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Abstract

This study investigates the linkages between the antecedents of the extended unified theory of acceptance and use of technology (UTAUT2) model and an individual demographics in four developing countries in Southeast Europe. Three unexplored linkages are introduced, such as employment status, living settlement and financial experience. The article applies the partial least square and non-parametric methods to examine the proposed associations from a dataset of 959 individuals. Evidence reveals that those individuals who are employed, live in urban areas, and have financial experience scored higher in the UTAUT2 constructs. The article suggests the inclusion of additional moderating factors into the UTAUT2 model, which gives a better understanding of the individuals’ technology adaption to scholars and practitioners.

Keywords

Mobile banking UTAUT2 gender employment status living settlement financial experience Southeast Europe

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References

Agarwal

, & Prasad

(1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences , 30(2), 361–391. https://doi.org/10.1111/j.1540-5915.1999.tb01614.x

Ahmad

, Bhatti

S. H.

, & Hwang

(2020). E-service quality and actual use of e-banking: Explanation through the Technology Acceptance Model. Information Development , 36(4), 503–519. https://doi.org/10.1177/0266666919871611

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes , 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Alavi

, & Joachimsthaler

E. A.

(1992). Revisiting DSS implementation research: A meta-analysis of the literature and suggestions for researchers. MIS Quarterly: Management Information Systems , 16(1), 95–113. https://doi.org/10.2307/249703

Alavi

, & Ahuja

(2016). An empirical segmentation of users of mobile banking apps. Journal of Internet Commerce , 15(4), 390–407. https://doi.org/10.1080/15332861.2016.1252653

Ashour

, Alzghool

, Iyadat

, & Abu Ai-ruz

(2012). Mobile phone applications in the university classroom: Perceptions of undergraduate students in Jordan. E-Learning and Digital Media , 9(4), 419–425. https://doi.org/10.2304/elea.2012.9.4.419

Asongu

S. A.

, & Nwachukwu

J. C.

(2018). Comparative human development thresholds for absolute and relative pro-poor mobile banking in developing countries. Information Technology and People , 31(1), 63–83. https://doi.org/10.1108/ITP-12-2015-0295

Asongu

S. A.

, & Odhiambo

N. M.

(2019). Mobile banking usage, quality of growth, inequality and poverty in developing countries. Information Development , 35(2), 303–318. https://doi.org/10.1177/0266666917744006

Baabdullah

A. M.

, Alalwan

A. A.

, Rana

N. P.

, Kizgin

, & Patil

(2019). Consumer use of mobile banking (m-banking) in Saudi Arabia: Towards an integrated model. International Journal of Information Management , 44, 38–52. https://doi.org/10.1016/j.ijinfomgt.2018.09.002

10.

Baptista

, & Oliveira

(2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior , 50, 418–430. https://doi.org/10.1016/J.CHB.2015.04.024

11.

Baptista

, & Oliveira

(2017). Why so serious? Gamification impact in the acceptance of mobile banking services. Internet Research , 27(1), 118–139. https://doi.org/10.1108/IntR-10-2015-0295

12.

Bhatiasevi

(2016). An extended UTAUT model to explain the adoption of mobile banking. Information Development , 32(4), 799–814. https://doi.org/10.1177/0266666915570764

13.

Çera

, Pagria

, Khan

K. A.

, & Muaremi

(2020). Mobile banking usage and gamification: The moderating effect of generational cohorts. Journal of Systems and Information Technology , 12(3), 243–263. https://doi.org/10.1108/JSIT-01-2020-0005

14.

Chaouali

, Souiden

, & Ladhari

(2017). Explaining adoption of mobile banking with the theory of trying, general self-confidence, and cynicism. Journal of Retailing and Consumer Services , 35, 57–67. https://doi.org/10.1016/J.JRETCONSER.2016.11.009

15.

Chawla

, & Joshi

(2018). The moderating effect of demographic variables on mobile banking adoption: An empirical investigation. Global Business Review , 19(3_suppl), S90–S113. https://doi.org/10.1177/0972150918757883

16.

Chawla

, & Joshi

(2019). Scale development and validation for measuring the adoption of mobile banking services. Global Business Review , 20(2), 434–457. https://doi.org/10.1177/0972150918825205

17.

