Moov: Break plateaus with your personalized fitness coach

Moov is one of the most innovating fitness wearable companies of today, by delivering the world’s most motivating and efficient fitness experience through a personalized fitness coach, based on artificial intelligence (Moov, 2018). Originally they were founded by three entrepreneurs with a feel for technology that had a same goal in life: get back in shape.

Efficiency criteria
Since there were already many companies competing in the fitness wearable industry, Moov had to find a segment of the market that shared a similar set of needs that had not been fulfilled yet, to provide a new value proposition for consumers. They did this by adding a new aspect to an already quite personalized product such as a Fitbit: training advice and tips based on artificial intelligence. Instead of just focusing on the amount of calories you burn, or the amount of steps you take in combination with a mobile application, Moov also gives you real-time feedback on the way you’re exercising. Whether it’s running, lifting weights or other activities, it will give you tips in order to improve your performance and remain injury free, which tailors experience to each consumer individually (Mobasher et al, 2000). By participating as a consumer, Moov will be able to better analyze your training and increase the overall consumer driven value, while also maintaining a quite personalized customer relationship. This joint profitability leads to maximized payoffs for both parties involved. The institutional arrangements and environment is hard to elaborate on, since Moov only interacts with the consumer directly, it has no other partnerships. One aspect of the institutional environment could be privacy of the users, but since Moove needs the data to be able to provide the best customer experience, users are probably okay with sharing their data.

The company is now working towards offering a platform of applications, specially made for the activities that are being tracked. They are continuously updating their wearable devices and coaching mechanisms to keep a leading edge on competition (Hardwick, 2017). Also, since Moov actively collect data from their users to improve their coaching technologies, they can also use this data for more insights about their users. This data can be seen as a key resource, since it can lead to further enhancement of their services, but also find new ways of providing value for their customers to increase lock-in.

Involving consumers
One of their first products consisted of an AI performance coach through a fitness tracker, which was an enormous success. Through the use of a crowdfunding page, they managed to reach their $40k goal within 90 minutes of the opening (Crook, 2014). One of the ways that Moov differentiated itself from competition, was by involving the consumers from the start of the project. They knew that identifying the right customer segment was not enough, but that active involvement of the consumer was necessary to come to the most value providing service possible (Kaulio, 1998). By using crowd sourcing as a tool for generating ideas for their new product and information pooling,  they made sure that their crowdfunding campaign was a success.

Not only have they found ways to gather funding for new projects from consumers, they also provide ways to interact with other users that use Moov. People can share their results with friends and people that are on the same level as them, while also keeping track of their progress. Furthermore, by using hashtags that are linked to Moov, users can link their photos to to the website to share it with others. Users of Moov can easily find each other through their Instagram accounts, and many people have used this as a way to stay motivated by keeping each other updated about their progress. By providing this platform to share and collaborate with other consumers, Moov can actively promote their product and service. One of the downfalls of the value proposition that Moov provides, is the constant need of data to be able to offer the best service possible. As long as people are okay with sharing their data, this should not become a problem, but it might be important for Moov to take this into account for their new products and services, when they further develop and enhance their business model. But until then, I invite you to give Moov a try to reach your fitness goals in 2018!


Crook, J. (2014, February 28). Moov Fitness Tracker Passes Its $40K Crowdfunding Goal In 90 Minutes. Retrieved February 18, 2018, from

Hardwick, T. (2017, February 23). Moov Fitness Coaching Tracker App Receives Major Update. Retrieved February 18, 2018, from

Kaulio, M. A. (1998). Customer, consumer and user involvement in product development: A framework and a review of selected methods. Total Quality Management, 9(1), 141-149.

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

Moov (2018). Moov Fitness Coach. Retrieved February 18, 2018, from


Is truly consumer-centric marketing policy possible in the modern world?

Osborne and Ballantyne, while skeptical of a modern firm’s capacity to practice authentic customer-centric marketing in favor of firm-centric, offer valuable insights on behaviors marketing managers must be wary of in order to validate this identity.

While there are many aspects of marketing whose strategies and practices can involve nearly all department of a large firm, it is believed by Osborne that four elements are majorly involved players:  the 4P’s marketing mix, market-based assets, relationship marketing, and customer equity.


Researchers believe, besides the 4ps, these other marketing frameworks are inherently firm-centric, preventing marketing managers to truly embrace meeting all conceivable needs of the customer in their full marketing approach.


One of the stronger points mentioned includes the requirement for managers who wish for their firms to incorporate customer-centric approach in all their day to day actions require internal marketing. In addition to Internal marketing efforts and the like, fostering a culture an identity with help from Human Resource managers can be the holistic team approach that catalyzes a company’s ability to focus on customer and stakeholder needs.  


With regards to Relationship marketing, the market can be a minefield depending on what key business relationship your firm or related firms may have. With regards to the fast pace nature of social media, a never-ending appetite for online content, and heightened sensitivities to nearly all social groups, relationships are very difficult to manage in today’s business world. One slip up can potentially lead to public relations nightmares, loss of profits and valuable strategic partnerships (possibly to competitors).


The Customer Equity framework mentioned is a more controversial way to establish customer-centric marketing. Although consumers may find identity and value by feeling represented by certain brands, the weak per dollar value by customer may fall flat depending on how exclusive and luxurious a customer the company targets their marketing towards.


While advances in technology have occurred and customer-centric marketing has risen in many unexpected ways, Osborne and Ballantyne certainly put forth worthwhile arguments that marketing managers can find a use for in campaign development.


Osborne, P. and Ballantyne, D. (2012). The paradigmatic pitfalls of customer-centric marketing. Marketing Theory, [online] 12(2), pp.155-172. Available at:


Intuit’s Design for Delight


Intuit is a well-known, US based, financial software company, that provides financial, accounting, and tax preparation software for small businesses, accountants and individuals. Their most notable products include the Quicken, TurboTax and Quickbooks (Intuit, 2017). The company has been existent for 35 years and Intuit has been known to pay great attention and focus on the needs of their customers. When the company first started, employees were encouraged to observe their customers in the so called “usability- labs”. The employees were encouraged to try to come up with real-time solutions for the problems the customers encountered. In addition, they also implemented the “Follow me Homes” project, where they would not only observe their customers at work but also at their homes. Further gathering of customer insights were also gathered from their annual big survey (Lester, 2016).

Based on their above-mentioned initiatives, Intuit was becoming quite successful and known as a software company with their own unique approaches. However, this approach did not come without its problems, after a few years the focus started to shift in another (unintended) direction. This approach lead to the company constantly focusing on solving the problems of their customers instead of focusing on determining their customer needs. Intuit was “fixing” instead of learning and innovating (Power & Stanton, 2015).


The New Business Model

Intuit was certain that it had to change their focus, to what is was supposed to be. The new focus would be on the customer-centricity that characterized their entrepreneurial past. Therefore, Intuit launched a new initiative called the “Design for Delight” which was meant to fulfill their vision of meeting its customer requirements. This initiative was based on three core principles as outlined by Power and Stanton (2015);

  • Deep Customer Empathy – Immerse yourself with customers to know them better than they know themselves. To understand what really matters to customers, you should watch them, talk with them, and put yourself in their shoes.
  • Go Broad to Go Narrow – Create options before making choices. There are lots of possible answers, so to get one great idea, you need to create lots. The first idea is rarely the best.
  • Rapid Experiments with Customers – Get customer feedback early and often to understand the pros and cons of options. Watching customers react to prototypes through trial and error is better than relying on our own opinions.
To implement these new initiatives, a cultural and operational transformation was a necessity. They trained their employees, held a large number of immersive experiential workshops and most importantly, they added the so-called “design thinking” to their leadership training programs (Power & Stanson, 2015). All of this enabled Intuit to be able to become a leader in customer experience and innovation. Their culture is constantly seeking to fulfill the customers’ needs by putting the customers first and finding new ways to improve their experiences (Intuit, 2017).


Efficiency Criteria 

Due to the new initiative, Intuit was able to become a frontier in the market of tax and financial software, the majority of the American consumers uses software made by Intuit for their tax return (Aquino, 2016). As the customer shopping behavior and experiences have changed in the past decade(s), to one in which they expect their needs to be be fulfilled as fast as possible, Intuit was also impacted since these same expectations were also for financial services. Fortunately, Intuit has been able to adapt to these changes by providing the customers with instant and easy services. Their “Design for Delight” initiative provides the business with constant customers’ feedback which ensures the company of staying up to date with the needs of their customers.

Therefore, looking at the efficiency criteria, they were also able to tackle the several challenges that come a long with a business like Intuit. As the taxes and accounting business is associated with the law and reporting rules that tend to change annually, Intuit needed to stay nimble. Moreover, Their customers demanded faster and simpler tools to help them prepare their financial and tax reports, therefore Intuit needed a constant stream of effective and relevant new products. Lastly, their traditional research process took a lot of time, making it unable for Intuit to be responsive, because the moment they finally gathered all the data, it was no longer actionable by the time it was received.

With the new community that was build, Intuit got access to insights from their users, as these led to rapid and sophisticated findings, which resulted in more appropriate product development. So the overall market research spend was dropped while the number of projects increased. Moreover, the company also got the ability to translate the feedback and requests of its customers into tangible product changes, in a quick and easy way, creating a revolutionary form of dialogue between its customers.



R. Lester (2016) “4 Succesful Businesses Following A Customer-Centric Model”, available at:

B. Power, S. Stanton (2015) “How IBM, Intuit, and Rich Products Became More Customer-Centric”, available at:

“Company”. (Accessed on 18 February 2018). (Accessed on 18 February 2018).

