Tag Archives: information

The swaying effects of online product reviews

February 18, 2018 maxoudenbroek Leave a comment

Based on the ‘wisdom of the crowd’ effect (Surowiecki, 2005), consumers make use of reviews to make accurate product evaluations. However, due to the large amount of information and conflicting opinions in reviews, it becomes difficult for them to identify and consider the attributes that are relevant to their consumer situation.

Imagine you are browsing a webstore, looking for a new camera to take on your backpacking trip. For this situation, you prefer a camera that is lightweight, easy to use, shock-resistant and cheap. You don’t have a lot of experience with camera’s, so you decide to look at the reviews of other consumers that bought Camera X. As you browse through several reviews, you start to notice that a lot of reviews mention things like FPS, image stabilization, Wi-Fi connection and GPS tracking. However, the reviews are in conflict about the quality of the image stabilization and many mention the lack of a Wi-Fi connection. After reading most of the reviews, you decide that you want to look for a camera that has better image stabilization and a Wi-Fi connection, attributes which you originally didn’t pick as relevant for your situation …

The scenario above, is what Liu & Karahanna (2017) describe as the ‘swaying’ effect. After reading reviews, people might over-weigh irrelevant attributes and under-weigh relevant attributes. They suggest that attribute preferences are more heavily influenced by characteristics of the online reviews rather than by the relevance of the attributes to the consumers decision context.

Theory development & methodology

Liu & Karahanna (2017) developed their theory from the constructive preference perspective theory (Bettman, Luce, & Payne, 1998; Payne, Bettman, Coupey, & Johnson, 1992). This theory suggests that preferences are shaped by the interaction between the properties of the information environment of the choice problem and the properties of the human information-processing system. Liu & Karahanna (2017) propose that three characteristics of online reviews affect the assessment of attribute preference and theorize that these characteristics together may ‘sway’ attribute preferences.

the amount of information about attribute level performance,
the degree of information conflict about attribute level performance
the overall numeric rating and the attribute-level performance information

They conducted three studies, in which they provided the participants with a consumer scenario, asked them to weigh different attributes in terms of relevance and made them evaluate a digital camera based on reviews.

In study 1 they manipulated the three hypothesized factors and examined their effects on the attribute preferences. In study 2, they reproduced this study but added a monetary incentive to induce high motivation to process review information. The third study was a free simulation experiment to provide more realism and to allow for higher generalizability, in which verbal protocol analysis was used to capture and measure the factors.

Main findings

When the participants were asked to weigh the attributed based on the provided scenario, they placed more weight on the relevant attributed than the irrelevant attributes (in the scenario above, the attributes cost, ease-of-use and weight are relevant attributes, whereas image stabilization is not). But when they had to evaluate the camera based on reviews (that contained an uneven amount of information across different attributes, varying degrees of information conflict, and a numeric overall rating), the relevance of the attributes did not have a significant impact on attribute preferences.

CCDC — Figure 1. Participants’ Constructed Attribute Preferences (Liu & Karahanna, 2017)

The amount of attribute information in the reviews had the greatest impact on attribute preferences. Study 2 showed that the degree of attribute information conflict only affects attribute preferences when people have high motivation to process information. Study 3 showed consistent results. The studies provided evidence that attribute preferences that result from reading the reviews are primarily driven by the review characteristics and not by attribute relevance, thus supporting the hypothesized ‘swaying’ effect of online product reviews.

Practical implication.

What implications can be derived from these results? To support informed consumer decision making, it should be investigated how reviews should be organized and presented and how making sense of information conflicts can become less cognitively demanding. The effectiveness of some practical suggestions, such as providing a short description of the reviewer’s background (newegg.com), displaying the amount of positive and negative comments on an attribute (Q. (Ben) Liu, Karahanna, & Watson, 2011) and allowing people to see the overall rating from reviewers who have similar decision context, need to be investigated. Implementation of these suggestions allows consumer to filter reviews from people in a similar consumer scenario, makes making sense of conflicts become less demanding and causes the numeric overall rating to make more sense.

Strengths, weaknesses, suggested improvements

By conducting multiple studies with consistent results, the article provides strong evidence for generalizability & robust hypotheses, which enhances the external validity of the results. Nevertheless, there are some limitations. The study only examines a single product category (camera) and a single scenario. Additionally, the samples only consisted of students with a similar expertise of cameras. It would be interesting to examine whether the effects differ based on the consumer’s level of expertise with the product category (camera) or the product category itself. Additionally, to increase the generalizability of this study, it would be interesting to see if these results also apply on a sample that is more representative of the population (not only students).

I would love to hear your opinions on this. Do you recognize yourself in the ‘swaying’ effect? Are reviews influencing your preferences?

References

Bettman, J. R., Luce, M. F., & Payne, J. W. (1998). Constructive Consumer Choice Processes. Journal of Consumer Research, 25(3), 187–217. https://doi.org/10.1086/209535

Liu, Q. (Ben), Karahanna, E., & Watson, R. T. (2011). Unveiling user-generated content: Designing websites to best present customer reviews. Business Horizons, 54(3), 231–240. https://doi.org/10.1016/j.bushor.2011.01.004

Liu, Q. Ben, & Karahanna, E. (2017). The dark side of reviews: The swaying effects of online product reviews on attribute preference construction. MIS Quarterly, 41(2), 427–448. https://doi.org/10.25300/misq/2017/41.2.05

Payne, J. W., Bettman, J. R., Coupey, E., & Johnson, E. J. (1992). A constructive process view of decision making: Multiple strategies in judgment and choice. Acta Psychologica, 80(1–3), 107–141. https://doi.org/10.1016/0001-6918(92)90043-D

Surowiecki, J. (2005). The Wisdom of Crowds. American Journal of Physics, 75(908), 336. https://doi.org/10.1038/climate.2009.73

