Skip to main content Skip to main navigation menu Skip to site footer
BUSINESS & MANAGEMENT STUDIES:
AN INTERNATIONAL JOURNAL
Published: 2020-09-25

THE EFFECTS OF COVID-19 PANDEMIC ON PRODUCT REVIEWS

Asisst. Prof. Dr., Akdeniz University
Product Reviews Pandemic COVID-19 Logistic Regression Probit Regression

Abstract

1. LITERATURE

1.1. RESEARCH SUBJECT
Due to the high rate of transmission and infection, the World Health Organization announced on March 11, 2020, that it defined the new coronavirus disease (COVID-19) as a pandemic (World Health Organization, 2020). COVID-19, which affects the whole world, has positive effects on some sectors. Many companies in the retailing industry encountered a significant increase in their online customer portfolio. The intensive involvement of customers in the pandemic period on online platforms has also carried product reviews or word-of-mouth communication to online platforms. During the pandemic period, the product reviews written by the consumers are expected to be affected by their mood because it has been discussed that people with negative emotions make product evaluations in order to reduce their anxiety (Hennig-Thurau et al., 2004; Sundaram et al., 1998). Bujisic et al. (2019) stated that the weather changes the mood of the person, and as a result, it affects the valence of online reviews. Many studies have demonstrated that the pandemic period affects the psychological and physical states of people, especially mood, weakness, and depression (Alonzi et al., 2020; El-Zoghby et al., 2020; Somma et al., 2020). Although the effects of the pandemic period on consumer behaviour have attracted the attention of researchers (Grashuis et al., 2020; Zwanka & Buff, 2020), the effects of the pandemic period on product reviews have not been explained in the literature yet.
1.2. RESEARCH PURPOSE AND IMPORTANCE
During COVID-19 pandemic, the fact that people are facing death threats (Song et al., 2020) and feeling like they are caged (Fullana et al., 2020) caused a great panic in the world and so in Turkey. Economic and health concerns caused by the COVID-19 pandemic also resulted in negative moods in people (Duan et al., 2020; El-Zoghby et al., 2020; Van Rheenen et al., 2020). It was discussed that COVID-19 pandemic changes the decision-making processes and purchasing behaviours of the consumers (Grashuis et al., 2020; Hall et al., 2020; Zwanka & Buff, 2020). It is expected that the increased negative mood due to the pandemic would also affect the product reviews written by consumers. This research aims to examine the effect of the pandemic on product evaluations on internet platforms.
1.3. CONTRIBUTION of the ARTICLE to the LITERATURE
The present research explores, for the first time, the effects of COVID-19 pandemic on product reviews. Thus, the effect of the pandemic was introduced to electronic word-of-mouth communication literature.
2. DESIGN AND METHOD

2.1. RESEARCH TYPE
Causal research is designed to draw inferences about the effects of COVID-19 pandemic on product reviews and to model the cause-and-effect relationship. Additionally, descriptive research is employed to understand the structure of the data set.
2.2. RESEARCH PROBLEMS
The research on COVID-19 pandemic is at its infant stage. Significantly, the number of studies in social sciences related to COVID-19 pandemic is very few. Little is known about consumer behaviour during COVID-19 pandemic, and it is not clear how COVID-19 pandemic affects product reviews.


2.3. DATA COLLECTION METHOD
The secondary data source is used for the research analysis. 5812 online reviews were extracted for 5 different types of products (toy, skirt, framed painting, coffee-making machine, T-shirt) from Trendyol web site (a famous Turkish e-commerce web site).
2.4. QUANTITATIVE / QUALITATIVE ANALYSIS
Quantitative analysis was adopted. First, descriptive statistics were used to draw inferences about the data. Second, logit and probit regression analysis was employed to test the hypothesis.
2.5. RESEARCH HYPOTHESES

H1: Product reviews written in the period from the pandemic announcement to the normalization are more damaging than the product reviews written in the pre-pandemic period.
H2: Product reviews written during the normalization period are more harmful than the product reviews written in the pre-pandemic period.
H3: The product type affects the length of the product review.
H4: The product reviews written in the period from the pandemic announcement to the normalization are longer than the product reviews written in the pre-pandemic period.
H5: Product reviews written in the normalization period are longer than the product reviews written in the pre-pandemic period.

