Academy of Indian Marketing International Conference - 2010 “Internet Banking and Consumer Behavior: Revisiting the Technology Acceptance Model” By AnkitKesharwani, IBS Hyderabad Shailendra S. Bisht, IBS Hyderabad
Transition from ‘Brick and Mortar Bank’ to ‘Mouse and Click Bank’
Virtual banking: Four forms
ATM, Phone banking, Internet banking, Mobile banking
Resistance to change
Increasing fraudulent activities
Source: IAMAI Report (i-cube 2009)
Technology Acceptance Model (TAM) Perceived usefulness Technology Characteristics Attitude towards use of system Behavioral Intention Perceived ease of use Source: Davis (1986, 1989)
Literature Review Models of Innovation Adoption
theory of reasoned action (TRA), technology acceptance model (TAM), motivational model (MM), theory of planned behavior (TPB), combined TAM and TPB (C-TAM-TPB), model of PC utilization (MPCU), innovation diffusion theory (IDT), and social cognitive theory (SCT).
Throughout its development, TAM has received extensive empirical support through………..
Applications (Karjaluoto et al. 2002; Chau and Li 2003; Lu et al. 2003; Chan and Lu 2004, Fusilier and Durlabhji 2005; Chiemeke et al. 2006, Guriting and Ndubisi 2006; Lee et al. 2007; Amin et al. 2008; etc)
Comparisons (Davis et al. 1989; Mathieson 1991; Taylor and Todd 1995; Venkatesh et al. 2003; etc.)
Extensions (Venkatesh and Davis 2000; Gafen et al. 2003; Pikakarainen et al. 2004; Venkatesh and Bala 2008; etc.)
Why Need to Revisit?
Literature Review Concept of perceived risk
(Tan 1999; Littler and Melanthiou 2006; Gerrard et al. 2006; Manzano et al. 2009; etc.)
(Suh and Han 2003; Mukherjee and Nath 2003; Dash and Saji 2007; etc.)
Importance of website design
(Venkatesha and Agarwal 2006; Cyr 2008; Alhudaithy and Kitchen 2009; Ganguly et al. 2009; Jiang et al. 2010; etc.)
Research Methodology Instrument development (35 items)
Perceived ease of use, perceived usefulness, perceived behavioral control, social influence, and behavioral intention (Venkatesh and Bala 2008); Trust and Perceived risk (Dash and Saji 2007); Website design (Cyr et al. 2008)
Sample size: 619 MBA students
78.6 % respondents fall under category of active users (i-cube 2009)
85.8 % respondents were using internet banking more than one year
Data analysis: Two-step Structural Equation Modeling (Anderson and Gerbing 1988)
Measurement model: 328 responses
Structural model: 291 respondents
Measurement Model Model fit Indices: chi-square=257.62, CMIN/df=1.600, GFI=0.931, AGFI=0.901, NFI=0.922, CFI=0.969, RMSR=0.070, RMSEA=0.047
Discussion The indirect effect of PEOU on BI was 0.453 (= 0.761*0.595). The total effect of SI on BI was 0.276 (= 0.169 + 0.128*0.595 + 0.173*181). WB has total effect of 0.333 (= 0.125*0.181 + 0.685*0.761*595) on BI. Result shows that WB has a greater influence on PEOU (H10: β=0.685, p<0.001) as compared to PR (H9: β=0.125, p<0.05).
Conclusion Integrate the fragmented theories and research on perceived risk, trust, website design and individual acceptance of technology acceptance and usage; Faced by skepticism and uncertainty, bank institutions need to reduce the trust gap to minimum possible in order to position e-banking as a viable medium of delivering banking service; The inclusion of website design would make contributions by enriching the extant literature and future research can provide a detailed investigation of the effects of specific feature of a website on internet banking adoption.
Beliefs about behavior Attitude Toward The Behavior Bi Evaluations of Behavioral Outcomes Ai αBi*Ei Ei Behavioral Intention Actual Behavior Normative Beliefs BIiα Ai*SNi ABi ≈ BIi Subjective Norms NBi Motivation to comply Source: Theory of Reasoned Action (Fishbein and Ajzen, 1975) SNiαBi*MTCi MTCi 16