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.

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

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.

Sources:

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.

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 Createmytattoo.com 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, Createmytattoo.com guarantees for satisfaction or you get your money back. Createmytattoo.com is unique in its sort. Several websites on tattoo inspiration exist (Tattoodo.com, lastsparrowtattoo.com), 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 Createmytattoo.com 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 Createmytattoo.com.  Firstly, the winning designers are paid through an eighty-five percent share of contest prices, which vary from $20 up to $800 (Createmytattoo.com). 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 Createmytattoo.com, 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 Createmytattoo.com, 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;

Createmytattoo.com (2017) Createmytattoo.com. Available at: http://www.createmytattoo.com/. 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.

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?

Bibliography

Feldman, A. (2016, October 19). Next-Billion Dollar Startups. Retrieved from Forbes: https://www.forbes.com/sites/amyfeldman/2016/10/19/next-billion-dollar-startups-2016/#49067b35549d

Gigster. (2017, February). Gigster is the world’s engineering department. Retrieved from About: https://gigster.com/about

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:

Strengths

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.

Weaknesses

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.

Sources:

Gurman, M., & Zaleski, O. (2016, December 7). Fitbit Buys Software Assets From Smartwatch Startup Pebble. Retrieved from Bloomberg: https://www.bloomberg.com/news/articles/2016-12-07/pebble-said-to-discuss-selling-software-assets-to-fitbit

Matas, D. &. (2017, March 05). Redefining Luxury Watches – Filippo Loreti. Retrieved from Kickstarter: https://www.kickstarter.com/projects/2050848594/redefining-italian-luxury-watches-filippo-loreti?ref=nav_search

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: https://www.pexels.com/photo/please-relax-steal-dance-flirt-smoke-wonder-feel-24897/

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, RateMyProfessors.com. 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 RateMyProfessors.com and 94% of them used the website to select a professor. The main reason why the students use RateMyProfessors.com 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.

Reference:

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

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 Amazon.com. 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.

References:

Enwemeka (2017) http://www.wbur.org/bostonomix/2017/02/28/amazon-bookstore-dedham

Hook and Whipp (2016) https://www.ft.com/content/6bb27dac-6b1e-11e6-a0b1-d87a9fea034f

McKinsey (2016) http://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/the-sales-practices-of-europes-leading-consumer-goods-companies

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

Zetlin (2016) http://www.inc.com/minda-zetlin/7-reasons-amazons-bricks-and-mortar-store-isnt-really-a-bookstore.html

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, <http://www.jstor.org.eur.idm.oclc.org/stable/pdf/4134013.pdf&gt;.

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).

Review

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.

Strengths

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.

Weaknesses

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.

References

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.

Introduction

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.

“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 (Quirky.com).

Success

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).

Struggles

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).

Why?

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).

Management

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

References

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 http://www.businessinsider.com/quirky-ben-kaufman-2015-4?international=true&r=US&IR=T

Einstein, B. (2015). The Real Reason Quirky Failed. Bolt Blog. Retrieved 2 March 2017, from https://blog.bolt.io/the-real-reason-why-quirky-failed-c362b3a3abd7#.6o9f8r6nk

Fixson, S. & Marion, T. (2016). A Case Study of Crowdsourcing Gone Wrong. Harvard Business Review. Retrieved 3 March 2017, from https://hbr.org/2016/12/a-case-study-of-crowdsourcing-gone-wrong

K, C. (2015). How Not to Crowdsource : The Demise of Quirky – Digital Innovation and Transformation. Harvard Business School. Retrieved 2 March 2017, from https://digit.hbs.org/submission/how-not-to-crowdsource-the-demise-of-quirky/

Lagorio-Chafkin, C. What Happened to Quirky?. Inc.com. Retrieved 2 March 2017, from http://www.inc.com/christine-lagorio/what-happened-to-quirky.html

Lohr, S. (2015). Quirky, an Invention Start-Up, Files for Bankruptcy. Nytimes.com. Retrieved 2 March 2017, from https://www.nytimes.com/2015/09/23/business/the-invention-start-up-quirky-files-for-bankruptcy.html?_r=0

Popper, B. (2015). Exclusive: the secret struggles of Quirky, a seemingly successful startup. The Verge. Retrieved 3 March 2017, from http://www.theverge.com/2015/4/24/8488531/quirky-invention-powered-by-quirky

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 http://nymag.com/daily/intelligencer/2015/09/they-were-quirky.html

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