3. FINDINGS AND DISCUSSION

3.1. FINDINGS as a RESULT of ANALYSIS
The descriptive statistics showed that the oldest product review among 5812 reviews in the data set was published on January 28, 2019, and the most recent on August 10 2020. Approximately 84% of the reviews in the data set received a rating of 4 stars and above. In other words, the data set contains very positive product reviews. More than 60% of the product reviews were written after the pandemic announcement. The average length of the product reviews in the pre-pandemic period is shorter than those posted after the pandemic announcement.
The results of logistic regression analysis demonstrated that there is no significant difference in the number of stars received by product reviews between the period from the pandemic announcement to the normalization and pre-pandemic period. However, the probability of negative product reviews to be posted in the normalization period is higher than the product reviews before the pandemic period. The results of probit regression showed that product type affects the length of the product review posted. Additionally, the product reviews that were the least likely to belong were those which were written before the pandemic (probability = 0.308). The probability of long online reviews written in the period from the pandemic announcement to the normalization period was 0.342. Among all the periods of the pandemic, the probability of extended product evaluations written in the normalization period was highest (probability = 0.432).
3.2. HYPOTHESIS TEST RESULTS
The first hypothesis of the research is rejected because there is no significant difference between the number of stars received for product reviews written in the period from the pandemic announcement to the normalization period and those written in the pre-pandemic period. The second hypothesis is accepted since product reviews written after normalization period are more damaging than those before the pandemic announcement. The third hypothesis is accepted as the product type influences the length of the online review. The fourth and fifth hypotheses are accepted because it was found that the product reviews posted in the pre-pandemic period are shorter than the ones posted after the pandemic announcement.

 

3.3. DISCUSSING the FINDINGS with the LITERATURE
Descriptive statistics showed that the number of positive product reviews (with a rating of 4 or more) is higher than the negative ones. This finding is in line with studies (Cabosky, 2016; Chen & Lurie, 2013) stating that individuals' tendency to write positive online reviews is higher than negative ones. The findings of the current research showed that the product reviews written after the normalization period were more negative than those in the pre-pandemic period. This result is consistent with studies in the literature (Sundaram et al., 1998; Bujisic et al., 2019) indicating that negative mood affects product reviews. According to another result of the current research, the length of product reviews was affected by the product type. This result coincides with the findings of other studies in the literature (Moore, 2015; Pan & Zhang, 2011) showing that the product type affects the length of the online review. The findings also demonstrated that the gloomy mood of people after the pandemic announcement increases the possibility of writing long online reviews. This result supports the results of studies (Hennig-Thurau et al., 2004; Sundaram et al., 1998) indicating that in order to reduce their anxiety, consumers tend to write product reviews.
4. CONCLUSION, RECOMMENDATION AND LIMITATIONS

4.1. RESULTS of the ARTICLE
The results showed that the pandemic period affects the valence of product reviews. Product type influences the length of product reviews. Furthermore, in the pandemic period, consumers tend to write longer product reviews.
4.2. SUGGESTIONS BASED on RESULTS

This research offers implications to practitioners about the strategies that companies will follow in times of disasters such as a pandemic to guide their consumers for posting more effective and positive product reviews. In disaster situations such as pandemic, earthquake and flood that may cause negative mood in consumers, e-commerce firms can send samples with their ordered products and make their customers surprised and make them feel good. Additionally, firms can also send disinfectants or medical masks with the ordered products that may be needed especially in case of a pandemic as a feel-good gift. Nevertheless, in response to negative product evaluations to be encountered, companies may write responses showing that they evaluate the customer's complaint, understand them, and deal with them.

4.3. LIMITATIONS of the ARTICLE
The data set was derived from a single e-commerce platform. Due to the effects of platform differences on product evaluations, a data set can be created by including different e-commerce platforms in future studies, and the effects of platform differences on product evaluations can be examined. Also, the present study examined the effect of the COVID-19 pandemic on product reviews by using secondary data and could not control other factors affecting the mood of consumers. Thus, in future studies, experimental design can be used only to observe the effect of the pandemic on product reviews by controlling other factors that affect the mood of consumers.

Downloads

Download data is not yet available.

