Determinants of emerging technology adoption (Artificial Intelligence, blockchain and machine learning) in credit analysis among Deposit-Taking SACCOs in Kenya

Simple item page
dc.contributor.authorAden, J.
dc.date.accessioned2026-02-02T09:16:36Z
dc.date.available2026-02-02T09:16:36Z
dc.date.issued2025
dc.descriptionFull - text thesis
dc.description.abstractEmerging technologies, including artificial intelligence (AI), blockchain, and machine learning (ML), are significantly transforming credit analysis; however, their adoption among deposit-taking SACCOs in Kenya remains limited. This study sought to examine the factors influencing the adoption of these emerging technologies in credit analysis among DTSs in Kenya. The specific objectives were to establish effect of financial capacity, board characteristics and technological characteristics on the adoption of emerging technologies in credit analysis. The study was guided by the Technology-Organization-Environment (TOE) theory. The descriptive cross-sectional design was used in this study wherein primary and secondary data was collected from 110 DTSs. Primary data was collected using questionnaires administered to managers of the DTSs in Kenya, including key roles such as credit managers, information technology managers, and operations managers, which focused on technology characteristics and extent of adoption. Secondary data was collected from 2024 annual reports of DTSs, and were used to obtain data on financial and board characteristics. Regression results confirmed these patterns, with board characteristics and financial capacity emerging as significant predictors, while technology characteristics showed no significant independent effect. The findings indicate that SACCOs with stronger financial positions (higher profitability, better asset quality, and capital adequacy) and robust board structures (independent, diverse boards) were more likely to adopt advanced technologies, regardless of their perceptions about the technologies themselves. These results have important theoretical and practical implications. Theoretically, they support but qualify the Technology-Organization-Environment (TOE) framework, demonstrating that organizational factors outweigh technological considerations in resource-constrained environments like SACCOs. Practically, the findings suggest that efforts to promote digital transformation should prioritize building financial capacity and board structures before addressing technological perceptions. Policymakers and SACCO managers should focus on improving financial management practices, strengthening board independence and diversity, and securing capital for technology investments. The study was limited to three categories of determinants and focused only on licensed deposit-taking SACCOs in Kenya using a cross-sectional design. Despite this, the study contributes to existing knowledge by extending the TOE framework to SACCOs, highlighting the primacy of organizational factors over technological perceptions in low-resource settings, and integrating both primary and secondary data to provide a comprehensive view of adoption dynamics.
dc.identifier.citationAden, J. (2025). Determinants of emerging technology adoption (Artificial Intelligence, blockchain and machine learning) in credit analysis among Deposit-Taking SACCOs in Kenya [Strathmore University]. http://hdl.handle.net/11071/16050
dc.identifier.urihttp://hdl.handle.net/11071/16050
dc.language.isoen_US
dc.publisherStrathmore University
dc.titleDeterminants of emerging technology adoption (Artificial Intelligence, blockchain and machine learning) in credit analysis among Deposit-Taking SACCOs in Kenya
dc.typeThesis
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Determinants of emerging technology adoption (Artificial Intelligence, blockchain and machine learning) in credit analysis among Deposit-Taking SACCOs in Kenya.pdf
Size:
5.32 MB
Format:
Adobe Portable Document Format
Description:
Full - text thesis
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
MBA Theses and Dissertations (2025)