With your graduation in mind a lot of questions about a potential future job arise. How to find the best job and how do you know what’s the best job for you? What qualities are businesses looking for and how can you best present yourself? It would be ideal to have someone assist you that already went through this process. A person to ask all your questions to in person instead of reading blogs on the Internet, hoping you will find the right answers. But how do you find that person?

There is nowadays a company that can help you find such a mentor, who will guide you through this exiting journey of getting your first job. Dwillo provides online mentor communities, which exist out of students and young professionals. Dwillo facilitates the relations between these groups and tries to help between finding the perfect match. A mentor (which could be a young professional in my case) can help mentees (which would then be me) in making difficult choices related to career and personal development. Exactly the person I am looking for!

Dwillo, (co-) founded by a RSM alumnus in 2012, is an online platform that allows students, professionals and entrepreneurs to connect in a mentor relation. By providing online mentor communities for universities, associations and enterprises they make it possible for people to connect. Dwillo provides a platform, which creates value through co-creation of mentors and mentees. Dwillo won several competitions such as the RSM I WILL Award and the Philips Innovation Award.

Some will be skeptical about the platform, as they might say you could easily ask someone that is close by (relatives, friends, classmates), but Dwillo markets itself with the fact that it saves time, engages users and their platform has a low organizational risk. Who actually uses Dwillo and whom can I find on the platform? As a student from Rotterdam School of Management, I can contact (experienced) alumni to find a mentor. But it also works the other way around, because as an experienced student (I think I can call myself that after living and studying almost 6 years in Rotterdam now), I can choose to also be a mentor for young students and future students to help them in return. The intrinsic satisfaction of helping young students, a rewarding volunteering experience and enhancing leadership and coaching skills are benefits that arise from mentoring (other) students.

Dwillo will only continue to exist if enough students are looking for a mentor and enough young professionals want to mentor students. Value is therefore created if the online platform is used and, according to Grönroos and Voima (2013), the customer’s well being increases through the process. The students and young professionals co-create value for each other, and Dwillo assists them in this process.

Unfortunately I am too late to register for the spring edition, but I will definitely register myself when the registration procedure is open again! Be it a mentor or a mentee, I definitely want to contribute to this beautiful volunteering experience provided by Dwillo.

Grönroos, C., & Voima, P. (2013). Critical service logic: making sense of value creation and co-creation. Journal of the Academy of Marketing Science, 41(2), 133-150

http://hi.dwillo.com/start-a-mentoring-program-for-your-organisation-short/?utm_expid=96377915-1.K3BiFYw4QeKO43sT7IQ0ig.3

https://www.dwillo.com/rsm

Pandora Internet Radio is a music streaming service that is doing a great job in terms of music recommendation. Their recommendation system is based on the Music Genome Project, which began in 2000 and is one of the most thorough analysis of popular music ever undertaken. It took 30 experts in music theory five years to complete. The Music Genome Project is based on an intricate analysis by actual humans (about 20 to 30 minutes per four-minute song) of the music of 10,000 artists from the past 100 years. This means that the analysis of new music continues every day. The difference with recommendation systems of other players in the market is that Pandora does not make use of the popular method of “people who like this also like this” or other users ratings.

It starts with entering a song or artist that you want to listen to and Pandora will generate a continuous playlist. While a song is playing, it is possible to provide positive or negative feedback for songs chosen by the service. Based on your feedback, Pandora is analyzing the musical structures present in the songs you like and it will add other songs that possess similar musical traits to your playlist.

But, after years of customizing playlists to individual listeners, the company has started data-mining users’ musical tastes for clues about the kinds of ads most likely to engage them. Eric Bieschke, Pandora’s chief scientist says: “It’s becoming quite apparent to us that the world of playing the perfect music to people and the world of playing perfect advertising to them are strikingly similar”.

A lot of companies make use of behavioral targeting. Behavioral targeting comprises a range of technologies and techniques used by online website publishers and advertisers aimed at increasing the effectiveness of advertising by using users’ web-browsing behavior and personal information. Amazon is one of the companies that is really differentiating themselves by using their huge amount of data to make a deeper understanding about individuals and try to influence their behavior.

Pandora adds another layer on behavioral targeting: music. They seek correlations between the listening habits of their users and the kinds of ads they might be most receptive to. The company beliefs that people’s music, movie or book choices may reveal much more than commercial likes and dislikes. Certain product or cultural preferences can give glimpses into consumers’ political beliefs, religious faith, sexual orientation or other intimate issues.

In time of elections, Pandora uses their political ad-targeting system. They are able to deconstruct users’ song preferences to predict their political party of choice. This system has already been used in former presidential and congressional campaigns. During these campaigns, Pandora users tuning into country music or Christian bands might see ads for republican candidates and others listening to hip-hop tunes or classical music might see ads for democrats.

I think providing ads based on music preferences is a new interesting way to target the right audience. Of course, the system will not be able to predict everyone’s political affiliations right, but I can imagine that the algorithm has improved a lot since the last presidential elections. So in 2016, Hillary Clinton might be winning the elections thanks to the ads on Pandora.

Deschene, L. (2008). What Is Behavioral Targeting?. Available: http://i.bnet.com/pdf/199800-What_Is_Behavioral_Targeting_.pdf. Last accessed 03-05-2015.

Layton, J. (2015). How Pandora Radio Works. Available: http://computer.howstuffworks.com/internet/basics/pandora1.htm. Last accessed 03-05-2015.

