 117–130.

    Article  Google Scholar 

  • Liébana-Cabanillas, F., N. Singh, Z. Kalinic, and E. Carvajal-Trujillo. 2021. Examining the determinants of continuance intention to use and the moderating effect of the gender and age of users of NFC mobile payments: A multi-analytical approach. Information Technology and Management 22 (2): 133–161.

    Article  Google Scholar 

  • Liu, C.L., and S. Forsythe. 2011. Examining drivers of online purchase intensity: Moderating role of adoption duration in sustaining post-adoption online shopping. Journal of Retailing & Consumer Services 18 (1): 101–109.

    Article  Google Scholar 

  • Loh, X.M., V.H. Lee, T.S. Hew, and B. Lin. 2022. The cognitive-affective nexus on mobile payment continuance intention during the COVID-19 pandemic. International Journal of Bank Marketing. https://doi.org/10.1108/IJBM-06-2021-0257.

    Article  Google Scholar 

  • Madan, K., and R. Yadav. 2016. Behavioural intention to adopt mobile wallet: A developing country perspective. Journal of Indian Business Research 8 (3): 227–244.

    Article  Google Scholar 

  • Madan, K., and R. Yadav. 2018. Understanding and predicting antecedents of mobile shopping adoption: A developing country perspective. Asia Pacific Journal of Marketing and Logistics 1: 139–162.

    Article  Google Scholar 

  • Majumdar, S., and V. Pujari. 2021. Exploring usage of mobile banking apps in the UAE: a categorical regression analysis. Journal of Financial Services Marketing. https://doi.org/10.1057/s41264-021-00112-1.

    Article  Google Scholar 

  • Marinković, V., and Z. Kalinić. 2017. Antecedents of customer perceived satisfaction in mobile commerce: Exploring the moderating effect of customization. Online Information Review 41 (2): 138–154.

    Article  Google Scholar 

  • McCrae, R.R., and P.T. Costa. 1988. Recalled parent-child relations and adult personality. Journal of Personality. 56 (2): 417–434.

    Article  Google Scholar 

  • Miltgen, C.L., A. Popovic, and T. Oliveira. 2013. Determinants of end-user acceptance of biometrics: Integrating the “Big 3” of technology acceptance with privacy context. Decision Support Systems 56: 103–114.

    Article  Google Scholar 

  • Oliveira, T., M. Thomas, G. Baptista, and F. Campos. 2016. Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior 61: 404–414.

    Article  Google Scholar 

  • Purohit, S., and R. Arora. 2021. Adoption of mobile banking at the bottom of the pyramid: An emerging market perspective. International Journal of Emerging Markets. https://doi.org/10.1108/IJOEM-07-2020-0821.

    Article  Google Scholar 

  • Rafdinal, W., and W. Senalasari. 2021. Predicting the adoption of mobile payment applications during the COVID-19 pandemic. International Journal of Bank Marketing. https://doi.org/10.1108/IJBM-10-2020-0532.

    Article  Google Scholar 

  • Ramos de Luna, I.R., F. Liébana-Cabanillas, F. Muñoz-Leiva, and J. Sánchez-Fernández. 2019. The adoption of mobile payment systems depending on the technology applied. Technological Forecasting & Social Change. https://doi.org/10.1016/j.techfore.2018.09.018 ((In press)).

    Article  Google Scholar 

  • Rogers, E.M. 2003. Diffusion of innovations, 5th ed. New York: The Free Press.

    Google Scholar 

  • Santosa, A.D., N. Taufik, F.H.E. Prabowo, and M. Rahmawati. 2021. Continuance intention of baby boomer and X generation as new users of digital payment during COVID-19 pandemic using UTAUT2. Journal of Financial Services Marketing 26 (4): 259–273.

    Article  Google Scholar 

  • Sharma, S., and M. Sharma. 2019. Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation. International Journal of Information Management 44: 65–75.

    Article  Google Scholar 

  • Sharma, S.K., S.K. Mangla, S. Luthra, and Z. Al-Salti. 2018. Mobile wallet inhibitors: Developing a comprehensive theory using an integrated model. Journal of Retailing and Consumer Services 45: 52–63.

    Article  Google Scholar 

  • Singh, N., S. Srivastava, and N. Sinha. 2017. Consumer preference and perceived satisfaction of M-wallets: A study on North Indian consumers. International Journal of Bank Marketing 35 (6): 944–965.

    Article  Google Scholar 

  • Singh, N., N. Sinha, and F.J. Liébana-Cabanillas. 2020. Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence. International Journal of Information Management 50: 191–205. https://doi.org/10.1016/j.ijinfomgt.2019.05.022.

