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Quantitative quality control from qualitative data: control charts with latent semantic analysis

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  • Triss Ashton
  • Nicholas Evangelopoulos
  • Victor Prybutok

Abstract

Large quantities of data, often referred to as big data, are now held by companies. This big data includes statements of customer opinion regarding product or service quality in an unstructured textual form. While many tools exist to extract meaningful information from big data, automation tools do not exist to monitor the ongoing conceptual content of that data. We use latent semantic analysis to extract concept factors related to service quality categories. Customer comments found in the data that express dissatisfaction are then considered as representing a non-conforming observation in a process. Once factors are extracted, proportions of nonconformities for service quality failure categories are plotted on a control chart. The results are easily interpreted and the approach allows for the quantitative evaluation of customer acceptance of system process improvement initiatives. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Triss Ashton & Nicholas Evangelopoulos & Victor Prybutok, 2015. "Quantitative quality control from qualitative data: control charts with latent semantic analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 1081-1099, May.
    • Handle: RePEc:spr:qualqt:v:49:y:2015:i:3:p:1081-1099
    DOI: 10.1007/s11135-014-0036-5
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    References listed on IDEAS

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    1. Michele Cocco & Arjuna Tuzzi, 2013. "New data collection modes for surveys: a comparative analysis of the influence of survey mode on question-wording effects," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(6), pages 3135-3152, October.
    2. Michael Scharkow, 2013. "Thematic content analysis using supervised machine learning: An empirical evaluation using German online news," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 761-773, February.
    3. Jane Fielding & Nigel Fielding & Graham Hughes, 2013. "Opening up open-ended survey data using qualitative software," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(6), pages 3261-3276, October.
    4. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    Cited by:

    1. Aykroyd, Robert G. & Leiva, Víctor & Ruggeri, Fabrizio, 2019. "Recent developments of control charts, identification of big data sources and future trends of current research," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 221-232.
    2. Zavala, Araceli & Ramirez-Marquez, Jose Emmanuel, 2019. "Visual analytics for identifying product disruptions and effects via social media," International Journal of Production Economics, Elsevier, vol. 208(C), pages 544-559.

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