A Brief Talk about Crowdfunding

Recently, crowdfunding has become one of the most well-known methods for a business owner to get his or her project funded through public contribution. Although, like any other fundraising methods, there are positive and negative sides to crowdfunding. The advantages and disadvantages of using crowdfunding platforms can be divided in two perspectives: firm-level and contributor-level (Tsekouras, 2019).

From a firm’s perspective, utilising crowdfunding platforms can be attractive for several reasons. Crowdfunding enables companies to collect funding in a short period of time. The companies can also tap into a larger pool of potential contributors. The community features offered by crowdfunding platforms (either through social media or its own social networks) help founders with constructive feedbacks for projects and online word of mouth. The tradeoff, though, would be the element of transparency that can disclose not only information about the company, but also personal information about the project creators (through social media). There is also the risk of damaged reputation should the project not meet the public’s expectations.

From a contributor’s perspective, crowdfunding platforms allow people access to exclusive products that better match their tastes more. The community aspect of the system also gives potential contributors a “place to belong”; enabling them to provide feedbacks and improve the projects. However, there are risks for an individual to support projects through crowdfunding. There have been cases of fraud in the past (Rio, 2017). There is also the risk of underperformance by the companies that do not deliver within the expected time frame.

The Minority Problem

Taking a closer look into the matter, with the intention to dig deeper regarding the potential of crowdfunding platforms, Younkin and Kuppuswamy (2016) conducted a research given the fact that there are problems of underrepresentation of minority founders in venture investment markets. This situation is relevant, knowing there is indeed discrimination in entrepreneurship against minorities. There are many examples of this. A research by Freeland and Keister (2014) mentioned that it is more difficult for minority founders to receive credit and another research conducted in 2011 saw that minorities must pay higher interest rates (Pope & Sydnor, 2011). Younkin and Kuppuswamy, relating the problem to several prior studies, explain the possible involvement of several types of discrimination as to why discrimination is prevalent even in the business sector:

1.       Statistical Discrimination: People devalue minorities because they expect that they are less likely to complete projects.

2.       Taste-based Discrimination: Preference to support or reject people of certain races/ethnicities, even if it is not economically efficient to do so.

3.       Implicit Bias Theory: Discrimination is described as subconscious acts reflecting cultural beliefs.

The research then recognises crowdfunding as a possible solution for this minority-founder problem. Crowdfunding, in this case, has several roles: (1) an alternate solution when traditional financial institutions show bias, (2) shifts the focus of funding decision away to the broader population, and (3) facilitates projects that are overlooked by “traditional” experts but valued by the wider population. However, to directly see the impact of crowdfunding on minority founders, Younkin and Kuppuswamy tested whether reward-based crowdfunding platforms like Kickstarter really improve the performance of projects run by black founders. They also attempt to identify which forms of discrimination are involved and ways to mitigate these biases.

Methodology & Results

In their study, the Younkin and Kuppuswamy (2016) combined an analysis of crowdfunding projects with an experimental design. In the former, they had collected a sample 7,617 Kickstarter projects launched between January 2012 and March 2014. Results of their statistical analysis showed that black founders were less likely to succeed as they received both fewer and smaller contributions. Moreover, black founders were not less capable in delivering their project than nonblack counterparts, thus suggesting that discrimination was present.

Subsequently, three experimental tests were conducted to identify the types of discrimination at play for which participants were recruited through Amazon’s Mechanical Turk Service. In the first one, the researchers tested for both types of conscious discrimination by asking participants questions about an example project and its founder. Moreover, subjects were randomly placed into one of four possible conditions with differing salience of founder race. Image 1 illustrates what participants saw during the test. After analysing their data, the researchers found that taste-based bias was driving discrimination.

The goal of the second experimental test was to distinguish between unconscious and conscious bias. This was done by testing whether funders altered their perception of project quality between white and black founder using project quality signals such as “Staff Pick”. Similarly to last time, participants were randomly placed into various conditions with differing degrees of quality signals. Results showed that discrimination was unconscious (i.e. implicit bias), thus invalidating taste-based bias as a driver of discrimination.