Aquino (2016) “How Intuit uses Customer Insights to ‘Design for Delight’ “, available at:

Finding Rover: Do not despair! Your dog/cat is waiting to be found

Nowadays, having a large amount of friends is a given due to the Internet’s expansion of our networks. However, a type of household friends that have stayed through this transition are our pets. And for some, these fury companions are more than just friends; they are family. Therefore, if one ever had a dog or cat that went missing, then they can tell you that the loss is an emotional experience for everyone involved.

This experience is exactly what drove John Polimeno in 2012 to find software developers who could help him build a facial recognition that would work even on our little companion’s furry faces. The end product was am algorithm that combines machine learning and computer vision to locate dogs and cats’ features. This tool was then launched with the app “Finding Rover”, which has been able to reunite more than a 1,000 missing dogs and cats with their owners in the US.

How does Finding Rover work?
The basic principle of the app is to connect the owner who lost his/her pet with anyone who has seen said pet. Thus, if you are the owner, you merely need to choose “Lost”, upload a photo of your pet, and select the location where you last saw it. The algorithm will then automatically compare your pet’s key features with its database of pets that were found. A similar procedure applies if you are a person who has found a stray pet. You just select “Found”, upload a picture of the pet you just saw, and select the location you found it in. The algorithm will then match your found pet with any picture of a pet that is missing. If the owner and finder are matched by the system, they get each others’ contact details. In case there is not match, the system will notify you if there is a match in the future. You can also go through the database of pictures yourself. Furthermore, as soon as you post a picture of your missing pet, anyone registered on the app in a 10-mile radius from you will be notified in order to keep an eye out for your pet.

Currently, individual users are not the only ones who employ this free lifetime membership app. Shelters across the entire US have partnered with the platform, in order to mitigate their own overflow of pets.

Which co-creation value does Finding Rover offer?

Screen Shot 2018-02-18 at 20.09.21
Value: The entire value that is generated by the app for its users is based on the main advantages it offers above all other options that help you find your pet.

  1.  Other apps: While other platforms exist to find your dog or cat, all of them are merely databases of information. In other words, the owners/finders upload a picture, some key words that define the pet, and their contact details. But no real connection between those two databases takes place unless a user scrolls themselves through the entire database. Thus, Finding Rover’s algorithm, which has a 98% accurate match rate, provides for a much quicker method to connect owners and finders. Additionally, most of the other apps build communities of people missing their pets in specific states or areas, while Finding Rover cross-checks the entire country and therefore allows for a much larger search radius.
  2. Chips: While the use of microchips is a common method to identify a missing dog/cat, there is a crucial aspect that usually prevents this method from working. Normally when a pet is found wandering on the street, they are incredibly aggressive towards anyone who wants to pick them up and bring them to a veterinarian who can scan their chip. Finding Rover significantly simplifies this activity, since the finder just needs to take a picture, something they can do from a secure distance.
  3. Flyers: While flyers are the most common go-to option in the US, the app provides for the larger radius advantage over them. Therefore, if a catastrophic event happens, such as hurricanes Harvey or Irma where pets were found sometimes 2,000miles from their owners’ homes, then the app allows for people there to also be informed about the missing pet. Furthermore, as mentioned, if you upload your missing pet, anyone registered in a 10-mile radius will be informed anyways, which replaces the need for a flyer in the first place.
  4. Shelters: The main reason for why more than 500 shelters have already committed to the app, is because it actually saves them a lot of money. If a dog has to be taken into a shelter, it will usually cost the shelter around $225 to house it for some days. Thus, a lot of money has been saved, since now dogs do not even have to reach the shelter stage. And all of this at a $0 cost!

Co-: Taking  into consideration that the Finding Rover company only supplies the platform as a intermediary, the value is created C2C, namely between the people who are missing their pets and those who have found pets.

Creation: The creation of the value is completely dependent on the users’ involvement, since they are the information sources of the entire platform.

Does Finding Rover fulfill the Efficiency Criteria?

  • Joint Profitability: The platform maximizes the joint payoffs of all partners involved. Not only does the app allow the owners to find their pets quickly and save shelters a lot of costs, but it simultaneously allows for the people who find the dogs to feel an ethical fulfilment for having helped out not only the owners but also in keeping their communities safe from any aggressive, stray pets.
  • Institutional Arrangement: The company sets an incredibly high value on privacy, in order to keep everyone’s contact details as safe as possible. Before the owner’s and finder’s contact details are shared to each other, the owner must confirm that the match is correct. This has allowed that until now no privacy issues related to the users’ information have emerged and, therefore, fulfilled the most important institutional arrangement.
  • Institutional Environment: Due to the fact that this app is not only helping the community by matching owners to their missing pets but also keeping the streets safe from aggressive dogs, it has been highly recognised by different government institutions.

For more information about Finding Rover, click here.

Finding Rover. (2018). Finding Rover | Let’s bring them all home.. [online] Available at:

Hartley, S. (2015). Shelter takes new approach to finding lost dogs. [online] Napa Valley Register. Available at:

Deneen, S. and Lau, E. (2015). Facial-recognition apps scout lost pets. [online] Available at:

Taylor, C. (2016). Dog gone? County shelters embrace Finding Rover app. [online] NACo. Available at:

Das, S. (2013). Finding Rover app tracks lost dogs using facial recognition. [online] CNET. Available at:

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).

Schermafbeelding 2018-02-18 om 22.13.33

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.

Personalized e-learning is on its way and we should be prepared


With the rise of digitalization has come the rise of digital personalization. Personalization has been existent for a couple of years now, in different kinds of industries, such as retail, cars and even perfumes (Randall, Terwiesch & Ulrich 2005). This means companies and researchers also already had quite some time to learn about both the benefits and the drawbacks of personalization. However, the drawbacks are harder to overcome nowadays, since the use of personalization has already been implemented on such a big, global, scale. Think, for instance, about privacy concerns that could have at least partly been prevented if legislation was set in place in time. However, we can all understand that it is hard to act upon potential drawbacks in advance if there is no prior experience whatsoever.

Nevertheless, it is always a good idea to be cautious and critical about upcoming trends such as personalization, before blindly implementing them without thinking about any potential consequences, either negative or positive. This means, foreseeing any potential drawbacks, as well as keeping in mind what you would like to reach as a goal by pursuing a trend such as personalization.

Personalized e-learning

Ashman et al. (2014) have presented a detailed discussion regarding personalization, but not in the field of, e.g., e-commerce, where it is already widely implemented, but rather in the field of e-learning. Personalization in e-learning is still in its beginning phase and therefore not yet widely implemented. Thus, the authors act in advance on warning e-learning providers and educational institutions on the potential drawbacks of the personalization of e-learning, including recommendations on how to overcome them, before it is too late. Especially since educational institutions are increasingly using such models as a way to gain as much as new students as possible, to increase their income, risking to lose their initial, most important goal out of sight: to enhance the quality of education. (Ashman et al. 2014)

But why is personalization of e-learning initially needed, then? The authors acknowledge where institutions’ interest in personalization of e-learning is coming from. E-learning is an upcoming trend on its own already to overcome the lack of time and resources to facilitate an increasing number of students globally. However, students might feel disenfranchised and their individual learning needs might become neglected by the use of e-learning. To overcome this issue, educational institutions are starting to implement the personalization of e-learning. However, then again, personalization comes with its setbacks.


The three main setbacks discussed by Ashman et al. (2014) are privacy concerns, serendipity issues and deskilling problems. The authors discuss these three setbacks in great detail. Privacy concerns is a recurring issue surrounding the topic of data gathering in general, which is also needed for personalization. Serendipity issues are about the reduced ability to learn and understand different beliefs, cultures and lifestyles, or to learn ‘out of your comfort zone’, as personalization leads to the targeted student to only be presented information that fits within his/her field of interest. Lastly, students can be deskilled in the sense that they do not learn how to critically assess and evaluate the information that they are given, as with personalization they are presented the results that most closely fit their needs, so they stop looking further very quickly. The authors emphasize, in order to overcome these issues, it is important to inform students about what and how data is gathered about them, and to give them the opportunity to control what information is presented to them. Additionally, they advise a clear and thorough understanding by e-learning providers and educational institutions of why personalization in e-learning is needed and what can be achieved by it, for which thorough experimentation is required.

In their paper, several universities, such as Harvard, St. Gallen and Ontario, are used as an example, from which data is analyzed very extensively by Google Analytics. Google Analytics tracks staff and students on the websites of the universities. This enhances the concern of privacy, as the user ID’s were visible.


Despite these discussed setbacks, the authors do see great value in personalized e-learning as “the system is genuinely able to interact with users, recognize when they need assistance and guide them to the appropriate information or educational activity” (Ashman et al., 2014). Unfortunately, the authors of this paper focus solely on education in well-established economies, which is only a small part of the world. It would be interesting to see the possibilities of personalized e-learning being enforced globally, and thus in poorer areas, too. Interestingly, founder of Facebook, Mark Zuckerberg, and his wife, are planning to donate 99% of their Facebook shares to invest in, amongst other things, personalized learning. He mentioned:

“Students around the world will be able to use personalized learning tools over the internet, even if they don’t live near good schools. Of course it will take more than technology to give everyone a fair start in life, but personalized learning can be one scalable way to give all children a better education and more equal opportunity.” (Strauss, 2015)


Let’s see what the future holds for us and the upcoming generations regarding a transformation in education, not only in well-established, advanced countries, but also in countries limited in access to good education. Although the negative consequences should not be forgotten and be acted upon well in advance…


Ashman, H., Brailsford, T., Cristea, A. I., Sheng, Q. Z., Stewart, C., Toms, E. G., & Wade, V. (2014). The ethical and social implications of personalization technologies for e-learning. Information & Management, 51, pp. 819–832.

Randall, T., Terwiesch, C., & Ulrich, K.T. (2005). Principles for user design of customized products. California Management Review, 47(4), 68. Links to an external site.

Straus, V. (2015). A primer for Mark Zuckerberg on personalized learning — by Harvard’s Howard Gardner. The Washington Post. 

