How social media changed the way we communicate

Social media has changed the way how consumers and marketers communicate. Word-of-mouth is more and more shifting from offline to online platforms. Social media websites respond to this shift by providing public forums where individual consumers can show their own voices. Thereby, the consumers have the accessibility to product information from other consumers that could influence their purchase decision. This kind of word-of-mouth increases the way that consumers process information and the way that marketing messages are distributed. Needless to say that this has an impact on consumer decision making and thus marketing strategies.

Why is this paper so important?

This paper is about how an individual becomes socialized through positive interaction on social media websites to use some product or service and what the impact of that is. This paper is important because it is distinguishing itself from existing literature by focussing on peer communication in online socialization processes. Particularly the influence of peer communication through social media web sites on consumers’ purchase decision. This has rarely been investigated and this research tries to fill the gap.


To test the research question, the researchers used the data set of one of the largest Internet portal in China, The researches placed an extra link on the homepage of the site, asking whether the visitor wanted to participate. To increase the incentive of the visitor, the participants had a chance to win a laptop. A total of 935 participants clicked the survey link, which makes the research quite dependable.

The most important result is that peer communication through social meida positively influences purchase intentions in two ways: a direct influence through conformity and an indirect influence by reinforcing precept involvement. This peer communication can be increased by strengthening individual-level tie strength with peers as well the group-level identification with a peer group.

Main strengths and Managerial Implications

One of the main strengths of this article is their data collection. The article used real life data to test their research, liked already discussed here above. Because of this real-fie data, the validity of the paper can be guaranteed.

This paper divides the managerial implications into three parties: implication for Marketers, implication for Online Advertisers and implication for Social Media Website Operators. I want to address the most important ones. This article can be used Online Advertisers for many reasons. Increasing activity of the users at social media platforms, may encourage visitors to build new kinds of relationships with the sponsoring organization. Besides, an advertiser that can use social media to respond effectively to consumer commentary on review sites gains a great advantage because it can engage customers in conversations to understand their needs and build relationships throughout the purchase and after purchase process.


X Wang, C Yu, Y Wei (2012) “Social media peer communication and impacts on purchase intentions: A consumer socialization framework”. Journal of Interactive Marketing, 26(4), 198-208

Social Influence Effects in Online Product Ratings

Before booking a vacation or a hotel, the first thing most people do is to check the reviews. A comScore Inc. survey (2007) found that 24% of consumers use online consumer reviews before purchasing a product. In the context of hotels, these online consumer reviews influenced the choices for 87% of the consumers.

Online ratings and reviews are a growing form of interpersonal communication that is not only outside a firm’s control but also exerts a strong influence on consumers’ purchase decisions. These reviews are influenced by the consumer’s product experience, which consist of positive features, regular negative features, (possible) product failure, and product recovery.

However, are these reviews also influenced by other consumers’ online ratings?

Sridhar & Srinivasan (2012) hypothesized the moderating effects from other consumers’ online ratings on the effects of a reviewer’s positive and regular negative features of product experience product failure and product recovery to address product failure on the reviewer’s online product rating.

They used data from 7499 consumers who filled in all the personal information (e.g., age, membership duration) while reviewing. They collected their online ratings on a five-point scale and text reviews of 114 hotels in Boston and Honolulu, which were posted on a third-party travel website between 2006 and 2010.  They also collected information about the reviewed hotel’s class and amenities (e.g., business centers, swimming pools) and personal information

With a model they tested the different hypotheses which results supported the hypotheses. They found that the higher other consumers’ online ratings were:

  • the weaker the positive effect of the positive features of product experience on a reviewer’s online product rating was.
  • the weaker the negative effect of the regular negative features of product experience on a reviewer’s online product rating was.
  • the stronger the negative effect of product failure on the reviewer’s online product rating was.

This would imply that consumers expect more of a product if the reviews are higher. They already expected the product to work well, so if it did, this would not ‘stand out’ and would affect the reviewers online rating less. But the negative features would also affect the reviewers rating less because these negative features did not bother the previous online reviewers that much as well considering the ratings were higher. However, if the product failed, consumers would react stronger and have a stronger negative effect, resulting in a lower online product rating. This is also logically explainable, because when the reviews are very high, you would certainly not expect to have a failure in the product.

This paper documented that online product reviewers are influenced by information provided by other online product reviewers. The findings of Sridhar & Srinivasan (2012) showed that online social influence can alter the marketing phenomena. For example, the marginal impact of product failure would be expected to be negative, but together with a superior product recovery and social influence effects, this negative marginal effect may be overturned.

The main managerial implication that this paper gives are that companies are well aware of the fact that high reviews can either strengthen or weaken the effects of product failure on a reviewer’s online product rating, companies should be well aware of the double-edged effects of high online ratings of their products. If such product failure occurs, companies can proactively contact such consumers requesting direct feedback, instead of letting them post negative reviews online.

Sridhar, S., & Srinivasan, R. (2012). Social influence effects in online product ratings. Journal of Marketing, 76(5), 70-88.

The effects of trust, security and privacy in social networking: A security-based approach to understand the pattern of adoption

It makes a few years that Social Network Services (SNS) are involved in our daily life. We share content, keep in touch with our friends and family members and build communities around common interests with total strangers. From Myspace, to Facebook and Twitter, people login on SNS every day to take an infinite amount of actions. Facebook alone records 1.86 billion monthly active users! The heavy use of those SNS engendered many different risks. Today hackers use the easy access to information to commit fraud, identity theft and many malicious actions; those causes both users and potential users to question the level of security and privacy that SNS offers and impacts their level of trust toward those platforms.

Today, it is important for SNS to understand what pushes consumers to adopt or not their services. User’s concerns may inhibit the growth of those platforms and this why Dong-Hee Shin examined users’ perception of security, trust, and privacy concerns regarding social networking platforms and intended to elaborate an SNS acceptance model based on those factors.

After conducting an intensive literature review about technology adoption and its relevant concepts, Dong-Hee Shin created a trust-based decision-making model and tested the model by collecting data from SNS users and running a structural-equation modeling process on those data.

Beforehand, Dong-Hee Shin conducted some pre-survey interviews in order to,

(1) cross-validate factors identified from the literature;

(2) learn about context-specific factors;

(3) to guide the survey question design.

From the results of those pre-survey interviews, an actual survey was created and sent to a survey agency which gathered responses from 370 participants. The answers of this survey were then used to test the model fit. Eight common model-fit measures were used to estimate the measurement model fit and results confirmed that the anticipated theoretical model explains and predicts user acceptance of SNS substantially well.



The results demonstrated the importance of perceived security and perceived privacy when influencing users’ intentions to use SNS. The effect of trust on attitude was also supported by the results, implying that privacy mediates the relation of security on trust. 66% of the variance in trust is explained by users perceived privacy and perceived security. Trust, combined with security and privacy, explain 51% of the variance in attitude toward behavior, which subsequently explain 23% of the variance of intention.

As the paper suggests, SNS managers can learn a lot from those findings. First of all, managers need to become more and more customer centric, focusing on their trust relationship with customers through the initiation and the promotion of comprehensive privacy standards. Furthermore, SNS should make sure that the offered level of security of their services is aligned to the one that consumers expects as security affects behavioral intention through attitude. Moreover, explicit policies and data protection mechanisms are needed so that users can experience a similar level of social privacy as the one they find offline.

Overall, SNS managers needs a full understanding of the security and privacy factors that influences users’ acceptance of their networks. Only this understanding will promise a bright future to their platforms which matures in such hostile environment.



