Due to recent technological developments in banking, the use of e-banking is becoming increasingly widespread. This study explored the effect of e-service quality on actual use of e-banking services and explained it through the Technology Acceptance Model. A survey was conducted in Pakistan and 493 questionnaires were collected from e-banking users and the data was analyzed using AMOS 20 and SPSS 20. The results of the study indicate that e-service quality positively affects perceived usefulness, perceived ease of use and intention to use e-banking and perceived usefulness positively affects attitude towards using e-banking and behavioral intentions to use e-banking. Perceived ease of use affects attitudes towards e-banking and attitudes positively affect behavioral intentions to use e-banking and behavioral intentions further positively affect actual use of e-banking. The sequential mediation of attitude and intention has been confirmed between e-service quality and actual use of e-banking. This study has significance for the managers of banking industry and will enable them to influence the attitude of customers in favor of their services and also help them devise strategies to improve their profitability.
AbdullahFSuhaimiRSabanGHamaliJ (2011) Bank service quality (BSQ) index: an indicator of service performance. International Journal of Quality and Reliability Management28(5): 542–555.
2.
AfshanSSharifA (2016) Acceptance of mobile banking framework in Pakistan. Telematics and Informatics33(2): 370–387.
3.
AgarwalRKarahannaE (2000) Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly665–694.
4.
AgarwalRRastogiSMehrotraA (2009) Customers’ perspectives regarding e-banking in an emerging economy. Journal of Retailing and Consumer Services16(5): 340–351.
5.
AhireS LGolharDYWallerMA (1996) Development and validation of TQM implementation constructs. Decision Sciences27(1): 23–56.
6.
AhnTRyuSHanI (2007) The impact of Web quality and playfulness on user acceptance of online retailing. Information and Management44(3): 263–275.
7.
AjzenI (1985) From intentions to actions: A theory of planned behavior. In Action Control (pp. 11–39) Springer.
AjzenI (2005) Attitudes, Personality and Behavior. McGraw-Hill Education (UK)
10.
AlalwanAADwivediYKRanaNPWilliamsMD (2016) Consumer adoption of mobile banking in Jordan: examining the role of usefulness, ease of use, perceived risk and self-efficacy. Journal of Enterprise Information Management29(1): 118–139.
11.
AlwahaishiSSnáselV (2013) Acceptance and use of information and communications technology: a UTAUT and flow based theoretical model. Journal of Technology Management and Innovation8(2): 61–73.
12.
ArmstrongJSMorwitzVGKumarV (2000) Sales forecasts for existing consumer products and services: Do purchase intentions contribute to accuracy?International Journal of Forecasting16(3): 383–397.
13.
BankoleFOBankoleOOBrownI (2011) Mobile banking adoption in Nigeria. The Electronic Journal of Information Systems in Developing Countries47(1): 1–23.
14.
BashirSAnwarSAwanZQureshiTWMemonAB (2018) A holistic understanding of the prospects of financial loss to enhance shopper’s trust to search, recommend, speak positive and frequently visit an online shop. Journal of Retailing and Consumer Services42169–174.
15.
BeiginiaARBesheliASSolukluMEAhmadiM (2011) Assessing the mobile banking adoption based on the decomposed theory of planned behaviour. European Journal of Economics, Finance and Administrative Sciences28(1): 7–15.
16.
BenamatiJServaMA (2007) Trust and distrust in online banking: Their role in developing countries. Information Technology for Development13(2): 161–175.
17.
BerryLLParasuramanA (2004) Marketing services: Competing through quality. Simon and Schuster.
18.
BobbittLMDabholkarPA (2001) Integrating attitudinal theories to understand and predict use of technology-based self-service: the Internet as an illustration. International Journal of Service Industry Management12(5): 423–450.
19.
BoltonRNDrewJH (1991) A multistage model of customers’ assessments of service quality and value. Journal of Consumer Research17(4): 375–384.
20.
BosnjakMObermeierDTutenTL (2006) Predicting and explaining the propensity to bid in online auctions: a comparison of two action-theoretical models. Journal of Consumer Behaviour5(2): 102–116.
21.
Brandon-JonesAKauppiK (2018) Examining the antecedents of the Technology Acceptance Model within e-procurement. International Journal of Operations and Production Management38(1): 28–42.
22.
BrunerII, GCKumarA (2005) Explaining consumer acceptance of handheld Internet devices. Journal of Business Research, 58(5): 553–558.
23.
CarlsonJO’CassA (2010) Exploring the relationships between e-service quality, satisfaction, attitudes and behaviours in content-driven e-service web sites. Journal of Services Marketing24(2): 112–127.
24.
