Nowadays, companies are really engaged with consumers as they participate in a company’s development process. Active contribution leads to ideas, solutions and positive word-of-mouth(WOM). Collaborative innovation creates a sense of community among the participants. However, it may not always live up to the expectations of its members or is seen as a success. So, are there only positive sides of co-creation? The goal of this paper was to explore triggers for positive and negative reactions from engagement in online innovation communities (Gebauer et al., 2013).
How did they study it?
A qualitative study was conducted to find triggers in online innovation communities and a quantitative study tested how triggers influence the behavior of members of online innovation communities. Continue reading The dark and the bright side of co-creation→
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.
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
We all have been in situations where you are browsing the internet and the advertising is targeted on the content on the website and they are shown to you. Hereby, advertising is targeted. And what is maybe even more intrusive, that the advertising pops up. It definitely gets your attention. However, are you more willing to click on the advertising and buy the product? This is what Goldfarb & Tucker (2011) researched. They study the effect of targeted display advertising and obtrusiveness on sales, and what the effect is when these two are combined.
This question is interesting. Because even with all these new techniques, display advertising success drops. People avoid online display advertising because they infer them in their browsing goals (Drèze & Hussherr 2003). Do obtrusive advertising works exactly in the opposite way and affects the effectiveness of advertising negatively?
To find out, this study uses data from a large randomized field experiment on 2,892 web advertising campaigns. For every campaign on average 852 surveys were distributed. Where the half of them were to consumers who have seen the advertising and the other half were on the website without the advertisement on it.
The main results of this study are that targeting the advertising improves the effectiveness of online display advertising and obtrusiveness does also. However, when these two techniques are combined the effectiveness decreases. This is because privacy concerns temper the appreciation of formativeness in targeted advertising. So, for advertising in categories where privacy matters more, the effect is tempered more than in categories where privacy matters less.
The strength of this paper is the fact that it, in contrast to earlier research, propose that obtrusive advertising is not very effective in the contextually targeted situations. Earlier research study the effect of the obtrusiveness on advertising recall, which is of course positive. By adding the privacy concerns and the feelings of manipulation to the fact that advertising can be perceived as useful makes advertising perceived as intrusive, and therefore result the effectiveness negatively.
This paper shows a reason for the unexpected success of search advertising, where the advertising is highly targeted on the context (the advertising is based on search keywords) but is absolutely not obtrusive or attractive. For managers, this means that in choosing the right way to advertise they must not only consider whether to target their audience with contextually targeted advertising, but also consider the negative influence if these advertisements are obtrusive. Economically, 5.3 percent of advertising spending could be cut, without affecting the effectiveness of advertising. This, solely because the wrong combination of advertising content and format is used.
In short terms, either choose to reach your audience with targeted advertising, or with obtrusive advertising. But don’t combine the two.
Drèze, X. & Hussherr, F.X., 2003. Internet advertising: Is anybody watching? Journal of Interactive Marketing, 17(4), pp.8–23.
Goldfarb, A. & Tucker, C., 2011. Online Display Advertising: Targeting and Obtrusiveness. Marketing Science, 30(3), pp.389–404.
Advertising as we know these days have changed so much from what it used to be a decode ago. First thing any marketing manager thinks of when it comes to devising a marketing strategy is to figure out the online advertising channels. But is it really bring in money? How much of the money invested in online advertising is actually getting converted to sales revenue? Paul R. Hoban and Randolph E. Bucklin, analyzed this same concept in a paper that we are going to dig deeper in this blog post.
Challenge with the study:
The main challenge in studying the effects of online ads on consumers is there is a widespread selection effect. Users browsing patterns and their preferences affect the impact of the advertising. To overcome these selection bias, the paper focuses on profitability or conversion during the purchase funnel. The life cycle of the user follows the four different stages: Non visitor, visitor, registered user and converted customer.
Method:
The method used here is to have two different groups: Control and Treatment group. Control group have similar ad displays as that of the treatment group, but their ads are of some charity firm whereas the treatment group had a focused firm for advertising. This is to help control the selection bias. During the experiment, for the first digital interaction the users were randomly selected into one of the two groups.
Findings from the experiment:
Based on the experiment, they have found the effectiveness for various stages of the users as follows:
Non Visitor: Helps in creating the brand image since the user has never visited the brand’s website. Here the ads
have a little effect in conversions.
Visitor: Users who have already visited the site before and had not created registered accounts, seeing the online ads will have little or no effect. If they hadn’t done that previously, there is a very little chance the ads will trigger them to do.
Registered Users: On the other hand, for registered users, the ads might trigger an emotion motivating them to make a purchase and in that case they have a good effect in conversion rates.
Converted Users: People who had already made a purchase with the brand, the ads might serve as a memory but might not have an effect in making them purchase again. There was no clear result obtained from the paper about the converted users.
Conclusion:
The experiment showcased how the online advertising will impact different customer group segments. By leveraging this and varying the frequency and carryover effects we can increase the marginal effects of exposure. For example, this effect is more prominent for non visitors and less prominent for already converted users. The main limitation of this paper is that the experiment is carried out only for one particular firm. So the results can’t be generalized. There could be same users opening multiple browsers viewing similar ads. That might hinder the estimation. Thus this paper provides numerous opportunities for further research in this domain.
References:
Paul R. Hoban Randolph E. Bucklin February 2015, “Effects of Internet Display Advertising in the Purchase Funnel: Model-Based Insights from a Randomized Field Experiment”
Manchanda, Puneet, Jean-Pierre Dubé, Khim Yong Goh, and Pradeep K. Chintagunta (2006), “The Effect of Banner Advertising on Internet Purchasing,” Journal of Marketing Research, 43 (1), 98-108.
Consumer Value Creation 