Senior Citizens and E-commerce Websites: The Role of Perceived Usefulness, Perceived Ease of Use, and Web Site Usability

Introduction Conducting business over the Web has become a key component of business strategies. According to the U. S. Department of Commerce (2004, 2006a, 2006b, 2007) total retail e-commerce sales in 2007 were $136 billion, an increase of 25.6% from the previous year and an amazing 207% increase compared to 2002. E-commerce sales as a percentage of total retail sales also continued to increase and, for 2007, were at 3.3% of total retail sales. Despite the continued growth in e-commerce and the potential for future growth in e-commerce, companies have reported problems in attracting new customers and retaining existing ones (Devaraj, Fan, & Kohli, 2002) and face challenges in converting online visitors to real purchasers (C. Chen, 2003). One of the ways to explain this is to apply the Technology Acceptance Model (TAM) (see Figure 1) which states that the success of a system can be deter mined by user acceptance of the system, measured by two variables: the Perceived Usefulness of the system and the Perceived Ease of Use of the system (Davis, 1989). According to the model, a user's perceptions about a system's usefulness and ease of use result in an intention to use (or not use) the system (Davis, 1989; Venkatesh, 2000). [FIGURE 1 OMITTED] The TAM has been tested in many empirical studies (Burton-Jones & Hubona, 2005), found to be highly valid and reliable (Koufaris, 2000), and is widely referenced (Devaraj et al., 2002). The external variables represent attributes or characteristics of the system, such as the overall design and features of the system, the user's computer skills, capabilities and abilities, and the user's knowledge, beliefs, and attitude toward computers. Perceived usefulness is defined as "the degree to which a person believes that using a particular system would enhance his or her job performance." Perceived ease of use refers to "the degree to which a person believes that using a particular system would be free of effort" (Davis, 1989, p. 320). Data from the U. S. Census Bureau (2004) indicate that the U.S. population, when compared to the 2000 census and then adjusted for fertility, mortality, and international migration rate estimates, will increase 9.8% by 2010, another 8.7% by 2020, and another 8.3% by 2030. Seniors, people 65 years of age and older (Fox, 2004), are estimated, based on this same data, to increase at a faster rate, a 15% increase by 2010 and a staggering 35.8% by 2020. What is even more significant is that by 2030 the senior population will double and become almost 20% of the U.S. population. This is important because the senior population is increasingly using the Internet to communicate via e-mail with family and friends and to get information and evaluate services (Hanson, 2001). Seniors also conduct e-commerce transactions, utilize online access to financial services such as banks and brokerage firms, obtain travel information, and research health-related services. A problem facing the designers of e-commerce Web sites is that seniors face challenges to using computers and navigating Web sites (Gregor & Newell, 2001; Hanson, 2001) since, as people age, their sight, their cognitive functions, and their motor-skills may change (Becker, 2004; Gregor & Newell, 2001; Hawthorn, 2000). Since the usability of an e-commerce Web site can be a predictor of success (Agarwal & Venkatesh, 2002; L. Chen, Gillenson, & Sherrell, 2004; Gefen, Karahanna, & Straub, 2003), these aging problems have an impact on the ability for seniors to effectively use the Web (Becker, 2004) and to conduct e-commerce transactions. It is the seniors' desire to use e-commerce Web sites and the design of e-commerce Web sites that were the focus of this research. Prior studies of the TAM and e-commerce Web sites have not been conducted with sole participation from the senior population. …

[1]  Carl L. Witte,et al.  Norwegian Internet Shopping Sites: An Application & Extension of the Technology Acceptance Model , 2007 .

[2]  Wynne W. Chin The partial least squares approach for structural equation modeling. , 1998 .

[3]  Detmar W. Straub,et al.  Trust and TAM in Online Shopping: An Integrated Model , 2003, MIS Q..

[4]  Detmar W. Straub,et al.  A Practical Guide To Factorial Validity Using PLS-Graph: Tutorial And Annotated Example , 2005, Commun. Assoc. Inf. Syst..

[5]  E. McKinney,et al.  Extending the Technology Acceptance Model and the Task-Technology Fit Model to Consumer E-Commerce , 2004 .

[6]  Rajiv Kohli,et al.  Antecedents of B2C Channel Satisfaction and Preference: Validating e-Commerce Metrics , 2002, Inf. Syst. Res..

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

[8]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[9]  Arthur D. Fisk,et al.  Human Factors Interventions for the Health Care of Older Adults , 2008 .

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

[11]  Vicki L. Hanson Web access for elderly citizens , 2001, WUAUC'01.

[12]  Nitish Singh,et al.  Understanding international web site usage , 2006 .

[13]  Julie A. Jacko,et al.  Older adults and visual impairment: what do exposure times and accuracy tell us about performance gains associated with multimodal feedback? , 2003, CHI '03.

[14]  Tapabrata Maiti,et al.  Principles and Practice of Structural Equation Modeling (2nd ed.) , 2006 .

[15]  E. McKinney,et al.  Extending the Technology Acceptance Model Extending the Technology Acceptance Model and the Task and the Task-Technology Fit Model to Technology Fit Model to Consumer E Consumer E- -Commerce Commerce , 2004 .

[16]  Lei-da Chen,et al.  Consumer acceptance of virtual stores: a theoretical model and critical success factors for virtual stores , 2004, DATB.

[17]  George A. Marcoulides,et al.  Modern methods for business research , 1998 .

[18]  Detmar W. Straub,et al.  Structural Equation Modeling and Regression: Guidelines for Research Practice , 2000, Commun. Assoc. Inf. Syst..

[19]  Viswanath Venkatesh,et al.  Assessing a Firm's Web Presence: A Heuristic Evaluation Procedure for the Measurement of Usability , 2002, Inf. Syst. Res..

[20]  Albert L. Lederer,et al.  The technology acceptance model and the World Wide Web , 2000, Decis. Support Syst..

[21]  Rex B. Kline,et al.  Principles and Practice of Structural Equation Modeling , 1998 .

[22]  D. Hawthorn,et al.  Possible implications of aging for interface designers , 2000, Interact. Comput..

[23]  Changfeng Chen,et al.  An investigation of significant factors affecting consumer trust in e -commerce , 2003 .

[24]  Ingoo Han,et al.  The Impact of Customer Trust and Perception of Security Control on the Acceptance of Electronic Commerce , 2003, Int. J. Electron. Commer..

[25]  Ting-Peng Liang,et al.  Effect of store design on consumer purchases: an empirical study of on-line bookstores , 2002, Inf. Manag..

[26]  Peter Gregor,et al.  Designing for dynamic diversity: making accessible interfaces for older people , 2001, WUAUC'01.

[27]  Shirley Ann Becker,et al.  A study of web usability for older adults seeking online health resources , 2004, TCHI.

[28]  Deborah J. Mayhew,et al.  The usability engineering lifecycle , 1998, CHI Conference Summary.

[29]  F. Paas,et al.  The efficiency of multimedia learning into old age. , 2003, The British journal of educational psychology.

[30]  Ruth M. Rettie,et al.  Net Generation Culture , 2002, J. Electron. Commer. Res..

[31]  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..

[32]  Stephen J. Cutler,et al.  Ageism and Technology , 2005 .