A Test of Two Models of Value Creation in Virtual Communities

CONSTANCE ELISE PORTER , SARV DEVARAJ, AND DAEWON SUN This article analyses the impact of virtual communities –with a close focus on trust- on the customer-company relation, or more specific: “…trust as the critical mediator of value creation in virtual communities (Porter, Devaraj, & Sun, 2013). A virtual community is defined as “an aggregation of individuals who interact around a shared interest, where the interaction is at least partially supported by technology and guided norms (Kannan, Chang, & Whinston, 2000). Specifically, they identify two types of virtual communities, (1) A virtual community that is customer-initiated and (2) a virtual community that is firm-sponsored. Prospective customers seek information in virtual communities in order to gain valuable information about a purchasing decision. In order to evaluate this information, prospective customers evaluate member-generated information (MGI) by three attributes: (1) Information Consensus, (2) Information Consistency and (3) Information. In Firm-Sponsored communities another element is added next to MGI: Sponsor efforts. Sponsor efforts consists of: (1) Content, (2) Embeddedness and (3) Interaction. One model (customer-initiated or firm-sponsored) is not by definition better than the other in creating trust and informedness of customers, it depends on the situation and stage of the customer process. However firms can take specific actions in order to optimize their firm-sponsored virtual communities. Many firms have enabled customers to provide summarized star reviews for products to provide a better consensus of customer-ratings, however this research shows that this only moderately helps in the customer. In order to improve on this, managers should focus on the consistency and distinctiveness dimensions of MGI in customer conversations. Example: to encourage perceptions of consistency, firms could focus on developing reliable customer management policies and business processes that are aligned across the organization (e.g. return policy, warranty management process) (Porter, Devaraj, & Sun, 2013). An important thought is that firms do not necessarily have to choose for one of these communities; firms can have both are even more than 2 (a customer-initialized, firm-sponsored and third-party virtual community) because they all have different positive and negative attributes. For example, a customer-initiated Virtual Community might be perceived as independent and therefore more credible (Gu, Park, & Konana , 2012). However firm-sponsored communities can provide both customers and the firm itself valuable information about each other (the opportunity to create direct relationships with customers). Also, virtual communities might follow an evolutionary path; firms might not have the resources initially to foster a well-organized, virtual community, but customer may have initiated one. Over time, this community might change in a firm-sponsored community because the firm decides to allocate resources to create a virtual community to improve relations and informedness of customers. An overview of the two models: Customer-Initiated and Firm-Sponsored Virtual Communities Schermafbeelding 2015-04-05 om 15.30.27 Schermafbeelding 2015-04-05 om 15.30.34


  • Gu, B., Park, J., & Konana , P. (2012). The impact of external word-of-mouth sources on retailer sales of high-involvement products. Information Systems Research , 182-196.
  • Kannan, P., Chang, A., & Whinston, A. (2000). Electronic communities in e-business: their role and issues. Information Systems Frontiers , 415-426.
  • Porter, C., Devaraj, S., & Sun, D. (2013). A test of two models of value cretion in virtual communities. Management Information Systems , 30 (1), 261-292.

Searching in Choice Mode: the faster and superior way?