Subscribe based business model revitalized

Subscription based business models have been around for ages. Newspapers and magazines pioneered the business model and have been relying on it ever since. However, in recent times the subscription model has seen a resurgence in an unexpected manner. A subscription model makes sense for a newspaper due its perishability and the recurring nature of its demand. Products such as shaving products, food and household products are increasingly more often sold on a subscription basis. Dollar shave club, an American men’s razor company that pioneered the selling of razor blades on a subscription basis, was acquired by Unilever in 2016 for a billion dollars.

Dollar shave club was founded in January of 2011 and grew to its billion dollar valuation in merely 5,5 years. This goes to show how disruptive subscription models can be to traditional brick and mortar and click and mortar dominated industries. Part of the success of the subscription business model in industries like the shaving industry is because of the predictable and recurring demand patterns products like razors have. Established companies that sell products with similar demand patterns are quick to adapt to the disrupting trend, offering subscriptions next to their individual sales. Amazon for example sells household products on a subscription basis, and even offer a discount for those who subscribe. Gillette started its own shave club as a response to the disruptive dollar shave club, offering its range of products on a subscription basis. The big brands respond in an attempt to preserve their market share.

One major advantage of the subscription business model is the elimination of the middle man. Which, in theory, allows for undercutting the competition. Companies that have managed to sell directly to consumers have traditionally been able to offer a better value proposition to the consumer. A noteworthy example of this is computer manufacturer Dell that started selling their personal computers directly to consumers around 2007, eliminating the need for retail stores.

By subscribing a customer effectively declares intention to buy a certain product on certain dates. This makes it very easy for the supplying company to forecast demand, resulting in a lower carrying costs of inventory and, in case of a highly perishable product such as food, a lower cost due to deterioration. Predictability of demand may not sound like a noteworthy advantage, but for a company like amazon that generates close to 200 billion dollars in revenue per year inventory management is one of the biggest business challenges.

Not only does the subscription model provide predictability of demand, it also provides an increased payment safety. Many subscription services require direct debit payments. This results in a more stable and more reliable cash flow for the company. Whereas large retail companies have to devote a substantial amount of resources to the collection of revenue, sometimes going as far as establishing debt collection departments. Collecting revenue from customers that haven not paid their bill not only takes time and effort, sometimes businesses are forced to settle for less than the original amount or to completely absolve a bill. With a subscription based business model this problem is less likely to occur and the costs associated with this problem are likely less severe.


Subscription boxes with a return policy is the subscription business model 2.0?

Subscription models that offer a themed box of products with changing contents on a predetermined schedule have been around for a while. This business model has many of the same advantages for consumers as the traditional subscription business model, with the added benefit of being exposed to new products that you otherwise would have missed out on. For the subscription box provider the added benefit is that companies might be willing to provide free of highly discounted promotional products to put into the subscription box. Subscription boxes are a new but growing business. Ipsy, a beauty related subscription box has over 2.5 million paying subscribers that are paying a monthly fee to receive beauty related products.

These subscription box services operate in a very similar way as the previously mentioned subscription based services. But recently subscription box providers in the fashion industry have been offering ways to personalize the content of subscription boxes, and have been offering refund policies. The personalization of subscription boxes is an interesting development that also seems to occur outside of the fashion industry. Vinebox for example offers a questionnaire that allows customers to discover their taste in wine, and adjusts the content of the subscription box to the taste of the customer. Trunk club offers the assistance of a personal stylist to pick out fashionable clothes that fit your style and body. These attempts to personalize the subscription boxes require the cooperation and attention of the customer, the customer needs to invest time into the customization. In return there is an increased chance that the customer enjoys the content of the box.

Trunkclub also offers a return policy, that allows customers to ship back unwanted items from their subscription box. This is odd because a return policy might eliminate one if the main advantage of the subscription based business model, namely the predictability of demand. This trend is most prevalent among the fashion themed subscription boxes, which is not surprising. Trunkclub for example is owned by Nordstrom, a large retail company in the fashion industry. Returned items could potentially be sold through the retail branch of the parent company before they go out of fashion.








Customized pricing; not always that effective

The researchers of this paper (David et al, 2017) conducted three tests to find out what the impact is of customized pricing on consumer evaluation, considering consumer’s interpersonal attachment orientation. First of all, they decided to run a survey with 172 U.S. participants. Half of them were randomly assigned to a situation in which they paid the shelf price, others also paid the shelf price or a lower, customized price. After manipulating the  prices for the consumers, they measured their level of satisfaction. They found significant evidence that there is a variation in the consumer evaluation of prices, in case of customized pricing. Thereby, this relation is influenced by someone’s attachment style, namely secure or anxious. It appears that anxiously attached consumers were satisfied paying the shelf price, whereas secure consumers were dissatisfied paying the shelf price. To find an explanation for this phenomenon, a second study was conducted with 270 students as participants. The results supported the earlier findings and showed an explanation for the mentioned phenomenon, namely that customized pricing programs create an enhanced expectation of receiving a discounting price among the secure consumers. This means that these customers are increasingly price sensitive and this could be disadvantageous for retailers, as this could affect their profits. As more and more retailers use customized pricing techniques based on the purchase pattern of their customers nowadays, this has implications; customized pricing is not always effective. A third and last survey extended the findings of the previous tests, as it showed that in the presence of customized pricing programs, securely attached consumers expect to receive discounted prices that are lower than the prices that other customers have to pay. Anxiously customers on the other hand are merely satisfied paying the shelf price despite the attendance of customized pricing systems, unless they are in a disadvantaged situation, meaning another customer receives a lower price.

As earlier research mostly highlighted the benefits of customized pricing as it encourages consumer satisfaction and purchase likelihood (Van den Bos, Peters, Bobocel and Ybema, 2006 & Xia and Monroe, 2010), the results of this study showed that there is also a downside to this sort of customization.  It is indeed the case that consumers positively evaluate customized pricing in which they receive advantage, but the effectiveness appeared to be heavily influenced by interpersonal attachments, as expectations are created among secure attached consumers for a discounted price. On the other hand, anxious attached consumers are decreasingly price sensitive. Altogether,  this paper did not only found the relationship between customized pricing and consumer valuation, which is moderated by interpersonal attachment, but they also found the specific explanation how that is possible, namely the mentioned increasing expectations. Knowing all this now, what are the (practical) implications now for managers for example, so that this information can be used in their advantage? For marketers, it is important information as understanding when and for which customers the customized pricing is effective. Thereby, it is likely that this also applies to other forms of customized offers and services, so not only customized pricing. Managers should segment their customers based on their attachment style. For example, they can offer customized products best to older people, who are securely attached and have a higher level of income, meaning they should try to individualize their marketing approach.

A strong point of this research is the fact that they conducted three tests, with many different participants, that all found the same significant results of the impact of customized pricing and the role of interpersonal attachment. This means there is strong evidence and the validity is high in my eyes. It has huge implications for managers and marketers, as they can adjust their way of customization now on an individual basis to enlarge the effectiveness. One of the weaknesses are the fact that only Americans were involved with the studies, while people from collectivistic cultures such as China might think different about customized prices, so that external generalization is more difficult. Another point of improvement is to have more data on the actual numbers a retailer could benefit from applying their implications. No real existing examples were mentioned, which could be beneficial to really test these implications and to know how much per cent their sales would increase for example.


David, M.E. Bearden, W.O. Haws, K.L. (2017) Priced just for me: The role of interpersonal attachment style on consumer responses to customized pricing. Journal of Consumer Behaviour

References :

Van den Bos, K., Peters, S. L., Bobocel, D. R., & Ybema, J. F. (2006) On preferences and doing the right thing: Satisfaction with advantageous inequity when cognitive processing is limited. Journal of Experimental Social Psychology, 42, 273-289

Xia, L., & Monroe, K. B., (2010) Is a good deal always fair? Examining the concepts of transaction value and price fairness Journal of Economic Psychology, 31, 884-894

Stronger together! How co-creation unveiled image recognition applications.

A short story of image recognition applications for long-established businesses

What does image recognition evoke to you ? Tesla’s automatic pilot mode ?  Google’s automated image organization or Facebook’s face recognition system ?

All these applications are state-of-the art image recognition applications but yet they might not be the more profitable ones. Traditional business are often considered as laggard when it comes to technologic innovation but they actually carry the most added-value applications for computer vision. From automatic quality control to predictive maintenance, deeply-rooted companies are operated by many simple but repetitive tasks than can easily be automated with computer vision. But why don’t we hear about them ?


Long-established companies are facing many challenges to adapt their operations to computer vision technology. Often handicapped by their unsexy corporate images, they don’t attract talented data scientist and fall behind to develop AI applications. For this reason, many solution-provider companies started to offer a variety of off-the-self image recognition API. But once again, this approach was not satisfying. Most APIs had a too much restricted scope and performed poorly once used in the business environment.

In response to the lack of success of these APIs, more and more image recognition API providers companies are pivoting towards custom image recognition applications and it might finally be the right approach to bring AI into traditional companies’ operations. In order to tailor each system to business needs, it appeared that a strong collaboration is required between solution providers and clients. Therefore, it is relevant to present this new approach with the spectrum of value co-creation.

Co-creation principles of real-world image recognition applications

1. Custom, the system will be

As mentioned above, custom applications proved to be more way more efficient to solve businesses’ problems. Image recognition applications are systems that take in input an image and give an information about it on output. This information can be a tag (eg : there is a dog in this image) or an object localization for instance.  They are highly specific to each company and therefore need to be adapted every time.

2. Client’s image, you will use

To ensure satisfying performances, each applications should be build with customers images. By that, I mean that later on the application’s system will predict information from specific images and the model used in production should be be created with extremely similar images. I won’t go into details but keep in mind, that AI learn by examples and the more relevant the examples are, the more accurate the results will be. Be careful, some images can be qualified as personal data and has to respect personal data directives.

