Abstract
Digital transformation has revolutionized business operations and customer service with the rise of automation. This research aims to quantify the influence of Digital Transformation (DT) on customer service automation and predict open innovation patterns in small and medium-sized enterprises (SMEs) using a hybrid approach. The study will use a combination of quantitative and qualitative analysis methods to assess the extent of DT in SMEs and its impact on customer service automation. Data will be obtained from a trial of SMEs through surveys and interviews to gather information on their adoption of digital technologies and the level of automation in their customer service processes. The convenience sampling method is used for the data collection. However, to successfully implement these technologies, SMEs need to adopt effective knowledge management practices that can enable them to acquire, store, transfer, and exploit the knowledge generated from their interactions with customers driven by a customer-centric approach. Furthermore, the enquiry will develop a predictive model to identify open innovation patterns in SMEs by combining machine learning algorithms with expert knowledge. This hybrid approach will leverage the power of automated data analysis while incorporating the domain expertise of professionals in the field. The utilization of Morphological-Linear Neural Network (MLNN) in conjunction with a Logistic Regression model for addressing complex pattern recognition and prediction tasks. This study predicts the high ratings of brands for the base of customer satisfaction. The proposed work involves a detailed analysis utilizing the Statistical Package for the Social Sciences (SPSS) software, specifically tailored for SMEs. Cronbach's Alpha is a quantity of internal consistency or reliability in established items in a questionnaire or survey. The sum of squares between people is 45189.032, with 944 degrees of freedom, resulting in a mean square of 47.870. The findings can help SMEs make informed decisions regarding their DT strategies and potentially drive more effective and efficient customer service operations.