Purpose: The current study was aimed at understanding the benefits and shortcomings of direct-to-consumer hearing devices (DCHDs) by analyzing the large text corpus of secondary data generated from Amazon customer reviews.
Method: Secondary data were generated manually by gathering user feedback for 62 different DCHDs (cost range: $9.95-$635) on the Amazon.com website, which included 11,258 unique Amazon-verified customer reviews. The data were analyzed using both quantitative and qualitative analyses methods.
Results: The cluster analysis of large data corpus resulted in 7 unique clusters, which were labeled as (a) Issues related to fit and comfort (15%), (b) Friends and family recommendations (11.8%), (c) Issues related to sound quality (11.9%), (d) Listening and conversation (16.1%), (e) Positive customer service (12.1%), (f) General usage and customer service (14.7%), and (g) Cost and affordability (17.3%). Exploratory analysis also revealed an association between customer ratings and cost in relation to these clusters (i.e., customer reviews). For example, customer reviews about cheaper DCHDs are related to issues about sound quality, whereas reviews about expensive DCHDs are related to cost and affordability of the device. The qualitative content analysis resulted in 8 main themes, which include (a) intrinsic factors, (b) extrinsic factors, (c) supplemental items, (d) ease of use, (e) interaction with support services, (f) reasons for purchase, (g) experiences, and (h) general information.
Conclusions: The study using the text mining techniques highlights the benefits and shortcomings of DCHDs that are currently available in the U.S. market. Our findings relate well to the published study results of electroacoustic analysis on similar products, which provide clinicians with knowledge related to DCHDs that they can convey to consumers during clinical consultations. The findings may also be of interest to the hearing instrument industry from the perspective of developing products based on user feedback.