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

Chan, Hing Kai, Wang, Xiaojun, Lacka, Ewelina and Zhang, Min (2016) A mixed-method approach to extracting the value of social media data. Production and Operations Management, 25 (3). 568–583. ISSN 1937-5956

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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 through probability weighting function. A case study is employed to test the proposed approach.

Item Type: Article
Additional Information: This article is protected by copyright. All rights reserved.
Uncontrolled Keywords: social media,mixed-method,product innovation,business intelligence,analytics,sdg 9 - industry, innovation, and infrastructure ,/dk/atira/pure/sustainabledevelopmentgoals/industry_innovation_and_infrastructure
Faculty \ School: Faculty of Social Sciences > Norwich Business School
UEA Research Groups: Faculty of Social Sciences > Research Groups > Innovation, Technology and Operations Management
Depositing User: Pure Connector
Date Deposited: 15 Mar 2016 10:04
Last Modified: 21 Oct 2022 00:52
URI: https://ueaeprints.uea.ac.uk/id/eprint/57446
DOI: 10.1111/poms.12390


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