Data collaboratives as “bazaars”? A review of coordination problems and mechanisms to match demand for data with supply

Abstract

Purpose

In “data collaboratives”, private and public organizations coordinate their activities to leverage data to address a societal challenge. This paper aims to focus on analyzing challenges and coordination mechanisms of data collaboratives.

Design/methodology/approach

This study uses coordination theory to identify and discuss the coordination problems and coordination mechanisms associated with data collaboratives. The authors also use a taxonomy of data collaborative forms from a previous empirical study to discuss how different forms of data collaboratives may require different coordination mechanisms.

Findings

The study analyzed data collaboratives from the perspective of organizational and task levels. At the organizational level, the authors argue that data collaboratives present an example of the bazaar form of coordination. At the task level, the authors identified five coordination problems and discussed potential coordination mechanisms to address them, such as coordination by negotiation, by third party, by standardization, to name a few.

Research limitations/implications

This study is one of the first few to systematically analyze the phenomenon of “data collaboratives”.

Practical implications

This study can help practitioners better understand the coordination challenges they may face when initiating a data collaborative and to develop successful data collaboratives by using coordination mechanisms to mitigate these challenges.

Originality/value

Data collaboratives are a novel form of data-driven initiatives which have seen rapid experimentation lately. This study draws attention to this concept in the academic literature and highlights some of the complexities of organizing data collaboratives in practice.

Citation

Susha, I., Janssen, M. and Verhulst, S. (2017), "Data collaboratives as “bazaars”? A review of coordination problems and mechanisms to match demand for data with supply", Transforming Government: People, Process and Policy, Vol. 11 No. 1, pp. 157-172. https://doi.org/10.1108/TG-01-2017-0007