ChangHHWangY-HYangW-Y (2009) The impact of e-service quality, customer satisfaction and loyalty on e-marketing: Moderating effect of perceived value. Total Quality Management20(4): 423–443.
25.
ChapmanKJ (2001) Measuring intent: There’s nothing “mere” about mere measurement effects. Psychology and Marketing18(8): 811–841.
26.
ChenCH (2008) Why do teachers not practice what they believe regarding technology integration?The Journal of Educational Research102(1): 65–75.
27.
ChenHZengDAtabakhshHWyzgaWSchroederJ (2003) COPLINK: managing law enforcement data and knowledge. Communications of the ACM46(1): 28–34.
28.
ChenJ-FChangJ-FKaoC-WHuangY-M (2016) Integrating ISSM into TAM to enhance digital library services: a case study of the Taiwan digital meta-library. The Electronic Library34(1): 58–73.
29.
ChenLGillensonLSherrellL (2002) Enticing online consumers: an extended technology acceptance perspective. Information and Management39 (8): 709–719. doi: 10.1016. S0378-7206 (01): 00127–6.
30.
ChenQCliffordSJWellsWD (2002) Attitude toward the site II: new information. Journal of Advertising Research42(2): 33–45.
31.
ChenQWellsWD (1999) Attitude toward the site. Journal of Advertising Research39(5): 27–38.
32.
ChiuY-BLinC-PTangL-L (2005) Gender differs: assessing a model of online purchase intentions in e-tail service. International Journal of Service Industry Management16(5): 416–435.
33.
ChongAYOoiKBLinBTanBI (2010) Online banking adoption: an empirical analysis. International Journal of bank marketing28(4): 267–287.
34.
ChuP-YLeeG-YChaoY (2012) Service quality, customer satisfaction, customer trust and loyalty in an e-banking context. Social Behavior and Personality: An International Journal40(8): 1271–1283.
35.
ComreyALLeeHB (1992) Interpretation and application of factor analytic results. Comrey AL, Lee HB. A First Course in Factor Analysis, 2, 1992.
36.
CrespoAHDel BosqueIR (2010) The influence of the commercial features of the Internet on the adoption of e-commerce by consumers. Electronic Commerce Research and Applications9(6): 562–575.
37.
CroninJr, JJBradyMKHultGTM (2000) Assessing the effects of quality, value and customer satisfaction on consumer behavioral intentions in service environments. Journal of Retailing76(2): 193–218.
38.
CurranJMMeuterML (2005) Self-service technology adoption: comparing three technologies. Journal of Services Marketing19(2): 103–113.
39.
DavisFD (1985) A technology acceptance model for empirically testing new end-user information systems: Theory and results (PhD Thesis) Massachusetts Institute of Technology.
40.
DavisFD (1989) Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology. MIS Quarterly13(3): 319–340. https://doi.org/10.2307/249008
41.
DavisFDBagozziRPWarshawPR (1989) User acceptance of computer technology: a comparison of two theoretical models. Management Science35(8): 982–1003.
42.
DavisFDBagozziRPWarshawPR (1992) Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology22(14): 1111–1132.
43.
DollWJXiaWTorkzadehG (1994) A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly18(4): 453–461.
44.
EgeaJMOGonzálezMVR (2011) Explaining physicians’ acceptance of EHCR systems: An extension of TAM with trust and risk factors. Computers in Human Behavior27(1): 319–332.
45.
FishbeinMAjzenI (1975) Belief, attitude, intention and behavior: An introduction to theory and research.
46.
FornellCLarckerDF (1981) Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research382–388.
47.
GanguliSRoySK (2011) Generic technology-based service quality dimensions in banking: Impact on customer satisfaction and loyalty. International Journal of Bank Marketing29(2): 168–189.
48.
GrandónEENascoSAMykytynJr, PP (2011) Comparing theories to explain e-commerce adoption. Journal of Business Research64(3): 292–298.
49.
HaSStoelL (2009) Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research62(5): 565–571.
50.
Ho CheongJParkM.-C (2005) Mobile Internet acceptance in Korea. Internet Research15(2): 125–140.
51.
HomburgCBaumgartnerH (1995) Beurteilung von Kausalmodellen. Bestandsaufnahme und Anwendungsempfehlungen. Marketing ZfP17(3): 162–176.
52.
HuPJChauPYShengORLTamKY (1999) Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems16(2): 91–112.
53.
HwangYKimDJ (2007) Customer self-service systems: The effects of perceived Web quality with service contents on enjoyment, anxiety and e-trust. Decision Support Systems43(3): 746–760.
54.
JacksonCMChowSLeitchRA (1997) Toward an understanding of the behavioral intention to use an information system. Decision Sciences28(2): 357–389.
55.