Singer, N. (2014). Listen to Pandora, and It Listens Back. Available: http://www.nytimes.com/2014/01/05/technology/pandora-mines-users-data-to-better-target-ads.html?_r=0. Last accessed 03-05-2015.

No money? Time enough? A new platform has been developed where people can exchange goods or services. Usually we pay in cash, by credit card, we trade or we can even pay by bitcoin nowadays. One of the disadvantages of money is that you need to have it in order to be able to buy something. On Marktplaats.nl, a famous Dutch platform where people can offer and buy second hand goods and services, some people also ask to trade something instead of using money. There are even platforms, such as Ruilen.nl and Swapit.nl that focus solely on trading instead of paying with money. One of the disadvantages of trading is that it can be quite unfair sometimes. If you paint your neighbors house for 10 hours and your neighbor takes care of your garden for 5 hours it is not such a fair deal. Besides, it is hard to measure exactly how many hours one spent on a service and if you make a trading deal with a stranger it is usually hard to find something worth exactly the same. There is now a solution for this: the tijdmunt (timecoin).

Tijdmunt makes it possible for people to do a service for other people and in return receive a tijdmunt on the platform. One hour of work equals one tijdmunt. This tijdmunt can be spent on a service of someone else, so not especially the person you did something for. It is a platform where demand and supply come together. Tijdmunt currently has 260 users, and expects to grow faster in the future. At this moment there are 63 advertisements of people that offer something on the website, from repairing a flat tire to a dvd box of Lord of the Rings. Currently there are only 19 advertisements of people looking for a service, from someone looking for a hairdresser to someone looking for a programmer. On the demand side of the platform there is nobody looking for goods, only for services. Users can save real money because now they can pay in tijdmunten instead of with money. New users have to register on the website www.tijdmunt.nl, where they can create a tijdmunt bank account. This bank account allows them to transfer tijdmunten to other users of the platform.

Similar platforms where people can exchange services or goods for time were developed, but most of these websites are location based and local people do things for other local people.

The vision of tijdmunt states that before money it was normal to exchange goods and services, but over time consumers and companies have become largely dependent on money. The financial crisis has shown that money also has its limitations and therefore tijdmunt could be used as good complement to the normal money, because it is independent of place, investments and systematic risks. To me, tijdmunt seems like a fair complement to money for people doing services for each other. Would you try it out?

Sources:

http://www.geeferom.nl/project/tijdmunt-sociaal-geld-voor-iedereen

http://www.makkie.cc/service/verdienen/

http://www.marktplaats.nl/z.html?query=ruilen&postcode=

http://www.metronieuws.nl/binnenland/2015/01/nieuw-betaalmiddel-rotterdam-werkt-op-basis-van-tijd

http://www.omroepwest.nl/nieuws/20-02-2015/geen-geld-wel-tijd-student-uit-naaldwijk-bedenkt-nieuw-betaalmiddel

http://www.ruilen.nl

http://www.swapit.nl

http://www.tbrabantskwartiertje.info

Picture: http://www.caseact.com/Features/images/1

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).

Do you feel unique? Well you certainly are, but your photos on Instagram sure aren’t. At least, according to a study conducted by Zheng and Wei (2013). These authors examined the way content is generated on social networking sites in relation with the social ties a user has. This research is of interest because the appeal of a social network site is highly dependent on the content generated by its users. With the increasing number of competing social media, it becomes more important to offer differentiating content. However, according to the authors of this paper, the content generated by a user is often similar to the content generated by his or her social ties, especially around the time of formation of this social tie.

The theory behind this research is that forming social ties is correlated with homophily and contagion. Homophily refers to the tendency of people to relate to each other. This feeling of relating can be caused by shared intrinsic characteristics, a shared opportunity or affinity. For instance, it is more likely that two persons will create a social tie, because they both have the same interest or are located near each other. After a social tie has been created, the individuals often become more alike to each other. This process is referred to as contagion.

Yet according to the uniqueness theory (Snyder and Fromlin, 1980), we all have a strong desire to be unique individuals. Being too similar to others will induce negative emotions. Therefore it would be expected that we all generate distinct content in an attempt to differentiate from others.

Zheng and Wei (2013) based their research on a sub network from photo-sharing community Flickr over a timespan of 41 days. The data was gathered by taking one user and its social connections as the sample group. Every new social contact connected to this network group within these 41 days was subsequently added to the sample group. At the end of these 41 days, the sample consisted of 116,237 dyad pairs and 434,102 photos with 351,985 unique tags. The authors also examined whether the social tie was one-way or two-way as well as the popularity of the users based on the number of favourites their photos receive. Similarity of the photos is measured by the amounts of shared tags assigned to the photos.

The results of this study indicate that similarity of the photos is largest around the time the social ties are created. After the social ties have been created, the similarity of the photos uploaded by the pair of dyads decreased when they both have same popularity levels. However, then there is a high discrepancy in popularity between the two connected individuals, then the similarity of photos uploaded by the dyad remains. As such, the popularity level of the individual acts as a moderating variable.

While the research was based on a sample from Flickr, the results can be generalized to other photo-sharing communities such as Instagram, but also to Youtube and Twitter. However, the study is highly limited by the fact that the measure of similarity of photos is based on tags, which is a textual characteristic. The study, which is based on photos, does not take visual characteristics into account at all.

References: