A meta-regression of task-technology fit in information systems research

被引:34
作者
Jeyaraj, Anand [1 ]
机构
[1] Wright State Univ, Raj Soin Coll Business, 3640 Colonel Glenn Highway, Dayton, OH 45435 USA
关键词
Task-technology fit; TTF; System use; Meta-regression; Information systems; SOCIAL COGNITIVE THEORY; WORLD-WIDE-WEB; ACCEPTANCE MODEL; BEHAVIORAL INTENTION; USER ACCEPTANCE; INDIVIDUAL-PERFORMANCE; STUDENT SAMPLES; INTEGRATING TTF; SELF-EFFICACY; USAGE;
D O I
10.1016/j.ijinfomgt.2022.102493
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
Task-technology fit (TTF) is of considerable importance in information systems research. TTF predicts individuals' perceptions, intentions, and behaviors as well as performance impacts due to technology use. While TTF is expected to have positive effects, empirical results have been mixed. This study hypothesizes that the variation in the effects of TTF may be attributed to study characteristics. Using a meta-regression of 104 findings in 100 studies, this study shows that the variation in TTF effects is explained by type of respondents, type of dependent variable, and type of TTF variable. Specifically, students reported positive effects of TTF on other variables more often than non-students, TTF had positive effects on behavioral variables less frequently than perceptual variables, and TTF had positive effects on other variables less frequently when it was computed and not measured. These imply that TTF should be directly measured, associated with perceptions rather than behaviors, and contextualized to specific types of technologies and users.
引用
收藏
页数:13
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