Aggregated model of ttf with utaut2 in an employment website context

10.6339/JDS.201704_15(2).0001

Kuo-Yu Huang；Yea-Ru Chuang

Journal of Data Science

15卷2期（2017 / 04 / 01）

187 - 204

英文

This study applied partial least squares (PLS) path modeling for quantifying and identifying the determinants of job seekers' acceptance and use of employment websites (EWs) by using an aggregate model that applied task-technology fit (TTF), consumer acceptance and use of information technology (UTAUT2). We propose that the most crucial constructs explaining EW adoption are habit, behavioral intention, performance expectancy, and facilitating conditions. This study verified that a job seeker's habits were a major predictor of intention and usage of EWs involving web-based technology and occasional usage. Thus, when job seekers perceive that their task is to fit the technology, they recognize the value of using the technology and use it habitually.