Future-proofing the public accounting profession: Developing a model of technology acceptance for public accounting firms

• Main Author: Tugas, Florenz C.
• Format: text
• Language: English
• Published: Animo Repository 2017
• Subjects: Accounting > Philippines; Accounting firms > Philippines; Accounting
• Online Access: https://animorepository.dlsu.edu.ph/etd_doctoral/514
• Institution: De La Salle University

This research study aims to develop a socio-technical model of technology acceptance for public accounting firms in the Philippines. To do this, human construct (teamwork competence [TC] and technology readiness [TR]) and ethical perception under the belief construct were incorporated in the 1989 technology acceptance model (TAM) of Davis which was composed of belief construct and intention to use construct. Data were collected from 609 external audit CPAs coming from 11 public accounting firms in the Philippines with international affiliations. Using partial least squares – structural equation modeling (PLS-SEM), it was determined that the structural model of the proposed STMTA, taken as a whole, is of good fit and quality indices which is an indication that theories considered in this research study fit the data. Path coefficients of links, direct and indirect, were then analyzed in terms of sign, magnitude, significance, and effect size (f2). To refine the proposed model, path links that returned insignificant path coefficients were dropped resulting in the refined model composed of one variable under the human construct (teamwork competence), three variables under the belief construct (ethical perception [EP], perceived usefulness [PU], and perceived ease of use [PEOU]), and one variable under the intention to use construct. At the point of model refinement, very glaring was the negative sign in the path coefficient of TC as a moderating variable in the PU-ITU link. To verify and investigate on this, the proposed model (model 1), the 1989 TAM of Davis (model 2), and the refined model (model 3) were compared and contrasted using the same set of data.