Exploring the Determinants and Consequences of Task-Technology Fit: A Meta-Analytic Structural Equation Modeling Perspective

Abstract

Objectives: Task-Technology Fit (TTF) is mainly used to determine the users’ performance based on the tasks and technological attributes. This study integrated and evaluated TTF with the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). This paper aims to compile and analyze the literature on task-technology fit (TTF) since 2000. Method: Through the meta-analytic structural equation modeling (MASEM) approach, understand the application of TTF in the last 20 years and explore future research directions. In addition, this paper employs subgroup analysis and sample sub-grouping to better understand the differences between these studies. The samples were divided into two categories: identity groups (employee, individual, and student) and voluntary groups (voluntary and non-voluntary). Findings: The relationship between the variables belonging to the original TTF model (including TASK, TEC, TTF, PI, and UT) was found to be relatively stable. After combining the variables of UTAUT2 (including PEOU, BI, and PE) and IC, all paths were also found to have a medium or high effect. The TTF-BI path was significant in the identity-based subgroup analysis, and the IC-TTF path was significant in the voluntary-based subgroup analysis. Novelty: Given that the traditional TTF literature is too subjective, this paper adopts MASEM as applied in management research. There are few similar studies so far. Therefore, this paper not only analyzes TTF objectively through MASEM but also provides some directions and suggestions for expanding the TTF model and hopes to give a stronger explanation for future research.