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Braineet: The Platform For Innovative Brand Ideas

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The Digital Revolution has placed the customer at the center. A company that responded to the Digital Revolution is Braineet. Braineet launched in 2014 and it is an innovative crowdsourcing platform. Braineet allows consumers and employees to share their ideas with brands. Thereby, the platform bridges the gap between consumers and companies and facilitate companies to improve the quality of its products and services.

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Figure 1: How does Braineet works?

Braineet is available on the web, tablet and mobile app (Android & iOS) and offers members of the platform the opportunity to share ideas. Ideas can be posted independently by the user or by participating in Challenges of a brand (figure 1). These Challenges enables every member of the Braineet community to compete to win prizes and promote their best ideas. Each idea can be up to 140 characters. Users of Braineet can like and promote each other’s ideas in order to show their appreciation for the content of the idea. Subsequently, users can interact with the brand throughout the development process until the ideas have been realized.

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Figure 2:  Opinions of companies regarding Braineet

More than 100 brands have interacted with their customers via Braineet, including Nespresso, Taco Bell USA, AX, Dove. From figure 2, it can be concluded that companies are quite optimistic about Braineet. Companies are motivated to utilize Braineet because they want to solve problems, generate ideas and outsource tasks (Tsekouras, lecture 3). But why do firms want to delegate innovation to users? One of the reasons is that it is quite costly to obtain detailed consumer information (Tsekouras, lecture 3). Through Braineet, companies have direct insight on the preferences of the consumer regarding their products and services. Furthermore, the platform enables the firms to have interaction with customers and employees.

UnknownOn the other hand, consumers prefer to be active on Braineet due to the feeling of glory, the possibility of improving products/services according to their own wishes and/or the monetary rewards that they receive from brands (Tsekouras, lecture 3; Majchrzak & Malhotra, 2013). The greater the size of the crowd, the greater the possibility of idea diversity put forth by the crowd (Boundreau, 2012). Tens of thousands of ideas have been shared from more than 160 countries on Braineet. However, the large quantity of ideas doesn’t necessarily means that the quality of the ideas is high. The crowd often fails to offer well-considered solutions that incorporate multiple perspectives, risks and needs (Majchrzak & Malhotra, 2013). User ideas score high in terms of novel and customer benefits, but lower in terms of feasibility as the crowd are not always experts in the fields (Poetz & Schreirer, 2012). Braineet tries to overcome this issue by also offering employees the possibility to share their ideas for products/services. Employees namely have knowledge about the culture of the company and are experts in the field, so their ideas will definitely be more feasible. The downside of Braineet is the 140 words limit that makes it difficult for companies to understand complicated ideas. Consequently, it is harder for the company to integrate multiple ideas into a more integrated solution (Fuller et al., 2007).

The efficiency of the business model of Braineet can be assessed in the following way.
Firstly, there are moderate switching costs as consumers and companies can both choose to be active on other crowdsourcing platforms to maximize their value. For companies, this decision will depend on the quality and quantity of members of the community. Larger communities may result in more ideas whereas a community with more experts will result in more feasible ideas. For consumers, this decision depends e.g. on the brands that are active on certain platforms. When Braineet doesn’t facilitate their favorite brand, they might search for other platforms that do so.
Secondly, the protection from the competition is moderate. There are other competitors active in the market that provides the same services as Braineet, for example IdeaScale and Seezers. However, more than 100 brands used Braineet and Braineet includes success quotes of these companies on the website. Companies then tend to prefer Braineet over other platforms due to this experience factor.
Thirdly, the business model of Braineet is highly profitable due to the growing community and effective partnerships. Braineet worked with Microsoft GTM services during the past two years and that enabled Braineet to grow a larger base of enterprise clients and to double its revenue. Braineet namely benefits from Microsoft’s unique network and expertise in the B2B software landscape. Moreover, due to the partnership, Braineet runs on Microsoft Azure and integrates with Office 365 and Yammer. This is an unique feature compared to the competition of Braineet (IdeaScale/Seezers)
Lastly, the business model of Braineet is highly scalable. Braineet has no inventory and the company requires few capital investment to serve a growing consumer base.

In conclusion, Braineet has good future prospects and it is an excellent response to the Digital Age where business models are more consumer-centric. Would you participate on Braineet?


Boudreau, K.J., (2012.) Let a thousand flowers bloom? An early look at large numbers of software app developers and patterns of innovation. Organization Science, 23(5), 1409–1427.

Füller, J., Jawecki, G., Mühlbacher, H., 2007. Innovation creation by online basketball communities. Journal of Business Research, 60(1), 60–71.

Majchrzak, A., & Malhotra, A. (2013). Towards an information systems perspective and research agenda on crowdsourcing for innovation. The Journal of Strategic Information Systems, 22(4), 257-268.

Boudreau, K.J., (2012.) Let a thousand flowers bloom? An early look at large numbers of software app developers and patterns of innovation. Organization Science, 23(5), 1409–1427.

