Most of the people have probably heard of AirBnB. AirBnB is a platform which has become a great competitor of the traditional hotel-industry. The platform gives home-owners the opportunity to rent out a room of their home. This offers tourist an alternative to the traditional hotels and bed and breakfasts. On AirBnB, customers can get rooms at sharper rates than they would get at regular hotels. Furthermore, the atmosphere is often different from hotels; you stay at someone’s home which is usually more informal and cosy than a hotel. On the other hand, the suppliers have the opportunity to take advantage of spare rooms. Therefore, you could say that this platform gives incentives to both sides of the market.

Taking on the example of AirBnB; there now is a platform which offers similar opportunities for customers and suppliers in the dinning industry. This new platform is called AirDnD; this stands for Air Drink´n Dine(http://www.airdnd.nl, 2017). On this platform the suppliers are hobbyist cooks which offer a full meal, which can be enjoyed in their living room. As with AirBnB, the platform itself does not own any restaurant or home where the people can enjoy a meal. Rather, they function as an intermediary between hobbyist cooks and potential customers looking for a nice meal.

AirDnD is created with the goal making the passion and qualities of hobbyist cooks accessible to the public. (http://www.airdnd.nl, 2017) This provides customers with a unique dining experience; due to the fact that the cooks are not professional and the dinner takes place in the cosiness of someone’s living room. On the other hand, it offers hobbyist cooks an opportunity to show other people how well they can execute their hobby. The cooks can get reckoning for their abilities without having to work in a restaurant. Through reviews on the platform the customers can get a look in the kitchen beforehand. This provides them with some sort of certainty about the quality of the food; just as you can read reviews about restaurants to get a sense of the quality provided.

Although the platform is relatively new, it seems to be a success already. Customers rate their dining experience with an average of 4.9/5. Currently, the platform only offers living room dining in the major cities of the Netherlands. However, if the experiences of customers will keep being this positive it will likely be a matter of the time until it is also offered in smaller cities.

The platform offers great opportunities to both sides of the market; hobbyist-cooks and customers looking for a nice meal. This platform has the potential to disrupt the entire dining industry, just as AirBnB did in the hotel industry. Therefore, restaurant-owners fear false competition of the hobbyist cooks.( http://binnenland.eenvandaag.nl , 2017) This seems logical as hobbyist cooks do not have the same fixed costs as restaurant owners do. In my opinion, the platform offers great potential and proves once again that crowd-based platform become increasingly important. Such initiatives will likely become more frequent as the role of ‘sharing economy’ is becomes bigger.

http://www.airdnd.nl/Wie-zijn-we

http://binnenland.eenvandaag.nl/tv-items/62883/horeca_vreest_valse_concurrentie_van_hobbykoks_airdnd

Nowadays you cannot find any product category on the internet without anyone giving their opinion on a specific product. There will be practically no products available which have not been rated by consumers through the use of product reviews. This data offers an insight in the perceived strengths and weaknesses of a certain product, also the value of the product as a whole can be obtained. With all this data available, what will be the implications for companies? In other words, how can companies use this wide variety of data to determine what to produce next or to improve their current product?

To begin with, the authors acknowledge that “the basic relevance of consumer preferences, e.g., in connection with new product development processes, is widely confirmed in marketing research and practice.”(R. Decker, M. Trusov, 2010) Therefore it will only be logical to attempt estimating the aggregate consumer preferences in order to produce a product which satisfies these preferences. Furthermore, Zhu and Zhang(2010) found out that 24% of online customers make use of online reviews prior to purchasing a product offline. This implies that the preferences can be used to determine future sales in both online and offline environments. It becomes clear that online reviews have become a major information source for consumer prior to making a purchase. With that in mind, it will be stupid to ignore the importance of online reviews in the development or improvement of products.

Online product reviews typically consist of the perceived strengths and weaknesses; an overall product rating; the formless comments and remarks (full text). One of the main strengths of online reviews compared to traditional consumer preference information; is the fact that the reviews have been written voluntarily instead of being requested. Therefore, companies can expect a high level of authenticity with these reviews. In this paper, the authors created an econometric framework which can be used to aggregate the plentitude of individual consumer opinions into aggregate consumer preference data. The suggested methodology proves to be useful in the collection of consumer preferences and also in reputation analysis.

As with every research, the implications are subject to some practical limitations. The authors assume that the reviews are written by real consumers, instead of professionals of the company itself to boost sales. Furthermore, the available reviews can be biased by self-selection. This means that consumers in a certain product category might be more willing to participate in the creation of reviews.

In conclusion, it would be fair to say that the authors make a useful contribution to the scientific body of knowledge. They provide managers with a framework which can be used to aggregate consumer preferences. This will give managers a handle on which they can build further. As the authors also state; “future research should be devoted to the development of powerful filters for detecting fake reviews and to the further automation of the time-consuming data pre-processing and attribute extraction steps.” (R. Decker, M. Trusov, 2010)

Decker, R., & Trusov, M. (2010). Estimating aggregate consumer preferences from online product reviews. International Journal of Research in Marketing, 27(4), 293-307.

Zhu, F., & Zhang, X. (2010). Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of Marketing, 74(2), 133−148.