Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Faculty of Business | en_US |
| dc.contributor.advisor | Cheng, Edwin (LMS) | en_US |
| dc.contributor.advisor | Choy, Petrus (LMS) | en_US |
| dc.creator | Tang, Kwok Kei Stanley | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/14263 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | An exploratory study of the critical success factors of enterprise artificial intelligence services providers | en_US |
| dcterms.abstract | This study aims to identify the critical success factors of Enterprise Artificial Intelligence Services Providers and their organizational performance implications which is driven by the emerging and swift adoption of artificial intelligence technologies in various sectors and the necessity for Enterprise Artificial Intelligence Services Providers to navigate the intricacies of digital transformation in the AI era, particularly in the commercial world. The research is grounded in four key theoretical frameworks: the Resource-Based View, which emphasizes the importance of leveraging unique organizational resources to achieve competitive advantage; Competence-Based Theory, which highlights the development and deployment of essential capabilities within organizations; Diffusion of Innovation Theory, which addresses how AI technologies are adopted and spread within and across organizations; and Ecosystem Theory, which recognizes the interconnected nature of AI service providers, partners, and markets and the role of ecosystem management in driving value creation. | en_US |
| dcterms.abstract | Empirical data were collected through the structured survey instrument administered to 127 respondent firms drawn from Enterprise Artificial Intelligence Services Providers at the organizational level, using a cross-sectional approach. The survey design of this study refers to the survey instrument that contained survey questions as transformed from the identified measurement indicators under the respective initial construct of critical success factors applicable to and organizational performance measures pertinent to Enterprise Artificial Intelligence Services Providers which were identified and developed based on the extensive literature review of previous and extant studies from academic perspective (comprising information technology industry from generic viewpoint and digital transformation from industry-specific viewpoint), together with consultancy reports and papers from practical perspective, and with reference to the specific real-world business and commercial contexts of Enterprise Artificial Intelligence Services Providers. | en_US |
| dcterms.abstract | Furthermore, this study adopted and conducted two main statistical tests, including (1) Exploratory Factor Analysis to determine and define the new sets of factor-models, which represented the potential critical success factors applicable to and organizational performance measures pertinent to Enterprise Artificial Intelligence Services Providers, and then followed by (2) Multiple Regression Analysis to examine and predict the relationships and associations between the potential critical success factors and organizational performance measures of this study in order to identify the critical success factors of Enterprise Artificial Intelligence Services Providers and their organizational performance implications. Finally, this study conducted a case study of Hong Kong Telecommunications (HKT) Limited (SEHK: 6823) to further validate, triangulate, and enrich the statistical results of this study from practical application perspective. | en_US |
| dcterms.abstract | Based on the statistical results, this study identified seven critical success factors, with supportive empirical and practical relevance, including (1) Risk Management and Governance, (2) Knowledge Management, (3) Ethics Initiative and Management, (4) Human Resource Management, (5) Customer-centric, (6) Quality Management, and (7) Ecosystem Management, which are statistically and positively associated with the four organizational performance measures, relating to (1) Risk Performance, (2) Governance Performance, (3) Technology and Innovation Performance, and (4) Marketing Performance. Among the seven identified critical success factors, Ethics Initiative and Management was found to be the most prominent critical success factors of Enterprise Artificial Intelligence Services Providers which was significantly and positively associated with all four organizational performance measures. | en_US |
| dcterms.abstract | To the best of the author’s knowledge, this study is the first of its kind to conduct a thorough and comprehensive approach to holistically review, identify, and developed the critical success factors and organizational performance measures with the special focus on an emerging research topic of Enterprise Artificial Intelligence Services Providers, which provides both theoretical implications to academic researchers and practical implications to industry practitioners. From an academic perspective, this study enriches the body of academic knowledge of critical success factors and organizational performance, as well as the relationships among them. Regarding the practical implications, this study sheds light and practical insights to industry practitioners of Enterprise Artificial Intelligence Services Providers seeking concrete and specific improvements of their strategic management, operational efficiency, resources allocation, customer satisfaction, and ultimately financial performance. By pinpointing and prioritizing the critical success factors and their relationships with the respective organizational performance measure, the senior management members of Enterprise Artificial Intelligence Services Providers can also formulate their unique and specific corporate, business, and operational strategies to attain sustainable business growth and competitive advantage in the evolving and dynamic Artificial Intelligence competitive landscape. | en_US |
| dcterms.extent | xii, 410 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2025 | en_US |
| dcterms.educationalLevel | D.B.A. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Industrial management | en_US |
| dcterms.LCSH | Success in business | en_US |
| dcterms.LCSH | Artificial intelligence | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 8765.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.39 MB | Adobe PDF | View/Open |
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