IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v171y2021ics0040162521004157.html
   My bibliography  Save this article

Forecasting the importance of product attributes using online customer reviews and Google Trends

Author

Listed:
  • Yakubu, Hanan
  • Kwong, C.K.

Abstract

During the early stage of product design, product manufacturers seek to identify the most relevant product features that will meet the demands and needs of consumers. Conventionally, several surveys have to be undertaken during the time interval between product design and the launch of anew product, to understand any changes on the importance of the product attributes. However, the process is time-consuming and costly. Recently, online customer reviews have been generated on many websites and can be used to analyse the change of the importance of the product attributes. Also, Google Trends has been adopted in previous studies to understand consumers interests in certain products over a period of time and can be considered in analysing the change in product attributes importance. However, no such kinds of studies have been reported. This study aims to present an empirical approach that uses online big data, to identify and predict product design attributes of products that will be relevant to consumers in the future. To achieve this aim, we propose a methodology for forecasting the future importance of product attributes based on online customer reviews and Google Trends. A case study on an electric hairdryer is presented to illustrate the proposed methodology. Validation tests on the proposed fuzzy rough set time series method were conducted. The test results indicate that the proposed method outperforms the fuzzy time series, the fuzzy k medioid clustering time series and the ANFIS method in terms of forecasting accuracy. Our results contribute to the processes of new product development and can potentially assist R&D managers to establish methodologies and processes for product designs capable of generating higher returns.

Suggested Citation

  • Yakubu, Hanan & Kwong, C.K., 2021. "Forecasting the importance of product attributes using online customer reviews and Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    • Handle: RePEc:eee:tefoso:v:171:y:2021:i:c:s0040162521004157
    DOI: 10.1016/j.techfore.2021.120983
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162521004157
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2021.120983?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German car sales using Google data and multivariate models," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 97-135.
    2. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    3. Abbas Mardani & Mehrbakhsh Nilashi & Jurgita Antucheviciene & Madjid Tavana & Romualdas Bausys & Othman Ibrahim, 2017. "Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature," Complexity, Hindawi, vol. 2017, pages 1-33, October.
    4. Pai Zheng & Xun Xu & Sheng Quan Xie, 2019. "A weighted interval rough number based method to determine relative importance ratings of customer requirements in QFD product planning," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 3-16, January.
    5. Jiunn-I Shieh & Hsin-Hung Wu, 2009. "Applying a hidden Markov chain model in quality function deployment to analyze dynamic customer requirements," Quality & Quantity: International Journal of Methodology, Springer, vol. 43(4), pages 635-644, July.
    6. Lohithaksha M. Maiyar & SangJe Cho & Manoj Kumar Tiwari & Klaus-Dieter Thoben & Dimitris Kiritsis, 2019. "Optimising online review inspired product attribute classification using the self-learning particle swarm-based Bayesian learning approach," International Journal of Production Research, Taylor & Francis Journals, vol. 57(10), pages 3099-3120, May.
    7. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    8. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    9. Lei Zhang & Xuening Chu & Deyi Xue, 2019. "Identification of the to-be-improved product features based on online reviews for product redesign," International Journal of Production Research, Taylor & Francis Journals, vol. 57(8), pages 2464-2479, April.
    10. Lee, In & Shin, Yong Jae, 2020. "Machine learning for enterprises: Applications, algorithm selection, and challenges," Business Horizons, Elsevier, vol. 63(2), pages 157-170.
    11. Weiqiang Jia & Zhenyu Liu & Zhiyun Lin & Chan Qiu & Jianrong Tan, 2016. "Quantification for the importance degree of engineering characteristics with a multi-level hierarchical structure in QFD," International Journal of Production Research, Taylor & Francis Journals, vol. 54(6), pages 1627-1649, March.
    12. Pai Zheng & Xun Xu & Sheng Quan Xie, 2019. "Correction to: A weighted interval rough number based method to determine relative importance ratings of customer requirements in QFD product planning," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 459-459, January.
    13. Chen, Yizeng & Fung, Richard Y.K. & Tang, Jiafu, 2006. "Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1553-1566, November.
    14. Manfred Besner, 2019. "Axiomatizations of the proportional Shapley value," Theory and Decision, Springer, vol. 86(2), pages 161-183, March.
    15. Ozge Cagcag Yolcu, 2013. "A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, November.
    16. Jun, Seung-Pyo & Yoo, Hyoung Sun & Choi, San, 2018. "Ten years of research change using Google Trends: From the perspective of big data utilizations and applications," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 69-87.
    17. Palma Lampreia Dos Santos, Maria José, 2018. "Nowcasting and forecasting aquaponics by Google Trends in European countries," Technological Forecasting and Social Change, Elsevier, vol. 134(C), pages 178-185.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lijie Feng & Kehui Liu & Jinfeng Wang & Kuo-Yi Lin & Ke Zhang & Luyao Zhang, 2022. "Identifying Promising Technologies of Electric Vehicles from the Perspective of Market and Technical Attributes," Energies, MDPI, vol. 15(20), pages 1-22, October.
    2. Yanlin Shi & Qingjin Peng, 2023. "Conceptual design of product structures based on WordNet hierarchy and association relation," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2655-2671, August.
    3. Fernando, Angeline Gautami & Aw, Eugene Cheng-Xi, 2023. "What do consumers want? A methodological framework to identify determinant product attributes from consumers’ online questions," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    4. Pal, Shounak & Biswas, Baidyanath & Gupta, Rohit & Kumar, Ajay & Gupta, Shivam, 2023. "Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach," Journal of Business Research, Elsevier, vol. 156(C).
    5. 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).

