We all browse on websites that contain user-generated content. As we discussed in class, a firm’s disadvantage of enabling users to generate its website’s content is the fact it has less control over their product offerings. Sometimes, this works out just fine. Firms relying on user-generated content may sometimes even acquire market positions that are not intended. For example, when Myspace, Friendster and Google’s Orkut were competing for the targeted U.S. market, Friendster became popular in Southeast Asian countries. Also, Orkut became one of the most visited websites in Brazil, India and Estonia, which are culturally complete distinct countries. The user-generated content had a key role in gaining these market positions and therefore proves to be able to let firms ‘spontaneously differentiate’ their products. The article “Differentiation with User-Generated Content” (Zhang et. al, 2015) examines the competitive implications of user-generated content.

In their first step, the writers assume firms’ offerings entirely depend on user-generated content. Consistent with previous research, when transportation cost is low (or in other words, the network effects are relatively global), the outcome typically is ‘winner-takes all’, where all consumers join a single, dominant firm. When transportation cost increases (or network effects are relatively local), ex ante identical firms can acquire horizontally differentiated market positions that spontaneously emerge from user-generated content. Moreover, this can result in patterns wherein a firm simultaneously attracts multiple distinct consumer segments that are isolated from each other, like in the Orkut example. The writers call this phenomena “segregation”. Interestingly, greater segregation also leads to smaller differentiation between platforms and increased competition. That may benefit users.

Secondly, Zhang et. al consider firms that have limited influence over their positioning: firms can generate content on their own, or take a certain target segment into consideration when designing their website features. When two firms are competing, given that their levels of advertising are fixed, the one with a smaller market share cannot compensate its users with a lower level of advertising. Therefore, consumers tend to migrate to the firm with a larger market share.

Thirdly, the writers review a model wherein consumers can join multiple websites by allocating their time between these firms. They call it multihoming and creates greater overlap between the firms’ content, because the consumer contributes to both firms’ websites. This leads to smaller product differentiation that makes coexistence more difficult and lowers profits. Thus, however tempting it is from a market share perspective, competing platforms should be careful with encouragement to join multiple platforms and share content across these platforms.

In conclusion, the writers found that with local network effects, user-generated content may trigger spontaneous differentiation among competing platforms. This may happen even if firms play an active role by designing their website especially for certain segments or generate content itself. On the other hand, hosting different segments does not automatically imply content differentiation. Ideally, content contributors build a community that is perceived as different as possible from competitors. This implies that the firm should measure differentiation based on consumer’s perception of content instead of on the number of unique content contributors.

Source: Zhang, Kaifu, and Miklos Sarvary. “Differentiation with User-Generated Content.” Management Science (2014).

When you are browsing the web in order to buy a certain product, you have got to process a lot of information. As we have been taught in class extensively, a website’s Recommendation Agent (RA) can help consumers to make purchasing decisions. Shoppers are loyal to e-stores that enable them to function efficiently.

Before M. Aljukhadar et. al (2012) conducted their research on consumer’s use of an RA to cope with information overload, little was researched about a consumer’s likelihood of using an RA or conforming to its recommendations. Therefore, they investigated several approaches to measurement of product information load; the relationship between delivered information load and perceived information overload; using an RA to indicate occurrence of information overload; effects on information overload on decision strategy while accounting for consumer’s need for cognition; and information overload and decision strategy on several performance measures.

The researchers created a fictitious retailer website that offered laptops. They chose three levels for the number of alternatives as well as for attributes. As consumer’s normally consider many attributes when shopping for complex search goods such as a laptop, the researchers included a high number of attributes. 466 participants were asked to choose a laptop they would seriously consider buying and needed to rate the importance of each laptop attribute. Afterwards, the participants could choose to click on a link to the recommendation according to their preferences.

Participants also were asked whether there was too much information to make a choice on two seven point scales and on two additional scales on how satisfied they were with the choice they made, in order to test perceived information load and choice confidence respectively. The need for cognition was measured by asking to fill in an 18-item scale, e.g. “I find satisfaction in deliberating hard and for long hours”.