Cohen

(1988). Statistical power analysis for the behavioral sciences (2nd ed.). L. Erlbaum Associates.

18.

Compeau

D. R.

, & Higgins

C. A.

(1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly , 19(2), 189. https://doi.org/10.2307/249688

19.

Dahlberg

, Guo

, & Ondrus

(2015). A critical review of mobile payment research. Electronic Commerce Research and Applications , 14(5), 265–284. https://doi.org/10.1016/J.ELERAP.2015.07.006

20.

Davis

F. D.

(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly , 13(3), 319. https://doi.org/10.2307/249008

21.

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. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x

22.

Oruç

Ege Ö.

, & Tatar

Ç.

(2017). An investigation of factors that affect Internet banking usage based on structural equation modeling. Computers in Human Behavior , 66, 232–235. https://doi.org/10.1016/J.CHB.2016.09.059

23.

Elmi

M. A.

, & Ngwenyama

(2020). Examining the use of electronic money and technology by the diaspora in international remittance system: A case of Somali remittances from Canada. The Electronic Journal of Information Systems in Developing Countries , 86(5). https://doi.org/10.1002/isd2.12138

24.

Čeperković

, Gaub

, Emini

, Nechev

, & Stakić

(2018). Balkan futures. Three scenarios for, 2025 , 5–7. https://doi.org/10.2815/82269

25.

Fall

F. S.

, Orozco

, & Akim

A. M.

(2020). Adoption and use of mobile banking by low-income individuals in Senegal. Review of Development Economics , 24(2), 569–588. https://doi.org/10.1111/rode.12658

26.

Fishbein

(2008). Theory of reasoned action. The International Encyclopedia of Communication, 1–4. https://doi.org/10.1002/9781405186407.wbiecr017

27.

Fishbein

, & Ajzen

(1975). Belief, attitude, intention, and behavior: An introduction to theory and research . Addison-Wesley.

28.

Goh

T. T.

, & Sun

(2014). Exploring gender differences in Islamic mobile banking acceptance. Electronic Commerce Research , 14(4), 435–458. https://doi.org/10.1007/s10660-014-9150-7

29.

Goularte

A. da C.

, & Zilber

S. N.

(2018). The moderating role of cultural factors in the adoption of mobile banking in Brazil. International Journal of Innovation Science , 11(1), 63–81. https://doi.org/10.1108/IJIS-11-2017-0119

30.

Gravetter

F. J.

, & Wallnau

L. B.

(2017). Statistics for the behavioral sciences (10th ed.). CENGAGE Learning.

31.

GSMA. (2019, July). Connected society the state of mobile Internet connectivity 2019 . GSMA. https://www.gsma.com/mobilefordevelopment/wp-content/uploads/2019/07/GSMA-State-of-Mobile-Internet-Connectivity-Report-2019.pdf

32.

Haider

M. J.

, Changchun

, Akram

, & Hussain

S. T.

(2016). Does gender differences play any role in intention to adopt Islamic mobile banking? An empirical study Abstract. Journal of Islamic Marketing , 9, 439–460.

33.

Hair

J. F.

, Risher

J. J.

, Sarstedt

, & Ringle

C. M.

(2019). When to use and how to report the results of PLS-SEM. European Business Review , 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

34.

Hanafizadeh

, Khosravi

, & Badie

(2019). Global discourse on ICT and the shaping of ICT policy in developing countries. Telecommunications Policy , 43(4), 324–338. https://doi.org/10.1016/j.telpol.2018.09.004

35.

Hoque

M. E.

, Kabir Hassan

, Hashim

N. M. H. N.

, & Zaher

(2019). Factors affecting Islamic banking behavioral intention: The moderating effects of customer marketing practices and financial considerations. Journal of Financial Services Marketing , 24(1–2), 44–58. https://doi.org/10.1057/s41264-019-00060-x

36.

Huang

C.-Y.

, & Kao

Y.-S.

(2015). UTAUT2 based predictions of factors influencing the technology acceptance of phablets by DNP. Mathematical Problems in Engineering , 2015(5), 1–23. https://doi.org/10.1155/2015/603747

37.

Hutjens

(2014). The influence of fear on the buying behaviour of consumers in case of an animal disease outbreak . Wageningen University.

38.