References

  1. Alonzi, S., La Torre, A., & Silverstein, M. W. (2020). The Psychological Impact of Preexisting Mental and Physical Health Conditions During the COVID-19 Pandemic. Psychological Trauma: Theory, Research, Practice, and Policy. https://doi.org/10.1037/tra0000840
  2. Anderson, E. W. (1998). Customer Satisfaction and Word of Mouth. Journal of Service Research, 1(1), 5–17. https://doi.org/10.1177/109467059800100102
  3. Bearden, W. O., & Etzel, M. J. (1982). Reference Group Influence on Product and Brand Purchase Decisions. Journal of Consumer Research, 9(2), 183. https://doi.org/10.1086/208911
  4. Bujisic, M., Bogicevic, V., Parsa, H. G., Jovanovic, V., & Sukhu, A. (2019). It's Raining Complaints! How Weather Factors Drive Consumer Comments and Word-of-Mouth. Journal of Hospitality & Tourism Research, 43(5), 656–681. https://doi.org/10.1177/1096348019835600
  5. Cabosky, J. (2016). Social media opinion sharing: beyond volume. Journal of Consumer Marketing, 33(3), 172–181. https://doi.org/10.1108/JCM-02-2015-1323
  6. Cameron, C., & Trivedi, P. (2009). Microeconomics using stata. Lakeway Drive, TX: Stata Press Books.
  7. Chen, Z., & Lurie, N. H. (2013). Temporal Contiguity and Negativity Bias in the Impact of Online Word of Mouth. Journal of Marketing Research, 50(4), 463–476. https://doi.org/10.1509/0022-2437-50.4.463
  8. Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345–354.
  9. Deloitte. (2020). Küresel Covid-19 Salgınının Türkiye’de Farklı Kategorilere Etkileri - II. Retrieved from https://www2.deloitte.com/content/dam/Deloitte/tr/Documents/consulting/kuresel-covid-19-salgininin-turkiyede-farkli-kategorilere-etkileri-sayi-2.pdf
  10. Duan, L., Shao, X., Wang, Y., Huang, Y., Miao, J., Yang, X., & Zhu, G. (2020). An investigation of mental health status of children and adolescents in china during the outbreak of COVID-19. Journal of Affective Disorders. https://doi.org/10.1016/j.jad.2020.06.029
  11. El-Zoghby, S. M., Soltan, E. M., & Salama, H. M. (2020). Impact of the COVID-19 Pandemic on Mental Health and Social Support among Adult Egyptians. Journal of Community Health, 45(4), 689–695. https://doi.org/10.1007/s10900-020-00853-5
  12. Floyd, K., Freling, R., Alhoqail, S., Cho, H. Y., & Freling, T. (2014). How online product reviews affect retail sales: A meta-analysis. Journal of Retailing, 90(2), 217–232. https://doi.org/10.1016/j.jretai.2014.04.004
  13. Fullana, M. A., Hidalgo-Mazzei, D., Vieta, E., & Radua, J. (2020). Coping behaviors associated with decreased anxiety and depressive symptoms during the COVID-19 pandemic and lockdown. Journal of Affective Disorders, 275, 80-81.
  14. Godes, D., & Silva, J. C. (2011). Sequential and Temporal Dynamics of Online Opinion. Marketing Science, 31(3), 448–473. https://doi.org/10.1287/mksc.1110.0653
  15. Grashuis, J., Skevas, T., & Segovia, M. S. (2020). Grocery shopping preferences during the COVID-19 pandemic. Sustainability (Switzerland). https://doi.org/10.3390/su12135369
  16. Hall, M. C., Prayag, G., Fieger, P., & Dyason, D. (2020). Beyond panic buying: consumption displacement and COVID-19. Journal of Service Management, ahead-of-p(ahead-of-print). https://doi.org/10.1108/JOSM-05-2020-0151
  17. Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing, 18(1), 38–52. https://doi.org/10.1002/dir.10073
  18. Huang, M., Cai, F., Tsang, A. S. L., & Zhou, N. (2011). Making your online voice loud: The critical role of WOM information. European Journal of Marketing, 45(7), 1277–1297. https://doi.org/10.1108/03090561111137714
  19. Ipsos. (2020). Covid-19 Salgını ve Kamuoyu: Normalleşiyoruz Ama Hala Endişeliyiz. Retrieved August 17, 2020, from https://www.ipsos.com/tr-tr/covid-19-salgini-ve-kamuoyu-normallesiyoruz-ama-hala-endiseliyiz
  20. Long, J. S., & Freese, J. (2014). Stata Bookstore: Regression Models for Categorical Dependent Variables Using Stata, Third Edition. Stata Press.
  21. McKinsey & Company. (2020). Consumer sentiment and behavior continue to reflect the uncertainty of the COVID-19 crisis. Retrieved August 16, 2020, from https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/a-global-view-of-how-consumer-behavior-is-changing-amid-covid-19#
  22. Mert, M. (2016). Yatay kesit veri analizi bilgisayar uygulamaları. Ankara: Detay Yayıncılık.
