Expertise-building as the engine of advanced generative artificial intelligence utilization
TL;DR
The findings suggest that Expertise-Building serves as a sufficient proxy for predicting user behavior, as the action cannot be completed without the essential Expertise-Building "gatekeeper", and contributes to both scholarly discourse and industry practice.
Abstract
The rapid advancement of near human Generative Artificial Intelligence (GAI) systems is reshaping the modern software engineering and development ecosystem, altering practitioners' workflows, cognitive processes, and technology engagement patterns. This research undertaking, through an empirical quantitative study, analyzed the structural drivers of Intended Use (IU) within a complex cognitive framework, challenging traditional technology acceptance paradigms that prioritize ease of use. Motivated by an industry rooted business problem of adoption variability in the fast-paced markets, competition and corporate dynamics, this study investigated the mechanisms through which software engineering and development practitioners build Expertise in GAI utilization and form Intentions to adopt GAI for algorithmic design and code generation and broader software engineering tasks. Thus, this study highlights the internalized logic--the mental model--that a developer builds when interacting with an AI. Grounded in extant theory and contextualized within business software engineering and development dynamics, the study examined how Familiarity, Trust, Perceived Usability, Perceived Behavioral Control, Deterrence, and Complexity influence GAI Expertise Building and Adoption decisions in the context of modern software engineering ecosystem. Therefore, this study intended to derive new findings and conclusions on what theory-based and industry factors contextualize and affect this process, with emphasis on the said ecosystem. The results revealed a highly efficient, parsimonious "engine" model. The negligible impact of deterrence (R²= 0.03) further underscored that user intent is driven by capability-based "pull" rather than an inhibitor-based "push". On a more granulated outlook, the major takeaway from this study is that although the predictors in our model would intuitively make sense individually, what I tried to unravel was to tell the story of the relative importance of each expected predictor within a parsimonious model, as well as the role of statistical controls in moderating these predictors. All predictors were anticipated to be important because we already know they affect the acceptance and adoption of other types of information technologies. The findings suggest that Expertise-Building serves as a sufficient proxy for predicting user behavior, as the action cannot be completed without the essential Expertise-Building "gatekeeper". GLM results in particular, emphasized that the "action" is a function of capability, not logistics. On an exploratory level, I also tested possible interaction among two important predictors, extending understanding of how adoption behaviors emerge in complex high-tech environments. Additional important implications, as well as study limitations and future research recommendations are also discussed. To enhance translational relevance, this study also addresses the practical challenges software engineering and development practitioners face during early-stage GAI adoption--particularly in enterprise grade software engineering contexts where GAI integration requires new models of learning, skill acquisition, trust calibration, and workflow adaptation. The findings offer actionable guidance and recommendations for developing corporate and organizational dynamic capabilities, such as GAI related learning curves, confidence in the utilization of GAI, and expertise development pathways. These insights support software engineers, development managers, R&D units, program leaders, and executive decision makers in designing GAI enabled workflows and methodologies, governance structures, and project strategies that enhance productivity, efficiency, and innovation across software manufacturing workflows. Collectively, the study contributes to both scholarly discourse and industry practice by illuminating how Expertise-Building in GAI technologies is cultivated and how such Expertise drives effective and confident Adoption in the software engineering and development ecosystem. Keywords: expertise-building of GAI utilization, GAI adoption for algorithm design, software engineering with AI, GAI for code building, trust, distrust, familiarity, perceived ease of use, perceived behavioral control, deterrence, complexity.