Evaluation of GenAI Usage for New Product Development in Digital Product MSMEs in Kazakhstan

dc.contributor.advisorTsakalerou, Mariza
dc.contributor.advisorAkhmadi, Saltanat
dc.contributor.authorRamatulla, Balausa
dc.contributor.authorBozbayev, Zhanybek
dc.contributor.authorKabdrakhmetova, Zhazira
dc.date.accessioned2026-06-09T06:52:02Z
dc.date.issued2026-05-05
dc.description.abstractDespite the growing importance of Generative Artificial Intelligence usage in New Product Development, its adoption among resource-constrained Micro, Small, and Medium Enterprises (MSMEs) in emerging countries still remains limited and understudied. To understand what may be influencing the extent to which digital MSMEs in Kazakhstan use GenAI to assist with their NPD processes, this study used the Technology-Organization-Environment (TOE) framework along with the Resource-Based View (RBV). A conceptual model with seven constructs based upon these frameworks was then tested through Partial Least Squares Structural Equation Modeling (PLS-SEM) and survey data gathered from 52 employees working within Kazakhstani digital MSMEs. Results indicated that Top Management Support had a significant impact upon both AI Technological Capabilities (β = 0.744, p < 0.001) and Organizational Readiness (β = 0.587, p = 0.004), and as such positively influenced GenAI integration across all stages of the NPD process. Additionally, the results showed that the proposed model accounted for 20.9 percent of the total variance in the overall level of adoption of GenAI for NPD, and in the experimental model, it rose to 28.7% for development and testing activities. Neither Government Regulation nor Personnel Capabilities were shown to have statistically significant direct influences on the adoption outcome. Therefore, it appears that at this early stage of Kazakhstan’s AI governance, external factors are not yet influencing firm behavior. The findings highlighted a strategic imbalance amongst Kazakhstani MSMEs, concluding that they tend to prioritize GenAI tools over human capital that is required for their effective use. Limitations exist due to a smaller sample size and a cross-sectional research design. Future studies should use longitudinal methodologies, expand sampling techniques, and assess attitudinal constructs (i.e., technology acceptance model) to provide further insight into GenAI adoption in MSMEs operating within emerging markets.
dc.identifier.citationBozbayev, Z., Kabdrakhmetova, Z., & Ramatulla, B. (2026). Evaluation of GenAI usage for new product development in digital product MSMEs in Kazakhstan. Nazarbayev University School of Engineering and Digital Sciences.
dc.identifier.urihttps://nur.nu.edu.kz/handle/123456789/18937
dc.language.isoen
dc.publisherNazarbayev University School of Engineering and Digital Sciences
dc.rightsAttribution 3.0 United Statesen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/
dc.subjectGenerative AI
dc.subjectNew Product Development
dc.subjectMSMEs
dc.subjectKazakhstan
dc.subjectTOE Framework
dc.subjectResource-Based View
dc.subjectPLS-SEM
dc.subjectTechnology Adoption
dc.titleEvaluation of GenAI Usage for New Product Development in Digital Product MSMEs in Kazakhstan
dc.typeMaster's Capstone project

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Master's Capstone project
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