Understanding Service Providers’ Competency in Knowledge-Intensive Crowdsourcing Platforms: An LDA Approach

Abstract

Knowledge-intensive crowdsourcing (KIC) is becoming one of the most promising domains of crowdsourcing by leveraging human intelligence and building a large labor-intensive service network. In this network, the service providers (SPs) constitute the backbone of the KIC platform and play an important role in connecting the platform and service requesters. The SPs are a group of distributed crowds with a complex composition and high level of uncertainty, resulting in great challenges in service quality and platform management. Understanding the SPs’ competency is an effective way for the platform to manage them. Therefore, we attempt to connect the competency analysis to the environment of KIC to investigate and identify the criteria of SPs’ competency (i.e., the competency factors and dimensions required for being competent for the SPs’ business). To this end, we leverage the Latent Dirichlet Allocation (LDA) model to explore and extract hidden competency dimensions from online interview records. We then introduce the competency theory to identify and label the competency factors and dimensions and construct the three-level KSAT competency model, which presents a comprehensive vision of the SPs’ performance standards in the context of KIC. Given the competency criteria in the KSAT competency model, we use the Best-Worst Method (BWM) to determine their weights, which reflect their importance when evaluating the SPs’ competency from the platforms’ perspective. The results show that skill and knowledge are the two most important competency factors, and customer relationship management and communication ability are the two most valuable competency dimensions when evaluating the SPs’ competency. Furthermore, the KSAT competency model can be applied to analyze the competency of individuals or organizations in many other industries as well.