The influence of demographic factors on consumer attitudes and intentions to use RFID technologies in the US hotel industry

Purpose – The purpose of this paper is to explore the influence of demographic factors (age, gender, education, income) on consumer attitudes and their intentions to use radio frequency identification (RFID) in the hotel industry.Design/methodology/approach – Quantitative research methodology was used in this study. The methods used for this study are both descriptive and causal modeling tests. This research study used web‐survey method for collecting and analyzing data. The measurement model was assessed using confirmatory factor analysis using the maximum likelihood method and structural equation modeling was used to estimate the parameters of the structural model.Findings – The results indicate that there are few differences in consumer attitudes and intentions in terms of the demographic factors. It can be concluded that consumer differences can be associated with consumer attitudes that are determined by age. The results for demographic factors, gender, income, and education levels indicate no differ...

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

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

[3]  Judy A. Siguaw,et al.  Adoption of Information Technology in U.S. Hotels: Strategically Driven Objectives , 2000 .

[4]  D. H. Dean Shopper age and the use of self‐service technologies , 2008 .

[5]  Vallabh Sambamurthy,et al.  Sources of Influence on Beliefs about Information Technolgoy Use: An Empirical Study of Knowledge Workers , 2003, MIS Q..

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

[7]  Charlotte H. Mason,et al.  An empirical study of innate consumer innovativeness, personal characteristics, and new-product adoption behavior , 2003 .

[8]  I. Ajzen,et al.  Prediction of goal directed behaviour: Attitudes, intentions and perceived behavioural control , 1986 .

[9]  Alan D. Smith,et al.  Exploring radio frequency identification technology and its impact on business systems , 2005, Inf. Manag. Comput. Security.

[10]  John Todman,et al.  Gender differences in computer anxiety among university entrants since 1992 , 2000, Comput. Educ..

[11]  E. Rogers Diffusion of Innovations , 1962 .

[12]  Naveen Donthu,et al.  Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics , 2006 .

[13]  Bo Rundh,et al.  Radio frequency identification (RFID): Invaluable technology or a new obstacle in the marketing process? , 2008 .

[14]  Dietram A. Scheufele,et al.  Exploring motivations for consumer Web use and their implications for e‐commerce , 2003 .

[15]  Sergio Román Relational Consequences of Perceived Deception in Online Shopping: The Moderating Roles of Type of Product, Consumer’s Attitude Toward the Internet and Consumer’s Demographics , 2010 .

[16]  W. Qualls,et al.  Towards a theoretical model of technology adoption in hospitality organizations , 2007 .

[17]  A. Fairhurst,et al.  The influence of consumer traits and demographics on intention to use retail self‐service checkouts , 2010 .

[18]  Deborah Compeau,et al.  Social Cognitive Theory and Individual Reactions to Computing Technology: A Longitudinal Study , 1999, MIS Q..

[19]  C. M. Schaninger,et al.  The Influence of Cognitive Personality Traits and Demographics on Consumer Information Acquisition , 1981 .

[20]  C. Hertzog,et al.  Metacognition in adulthood and old age. , 2000 .

[21]  I. Ajzen,et al.  Attitude-behavior relations: A theoretical analysis and review of empirical research. , 1977 .

[22]  Peter Jones,et al.  The benefits, challenges and impacts of radio frequency identification technology (RFID) for retailers in the UK , 2005 .

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

[24]  Icek Ajzen,et al.  From Intentions to Actions: A Theory of Planned Behavior , 1985 .

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

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

[27]  E. Rogers New Product Adoption and Diffusion , 1976 .

[28]  Raymond R. Burke Technology and the customer interface: What consumers want in the physical and virtual store , 2002 .

[29]  Pedro M. Reyes,et al.  Is RFID right for your organization or application , 2007 .

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

[31]  Prashanth U. Nyer,et al.  The role of emotions in marketing , 1999 .

[32]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[33]  David Wyld,et al.  RFID 101: The Next Big Thing for Management , 2006, IEEE Engineering Management Review.

[34]  J. Hair Multivariate data analysis , 1972 .

[35]  Daniel J. Connolly,et al.  Technology Strategies for the Hospitality Industry , 2004 .

[36]  Jane M. Howell,et al.  Personal Computing: Toward a Conceptual Model of Utilization , 1991, MIS Q..