Search results

The aim of this research is to theorize and demonstrate that analyzing consumers’ text product reviews using text mining can enhance the explanatory power of a product sales model, particularly for hedonic products, which tend to generate emotional and subjective product evaluations. Previous research in this area has been more focused on utilitarian products.

Our text clustering-based procedure segments text reviews into multiple clusters in association with consumers’ numeric ratings to address consumer heterogeneity in taste preferences and quality valuations and the J-distribution of numeric product ratings. This approach is novel in terms of combining text clustering with numeric product ratings to address consumers’ subjective product evaluations.

Using the movie industry as our empirical application, we find that our approach of making use of product text reviews can improve the explanatory power and predictive validity of the box-office sales model.

Marketing scholars have actively investigated the impact of consumers’ online product reviews on product sales, primarily focusing on consumers’ numeric product ratings. Recently, studies have also examined user-generated content. Similarly, this study looks into users’ textual product reviews to explain product sales. It remains to be seen how generalizable our empirical results are beyond our movie application.

Whereas numeric ratings can indicate how much viewers liked products, consumers’ reviews can convey why viewers liked or disliked them. Therefore, our review analysis can help marketers understand what factors make new products succeed or fail.

Primarily our approach is suitable to products subjectively evaluated, mostly, hedonic products. In doing so, we consider consumer heterogeneity contained in reviews through our review clusters based on their divergent impacts on sales.

The first case of coronavirus disease 2019 (COVID-19) was documented in China, and the virus was soon to be introduced to its neighboring country – South Korea. South Korea, one of the earliest countries to initiate a national pandemic response to COVID-19 with fairly substantial measures at the individual, societal and governmental level, is an interesting example of a rapid response by the Global South. The current study examines contact tracing mobile applications (hereafter, contact tracing apps) for those who were subject to self-quarantine through the lenses of dataveillance and datafication. This paper analyzes online/digital data from those who were mandatorily self-quarantined by the Korean government largely due to returning from overseas travel.

This study uses an Internet ethnography approach to collect and analyze data. To extract data for this study, self-quarantined Korean individuals' blog entries were collected and verified with a combination of crawling and manual checking. Content analysis was performed with the codes and themes that emerged. In the COVID-19 pandemic era, this method is particularly useful to gain access to those who are affected by the situation. This approach advances the author’s understandings of COVID-19 contact tracing mobile apps and the experiences of self-quarantined people who use them.

The paper shows Korean citizens' understandings and views of using the COVID-19 self-tracing application in South Korea through examining their experiences. The research argues that the application functions as a datafication tool that collects the self-quarantined people's information and performs dataveillance on the self-quarantined people. This research further offers insights for various agreements/disagreements at different actors (i.e. the self-quarantined, their families, contact tracers/government officials) in the process of contact tracing for COVID-19.

This study also provides insights into the implications of information and technology as they affect datafication and dataveillance conducted on the public. This study investigates an ongoing debate of COVID-19's contact tracing method concerning privacy and builds upon an emerging body of literature on datafication, dataveillance, social control and digital sociology.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2020-0377