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Personalized offers without compromising the privacy of personal information

February 18, 2018 lasdip Leave a comment

Searching on the internet for information is one of the most common activities these days. This behavior varies from searching for the closing time of your favorite shop, to finding out how cognitive processes work. Consumers are generating and sharing data when they search on the web. The knowledge of this process is not always present. Even if consumers knew their steps were being followed, would it change their daily behavior? Privacy is not a new term. Yet, it disclosed itself more than ever the last couple of decades, together with the introduction of the Internet. The general definition is the right to be let alone. It is based on the principle of protection of the individual in both person and property (Warren & Brandeis, 1890). However, this definition is too broad. Informational privacy fits more in the context of these days. The definition is the right to control of access to personal information (Moor, 1991).
It is clear that privacy concern is an issue for companies. Companies want to use personal information to offer personalized adverts which is often considered as something positive by the consumers. A business example is Amazon. The well-known E-commerce company continually updates the user’s personal page to create more tailored experiences. This is done based on past purchases and browsing history and the objective is to stimulate impulse buys (Reverte, 2013). However, personalization has some concerns.

Sutanto, Palme, Tan and Phang (2013) wrote an article in which they study the so called personalization-privacy paradox. This is the tension between how IT developers and marketers of applications exploit personal users’ information to offer personalized products or services and these users’ increasing concern regarding the privacy of that information. Eventually, this may restrain the use of these applications. The purpose of this paper is to study whether a personalized privacy-safe application works. This application stores and processes information within the user’s smartphone, but does not transmit it to the marketers. This way, personalized information can be offered, without compromising the privacy of personal information (Sutanto, 2013).

To understand the personalization-paradox, the authors build on two theories; the use and gratification theory (UGT) and information boundary theory (IBT). UGT suggests that consumers use a medium either for the experience of the process or for the content it offers. These two dimensions are called process gratification and content gratification (Sutanto et al., 2013). While the latter refers to the messages carried by the medium, the first one relates to the enjoyment of using the medium.

Next, IBT gives a better understanding in which factors influence process and content gratification. This theory suggests that consumers form so-called physical or virtual information spaces around themselves. These spaces have boundaries and an attempt by third parties to cross these boundaries will be considered invasive which makes consumers uncomfortable. In case of an intrusion, consumers will apply a risk-control assessment, weighting the risk of disclosing personal information and the benefits they gain when doing so (Stanton, 2003).

This study conducted a field experiment with three mobile advertising applications. The first mobile application broadcasts adverts generally (i.e. non-personalized application). The second application filters and displays adverts based on the profile information of users, stored in a central server (i.e. personalized, non-privacy-safe application). The last application filters and displays adverts on the profile information of users, stored on their smartphone (i.e., personalized, privacy-safe application). In this context, process gratification is measured with the number of application launches. On the other hand, if a user is interested in the content offered by the application, they are more likely to save the advert with the purpose of retrieving it later, thus content gratification is measured in terms of the frequency of saving adverts (Sutanto et al., 2013).

The results of the field experiment showed that there is indeed a difference in process and content gratification between the three different applications. Process gratification increased by 64.5% when the adverts were personalized compared to when adverts were general. However, there was no significant difference in content gratification. This may be explained by the fact that saving adverts explicitly indicates interest in a specific product, thus it requires the user to reveal deeper levels of information than their own boundaries allow. It is likely that this situation causes an uncomfortable feeling and which eventually will lead to a hesitation to save adverts. Next, the local privacy-safe personalization design increased both process and content gratification. Application use increased by 9.6% compared to personalized, non-privacy-safe application and by 79.1% compared to the non-personalized application. Respectively, advert saving increase by 24.5% and 55.1%.

However, there is an important limitation in this paper. There is a possibility that some users launched the application, but already were interested in a certain ad. This makes it more difficult to disentangle process gratification from content gratification.

Concluding, this article proposes a personalized privacy-safe application. The results show significant differences between the three applications in favor of the local privacy-safe personalization application. Thus, offering personalized adverts without compromising the privacy of personal information is possible.

References:

Moor, J.H. (1991) ‘The ethics of privacy protection’, , pp. 69–82

Reverte, C. (2013) Personalization Innovators: Amazon, Netflix, and Yahoo! | Available at: https://www.addthis.com/blog/2013/08/28/personalization-innovators-amazon-netflix-and-yahoo/#.WomB3OciHIU. [Accessed 18 February 2018].

Stanton, J. M. (2003). Information technology and privacy: A boundary management perspective. In Socio-technical and human cognition elements of information systems (pp. 79-103). Igi Global.

Sutanto, J., Palme, E., Tan, C. H., & Phang, C. W. (2013). Addressing the Personalization-Privacy Paradox: An Empirical Assessment from a Field Experiment on Smartphone Users. Mis Quarterly, 37(4).

Warren, S.D. and Brandeis, L.D. (1890) ‘The right to privacy’, Harvard Law Review, 4(5), pp. 193–220. doi: 10.2307/1321160

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Does privacy still exist?

February 12, 2016 oscarchongis Leave a comment

Is it still possible to maintain privacy nowadays when purchasing products from webshops? There is a lot of debate regarding this subject, lately privacy issues are often in the headlines. Almost everyone in America en Europe uses the search engine Google nowadays, it is the most visited website in the entire world. As you can imagine this is a sexy target for cybercriminals, in the past Google has been hacked and personal information of more than 300,000 website owners have been leaked (Kirk, 2015). Facebook, one of the largest social media platforms in the world also leaked personal information. Personal information of over 6 million user accounts were leaked, the issue was caused by a bug in the social platform (Guarini, 2013). Remember Edward Snowden? Some call him a traitor, but I would call him a hero.

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