Shin, D. (2010). The effects of trust, security and privacy in social networking: A security-based approach to understand the pattern of adoption. Interacting with Computers, 22(5), pp.428-438.


Non-Public Versus Public Online Community Participation


Since online communities have become far more popular than ever before, their members are being paid close attention to for being an essential study object. In general, they could be divided into two distinctive groups, namely posters, who post text and non-text threads, comments and engage in conversations, and lurkers, who would not participate like posters in public but still read threads and observe others’ behaviors.

Continue reading Non-Public Versus Public Online Community Participation

A Design For Life:

In recent years multiple crowdsourcing platforms regarding logo designs have emerged. An infinite list from 48HoursLogo, Logomyway, DesignCrowd to 99designs, all guaranteeing professional logo designs for business use. But, what about designs on your body? Yes you heard me right: a crowdsourcing platform for tattoo ideas.


I have to be honest here. I do not have tattoos at the moment, nor am I planning on getting one in the future. Having something put on your body today and still being happy with it fifty years later seems impossible to me. Either way, I can imagine when planning on getting a tattoo, having a unique design is of great importance.  However, when going to a tattoo shop most likely you are limited to the imagination and skills of one tattoo designer and therefore the actual result may not always be how you envisioned it in the first place…


The owners of understand this problem and thus created a platform on which you are able to connect with a community of tattoo designers, drawing designs based on your wishes and budget. With over twenty thousand designers and twenty-two designs per contest on average, guarantees for satisfaction or you get your money back. is unique in its sort. Several websites on tattoo inspiration exist (,, however none of these platforms offer competition possibilities.

So How Does it Work?

The process is simple and straight forward. When creating a contest for your next tattoo design, you are asked to write a short summary on your wishes and pick your price, which you pay the platform upfront. Then just wait; during the next ten to sixteen days designers are working on their designs and submissions will pop up. Throughout the competition you are then able to rate submissions, give feedback and answer questions if needed. After choosing the winning design, you are provided with the design on stencil, which you can take with you to your local tattoo shop. The quality of the final product once on your body, therefore depends on the performing artist of course.


Besides the contest holder no one is able to see the designs of other competitors throughout the contest. Only after the contest has ended, submissions are made public for everyone to see. This blind setting may be applied to prevent free-riding amongst designers or highly similar submissions. However, Bockstedt et al. (2016) researched contestant’s behavior in unblind innovations contests and concluded that an unblind setting does not prevent contestant’s from submitting early in the contest due to free-riding scares, but instead provides the advantage of emerging information. These early birds are also more likely to be successful. Making submissions on open for everyone to see may have a positive impact on the overall quality.

Who’s to profit?

 Joint profitability is gained as all three participating parties profit from and on  Firstly, the winning designers are paid through an eighty-five percent share of contest prices, which vary from $20 up to $800 ( But, also the satisfaction of creating and being able to show of skills can be seen as profits for the designers. The remaining fifteen percent of the price money  stays with the platform owner. Although not directly monetary profiting from, indirect profits may also be assigned to the contest holder. By not having to search for the perfect tattoo design on different websites or tattoo shops, search costs are minimalized. In addition, one could imagine that due to the fact that the contest holder can give feedback and therefore optimize the final product, the possible costs of regret or even removing a disappointing tattoo are much lower.

Lastly, institutional arrangements are mentioned. As intellectual property is put up for sale on, the website provides an extensive user agreement list. This, in order to protect designers and prevent any claims on issues regarding copy rights, trademarks and publicity rights. This, to prevent designers from not wanting to post any content, as they may not trust their work being handled with care and is being protected.

So what about you? Would use this platform or stick to your local tattoo shop when contemplating on a new design?



Sources; (2017) Available at: Accessed on 04/03/2017

Bockstedt, J., Druehl, C., & Mishra, A. (2016). Heterogeneous submission behavior and its implications for success in innovation contests with public submissions. Production and Operations Management.



Gigster: Affordable Mobile & Web Applications

Building robust web and mobile applications quickly is complex and resource intensive. In many cases this is simply not viable for smaller companies as they do not have the human capital nor the cash flow to afford the right developers. Creating significant problems in business innovation.

Gigster wants to change that, they want to revolutionize the software industry through democratizing access to great software at affordable prices. It follows a service-for-hire business model, whereby customers plainly indicate what they want to get developed and at what price. Gigster then connects them to the most appropriate developer who has the right skill set. Simultaneously, Gigsters softwares creates pre-written code based on keywords of the customer’s proposal, through its AI based technology. This makes the job easier and quicker for the freelancers, saving both time and money for the customer and effort from the developer.

Gigster has many professional developers in its network including MIT, Stanford and Caltech graduates. So far they have executed over 80 projects for large conglomerates like google and square to smaller incubator backed startups. Andreesen Horowitz one of the largest VC funds in Silicon Valley invested $10 million in Gigster after just having come out of the Y-combinator accelerator program two years earlier in 2013.

Gigster is here to stay, through an innovative business model that combines smart recommendation systems with a pay as you want scheme, they provide access to affordable personalized and creative web and mobile applications.

Their business model can best be described as an economic multi-sided platform; the developer side of the network grows through an invite only strategy, this ensures the quality of the applicants and motivates the network to be organically grown. Currently they have over 700 developers, 300 product managers and 100 designers, people inside the network refer to them as “Gigsters”. Gisgsters have studied at respected intuitions and have worked at large conglomerates. This in turn also helps feed the demand side of the platform, as current developers already have existing experience with client certain clients providing a seal of approval for the platform itself.


The platform performs many functions and ensures joint profitability across the value chain. The platform provides high quality web applications at competitive prices, and provides interesting, well earning work for developers across the world. The platform is capable of reducing the overall costs for all stakeholders through its AI machine learning technology increasing the systems welfare, by reducing time spent designing and building the application. Gigster has created a strong institutional environment to mediate the network, by an invite only method of developers the platform ensures a form of social control through reputational and relational elements. There are also forms of contractual ownership that is managed by a team of customer experience Gigsters, they develop the system requirements and mediate contract relations between the customer and the developer. Intellectual property rights are strongly protected, Gigster even requires customers to sign a non disclosure agreement before signing up.

Will companies ever need developers again? How viable is this business model in the long run?


Feldman, A. (2016, October 19). Next-Billion Dollar Startups. Retrieved from Forbes:

Gigster. (2017, February). Gigster is the world’s engineering department. Retrieved from About:



The Language that Gets People to Give: Phrases that Predict Success on Kickstarter

Crowdfunding and the dynamics behind it have dynamically evolved year-over-year. It is difficult for researchers to study crowdfunding and have the ability to suggest recommendations that will hold valid for more than 6 to 12 months.

A prominent example of this is  the most funded project at the time of the paper written by Mitra & Gilbert (2014) – the Pebble Watch. I won’t go deep into how Pebble achieved this success, especially since it seems that it is the only crowdfunding project that deserves attention and is repeatedly mentioned across the body of research on crowdfunding. Rather, I’d like to point out a couple developments that have occurred since  the year that this paper was published (2014):

1) Pebble went from a valuation of $740 million to less than $40 million

2) There is no more technical support for Pebble, since it’s assets have been acquired by Fitbit.

3) in 2016, Filippo Loretti (the most funded watch project currently) was funded 480.000% while Fitbit was funded “only” 100.000%

This points to two important insights: (1) Having a successful Kickstarter does not guarantee a brand’s success (even in the example of the most funded Kickstarter project which authors present in their introduction as the “status quo”) and (2) There other more interesting numbers that might hint towards why a project is setup in a way towards funding success.