3. Involved, your client have to be

Unlike some others IT applications, defining requirement specifications won’t be enough to build a custom applications. Customers should be involved during the whole process in order to ensure that the final application match correctly the operations. For instance, if one company wish to automate quality control, it will need to define what tags are the best to represent the different type of defect on spare parts.

4. Labelling, your client will be in charge of

Finally, in cases where the customer is the expert, the only way to create custom systems implies to put client at work. As briefly mentioned before, to build image recognition model you need to show as many example as possible. To do so, you need to annotate every images with its corresponding tags and some tags requires an expertise only possessed by operators. For instance, there is a lot of excitements around automatic cancerous cell detection on medical images. To create an auto-diagnosis system, doctors need to teach algorithm to differentiate sane and cancerous of cells and it requires a specific annotation expertise that cannot be outsourced.


Information asymmetry has inhibited computer vision applications’s development as traditional companies have struggled to understand how it could benefit their business and AI companies to uncover potential use cases for them. Establishing co-creation relationship to build image recognition application might finally allows a faster integration of AI in traditional businesses. 

Deepomatic, making vision AI accessible to every businesses

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Let’s illustrate how these principles can be applied to a business model. French start-up deepomatic edits a software platform enabling businesses to build custom image recognition system. Starting from simple licence plan to more project-based sales, the start-up offers support to guide clients from use case ideation to application deployment. The relationship between them and their clients is structured around step by step meetings to define scope and tags, to collect images etc. The platform they designed helps to manage dataset and performance but also bridge deepomatic’s actions to its client’s. As it is possible to improve system’s performances over time, deepomatic designed the software as a human-in-the-loop platform : once in production, the system can still return images where the system is unconfident and client’s experts can annotate again and deploy a new version. This way, system can evolve over time to match operational changes and represent a strong example of a dynamic and customized product. 

For more information about deepomatic’s platform, click here.


deepomatic’s website :

Saarijävi et al (2013), “Value co-creation: theoretical approaches and practical implications”, European Business Review

Kohtamäki, Rajala (2016), “Theory and practice of value co-creation in B2B systems”, Industrial Marketing Management

The Effects of Web Personalization on User Attitude and Behaviour: An integration of the ELM and CST

In general web personalization has the capability to achieve two important business goals: increase advertising revenue and increase sales revenue. The realization of these two business goals is directly related to item sampling and item selection. Where Item Sampling takes the form of a user’s clicks on personalized recommendations, Item selection involves the user choosing one of the personalized recommendations as the final choice.

In this paper by Ying Ho and Bodoff (2013) a scientific gap is identified which limits the understanding of how web personalization can be used to increase advertising and/or sales revenues. In order to fill this gap the researchers try to develop a theoretical model of these behaviours, item sampling and item selection, and their attitudinal antecedents. Additionally, this model is based on the integration of two theories:  the Elaboration Likelihood Model and the Consumer Search Theory.

Elaboration Likelihood Model

The Elaboration Likelihood Model, ELM for short, models the effects of a user’s elaboration of individual persuasive items on his or her overall attitude. The model thus shows how a user’s elaboration, defined as the extent to which a person carefully thinks about an argument (Petty and Cacioppo 1986a), of individual recommendations influence a user’s attitude towards the personalization agent, in turn influencing the users decisions to select a personalized recommendation (Petty and Cacioppo 1986a, 1986b; Tam and Ho 2005).

In lay man terms, with the Elaboration Likelihood Model one is able to define the degree to which a user will cognitively process a given recommendation. This is quite limiting as it does not allow for the investigation of the number of recommendations the user investigates, something important if the business goal is to maximize user clicks. Hence the ELM model is combined with CST, Consumer Search Theory.

Consumer Search Theory

Where ELM is limited to individual recommendations the CST model models the umber of a items a user inspects on his way to the completion of a search task. Thus, in CST the item source is seen as a distribution where each act of inspecting an item is viewed as sampling an item from the original distribution. Where ELM thus focusses on an individual recommendation CST is able to capture the entire array of inspected elements prior to the completion of a search task. This breadth of sampling of CST and depth of processing from ELM allows for the exploration of influence attitude formation, which in turn affects item selection.


In order to develop a theoretical model of item sampling and item selection, the researchers employ both a lab and a field study. In the lab study a personalized online bookstore is developed and used in combination with a thought-listening technique in order to capture participants’ depth of processing. Despite being a widely used method in order to capture ELM and depth of processing thought-listening technique is an obtrusive measure and hence a field study was used to increase external validity.

During the field study the researchers collaborated with a large music provider in the Asia Pacific region in order to develop a personalized music website. During this 6 month period participants visiting this music website either received good or bad recommendations and where hence tracked on their behaviour.


Table 1. Summary of Findings    
Hypotheses Lab Field
H1: Depth of processing has a positive effect on the persistence of attitude that user form toward the personalization agent Supported
H2: Confidence in one’s attitude toward the personalization agent has a negative effect on subsequent breadth of sampling from the personalization agent. Supported Supported
H3: Cumulative breadth of sampling from the personalization agent positively influences confidence in one’s attitude toward the personalization agent Supported Supported
H4: Item variance negatively moderates the positive effect of cumulative sampling breadth on attitude confidence Not Supported Not Supported
H5: Attitude persistence moderates the effect of cumulative breadth of sampling from the personalization agent on attitude confidence. Supported Supported
H6: Attitude confidence moderates the relationship between attitude valence toward a personalization agent and actual selection from the agent Supported Not Supported

As visible in Table 1. one can see that for hypothesis 1-5 there are consistent results for both the lab and field study. Whereas hypothesis 6 was significant in the lab study such a significance could not be observed in the field study. This difference in observation however, could be explained due to the large difference in available alternatives and hence a possible higher required attitude confidence for  significance to be visible.

Apart from acknowledging several hypotheses the researchers also discovered two significant predictors of item section they had not hypothesized about. First, the NFC (Need for Cognition), the extent to which individuals are willing towards cognitive activities, exerts a negative effect on the person’s actual selection of a personalized item in the lab study. Second, attitude confidence, the degree of certainty a person has in a choice, has a positive effect on item selection in both the lab and field studies.

Strengths and Weaknesses

First of all, in the lab study the researchers made use of a though listing method, this method although widely used might have altered participants behaviour and thus jeopardize the result of hypothesis 1. In order to compensate for this result, the researchers used a second, field experiment, to increase external validity. Apart from using a thought listing method the researchers also made sole use of experience goods in their study. Arguably they did make use of these goods because clicks are meaningful in such a setting, however one may argue this limits generalizability as the developed model might be inconsistent for other types of goods.


Ho, S. Y., & Bodoff, D. (2014). The effects of Web personalization on user attitude and behavior: An integration of the elaboration likelihood model and consumer search theory. MIS quarterly, 38(2).

Lavie, T., Sela, M., Oppenheim, I., Inbar, O., and Meyer, J. 2010. “User Attitudes Towards News Content Personalization,” International Journal of Human-Computer Studies (68:8), pp. 483-495.

Petty, R. E., and Cacioppo, J. T. 1986a. “The Elaboration Likelihood Model of Persuasion,” Advances in Experimental Social Psychology (19), pp. 124-205.

Petty, R. E., and Cacioppo, J. T. 1986b. Communication and Persuasion: Central and Peripheral Routes to Attitude Change, New York: Springer.

Tam, K. Y., and Ho, S. Y. 2005. “Web Personalization as a Persuasion Strategy: An Elaboration Likelihood Model Perspective,” Information Systems Research (16:3), pp. 271-293.

Great ideas from ordinary people

Article discussed: Schemmann, B., Herrmann, A. M., Chappin, M. M., & Heimeriks, G. J. (2016). Crowdsourcing ideas: Involving ordinary users in the ideation phase of new product development. Research Policy, 45(6), 1145-1154.


Crowdsourcing ideas from customers is a contemporary phenomenon that has enjoyed widespread attention from both scholars and business practitioners. These events, albeit temporary or long-term, generate countless of ideas for the focal company, but only a handful are actually implemented. This is particularly true when ideas originate from ordinary users as opposed to experts, or lead users. It is thus pivotal to scrutinize this ordinary user group in order to improve efficiency of the yet expensive and time-consuming filtering process.

With this in mind, Schemmann et al. (2015) conducted a cross-sectional research in an effort to derive ideator and idea-related characteristics that could explain whether a crowdsourced idea from ordinary users is implemented. Based on prior studies and their analysis, they conclude that the following factors all positively relate to actual implementation:

  • Attention paid to crowdsourced ideas of others (H2)
  • Idea popularity (H3)
  • Idea potential innovativeness (H4)

Synthesis of theory: value from juxtaposing user bases

The most interesting theoretical contribution of this paper stems from its focus on the ordinary user, contrasting most existing research, which emphasized the role of lead users. That is, whether ideator and idea-related characteristics can determine actual adoption by the organization. Motivation is found to result in more successful ideas due to individuals being stimulated to develop a qualitative strong idea (Bayus, 2013). However, no such finding is found to be significant within the current research. In addition to the ideator-based characteristic, idea-related characteristics also influence the eventual adoption of the idea. Whereas lead users are valued for their knowledge and expertise, ordinary users leverage an even more powerful mechanism called Wisdom of the Crowds (Surowiecki, 2004). The sheer number of ordinary users results in an extremely accurate prediction of whether an idea is likely to be successful or not. Furthermore, given that innovative ideas are positively related to implementation (Witell et al., 2011), we can infer that the increased diversity within ordinary users is likely to produce more novel ideas than lead users.

