User:Vega2787/sandbox

Technological self-efficacy is “the belief in one’s ability to successfully perform a technologically sophisticated new task”. This is a specific application of self-efficacy, which is defined as the belief in one’s ability to engage in specific actions that result in desired outcomes. Self efficacy does not focus on the skills one has, but rather the judgments of what one can do with their skills. Traditionally, a distinguishing feature of self efficacy is its domain-specificity. In other words, judgments are limited to types of performances. There are numerous possible applications; however, considering the rapid rate of technological advancements and unprecedented increases in globalization, technological self efficacy merits special consideration.

Origins
In the original definition of technological self-efficacy, the specific technological task is left purposely vague. While one may criticize this lack of specificity, this broad definition was intended to describe a general feeling toward the adaption of new technology and thus is more generalizable across a number of specific domains. Furthermore, the definition is so broad that it can be applied to technologies that have not been invented yet. Although the breadth of this definition has allowed the construct to remain relevant in spite of dramatic technological improvements since 1992, this breadth has also created confusion within the literature about how to utilize technological self-efficacy. Specifically, a number of studies have been published about self-efficacy feelings towards specific types of technology; for example, computer self-efficacy, internet self-efficacy , and information technology self-efficacy. In order to offer some clarity to this literature, we suggest that technology specific self-efficacies can be considered sub-dimensions under the overall heading of technological self-efficacy.

Importance
Today’s modern society is completely embedded within a technological context, which makes the understanding and evaluation of technological self efficacy critical. Indeed, nearly half of Americans own smartphones and this trend towards technology use is not limited to the United States; instead cell phone, computer, and internet use is becoming increasingly common around the world. Two arenas that technology has become a foundation for which common operations occur are the workplace and learning environments. At work, 62% of employed Americans use the internet and email, but interestingly workplace internet users either use the internet everyday (60%) or not at all (28%). Internet and email use is obviously influenced by the type of job one holds, but 96% of employed Americans use some sort of new communication technology on the job. Successful investment in technology is associated with enhanced productivity; however, full realization of technological potential commonly plagues organizations. In learning environments, college courses are more frequently being offered online. This is commonly referred to as distance education and implementation ranges from courses being supported by the web (teaching occurs predominantly through face-to-face instructor interactions with supplemental materials being offered on the web) to blended learning (significantly less face-to-face instructor interactions and more online instruction) to fully online (all instruction is conducted virtually with no face-to-face instructor interactions). A number of advantages are associated with distance learning such as flexibility and convenience, which allows individuals the opportunity to enroll in classes that would otherwise be off-limits due to geographical or personal reasons. Another commonly cited advantage is that instruction is self paced, which allows for personalized tailoring based on individual needs. However, these advantages are not likely to be realized if the individual is anxious about the method of instructional delivery and/or their expectation of success is low due to its technological component. Taken together, these two critical arenas discussed above (workplace and learning) reinforce the extent to which technology has impacted modern activities and consequently the importance of perceived beliefs in one’s ability to master new technology. Success in everyday life often hinges on the utilization of technology and by definition, new technology will always be new. Therefore this construct warrants review.

Measurement
By strictly following the definition of self-efficacy set forth by Bandura, the construct is something that is defined by an individual’s belief in him or herself. This property has important implications for the measurement of any type of self-efficacy. Specifically, measures of self-efficacy must be self-report because the only person who can accurately portray beliefs in one’s ability is the target of investigation. In other words, self-report measures of self-efficacy have definitional truth. While a number of problems exist with the sole use of self-report inventories, in the case of self-efficacy (and other constructs that are defined as internal beliefs and cognitions) this measurement approach is unavoidable.

While the type of measurement approach is defined by the construct, the process of developing and validating these scales has varied considerably over the course of the technological self-efficacy literature. One difference between measures is the scoring of items. Previously, research has noted these differences in results when different scoring approaches were used. . Specifically, there are two main ways of scoring self-efficacy items. The first type is called self-efficacy magnitude. Items would be worded so participants would respond whether or not they felt they could accomplish a certain task (yes or no). The second type is self-efficacy strength. This scoring approach would ask participants to rate how confident they were in completing the tasks on a numerical scale and average across all items. All other scoring types are simply composites of these first two approaches. Another difference between measures is similar to the previous differentiation of technology specific self-efficacy and technological self-efficacy as a broader concept and measurement approaches reflect this distinction. Measurement attempts of the broader concept of technological self-efficacy will be considered first. McDonald and Siegall developed a five-item likert scale of technological self-efficacy based on the consideration of previous theoretical studies. This scale was scored using the strength approach to self-efficacy scales. The specific items in this scale were not referring to any specific item and instead focused on technology as a general concept. The specific wording and development of this scale differed from another attempt to measure technological self-efficacy made by Holcomb, King and Brown. In this study, the authors used a factor analytic approach to differentiate three factors. Following the factor analysis, researchers observe the factors resulting from the statistical test and determines what each factor should represent. The resulting technological self-efficacy scale contained 19 likert type items, which also was scored according to the strength scoring system. In contrast to the McDonald and Siegall scale, the items in this scale contained references to certain technologies (specifically computers and software packages). The two studies mentioned above are representative of attempts to measure technological self-efficacy as a broader concept.

