IDEAS home Printed from https://ideas.repec.org/a/ibf/ijmmre/v15y2022i1p1-13.html
   My bibliography  Save this article

What Drives Higher Beer Ratings? Evidence From Big Data

Author

Listed:
  • Andrew Manikas
  • Michael Godfrey
  • Jason Woldt

Abstract

The purpose of this paper is to present a data analytics model utilizing a large database of beer reviews. We are witnessing a big data revolution with companies’ ability to capture and analyze large volumes, velocity, and variety of consumer spending patterns and purchase factors. Analysis of consumer reviews recently has shifted from focusing only on consumer ratings to also mining the content of those reviews. Using a sample of approximately 9 million reviews over 22 years from BeerAdvocate, we developed a multiple regression model to test the relationship of alcohol by volume (ABV) and review word length to the rating that a beer received. Our main finding was that the more (ABV) that a beer had, the higher was that beer’s average rating. Although increased ABV was associated with higher consumer scores, we found diminishing returns. We also confirmed that negativity bias occurred in these reviews—lower-scoring beers tended to have longer reviews than higher scoring beers had. This suggests that breweries would be advised to have higher alcohol content in their products to meet consumer preferences (even if those preferences are subconscious). Given the supply chain disruptions plaguing the world today, big data analysis of consumer preferences for beer could enable both manufacturers with constrained capacity to better align product offerings with consumer tastes and preferences.

Suggested Citation

  • Andrew Manikas & Michael Godfrey & Jason Woldt, 2022. "What Drives Higher Beer Ratings? Evidence From Big Data," International Journal of Management and Marketing Research, The Institute for Business and Finance Research, vol. 15(1), pages 1-13.
    • Handle: RePEc:ibf:ijmmre:v:15:y:2022:i:1:p:1-13
    as

