Digital Citizen Co-Production and the Role of Public Service Motivation

Abstract

In recent times, a growing involvement of public organizations in the process of digitalization can be observed (Mergel, Rethemeyer & Isett, 2016). In this context, one area of particular importance is the increasing use of information and telecommunications technology (ICT) as a tool to facilitate citizen-government interaction. This development holds substantial opportunities for government to have more people engage in the co-production of public services. Simultaneously, government can improve its relationship with citizens through intensified exchange, greater openness, and more transparency (Harrison et al., 2012). Furthermore, ICT-based co-production can help improve the quality and effectiveness of public services (Chun et al., 2010; Fang, 2002). Consequently, a research question of paramount importance is what motivates individuals to engage in co-production with government. While this question has received a fair amount of scholarly attention in the past (e.g. Jakobsen, 2013; Thomsen, 2015; Van Eijk & Steen, 2015), to our knowledge, no studies exist on what the motivational drivers of co-production are when carried out though the intensifying digital links between government and citizens. This study seeks to fill this void by investigating the motivational specificities of digital co-production. To this end, we have collected data from over 800 users of an online tool run by the city of Zurich in Switzerland which allows users to digitally report damages in the city’s infrastructure via their smartphones. In this survey, we asked participants about their motivations to use the app, particularly focusing on the concept of public service motivation (PSM) (Perry & Wise, 1990). We now plan to link the survey data to a dataset containing the actual number of reports each user has filed and analyze how this measure of a behavioral outcome relates to the various motivations. Thus, as the response variable is count data, we will rely on poisson regression as an analytical method.