  23. Moore, S. G. (2015). Attitude predictability and helpfulness in online reviews: The role of explained actions and reactions. Journal of Consumer Research, 42(1), 30–44. https://doi.org/10.1093/jcr/ucv003
  24. Pan, Y., & Zhang, J. Q. (2011). Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews. Journal of Retailing, 87(4), 598–612. https://doi.org/10.1016/j.jretai.2011.05.002
  25. Plotkina, D., & Munzel, A. (2016). Delight the experts, but never dissatisfy your customers! A multi-category study on the effects of online review source on intention to buy a new product. Journal of Retailing and Consumer Services. https://doi.org/10.1016/j.jretconser.2015.11.002
  26. Somma, A., Gialdi, G., Krueger, R. F., Markon, K. E., Frau, C., Lovallo, S., & Fossati, A. (2020). Dysfunctional personality features, non-scientifically supported causal beliefs, and emotional problems during the first month of the COVID-19 pandemic in Italy. Personality and Individual Differences, 165, 110139. https://doi.org/10.1016/j.paid.2020.110139
  27. Song, W., Jin, X., Gao, J., & Zhao, T. (2020). Will buying follow others ease their threat of death? An analysis of consumer data during the period of COVID-19 in China. International Journal of Environmental Research and Public Health. https://doi.org/10.3390/ijerph17093215
  28. Sundaram, D. S., Kaushik, M., & Webster, C. (1998). Word-of-Mouth Communications: A Motivational Analysis. Advances in Consumer Research. https://doi.org/Article
  29. T.C. Sağlık Bakanlığı. (2020a). COVID-19 Durum Raporu Türkiye. Retrieved from https://dosyamerkez.saglik.gov.tr/Eklenti/37778,covid-19-durum-raporupdf.pdf?0&_tag1=B647A4A46C8B41228B2C445361452762CAEFD728
  30. T.C. Sağlık Bakanlığı. (2020b). Türkiye’deki Günlük COVID-19 Vaka Sayıları. Retrieved August 19, 2020, from https://covid19bilgi.saglik.gov.tr/tr/haberler/turkiye-deki-gunluk-covid-19-vaka-sayilari.html
  31. Tang, T. (Ya), Fang, E. (Er), & Wang, F. (2014). Is Neutral Really Neutral? The Effects of Neutral User-Generated Content on Product Sales. Journal of Marketing, 78(4), 41–58. https://doi.org/10.1509/jm.13.0301
  32. Türkiye Cumhuriyeti Cumhurbaşkanlığı. (2020a). Normal hayata dönüşü kademe kademe başlatacağız. Retrieved August 19, 2020, from https://www.tccb.gov.tr/haberler/410/119206/-normal-hayata-donusu-kademe-kademe-baslatacagiz-
  33. Türkiye Cumhuriyeti Cumhurbaşkanlığı. (2020b). Türkiye’nin salgının önlenmesinde örnek alınan bir konuma gelmesi hepimizin ortak başarısıdır. Retrieved August 19, 2020, from https://www.tccb.gov.tr/haberler/410/120316/-turkiye-nin-salginin-onlenmesinde-ornek-alinan-bir-konuma-gelmesi-hepimizin-ortak-basarisidir-
  34. Van Rheenen, T. E., Meyer, D., Neill, E., Phillipou, A., Tan, E. J., Toh, W. L., & Rossell, S. L. (2020). Mental health status of individuals with a mood-disorder during the COVID-19 pandemic in Australia: Initial results from the COLLATE project. Journal of Affective Disorders, 275, 69–77. https://doi.org/10.1016/j.jad.2020.06.037
  35. Wasserman, D., van der Gaag, R., & Wise, J. (2020). The term "physical distancing" is recommended rather than "social distancing" during the COVID-19 pandemic for reducing feelings of rejection among people with mental health problems. European Psychiatry, 63(1), e52. https://doi.org/10.1192/j.eurpsy.2020.60
  36. Webrazzi. (2020). Günde 15 milyon tekil ziyaretçiye ulaşan Trendyol’un 18 milyon aktif müşterisi bulunuyor. Retrieved August 20, 2020, from https://webrazzi.com/2020/06/30/gunde-15-milyon-tekil-ziyaretciye-ulasan-trendyolun/
  37. World Health Organization. (2020). WHO Director-General's opening remarks at the media briefing on COVID-19 - March 11 2020. Retrieved May 16, 2020, from https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020
  38. Zhang, L., Gao, Y., & Zheng, X. (2020). Let's Talk About This in Public: Consumer Expectations for Online Review Response. Cornell Hospitality Quarterly. https://doi.org/10.1177/1938965519864864
  39. Zwanka, R. J., & Buff, C. (2020). COVID-19 Generation: A Conceptual Framework of the Consumer Behavioral Shifts to Be Caused by the COVID-19 Pandemic. Journal of International Consumer Marketing. https://doi.org/10.1080/08961530.2020.1771646

How to Cite

YETKİN ÖZBÜK, R. M. (2020). THE EFFECTS OF COVID-19 PANDEMIC ON PRODUCT REVIEWS. Business & Management Studies: An International Journal, 8(3), 3471-3494. https://doi.org/10.15295/bmij.v8i3.1595