The researchers found that overload was actually experienced by consumers and that the relationship between information load and perceived overload was curvilinear. Participants who experienced high information overload, consulted the RA and accepted its recommendation more often than those who did not experience overload. When overloaded, a consumer with low need for cognition is more likely to consult an RA and vice versa. Moreover, as information overload increases, choice quality improves when consulting an RA. Thus, particularly in complex situations, the use of an RA has a positive effect on choice quality.

While using an RA upholds choice confidence, confidence will decrease for a consumer who contradicts the recommendation. The relationship between perceived information overload and e-store interactivity is curvilinear as well. The researches propose two possible explanations for this: first, the use of an RA makes choice accuracy feedback immediate and tangible. Second, rejecting a personalized and accurate recommendation leads a user to face more choice difficulty and cognitive dissonance, which results in less choice confidence and lower satisfaction with the performance of the webstore.

Given these results, it would be advisable to webstores to proactively show product recommendations when information overload is high. Also, RAs should provide accurate advice. Lastly, apply other measures to increase the number of shoppers that conform to the recommendations.

Source
Aljukhadar, Muhammad, Sylvain Senecal, and Charles-Etienne Daoust. “Using recommendation agents to cope with information overload.” International Journal of Electronic Commerce 17.2 (2012): 41-70.

At the moment, 3D printers become a more common phenomena. The use of 3D printers enables both consumers and companies to fabric products that before either could only be produced in massive amounts or would be very expensive or time consuming to make.

A very basic 3D printer for consumers would cost you around $1,000.-, whereas more professional printers that are able to print more complex materials in high quality can cost up to $100,000.-. In other words, it does not seem worth it to own such an expensive device on your own.

That is exactly what the founders of 3D Hubs thought. 3D Hubs is a startup company that enables users to connect with owners of 3D printers in order to print a certain design of choice. The company claims that at this very moment, 14,399 printers are connected to the platform. To some extent one could say that by connecting with 3D Hubs, a printer’s owner is co-creating value. Moreover, the platform includes so-called ‘Talks’, where users can start threads about anything related to 3D printing. By enabling upvoting to sort out the popularity of comments on threads, users get actively involved as well. Also, 3D Hubs organizes events related to 3D printing in order to meet up with fellow 3D printed object fanatics. The founders have the ambition to eventually make 3D Hubs the ‘Facebook of 3D printing’.

What would be needed in terms of value co-creation to reach this goal? To start with, the design of the object wanted to be printed, is designed with software outside of the 3D Hubs platform. Designing a 3D object can be rather difficult. Therefore, if you do not have a design already, 3D Hubs recommends to have a look on Thingiverse or 123DGallery. Thingiverse and 123DGallery are community platforms where you can design and post and share your 3D design on. People can actively participate with these platforms by using existing designs of others, making new designs on their own, or edit designs and customize these into different versions of the product. That does sound pretty useful.

However, when having a look at the designs posted on these platforms it seems that the majority consists of designs that are either really simple products or products that do not have a purpose (e.g. weird looking jewellery). In order words, these designs do not seem to address problems that can be solved with mass market kind of products and therefore it would be more convenient to buy such products in any kind of shop.

In my opinion, in order to become ‘Facebook of 3D printing’, a platform should try to up with ways to let people share only their most useful designs that really are an improvement to mass market or mass customized products. Let’s create real value co-creation. I believe these platforms would be used way more when some mechanisms would be added:

– By giving additional incentive to designers next to ‘name and fame’, e.g. a monetary incentive or prices for best designs;

– By recommending 3D designs to users related to their interest.

www.3dhubs.com/talk

http://www.emerce.nl/interviews/brian-garret-via-ons-platform-7000-3dprinters-140-landen-beschikbaar

http://www.123dapp.com/about

http://www.thingiverse.com/explore/collections