Mobile crowdsourcing-based data collection for user-centered facility maintenance management

Abstract

Current facility maintenance management (FMM) practices rely heavily on data collection by facility management professionals and FMM systems and, at times, results in inefficiency in facility condition data collection and decision-making for FMM. This is partially because data collection enabled by existing FMM systems lacks (1) top-down information solicitation on facility conditions, such as crowdsourcing task division, and (2) geo-referenced occupant feedback data. Mobile crowdsourcing has great potential to improve current FMM practices, especially in terms of timely data collection. In this context, this study explores the feasibility of mobile crowdsourcing for FMM data collection and highlights the associated opportunities and challenges. A survey was conducted on a university campus to gain an understanding of the human, data, system, geospatial, and automation characteristics of mobile crowdsourcing for FMM data collection on post-secondary campuses. The survey results were confirmed by FM professionals through a focus group discussion and analyzed to reveal the challenges and recommendations for mobile crowdsourcing for user-centered FMM. A conceptual framework is proposed to apply mobile crowdsourcing to the FMM. This research contributes to the body of knowledge by synthesizing the challenges and opportunities associated with mobile crowdsourcing-based data collection for facility maintenance and providing a framework for its application.