Hyde-Clarke

, & Mnisi

(2015). A gender gap? Uses and perceptions of mobile phone banking in South Africa. International Journal of Humanities and Social Science , 4(9(1)), 10.

39.

ITU. (2019) Measuring digital development . ITU.

40.

Jambulingam

(2013). Behavioural intention to adopt mobile technology among tertiary students. World Applied Sciences Journal , 22(9), 1262–1271. https://doi.org/10.5829/idosi.wasj.2013.22.09.2748

41.

Jayasiri

, Kariyawasam Graduate

N. J.

, & Jayasiri

N. K.

(2016). Awareness and usage of Internet banking facilities in Sri Lanka. International Journal of Scientific Research and Innovative Technology , 3(6), 2313–3759.

42.

Kim

, Zoo

, Lee

, & Kang

(2018). Mobile financial services, financial inclusion, and development: A systematic review of academic literature. The Electronic Journal of Information Systems in Developing Countries , 84(5), e12044. https://doi.org/10.1002/isd2.12044

43.

Kirgiz

(2014). Hedonism, a consumer disease of the modern age: Gender and hedonic shopping in Turkey. Global Media Journal , 4(8), 200–213.

44.

Lee

H. J.

, Cho

H. J.

, Xu

, & Fairhurst

(2010). The influence of consumer traits and demographics on intention to use retail self-service checkouts. Marketing Intelligence and Planning , 28(1), 46–58. htpps://doi.org/10.1108/02634501011014606

45.

Leppäniemi

, & Karjaluoto

(2008). Exploring the effects of gender, age, income and employment status on consumer response to mobile advertising campaigns. Journal of Systems and Information Technology , 10(3), 251–265. https://doi.org/10.1108/13287260810916943

46.

Lim

J. S.

, Heinrichs

J. H.

, & Lim

K. S.

(2017). Gender and hedonic usage motive differences in social media site usage behavior. Journal of Global Marketing , 30(3), 161–173. https://doi.org/10.1080/08911762.2017.1308615

47.

Lin

H.-H.

(2011). Gender differences in the linkage of online patronage behavior with TV-and-online shopping values. Service Business , 5(4), 295–312. https://doi.org/10.1007/s11628-011-0115-9

48.

Liu

, Zhao

, Chau

P. Y. K.

, & Tang

(2015). Roles of perceived value and individual differences in the acceptance of mobile coupon applications. Internet Research , 25(3), 471–495. https://doi.org/10.1108/IntR-02-2014-0053

49.

Lwoga

E. T.

, & Sangeda

R. Z.

(2019). ICTs and development in developing countries: A systematic review of reviews. The Electronic Journal of Information Systems in Developing Countries , 85(1), e12060. https://doi.org/10.1002/isd2.12060

50.

Magni

, Taylor

M. S.

, & Venkatesh

(2010). ‘To play or not to play’: A cross-temporal investigation using hedonic and instrumental perspectives to explain user intentions to explore a technology. International Journal of Human Computer Studies , 68(9), 572–588. https://doi.org/10.1016/j.ijhcs.2010.03.004

51.

Mago

, & Chitokwindo

(2014). The impact of mobile banking on financial inclusion in Zimbabwe: A case for Masvingo province. Mediterranean Journal of Social Sciences , 5(9), 221–230. https://doi.org/10.5901/mjss.2014.v5n9p221

52.

Martins

, Oliveira

, & Popovič

(2014). Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management , 34(1), 1–13. https://doi.org/10.1016/J.IJINFOMGT.2013.06.002

53.

Meyers

L. S.

, Gamst

G. C.

, & Guarion

A. J.

(2013). Performing data analysis using IBM SPSS . Wiley.

54.

Moghaddam

B. K.

, & Khatoon-Abadi

(2013). Factors affecting ICT adoption among rural users: A case study of ICT Center in Iran. Telecommunications Policy , 37(11), 1083–1094. https://doi.org/10.1016/j.telpol.2013.02.005

55.

Mtambalika

, Davis Manda

, Gombachika

, & Kunyenje

(2016). Branchless banking in rural Malawi: Potential customers’ perspective on bank-led mobile banking. 2016 IST-Africa Conference, IST-Africa (pp. 1–11). IIMC. https://doi.org/10.1109/ISTAFRICA.2016.7530701

56.