In their paper, Mitra & Gilbert (2014) focus on a particular interest component of Kickstarter campaigns – the phrases used on the project’s page (independent variable) and how it affects a project being successfully funded (dependent binary variable). After presenting past research that has confirmed variables such as including a video (which is now mandatory on Kickstarter) or the size one’s social network having an effect on success of a crowdfunding campaign, the authors delve into exploring whether and how particular language used affects a project’s campaign on crowdfunding platforms.


Below is a breakdown of the strengths and weaknesses of this paper:


First, the authors used a very clear method in identifying which phrases from Kickstarter pages would be assessed. By removing “niche” phrases that were often category-specofic, Mitra & Gilbert were able to compile a list of common general phrases and measure their effect on a project (not) being fully funded.

Second, to avoid the potential distorting effects of confounding variables, the authors included a large variety of control variables – namely 59 Kickstarter variables that were identified as possible predictors of funding. This then allows the authors to identify whether there is a direct correlation between phrases used in Kickstarter campaigns and their eventual funding success.

Third, using big data methodology, the authors engaged in creating a predictive model – one that does not over-fit to the data (since there is a large data set). Using a ten fold cross validation method and expanding the features list until there is no more substantial gain in the explanatory power, they were able to effectively asses the impact of the independent variables on the binary dependent variable.


First, based on the industry observers, the author believes that there will be several crowdfunding sites that will emerge and join ones that are already on the internet. However, such a statement does not say much about the competitive environment of these platforms and that although copycat businesses will evolve, eventually only a one or two will be “prominent,” similarly to what has occurred in the space of social media. To fix this, the authors should focus more on explaining why new emerging crowdfunding sites will be relevant to the space of crowdfunding.

Second, the authors used a binary response variable for their dependent variable. Although this is interesting in general, it does not explore the phenomenon of over funded projects (and how much they got over funded). This particularly related to the aforementioned comparison of Pebble and Filippo Loretti. To fix this, the author could have included % funded as a moderating or mediating variable.

One of the key takeaways from the paper by Mitra & Gilbert (2014) is that reciprocity – the tendency to return a favor after receiving one – plays an important role in persuading potential backers to support a project. Phrases such as mention your, also receive two, we can afford are all examples of reciprocity in action within projects. Other important categorizations of phrases that were found by the authors include scarcity, social proof, social identity, liking and authority.

The theoretical implications of the paper predominantly affect two bodies of research: emerging studies on crowdfunding platforms & computerized text analysis on drawing inferences from real-world examples (in this case, Kickstarter). However, the authors go on explaining that they do not claim the results to fully explain a guaranteed success of a Kickstarter project. They suggest that there is much more research required into additional attributes such as project categories, no. of project updates and pledge levels. Additionally, I would add the following to the list: percentage funded, type of reward stacking, type of video content & presence of voice over in video content, social media advertising (predominantly on Facebook) and the presence of crowdfunding-specific social media agencies (such as Jeloop or The Crowd Mafia) that have a incredibly strong effect on how much a successful project gets funded over the desired goal.


Gurman, M., & Zaleski, O. (2016, December 7). Fitbit Buys Software Assets From Smartwatch Startup Pebble. Retrieved from Bloomberg:

Matas, D. &. (2017, March 05). Redefining Luxury Watches – Filippo Loreti. Retrieved from Kickstarter:

Mitra, T., & Gilbert, E. (2014, February). The language that gets people to give: Phrases that predict success on kickstarter. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing (pp. 49-61). ACM.

Unsplash. (2016, March 05). Pexels. Retrieved from Pexels:





Which products are best suited to mobile advertising?

No doubt it is the first thing you look at when you wake up in the morning and the last thing you see before you go to sleep: your mobile phone. Imagine when checking the weather app before you leave the house a banner pops-up showing an ad for a new movie. What will be the chance that this banner will have any effect on you?

Mobile phones have become a part of our lives. According to eMarketer (2015a) US adults spend an average of 3 hours per day on mobile devices. Therefore, it is not surprising that global spending on mobile advertising has been rapidly growing up to more than $100 billion in 2016 (eMarketer, 2015b). Marketers want to grab the opportunity to capture your attention while browsing on your phone. However, marketers are not very happy with the effectiveness of their mobile advertising campaigns. Most of the time, they do not know what they are doing and are using a so-called ‘spray-and-pray’ mentality.

Because of the large-scale investments that are being made in mobile advertising, a better understanding of factors affecting mobile advertising campaign performance is needed. This is where Bart et al. (2014) saw an opportunity for research. Their main question is: ‘Under what product-related conditions are mobile display advertisements effective in changing consumers’ product related attitudes and purchase intentions?’.

Bart et al. (2014) used a multi-campaign, multi-industry data set from a large U.S. market research agency. 39,946 consumers participated in their field study and completed a questionnaire about products featured in 54 mobile display ads from 2007 to 2010. This large and double mixed data set is one of the main strengths of the paper. The authors estimated Average Treatment Effects (the difference between the mean attitude or intention ratings in the exposed and control conditions) for attitude and intention, moderated by product involvement (high or low) and product type (utilitarian or hedonic).

The most striking result from their study is that mobile display advertisements tend to only be effective for products that are both utilitarian and have high involvement, such as washing machines and family cars. These ads, on average, increased positive attitude by 4.5% and intention to buy by 6.7%, while hedonic and low involvement product ads (such as a sports car or toilet paper) had no effect.

An underlying psychological reason for this result is that high involvement goods are relevant and consumers are likely to have retained information about them. Even though mobile display ads do not include a lot of information they do have the ability to influence consumers by triggering their memory about a product that has already been assessed. In addition, a higher involvement with the product tends to be processed cognitively rather than emotionally, which is why mobile display ads work better for utilitarian products rather than hedonic products.

The main managerial implications of this study are that given a product, marketers can now better understand whether a mobile display ad will be effective or not. Also, given the decision of a marketer to invest in a mobile display advertisement campaign, they can have a better sense of what product to use and how to position this product in order to maximize campaign effectiveness.

So after reading these results and interesting findings, what do you think, will a banner on your phone showing a movie ad have an effect on you?


Bart, Y., Stephen, A. T., & Sarvary, M. (2014). Which products are best suited to mobile advertising? A field study of mobile display advertising effects on consumer attitudes and intentions. Journal of Marketing Research51(3), 270-285.

eMarketer (2015a) Growth of Time Spent on Mobile Devices Slows available at:

eMarketer (2015b) Mobile Ad Spend to Top 100 Billion Worldwide in 2016, 51% of Digital Market available at: (

Ideal Flatmate: how to avoid roomates from hell


Ideal Flatmate

“Imagine…after months of searching for the perfect apartment, you finally did it!!you found the perfect house, close to work, recently renovated and within the budget! It even has a roof terrace!!but oh wait…it also comes with a flat mate! who is sloppy… and forgets to pay the bills…and he ate your sandwich! your SANDWICH!!!”

raw (220×120)

It is this kind of situations that Ideal Flatmate aims to eliminate.

Ideal Flatmate is a UK startup which takes house hunting to another level. In similar housing platforms, users would search among all the available properties or rooms trying to pinpoint the most appropriate one. On the other hand, landlords and flat sharers would upload a listing of their property (along with rules and preferences), hoping to increase the chances of renting it. Indeed, renting an apartment or room became easier with these platforms. However, what they could not guarantee was the success of cohabitation. The launch of Ideal Flatmate might mitigate this tricky situation. The platform came to shake things up, by adding a little of “matchmaking” in the mix. Users are asked to complete a 20-question survey (devised by 2 Cambridge professor) with questions focused lifestyle, living habits and personality traits. Then though a dating style algorithm, Ideal Flatmate pairs users with compatible flat mates. The idea behind it is not completely novel. Some roommate matching startups already exist in the US; although, they are targeted to students only. The platform is the first to enter the UK market and for the time being it is available for London flat sharers and landlords.

