Decoding internal customer satisfaction in services firms through the lens of ability, motivation and opportunity framework using text mining approaches

This study explores the key factors driving internal customer satisfaction in service firms by integrating the Ability-Motivation-Opportunity (AMO) framework with advanced Natural Language Processing (NLP) techniques. Using employee-generated online reviews as the primary data source, the research examines how high-performance work practices centered on ability, motivation, and opportunity influence satisfaction. The study employs Latent Dirichlet Allocation (LDA) for topic modeling to uncover major themes, which are then aligned with the dimensions of the AMO framework. Sentiment analysis further evaluates the relationships between these themes and satisfaction levels. By focusing on the internal customer perspective, this research addresses a critical gap in the literature, offering insights that extend beyond traditional organizational outcomes. Methodologically, it introduces innovative approaches to capture a more authentic and nuanced understanding of employee sentiments and experiences, which are essential for fostering desirable service delivery behaviors. The study also incorporates emotion analysis to examine the associations between various emotions and the AMO factors, providing deeper insights into how employees emotionally respond to high-performance work practices. The findings highlight that practices such as intrinsic and extrinsic motivation, training and development, recruitment strategies, thoughtful job design, and active employee involvement significantly enhance internal customer satisfaction in service firms. Moreover, the findings from emotion analysis highlight that trust is associated with training and development, fear with knowledge sharing and anger with extrinsic motivation, autonomy and recruitment.