Website design quality and usage behavior: Unified Theory of Acceptance and Use of Technology

Firms gain many benefits from well-designed websites. But which elements of website design quality really matter, and how do these elements influence usage behavior? With the Unified Theory of Acceptance and Use of Technology (UTAUT) as the theoretical foundation, this paper proposes that website design quality is a multi-dimensional construct with a higher-order structure that, when successfully incorporated into the UTAUT model, outperforms existing models. Results are based on a survey of 216 users of internet banking. Findings indicate that the technical, general content and appearance dimensions of a website are most important for users. These dimensions are significantly related to usage behavior directly and indirectly. A halo effect may influence overall evaluation of a website because the dimensions of website design quality are interrelated. The implication is that improvements to the appearance of a website should enhance the overall evaluation of the site, leading to greater usage intentions.

[1]  Natalia Vila-López,et al.  Consumer feelings and behaviours towards well designed websites , 2011, Inf. Manag..

[2]  Ephraim R. McLean,et al.  The DeLone and McLean Model of Information Systems Success: A Ten-Year Update , 2003, J. Manag. Inf. Syst..

[3]  Shaohan Cai,et al.  The key determinants of Internet banking service quality: a content analysis , 2001 .

[4]  Prashant C. Palvia,et al.  Developing and validating an instrument for measuring user-perceived web quality , 2002, Inf. Manag..

[5]  Magid Igbaria,et al.  Personal Computing Acceptance Factors in Small Firms: A Structural Equation Model , 1997, MIS Q..

[6]  Pingjun Jiang.  Consumer Adoption of Mobile Internet Services: An Exploratory Study , 2009 .

[7]  Donna L. Hoffman,et al.  Measuring the Customer Experience in Online Environments: A Structural Modeling Approach , 2000 .

[8]  C. Ruiz-Mafé,et al.  The role of consumer innovativeness and perceived risk in online banking usage , 2009 .

[9]  Diane M. Strong,et al.  EXTENDING THE TASK-TECHNOLOGY FIT MODEL WITH SELF-EFFICACY CONSTRUCTS , 2002 .

[10]  Petrus Guriting,et al.  Borneo online banking: evaluating customer perceptions and behavioural intention , 2006 .

[11]  Geoffrey S. Hubona,et al.  Individual differences and usage behavior: revisiting a technology acceptance model assumption , 2005, DATB.

[12]  Barbara M. Byrne,et al.  Structural equation modeling with AMOS , 2010 .

[13]  Weiguo Fan,et al.  Determining Success for Different Website Goals , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[14]  Gwo-Guang Lee,et al.  Customer perceptions of e‐service quality in online shopping , 2005 .

[15]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[16]  Viswanath Venkatesh,et al.  Predicting Different Conceptualizations of System Use: The Competing Roles of Behavioral Intention, Facilitating Conditions, and Behavioral Expectation , 2008, MIS Q..

[17]  Viswanath Venkatesh,et al.  Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model , 2000, Inf. Syst. Res..

[18]  Hans H. Bauer,et al.  eTransQual: A Transaction Process-Based Approach for Capturing Service Quality in Online Shopping , 2006 .

[19]  Richard,et al.  Extrinsic and Intrinsic Motivation to Use Computers in the Workplace , 2022 .

[20]  Fred D. Davis,et al.  Extrinsic and Intrinsic Motivation to Use Computers in the Workplace1 , 1992 .

[21]  C. Jayawardhena,et al.  e-Consumer Behaviour , 2009 .

[22]  Jane M. Howell,et al.  Influence of Experience on Personal Computer Utilization: Testing a Conceptual Model , 1994, J. Manag. Inf. Syst..

[23]  Charles Dennis,et al.  Interviews of deshopping behaviour: an analysis of theory of planned behaviour , 2003 .

[24]  Judy Chuan-Chuan Lin,et al.  Towards an understanding of the behavioural intention to use a web site , 2000, Int. J. Inf. Manag..

[25]  Fred D. Davis,et al.  A Model of the Antecedents of Perceived Ease of Use: Development and Test† , 1996 .

[26]  Heikki Karjaluoto,et al.  Consumer acceptance of online banking: an extension of the technology acceptance model , 2004, Internet Res..

[27]  Elissar Toufaily,et al.  Customer loyalty to a commercial website: Descriptive meta-analysis of the empirical literature and proposal of an integrative model , 2013 .

[28]  Richard D. Johnson,et al.  Research Report: The Role of Behavioral Modeling in Computer Skills Acquisition: Toward Refinement of the Model , 2000, Inf. Syst. Res..

[29]  Hyun-Jung Park,et al.  Effects of social influence on consumers' voluntary adoption of innovations prompted by others , 2011 .

[30]  Namho Chung,et al.  A unified perspective on the factors influencing usage intention toward mobile financial services , 2012 .

[31]  Youngchan Kim,et al.  Driving factors of post adoption behavior in mobile data services , 2011 .

[32]  Hans H. Bauer,et al.  Measuring the Quality of E-Banking Portals - an Empirical Investigation , 2005 .

[33]  Gurpreet Dhillon,et al.  TORKZADEH AND DHILLON Measuring Factors that Influence the Success of Internet , 2015 .

[34]  Kimberly A. Neuendorf,et al.  Tailoring new websites to appeal to those most likely to shop online , 2005 .