How it works


The perfect roommate is 5 steps way:

  1. Register
    • Users can either advertise their available room or search for one among the listings.
  2. Ideal Matching
    • Users fill in the on-site survey. It takes 20 questions for the personality matchmaking algorithm to find and suggest the most suitable potential flat mates.
  3. Get to know each other
    • Users can chat and discuss with their matches through the message board. They can also create groups to find people to fill larger properties.
  4. Meet up
    • Users are now ready to take that “relationship” further and arrange to meet face to face!
  5. Secure your place
    • Users can move in and start enjoying the shared living experience.

Efficiency Criteria

Users primarily save time. Finding the right place to leave is like searching for a needle in a haystack. That applies especially to the London rental market where supply is currently on the rise(,2017). Browsing through a dozen of listings and subsequently meeting with numerous applicants,is time consuming. The tailormade suggestions of the platform mitigate this issue. The invested effort is also low, the platform is easy to use and even the survey is simple and quick to fill in. Furthermore, users are relieved from the stress and discomfort of ending up with unsuitable flat mates. In general, the platform comes at a low cost for the majority of users since browsing properties and flat mates is free of charge. However, in order to contact potential flat mates, users have to pay a subscription (either £4.99 for a week or £8,99 for a month). The subscription fee is a little bit pricy, albeit is more economical than the fees of competitive platforms (e.g Rival Spareroom charges £10.99/week and £23.99/month for complete access to the listings).Last but not least, specifically for landlords, placing a listing is also cost free.

Letting agents apart from being advertised, they also enjoy a grace period, since they also pay no fee.

To this point, Ideal Flatmate has not reached its full potential when it comes to maximizing its pay offs. The only source of revenue comes from the users that opt for the full functionality subscription. The platform is also available only on the London market, so the pool of potential users(and subscriptions) is also limited. However, the platform is backed up by government seed funding,which has probably covered at least the development costs.

Value Co – Creation

  • Users co-create by providing all the necessary information to facilitate the house/flat mate hunting process. By completing the survey, both flat sharers and flat seekers, provide the data (property/personality/preference data) for the matchmaking algorithm to work. Provided that the algorithm is accurate in pairing the right people, the chances of finding the perfect property with the most suitable flat mate increase. On the other hand, it becomes possible for landlords and letting agents to pinpoint the appropriate tenants (and essentially avoid short term contracts or early terminated contracts).
  • Ideal Flatmate benefits either way. The company does not own any property. It is essentially the intermediary that facilitates the whole process, by connecting and matching landlords with tenants and flat seekers with flat mates. Moreover, an amount of revenue is guaranteed since they have a subscription model (the company will have an income even if users do not find what they wish for)


Regarding the institutional environment the company adhere to EU regulation (privacy,cookies,age limit and use of personal data).Since the company does not own any property it also not liable for any damage that was not foreseeable or caused by other user(Terms and Conditions).Overall,even though the idea might not be entirely unique,it is quite promising since it could solve a long-standing problem.To reach its full potential the company ought to start charging a fee for landlords’ listings and letting agents’ advertisements (company owners have stated that they will soon begin to do so). Currently, the platform is limited to the London market although within the year it is likely to be launched across the UK. If it proves successful in accurately matching people and Londoners indeed embrace it, the platform could potentially expand to other European capitals facing the same problem…hello Rotterdam!!


Ideal Flatmate wants to be a matchmaking platform for UK flatshares


The Impact of User Review Volume on Consumers’ Willingness-to-Pay: A Consumer Uncertainty Perspective

With the increasing product offers on the internet, product reviews become more important. Subsequently, the statistics towards these reviews act as criterion for consumers. Important statistics include review volume, review valence, and review variance (Tsekouras, D., 2017). According to Jang et al. (2012) these statistics serve as a decision tool for consumers. More specifically, high review volume can increase the exposure of a business or product offering and high review valence also increases product consideration (Jang et al., 2012). There is a lot of research done towards these important statistics, however the findings were inconsistent. The most important reason for this, is the wrong assumption in previous research that online reviews have equal impact on different consumers. This study will fill this gap.

This study includes two different methods of research: an experiment and an empirical study. This is the main strength of the article. Both research methods are conducted in order to investigate how consumers use statistics in online reviews to form their WTP toward different online sellers. The authors focus on WTP because the impact of user reviews on price is inconclusive and a fuller understanding of this relationship contains direct implications for enhancing targeted pricing and promotion strategies.

First of all, an experiment was conducted in order to test the internal validity of the framework. The authors asked 143 undergraduate students with a scenario where they needed to purchase a new LCD TV of $800. The researchers showed the subjects a list of sellers with different review profiles and asked them to report the

maximum price they were willing to pay each seller for the TV. Results of this experiment support, except for the hypothesis H4a, the whole theoretical framework. The relationship between review volume and WTP varies not only by individual, but also by review valence (Wu & Wu, 2016).

The empirical study is conducted for two reasons. establishing external validity and sterile lab setting may not perfectly reflect the consumer decision process as it naturally occurs in online markets (Wu & Wu, 2016). They select the online auction site for use in our empirical study because the prices that consumers bid on

products are directly observable on the site and because user reviews are critical for eBay sellers’ success. Results of this empirical research emphasized that consumers differ in their preferences toward review volume. More specifically, this research shows review volume positively influences consumer WTP. The authors provide evidence that consumer preferences for review volume and review variance not only differ across individuals, but also change with review valence within individuals.

In conclusive the experiment and the empirical study shows that consumers’ preferences regarding review statistics are different. Moreover, the study shows that a consumer’s preference regarding review volume may shift: a consumer may be willing to pay more to a seller with a higher volume, but only when valence reaches a certain level. An implication of this research is the short period of data collection. To provide more reliable evidence towards the phenomenon of the heterogeneity of consumers regarding the review statistics, further research has to select more data over a longer period.


Jang, S., Prasad, A. and Ratchford, B.T. (2012) How Consumers Use Product Reviews in the Purchase Decision Process. Marketing letters. Vol. 23 (3), pp. 825-838

Tsekouras, D. (2017). Customer Centric Digital Commerce. Session 5 slide 13.

Wu, Y. and Wu, J. (2016). The Impact of User Review Volume on Consumers’ Willingness-to-Pay: A Consumer Uncertainty Perspective. Journal of interactive marketing. Vol 33, pp. 43-56



The Effect of Perceived Impact on Crowd-Funding Contributions

By Madeleine van Spaendonck (365543ms)

When deciding to fund a project on a crowd-funding platform, does it matter to you how close it is to its target? This is what researchers Kuppuswamy and Bayus (2017) investigated in their study “Does my contribution to your crowdfunding project matter?”. Prior scholarly work in this field has focused mostly on the significance of early contributions, and their ability to signal quality and lessen project uncertainty (Colombo et al., 2015). They found that people financially support projects when they believe their contribution will have an impact. Using a panel-data approach, the study examined 10,000 randomly-selected funded and unfunded Kickstarter projects (posted between 2012-2014), with the following variables.

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The results reveal the following phenomena, forming the basis of the proposed “impact theory”:

  1. Additional backer support for a project will be higher as its cumulative funding approaches its target goal.
  2. Additional backer support for a project will drop sharply after the target is reached. After this point, people are likely to prefer other projects that do not have sufficient funding and where their financial help is thus perceived to have more impact. Results 1 and 2 combined form the ‘goal gradient’ effect.
  3. Moderating factors: this effect is strongest when backer support is likely to have the highest impact; this is when the project is close to its funding deadline, has a small funding goal, or has limited early support [figures 1, 2 and 3].