Mukerjee

H. S.

, Deshmukh

G. K.

, Mukherjee

, & Chawla

(2020). Investigating influence of moderators in adopting Internet: Indian seniors perspective. Global Business Review (in press). https://doi.org/10.1177/0972150920908690

57.

Muñoz-Leiva

, Climent-Climent

, & Liébana-Cabanillas

(2017). Determinants of intention to use the mobile banking apps: An extension of the classic TAM model. Spanish Journal of Marketing—ESIC , 21(1), 25–38. https://doi.org/10.1016/j.sjme.2016.12.001

58.

Myovella

, Karacuka

, & Haucap

(2020). Digitalization and economic growth: A comparative analysis of Sub-Saharan Africa and OECD economies. Telecommunications Policy , 44(2). https://doi.org/10.1016/j.telpol.2019.101856

59.

Niehaves

, & Plattfaut

(2014). Internet adoption by the elderly: Employing IS technology acceptance theories for understanding the age-related digital divide. European Journal of Information Systems , 23(6), 708–726. https://doi.org/10.1057/ejis.2013.19

60.

O’Cass

, & Fenech

(2003). Web retailing adoption: Exploring the nature of Internet users Web retailing behaviour. Journal of Retailing and Consumer Services , 10(2), 81–94. https://doi.org/10.1016/S0969-6989(02)00004-8

61.

OECD. (2018). Financial markets, insurance and private pensions: Digitalisation and finance . OECD.

62.

OECD. (2019). Policy insights GLOBAL RELATIONS unleashing the transformation potential for growth in the Western Balkans Ministry . OECD.

63.

Omotoso

K. O.

, Dada

A. D.

, Adelowo

C. M.

, & Siyanbola

W. O.

(2012). Linking innovations with productivity in a Nigeria banking firm: What roles for ICT? Management , 2(5), 204–213. https://doi.org/10.5923/j.mm.20120205.09

64.

Onyia

O. P.

, & Tagg

S. K.

(2011). Effects of demographic factors on bank customers’ attitudes and intention toward Internet banking adoption in a major developing African country. Journal of Financial Services Marketing , 16(3–4), 294–315. https://doi.org/10.1057/fsm.2011.28

65.

Pallant

(2016). SPSS survival manual: A step by step guide to data analysis using IBM SPSS (6th ed.). McGraw-Hill Education.

66.

Pappas

I. O.

, Kourouthanassis

P. E.

, Giannakos

M. N.

, & Chrissikopoulos

(2014). Shiny happy people buying: The role of emotions on personalized e-shopping. Electronic Markets , 24(3), 193–206. https://doi.org/10.1007/s12525-014-0153-y

67.

Park

, Yang

, & Lehto

(2007). Adoption of mobile technologies for Chinese consumers. Journal of Electronic Commerce Research , 8(3), 196–206. https://doi.org/10.1.1.92.2520

68.

Petersen

, Baker

, Pather

, & Tucker

W. D.

(2020) Impact of socio-demographic factors on the acceptance of information communication and technology (ICT) for diabetes self-care . Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) . Springer International Publishing. https://doi.org/10.1007/978-3-030-45002-1_7

69.

Prenaj

(2017). Online and mobile banking usage: The effects of e-skills and personal innovativeness. Balkan Economic Review , 16(1), 1–15.

70.

Ramavhona

T. C.

, & Mokwena

(2016). Factors influencing Internet banking adoption in South African rural areas. SA Journal of Information Management , 18(2), 1443–1455. https://doi.org/10.4102/sajim.v18i2.642

71.

Ringle

C. M.

, Wende

, & Becker

J.-M.

(2015). SmartPLS. 3.0. SmartPLS GmbH. http://www.smartpls.com

72.

Riquelme

H. E.

, & Rios

R. E.

(2010). The moderating effect of gender in the adoption of mobile banking. International Journal of Bank Marketing , 28(5), 328–341. https://doi.org/10.1108/02652321011064872

73.

Rogers

E. M.

(1995). Diffusion of innovations (3rd ed.). The Free Press.

74.

Rowntree

(2019). Methodology the mobile gender gap report 2019 . London.

75.

Senou

M. M.

, Ouattara

, & Acclassato Houensou

(2019). Financial inclusion dynamics in WAEMU: Was digital technology the missing piece? Cogent Economics and Finance , 7(1), 1–16. https://doi.org/10.1080/23322039.2019.1665432

76.