    Article  Google Scholar 

  • Sinha, N., and N. Singh. 2022. Revisiting expectation confirmation model to measure the effectiveness of multichannel bank services for elderly consumers. International Journal of Emerging Markets. https://doi.org/10.1108/IJOEM-03-2021-0361.

    Article  Google Scholar 

  • Slade, E., Y. Dwivedi, M. Williams, and N. Piercy. 2016. An empirical investigation of remote mobile payment adoption. In Let’s get engaged! Crossing the threshold of marketing’s engagement era, 441–442. Cham: Springer.

    Chapter  Google Scholar 

  • Sleiman, K.A.A., L. Juanli, H. Lei, R. Liu, Y. Ouyang, and W. Rong. 2021. User trust levels and adoption of mobile payment systems in China: An empirical analysis. SAGE Open 11 (4): 21582440211056600.

    Article  Google Scholar 

  • Statista. 2018. Share of consumers using mobile apps for online shopping across India as of January 2018, by age group. Retrieved from Statista: https://www.statista.com/statistics/870610/india-consumer-payments-for- online-shopping-with-mobile-apps-by-age-group/. Accessed on Dec 23, 2018.

  • Sunarjo, W.A., S. Nurhayati, and A. Muhardono. 2021. Consumer behavior toward adoption of mobile payment: A case study in Indonesia during the COVID-19 pandemic. The Journal of Asian Finance, Economics and Business 8 (4): 581–590.

    Google Scholar 

  • Tajvidi, M. Y. Wang, N. Hajli, and P.E. Love. 2017. Brand value Co-creation in social commerce: The role of interactivity, social support, and relationship quality. Computers in Human Behaviour 115 (2): 12–31.

  • Tan, G.W.-H., and K.-B. Ooi. 2018. Gender and age: Do they really moderate mobile tourism shopping behavior? Telematics and Informatics 35 (6): 1617–1642.

    Article  Google Scholar 

  • Taylor, S., and P.A. Todd. 1995. Assessing IT usage: The role of prior experience. MIS Quarterly 19 (4): 561–570.

    Article  Google Scholar 

  • Thakur, R. (2013). Customer adoption of mobile payment services by professionals across two cities in India: An empirical study using modified technology acceptance model. Business Perspectives and Research, 1(2), 17–30.

  • Venkatesh, V., M.G. Morris, F.D. Davis, and G.B. Davis. 2003. User acceptance of information technology: Toward a unified view. MIS Quarterly 27 (3): 425–478.

    Article  Google Scholar 

  • Venkatesh, V., J. Thong, and X. Xu. 2012. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly 36 (1): 157–178.

    Article  Google Scholar 

  • Wang, M.H. 2016. Factors influencing usage of e-learning systems in Taiwan's public sector: Applying the UTAUT model. Advances in Management and Applied Economics 6 (6): 63.

  • Xu, F., and J.T. Du. 2018. Factors influencing users’ perceived satisfaction and loyalty to digital libraries in Chinese universities. Computers in Human Behavior 83: 64–72.

    Article  Google Scholar 

  • Yang, S., Y. Lu, S. Gupta, Y. Cao, and R. Zhang. 2012. Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits. Computers in Human Behavior 28 (1): 129–142.

    Article  Google Scholar 

  • Yuan, S., Y. Liu, R. Yao, and J. Liu. 2016. An investigation of users’ continuance intention towards mobile banking in China. Information Development 32 (1): 20–34.

    Article  Google Scholar 

  • Zhang, Y., J. Sun, Z. Yang, and Y. Wang. 2018. What makes people actually embrace or shun mobile payment: A cross-culture study. Mobile Information Systems 2018: 13.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nidhi Singh.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix I

Source

Dependent variables

Independent variables

Models used

Findings

Survey country

June (2014)

Continuance intention

Social influence, perceived usefulness, perceived ease of use, personal innovativeness

TAM, UTAUT

personal innovativeness and perceived usefulness are the strong determinants

Texas, the USA

Liébana-Cabanillas et al (2014)

Intention to use

Ease of use, attitude, usefulness, trust, and risk

TAM

Ease of use is the most influential determinant followed by usefulness

Spain, Europe

Lee et al. (2016)

Intention to use

Openness to experience, extraversion, agreeableness, performance expectancy

TAM, Big 5 personality traits

Openness to experience and agreeableness are the most important determinants

Korea

Kim et al. (2016)

Intention to use

Openness to experience, extraversion, agreeableness, ease of use, neuroticism, conscientiousness

TAM, Big 5 personality traits

neuroticism and extraversion have the highest influence

Korea

Thakur (2013)

Continuance Intention

Subjective norms, attitude, perceived usefulness, perceived ease of use

TAM and TPB

performance expectancy, effort expectancy, have the most significant influence

India

Wang (2006)