JaruwachirathanakulBFinkD (2005) Internet banking adoption strategies for a developing country: the case of Thailand. Internet Research15(3): 295–311.
56.
KeeneyRL (1999) The value of Internet commerce to the customer. Management Science45(4): 533–542.
57.
KimTGLeeJHLawR (2008) An empirical examination of the acceptance behaviour of hotel front office systems: An extended technology acceptance model. Tourism Management29(3): 500–513.
58.
KimYJChunJUSongJ (2009) Investigating the role of attitude in technology acceptance from an attitude strength perspective. International Journal of Information Management29(1): 67–77.
59.
Koenig-LewisNPalmerAMollA (2010) Predicting young consumers’ take up of mobile banking services. International Journal of Bank Marketing28(5): 410–432.
60.
KuismaTLaukkanenTHiltunenM (2007) Mapping the reasons for resistance to Internet banking: A means-end approach. International Journal of Information Management27(2): 75–85.
61.
KumarMTat KeeFCharlesV (2010) Comparative evaluation of critical factors in delivering service quality of banks: An application of dominance analysis in modified SERVQUAL model. International Journal of Quality and Reliability Management27(3): 351–377.
62.
LedererALMaupinDJSenaMPZhuangY (2000a) The technology acceptance model and the World Wide Web. Decision Support Systems29(3): 269–282. https://doi.org/10.1016/S0167-9236(00)00076-2
63.
LedererALMaupinDJSenaMPZhuangY (2000b) The technology acceptance model and the World Wide Web. Decision Support Systems29(3): 269–282.
64.
LiaoZCheungMT (2001) Internet-based e-shopping and consumer attitudes: an empirical study. Information and Management38(5): 299–306.
65.
LinC-HShihH-YSherPJ (2007) Integrating technology readiness into technology acceptance: The TRAM model. Psychology and Marketing24(7): 641–657.
66.
LiuCArnettKP (2000) Exploring the factors associated with Web site success in the context of electronic commerce. Information and Management38(1): 23–33.
67.
MacKenzieSBLutzRJ (1989) An empirical examination of the structural antecedents of attitude toward the ad in an advertising pretesting context. The Journal of Marketing48–65.
68.
MallatNRossiMTuunainenVKOorniA (2006) The impact of use situation and mobility on the acceptance of mobile ticketing services. In System Sciences 2006. HICSS’06. Proceedings of the 39th Annual Hawaii International Conference on (Vol. 2, p. 42b–42b) IEEE.
69.
MarafonDLBassoKEspartelLBde BarcellosMDRechE (2018) Perceived risk and intention to use Internet banking: The effects of self-confidence and risk acceptance. International Journal of Bank Marketing36(2): 277–289.
70.
MathiesonK (1991) Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information Systems Research2(3): 173–191.
71.
McFarlandDJHamiltonD (2006) Adding contextual specificity to the technology acceptance model. Computers in Human Behavior22(3): 427–447.
72.
MorwitzVG (2001) Methods for forecasting from intentions data. In Principles of forecasting (pp. 33–56) Springer.
73.
MorwitzVGSchmittleinD (1992) Using segmentation to improve sales forecasts based on purchase intent: Which “intenders” actually buy?Journal of Marketing Research391–405.
74.
NasriWCharfeddineL (2012) Factors affecting the adoption of Internet banking in Tunisia: An integration theory of acceptance model and theory of planned behavior. The Journal of High Technology Management Research23(1): 1–14.
75.
NysveenHPedersenPEThorbjørnsenH (2005) Intentions to use mobile services: Antecedents and cross-service comparisons. Journal of the Academy of Marketing Science33(3): 330.
76.
OliveiraTFariaMThomasMAPopovicA (2014) Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management34(5): 689–703
77.
ParasuramanAGrewalD (2000) The impact of technology on the quality-value-loyalty chain: a research agenda. Journal of the Academy of Marketing Science28(1): 168–174.
78.
ParasuramanAZeithamlVAMalhotraA (2005) ES-QUAL: A multiple-item scale for assessing electronic service quality. Journal of Service Research7(3): 213–233.
79.
ParkEPobilAP (2013) Technology acceptance model for the use of tablet PCs. Wireless Personal Communications73(4): 1561–1572.
80.
ParkEKwonSJ (2016) The adoption of teaching assistant robots: a technology acceptance model approach. Program50(4): 354–366.
81.
PatelKJPatelHJ (2018) Adoption of Internet banking services in Gujarat: An extension of TAM with perceived security and social influence. International Journal of Bank Marketing36(1): 147–169.
82.
PearsonATadisinaSGriffinC (2012) The role of e-service quality and information quality in creating perceived value: antecedents to web site loyalty. Information Systems Management29(3): 201–215.
83.