Poetz, M. K., & Schreier, M. (2012). The value of crowdsourcing: can users really compete with professionals in generating new product ideas?. Journal of product innovation management, 29(2), 245-256.

Privacy calculus and its utility for personalization services in e-commerce

You are probably familiar with the Amazon’s personalized recommendations that induce consumers to purchase products. The personalized recommendations are generated by using the detailed records of an individual consumer and corresponding analysis about webpage browsing and consumption. But where is the calculation of the privacy of the consumer?

According to (Mobasher et al., 2000) personalization is any action that tailors experience to a particular individual. The study of Tam and Ho (2006) is more specific by mentioning that personalization is the process of adapting web content to meet the specific needs of users and to maximize business opportunities. Both definitions have in common that personalization involves the tailoring/adapting of certain “subjects” that can be distinguished into products, website features, advertising or communication (Tsekouras, lecture 2).

The benefits of personalization can be analysed from different perspectives. From the consumer perspective, personalized recommendations allows them to see products with more relevance and less effort. From the perspective of the company, personalized recommendations leads to higher conversion and loyalty. However, there are also potential drawbacks: overpersonalization and privacy concerns (Tsekouras, lecture 2). Privacy concerns are increasing because consumers fear that their information will be misused and they don’t like the feeling of being tracked. Due to the drawbacks and benefits, this blog post aims to broaden your knowledge by highlighting both the privacy concerns and the utility of personalization services in e-commerce.

The paper of (Zhu et al., 2017) concentrates on the personalization of services in e-commerce. Although there are benefits associated with personalization, the privacy concerns may drive consumers away. This is referred as the personalization-privacy paradox. Consumers base their decision to disclose information on the trade-off personalization-privacy.


The paper uses the multi-attribute utility theory to analyse the combined outcome of the personalization-privacy paradox (utility). Subsequently, the model is tested by a simulation in Matlab R2012b using random data that depicts the random behavior of consumers. The simulation example is based on the single side of benefit or cost, the benefit-cost analysis and reputation scores. The main findings shows that the value of utility is sensitive to the factors of costs and benefits of information disclosure, the level of privacy concern and company reputation. Company reputation can effectively reduce the perceived risk in the information disclosure for privacy fundamentalists.

As a strength, the paper of (Zhu et al., 2017) is academically relevant: the paper addresses the lack of balanced research that analyses and reconciles the contradiction between privacy and personalization service. The paper therefore uniquely classifies consumers on the basis of their privacy concerns and analyse their behavioural differences. This is depicted in figure 2 of (Zhu et al., 2017) where the following three consumer segments are identified: “privacy unconcerned” (least concerned about privacy and most willing to disclose their personal information), “privacy pragmatist” (they both consider the risk of privacy and benefit of personalization) and “privacy fundamentalist” (mostly considers privacy concerns and have a minimum care about personalization).

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Besides the academic relevance, the second strength is managerially relevance. Firstly, the findings of this paper facilitates managers in the creation of a more accurate personalization strategy and privacy management. Secondly, through the use figure 2 managers learn that consumer segments should all be addressed differently to maximize the utility of the product/service: “privacy unconcerned” with the best personalization business model and highest data collection, “privacy pragmatist” with the medium personalization business model and lesser data collection, “privacy fundamentalist” with standard products and utmost least data collection. Thirdly, managers can improve their brand image to address the privacy fundamentalist because the findings show that company reputation can effectively reduce the perceived risk in the information disclosure.

Let’s illustrate the managerial findings with an instance, Consumers receive location-based promotion but they dislike the feeling that their online footprints are hacked. By weighting the personalization-privacy paradox, they ultimately decide to cancel their account. Through the use of the findings of (Zhu et al., 2017), the cancellation of privacy fundamentalist can be prevented by improving the corporate reputation of


The weakness of the findings of paper of (Zhu et al., 2017) is the low external validity. The researchers used a simulation to test their framework and it is therefore questionable whether the findings are fully applicable to the real-world. Future research should therefore use real data that quantifies consumer’s preference on the basis of past transaction records, so that the consumer is placed in the proper customer segment. Furthermore, future research could also be done by examining how the website should be designed to address the personalization needs of different customer segments. Thirdly, the relationship between the identified customer segments and profit can be examined.

In what consumer segment do you think that you belong? According to your customer segment, do you agree with your levels of personalization and privacy as suggested by (Zhu et al., 2017)?

Mobasher, B., Cooley, R., & Srivastava, J. (2000). Automatic personalization based on web usage mining. Communications of the ACM43(8), 142-151.

Tam, K. Y., & Ho, S. Y. (2006). Understanding the impact of web personalization on user information processing and decision outcomes. MIS quarterly, 865-890.

 Zhu, H., Ou, C. X., van den Heuvel, W. J. A. M., & Liu, H. (2017). Privacy calculus and its utility for personalization services in e-commerce: An analysis of consumer decision-making. Information & Management54(4), 427-437.