Such a juxtaposition of user base enables firms to tailor crowdsourcing initiatives to their respective objectives and move away from crowdsourcing failure rates that have been observed to be as high as 40% (Castellion & Markham, 2013). That is, firms can tweak two mechanisms that naturally emerge from this study’s insights. First, crowdsourcing participants can be limited to a certain user base. Secondly, crowdsourcing platforms can be designed to includes functions such as peer evaluations and idea visibility. The latter brings us to the study by Bockstedt et al. (2016) who distinguishes between blind and unblind innovation contests. Specifically, unblind contests are likely to enhance the positive relation of hypothesis 2 as the platform facilitates learning from others.

Methods used: weaknesses and improvements

The present study was conducted on a sample of 1456 ideas for which the company’s review process was completed. The data stems from an open idea call of a large beverage producer, which was publicly available. Although such a method bears generalizability issues, it can still be justified from a practical perspective. However, in addition to the limitations stated by the authors, another weakness relates to the codification of variables. That is, all variables are examined as dichotomous variables, with exception of ideator motivation. All hypotheses measured with dichotomous values are found to be significant, whereas the continuous variable used to test hypothesis 1 is rejected. Not only does such an outcome arouse suspicion, there is much room for improvement. As illustration, the independent variable attention paid to other ideas could also be coded as a continuous or count variable, measured by relatively time spent on other’s idea page or the number of other’s idea pages visited, respectively. Although arguments for both methods are evident, the authors could at least include such an empirical analysis as a robustness check. As a result, findings become more credible, increasing the likelihood of actual utilization when managing crowdsourcing platforms.


Thus, the present study presents insights into ideator and idea-related characteristics that determine idea implementation in long-term crowdsourcing initiatives. By analysis of ordinary users, the authors complement existing views with another relevant dimension that could help business practitioners manage difficult socio-technical dynamics inherent to crowdsourcing. The higher success rate of crowdsourcing is in turn, likely to spur innovation across a multitude of industries. Although such aspirations seem distant, achieving great feats is realized by taking one step at a time.


Bayus, B.L., 2013. Crowdsourcing new product ideas over time: an analysis of the Dell IdeaStorm Community. Manage. Sci. 59 (1), 226–244

Surowiecki, J., 2004. The Wisdom of Crowds: Why the Many are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations. Doubleday, New York, NY.

Witell, L., Löfgren, M., Gustafsson, A., 2011. Identifying ideas of attractive quality in the innovation process. TQM J. 23 (1), 87–99.

Castellion, G., Markham, S.K., 2013. Perspective: new product failure rates:influence of argumentum ad populum and self-interest. J. Prod. Innov. Manage.30 (5), 976–979.

Bockstedt, J., Druehl, C., & Mishra, A. (2016). Heterogeneous submission behavior and its implications for success in innovation contests with public submissions. Production and Operations Management25(7), 1157-1176.

From e-commerce to social commerce

A matter of trust

The advancement of Web 2.0 social networks brought new developments to e-commerce. In recent years, e-commerce has transformed to social commerce. Social commerce is a new stream and subset of traditional e-commerce, which combines e-commerce with Web 2.0 social networks.

Social commerce, trust & buying intention

Thanks to social networks consumers can now communicate, rate other products, review others’ opinions, participate in forums, share their experiences and recommend products and services. By bringing the features of Web 2.0 social networks to e-commerce, consumers can support each other in the acquisition of products and services in an online context. This results in more customer-oriented business models where customers can share knowledge, experiences and information about their products and services.

Social commerce has three main constructs that empower customers and increase the sociability of e-commerce:

  • Forums and communities: Online discussion sites that support information sharing;
  • Ratings and reviews: Provide comprehensive information about a product for potential customers;
  • Referrals and recommendations: Unlike brick and mortar stores, in online stores it is not possible to interact with staff, so customers rely more on other customers’ recommendations.

Trust is a central issue in e-commerce. Social commerce has helped to establish more trust in e-commerce platforms. Customers experience higher levels of trust as they can support each other through information exchange. This is because interactions and interconnectivity reduce the perceived risk in online transactions. Reviews, ratings and recommendations can indicate the trustworthiness of an online seller as customers consider reviews from other customers to be more reliable than information from a commercial website.

Hajli (2015) found that the three social commerce constructs significantly positively influence the user’s intention to buy. Trust appeared to be a mediating variable. Social commerce constructs have a positive effect on user’s trust, which in turn positively influences the intention to buy (Figure 1). To arrive at these findings, Hajli (2015) conducted a survey study with four constructs: intention to buy, social commerce constructs, perceived usefulness and trust. A five point Likert-scale was used in the questionnaire. Data was collected at universities in the UK. The final sample consisted of 243 completed and usable questionnaires. Next, Structural Equation Modelling (SEM) was used for data analysis. The hypotheses were tested with the Partial Least Squares (PLS) method. The findings underline that social commerce constructs, like customer reviews, are more likely to increase trust, and in turn increase customers’ intention to buy.

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Amazon customer reviews

From a practical perspective, this study encourages online businesses to make a plan for reviews and to manage social networks effectively as it significantly impacts customers’ purchasing decisions. It recommends them to engage with their customers through reviews to develop trust. Other research indeed shows that 91 percent of customers read online reviews and that 84 percent trusts online reviews as much as a personal recommendation (Bloem, 2017) In practice, this implies that not offering customer reviews is similar to ignoring 84 percent of your buying population by not giving them the information they want to support them in their buying decision (DeMers, 2015).

To illustrate, Amazon optimised its business model based on customer reviews and ratings. Customer reviews are one of the most important ranking factors in Amazon’s A9 algorithm. It ranks product search results based on the positivity of customer reviews and rating. (Grosman, 2017)

Fake review problem

A weakness in the study of Hajli (2015) is that it does not consider that information related to the identity of the reviewers influences the perceived trustworthiness of a review.  The paper simply finds that more reviews increases trust, which in turn increases the buying intention.  However, in reality, it might not be that straight forward anymore with the rise of fake product reviews. Nowadays, it is hard for customers to decide which reviews to trust. There is looming crisis of confidence in online product reviews, which used to be a key factor in customers’ buying decision. (Silverman, 2017) As customers cannot trust reviews anymore, it can be questioned whether the positive relation between the number of reviews, trust and buying decision still holds.

Increasingly, customers pay careful attention to reviews, e.g. looking for reviews with a Verified Purchase tag. Nearly 66.3 percent of Amazon reviews are five-star ratings, which is highly unrealistic. Reviews on Amazon are a key factor when making a purchasing decision and without reviews it is difficult for online retailers to gain sales. In an attempt to boost sales, retailers offer reviewers free or discounted samples in return for a positive customer review. So, it is no surprise that 96 percent of paid reviews on Amazon is rated four- or five-star.  (Cipriani, 2016)

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Source credibility

Many authors have investigated the positive impact of online reviews on sales of products and services. However, given the importance of source credibility, I believe more research is needed on trustworthiness of reviewers as an important construct. The source credibility theory explains how a recommendation persuasiveness is affected by the perceived credibility of its source. Actually, customers accept reviews depending on the perceived trustworthiness of the reviewer, which consequently impacts the buying decision. Reviewer trustworthiness is therefore an important moderating variable that positively moderates the impact of review-based online reputation. (Banerjee, Bhattacharyya, & Bose, 2017)

Concluding, instead of solely increasing the number of (positive) customer reviews, online retailers should also build a good review-based online reputation that encourages and identifies top trustworthy reviewers and that ranks reviews based on reviewer trustworthiness.

This post was inspired by: Hajli, N. (2015). Social commerce constructs and consumer’s intention to buy. International Journal of Information Management, 183-191


Banerjee, S., Bhattacharyya, S., & Bose, I. (2017). Whose online reviews to trust? Understanding reviewer trustworthiness. Decision Support Systems, 17-26.

Bloem, C. (2017, July 31). 84 Percent of People Trust Online Reviews As Much As Friends. Here’s How to Manage What They See. Opgehaald van Inc.:

Cipriani, J. (2016, March 14). Why You Shouldn’t Trust All Amazon Reviews. Opgehaald van Fortune:

DeMers, J. (2015, December 28). How Important Are Customer Reviews For Online Marketing? Opgehaald van Forbes:

Grosman, L. (2017, February 28). Five Tips To Improve Your Ranking On Amazon. Opgehaald van Forbes:

Hajli, N. (2015). Social commerce constructs and consumer’s intention to buy. International Journal of Information Management, 183-191.

Silverman, D. (2017, April 20). A Matter of Trust: Amazon Declares War on Fake Product Reviews. Opgehaald van Clavis Insight:

Beyond Omnichannel: Alibaba’s “New Retail” strategy


Alibaba Founder and Chairman Jack Ma coined the term “New Retail” in a letter to Alibaba’s shareholders in October, 2016. Ma said, “In the future, pure e-commerce will be reduced to a traditional business and replaced by the concept of New Retail-the integration of online, offline, logistics and data across a single value chain.”

Retail customer interaction has been rapidly changing over the years, from the traditional way of visiting storefronts to shopping in the comfort of our homes via e-commerce, and now to New Retail. Although the integration of Online and Offline (O2O) commerce is not entirely new concept, “New Retail” demands for greater customer-centric engagement seamlessly  through leveraging data technology.


Fresh-food focused HEMA (盒马鲜生, which means “Boxed/packaged freshness and liveliness”) – a real example of online-offline hybrid, where carries a curated selection of 3,000 products from 100 countries, ranging from fresh food, grocery to catering and fine-dining. HEMA offered customers to choose order online via HEMA mobile app and receive free delivery within half an hour in a 3km radius, or scan barcodes at the store, pay via HEMA app and set up delivery, blending the online and offline shopping experience. From ordering to delivery, customer shopping behavior is captured all along the way via its mobile app. Accordingly, Alibaba is able to personalize the entire experience such as organizing promotion, offering tailored suggestions based on specific consumers’ wants and needs, and dedicated food booths where customers can easily get the food cooked on the spot.


What value HEMA brings to customers?