    Download full text from publisher

    File URL: http://www.theibfr2.com/RePEc/ibf/ijmmre/ijmmr-v15n1-2022/IJMMR-V15N1-2022-1.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liesbeth Colen & Johan Swinnen, 2016. "Economic Growth, Globalisation and Beer Consumption," Journal of Agricultural Economics, Wiley Blackwell, vol. 67(1), pages 186-207, February.
    2. Meyerding, Stephan G.H. & Bauchrowitz, Alexander & Lehberger, Mira, 2019. "Consumer preferences for beer attributes in Germany: A conjoint and latent class approach," Journal of Retailing and Consumer Services, Elsevier, vol. 47(C), pages 229-240.
    3. Shalini Talwar & Puneet Kaur & Samuel Fosso Wamba & Amandeep Dhir, 2021. "Big Data in operations and supply chain management: a systematic literature review and future research agenda," International Journal of Production Research, Taylor & Francis Journals, vol. 59(11), pages 3509-3534, June.
    4. Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    5. Erevelles, Sunil & Fukawa, Nobuyuki & Swayne, Linda, 2016. "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, Elsevier, vol. 69(2), pages 897-904.
    6. Gandomi, Amir & Haider, Murtaza, 2015. "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, Elsevier, vol. 35(2), pages 137-144.
    7. Alain Yee Loong Chong & Eugene Ch’ng & Martin J. Liu & Boying Li, 2017. "Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5142-5156, September.
    8. Boone, Tonya & Ganeshan, Ram & Jain, Aditya & Sanders, Nada R., 2019. "Forecasting sales in the supply chain: Consumer analytics in the big data era," International Journal of Forecasting, Elsevier, vol. 35(1), pages 170-180.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Terrance Jalbert & Jonathan D. Stewart, 2022. "A Comprehensive Retirement Financial Planning Tool," International Journal of Management and Marketing Research, The Institute for Business and Finance Research, vol. 15(1), pages 47-76.
    2. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    3. Pan, Qiaohong & Luo, Wenping & Fu, Yi, 2022. "A csQCA study of value creation in logistics collaboration by big data: A perspective from companies in China," Technology in Society, Elsevier, vol. 71(C).
    4. Tanzeela AQIF & Abdul WAHAB, 2022. "Reshaping The Future Of Retail Marketing Through Big Data: A Review From 2009 To 2022," Management Research and Practice, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 14(3), pages 5-24, September.
    5. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    6. Harkaran Kava & Konstantina Spanaki & Thanos Papadopoulos & Stella Despoudi & Oscar Rodriguez-Espindola & Masoud Fakhimi, 2021. "Data Analytics Diffusion in the UK Renewable Energy Sector: An Innovation Perspective," Post-Print hal-03781046, HAL.
    7. Boccali, Filippo & Mariani, Marcello M. & Visani, Franco & Mora-Cruz, Alexandra, 2022. "Innovative value-based price assessment in data-rich environments: Leveraging online review analytics through Data Envelopment Analysis to empower managers and entrepreneurs," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    8. Jain, Geetika & Paul, Justin & Shrivastava, Archana, 2021. "Hyper-personalization, co-creation, digital clienteling and transformation," Journal of Business Research, Elsevier, vol. 124(C), pages 12-23.
    9. Miikka Blomster & Timo Koivumäki, 2022. "Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing," Information Systems and e-Business Management, Springer, vol. 20(1), pages 123-169, March.
    10. Francesco Cappa & Stefano Franco & Federica Rosso, 2022. "Citizens and cities: Leveraging citizen science and big data for sustainable urban development," Business Strategy and the Environment, Wiley Blackwell, vol. 31(2), pages 648-667, February.
    11. Erkip, Nesim Kohen, 2023. "Can accessing much data reshape the theory? Inventory theory under the challenge of data-driven systems," European Journal of Operational Research, Elsevier, vol. 308(3), pages 949-959.
    12. Pascucci, Federica & Nardi, Lorenzo & Marinelli, Luca & Paolanti, Marina & Frontoni, Emanuele & Gregori, Gian Luca, 2022. "Combining sell-out data with shopper behaviour data for category performance measurement: The role of category conversion power," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    13. Wang, Le & Yan, Jie & Lin, Jun & Cui, Wentian, 2017. "Let the users tell the truth: Self-disclosure intention and self-disclosure honesty in mobile social networking," International Journal of Information Management, Elsevier, vol. 37(1), pages 1428-1440.
    14. Vaibhav S. Narwane & Rakesh D. Raut & Sachin Kumar Mangla & Manoj Dora & Balkrishna E. Narkhede, 2023. "Risks to Big Data Analytics and Blockchain Technology Adoption in Supply Chains," Annals of Operations Research, Springer, vol. 327(1), pages 339-374, August.
    15. Božič, Katerina & Dimovski, Vlado, 2019. "Business intelligence and analytics for value creation: The role of absorptive capacity," International Journal of Information Management, Elsevier, vol. 46(C), pages 93-103.
    16. Conboy, Kieran & Mikalef, Patrick & Dennehy, Denis & Krogstie, John, 2020. "Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda," European Journal of Operational Research, Elsevier, vol. 281(3), pages 656-672.
    17. Xu, Jinou & Pero, Margherita & Fabbri, Margherita, 2023. "Unfolding the link between big data analytics and supply chain planning," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    18. Falana, Gbenga Ayodele & Olusola Esther (PhD) & Dagunduro, Muyiwa Emmanuel, 2023. "Effect of Big Data on Accounting Information Quality in Selected Firms in Nigeria," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 7(3), pages 789-806, March.
    19. Ragmoun Wided, 2023. "IT Capabilities, Strategic Flexibility and Organizational Resilience in SMEs Post-COVID-19: A Mediating and Moderating Role of Big Data Analytics Capabilities," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(1), pages 123-142, March.
    20. Cristescu Marian Pompiliu & Nerişanu Raluca Andreea & Mara Dumitru Alexandru, 2022. "Using Data Mining in the Sentiment Analysis Process on the Financial Market," Journal of Social and Economic Statistics, Sciendo, vol. 11(1-2), pages 36-58, December.

    More about this item

    Keywords

    L31; L84; M11;
    All these keywords.

    JEL classification:

    • L31 - Industrial Organization - - Nonprofit Organizations and Public Enterprise - - - Nonprofit Institutions; NGOs; Social Entrepreneurship
    • L84 - Industrial Organization - - Industry Studies: Services - - - Personal, Professional, and Business Services
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ibf:ijmmre:v:15:y:2022:i:1:p:1-13. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mercedes Jalbert (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.