Exploratory Factor Analysis of Financial Modeling Chatbot Features Factor

Overview

This study examines the factors that determine the effectiveness of chatbots designed to create financial models for startup enterprises. Financial modeling represents a critical operational requirement for startups, particularly in business planning and capital acquisition contexts, yet many startup teams demonstrate insufficient financial expertise to construct adequate models independently. While chatbot technology has been deployed across numerous functional domains, its application to financial modeling has received minimal empirical attention. The research addresses this gap by identifying and analyzing the specific chatbot characteristics that enable effective financial model generation, with particular focus on startup environments in Thailand where resource constraints and knowledge deficits are common operational challenges.

Methods and approach

The investigation employed a mixed-methods research design to collect data from Thai startup organizations. Exploratory Factor Analysis served as the primary analytical technique to identify and structure the latent factors that influence chatbot effectiveness in financial modeling applications. The sample comprised startup founders and personnel who constitute the primary user population for financial modeling tools. The EFA methodology enabled the researchers to distill the complex array of potential chatbot features and capabilities into coherent, interpretable factor structures without imposing predetermined theoretical constraints on the data. This exploratory approach was appropriate given the nascent state of research on chatbots in financial modeling contexts.

Results

The Exploratory Factor Analysis identified three principal factors that determine chatbot capability in financial modeling tasks. The first factor, ease of use, encompasses the accessibility and usability dimensions of the chatbot interface and interaction design. The second factor, recommendation capability, reflects the chatbot's capacity to provide guidance and suggestions throughout the financial modeling process. The third factor consists of advanced features, which includes sophisticated functionalities that extend beyond basic financial model construction. These factors emerged as statistically significant and conceptually distinct dimensions that collectively characterize effective financial modeling chatbots. The factor structure provides an empirical foundation for understanding which chatbot attributes matter most to startup users engaged in financial modeling activities.

Implications

The identification of these three factors provides actionable guidance for chatbot developers working on financial modeling applications. Development priorities can be organized around enhancing usability, strengthening recommendation algorithms, and implementing advanced analytical capabilities. The findings suggest that effective financial modeling chatbots require a balanced approach that addresses both fundamental usability requirements and sophisticated analytical functions. For startup ecosystems, particularly in emerging markets where financial literacy and access to expertise may be limited, chatbots that embody these factors could reduce barriers to sound financial planning and improve the quality of financial projections used in operational decision-making and investor communications. The research framework established through this exploratory analysis can be extended to other contexts and refined through confirmatory studies that test the stability and generalizability of the identified factor structure across different geographical markets and startup sectors.

Disclosure

• Research title: Exploratory Factor Analysis of Financial Modeling Chatbot Features Factor
• Authors: Patarachet Soodsanguan, Satidchoke Phosaard
• Publication date: 2026-01-08
• DOI: https://doi.org/10.14416/j.it.2026.v1.006
• OpenAlex record: View
• Image credit: Photo by Ahmed Khan on Freepik (Source • License)
• Disclosure: This post was generated by artificial intelligence. The original authors did not write or review this post.