Introduction

Online reviews and ratings

 

The impact on individual decision-making and product demand

 

  

 Introduction
 

 

A dramatic technological change in the past decade resulted in the rise of Web 2.0 and user-generated content. A new way in which software developers and end-users utilize World Wide Web concludes in platforms where content and applications are continuously modified by all users in a participatory and collaborative fashion. Users can now exchange various forms of media content that are publicly available and created by end-users (Kaplan and Haenien, 2010: p. 61). Specifically, user-friendly technologies (e.g. smartphones and tablets) make it more convenient for consumers to provide reviews and recommendations (Mangold & Smith, 2012: p. 150).

 

Both consumers and businesses observe a major change in social environment. A rise of a generation of digital natives with substantial technical knowledge and willingness to engage online dictates how firms should market products. Millennials have traditionally been viewed as innovators and early adaptors of new technologies (Mangold & Smith, 2012: p. 151). Consumers’ choice is now influenced by actions taken by others. Social interaction takes place in a direct and meaningful way. Consumers demand a credible channel, content that is objective and has valence. This ultimately affects other’s actions (Godes et. al., 2005: p. 416).

 

Engagement with online reviews and ratings is phenomenal. BrighLocal research concluded that 88% of consumers have read online reviews for local businesses in 2014 (BrightLocal, 2014). Evidence in a number of websites dedicated to this type of eWOM alone is clear. Many of these focus on specific industry such as travel (Trip Advisor) and restaurants (Yelp). Reviews and ratings are very important for a number of eCommerce websites such as Amazon and eBay.

 

Research focusing on this type of eWOM is relatively new and small. Academics investigated areas such as online review systems design (e.g. Chou et. al., 2013: p. 134), how marketers can encourage customers to provide recommendations (e.g. Li & Bernoff, 2008) and the impact of reviews on different product categories (e.g. Duan et al., 2008). However, a few studies were found that specifically concentrate on consumer purchase decision-making and the impact on product demand.

 

In terms of decision-making, researchers investigated the effects of online recommendations with heuristic cue such as popularity of the event (e.g. Chevalier & Mayzlin, 2006) and average ranking (e.g. Luca, 2016) but failed to evaluate the level of impact of these heuristic cues. In terms of online reviews and ratings contribution to product sales, academics previously analyzed the effects of positive reviews (e.g. Barton, 2006: p. 48) and negative reviews (e.g. Lucking-Reley et. al., 2000) but disagree on their tangible impact. Furthermore, no previous research addressed online reviews effects on both individual decision making level and market level in a single study.

 

This study involves a field experiment that contributes to the assessment of online reviews causality. Observational studies are limited because of variable problems. For instance: variations in price, more attractive presentation and better product descriptions may be willingness-to-pay-effects compared to the impacts of reviews (Resnick et al., 2006). Field experiments allow combining control of lab experiments and behavior in natural settings. Recent studies already applied this to research design. For example Resnick et al. (2006) investigated the value of eBay reputations in the natural setting of actual eBay auctions. This dissertation designs an A / B test experiment that involved similar, yet different websites. One of the prototypes did not have any kind of eWOM and the other one incorporated additional product attributes, namely peer recommendations.

 

Furthermore, this project included a novel data collection method. Website analytic tools automatically collected statistics with detailed information on consumer behavior that allowed evaluating the effects of online reviews and rating. This methodology is affordable for non-expert data miners and any academics (Plaza, 2009: p. 475). Website analytics tools were employed to evaluate other digital-technology related research such as e-commerce websites, and proved to be useful in identifying opportunities to improve its performance (Hasan et. al., 2009: p. 697).


Online recommendations are becoming more debated and an area that requires further research. This dissertation taps into its impact on decision-making and product demand. Moreover, it applies novel research methods. 

  

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