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A Novel Approach Of Social Media Analytics For Predicting National Consumer Confidence Index

Author

Listed:
  • MUHAMMAD ASHRAF

    (Department of Management Sciences, COMSATS University Islamabad, Vehari Campus)

  • ARSLAN ALI RAZA

    (Department of Computer Science, COMSATS University Islamabad, Vehari Campus)

  • MUHAMMAD ISHAQ

    (Department of Mathematics, COMSATS University Islamabad, Vehari Campus)

Abstract
Consumer confidence captures the difference in consumer attitudes when making buying decisions. The consumer confidence index (CCI) is referenced by businesses, governments and other institutions when they make strategic decision. Analysis of Pakistani official consumer confidence index in comparison with the actual data reveals inconsistency between the official index and actual state of the economy in Pakistan. The data used in computing the official CCI has been collected using cross-sectional survey which has inherent limitations which spur research efforts to clarify and develop evidence on associations that form the core of the economic indicator of consumer confidence. It poses challenges to industry with regards to measuring the CCI as it is subjected to rigorous analysis. One aspect of such dynamic has to do with the emergence of new sources of digital data that could be used to measure consumer confidence. However, it is conjectured that using consumer sentiment data on social media might show greater marginal significance for consumer confidence, because the surveys might capture effects that will not appear in the data. Social media can offer a huge volume of data on consumer confidence, the analysis of which can be conducted at a more rapid time and can also refine the accuracy of the CCI using data from far larger populations. However, this study aims to propose a novel method of social media analytics for predicting national consumer confidence index (NACOP) that reflect true state of the economy. The proposed NACOP utilizes big data and data science to predict the national consumer confidence using large data sets of purchasing behaviour, jobs/employment, consumer price and personal finance from social media platforms. The study also explains the architecture of proposed NACOP and provides significant implications to academicians and practitioners.

Suggested Citation

  • Muhammad Ashraf & Arslan Ali Raza & Muhammad Ishaq, 2022. "A Novel Approach Of Social Media Analytics For Predicting National Consumer Confidence Index," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 11(2), pages 220-234, June.
    • Handle: RePEc:rfh:bbejor:v:11:y:2022:i:2:p:220-234
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    References listed on IDEAS

    as
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    Cited by:

    1. Muhammad Ashraf & Arslan Ali Raza & Muhammad Ishaq & Wareesa Sharif & Asad Abbas, 2022. "Real-Time Extraction and Annotation of Social Media Contents for Predicting National Consumer Confidence Index," Journal of Policy Research (JPR), Research Foundation for Humanity (RFH), vol. 8(4), pages 292-309, December.

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    More about this item

    Keywords

    Big data; Consumer confidence index; NACOP; Sentiment analysis; Social media analytics;
    All these keywords.

    JEL classification:

    • L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media

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