good bad choice

With the rise of the internet, consumers can consider an ever growing amount of alternatives of a certain product they intend to buy.  Sequentially, recommendation systems have become a widely observed phenomenon in electronic commerce. Much is known about the effect of these product recommendation on the outcome of the purchase decision of the consumer, but research now also concludes that the process of the product search is transformed in the presence of recommendation systems. EUR Professor Benedict Dellaert  and Gerard Haubl from the University of Alberta explain how we traditionally approach searching for a product and how RAs transform the way we search. We trade in the well documented normative model of consumer search (i.e. Hauser & Wernerfelt, 1990), where we continue to search for additional alternatives until we believe that the potential improvement of our product selection, which the newly examined alternative might represent, does not weigh up against the effort of search we need to put in, for a model where we search in choice mode. In choice mode, we let the recommendations guide us on which alternatives to consider and we continuously compare the alternatives we have assessed among each other. Furthermore, we consider a smaller set of alternatives than we would without the RAs and consider these in more depth. The higher the variability in the (perceived)  attractiveness of the alternatives, the stronger this effect becomes, a finding that contrasts the prediction of normative search theory (see Weitzman, 1979). grfiek Luckily for us, the research indicates that the choices we make using the choice mode match our preferences a lot better than the normative model search, following the average match of 82% with the RA versus a mere 47% without the RA. On top of that, we save time as we do so. Though, there might be a fly in the ointment; more recent research from Adomavicius, Bockstedt, Curley & Zhang (2013) reports that the recommendation system can manipulate consumer preferences and can give rise to a bias. The consumer may choose for a particular alternative, because he believes that its high position on the recommendation system means that that particular alternative is a ‘correct’ answer to the uncertainty he faces. Whether that is true depends greatly on the quality of the recommendations. Also, the consumer might be biased when reporting its satisfaction with the product, as subconsciously he adapts his preferences to the product characteristics of the product bought using a recommendation system. In the end, it seems we can make truly better and more time-efficient product choices with the help of recommendation systems, on the conditions that we critically asses the quality of these systems and remain loyal to our original preferences.


Resources :

Adomavicius, G., Bockstedt, J. C., Curley, S. P., & Zhang, J. (2013). Do recommender systems manipulate consumer preferences? A study of anchoring effects. Information Systems Research, Vol. 24, No. 4, pp. 956-975.

Dellaert, B. G.C.,  Häubl, G., (2012) Searching in Choice Mode: Consumer Decision Processes in Product Search with Recommendations. Journal of Marketing Research, Vol. 49, No. 2, pp. 277-288.

Hauser, J.R., & Wernerfelt, B ., An Evaluation Cost Model of Consideration Sets,  Journal of Consumer Research, Vol. 16, No. 4 (Mar., 1990), pp. 393-408 Weitzman, M., Optimal Search for the Best Alternative, (1979), Econometrica, Vol.  47, No. 3, pp. 641–54.

Are Advertisement Agents Out of a Job?


Why spend millions on promotion and advertisement campaigns, when consumers are willing to do it for way less? There has been an increasing trend of consumer-generated advertisement which is promotion through the input of the consumers. By using consumers to create advertisements, there is more engagement of the consumer with the business allowing for communicative participation.

Through the use of consumer-generated ads, much of the research about the brand and product is conducted by the consumer. However, we have always been taught that people are inherently lazy. Therefore, why are consumers participating in the production of ads? According to Campbell, Pitt, Parent & Berthon (2011), besides the inherent monetary reward, there are three main motivations as to why consumers generate in the promotion process, namely, (1) intrinsic enjoyment, (2) self-promotion and (3) change perception.

LayFrito was one of the leading companies to use consumer-generated advertisements successfully through extracting all the motivational concepts. Doritos, the nacho chips brand, started the campaign ‘Crash the Super Bowl’ in the fall of 2006. This campaign gave consumers the chance to create an advertisement for Doritos that would be aired during the Super Bowl with about 141.1 million viewers. In 2006, there were 1065 advertisements of 30 seconds sent to Doritos for the reward money of 10,000 dollars and two tickets to see the finals in Detroit. Doritos furthermore, motivated their consumers through intrinsic enjoyment of the creation of the advertisement as well as sell-promotion as they are the ones staring in the advertisement.

However one of the main fears of letting consumers create advertisement is that there might be less consistency in the company’s message (Thompson & Malaviya, 2013). Since the advertisement might not be in line with the overall vision of the company, there are some downsides in handing the responsibility to consumers. However during the ‘Crash the Super Bowl’ campaign, the submitted advertisements juried by the organization funneling them down to the 10 best advertisements.