HEMA is not only a one-stop shop, but also its entertaining layout and display is definitely a game-changer, especially at fresh seafood section. HEMA has a pop-up “Seafood Exhibition” in Shanghai, in an aquarium-style glass house, where consumers are able to check out expensive and live seafood products such as Boston lobster and Alaskan king crab, and can choose to have it cooked right away for carry-out, delivered to assigned places, or eat it on the spot at the store’s dining area.

Secondly, with the technology of Cainao delivery and logistics business under its belt, HEMA itself is able to do pre-packaging and electronic tagging, so that staff can sort out goods from different food sections through scanning codes. HEMA is using algorithms to solve problems concerning sorting time, binning, combining bills and logistics route. The stipulation so-called “arrival within 30 minutes” is the superlative service achieved through the operations management and data technology.

Thirdly, Alibaba takes its dedication to building customer trust. China is considered as a comparably low-trust society, especially with regards to food issue, which raised great concern since 2008. It was precisely because of the poor transparency in China and society’s lack of trust in the government. HEMA’s transparency provides multi-level reassurance, such as easy access to all the information via a QR code, which reveals the origin of every product as a guarantee of quality, price comparison, personalized product recommendations, etc. In addition, HEMA’s advertising is also more adventurous than other supermarket brands. For example, HEMA redefined the “Home Kitchen” as a liberation campaign for the housewives. This has boosted HEMA’s high reputation among women with hectic lifestyles.


How to make HEMA more efficient?

With the usage of HEMA App and Alipay as the only accepted payment method at HEMA stores, Alibaba successfully engages consumers into its ecosystem. While customers gained convenience, Alibaba benefits from an increase in users. The new comers joined the steady stream of its customer data and can provide customer insights from different perspectives. Alibaba further boost its data and smart logistics technology to enhance greater consumer experiences; meanwhile, the database of customers insights will apparently enable Alibaba to market its products to consumers better.

With two years store operation, statistics cited by Alibaba look relatively promising at first glance. On average, customers make 4.5 purchases per month and 50 times a year, and among users who have HEMA app, the conversion rate for making a purchase is as high as 35%. Online orders account for more than 50% of total orders and in some premium locations, online orders are as high as 70% of total orders. For now, HEMA opened more than 20 locations in major commercial districts in Shanghai and Beijing, focusing increasingly on the lucrative fresh-food retail sector.

However, HEMA’s expansion continues to incur high operating costs due to the prime locations of its supermarkets. The exorbitant cost of expansion may discourage its growth in the future. In the long run, it could be more profitable for Alibaba to expand its delivery network or partner with existing partners, merging quality brands to open up more supermarkets around the urban areas. Above all, Alibaba can keep encouraging the adoption of “New Retail” concept amongst partners with existing online stores, for that will minimize the need to invest in more supermarkets.

In conclusion,  HEMA presents a “New Retail” model to redefine commerce by enabling seamless engagement between the online and offline world. Nevertheless, it’s not all about converting online users to offline customers or vice versa. It’s about building a retail ecosystem that combines online and offline channels in a cohesive way that features the consumer at its core. While Amazon Go is calling its strategy as a “Just Walk Out” shopping experience, I’d like to propose that HEMA is creating a “Just Be There” strategy to increase customer stickiness, offering a more efficient, flexible and fun shopping experience.


References: the start of your career

The rise of the Internet has drastically changed the recruitment industry. The Internet made recruitment faster, cheaper and easier (Revell 2014). Companies can post job applications online for the world to see, while jobseekers can reply in seconds. On top of that, communication between companies and job applicants changed; companies can now for instance reach job applicants through e-mail or Skype, diminishing recruitment costs and increasing the reach of the company. The Internet therefore increased the range and the competition for talent amongst companies, for example through recruitment platforms. One of these recruitment platforms that makes locating and acquiring of job applicants easier, is 

What is was launched in 2012 in Rotterdam by three students as a recruitment platform that connects students (and recent graduates) with companies. Currently, the platform has over 1,500 companies and 150,000 students using it, creating more than three million connections ( n.d. a). So, how does it exactly work?

Companies create a profile, where they describe their business, selection criteria for students and add relevant news, jobs and other possibilities. Students, create a profile with their skills and interests and upload their resume. then checks if the student’s profile matches any companies. If so, the student will automatically receive a network request from the company. If the student accepts that request (and only in that case), the student will receive updates and information on available jobs, internships, business courses and traineeships offered by the company. The company can view the connected students’ profiles, send them messages or invitations to directly apply for job opportunities. In that way, a company can build a network of suitable candidates ( n.d. a).

The video below summarises the idea behind as described above.

Thus, is a two-sided market, providing structure and rules that facilitate the interaction between students and companies (Eisenmann et al. 2006). The two groups attract one another, meaning that if the number of students grows, it will attract more companies, and vice versa. This is called the network effect, which suggests that the higher the number of users, the more attractive will be and the higher its value.

Business model uses a freemium business model, meaning that basic functions are free for students and companies, but companies must pay a subscription fee for additional functions (Kumar 2014). Companies pay €190 per job per month to put their job offer on the homepage of all students who meet their selection criteria ( n.d. b). Other premium options are the ability to approach and invite the best suited candidates to apply for jobs and user data insights. For these additional options, an inquiry must be done to determine the fee. However, the creation of a company profile and posting job offers is free.

This business model is beneficial for all platform players and thus creates joint profitability (Carson 1999). has many users and thus companies can benefit by recruiting more students that are better suited with less effort. is payable for both small and large companies since the subscription fee is relatively low compared to alternatives, such as recruitment agencies. Hence, switching costs are substantial since the convenience of is high; you can reach many suitable students at a relatively low cost. On the other hand, students benefit by the many job options offers and the ease with which they can look up jobs, while not having to pay a subscription fee. Finally, itself benefits from an increase of users since in that case it can collect more subscription fees. is very customer-centric since students and companies can set selection criteria indicating what they are looking for. For instance, a student can filter job applications for the type of job, function, industry, educational level and company size, making parameter-based (Randall 2005). Thus, the platform takes care of institutional arrangements by giving power to platform players; companies and students can indicate what they are looking for and they can directly communicate, which makes recruitment more efficient.

In terms of the institutional environment, complies to social norms and regulations. hosts many well-known companies such as Unilever, KLM and Heineken and recently acquired one million euro from investors to grow the company, suggesting trust and enormous potential (Thole 2017). The company is also registered at the chamber of commerce and complies to the Dutch (Personal) Data Protection Act ( n.d. c).

All in all, meets efficiency criteria and is still growing. Although there are many other recruitment platforms (e.g. LinkedIn), differentiates itself by focusing on students and matching them with companies instead of individuals. The potential of is enormous, last year the company expanded to the U.K., where it is now the number one student career website ( 2017). So, one may wonder how far can go.

Carson, S.J., T.M. Devinney, G.R. Dowling and G. John (1999) ‘Understanding institutional designs without marketing value systems’, Journal of Marketing 63(4): 115-130.

Eisenmann, T., G. Parker and M.W. Van Alstyne (2006) ‘Strategies for Two-Sided Markets’, Harvard Business Review 84(10): 92-101. (2017) ‘2017: Our year in numbers’. Accessed on 18 February 2018 on (n.d. a) ‘About’. Accessed on 16 February 2018 on (n.d. b) ‘Attract talent, save time’. Accessed on 17 February 2018 on (n.d. c) ‘Terms of Service (User Agreement)’. Accessed on 17 February 2018 on

Kumar, V. (2014) ‘Making ‘’freemium’’ work’, Harvard Business review 92(5): 27-29.

Randall, T., C. Terwiesch and K.T. Ulrich (2005) ‘Principles for user design of customized products’, California Management Review 47(4): 68-85.

Revell, S. (2014) ‘5 Ways the Internet has Changed Recruitment Forever’. Accessed on 17 February 2018 on

Thole, H. (2017) ‘Deze Nederlandse startup ziet de Brexit als grote kans – en krijgt €1 miljoen om te groeien in het VK’. Accessed on 17 February 2018 on

Pakkie – the new trustworthy transaction platform

Imagine you found a nice second-hand television online, but the seller lives in Groningen and you live in Rotterdam. Driving back and forth is not easy for everyone, but transferring money in advance and waiting to get your product delivered perhaps does not feel good either. How do you really know if someone does as he or she promised? And whether the product meets the expectations? This is where Pakkie comes in: a safe transaction platform arranging both payment and shipping.

The startup, launched one month ago (January ’18), combines payment via a third-party account ( and sending from one of the 5,500 connected parcel points of PostNL, DHL and DPD. This way, you can safely buy second-hand items via Facebook Marketplace or ‘’, because the money is only transferred from the third-party account once the package has arrived at the recipient. As both payment and shipment are arranged via one party, there is control over every step of the transaction. Hence, Pakkie makes selling and buying online much easier (Pakkie, 2018).

How does it work?
Within a transaction, there are always two parties involved: a seller and a receiver. The transaction works differently for both. When a seller has made a deal online, the other party can pay via Pakkie. At this moment the seller will receive a shipping label. Afterwards, the seller can drop his or her package at PostNL, DHL or DPD and once the package is delivered the seller will receive his or her money. The shipment can be followed via the Pakkie app. On the other hand, Pakkie keeps the receiver informed about the shipping process and after the package is received, the seller gets paid (Pakkie, 2018).

Sending a large package (larger than standard letter post) with Pakkie costs 6,95 euros and you have to pay 4,25 euros for a small-volume order (standard letter post, shipped in an envelope). Sending a Pakkie costs as much as sending a package from a store, without additional service costs (Pakkie, 2018).

