An Information Management Model for Addressing Residents’ Complaints through Artificial Intelligence Techniques

Abstract

Construction companies usually record customer complaints as unstructured texts, resulting in unsuitable information to understand defect occurrences. Moreover, complaint databases are often manually classified, which is time-consuming and error-prone. However, previous studies have not provided guidance on how to improve customer complaint data collection and analysis. This research aims to devise an information management model for customer complaints in residential projects. Using Design Science Research, a study was undertaken at a Brazilian residential building company. Multiple sources of evidence were used, including interviews, participant observations, and analysis of an existing database. Natural language processing (NLP) was used to build a word menu for customers to lodge a complaint. Moreover, a recommendation system was proposed based on machine learning (ML) and hierarchical defect classification. The system was designed to indicate which defects should be investigated during inspections. The main outcome of this investigation is an information management model that provides an effective classification system for customer complaints, supported by artificial intelligence (AI) applications that improve data collection, and introduce some degree of automation to warranty services. The main theoretical contribution of the study is the use of advanced data management approaches for managing complaints in residential building projects, resulting in the combination of inputs from technical and customer perspectives to support decision-making.