The Comparison of Three Major Occupations for User Acceptance of Information Technology: Applying the UTAUT Model

This study investigated whether the differences of gender, age, and occupation for m-learning showed significance on the utilization of the mobile devices and to figure out if the variation may influence the performance expectancy, effort expectancy and the social influence to the behavioral intention or even to the behavior of usage. When the employees’ behavioral intention was low, the director of managers or HR department can suggest the employees’ colleagues, superior manager or friends to communicate with them to enhance their behavioral intention and to use it. And it suggested that male employees and elder employees should be put more emphasis on the communication to enhance their behavioral intention. UTAUT model with different kinds of businesses for m-learning but the conclusion did not investigate the differences of the adoption of the mobile devices in each industry. Basing on this, this study attempted to investigate whether the difference occupations showed significance on the utilization of the mobile devices.

[1]  Fred D. Davis,et al.  A critical assessment of potential measurement biases in the technology acceptance model: three experiments , 1996, Int. J. Hum. Comput. Stud..

[2]  A. Morrison,et al.  A Model of Traveller Acceptance of Mobile Technology , 2008 .

[3]  Gordon C. Bruner,et al.  Toward a unified theory of consumer acceptance technology , 2007 .

[4]  Dong-Hee Shin,et al.  Towards an understanding of the consumer acceptance of mobile wallet , 2009, Comput. Hum. Behav..

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

[6]  Timo Koivumäki,et al.  The perceptions towards mobile services: an empirical analysis of the role of use facilitators , 2006, Personal and Ubiquitous Computing.

[7]  John Sabini,et al.  The roles of empathy, anger, and gender in predicting attitudes toward punitive, reparative, and preventative public policies , 2000 .

[8]  Venkatesh,et al.  A Longitudinal Field Investigation of Gender Differences in Individual Technology Adoption Decision-Making Processes. , 2000, Organizational behavior and human decision processes.

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

[10]  Michael Ahearne,et al.  Effect of Technology on Sales Performance: Progressing from Technology Acceptance to Technology Usage and Consequence , 2004 .

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

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

[13]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

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

[15]  Venkateshviswanath,et al.  A Theoretical Extension of the Technology Acceptance Model , 2000 .

[16]  Tao Zhou,et al.  Integrating TTF and UTAUT to explain mobile banking user adoption , 2010, Comput. Hum. Behav..

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

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

[19]  Yu-Cheng Lee,et al.  Analysis of adopting an integrated decision making trial and evaluation laboratory on a technology acceptance model , 2010, Expert Syst. Appl..

[20]  Yi-Shun Wang,et al.  Investigating the determinants and age and gender differences in the acceptance of mobile learning , 2009, Br. J. Educ. Technol..

[21]  Izak Benbasat,et al.  Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation , 1991, Inf. Syst. Res..

[22]  Alexander Serenko,et al.  Moderating Roles of User Demographics in the American Customer Satisfaction Model within the Context of Mobile Services , 2006, AMCIS.

[23]  A. Bandura Social Foundations of Thought and Action: A Social Cognitive Theory , 1985 .

[24]  R. Bagozzi,et al.  On the evaluation of structural equation models , 1988 .

[25]  J. Maltby,et al.  Forgiveness of Self and Others and Emotional Empathy , 2002, The Journal of social psychology.

[26]  CompeauDeborah,et al.  Social cognitive theory and individual reactions to computing technology , 1999 .

[27]  Rolph E. Anderson,et al.  Multivariate Data Analysis: Text and Readings , 1979 .

[28]  J. McCubbin,et al.  Social influence and pain response in women and men , 2008, Journal of Behavioral Medicine.

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

[30]  E. Rogers,et al.  Diffusion of Innovations, 5th Edition , 2003 .

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

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

[33]  J. Nunnally Psychometric Theory (2nd ed), New York: McGraw-Hill. , 1978 .

[34]  E. Rogers,et al.  Diffusion of innovations , 1964, Encyclopedia of Sport Management.

[35]  Geoffrey W. Sutton,et al.  Sexual Orientation, Mental Health, Gender, and Spirituality: Prejudicial Attitudes and Social Influence in Faith Communities , 2008 .

[36]  Chih-Chung Chen,et al.  An extension of financial cost and TAM model with IDT for exploring users’ behavioral intentions to use the CRM information system , 2009 .

[37]  A ToddPeter,et al.  Understanding Information Technology Usage , 1995 .

[38]  F. Fisher Tests of Equality Between Sets of Coefficients in Two Linear Regressions: An Expository Note , 1970 .

[39]  Viswanath Venkatesh,et al.  Why Don't Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior , 2000, MIS Q..

[40]  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..

[41]  Paul Jen-Hwa Hu,et al.  Investigating healthcare professionals' decisions to accept telemedicine technology: an empirical test of competing theories , 2002, Inf. Manag..

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

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

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

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

[46]  Wynne W. Chin,et al.  A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic - Mail Emotion/Adoption Study , 2003, Inf. Syst. Res..

[47]  Ritu Agarwal,et al.  A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology , 1998, Inf. Syst. Res..

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

[49]  Ritu Agarwal,et al.  Are Individual Differences Germane to the Acceptance of New Information Technologies , 1999 .

[50]  Deborah Compeau,et al.  Computer Self-Efficacy: Development of a Measure and Initial Test , 1995, MIS Q..