CLASSIFICATION MODEL FOR HOW LONG UNTIL EMPLOYEES ADOPT AGILE TRANSFORMATION: THE CASE OF A PRE-DIGITAL ORGANIZATION IN THAILAND

Abstract

[Purpose]: Pre-digital organizations have transformed their organizations for more agility. Agile transformation requires a significant investment of time and effort. The primary challenge encountered in agile transformation is employees' absence of an Agile mindset. If pre-digital organizations can identify employees who can adopt agile methodologies quickly, companies may expedite these employees' transition to an agile approach earlier. Thus, the research question is how pre-digital organizations can predict how long it will be until employees adopt agile. The primary research aimed to identify essential human factors affecting agile adoption.

[Design]: This research utilizes classification model development to classify employees who tend to embrace agile methodologies faster by relying on three algorithms, namely Decision Tree, Naïve Bayes, and k-Nearest Neighbors. The research mainly utilizes a quantitative approach, in which questionnaires collected data from 80 participants.

[Findings]: Research findings indicate that the classification model from the Naïve Bayes algorithm with evolutionary feature selection and range transformation normalization provides the highest accuracy at 53.75%. At the same time, k-NN and decision tree algorithms can provide accuracy at 47.50% and 42.50%, respectively. The finding also reveals that human factors, including collaboration, communication, trust, and administration, influence the duration of Agile adoption at a significant level. Moreover, researchers found a substantial correlation between Administration and Trust, Communication and Collaboration, and Administration and Communication.

[Originality]: This research fills the academic gap by demonstrating that organizations can develop the classification model to predict the duration of agile adoption for each employee using their human factors score.