Balancing Customer Effort and Communication Outcomes in Chatbot and Human-Agent Customer Interactions: A Quantitative Study

Abstract

In digital era, organizations continue to face a significant dilemma between the efficiency of automation and the quality of human-centered service. Existing literature has extensively studied chatbot adoption, customer satisfaction, and technology acceptance; yet, there is limited empirical evidence on the balance between customer effort and communication outcomes in chatbot versus human-agent interactions. This research aimed to address this gap by exploring the extent to which chatbots, compared with human agents, provide a more effective balance between customer effort and communication outcomes. Four hypotheses were developed and tested using a quantitative research design. Data were collected through an online survey administered via Qualtrics, yielding 291 valid responses. SPSS version 30 was used to conduct descriptive and inferential statistical analyses. Additionally, this research was grounded in the Technology Acceptance Model (TAM). The results revealed that chatbot interactions were perceived as more effective for low-complexity, transactional service tasks because they reduced response times and increased convenience. In contrast, human-agent interactions yielded stronger outcomes in high-complexity and emotionally sensitive contexts, especially regarding trust, reassurance, and perceived resolution quality. The results further suggest that hybrid chatbot–human service models may provide the strongest effort–outcome balance by combining the efficiency of automation with human empathy and deeper problem-solving capabilities.