The 10 best advertisements were then announced and the consumers had the opportunity to vote for their favorite advertisement to be aired during the Super Bowl. This again created consumer participation in the advertisement process by motivating consumers to have the perception that they can change the outcome of the advertisement. Through voting, the ‘Live the Flavor’ ad won the campaign and was the first ever consumer-generated advertisement to be aired during the Super Bowl. According to the USA Today Ad Meter poll, the ‘Live the Flavor’ ad was ranked the number 4 best commercial during the Super Bowl (USA Today, 2007).

Ever since Doritos has repeatedly created the ‘Crash the Super Bowl campaign to result in 4900 submitted advertisements between 2014-2015 to win the grand prize of 1 million dollars and a year-long contract at Universal Studies for the so-called dream job. Doritos is one of the best examples of how to successfully engage the consumer in the generation of advertisements.


Campbell, C., Pitt, L. F., Parent, M., & Berthon, P. R. (2011). Understanding consumer converstations around ads in a Web 2.0 world. Journal of Advertising, 40(1), 87-102.

Thompson, D. V., & Malaviya, P. (2013). Consumer-Generated Ads: Does awareness of advertising co-creation help or hurt persuasion? Journal of Marketing, 77, 33-47.

USA Today. (2007, February 5). USA Today. Retrieved from Anheuser-Busch Wins USA TODAY Ad Meter: http://www.usatoday.com/


Find information through people

Nowadays, a lot of start-up pitches start with: “We are the Google of “fill in…”. Zeef.com did not, Zeef.com is saying they will do/are doing (a bit) the same as Google, only better. That sounds not realistic, but is it though?

Zeef.com is competing with algorithms by using us, using our knowledge. The core-thought of Zeef.com is, that people are able to come up with better suggestions than Google’s algorithm does, everyone for a specific topic/subject within his or her knowledge domain. So, they, the founders of Zeef.com, asked themselves: why aren’t people with specific knowledge of topics doing the searching and filtering (Zeef = sieve) for us with regard to web-search: “It is time for human knowledge to advance where algorithms have reached their limitations.” (Klaas Joosten – founder, 2015)

How it works? Everyone can set up a page about a specific topic. Within this topic page you can create different lists for subtopics; for example a HTML list (subtopic) within the topic web-development (example). Within this list you are able to rank different web pages based on content of HTML. Finally, if someone is searching for programming information on Zeef.com, it proposes a specific page about programming based on views, rating, etc. all in order to let him/her find the information.

So, why do they think someone will create a page? Zeef.com integrates affiliate marketing within the pages; you can earn money by creating pages without using banners and other adds. If someone will buy something or clicks on specific content redirected by your Zeef page, both you and zeef.com will get a fee (if the specific webshop is using affiliate marketing).

In first instance it sounds a bit like startpagina.nl to me, doesn’t it? The concept is the same, definitely. However, within startpagina.nl you cannot compete within topics. This competition needs to increase the quality of the pages. Besides, you can embed a Zeef-list into your own (blog) website, totally adjusted to your design (example). Zeef.com wants to concur with google Adsense in this way (frido van Driem – co-founder, 2015).


We are better than google Adsense (Rick Boerebach – co-founder, 2015)

Does it have a chance to survive? They attracted over 8000 curators/list makers within one year, besides they raised an investment of 1,2 million euro, end of 2014 in order to “take over” the US market. On top of that the adjusted lists are a lot more inviting than AdSense banners, resulting in a 15x higher click-through rate (CTR) than those AdSense banners. However, there is a huge critical mass within this market, you definitely need to collect a lot of curators in order to be the standard for someone within web searching. Besides, there is a chance that people only create pages that results in earning money for themselves, instead of sharing the “right” content of their topic (Abuse their knowledge).

All in all, I like the idea and the opportunism of Zeef.com. I often think myself, wouldn’t it be great if someone who knows everything within this subject could help me out. However, maybe I am skeptical because I really like start-ups that want to beat the big boys by focusing on quality… Do you think I am?

