All posts by jeroenaelen

With my energy I help businesses and individuals create opportunities in the digital world. I do this by leveraging my knowledge and skills in Business, IT, Psychology and Philosophy. My tools are: Agile Scrum 🔄 Growth Hacker Marketing 🚀 Sustainable Investing 🌳 Gamification 🎳 Connection & Inspiration ⚡️

Scoop your best shot

Always felt like a photographer, but never had the time, knowledge or equipment to go professional? With Scoopshot, you can join a community of mobile photographers from all over the world and participate in daily photo contests. The good thing is: you can even earn money with it!

Scoopshot was released at the end of 2011 as a platform for brands and publishers to crowdsource pictures for their magazines, newspapers and other media. Once the company buys a subscription for the application they can upload new contests every day. These contests last for that day only, to keep the content in the application up to date. For 2000 dollars per year, an organisation can upload as many tasks as it wants.  Users can download it for free and earn from around 10-20 euros all up to hundreds of euros for special contests. By now, the application is used by more than 600.000 people in 200 countries.

In the Netherlands the free newspaper Metro uses the application. For certain events, for instance a big autumn storm in 2013 (Metro, 2013), they create crowdsourcing contests (see picture below for example). From then on, users can submit their own photos (‘scoops’) and thereby help to develop the company’s product. which corresponds to phase one of the consumer value creation functions. As pictures are submitted the community comes into play and votes for a daily winner.


The application does not only leverages extrinsic motivation, it also uses intrinsic motivation. Users can gain reputation when they get more votes and win contests, the so called ‘love and glory’ element of crowdsourcing (Tsekouras, 2015). They can follow the brands that they like, and help to co-brand these firm, making it a small brand community. This corresponds to phase two of the consumer value creation functions ‘compose and co-brand product’. By submitting scoops the user contributes to the brand. We can identify two types of consumer contributions, namely user-generated content observation and contribution of user-generated content (Tsekouras, 2015).

So how big can this application become? I mentioned that it already has quite a lot of users worldwide, but it is not used a lot in the Netherlands yet. In fact, Metro is the only company that is active on the platform. Robert van Brandwijk, Editor in Chief of Metro Netherlands has stated that their goal is to have at least 50% of their photos delivered by Scoopshot (Scoopshot, 2015). Will other companies join in as well? Popularity of the Dutch application ‘Happening’, shows that people like to participate in photo challenges (Tuenter, 2015). Happening allows photo challenges within friend groups, without an extrinsic motivation and is very popular nonetheless. What do you think about Scoopshot? Can it become as popular as Happening and could it be the future of news photography?


Android vs Apple 2.0?

When browsing the internet you normally encounter dozens of news items, blogs and other content. It is no exception that a catchy title usually makes you decide to click through and see what’s out there. And of course when the item ‘Android takes a piss on Apple on Google Maps. Seriously’ popped up on my Facebook news feed I decided to take a look at it.

If you would have searched on Google Maps on the 24th of April for certain coordinates just south of Rawalpindi, Pakistan, a giant Android could be seen urinating on the Apple logo. First thought was that it was an Android developer fuelling the old rivalry again, although later Google released that it was user-created content which was slipped through the approval filter. Because of this item I decided to dig into Google Map Maker, the tool that allows you to edit Maps, which was unknown to me since I had read the blog.

The official goal of Google Map Maker is to share information about places in user’s neighbourhood, like companies or university campuses. Places which are inaccessible with Google street view cars can thereby be edited by Map Maker users. It is actually even more elaborate, because users are also able to add roads, railways or other places and add new languages. Once an edit is sent to Google, it can be reviewed by other users by giving a thumb up or thumb down. This score is considered by the Google algorithm to accept or reject the edit. As we can see with this case users can, with enough peer user support, fool the algorithm.

If we look into the business model of Map Maker more closely we can link it to the first phase of consumer co-creation, namely recommend and develop products. Instead of only browsing through Google Maps to find by Google pre-defined places users can now develop new elements and recommend them to each other. The wisdom of the crowd is the most prominent reason for delegating (crowdsourcing) products. Best known comparable platform that uses this is Wikipedia. At Wikipedia every edit is implemented immediately and can be removed by higher ranked users if they incorrect, offensive or silly.


During our course we learend that controlling quality of submissions can be done by having specific terms and conditions, clear guidelines peer evaluation of content and punishment or public shaming (Tsekouras, 2015). However, the first thing that teenagers do when joining Wikipedia is to make a page about themselves or another try to edit a celebrity’s page. We now have seen that this can also be the case with Google Maps. Google claims that ‘the vast majority of users who edit Maps provide great contributions’, however internet users show that a manipulation is easy to make. What do you think of user generated content on the internet? Should it be better monitored or are we allowed to have a joke every once in a while?


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:

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