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Not your fault, but your responsibility: worsened consumer sentiment on work-from-home products

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Abstract

This study analyses the evolution of people’s sentiment towards Work from Home (WFH)-related products during the pandemic, using user-generated content from social media platform X on responses for the largest US online furniture stores. We find that people interacted more about WFH products during the Covid-19 lockdowns, but sentiment towards WFH products worsened. For some online furniture stores, Covid-19 restrictions may explain the changes in sentiment, but firms’ idiosyncrasies also play a role. The methodology of this study allows companies to assess the impact of external effects on customers’ sentiments, allowing them to identify specific problems and to connect more naturally with their customers.

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Fig. 1
Fig. 2

Source: Developed by the author

Fig. 3

Source: Data consolidated by the author and eCommerceDB (2022)

Fig. 4

Source: Developed by the author

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Data Availability

Data is available upon request.

Notes

  1. https://www.bloomberg.com/news/articles/2021-04-16/amazon-is-working-on-furniture-assembly-service-to-catch-wayfair.

  2. While incorporating data from additional platforms might enrich our insights, Twitter’s specific constraints and features create an ideal environment for our analysis for two reasons. First, Twitter’s text-centric nature, unlike Instagram and Facebook, enhances our text analysis algorithm’s ability to interpret the messages. Second, the brevity enforced by Twitter’s character limit streamlines our algorithmic analysis.

  3. It is worth mentioning that “work from home” and “home office” returned more than 10,000 tweets but were removed from the database because they are not directly related to products. In addition, we removed the duplicate tweets from the keywords that contained the word “desk” to avoid biasing the results. We acknowledge that this decision may potentially increase the dominance of popular terms, but it ensures a robust and continuous dataset, reducing the potential for temporal outliers in our sample.

  4. The decision to focus on these specific references was aimed at enhancing the precision of our analysis. By doing so, we avoid including generic furniture discussions and conversations about items unrelated to WFH scenarios.

  5. See, for example, Zhang et al. (2011) for a more detailed description of these algorithms.

  6. In this database, both models (SVM and MNB) have an accuracy of around 80%, indicating good predictive performance.

  7. This coefficient is comparing the sentiment towards each top-4 online store against the average sentiment towards the other 16 online stores.

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Acknowledgements

Filipe Grilo acknowledges that his research has been financed by Portuguese public funds through FCT—Fundação para a Ciência e a Tecnologia, I.P., in the framework of the project UIDB/04105/2020.

Funding

This work is supported by the Fundação para a Ciência e a Tecnologia (Grant No. UIDB/04105/2020) to Filipe Grilo.

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Authors and Affiliations

  1. School of Economics and Management, University of Porto, Porto, Portugal

    Giovanni Cintra

  2. Porto Business School, CEF.UP, CEAD, University of Porto, Porto, Portugal

    Filipe Grilo

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  1. Giovanni Cintra
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Cintra, G., Grilo, F. Not your fault, but your responsibility: worsened consumer sentiment on work-from-home products. J Market Anal (2024). https://doi.org/10.1057/s41270-024-00315-0

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