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

Hing Kai Chan, Xiaojun Wang, Ewelina Lacka, Min Zhang

Research output: Contribution to journalArticlepeer-review

94 Citations (Scopus)
15 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)568–583
Number of pages16
JournalProduction and Operations Management
Issue number3
Early online date29 May 2015
Publication statusPublished - Mar 2016


  • Social media
  • mixed-method
  • product innovation
  • business intelligence
  • analytics

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