Schermafbeelding 2018-02-16 om 11.42.35

Efficiency criteria
Pakkie fulfils several efficiency criteria. Sellers can safely sell their goods online via Pakkie, without having to pay additional costs. Moreover, buyers can buy everything they like online in a safe manner. This way Pakkie removes the distrust of buyers and sellers and encourages people to buy and sell more second-hand instead of new products. In addition, Pakkie makes online trading easier, because it takes less time and effort to sell and buy second-hand products. Parcel deliverers could also benefit from Pakkie. With every distance of more than 30 kilometers, it is cheaper and more sustainable to send the purchase instead of picking it up yourself. On the other hand, users provide value for Pakkie as well by creating a bigger user base. Besides, Pakkie works with a commission business model: they charge a commission from each transaction. For every parcel that is traded via the service, the startup receives a fixed amount from the relevant parcel delivery service. This commission is below 1,00 euro per product sent, hence it is very important for Pakkie that more people will use this service. Furthermore, users are an essential part of Pakkie’s business model because without users the company would not even exist. This is the principle of value co-creation, where value is created in a multi directional way (Saarijärvi et al., 2013). Thus, the joint profitability criteria are met as both company (Pakkie) and users can benefit from the app (Carson et al., 1999).

Pakkie is feasible and takes care of several institutional arrangements. The company is fair, since it stimulates people to treat others the way they want to be treated as well.  In addition, the platform takes care of several concerns regarding security, fraud and scams, which no other marketplace platform has succeeded to do. Finally, the platform is transparent in a way that users can see where their parcel is and have full control in the transaction.

The future
Pakkie offers a solution to many people who want to buy and sell second-hand items online, but are reluctant towards the transaction process. The platform could lead to a more sustainable world with more second-hand trading. However, in a world where globalization is becoming increasingly important, especially within the European market, Pakkie should also start focusing on expanding abroad. Only if Pakkie gains enough users, the platform could expand all over the world.

Carson, S. J., Devinney, T. M., Dowling, G. R., & John, G. (1999). Understanding institutional designs within marketing value systems. Journal of Marketing, 115-130.

Pakkie. (2018). Retrieved from

Saarijärvi, H., Kannan, P. K., & Kuusela, H. (2013). Value co-creation: theoretical approaches and practical implications. European Business Review, 25(1), 6-19.





Farmers First: the Farmers Business Network


Farming is one of the oldest professions in the world, but one that has markedly evolved in the past century. Gone are the days where 41% of the U.S. workforce was employed in agriculture; as of 2000, a mere 1.9% remain, of which 93% has to rely on off-farm income to survive (Dimitri et al., 2005).

Farm income above avg income

And although graphs such as the one depicted to the left paint a pretty picture concerning the average farm household income, these numbers do not reflect that more than half of the households operating small farms (gross cash farm income < $350 000) typically incur losses from farming (Prager et al., 2017). This issue is made even more relevant by the fact that 90% of the U.S. farms actually consist of those small family farms. Evidently, only the largest players have the bargaining power and economies of scale to make serious profits in this cut-throat, behemoth-dominated industry.

Continue reading Farmers First: the Farmers Business Network

What motivates consumers to contribute to a Kickstarter campaign?

There have been many research developments in the field of crowdfunding, in the past decade. While the focus for IS practitioners has been on optimising crowdfunding efforts in order to reach a specific measure of success (usually financial success eg: target funding amount), less focus has been placed on identifying the psychological and motivational queues that lead individuals to contribute monetarily to campaigns.


What makes people want to contribute in the first place, to a crowdfunding campaign?


Zvilichovsky et al (2017) have devoted their research efforts on investigating the motivational factors that lead individuals to contribute to crowdfunding campaigns. Their research analyses how consumers increase their participations when they believe their contribution is pivotal to product creation. In crowdfunding platforms, consumers consciously and willingly fund a product that does yet no exist in the market. The essence of crowdfunding platforms is to involve individuals in early stages of the product development process. This context sparks consumer motivations that transcend traditional transactional interactions.


Rather than being motivated by a sense of generosity towards the laborious efforts of the entrepreneur, consumers are driven by the notion of seeing the product become a reality and become in possession of it. The authors conceptualise making-the-product-happen motivation as an attempt by consumers to contribute to the creation and commercialisation of a product by means of monetary funding and making-it-happen motivation as an attempt by consumers to contribute to the realisation of the campaign’s financial goal. When consumers feel that the existence of the product depends on the success of the crowdfunding campaigns and that their financial backing is pivotal to the creation of the product, making-the-product-happen motivation peaks.


The authors conducted a total of four studies controlling for future product availability, funding mechanism and funding stage target to provide empirically validated answers to the question above.


  • The first study, tests whether all-or-nothing funding mechanisms vs keep-it-all funding mechanisms see differences in the willingness to back propensity of consumers. The participants were provided information regarding an air cleaning system and asked using a 7-item likert scale how likely they would be to pay $50 to back the campaign. The result of the first study determined that all-or-nothing funding mechanisms drove the participants’ making-it-happen motivation and willingness to back the campaign.


  • The second study tested whether consumers were more willing to back a campaign that was closed to reaching its target vs one that had already reached it target. The results found that consumers’ perceived impact on making-the-product happen, led to a higher willingness to participate in a project whose financial target is close to its goal.


  • In the third and fourth study, the researchers tested whether future availability of the product would affect their willingness to contribute. They found that when future availability of the product depends exclusively on the success of the campaign (thus that the backer’s financial donations are pivotal in making-the-product happen) consumers are more willing to back the campaign.


To test for generalisability the authors of the study conducted a field experiment on 193,312 campaigns by analysing the average contribution per backer compared to the length of the campaign.

Apart from validating the results found in the lab study, they discovered that most of successful campaigns achieve their goal only by a slight margin above the target (computed to be $195 or three $65 donations) indicating the importance of communicating the importance of the backer’s role in product realisation process.

The authors found very interesting insights that serve as insights to campaign designers to optimise their probability of success. If entrepreneurs are able to effectively and sincerely communicate to consumers the importance of their role as backers in making-the-product happen then the results of the study indicate a positive correlation with financial success (for the entrepreneur) and future product availability for the consumer.


While the study did provide insights as to which motivational factors lead consumers to back campaigns, it is not short with its limitations. The data collected from Kickstarter does not report the amount contributed by backers, which had to be assumed by the authors. Furthermore, the results applied only to consumer products and not to public goods, who see crowdfunding as a viable source of funding.


The above limitation set a precedent in the direction that future research should take in further developing the crowdfunding literature. The researchers hint that studying the interactions that occur in the post-crowdfunding stage would provide a more complete portrait of crowdfunding literature.


Zvilichovsky, D., Danziger, S. and Steinhart, Y. (2018). Making-the-Product-Happen: A Driver of Crowdfunding Participation. Journal of Interactive Marketing, 41, pp.81-93.

Studio – Have fun running on a treadmill

Running on a treadmill in your living room or at the gym can be rather boring. Also, there is no one there to motivates and monitors you. Many people, therefore, hire a professional coach to help them go through some training sessions. However, not everyone can afford a 40 euro/hour sessions guided by professional coaches. Another option would be attending group classes, which is much cheaper than having a personal coach; however, the timing of these group classes don’t always fit your schedule. These will eventually become excuses for us to skip a fitness session. Is there a solution?


CEO and founder Jason Baptiste had a goal to lose 50 extra pounds after he finished school and decided to run 5km every day on the treadmill. However, after hundred days of running on a treadmill, he found it extremely boring and discouraging. This gives him the idea to develop a platform which will improve the whole treadmill experience. Meet Studio, a treadmill app on your smartphones or smartwatches that will bring motivation convenience and excitement to each of your training session. After subscribing to their services, users gained access to classes coached by professional coaches that motivate and guides users through their training session. Every class is accompanied by playlists that users can choose from. In addition, classes are offered in different difficulties and in different lengths; users can choose the corresponding classes according to their own needs. The classes can be taken anytime and anywhere.

Engaging the users

It is not only about running on the treadmill, Studio collects distance, speed and biometric data from every user that took the classes and these will be imported into the Real-time Leaderboard. Every user is rewarded with the in-app virtual currency Fitcoin after each session depending on how long and how far he/she has run. Fitcoin will defined users position on the leaderboard. Furthermore, Studio elevates its gamification experience to another level by making all users start with the basic level – Basic bear. Earning more Fitcoin, by running more, will allow users to rank up. To further motivates users during their training session, the real-time heartbeat of the users will be shown on the leaderboard and so as their real-time Fitcoin balance. In addition, users can choose to click on the cheer button during the session to motivates other users that are also doing the same exercise at the same time. In the future, Studio is planning to enable users to redeem Fitcoin for real-world prizes. Even though Studio targets users that would prefer flexible classes as opposed to physical group classes, the app still managed to provide users the same motivation they would get in a group-based training session with the help of its real-time leaderboard.

Screen Shot 2018-02-18 at 15.54.07.pngScreen Shot 2018-02-18 at 17.20.58.png

The business

Studio currently operates under a subscription-based model, which requires users to either pay $15 per month or a $100 upfront payment a year for unlimited access. The success of such Netflix and Spotify alike subscription model is dependent on the number of subscribers. To ensure that there is a sufficient and increasing amount of subscribers, Studio has to make sure that it keeps satisfying its existing users and at the same time keeps attracting new users. Like many other subscription-based business models, Studio offers new users with the opportunity to try out the services for free for a period of two weeks. Also, users can cancel their subscription at any time (for monthly subscribers). Every day, new trainings will be available for users to choose from. Besides, Studio has purchased full music licenses and therefore is able to offer a wide range of music for users to choose from. Studio is targeting both busy working professional and parents who often do not have time to visit the gym and own a treadmill at home. As for the professional coaches, Studio incentivized them to work for the platform by paying a commission of every listed class depending on the popularity of the class. Coaches are not limited to the number of participants in such platform compared to a physical classroom, there can be up to 20000 people taking their classes which is nearly impossible in a normal group training session.