Screen Shot 2017-03-05 at 10.29.49.pngScreen Shot 2017-03-05 at 10.33.39.pngScreen Shot 2017-03-05 at 10.34.34.png

The figures illustrate U-shaped patterns for funding contributions over time. From a customer-centric perspective, backers are thus motivated by the target goal of the project and its proximity. For both pro-social reasons and the opportunity to receive the promised rewards, backers want the project to succeed (Gerber & Hui, 2013).

Strengths, Weaknesses and Future Research Directions

The paper offers new insights into crowd-funding behaviours by empirically studying its dynamics over time. For example, the ‘impact theory’ can explain the “Kickstarter effect”, which is the observation that more than 90% of projects that achieve at least 30% of their goal will eventually reach their target. People want to make an impactful contribution, which means that projects that are near – but not past – their target are most likely to receive support. Other crowd-funding phenomena, such as herding, cannot account for this by themselves. A weakness of the study is that the outcome measure is only focused on whether or not a contribution was made. To determine whether people voluntarily contribute more when they believe it will make an impact, a future research direction would be to measure contribution amounts.

Managerial Implications

The study highlights several practical implications for entrepreneurs. Setting the appropriate goal has a high impact on potential funding. A too-high goal makes it challenging to get close enough to the target for the goal gradient effect to arise. However, a too-low goal may prematurely halt contributions, because support declines after the target is reached. Furthermore, communicating the target goal and the goal process in the form of updates/reminders can increase contributions, as this also triggers the goal gradient effect.


Colombo, M., Franzoni, C., & Rossi-Lamastra, C., (2015). Internal social capital and the attraction of early contributions in crowdfunding projects. Entrep. Theory Pract. 39(1), 75–100.

Gerber, E., Hui, J., (2013). Crowdfunding: motivations and deterrents for participation. ACM Trans. Comput. Hum. Interact. 20 (6), 1–32.

Kuppuswamy, V., & Bayus L, B. (2017). Does my contribution to your crowdfunding project matter?. Journal of Business Venturing, 32(1), 72-89.

Cover photo:

Gil C. via for VentureBeat, (2017), Kickstarter Headline [ONLINE]. Available at: [Accessed 3 March 2017].

Do traditional findings on social ties and WOM hold for eWOM?

Different from Word-of-mouth, online word-of-mouth is on a one-to-world platform. It has access to the unlimited reach of the Internet to share opinions and experiences. Another difference is that the electronic nature of eWOM makes the reader unable to judge the credibility of the writer, anonymous posts exist, which means profit-motivated messages can be posted. This paper focusses on the influence social effects have on the value that consumers place on information gathered in their search. It is interesting to know what are the differences between the influence of social ties on WOM and eWOM, because in this way it is possible to understand the value of the source in eWOM environments.

They investigate the data from a website where you can rate your professors, In previous research, academics believe that this website is rather used for entertainment than as a source of information to choose a professor. In this research they argue that the website is used for its intentional purpose, as statistics show that 6 million ratings have been posted, which means there is evidence that students invest time in rating the professors.

482 US college students participated in a survey related to RMP usage, course selection, professor selection and demographic information. The survey resulted in another reason to believe the website is used as a source of information. 96 percent of the students was aware of and 94% of them used the website to select a professor. The main reason why the students use is to reduce risk. For example, the risk of a decrease in GPA and a less interesting class, because of the way of teaching. They also found that just 36% of the students had ever rated a professor themselves and most part of them did not rate more than 2 professors. Most part of the users is passive and reads the content generated by other users. This is a low number, but in line with studies of online behavior.  The results show that the website is more important in the decision making than talking with friends and an academic advisor.

In contrary with WOM, in eWOM situations users find anonymous online forum sources more important than strong friendship ties or weak tie sources. On the other side, the theory for homophily holds, people utilize more information from homophilic sources than from heterophilic sources. Which means that people use more frequently the information from people with the same gender, age and interests. This applies for both WOM and eWOM according to this research.

For websites in the same area or with the same purpose the findings above are important to know how to provide the best and most useful information to its users. By this, they can understand that suggestions based on strong friendships are not useful in eWOM situations, but that people with the same interests are more likely to use eachothers information.




Erin M. Steffes, Lawrence E. Burgee, (2009) “Social ties and online word of mouth”, Internet Research, Vol. 19 Iss: 1, pp.42 – 59. 

Hey neighbour, can I rent your drone?

Drones are becoming increasingly popular, cities are filmed from above with drones, drones can send packages and drones are even used in the army (, 2017). More and more companies and individuals are interested in using drones, but what to do when you do not own a drone or in contrary, when you have a drone, but you do not use it that often? The sharing economy is already present in various aspects of society and this is exactly where the new peer-to-peer drone rental marketplace Up Sonder responds to.

Up Sonder
Just like renting a room through AirBnB, it is now possible to rent a drone. Up Sonder is a free platform and takes only a 5% provider service fee as revenue and on top of that, a small portion of the revenue is donated to help deliver access to clean drinking water to Africa (, 2017).


Everyone who owns a drone can create a free profile and list their drone at their own price to become a provider and certified FAA remote drone pilots can also list themselves by adding their service. On the other hand, companies and individuals are able to filter and rent different kinds of drones and/or services in their direct area. The platform is easy to use and providers can manage and accept bookings from within the platform. Also, they are able to access payments, scheduling, inventory management, customer messaging and sales through the online portal. The renters on the other hand can schedule and make their payments quickly. Up Sonder collaborates with UberRUSH that picks up and drops of the drones when the rent is scheduled (, 2017).


Efficiency Criteria
The utility for the consumers of the platform, the renters, is the fact that the platform is simple and fast and can be used anywhere at anytime. It is free to sign up and create a profile. Renters will switch to Up Sonder, because they do not have to buy a drone themselves, so they save money, and providers will switch to Up Sonder, because they can still make money of (unused) drones at their own price in an easy way. Besides that, the renters and providers become part of a larger community, which is fun and in which they do not have to exchange the money and drone themselves. It saves them money and time and maximizes the joint profitability.

On the other hand Up Sonder is feasible, because the platform takes care of several institutional arrangements. Firstly, to make the platform more reliable, etiquettes are present. The renters and providers can see photos of each other on their profiles and afterwards, both parties are able to write a review. At the same time, providers are protected by the platform from damage with insurance up to 2,500 dollars and renters are offered a refund policy with three different cancellation policies. Additionally, Up Sonder has a non-discrimination policy to make sure that people from all backgrounds are treated equally. The platform takes also care of the institutional environment. When providers earn more than 600 dollars in a tax year they have to fill in a tax form and payments are made by means of established methods.

Up Sonder meets several efficiency criteria and is rapidly growing. Having a drone and using its services is made available for everyone!


From on-line to off-line successes, Amazon Books.

In this post, I would like to follow-up on the earlier essay named ‘Amazon go: invisible interaction yet visible personalization’ as well as focus on certain aspects of the future of retail. I believe the Amazon Go concept is not entirely new. Of course, the aspect of no cashiers and no lines is innovative, however the movement from pure online players to owning brick-and-mortar stores successfully is not new. In a Harvard Business Review article by Porter (2001), it was already indicated that that off- and on-line practices complement one another. More recent studies such as a consumer goods report by McKinsey (2016) on the sales practices of Europe’s leading consumer-goods companies indicates that one of the successful criteria of a company is to make bold omnichannel investments.