Van Driem, F. Co-founder Zeef (2015), Zeef – Waar hebben wij het over?, In: http://articulum.nl/algemeen/zeef-waar-hebben-wij-het/, By: Van Breda, N.

Boerebach, R. Co-founder Zeef (2015), “Zeef”, in: https://fastmovingtargets.nl/episodes/rick-boerebach-zeef-wij-zijn-beter-dan-google-adsense/, By: Blom & Stekelenburg


Joosten, K. Founder Zeef (2015) “Zeef: About”, in: https://zeef.com/about


Justin got fired, kept his job and earned more. WHAT? (Incl. Video)

The story in this blog is pure fiction. The fundamentals of this blog is inspired by the following academic article: Schreier, Martin, Christoph Fuchs, and Darren W. Dahl. “The innovation effect of user design: Exploring consumers’ innovation perceptions of firms selling products designed by users.” Journal of Marketing 76.5 (2012): 18-32.

A couple of days ago I experienced the most impressive thing in my life. A way to earn money for free, for doing nothing. No, I am not talking about the scams advertised in web-popups or the unique offers from ambiguous men on the corner of the street. This time was different. This time I faced the real-thing and the good part of it is that you, as a business owner, can earn money for free too! Let me share this have-to-know story with you…

It was the first Monday morning after my 3-week during holiday. I jumped into my car and head to work, kicking off my working-week at Threadless ltd. Arrived at work, I noticed something was different, my colleagues where not there. Literally, no cars on the parking lot, no bikes, no nothing. The entrance of my office was still open, but the receptionist wasn’t there either. The entire “freaking” office was empty, non of my fellow product developers where present. At first I thought, is this a dream? But then, a couple of minutes later, I saw the Porsche of my boss driving towards the office. He jumped out and spoke the following words to me: “Hi Justin! You can go home, our customers have taken your job, for free!” My natural reply was: “Our customers?” , leaving alone my thoughts “I am fired!?”.

Let me explain, Schreierer et al. (2012) found that common design by users (products developed by customers) enhances the perceived innovation ability of a firm, leading to greater purchase intentions. In other words, by empowering a firm’s customers to build their own products, a company saves money while increasing their future sales. In the extremes, a firm, such as where (I) Justin worked, entirely outsource their product development and fires their employees of the design, innovation or product development department. Examples are LEGO (customers partly develop new LEGO models), Threadless (fashion items designed by users) and Linux (entirely open-sources co-created operating software). The authors describe that products that best-fit for outsourcing product design to users is with products with low-complexity and in markets where users are familiar with user innovation. A large downside is found in the field of luxury products (Fuchs, Christoph, et al. 2013). For each individual firm it is therefore (according to literature) a trade-off between perceived innovativeness and luxury.

Coming back at the case of Justin and Threadless ltd, the business owner and boss determined to fire the entire product development department. Instead of letting employees design their shirts, they outsourced design tasks towards the customer themselves. Interesting to experience is the way the business owner could cut costs and increase their sales. Less costs, more sales: free money, for nothing.

As most fair tales have a happy ending, this was the case for Justin too. He was one of the best product developers of his company Threadless. He determined to continue working on new designs, but now from as a customer / co-creator. In the end his experience and feeling for fashion let to the result of being the best-performing artists on Threadless, earning even more than his former salary [CHECK THE VIDEO OF JUSTIN].

1. Fuchs, Christoph, et al. “All that is users might not be gold: How labeling products as user designed backfires in the context of luxury fashion brands.”Journal of Marketing 77.5 (2013): 75-91.
2. Schreier, Martin, Christoph Fuchs, and Darren W. Dahl. “The innovation effect of user design: Exploring consumers’ innovation perceptions of firms selling products designed by users.” Journal of Marketing 76.5 (2012): 18-32.