Furthermore, it has been announced that Studio will partner up with treadmill maker Life Fitness to incorporate the Studio’s classes on Life Fitness treadmills in gyms. This will enable Studio to connect to an even larger audience as Life fitness is one of the largest and top manufacturers of fitness equipment. At the same time, having Studio integrated into the Life fitness treadmill will help Life fitness step into the digital fitness business.






The swaying effects of online product reviews

Based on the ‘wisdom of the crowd’ effect (Surowiecki, 2005), consumers make use of reviews to make accurate product evaluations. However, due to the large amount of information and conflicting opinions in reviews, it becomes difficult for them to identify and consider the attributes that are relevant to their consumer situation.

Imagine you are browsing a webstore, looking for a new camera to take on your backpacking trip. For this situation, you prefer a camera that is lightweight, easy to use, shock-resistant and cheap. You don’t have a lot of experience with camera’s, so you decide to look at the reviews of other consumers that bought Camera X. As you browse through several reviews, you start to notice that a lot of reviews mention things like FPS, image stabilization, Wi-Fi connection and GPS tracking. However, the reviews are in conflict about the quality of the image stabilization and many mention the lack of a Wi-Fi connection. After reading most of the reviews, you decide that you want to look for a camera that has better image stabilization and a Wi-Fi connection, attributes which you originally didn’t pick as relevant for your situation …

The scenario above, is what Liu & Karahanna (2017) describe as the ‘swaying’ effect. After reading reviews, people might over-weigh irrelevant attributes and under-weigh relevant attributes. They suggest that attribute preferences are more heavily influenced by characteristics of the online reviews rather than by the relevance of the attributes to the consumers decision context.

Theory development & methodology

Liu & Karahanna (2017) developed their theory from the constructive preference perspective theory (Bettman, Luce, & Payne, 1998; Payne, Bettman, Coupey, & Johnson, 1992). This theory suggests that preferences are shaped by the interaction between the properties of the information environment of the choice problem and the properties of the human information-processing system. Liu & Karahanna (2017) propose that three characteristics of online reviews affect the assessment of attribute preference and theorize that these characteristics together may ‘sway’ attribute preferences.

  1. the amount of information about attribute level performance,
  2. the degree of information conflict about attribute level performance
  3. the overall numeric rating and the attribute-level performance information

They conducted three studies, in which they provided the participants with a consumer scenario, asked them to weigh different attributes in terms of relevance and made them evaluate a digital camera based on reviews.

In study 1 they manipulated the three hypothesized factors and examined their effects on the attribute preferences. In study 2, they reproduced this study but added a monetary incentive to induce high motivation to process review information. The third study was a free simulation experiment to provide more realism and to allow for higher generalizability, in which verbal protocol analysis was used to capture and measure the factors.

Main findings

When the participants were asked to weigh the attributed based on the provided scenario, they placed more weight on the relevant attributed than the irrelevant attributes (in the scenario above, the attributes cost, ease-of-use and weight are relevant attributes, whereas image stabilization is not). But when they had to evaluate the camera based on reviews (that contained an uneven amount of information across different attributes, varying degrees of information conflict, and a numeric overall rating), the relevance of the attributes did not have a significant impact on attribute preferences.

Figure 1. Participants’ Constructed Attribute Preferences  (Liu & Karahanna, 2017)

The amount of attribute information in the reviews had the greatest impact on attribute preferences. Study 2 showed that the degree of attribute information conflict only affects attribute preferences when people have high motivation to process information. Study 3 showed consistent results. The studies provided evidence that attribute preferences that result from reading the reviews are primarily driven by the review characteristics and not by attribute relevance, thus supporting the hypothesized ‘swaying’ effect of online product reviews.

Practical implication.

What implications can be derived from these results? To support informed consumer decision making, it should be investigated how reviews should be organized and presented and how making sense of information conflicts can become less cognitively demanding. The effectiveness of some practical suggestions, such as providing a short description of the reviewer’s background (, displaying the amount of positive and negative comments on an attribute (Q. (Ben) Liu, Karahanna, & Watson, 2011) and allowing people to see the overall rating from reviewers who have similar decision context, need to be investigated. Implementation of these suggestions allows consumer to filter reviews from people in a similar consumer scenario, makes making sense of conflicts become less demanding and causes the numeric overall rating to make more sense.

Strengths, weaknesses, suggested improvements

By conducting multiple studies with consistent results, the article provides strong evidence for generalizability & robust hypotheses, which enhances the external validity of the results. Nevertheless, there are some limitations. The study only examines a single product category (camera) and a single scenario. Additionally, the samples only consisted of students with a similar expertise of cameras. It would be interesting to examine whether the effects differ based on the consumer’s level of expertise with the product category (camera) or the product category itself. Additionally, to increase the generalizability of this study, it would be interesting to see if these results also apply on a sample that is more representative of the population (not only students).

I would love to hear your opinions on this. Do you recognize yourself in the ‘swaying’ effect? Are reviews influencing your preferences? 


Bettman, J. R., Luce, M. F., & Payne, J. W. (1998). Constructive Consumer Choice Processes. Journal of Consumer Research, 25(3), 187–217.

Liu, Q. (Ben), Karahanna, E., & Watson, R. T. (2011). Unveiling user-generated content: Designing websites to best present customer reviews. Business Horizons, 54(3), 231–240.

Liu, Q. Ben, & Karahanna, E. (2017). The dark side of reviews: The swaying effects of online product reviews on attribute preference construction. MIS Quarterly, 41(2), 427–448.

Payne, J. W., Bettman, J. R., Coupey, E., & Johnson, E. J. (1992). A constructive process view of decision making: Multiple strategies in judgment and choice. Acta Psychologica, 80(1–3), 107–141.

Surowiecki, J. (2005). The Wisdom of Crowds. American Journal of Physics, 75(908), 336.


Personalization or Standardization – A review of “Customers do not always prefer personalized products: The role of personalized options range in personalization”

Nowadays firms have increasingly adopted personalization strategy to provide customers with the option to choose on important feature parameters at the price similar to the standardized products. For example, Dell is well known for its success in mass customization. (Randall, et al., 2005) Subway is also a kind of customization and received huge success. (Choi & LEE, 2015) However, the academic defines the personalization in a different way. It has been suggested as a revolutionary approach to market segmentation. The company now treat the individual customer as individual segments by satisfying their very specific need. The strategy can boost sales and in such bring competitive advantage to the company.

However, it is not necessarily always true for every company. There are many attributes can influence the attractiveness of personalization option, for example, price premium, effort and monitoring quality. If we take a closer look in the effect of effort that the customer needs to take in the personalization process, we can find that the more complex the personalized process is, the less the customer will likely to personalize the item. (Choi & LEE, 2015) Especially the company failed to convey information to the different type of customer. Just like the example of Dell, the inexperienced user cannot tell the meaning of “Memory”. (Randall, et al., 2005) Some of those customers just decided not to make the choice because the complexity exceeds the optimal level.  The product utility that consumer could get through personalization is not strong enough to cover the inconvenience.

This research argues that the customers prefer standard products over the personalized product when the range of personalization is perceived as excessive. The customer is more likely to select standard products over personalized alternatives when faced with inordinately complex decision-making. In order to test the pattern of consumer responses regarding the personalized product and the standardized product, the author decided to use the attitude of customers towards product and the purchase intention as the key factor.

H3: Customers will demonstrate a more positive attitude for standard products over personalized products with a higher level of personalizing options beyond a customer-perceived optimal point.

H4: the customer will demonstrate a higher purchase intention for standard products over personalized products with a higher level of personalizing options beyond a customer-perceived optimal point.

The hypothesis is tested by using stimulation experiment. There are 195 undergraduate students in Korea are recruited. They have been randomly assigned to one of the seven sub-group according to the extent of personalization options. The basic setting is the standardized product, and the other six have the different level of personalization in an ordinal ranking. The incremental increase in the personalization level allows the author to compare between groups and examine the relationship between personalization level and customer response.

The result shows that the customer product attitude shows an inverted U shape as the number of personalizing options increased. This is also the case for the other factor, purchase intention, as the number of personalization option increase, the purchase intention increase until the optimal level and then decrease. (Figure 1 and Figure 2)

The author then conducted an ANOVA analysis on the product attitude and purchase intention respectively for the standardized product and the personalized product (with most extensive personalization options). The results support the Hypothesis 3 while the result of Hypothesis 4 was not statistically significant. To test the mechanism of complexity as a mediator, they also test the hypothesis based on Baron and Kenny (1986). The perceived complexity mediated the effect of product type on product attitude. The mediation tests show that perceived complexity is the influence factor for the preference over standard products and the personalized products when comparing the standard product with the overwhelming personalization options.

The logic of this research is very easy to follow, they successfully demonstrated the importance of setting an appropriate level of personalization to companies that wanted to implement the personalization business model. However, the question arises when evaluating the regression model. Has the author controlled enough factors to isolate the effect of independent variables on the dependent variable? For example, if we look at the product nature, the result doesn’t necessarily apply to the experienced user with the clear expected product. They will likely to appreciate the extensive personalization options to fine tune the product to maximize the fit with their expectation. In addition, the author used to watch as the testing product. It is a relatively straightforward functional product. The personalization option does not include any configuration regarding the function itself but only the design. Will the research show a different result if we test it on Dell computer? The answer is unknown. If we look at a further research in the background of web companies. There is a phenomenon called “filter bubble”. It suggests that with those filter bubbles people are restricted to the filter options and shapes our view to the worldview.  If you are interested, please have a look at the following video.


Choi, J.-E. & LEE, D.-H., 2015. Customers do not always prefer personalized products: The role of personalized options range in personalization. International Academy of Marketing Studies Journal, 19(2).

Randall, T., Terwiesch, C. & Ulrich, K. T., 2005. Principles for User Design of Customized Products. California Management Review, 47(4), pp. 68-85.