PurposeThe purpose of this paper is to examine gender differences in predictors of technology threat avoidance motivation and behavior among working US adults. Implications were considered in regard to cybersecurity awareness training motivation and perceptions of need for protective cybersecurity behavior in the workplace.Design/methodology/approachA single-shot regression-based study used ordinal regression supported by K-means clustering to evaluate the moderating effects of gender on predictors of technology threat avoidance motivation and behavior on a sample of n = 206 US adult workers.FindingsThe regression model explained 47.5% of variance in avoidance motivation and 39% of avoidance behavior variance. Gender moderated predictive associations between several independent variables and avoidance motivation: perceived susceptibility, perceived effectiveness, perceived cost and self-efficacy. Gender also moderated the association between avoidance motivation and avoidance behavior.Research limitations/implicationsThe predictive impact of gender extends beyond the main effects in technology threat avoidance. Data frequency distributions and inter-variable relationships should be routinely considered in threat avoidance studies, especially if sample variables exhibit non-normal frequency distributions and nonlinear associations.Practical implicationsGender was significantly associated with threat avoidance motivation and avoidance behavior and exhibited notable associations with antecedents of avoidance motivation. Related insights can inform the design and delivery of training content relating to technology threat avoidance as organizations strive to more effectively leverage information technology end-users as protective assets for the enterprise.Originality/valueThe uniqueness of this study derives from its focus and findings regarding the moderating effects of gender on technology threat avoidance factors and techniques used to measure and evaluate the associations between them.
Read full abstract