[35]  Ingoo Han,et al.  The impact of Web quality and playfulness on user acceptance of online retailing , 2007, Inf. Manag..

[36]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[37]  Shu-Sheng Liaw,et al.  An Internet survey for perceptions of computers and the World Wide Web: relationship, prediction, and difference , 2002, Comput. Hum. Behav..

[38]  Heshan Sun,et al.  The role of moderating factors in user technology acceptance , 2006, Int. J. Hum. Comput. Stud..

[39]  Viswanath Venkatesh,et al.  Model of Adoption and Technology in Households: A Baseline Model Test and Extension Incorporating Household Life Cycle , 2005, MIS Q..

[40]  I. Ajzen.  The theory of planned behavior , 1991 .

[41]  O. Byung Kwon,et al.  Impact of website information design factors on consumer ratings of web-based auction sites , 2002, Behav. Inf. Technol..

[42]  C. Jayawardhena,et al.  An empirical investigation into e‐shopping excitement: antecedents and effects , 2009 .

[43]  Zhenhui Jiang,et al.  The Antecedents of Online Consumers' Perceived Usefulness of Website: A Protocol Analysis Approach , 2007, HCI.

[44]  L. Stoel,et al.  Consumer e-shopping acceptance: Antecedents in a technology acceptance model , 2009 .

[45]  S. West,et al.  Teacher's Corner: Testing Measurement Invariance of Second-Order Factor Models , 2005 .

[46]  H. Winklhofer,et al.  Applying the technology acceptance model to the online retailing of financial services , 2006 .

[47]  Gordon B. Davis,et al.  Testing the Determinants of Microcomputer Usage via a Structural Equation Model , 1995, J. Manag. Inf. Syst..

[48]  George D. Deitz,et al.  Cross-cultural examination of online shopping behavior: A comparison of Norway, Germany, and the United States , 2013 .

[49]  V. Venkatesh,et al.  AGE DIFFERENCES IN TECHNOLOGY ADOPTION DECISIONS: IMPLICATIONS FOR A CHANGING WORK FORCE , 2000 .

[50]  Walfried M. Lassar,et al.  The relationship between consumer innovativeness, personal characteristics, and online banking adoption , 2005 .

[51]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[52]  Vazifehdust Housein,et al.  CUSTOMER PERCEPTIONS OF E-SERVICE QUALITY IN ONLINE SHOPPING , 2012 .

[53]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[54]  S. Gounaris,et al.  Person-Place Congruency in the Internet Banking Context , 2010, SSRN Electronic Journal.

[55]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[56]  Per E. Pedersen,et al.  An exploratory study of customers' perception of company web sites offering various interactive applications: moderating effects of customers' Internet experience , 2004, Decis. Support Syst..

[57]  Monika Koller,et al.  Customer segmentation using unobserved heterogeneity in the perceived value - loyalty-intentions link , 2014 .

[58]  Horst Treiblmaier,et al.  What Keeps the E-Banking Customer Loyal? A Multigroup Analysis of the Moderating Role of Consumer Characteristics on E-Loyalty in the Financial Service Industry. , 2006 .

[59]  Xianggui Qu,et al.  Multivariate Data Analysis , 2007, Technometrics.

[60]  B. Morris.  The Service Profit Chain: : How Leading Companies Link Profit and Growth to Loyalty, Satisfaction, and Value , 1998 .

[61]  Narongsak Thongpapanl,et al.  Enhancing Online Performance through Website Content and Personalization , 2011, J. Comput. Inf. Syst..

[62]  J. Heller,et al.  Some men like it black, some women like it pink: consumer implications of differences in male and female website design , 2006 .

[63]  Dieter Fink,et al.  Internet banking adoption strategies for a developing country: the case of Thailand , 2005, Internet Res..

[64]  Shumaila Y. Yousafzai,et al.  Understanding customer‐specific factors underpinning internet banking adoption , 2012 .

[65]  C. Dennis,et al.  Internet banking acceptance model: Cross-market examination , 2010 .

[66]  I. Ajzen,et al.  Understanding Attitudes and Predicting Social Behavior , 1980 .

[67]  R. Fazio,et al.  Attitude accessibility, attitude-behavior consistency, and the strength of the object-evaluation association , 1982 .

[68]  Mónica Cortiñas,et al.  Understanding multi-channel banking customers , 2010 .

[69]  Detmar W. Straub,et al.  The psychological origins of perceived usefulness and ease-of-use , 1999, Inf. Manag..

[70]  Adel M. Aladwani.  An empirical test of the link between web site quality and forward enterprise integration with web consumers , 2006, Bus. Process. Manag. J..

[71]  Charles D. Barrett.  Understanding Attitudes and Predicting Social Behavior , 1980 .

[72]  Peter A. Todd,et al.  Understanding Information Technology Usage: A Test of Competing Models , 1995, Inf. Syst. Res..

[73]  João F. Proença,et al.  A comparison of users and non‐users of banking self‐service technology in Portugal , 2011 .

[74]  C. Bianchi,et al.  Consumer internet purchasing behavior in Chile , 2013 .

[75]  Astrid Dickinger,et al.  Website performance and behavioral consequences: A formative measurement approach , 2013 .

[76]  Peter A. Todd,et al.  Assessing IT usage: the role of prior experience , 1995 .

[77]  Shirley Taylor,et al.  Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions , 1995 .

[78]  E. Rogers,et al.  Diffusion of Innovations , 1964 .