PikkarainenTPikkarainenKKarjaluotoHPahnilaS (2004) Consumer acceptance of online banking: an extension of the technology acceptance model. Internet Research14(3): 224–235.
84.
RobinsonJr, LMarshallGWStampsMB (2005) Sales force use of technology: antecedents to technology acceptance. Journal of Business Research58(12): 1623–1631.
85.
ShaikhAAGlavee-GeoRKarjaluotoH (2017) Exploring the nexus between financial sector reforms and the emergence of digital banking culture–Evidences from a developing country. Research in International Business and Finance42, 1030–1039.
86.
ShihY-YHuangS-S (2009) The actual usage of ERP systems: An extended technology acceptance perspective. Journal of Research and Practice in Information Technology41(3): 263.
87.
SinghSSrivastavaRK (2018) Predicting the intention to use mobile banking in India. International Journal of Bank Marketing36(2): 357–378.
88.
SongYHanJ (2009) Is enjoyment important? An empirical research on the impact of perceive enjoyment on adoption of new technology. In Information management, innovation management and Industrial Engineering, 2009 International Conference on (Vol. 4, pp. 511–514) IEEE.
89.
State Bank of Pakistan (2018) Payment Systems Review. January - March, 2018 (Quarter-3, FY18)
90.
SuhBHanI (2002) Effect of trust on customer acceptance of Internet banking. Electronic Commerce Research and Applications1(3–4): 247–263.
91.
SzymanskiDMHiseRT (2000) E-satisfaction: an initial examination. Journal of Retailing76(3): 309–322.
92.
TaherdoostH (2018) Development of an adoption model to assess user acceptance of e-service technology: E-Service Technology Acceptance Model. Behaviour and Information Technology1–25.
93.
TanMTeoTS (2000) Factors influencing the adoption of Internet banking. Journal of the AIS1(1es): 5.
94.
TaylorSToddPA (1995) Understanding information technology usage: A test of competing models. Information Systems Research6(2): 144–176.
95.
Van BirgelenMJWetzelsMGvan DolenWM (2008) Effectiveness of corporate employment web sites: How content and form influence intentions to apply. International Journal of Manpower29(8): 731–751.
96.
VatanasakdakulSAounCLiY (2010) AIS in Australia: UTAUT application and cultural implication.
97.
VenkateshV (2000) Determinants of perceived ease of use: Integrating control, intrinsic motivation and emotion into the technology acceptance model. Information Systems Research11(4): 342–365.
98.
VenkateshVBalaH (2008) Technology acceptance model 3 and a research agenda on interventions. Decision Sciences39(2): 273–315.
99.
VenkateshVDavisFD (1996) A model of the antecedents of perceived ease of use: Development and test. Decision Sciences27(3): 451–481.
100.
VenkateshVMorrisMG (2000) Why don’t men ever stop to ask for directions? Gender, social influence and their role in technology acceptance and usage behavior. MIS Quarterly115–139.
101.
VijayasarathyLR (2004) Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model. Information and Management41(6): 747–762.
102.
WangC-CLoS-KFangW (2008) Extending the technology acceptance model to mobile telecommunication innovation: The existence of network externalities. Journal of Consumer Behaviour7(2): 101–110.
103.
WesselsLDrennanJ (2010) An investigation of consumer acceptance of M-banking. International Journal of Bank Marketing28(7): 547–568.
104.
WheatonBMuthenBAlwinDFSummersGF (1977) Assessing reliability and stability in panel models. Sociological methodology8, 84–136.
105.
WolfinbargerMGillyM C (2003) eTailQ: dimensionalizing, measuring and predicting etail quality. Journal of Retailing79(3): 183–198.
106.
WuJ.-HWangS.-C (2005) What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information and Management42(5): 719–729.
107.
WuS.-I (2006) A comparison of the behavior of different customer clusters towards Internet bookstores. Information and Management43(8): 986–1001.
108.
YadavRChauhanVPathakG S (2015) Intention to adopt Internet banking in an emerging economy: a perspective of Indian youth. International Journal of Bank Marketing33(4): 530–544.
109.
YangHYooY (2004) It’s all about attitude: revisiting the technology acceptance model. Decision Support Systems38(1): 19–31.
110.
YooBDonthuN (2001) Developing a scale to measure the perceived quality of an Internet shopping site (SITEQUAL)Quarterly Journal of Electronic Commerce2(1): 31–45.
111.
ZameerHTaraAKausarUMohsinA (2015) Impact of service quality, corporate image and customer satisfaction towards customers’ perceived value in the banking sector in Pakistan. International Journal of Bank Marketing33(4): 442–456.
112.
ZeithamlV A (1988) Consumer perceptions of price, quality and value: a means-end model and synthesis of evidence. The Journal of Marketing2–22.