In addition to the attempts to measure technological self-efficacy broadly, a number of studies have developed measures of technology specific self-efficacy. One of the most cited measures of computer self-efficacy comes from Compeau and Higgins. These authors reviewed previous attempts to measure computer self-efficacy and theoretically derived a 10 item scale. Unlike the previously mentioned scales, this study employed a “composite” scoring approach. For each item, participants were first asked whether they could complete a specific task related to computers using a dichotomous yes/no scale. Following this answer, participants were then asked to rate their confidence they had about completing the task from 1 (‘’not at all confident’’) to 10 (‘’totally confident’’). The final score was calculated by counting the number of “yes” answers which reflects self-efficacy magnitude and the average of the confidence ratings which represents self-efficacy strength. The authors then validated this measure in a nomological network of related constructs. Another related type of self-efficacy that has been measured is internet self-efficacy. Similar to previous measurement approaches, internet self-efficacy was developed using a theoretical approach that considered previous measures of related topics and developed novel items to fill in the missing construct space. This scale showed a high level of reliability and validity. A final related measurement of specific technology self-efficacy is information technology (IT) self-efficacy. The measurement of IT self-efficacy is similar to the Holcomb, King and Brown conceptualization of technological self-efficacy. Participants rate the degree to which they feel comfortable using specific IT technology, reflecting self-efficacy strength. While other measures of technological self-efficacy and related technology specific self-efficacy exist, this list provides a start to the most commonly used measures.

Antecedents
Bandura proposes that the four primary sources of self efficacy beliefs are (1) prior experience, (2) modeling, (3) social persuasions, and (4) physiological factors. Past research supports that many of the sources for technological self efficacy are the same; however there are also additional antecedents.

Prior experience
Prior experience with technology is repeatedly found to be influential on technology related self efficacy beliefs. If an individual has had the opportunity to interact with new technologies and, more importantly, has had success with mastering new technologies then individuals are more likely to hold more positive beliefs for future performance.

Modeling or participation in technological training
Modeling or participation in technological training are also found to be significant predictors of technological self efficacy. Although different types of training interventions have been associated with different gains; in general, research supports that seeing other individuals successfully perform the task at hand (for example, the instructor) and then providing the learner with some opportunity for reinforcement and demonstration (for example, trying to successful utilize the technology on their own) increases technology related self efficacy beliefs.

Social persuasions
Social persuasions such as encouragement by others and organizational support are also important contributors to technology related self efficacy beliefs. The actions and statements of others can significantly alter perceptions of their likelihood for success. Organizational support typically includes management’s encouragement and assistance. If management does not appear to enthusiastically support their employees’ attempts to utilize technology then employees are unlikely to accept technology.

Resources
Resources are commonly cited as one of the largest barriers to adoption of technology. This includes, but is not limited to, sufficient computers, sufficient software licenses, out-of-date hardware/software, and slow or intermittent Internet connections. The success of proper technology use is first and foremost limited by the capabilities of the technology in question.

Gender
Gender is significantly related, such that men tend to have higher levels of technology related self efficacy beliefs than women.

Age
Age is also significantly related, such that younger individuals tend to have higher levels of technology related self efficacy beliefs than older individuals. This finding is not surprising given the widespread stereotype of older adults inability to learn new material, especially when the material is technology related. However, older adults’ low technological self efficacy beliefs suggest that older adults may internalize the ‘old dogs can’t learn new tricks’ stereotype, which consequently affects expectations about future performance in technology related domains.

Consequences
Technology related self efficacy beliefs have been linked with a number of consequences.

Task Performance
Task performance is negatively affected, such that lower technology related self efficacy beliefs are related to poorer performance   This is extremely important, because these findings suggest that positive perceptions of individuals’ technological capabilities may need to be present before successful performance can be achieved.

Perceived ease of use and usage
Perceived ease of use and usage is found to be positively related with technology related self efficacy beliefs. According to the Technology Acceptance Model, perceived ease of use and perceived usefulness influences behavioral intentions and ultimately technology related behaviors.

Anxiety
Anxiety is negatively related, such that lower technology related self efficacy beliefs are associated with higher level of anxiety.