Evaluating Chatbot Responsiveness and Service Quality in E-Commerce: Evidence from Daraz Pakistan

https://doi.org/10.5281/zenodo.18204176

Abstract

The increasing reliance on artificial intelligence (AI)-driven chatbots in e-commerce platforms has raised critical questions about their effectiveness in delivering service quality and meeting consumer expectations. This study investigates the responsiveness and service quality of chatbots in Daraz Pakistan, with specific focus on the SERVQUAL dimensions of reliability, responsiveness, empathy, assurance, and tangibility. Data were collected from 200 active Daraz users through a structured online survey and analyzed using SPSS. Results indicated that chatbot responsiveness was rated moderately high, with 68% of respondents reporting timely replies within one minute, yet only 54% considered the information provided accurate. Reliability scores averaged 3.41 on a 5-point Likert scale, suggesting moderate satisfaction, while empathy and assurance dimensions scored lower at 2.89 and 2.96, reflecting a lack of personalized interaction and trust. Regression analysis demonstrated that responsiveness had the strongest positive effect on overall customer satisfaction (β = 0.42, p < 0.01), followed by reliability (β = 0.36, p < 0.05). However, empathy showed a weak and statistically insignificant effect (β = 0.11, p > 0.05). These findings suggest that while chatbots provide quick and consistent responses, they struggle to replicate the human-like qualities of empathy and assurance, limiting their effectiveness as standalone service tools. The study concludes that improving chatbot reliability and incorporating culturally sensitive empathy frameworks are essential for enhancing customer satisfaction in Pakistan’s e-commerce sector. The findings offer a practical implication to the platform managers who are interested in maximizing chatbots systems and balancing automation and human support.