Intention to use

Self-efficacy, perceived usefulness, perceived ease of use, perceived financial sources, perceived credibility

TAM and TPB

Self-efficacy and perceived credibility found to be most significant

Taiwan

Appendix II

Constructs

Items statements

References used

Attitude

A1: I perceive that mobile payment is a new and unique idea

A2: Mobile payment is advantageous in every domain of transactions

A3: Mobile payment provides convenience, and it is very trendy

A4: Use of mobile payment is the really thrilling and nice experience

Venkatesh et al. (2012)

Subjective norms

SN1: People that are important to me (family, friends) would purchase through mobile payment

SN2: People that are important to me (family, friends) would recommend me to purchase through mobile payment

Ajzen and Fishbein (1980)

Perceived behavior control

PBC1: I know how to buy through mobile payment

PBC2: I feel that buying through mobile payment is not problematic

PBC3: I have enough time to purchase through mobile payment

Taylor and Todd (1955 a, b)

Customer satisfaction

S1: I would feel satisfied with the features of mobile payment

SI2: I would feel contented with the features of mobile payment

SI3: I would feel comfortable with mobile payments usage

SI4: I would feel pleased because it potentially fulfills my needs

Madan and Yadav (2016)

Continuance behavioral intention

CI1: I intend to continue using mobile money services in the future

CI2: I will continue using mobile money services in the future

CI3: I will regularly use mobile money services in the future

CI4: I want to continue using mobile money services rather than discontinue its use

CI5: My intentions are to continue using mobile money services rather than any alternative means

CI6: I intend to continue using mobile money services in the future

CI7: I intend to continue using mobile money services

CI8: Next time I am willing to use the mobile money services. (removed)

CI9: I will recommend other people to continue using mobile money services. (removed)

Chiu et al. (2005)

Recommendation to use

R1: I would recommend mobile payment to my friends and family to use it, if it is available

R2: If I have a worthy experience with mobile payment, I would recommend friends to download the apps of services

R3: I would recommend the apps on social platforms if it is worth using. (removed)

Oliveria et al. (2016)

Electronic word of mouth

e-word of mouth 1: I speak of mobile payment to purchase

e-word of mouth 2: I am proud to say to others that I am using mobile payment

e-word of mouth 3: I strongly recommend people to use mobile payment

e-word of mouth 4: I have spoken favorably for mobile payment to others (removed)

Oliveria et al. (2016)

Ease to use

U1: Interaction with mobile payment is clear and understandable

U2: Interaction with mobile payments does not require mental effort

U3: I think it is easy to use mobile payment to do what I want to do

U4: In general, mobile payment is easy to use (removed)

Venkatesh et al. (2012)

Personality

P1: I see myself as someone who is talkative

P2: I see myself as someone who is reserved

P3: I see myself as someone who is full of energy

P4: I see myself as someone who is full of energy

P5: I see myself as someone who tends to be quiet

P6: I see myself as someone who has an assertive personality

P7: I see myself as someone who is sometimes shy, inhibited

P8: I see myself as someone who is outgoing, sociable

P9: I see myself as someone who is original, comes up with new ideas

P10: I see myself as someone who is curious about many different things

P11: I see myself as someone who is ingenious, a deep thinker

P12: I see myself as someone who has an active imagination

P13: I see myself as someone who is inventive (removed)

P14: I see myself as someone who values artistic, esthetic experiences (removed)

P15: I see myself as someone who prefers work that is routine. (removed)

P16: I see myself as someone who likes to reflect, play with ideas. (removed)

P17: I see myself as someone who has few artistic interests. (removed)

P18: I see myself as someone who is sophisticated in art, music, or the literature. (removed)

Costa et al. (1986)

Appendix III

Variables

Frequency

Percentage

Gender

Males

149

53.3

Females

131

46.7

Age

21–30 years

31

11.1

31-40 years

84

30.1

41-50 years

88

31.4

51–60 years

52

18.5

Above 60 years

25

8.9

Education

Graduate

62

22.1

Post-Graduate

155

55.3

Others

63

22.8

Occupation

Students

59

21.0

Service

113

40.3

Business

65

23.2

Others

43

15.3

How long you have been using mobile payment services

0–2 years

40

14.3

2–4 years

83

29.6

4–6 years

87

31.1

6–8 years

37

13.2

More than 8 years

33

11.8

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Srivastava, S., Singh, N. An integrated model predicting customers’ continuance behavioral intention and recommendations of users: a study on mobile payment in emerging markets. J Financ Serv Mark 28, 236–254 (2023). https://doi.org/10.1057/s41264-022-00147-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/s41264-022-00147-y

Keywords

Navigation