Do customer reviews drive purchase decisions? The moderating roles of review exposure and price

Masłowska, Ewa; Malthouse, Edward C.; Viswanathan, Vijay

doi:10.1016/j.dss.2017.03.010

Cited by 104 publications

(61 citation statements)

References 48 publications

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“…6 Some studies have shown that hospitals with higher patient satisfaction scores have lower readmission rates for certain medical conditions, 7 improved adherence to guidelines and improved mortality in myocardial infarction 8 and shorter inpatient stays, and lower readmission and lower mortality in surgical patients. 9 More and more users now rely on internet sources and client feedback for choosing a business or a product 10 and hospitals are no different. Health care facilities rely on survey methodologies such as Press Ganey, which uses a third party to contact patients after discharge or visit date to have them complete a survey.…”

Section: Background and Significancementioning

confidence: 99%

Natural Language Processing to Extract Meaningful Information from Patient Experience Feedback

Nawab¹,

Ramsey²,

Schreiber

2020

Appl Clin Inform

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Background Due to reimbursement tied in part to patients' perception of their care, hospitals continue to stress obtaining patient feedback and understanding it to plan interventions to improve patients' experience. We demonstrate the use of natural language processing (NLP) to extract meaningful information from patient feedback obtained through Press Ganey surveys. Methods The first step was to standardize textual data programmatically using NLP libraries. This included correcting spelling mistakes, converting text to lowercase, and removing words that most likely did not carry useful information. Next, we converted numeric data pertaining to each category based on sentiment and care aspect into charts. We selected care aspect categories where there were more negative comments for more in-depth study. Using NLP, we made tables of most frequently appearing words, adjectives, and bigrams. Comments with frequent words/combinations underwent further study manually to understand factors contributing to negative patient feedback. We then used the positive and negative comments as the training dataset for a neural network to perform sentiment analysis on sentences obtained by splitting mixed reviews. Results We found that most of the comments were about doctors and nurses, confirming the important role patients ascribed to these two in patient care. “Room,” “discharge” and “tests and treatments” were the three categories that had more negative than positive comments. We then tabulated commonly appearing words, adjectives, and two-word combinations. We found that climate control, housekeeping and noise levels in the room, time delays in discharge paperwork, conflicting information about discharge plan, frequent blood draws, and needle sticks were major contributors to negative patient feedback. None of this information was available from numeric data alone. Conclusion NLP is an effective tool to gain insight from raw textual patient feedback to extract meaningful information, making it a powerful tool in processing large amounts of patient feedback efficiently.

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Section: Background and Significancementioning

confidence: 99%

Natural Language Processing to Extract Meaningful Information from Patient Experience Feedback

Nawab¹,

Ramsey²,

Schreiber

2020

Appl Clin Inform

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“…A central question of strategic importance in e-retailing is what makes an online review persuasive. Past research suggests that effective online reviews are driven by a combination of affective linguistic elements (Ludwig et al 2013), source credibility cues (Kareklas, Muehling, and Weber 2015), inferences of manipulative intent (Weber, Kareklas, and Muehling 2019), exposure to reviews and price (Maslowska, Malthouse, and Viswanathan 2017), and the perceived valence and volume of consumer ratings (Chevalier and Mayzlin 2006;Tata, Presha, and Gupta 2020). In the present study, we focus on review valence, and consider the impact of source credibility, persuasion knowledge, and manipulative intent on consumer response to products appearing on e-retailer websites.…”

Section: Conceptual Developmentmentioning

confidence: 99%

The impact of review valence and awareness of deceptive practices on consumers’ responses to online product ratings and reviews

Karabas

Kareklas

Weber

et al. 2020

Journal of Marketing Communications

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Many online retailers and some manufacturers/service providers have recently been engaging in questionable practices, where product reviews are often fabricated and/or posted without sufficient clarity and objectivity. Across an exploratory study and two main studies, we empirically examine this phenomenon and observe a pattern of effects that suggests that review valence (i.e., the average number of rating-stars a product receives) influences product attitudes and intentions, but that these outcomes are significantly impacted by the extent to which consumers are aware of potentially deceptive online review practices. Awareness of deceptive practices was found to differentially influence attitudes and intentions, depending upon whether the star-ratings were perfect (5/5 stars), highly positive (4.9/5 stars), or generally positive (4.5/5 or 4.7/5 stars). Participants' perceptions of the e-retailer's manipulative intent were also shown to mediate these effects, with higher perceptions of perceived manipulative intent yielding less favorable product attitudes and reduced purchase intentions.

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“…Previous research has mainly focussed on investigating the effects of the different review features on review helpfulness, consumers' attitudes and sales (rank) (eg Amblee and Bui 2011;Chevalier and Mayzlin 2006;Purnawirawan et al 2012; or their effects in conjunction with other elements of the product web page, such as price, product description, etc. (eg Maslowska et al 2017).…”

Section: Introductionmentioning

confidence: 99%

How consumers attend to online reviews: an eye-tracking and network analysis approach

Masłowska

Segijn

Vakeel³

et al. 2019

International Journal of Advertising

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Online reviews can play a significant role in consumer decisionmaking processes. Previous research has focused on investigating the effects of different review elements, such as valence or volume, on sales rank and perceived helpfulness. However, very little is known about how consumers actually attend to and process reviews. Moreover, reviews exist in the context of other elements on product pages, but previous studies have not investigated the combined effects of the different product page elements. This study uses eye tracking to investigate how consumers attend to product pages. Participants (N ¼ 74) were asked to review product pages of five different brands of either a search or an experience product. The results show that although product-related information is most important for consumers, consumers also spend time on review-related information, particularly the review text and reviewer information. Also, we observe differences in attention paid to different page elements for search versus experience products.

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Do customer reviews drive purchase decisions? The moderating roles of review exposure and price

Cited by 104 publications

References 48 publications

Natural Language Processing to Extract Meaningful Information from Patient Experience Feedback

Natural Language Processing to Extract Meaningful Information from Patient Experience Feedback

The impact of review valence and awareness of deceptive practices on consumers’ responses to online product ratings and reviews

How consumers attend to online reviews: an eye-tracking and network analysis approach

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