Amazon would be a great example of extensive omnichannel investments. Aside from Amazon Go, another example would be Amazon Books. A previous pure online player opening physical sources is outstanding in the book or publish industry where most players, like Barnes and Nobles, are actually closing their doors and disappearing in shopping areas (Enwemeka, 2017). Ironically, it comes across as if the Amazon online store has facilitated for the (re-)opening of Amazon Books shops. Just a couple of days ago, another new Amazon Books has been opened in Massachusetts (Enwemeka, 2017). All, related to a long-term plan introduced earlier last year to continue opening the successful bookstores of Amazon (Hook and Whipp, 2016).

What makes these Amazon Books so unique? How do they compete against other players?

One of the most interesting and innovative features of the store is the usage of online aspects implemented in the physical stores. More specifically, the user reviews and ratings as displayed in the web shop (and in the picture below) are also provided in the bookshop and therefore, contributing their service effortlessly to off-line customers as well. Moreover, differentiating themselves from other bookstores.


Second, only top-rated books are offered in the physical store, all provided enough space for customer to easily identify them resulting in higher convenience. Then, a third difference would be that no prices are given instore in order to provide the fluctuating prices which are also on Customers can access prices through usage of mobile applications, a real omnichannel approach (Zetlin, 2016).

From current experience in retail, I expect most difficulties to occur in operational tasks. For example, the updating of reviews and ratings for all books in store. Not to forget, these books might also be rapidly changing due to the business model’s need of providing top-rated books only. These concerns will increase when Amazon Books shops will continue to expand. Lastly, how could off-line reviews be implemented in the current concept?

To conclude, I believe that customers of Amazon Books truly experience an omnichannel world, where both off- and on-line is integrated. Ready for the future of retail.


Enwemeka (2017)

Hook and Whipp (2016)

McKinsey (2016)

Porter, M.E. (2001) Strategy and the Internet. Harvard Business Review 79(3) 62-79.

Zetlin (2016)


The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms

Let your fingers do the talking

With the rise of the internet came a distinct and new opportunity – not only could organizations more easily reach their, but customers could express their thoughts directly to the world with online feedback mechanisms.

Word-of-mouth has become digitized into a large-scale internet-based feedback network, enabling individuals to share their reviews of companies, products, services, individual sellers and more. Not long ago, consumers would make their purchasing decisions based on advertisements or advice from experts. Now, however, the author suggest that evidence points to consumers are increasingly relying on the reviews of others online.

As a result of this, the author (Dellarocas 2003) suggest that management now need to understand how these online feedback mechanisms affect their organizational activities, including:

  • Brand building and customer acquisition
  • Product development and quality control
  • Supply chain quality assurance

This paper discusses the new possibilities and challenges these feedback mechanisms pose and identifies how these online feedback mechanisms differ from word-of-mouth networks. It also provides a perspective from game theory and economics, focusing on feedback systems in online marketplaces, and identifies opportunities this new area brings.

Online feedback vs. word-of-mouth networks

The author propose the following difference between online and traditional feedback systems, which includes:

  • The unprecedented scale and reach of online systems
  • The ability of their designers to precisely control and monitor their operation
  • New challenges, such as the unpredictability and unreliability of online identities and the almost complete, and lack of contextual cues of subjective information

Case Study: eBay

Following an analysis of the literature available on the eBay’s feedback mechanism, the author found that:

  • Feedback profiles affect both the prices and likelihood of sale
  • The effect of feedback on prices and likelihood sale is increased for more risky sale transactions and for items that cost more.
  • The components of eBay’s feedback mechanism that influence buyer behaviour most are the total number of positive and negative ratings, and the number of recently posted negative reviews.

Reputation in Game Theory & Economics

Based on a thorough analysis and application of game theory on reputation, the author put forward the results that are most relevant for online feedback mechanism designs.

The author suggest that incentives to maintain a reputation reduce over time and eventually completely diminish. The author attribute this to the “trade-off between current restraints and the promise of future gains”, in a limited number of repeated games.

Solutions proposed for this includes: (1) Establish community membership rules that produce good behaviour, and (2) assigning a value to reputation that can be bought and sold, such that ncourages the maintenance of good reputation.

What’s good about this paper?

In addition to conducting an extensive literature review on the topic, the author of this paper collected the most valuable results of previous work and propose solutions to problems detected. It was also interesting that the author used a different perspective, game theory & economics to evaluate reputation, as opposed to the usual marketing and branding perspectives.

A suggestion for future research to extend on this paper would be to expand the focus from solely online feedback in online marketplaces to more types of online feedback mechanisms.


Dellarocas, C 2003, ‘The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms’, Management Science, vol. 49, no. 10, pp. 1407 – 1424, viewed 4 March 2017, <;.

How communication channels impact word of mouth

Word of mouth has become an important aspect of marketing. More specifically, word of mouth marketing is the most trusted form of marketing among consumers. According to Nielsen – a leading global provider of information and insights into what consumers watch and buy, 92 percent of consumers trust word of mouth and recommendations from friends and family above all other advertising forms (, 2012). Moreover, there are different channels through which word of mouth can be communicated. Examples of communication channels include social media, e-mail, face to face, or instant messaging, to name a few. Berger and Iyengar (2013) aimed to study the effects of how the communication medium can shape the message of word of mouth.

The main aim of their study was to uncover whether self-enhancement motives (i.e. wanting to seem cool or smart) and synchronicity (i.e. level of delay between the statement and response) of the communication channel impacts the brand or product being discussed. Furthermore, the authors distinguished the communication channels according to their modality (i.e. spoken or written). After conducting five related studies – three experiments and two field studies, the authors found that modality in fact influences what is discussed. Writing rather than oral communication leads to more interesting products and brands being mentioned. Regarding the impact of synchronicity, the studies showed that the asynchronous nature of written communication allows for greater construct and refinement of the discussion. Furthermore, asynchrony provides the opportunity to self-enhance, which in turn affect topic selection. On the other hand, when consumers have very little time to construct and refine their message (e.g. in oral communication), or have little urge to self-present, accessibility drives the topic of discussion. In these situations, consumers are more inclined to mention brands or products that are top of mind, regardless of how interesting these brands or products may be. (Berger and Iyengar, 2013)

In summary, these findings indicate that written word of mouth is the communication channel that naturally leads to the discussion of more interesting brands and products. This includes written discussions in blog posts, online reviews, and social networking sites, among others. The findings of this study are more relevant for explaining consumer behavior, however some managerial implications can be considered. Taking these findings, and the aforementioned fact that word of mouth and recommendations are regarded as the trustworthiest marketing channel, into account, impacts companies’ word of mouth strategy. Corporate blogs, in which employees interact with consumers in a more informal setting, have been around for a while, and many companies have attempted to launch viral campaigns, in order to stimulate word of mouth among consumers. However, some of these viral campaigns have caused backlash. For instance, McDonalds attempted to stimulate word of mouth by asking their followers on Twitter to share their #McDStories. However, this did not pan out the way they expected, as one user tweeted the following #McDStories:


All in all, it is important for companies to carefully consider their word of mouth marketing campaigns, as online communication channels have allowed for more elaborate and witty responses among consumers. This links back to the theory suggested by Berger and Iyengar (2013) that written communication channels provide consumers for better construct and review of their statement. Despite good intentions, a campaign can always pan out differently, as #McDstories has showed.



References: (2012). Nielsen: Global Consumers’ Trust in ‘Earned’ Advertising Grows in Importance | Nielsen. [online] Available at: [Accessed 5 Mar. 2017].