    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. van der Wielen, Wouter & Barrios, Salvador, 2021. "Economic sentiment during the COVID pandemic: Evidence from search behaviour in the EU," Journal of Economics and Business, Elsevier, vol. 115(C).
    2. Juan Camilo Anzoátegui-Zapata & Juan Camilo Galvis-Ciro, 2020. "Disagreements in Consumer Inflation Expectations: Empirical Evidence for a Latin American Economy," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 16(2), pages 99-122, November.
    3. Zhongchen Song & Tom Coupé, 2023. "Predicting Chinese consumption series with Baidu," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 21(3), pages 429-463, July.
    4. Serhan Cevik, 2022. "Where should we go? Internet searches and tourist arrivals," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4048-4057, October.
    5. Tsoyu Calvin Lin & Shih-Hsun Hsu, 2020. "Forecasting Housing Markets from Number of Visits to Actual Price Registration System," International Real Estate Review, Global Social Science Institute, vol. 23(4), pages 505-536.
    6. Jolana Stejskalova, 2023. "We investigated the link between stock returns of automobile companies, Fama French factors, and behavioral attention, represented by demand for a selected car brand belonging to an automobile company," Journal of Economics / Ekonomicky casopis, Institute of Economic Research, Slovak Academy of Sciences, vol. 71(3), pages 202-221, March.
    7. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    8. Jacques Bughin, 2015. "Google searches and twitter mood: nowcasting telecom sales performance," Netnomics, Springer, vol. 16(1), pages 87-105, August.
    9. Chien-jung Ting & Yi-Long Hsiao, 2022. "Nowcasting the GDP in Taiwan and the Real-Time Tourism Data," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 12(3), pages 1-2.
    10. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 2-12, April.
    11. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    12. Zeynalov, Ayaz, 2014. "Nowcasting Tourist Arrivals to Prague: Google Econometrics," MPRA Paper 60945, University Library of Munich, Germany.
    13. Long Wen & Chang Liu & Haiyan Song, 2019. "Forecasting tourism demand using search query data: A hybrid modelling approach," Tourism Economics, , vol. 25(3), pages 309-329, May.
    14. Chien-jung Ting & Yi-Long Hsiao & Rui-jun Su, 2022. "Application of the Real-Time Tourism Data in Nowcasting the Service Consumption in Taiwan," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(4), pages 1-4.
    15. Oestmann Marco & Bennöhr Lars, 2015. "Determinants of house price dynamics. What can we learn from search engine data?," Review of Economics, De Gruyter, vol. 66(1), pages 99-127, April.
    16. Dean Fantazzini & Julia Pushchelenko & Alexey Mironenkov & Alexey Kurbatskii, 2021. "Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg," Forecasting, MDPI, vol. 3(4), pages 1-30, October.
    17. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
    18. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    19. Jaroslav Pavlicek & Ladislav Kristoufek, 2015. "Nowcasting Unemployment Rates with Google Searches: Evidence from the Visegrad Group Countries," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-11, May.
    20. Niesert, Robin F. & Oorschot, Jochem A. & Veldhuisen, Christian P. & Brons, Kester & Lange, Rutger-Jan, 2020. "Can Google search data help predict macroeconomic series?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1163-1172.

    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:eee:tefoso:v:171:y:2021:i:c:s0040162521004157. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.