The development and validation of computer self-efficacy and outcome expectancy scales in a nonvolitional context

Abstract

Computer self-efficacy and outcome expectancy scales were developed using 306 responses to a questionnaire distributed by a national mail survey to end users of computer systems in a variety of functional business areas, Confirmatory factor analysis using a structural equations approach was used to develop three scales. The scales were found to demonstrate satisfactory psychometric properties. The reliability coefficients for these scales were as follows: .85 for computer self-efficacy; ,88 for work-related outcome expectancy; and .89 for personal outcome expectancy. The scales provide a strong foundation from which to refine the measurement of computer self-efficacy and outcome expectancy. From these refinements, empirical models that include self-efficacy and outcome expectancy as determinants of information technology acceptance at the individual level of analysis can be improved.The introduction of computers into the workplace has elicited a large number of studies in the past 20 years. The information technology (IT) literature is replete with reports ofcomputer attitude studies and how these attitudes affect computer use. In addition, anxiety (Igbaria & Chakrabarti, 1990;Igbaria & Parasuraman, 1989; Jacobson, Holder, & Deamer, 1989) and vague concepts such as "computerphobia" and "technophobia" have been related to resistance to IT (Jay, 1981). Specifically, IT refers to any product whose underlying technological base is composed of computer or communications hardware and software (Cooper & Zmud, 1990).Attitudes have received the most attention in IT research (LaLomia & Sidowski, 1991). The notion that attitudes affect behavior has been proposed for years (Oskamp, 1977). For example, the work of Fishbein and Ajzen (1975) identified specific conditions under which attitudes should predict behavior. Fishbein and Ajzen developed the theory of reasoned action (TRA), which proved successful in predicting behavior across numerous contexts. The TRA model posits that actual behavior (e.g., computer system use) is influenced by behavioral inten- tions regarding the behavior, while behavioral intentions are affected by attitudes toward the behavior, subjective norms, beliefs and evaluations, and normative beliefs and motivation to comply (Davis, Bagozzi, & Warshaw, 1989). This work was later adapted to IT research in the form of the technology acceptance model (TAM). The TAM theoretically links external variables, perceived usefulness, and ease of use of a computer system to actual computer system use through attitudes and behavioral intentions toward using the computer system (Davis, 1986;Davis et al., 1989). The TAM was relatively successful in predicting computer usage in a number of studies in which IT use was voluntary (Davis et aI., 1989). However, over time, inconsistencies in studies using attitude scales were noticed. Gutek, Winter, and Chudoba (1992) have posited that these inconsistencies may be due to the increase in nonvolitional IT use. Doll and Torkzadeh (1996) also stated that "where use is manda...

Product Meaning, Affective Use Evaluation, and Transfer:

“…Furthermore, the increase in the pace and degree with which technological aids pervade people's everyday lives has brought more attention to the downsides of the user experience. User frustration, computer anxiety, and technophobia have all been identified as plausible antagonists of effective, efficient, and satisfying use (Beckers & Schmidt, 2001, 2003Bowers & Bowers, 1996;Bradley & Russell, 1997;Henry & Stone, 1997). Again, this is especially true for transfer settings, for example, the resistance to developmental changes of the technology (Toffler, 1970), the aversion to certain types of systems and applications and attachment to others, and the transfer of negative affective content among different contexts and purposes of IT use.…”

Educator Attitudes Toward Electronic Grading Software

“…Toffler (1970) states that people are more resistant to change with increasing age. Baak et al (1991), Henry andStone (1997), Piña (1993), and Applebaum (1990) state that older people have less confidence and more anxiety toward technology than do younger people.…”

“…This also appears to be consistent with previous research. Henry and Stone (1997) linked years of teaching experience with teacher age, stating that typically, teachers with more years of experience tend to have more trouble with the integration of technology. The results realized in this study regarding relative years of computer experience also maintain consistency with previous research.…”

“…According to George & Sleeth (1996), Harris (1985) coined the term "cyberphobia," an aversion or anxiety caused by technology. Other terms com-monly used when describing people who resist technology in different ways are "computerphobia" and "technophobia" (Henry & Stone, 1997). We have to come to the realization that educators may harbor some form of fear or anxiety about using technology in their classrooms when they are the focus of students' attention (George & Sleeth, 1996).…”

Factors Analysis of Self-Efficacy Outcome Expectations In Influencing The Commitment And Performance Of Information Technology End Users

“…The relationship between self-efficacy and outcome expectations also treated intensively (Henry and Stone, 1997). Figure 2.1 illustrates a model where e1 and e2 are the effects of self-efficacy and outcome expectations on behavior.…”