Quantifying Digital Transformation Impact on Customer Service Automation in SMEs Using Knowledge Management Approach

Abstract

Digital transformation (DT) has significantly impacted business operations and customer service, particularly through the rise of automation. This study quantified the influence of DT on customer service automation in small and medium-sized enterprises (SMEs) and predicted open innovation patterns using a hybrid approach. A combination of quantitative and qualitative analysis methods was employed to assess the extent of DT in SMEs and its impact on automating customer service processes. Data were collected from SMEs through surveys and interviews, using convenience sampling to gather insights on their adoption of digital technologies and automation levels. The study found that effective knowledge management practices are crucial for SMEs to successfully implement these technologies, enabling them to acquire, store, transfer, and utilize knowledge generated from customer interactions. Additionally, a predictive model was developed to identify open innovation patterns, combining machine learning algorithms with expert knowledge. The hybrid approach leveraged the power of automated data analysis alongside professional domain expertise. The utilization of a Morphological-Linear Neural Network (MLNN) in conjunction with a Logistic Regression model demonstrated effectiveness in addressing complex pattern recognition and prediction tasks. The analysis, conducted using SPSS software, revealed that SMEs could enhance customer satisfaction by aligning their DT strategies with customer-centric approaches. The findings offer valuable insights for SMEs, guiding their DT strategies to drive more effective and efficient customer service operations.