Chatbots for Robotic Process Automation: Investigating Perceived Trust and User Satisfaction

TL;DR

This study investigates how perceived trust and user satisfaction are influenced by task complexity in human-chatbot interactions within robotic process automation, highlighting that simpler, multi-step conversations enhance user experience.

Contribution

It provides empirical evidence on the impact of task complexity on trust and satisfaction, emphasizing conversation structure in chatbot design for automation.

Findings

Higher task complexity reduces trust and satisfaction.

Short, multi-step conversations are perceived as less complex.

Descriptive, broad answers increase perceived complexity.

Abstract

Driven by ongoing improvements in machine learning, chatbots have increasingly grown from experimental interface prototypes to reliable and robust tools for process automation. Building on these advances, companies have identified various application scenarios, where the automated processing of human language can help foster task efficiency. To this end, the use of chatbots may not only decrease costs, but it is also said to boost user satisfaction. People's intention to use and/or reuse said technology, however, is often dependent on less utilitarian factors. Particularly trust and respective task satisfaction count as relevant usage predictors. In this paper, we thus present work that aims to shed some light on these two variable constructs. We report on an experimental study ($n=277$), investigating four different human-chatbot interaction tasks. After each task, participants were asked to complete survey items on perceived trust, perceived task complexity and perceived task satisfaction. Results show that task complexity impacts negatively on both trust and satisfaction. To this end, higher complexity was associated particularly with those conversations that relied on broad, descriptive chatbot answers, while conversations that span over several short steps were perceived less complex, even when the overall conversation was eventually longer.