Designing Engaging Personalities for Conversational AI Agents to Enhance Employee Interaction in an Enterprise Crowdsourcing Context

Abstract

Conversational agents (CAs) are increasingly being adopted across various domains, with enterprises particularly interested in leveraging these technologies to explore new opportunities. CAs, such as Copilot, are transforming human-computer collaboration in the workplace by serving as effective work assistants. Additionally, their advanced natural language understanding capabilities position them as powerful tools for facilitating crowdsourcing within enterprises.
This thesis investigates the dual role of CAs in enterprises: as work assistants for employees and as facilitators of crowdsourcing. By acting as work assistants, CAs interact with a broad range of employees, making them well-suited to gather valuable insights that contribute to the company’s knowledge base and support collaborative problem-solving. The central research question explores how to effectively engage and motivate employees in this crowdsourcing process through CA personality design, which is relatively unexplored. Drawing on the "Computers Are Social Actors" (CASA) framework by Nass et al. (1994), this thesis examines CA personality design, aiming to enhance human-CA interaction by eliciting social responses from employees and thereby increasing their engagement.
We approached these research questions from three perspectives: (1) designing and specifying CA personalities for effective implementation in enterprises, (2) identifying the most and least promising qualities of CA personality designs in this context, and (3) designing an empirical study within the enterprise to measure which personality archetype design is the most effective.
We reviewed literature on the context of conversational crowdsourcing in enterprise settings, and CA design methods with a focus on CA anthropomorphism. These findings informed the initial design phase, where CA personality archetypes were tested in an enterprise environment. Based on these insights, we developed design guidelines for enterprise CA personality design. After refining the archetypes, we proposed three unique CA personalities aimed at enhancing employee participation in enterprise crowdsourcing through their interactions with CAs. These archetypes were then implemented in the enterprise's internal chat platform for an experiment, where employee interactions with the CAs were collected and analysed both quantitatively and qualitatively.
The results of our experiment reveal how different CA personalities impact user engagement in enterprise crowdsourcing, demonstrating how distinct CA personalities can be effectively translated into LLM prompts and perceived by users. The study also highlights the relationship between CA personality and the triggering of users' social responses, as observed through sentiment analysis. Additionally, our research identifies the challenge of balancing social engagement with maintaining professionalism in enterprise CAs to build user trust.
This thesis concludes with a discussion of the findings and their implications for future research in enterprise CA design.