Berger, J. and Iyengar, R. (2013). Communication Channels and Word of Mouth: How the Medium Shapes the Message. Journal of Consumer Research, 40(3), pp.567-579.

Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics

Considering the ever increasing popularity of online consumer generated reviews, companies realize they can benefit a lot from this trend. One very relevant belief is that online consumer reviews can significantly influence a consumer’s purchasing decision via an online form of word-of-mouth promotion (WOM).


Several researchers have studied the average effect of reviews on a product’s sales, but the article by Zhu & Zhang (2010) adopts the view that product- and consumer-specific characterisics can moderate this relationship. In their developed conceptual model (Figure 1), this governing effect is examined. Via an emperical analysis in the gaming industry based on an extensive data set, the researchers came up with some interesting results. One suggestion that arose, is that the role of reviews is crucial if other information sources are relatively scarce. Therefore, an absolute ‘nail’ for an online marketing strategy of less popular products that reside in the “long tail”, is to intensively manage review possibilities. Mainly because superior WOM via online reviews strongly influences its sales. Furthermore, since people are nowadays relatively experienced with the internet, companies should be aware about the notion that online reviews are more influential when a consumer has greater internet experience.



Although prior studies focused on the effects of online reviews on purchase decisions, they did not consider any other external factors. Therefore, this study was the first to empirically examine the contextual factors that can moderate this relationship. In order to do so the researchers were able to collect a lot of reliable sales-data via a leading market research firm and review-data from established and well-known reviewing websites. This enabled the researchers to carefully analyze the obtained set containing five years of data! This amount of data definitely improved the validity of the results. Besides, the researchers were able to account for certain biases due to promotion actions or popular purchasing periods. Moreover, the researchers resolved a bias that occurred in the study by Chevalier & Mayzlin (2006) since they calculated for potential differences in the amount of sales caused by a higher level of competition per game.


A major weakness of the article is that some questionable assumptions are made. The most relevant one is that ‘online game players’ have greater internet experience than offline game players. Although these games make use of the internet, this is unrelated to reviewing and surfing on the internet via a computer. This requires completely different skills, and although these people may be more likely to have this experience, it does not say anything about relative levels of experience. Furthermore, players of offline games could simply prefer these games over online games, while these players also have great internet experience.

Discussion Points

The researchers claim a correlation between increased online reviews that result in higher incremental sales for products that currently have relatively high sales. Since this claim rests on a correlated effect, this does not mean there is a causal relationship. Therefore, the effect could also be the other way around, so the increase in online reviews could be a result of higher incremental sales.

To increase the generalizability of the results, other product categories need to be studied. As the researchers discuss inconsistent prior findings that resulted from other product categories, this could be a reason for the different outcomes (Jiang & Guo, 2015). As they propose themselves, products that are closer related to internet sales and online communities could have significant different reactions.


Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online Book Reviews. Journal of Marketing Research, August(43), 345-354.

Jiang, Y., & Guo, H. (2015). Design of consumer review systems and product pricing. Information Systems Research, 26(4), 714-730.

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

Crowdsourcing Invention – The tale of Quirky


Crowdsourcing is a great method of bringing people together who can achieve more when they cooperate than they could ever have accomplished alone. A great invention is a combination of a good idea and a good execution. This is the idea behind Quirky, an interesting idea that had a dream start, then (spoiler alert) fails but in the end gets a second life with new owners.

What is Quirky?

Some people overflow with (great) ideas but don’t have the resources or know-how to bring these ideas to life. On the other hand, some people have the capabilities to bring ideas to the marketplace and make them successful but aren’t terribly creative. Quirky was created in 2009 by Ben Kaufman as a marketplace to bring these people together.


quirky-logo“We’re making invention accessible”

 Crowdsourcing innovation is a way to get an audience, potentially even buyers, for a product before it is even in production. Quirky retains the rights to ideas and facilitates communication between inventor and executioners. It pulls invention out of isolation and created a community in which people work together to create new products (


The platform succeeded in creating a community and successful products.


For example, Garthen Leslie came up with a smart air conditioner, uploaded a rough idea to Quirky and with the help of the community came up with extra features and a name: Aros. Quirky then patented the idea, manufactured it and sold the product to big retailers such as Walmart and Amazon. Leslie’s return for his idea was 10% which in this particular instance amounted to more than $400.000, but also the community also benefitted with $200.000 that was distributed to all of the members that contributed to Aros (Silvester, 2015).


However, the invention business turned out to be difficult. Crowdsourcing as a method of quality control turned out to be flawed. For every Aros there were many examples that did not do too well. These cost the company money, a lot of money. For instance, the Beat Booster that cost the company $388.000 but only ever sold 30 units (Popper, 2015).


There is almost never one single reason a company fails. In the case of Quirky there are several, with these being the main ones:

Crowdsourcing as Market Validation

Quirky operated under the assumption that when the community ‘upvoted’ a product enough it would be a success in the market. However, this turned out not to be the case. In real life companies, an R&D department would thoroughly test ideas with pilot runs etc. However, Quirky has a fast-to-market mentality and it negatively impacted quality. The company started to receive many complaints by inventors feeling rushed and buyers feeling deceived with faulty products that did not live up to the hype (K, 2015).

Capital Risks & Brick-and-mortar retail

The company’s margins were thin, they took everything on themselves including manufacturing and distribution resulting in high upfront costs. Quirky decided not to solely focus on its core competency (facilitating ideas) but also on logistics, supply chain management etc. Especially when dealing with large brick-and-mortar retailers (with high minimum inventory demands) inventory costs are high, very high resulting in high capital risks when products were not successful (K, 2015).

Lack of Economies of scale/iterations

Where ‘normal’ companies generate and execute 2 or 3 ideas per year, Quirky aimed for more than 50. Resulting in many, but incoherent products. This resulted in a lack of recognition of Quirky as a brand. This absence of focus also led to an absence of iterations of successful products (Einstein, 2015). Aros, one of the company’s major successes could have been a multi-million company on its own if the company had decided to improve the first version with feedback from its customers. Yet, the company decided to invest time in putting, even more, products on the shelf instead, while not creating economies of scale (Fixson & Marion, 2016).


The founder of Quirky, Ben Kaufman, was only 25 when in 2015 his company grew to 300 employees and raised over $185 milben-kaufmanlion in capital.

He was lovable, ambitious and well on its way to make it in the business world since he was a good salesperson (Lagorio-Chafkin, n.d.). One Quirky member said: “I’m pretty sure he could sell ice to Eskimos” (D’Onfro, 2015). He was excellent at spotting talent and persuading them to come work for him. However, it has also been said he failed to listen to their knowledge soon after. For example, Kaufman demanded a team to work on Egg Minder. A smart egg tray which notified people on their smartphone that they were almost out of eggs even though a team of engineers claimed it did not make much economic sense (Popper, 2015).

Bankruptcy & Restart

After 6 years, in 2015, Quirky filed for bankruptcy. The before mentioned problems turned out to be too big to handle and the company could no longer pay its obligations. The company, at that point, had gathered more than 1 million registered users and brought more than 400 products to market (Lohr, 2015). However, this turned out not be the end for Quirky. On February 8 2016, it was announced that Quirky acquired new financiers and owners and the company relaunched in May 2016. The company redefined its definition of ‘public’ and made the process more private requiring people to sign up and log in before contributing to society. Furthermore, it has changed its evaluation methods, however not much is communicated in detail on this change (Quirky Blog).

Only the future can tell if this new and improved version of Quirky will last as it is too soon to tell right now. However, it is clear that crowdsourcing innovation can yield great revenues the only question is how to manage this exchange of responsibilities to great joint profitability for all parties involved.


