A mixed-method approach to extracting the value of social media data

Chan, H. K., Wang, X., Lacka, E. and Zhang, M. (2016) A mixed-method approach to extracting the value of social media data. Production and Operations Management, 25(3), pp. 568-583. (doi: 10.1111/poms.12390)

Abstract

In the last decade, social media platforms have become important communication channels between businesses and consumers. As a result, a lot of consumer - generated data are available online. Unfortunately, they are not fully utilised, partly because of their nature: they are unstructured, subjective, and exist in massive databases. To make use of these data, more than one research method is needed. This study proposes a new, multiple approach to social media data analysis, which counteracts the aforementioned characteristics of social media data. In this new approach the data are first extracted systematically and coded following the principles of content analysis, after a comprehensive literature review has been conducted to guide the coding strategy. Next, the relationships between codes are identified by statistical cluster analysis. These relationships are used in the next step of the analysis, where evaluation criteria weights are derived on the basis of the social media data th rough probability weighting function. A case study is employed to test the proposed approach.

• Item Type: Articles
• Additional Information: This is the peer reviewed version of the following article: Chan, H. K., Wang, X., Lacka, E. and Zhang, M. (2016), A Mixed-Method Approach to Extracting the Value of Social Media Data. Production and Operations Management, 25: 568?583. doi: 10.1111/poms.12390, which has been published in final form at http://dx.doi.org/10.1111/poms.12390. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
• Keywords: social media, m ixed method, product innovation, business intelligence, analytics, Marketing. Distribution of products, Marketing
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lacka, Dr Ewelina
• Authors: Chan, H. K., Wang, X., Lacka, E., and Zhang, M.
• College/School: College of Social Sciences > Adam Smith Business School > Management
• Journal Name: Production and Operations Management
• Publisher: Wiley
• ISSN: 1059-1478
• ISSN (Online): 1937-5956
• Published Online: 29 April 2015
• Copyright Holders: Copyright © 2017 The Authors2015 Production and Operations Management Society
• First Published: First published in Production and Operations Management 25(3):568-583
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher