The missing links: cultural, software, task and personal influences on computer self-efficacy

Abstract

Culture exerts a fundamental effect on employees and their use of technologies. We examine the influence of culture (and other factors) on computer self-efficacy (CSE). CSE, or employees' judgments about their capabilities to use a specific software system, is important given its relationship with work performance. By drawing a sample from two different countries, we show that culture affects CSE indirectly through employees' preferences for individualism and task interdependence. Furthermore, individualism, task interdependence and software personal innovativeness relate positively, whereas task ambiguity and software complexity associate negatively with CSE. Finally, we discuss several implications for human resource management.

Keywords::

• ambiguity
• complexity
• computer use
• culture
• individualism
• interdependence

Notes

^1. The Human Development Index is a measure of the degree of development of a given country. It combines three statistics: life expectancy, knowledge and education (measured by the adult literacy and the gross enrolment ratio, which is an indication of the level of education from kindergarten to postgraduate education), and standard of living (as measured by the natural logarithm of gross domestic product per capita at purchasing power parity).

^2. Lindell and Whitney (2001) ideally recommend the inclusion of a marker variable in the design that is theoretically unrelated to the variables in the model and the use of the lowest correlation between this variable and the rest of variables as the smallest correlation (r_s). However, they also state that common method variance can be tested without a marker variable in the way we did here.

^3. We also tested the model separately for each country. For both countries, we found that all paths were in the same direction as hypothesized. We also carried out comparisons based on the recommendations of Cohen, Cohen, West and Aiken (2003) and found differences in only two paths (the path for personal innovativeness was stronger for Spain, and the path for task ambiguity was stronger for the USA).

^4.; where is the variance accounted by a set of one or more independent variables A, and is the combined variance accounted for by A and another set of one or more independent variables B.

^5. Cohen (1988) classifies 0.02 as a small effect size, 0.15 as a medium, and 0.35 as large.

^6.; where R² is the variance accounted by all independent variables.

^7. The formulas in footnotes 4 and 6 are similar: the former is used to calculate the effect size of one independent variable, whereas the latter is used to calculate the effect size of all the independent variables together.