D’Onfro, J. (2015). How a quirky 28-year-old plowed through $150 million and almost destroyed his start-up. Business Insider. Retrieved 3 March 2017, from

Einstein, B. (2015). The Real Reason Quirky Failed. Bolt Blog. Retrieved 2 March 2017, from

Fixson, S. & Marion, T. (2016). A Case Study of Crowdsourcing Gone Wrong. Harvard Business Review. Retrieved 3 March 2017, from

K, C. (2015). How Not to Crowdsource : The Demise of Quirky – Digital Innovation and Transformation. Harvard Business School. Retrieved 2 March 2017, from

Lagorio-Chafkin, C. What Happened to Quirky?. Retrieved 2 March 2017, from

Lohr, S. (2015). Quirky, an Invention Start-Up, Files for Bankruptcy. Retrieved 2 March 2017, from

Popper, B. (2015). Exclusive: the secret struggles of Quirky, a seemingly successful startup. The Verge. Retrieved 3 March 2017, from

Silvester, J. (2015). The Rise and Fall of Quirky — the Start-Up That Bet Big on the Genius of Regular Folks. New York Magazine. Retrieved 1 March 2017, from

Quirky Blog

Fashion and the sharing economy: how collaborative consumption could shake up the fashion industry

The sharing economy (alternately also called the “collaborative” or “access” economy) refers to the activity of many individual sharing goods and services, as well as to the software platforms that make this practice possible (Hamari et al., 2015)– think Lyft and carpooling, or Appetit and food-sharing. One industry that is only now starting to get on board? Fashion.

While sharing economy platforms often emerge as alternative business models that co-exist with traditional channels, at times they become popular enough to go from a simple alternative to a serious disruption that steals market share and revenue from conventional businesses – such as AirBnB’s growing popularity as a substitute for hotels, or Uber’s detrimental impact on taxi services.

The most popular access business model – collaborative consumption – can serve as an alternative to ownership regarding items which an individual plans to get limited use out of. Many types of  fashion goods are characterised by high prices, but relatively low usage – two key traits shared by goods that have proven popular for implementing collaborative consumption.

And while access business models have not taken off in the fashion industry as quickly as in other areas, with only 2% of US shoppers currently having rented clothing (Pike, 2016), the area is projected to become increasingly influential – and to have a particularly profound impact on fast fashion, with more and more consumers choosing to rent higher quality trendy/special occasion items instead of purchasing them at cheaper stores.

Figure 1. Access business models in the fashion industry


Rent the Runway is the most popular example of a rental model with 6 million subscribers as of October 2016. It bypasses the typically peer-to-peer nature of collaborative consumption, and instead offers consumers the chance to rent expensive designer goods for 10% of retail value.

The company has been particularly successful not only because it allows users to bypass paying full-price for an item they might only wear once, but also because it combines this with the opportunity to rent aspirational items that most would otherwise not be able to afford – and all in a hassle-free manner, with cleaning, back-up sizes and liability insurance all handled by the company.

Perhaps taking inspiration from similar services like Amazon Prime, the company has recently introduced a membership plan that allows users to order unlimited items for a monthly flat rate, and it has also added reviews to help users pick the most successful items (Pearson, 2016).

While a promising concept, many peer-to-peer clothing rentals such as Rentez-Vous and StyleLend have run into logistical issues that have made sustaining business growth a difficult ordeal.

Renting out clothing is fundamentally different than renting out apartments or cars. How can sizing differences be handled? Who is responsible for shipping items? Should renters, owners or the company handle the dry-cleaning? Who is liable if an item is damaged or not returned on time?

If a company is not able to make clear arrangements for these issues without inflating costs for item renters, owners and the business, problems are bound to arise.



Hamari, J., Sjoklint, M., & Ukkonen, A. (2015). The sharing economy: why people participate in collaborative consumption. Journal of the Association for Information Science and Technology, 67 (9), 2047-2059.

Pearson, B. (2016). Where is the Uber of fashion? Forbes. Retrieved from

Pike, H. (2016). Will the “sharing economy” work for fashion? Business of Fashion. Retrieved from

There is nothing permanent except change—analyzing individual price dynamics in “pay-what-you-want” situations.

The innovative pricing mechanism Pay-What-You-Want (PWYW) has received increased attention in both research and practice during the last years. Museums and restaurants such as Brooklyn Museum and Little bay in London already implemented it. With PWYW pricing, buyers determine prices, and sellers need to accept every price, even if it is below zero. This can create a positive consumer experience, since it eliminates fair and related risks. (Kim et al., 2009)

PWYW can either be used as a promotion tool (e.g., when introducing a new product) or as a sustainably pricing mechanism. However, when buyers determine the price paid, it is important to consider individual price dynamics. This is what Schons et al., (2013) looked at. They examined the dynamics in prices paid in PWYW situations over multiple- customer-seller transactions on an individual customer level.

How did they measure this?

In order to test this, they conducted a field experiment in combination with a survey. They collected data by selling iced coffee at PWYW prices in an outdoor coffee bar frequently patronized by young people for over eight weeks. The iced coffee was added to the bar’s product portfolio for the purpose of this study, which enabled the researchers to track customers’ pricing decisions without existing supplier-specific internal reference prices (IRP). To monitor repeat purchases, they added a loyalty card that contained an ID that customers used to code their questionnaires at every purchase. The resulting sample comprised 966 first-time customers and includes 333 customer with two, 183 customers with three, and 128 customers with four purchases.

What did they find?

This paper elaborates on the importance of individual dynamics within PWYW pricing. First, first-time transaction prices are based on IRP. However, the influence of IRP on prices paid decreased over time. This is mainly because individual dynamics change over time. Buying frequently results in a decreasing downward slope in prices paid and IRPs because price paid in the first transaction calibrate IRPs to a level that already reflects customers’ fairness discounts. Second, customers with higher preferences for fairness pay on average higher prices. Third, repetition negatively affects customers’ price behavior, especially along the very first purchases. Lastly, it is important to note that within this study, the prices paid reached a steady state after the third transaction, only nine customers paid zero prices supporting the findings of Kim et al., (2009) and Kim et al., (2010), and customers underestimated the product’s cost by 16%. Can you imagine: buying the same product for the second time and pay a lower price or no price at all, while if you buy it for the fourth time, the price will not differ from your last price paid?

So what does this mean?

Sellers need to be aware that implementing PWYW for frequently purchased products does not ensure profits over repeated transactions. The profitability of a PWYW application depends on whether the long-term price paid after three transactions is still above the suppliers’ cost. In addition, this study confirms that customers have difficulty determining actual seller cost and hence consistently make lower estimations. PWYW pricing practitioners should therefore provide cost information to adjust customers’ initial estimates. Since this study investigated in PWYW prices at a local coffee bar, it would be interesting to see whether other larger settings such as clothing or electronic equipment result in the same conclusions. What is your opinion? Would you rather go to a coffee bar with PWYW pricing or to one with fixed prices? And if so, would you pay more than zero euro?



Kim, J.-Y., Natter, M., & Spann, M. (2009). Pay-What-You-Want—a new participative pricing mechanism. Journal of Marketing, 73(1), 44–58.

Kim, Ju-Young, Natter, M., & Spann, M. (2010). Where customers pay as THEY wish. Review of Marketing Science, 8(2)

Schons, L.M. & Rese, M., Wieseke, J., Rasmussen, W., Weber, D. & Strotman, W. (2012) There is nothing permanent except change—analyzing individual price dynamics in “pay-what-you-want” situations. Marketing Letters, 25, 25-36