An emergent grounded theory of AI-driven digital transformation: Canadian SMEs' perspectives

被引:6
|
作者
Taherizadeh, Amir [1 ]
Beaudry, Catherine [2 ]
机构
[1] McGill Univ, Desautels Fac Management, Montreal, PQ, Canada
[2] Polytech Montreal, Dept Math & Ind Engn, Montreal, PQ, Canada
关键词
Artificial intelligence; digital transformation; dynamic capability; grounded theory; industry; 4.0; technological innovation; C80; D20; L60; O30; DYNAMIC CAPABILITIES; TECHNOLOGY; INNOVATION; READINESS; STRATEGY;
D O I
10.1080/13662716.2023.2242285
中图分类号
F [经济];
学科分类号
02 ;
摘要
Artificial intelligence (AI) empowers traditional firms to transform into Industry 4.0, enabling them to compete in an era of rapid technological advancements. However, AI adoption remains limited among Canadian firms. This research aims to identify the key dimensions of AI-driven digital transformation (AIDT) and develop a grounded theory that provides a rich and nuanced understanding of how the AIDT process unfolds within Canadian SMEs. The study reveals that the AIDT process is shaped by the interplay of five core dimensions: evaluating transformation context, auditing organisational readiness, piloting the AI integration, scaling the implementation, and leading the transformation. The first four dimensions follow a sequential, stage-like progression, while the fifth dimension is recurring and omnipresent, exerting a continuous impact on the other phases. AIDT is characterised as a path-dependent, slow evolutionary change spectrum that demands firms adapt by developing their sensing, seizing and reconfiguration capacities to evolve and sustain their evolutionary fitness. The study explores several theoretical and managerial implications that arise from the findings.
引用
收藏
页码:1244 / 1273
页数:30
相关论文
共 50 条
  • [31] AI-driven generalized polynomial transformation models for unsupervised fundus image registration
    Chen, Xu
    Fan, Xiaochen
    Meng, Yanda
    Zheng, Yalin
    FRONTIERS IN MEDICINE, 2024, 11
  • [32] Theory of AI-driven scheduling (TAIS): a service-oriented scheduling framework by integrating theory of constraints and AI
    Khakifirooz, Marzieh
    Fathi, Michel
    Dolgui, Alexandre
    INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2024,
  • [33] Transforming the NHS through AI-driven solutions: a new era of digital health
    Imam, Mohamed A.
    Elgebaly, Ahmed
    Zumla, Adam
    Kolvekar, Shyam
    Ahmed, Rizwan
    Zumla, Alimuddin
    POSTGRADUATE MEDICAL JOURNAL, 2025,
  • [34] A Review of AI-Driven Digital Twin Frameworks for Cardiovascular Disease Diagnosis and Management
    Narigina, Marta
    Romanovs, Andrejs
    Merkuryev, Yuri
    2024 IEEE 65TH INTERNATIONAL SCIENTIFIC CONFERENCE ON INFORMATION TECHNOLOGY AND MANAGEMENT SCIENCE OF RIGA TECHNICAL UNIVERSITY, ITMS 2024, 2024, : 86 - 91
  • [35] The critical role of HRM in AI-driven digital transformation: a paradigm shift to enable firms to move from AI implementation to human-centric adoption
    Fenwick A.
    Molnar G.
    Frangos P.
    Discover Artificial Intelligence, 2024, 4 (01):
  • [36] Implementing AI-Driven Bed Sensors: Perspectives from Interdisciplinary Teams in Geriatric Care
    Acosta, Cromwell G.
    Ye, Yayan
    Wong, Karen Lok Yi
    Zhao, Yong
    Lawrence, Joanna
    Towell, Michelle
    D'Oyley, Heather
    Mackay-Dunn, Marion
    Chow, Bryan
    Hung, Lillian
    SENSORS, 2024, 24 (21)
  • [37] TEACHER PERSPECTIVES ON AI-DRIVEN GAMIFICATION: IMPACT ON STUDENT MOTIVATION, ENGAGEMENT, AND LEARNING OUTCOMES
    Alenezi, Abdullah
    INFORMATION TECHNOLOGIES AND LEARNING TOOLS, 2023, 97 (05) : 138 - 148
  • [38] Exploring Older Adults' Perspectives and Acceptance of AI-Driven Health Technologies: Qualitative Study
    Wong, Arkers Kwan Ching
    Lee, Jessica Hiu Toon
    Zhao, Yue
    Lu, Qi
    Yang, Shulan
    Hui, Vivian Chi Ching
    JMIR AGING, 2025, 8
  • [39] AI-Driven Smart Transformation in Physical Education: Current Trends and Future Research Directions
    Hu, Zhengchun
    Liu, Zhaohe
    Su, Yushun
    APPLIED SCIENCES-BASEL, 2024, 14 (22):
  • [40] A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics
    Huang, Ziqi
    Shen, Yang
    Li, Jiayi
    Fey, Marcel
    Brecher, Christian
    SENSORS, 2021, 21 (19)