Data-Driven Service Systems: Applications of Social Media Dialogue Mining

Abstract

This thesis introduces the concept data-driven service systems (DDSS) to clarify the increasing adoption of big data and analytics for changing and improving the service systems where a configuration of entities (e.g. companies, customers, competitors, and diverse stakeholders) performs service exchanges and resource integration to co-create value. Grounded in a value co-creation perspective (e.g. Vargo and Lusch 2004, 2008; Gronroos 2008, 2011), this thesis proposes a DDSS framework. The framework can serve as a set of theoretical constructs, a strategic tool, and an IT prototype for researchers and practitioners to specify, design, implement, and evaluate the use of data in promoting positive system transformation. This thesis includes three prepared journal manuscripts representing three applications that evaluate the utility of the DDSS framework. The first and second paper were conducted to examining dialogue data collected from Twitter customer care platform for addressing the research issue of service recovery. The first paper aims to provide a better understanding of customer complaint management in the dynamic service environment and uncover important activities and contexts that influence customer satisfaction. The second paper theorised a data analytical model for mining service recovery dialogues and advanced the dialogue analysis that used to be done by qualitative data analysis methods. The dialogue-mining model facilitates text mining and process mining to investigate three dimensions of dialogue including linguistic and semantic, process and relationship, and thus allows researchers to capture more insightful knowledge from a vast volume of dialogue data. Finally, Twitter dialogue data was further applied to investigate the issue of corporate social innovation in the third paper. This paper demonstrated a data-driven approach to extract and internalise stakeholder knowledge embedded in dialogues and thus, indicate opportunities for social innovation. For each paper, novel data analytical approaches were developed to analyse unstructured data that comprises 95% of big data. In particular, text mining was used to automate information extraction in unstructured dialogue data based on specific domain knowledge (e.g. ontologies, dictionaries). In this way, text-mining approaches can provide contributions beyond the methodological and shed light into focal research domains.

Date of Award	1 Aug 2018
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Babis Theodoulidis (Supervisor) & Jamie Burton (Supervisor)