Artificial intelligence adoption in a professional service industry

A multiple case study

Datos Bibliográficos

ID21401385
AutoresJiaqi Yang (0000-0002-5282-2722, Macquarie University), Yvette Blount (0000-0003-0129-7400, Deakin University), Alireza Amrollahi (0000-0002-3130-8185, Macquarie University, autor de correspondencia)
Año2024
Volumen201
Páginas123251
Fecha de publicación2024-04-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaTechnological Forecasting and Social Change (JOURNAL)
Identificadores de la revistaISSN: 0040-1625 • E-ISSN: 1873-5509
EditorialElsevier BV (PUBLISHER)
DOI10.1016/j.techfore.2024.123251
OpenAlexW4391707497
IdiomaEN
Citas recibidas23
Referencias citadas98

This study explores the factors influencing AI adoption in professional service firms. Grounded in the Technological-Organizational-Environmental (TOE) framework, we employed a qualitative, multiple case study approach, investigating three auditing firms of varying sizes through interviews and secondary document reviews. Our findings reveal six factors influencing AI adoption, including technology affordances and constraints, the firm's innovation management approaches and AI readiness, the competition environment, and the regulatory environment. Noteworthily, these factors vary significantly among the three firms. Larger firms, often operating in an environment with high AI penetration, primarily perceive the operating affordance of AI rather than marketing affordance. This means their AI adoption encompasses greater scale and depth than smaller firms. However, this expansive adoption exposes them to a widening gap in regulatory frameworks, hindering AI adoption. Moreover, smaller firms are characterized by weaker AI readiness, positioning them disadvantageously to mitigate the constraints imposed by AI. This study contributes to existing literature by offering a more holistic perspective on AI adoption in professional services

Business · Industrial organization · Knowledge management · Service (business) · Tertiary sector of the economy · Big Data and Business Intelligence · Business Process Modeling and Analysis · Computer Science · Digital Transformation in Industry · Marketing