Written by: Matthijs van de Grift (416083mg)

Social influence bias in online reviews

(This academic blog post is based on Muchnik, L., Aral, S., & Taylor, S. J. (2013). Social influence bias: A randomized experiment. Science, 341(6146), 647-651.)

During our course we learn that there are four functions of customer value creation: recommend & develop products, compose & co-brand products, sell products & digital distribution and P2P support & product evaluations. In this post I want to focus on the fourth function, namely product evaluations.

When consumers make an online purchase decisions, they tend to rely on online reviews generated by other consumers. Consumers regard them as more persuasive than traditional advertisement from marketers and companies, and reports from third party consumer report companies. This is because online reviews focus more on experience than on technical specifications (Lu et al., 2014). Industry reports state that 61% of consumers consult online reviews before making a new purchase (Cheung et al., 2012).

So we know that consumers base their buying decision on online reviews. Muchnik et al. (2013) research if online reviews accurately represent individual opinions about the quality of a product or service. They suspect that social influence create irrational herding effects, where users follow the decisions of prior users. This can lead to suboptimal decisions and a thereby disrupt the wisdom of the crowds. If that is the case, it means that online reviews could easily be manipulated and disturb our decision behaviour.

To research the social influence bias on individual rating behaviour Muchnik et al. (2013) did a large-scale randomized experiment in a news aggregation web site. They find that negative social influence were corrected by other users by giving a positive rating, so there is no significant herding effect there. However, they did find evidence for herding effects by positive social influence. Positive social influence increased the likelihood of giving a positive rating by 32%. Overall, this increased the final ratings by 25% on average.

An important theoretical contribution of this article is that it confirms prior hypotheses on a tendency towards positive ratings, which makes these results more generalizable. This applies to all different kinds of users (e.g. frequent or infrequent voters) that could be distinguished in the experiment. Future research will need to research about the mechanisms that drive individual and aggregate ratings.

Managerial implications can be interesting for companies who want to use reviews as a marketing tool. If they can up vote positive reviews it can lead to herding effects and thereby positively increase sales. Taking the findings of this article in mind, would you be more critical about online reviews? Or are they too important for your decision making process?


Cheung, C. M. K., & Thadani, D. R. (2012). The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decision Support Systems, 54(1), 461-470. Available: http://dx.doi.org/10.1016/j.dss.2012.06.008

Lu, X., Li, Y., Zhang, Z., & Rai, B. (2014). CONSUMER LEARNING EMBEDDED IN ELECTRONIC WORD OF MOUTH. Journal of Electronic Commerce Research, 15(4).

Muchnik, L., Aral, S., & Taylor, S. J. (2013). Social influence bias: A randomized experiment. Science, 341(6146), 647-651

You and Me will Shake Up the News Industry

Using your Twitter account to create your own TV channel, something that became reality last February with the introduction of the Meerkat app. It’s very easy: open the Meerkat app, log in with your Twitter account and press ‘stream’ to start broadcasting. The broadcast will be shared via your Twitter account and can be followed by any other Twitter users.[1]

Meerkat is a promising application: the start-up got over 4.2 million dollar in funding and has over 120,000 users already.[2] Furthermore, after the successful introduction of Meerkat, Twitter launched its own streaming application called Periscope. Both Meerkat and Periscope allow Twitter users to broadcast anything they like. I’m conviced that these streaming apps will shake up the news industry.

Already, news organisations started experimenting with both Meerkat and Periscope. The Economist correspondent Henry Curr answered questions send in via Twitter, using a Meerkat stream. According to the Economist, ‘Meerkatting’ is perceived more informal and a great way to engage with their Twitter audience.[3]

Twitter already got a great impact on the news industry. 78% of all journalists use social media on a daily basis (of which Twitter is used the most) and 74% of all journalists believe that social media have more rapid impact than traditional media. But it’s not the news organisations that will shake up the industry; it’s going to be you and me.