Shaw

, & Kesharwani

(2019). Moderating effect of smartphone addiction on mobile wallet payment adoption. Journal of Internet Commerce , 18(3), 291–309. https://doi.org/10.1080/15332861.2019.1620045

77.

Siregar

J. J.

, Wardaya Puspokusumo

R. A. A.

, & Rahayu

(2017). Analysis of affecting factors technology acceptance model in the application of knowledge management for small medium enterprises in industry creative. Procedia Computer Science (pp. 500–508). Elsevier B.V. https://doi.org/10.1016/j.procs.2017.10.075

78.

Steimer

(2002). The biology of fear- and anxiety-related behaviors. Dialogues in Clinical Neuroscience , 4(3), 231–249.

79.

Sun

, & Zhang

(2006). The role of moderating factors in user technology acceptance. International Journal of Human-Computer Studies , 64(2), 53–78. https://doi.org/10.1016/j.ijhcs.2005.04.013

80.

Tabetando

, & Matsumoto

(2020). Mobile money, risk sharing, and educational investment: Panel evidence from rural Uganda. Review of Development Economics , 24(1), 84–105. https://doi.org/10.1111/rode.12644

81.

Taylor

, & Todd

P. A.

(1995). Understanding information technology usage: A test of competing models. Information Systems Research , 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144

82.

Thompson

R. L.

, Higgins

C. A.

, & Howell

J. M.

(1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly , 15(1), 125–143. https://doi.org/10.2307/249443

83.

Todman

(2000). Gender differences in computer anxiety among university entrants since 1992. Computers and Education , 34(1), 27–35. https://doi.org/10.1016/S0360-1315(99)00036-6

84.

Tosheva

, & Dimeski

(2019). The competition policy in Western Balkan countries: How far they have come on the EU accession ‘Road’? Balkan Social Science Review , 14(14), 31–55.

85.

Vekiri

, & Chronaki

(2008). Gender issues in technology use: Perceived social support, computer self-efficacy and value beliefs, and computer use beyond school. Computers and Education , 51(3), 1392–1404. https://doi.org/10.1016/j.compedu.2008.01.003

86.

Venkatesh

, Morris

M. G.

, Davis

G. B.

. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly , 27(3), 425. https://doi.org/10.2307/30036540

87.

Venkatesh

, Thong

J. Y. L.

, & Xu

(2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly , 36(1), 157. https://doi.org/10.2307/41410412

88.

Vesnic-Alujevic

(2012). European integration of Western Balkans: From reconciliation to European future . Centre for European Studies.

89.

Vokshi

M. C.

, Dedaj

, Ben Youssef

. (2019). Mobile phone penetration and its impact on inequality in the Western Balkan countries. Zagreb International Review of Economics and Business , 22(2), 111–131. https://doi.org/10.2478/zireb-2019-0023

90.

Vrček

, & Klačmer

(2014). Intention to use and variables influencing intention to use electronic government services among citizens. Journal of Information and Organizational Sciences , 38(1), 55–69.

91.

Warsame

M. H.

, & Ireri

E. M.

(2018). Moderation effect on mobile microfinance services in Kenya: An extended UTAUT model. Journal of Behavioral and Experimental Finance , 18, 67–75. https://doi.org/10.1016/j.jbef.2018.01.008

92.

Wekeza

S. V.

, & Sibanda

(2019). Factors influencing consumer purchase intentions of organically grown products in shelly centre, port shepstone, South Africa. International Journal of Environmental Research and Public Health , 16(6), 26. https://doi.org/10.3390/ijerph16060956

93.

Zabukovšek

S. S.

, Picek

, Bobek

, & Tominc

(2019). Technology acceptance model based study of students’ attitudes toward use of enterprise resource planning solutions. Journal of Information and Organizational Sciences , 43(1), 49–71. https://doi.org/10.31341/jios.43.1.4

94.

Zhang

, Nyheim

, & Mattila

A. S.

(2014). The effect of power and gender on technology acceptance. Journal of Hospitality and Tourism Technology , 5(3), 299–314. https://doi.org/10.1108/JHTT-03-2014-0008

Linking Individual Demographics to Antecedents of Mobile Banking Usage: Evidence from Developing Countries in Southeast Europe

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