Social Collaboration Analytics Framework: A framework for providing business intelligence on collaboration in the digital workplace

作者:

Highlights:

• A novel framework for analyzing Enterprise Collaboration Systems data is proposed.

• Applies a design science approach supported by mixed-methods data collection.

• Collaboration system log data is analyzed to conclude on workspace types.

• Demonstrates deriving business intelligence about the digital workplace.

摘要

Enterprise collaboration systems (ECS) have become the core of the digital workplace in many organizations. More and more companies have introduced this new business software for supporting computer-mediated collaboration among employees. The emergence of ECS in the technical and strategical landscape of companies leads to new challenges in business intelligence and reporting. In contrast to traditional business software such as ERP systems, there are no established methods for decision-making in ECS projects. The collaboration professionals responsible for managing the ECS require insights into the use of an ECS to understand how employees use these systems. By developing the Social Collaboration Analytics Framework, this work provides a framework for establishing the analysis of collaboration activities in the digital workplace (social collaboration analytics) as part of organizations' business intelligence. The Social Collaboration Analytics Framework consists of distinct phases and guides the analysis of ECS data. It includes working steps, recommendations, and guiding questions developed based on the findings from longitudinal research in a university-industry collaboration context and a comparison of established data mining process models.

论文关键词:Social collaboration analytics,Business intelligence,Analytics,Enterprise collaboration systems,Design science,Mixed method

论文评审过程:Received 20 July 2020, Revised 28 March 2021, Accepted 4 May 2021, Available online 6 May 2021, Version of Record 7 July 2021.

论文官网地址:https://doi.org/10.1016/j.dss.2021.113587