Role of Age in Technology Adoption Decisions in Organizations
Aashish Jagini
University of Missouri

Technology has become a vital and integral part of every organization. From multi-national corporations who maintain mainframe systems and databases to small businesses that own a single computer, technology plays a role. Technology has become indispensable because it has made its way into all the areas of an organization.
Adoption of technology in an organization may influence performance and growth through improvement in productivity, competitiveness, efficiency, and effectiveness. Technology helps an organization to re-engineer work practices, improve speed, maintain consistency and accuracy and increase reliability. In the past two decades, research has focused on the notion of technology adoption. Studies have examined various aspects of technology adoption at an individual and organizational level and in this paper will analyze if age can be a differentiating factor in adoption of technology in the workplace.
Research examining age differences in technology adoption decisions A research study was conducted by Micheal G.Morris and Viswanath Venkatesh in the year 2000 to investigate age differences in individual adoption and sustained usage of technology in the workplace using the theory of planned behavior. The theory of planned behavior is a theory which links beliefs and behavior. The study was done over a period of 5 months among 118 workers. User reactions and technology usage behavior were studied majorly in this experiment by introducing a new software system to the workers.

Research Method:
The setting for the research done by Morris and Venkatesh was a medium-size financial accounting firm in a large mid-western city with approximately 300 employees. The firm was well established and had been in business for about 15 years. A total of 130 customer account representatives who were in the process of implementing a new technology participated in the study out of which 118 usable responses were obtained at all points of measurement. The new software being introduces was an organization-wide system for data and information retrieval. Usage of the new system was voluntary because the participants could use either the new system or the existing system. None of the participants had any prior knowledge about the new technology being introduced. All participants received a 2-day training session on the system which was a combination of training, interactive lecture, and hands-on use.
Potential Confounding factors:
Three potential confounds associated with age include income, occupation, and education. Specifically, older individuals are overrepresented in categories of higher income, higher occupational positions, and higher educational qualification. Thus, in the research done by Morris and Venkatesh, it was deemed important to initially evaluate the effects of income level, occupation level, and education level.
Key Determinants in the theoretical model: 1. Attitude towards behavior (A): Refers to the degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior in question (Ajzen, 1991, p.188) 2. Subjective norm (SN): Refers to the perceived social pressure to perform or not to perform the behavior (Ajzen, 1991, p.188) 3. Perceived behavioral control (PBC): Defined as people’s perception of the ease or difficulty of performing the behavior of interest (Ajzen, 1991, p.183)
Procedure and Measurement:
Employees’ reactions to the technology were gathered at two points in time: immediately after the initial training (t1) and after 3 months of experience (t2). Actual usage behavior (USE) was measured over a 5 month period from the time of the introduction of the technology. For purposes of this research, t1 represented the measurement point to study short-term effects (initial user reactions), and t2 represented measurements to study long-term effects (situations of significant direct experience with the technology). Attitude, subjective norm, perceived behavioral control measured in a specific time period were used to predict subsequent usage behavior. Figure 1 presents a summary of the design and points of measurement for the research. Validated items were used to measure attitude toward using technology, subjective norm, and perceived behavioral control (Davis, 1989; Davis et al., 1989; Mathieson, 1991; Taylor & Todd, 1995a, 1995b). Actual usage behavior, operationalized as the frequency of use, was gathered from system logs. Age, income, organizational position, and education were measured.
Preliminary Analysis:
A combination of factor analysis and reliability analysis was performed in order to evaluate the psychometric properties of the measures of the study.
Hypothesis Testing:
Hierarchical regression was used to analyze the data. Hierarchical regression is an effective technique in this context as it allows to keep the age in a continuous form along with evaluating the influence of potential confounds already discussed. Neither direct nor indirect effects of any of the three confounding variable were found to be significant suggesting that these variables did not play a role in influencing usage over and above age.
Discussion of the research and its results:
From the results of the regression models, it is evident that age does have an influence on short-term technology usage (p< .001). Although not explicitly tested, age has an influence on attitude, subjective norm and perceived behavioral control which is clearly noticeable from the correlations in Table 1. Over the long term, the pattern of results was largely consistent with the short term results (p<.001). The only exception was that in the long-term subjective norm was not significant as a determinant of usage, both as a direct effect and an interaction term.
The results suggest that there are clear differences with age in the importance of various factors in technology adoption and usage in the workplace. Initial acceptance decisions of younger workers found attitude toward using a new technology to be more salient than old workers. For long term usage decisions, the pattern of results for attitude toward using technology and perceived behavioral control was consistent with the initial adoption decision. The results were present even after controlling for potential confounding variables. In addition, an age-squared term was added to the preliminary analysis of the research to examine whether age-related changes might be asymptotic; however the term was not significant suggesting that the influence of age is linear for this sample. Therefore, it appears that age does have important influences on technology adoption and sustained usage decisions.

References:
Morris, M., & Venkatesh, V. (n.d.). Age Differences In Technology Adoption Decisions: Implications For A Changing Work Force. Personnel Psychology, 375-403.
Hameed, M., & Counsell, S. (n.d.). Assessing the influence of Environmental and CEO Characteristics for Adoption of Information Technology in Organizations. Journal of Technology Management &amp; Innovation, 64-84.
Ajzen, I. (n.d.). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 179-211.
Davis, F. (n.d.). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339.
Mathieson, K. (n.d.). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2, 173-191.
Taylor, S., & Todd, P. (n.d.). Understanding information technology usage: A test of competing model. Information Systems Research, 6, 144-176.

Appendix:
Table 1:

Table 2:

Figure 1:
Research Design and Timing of Measurement