Already, consumers are adding value to the news industry by sharing information about any kind of occurrences on social media (both text and pictures). This is already being used by journalists to pick-up the latest news flashes: 45% of all journalists put out 60%-100% of all they publish as soon as possible – without checking facts – and correct later if possible. Just 20% of the journalists always check the facts before publishing.[4] Via the streaming apps, consumers can start adding value to the news industry by sharing directly what they see: it’s an additional point of view next to traditional news organisations and, moreover, viewers can interact with the broadcasters. Concluding, you and me can help sharing news quicker and more reliable.

So from now on, news organisation are becoming of less importance in providing news to societies? No, that’s a misunderstanding. Meerkat and Periscope were widely used after an explosion in York City. Some were stating that these broadcasts were introducing a new era of journalism, while others were less convinced by the usage of the streaming apps. Jacob Brogan, Future Tense research associate, stated that “People weren’t getting information from either that they couldn’t have found more easily and more clearly on Twitter” because “it was too far from the scene to reveal more than the fact that the fire was still burning”.[5] I do not think that Brogan isn’t right there, but the ‘Meerkatters’ aren’t replacing journalists. While news organisations will remain the most reliable source for news, ‘Meerkatters’ can show news from a different angle and, moreover, followers can interact with the Meerkatters.

You and me are not going to take over news organisations – we shouldn’t even want to do that – but we are going to add value to the news industry!

[1] http://www.emerce.nl/nieuws/nieuwste-sensatie-sociale-media-meerkat

[2] http://mashable.com/2015/03/15/twitter-meerkat-graph-users/

[3] http://www.theguardian.com/media/2015/mar/30/meerkat-periscope-live-streaming-apps-news-twitter

[4] http://www.ing.com/Newsroom/All-news/NW/2014-Study-impact-of-Social-Media-on-News-more-crowdchecking-less-factchecking.htm


ZEEF: Taking on Google by using… humans.

Recently a new Dutch start-up has been getting some media attention. They have given themselves the name Zeef (the Dutch word for a sieve), a name that will start to make sense if you continue on reading. Zeef has taken on a rather ambitious goal; challenging Google by changing the way people search for information online. Their trick essentially revolves around sieving the information on the web to only show the relevant bits to people searching for a specific keyword. The interesting thing about Zeef is that this sieving is done by humans.

On Zeef, the information you can search through is managed by so-called curators. As a curator, you can create a page about a topic you like, let’s say backpacking. All the content on this page about backpacking is managed exclusively by you, the curator of the page. Curators can then add links to other websites with relevant information on backpacking on this page, and categorize these links into blocks on the Zeef page. So you might find a block with all kind of links about things to take on your backpacking trip and another block will show you all kinds of websites you can use to find hostels. It is also possible to add images or just blocks of text to your Zeef page, but the main aspect is the collection of links to websites relevant to the topic. The idea behind this is that humans are far better capable of deciding whether a website is relevant to this topic than algorithms, such as Google’s, ever will be. Now you might think “How do I know if this random person who created the page on backpacking is actually knowledgeable on the topic?”, and this would be a fair question since anyone can become a curator and setup a page within minutes. Zeef has tackled this problem by allowing other curators to ‘challenge’ a already existing page on the topic by creating their own page on the same topic. So let’s say you come across the backpacking page on Zeef and think you can do better. You can then simply create a page on the topic on backpacking as well, and when someone then searches on backpacking he/she will be able to choose between the two versions. If you like a page you can vote for it, and this way a ranking of multiple posts on the same topic is created.

(A page I created on Zeef: https://the-grid.zeef.com/axel.persoon)


Zeef has a really interesting approach, as it basically argues that a recommendation system by humans is better than a recommendation system based on a algorithm. And there might be some truth in that statement. As mentioned in the paper by Tsekouras & Li, people appreciate the effort made by recommendation agents. I can imagine that this appreciation of effort is even larger if people know the recommendation agents are human. The strength of Zeef lies in its numbers, and perhaps over time we will see a extensive hub of information completely curated by humans instead of algorithms.