In the final experiment, the researchers wanted to test the efficacy of 3 intervention methods in mitigating bias. In particular, they looked at (Younkin and Kuppuswamy, 2016):

• Debiasing Intervention; involves counteracting stereotypes about how a group acts
• Group Success Intervention; involves counteracting stereotypes about how a group performs
• Whitewashing Intervention; involves eliminating any indication of race

The usefulness of each method was once again tested by placing subjects into 9 different conditions. Ultimately, the results of this test reveal that both group success and whitewashing are effective at mitigating discrimination towards black founders.

Strengths

The main strength of this study is its pioneering efforts of studying which forms of discrimination are responsible for the underrepresentation of minorities in entrepreneurship. In particular, the subjective evaluation of investment risk and the capabilities of a founder make it rather difficult to study entrepreneurial settings. As a result, researchers turned towards audit or correspondence studies to assess biases. However, the authors tackled this issue by developing a novel research approach that involves the combination of observational and experimental data.

Managerial Implications

While crowdfunding does not by itself mitigate biases towards black founders, the study lists three methods to mitigate discrimination on the platform (Younkin and Kuppuswamy, 2016). The first two are examples of group success intervention, while the final one is an example of whitewashing intervention. First of all, the authors recommend displaying overt support, such as “Staff Pick”, towards African-American founders. They also advocate showing a list of successful founders on the platform, which includes black founder. Last but not least, they recommend reducing visibility of race on the crowdfunding platform’s project pages.

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References

Freeland, R. E., & Keister, L. A. (2014). How Does Race and Ethnicity Affect Persistence in Immature Ventures? Journal of Small Business Management, 54(1), 210-228. doi:10.1111/jsbm.12138

Pope, D. G., & Sydnor, J. R. (2011). Implementing Anti-Discrimination Policies in Statistical Profiling Models. American Economic Journal: Economic Policy, 3(3), 206-231. doi:10.1257/pol.3.3.206

Rio, C. (2017, April 10). 6 Stupid Crowdfunding Scams That Should Have Been Obvious. Retrieved February 27, 2019, from http://www.cracked.com/blog/6-incredibly-obvious-crowdfunding-scams-people-fell-for/

Tsekouras, D. (2019). CCDC.

Younkin, P., & Kuppuswamy, V. (2016). Is the Crowd Colorblind? Founder race and performance in crowdfunding. Academy of Management Proceedings, 2016(1), 11665. doi:10.5465/ambpp.2016.11665abstract

An infamous adage states “Birds of a feather flock together”. This implies that individuals favor other individuals that are similar to them. This not only applies to one’s personal life, but also to his professional one. As such, with this logic in the workplace people should try to help and promote others that share identical traits to them.  However, is that truly the case? Cohen, Broschak & Haveman, (1998) conclude that when women are numerous in higher-level position, promotion for other women is less likely. The researchers argue that the women managers do not feel the need to push for the advancement of their women subordinates. What motivates such a behavior?

Greenberg & Mollick (2017) chose to investigate this question by further researching homophily and more specifically its underlying mechanisms. The authors defined homophily as the “tendency of individuals to associate with others based on shared characteristics”. While homophily had previously been researched, the underlying causes behind the phenomenon were still unclear. The researchers distinguished between two kinds of homophily: induced and choice homophily. On the one hand, induced homophily is a result of homogenous networks that perpetuate themselves. Thus, the offered opportunities of interaction, such as schools for instance, are homogeneous and led similar individuals to associate with one another (Kossinets & Watts, 2009). Greenberg & Mollick (2017) were interested in researching choice homophily, which according to Kossinets & Watts, (2009) is more related to individual preferences and choices we make as a result. However, Greenberg & Mollick (2017) argue that choice homophily can in fact be further divided into two different constructs that would explain the conflicting results in previous research concerning homophily.

• Interpersonal choice homophily operates on a personal level and implies that the affinity for similar people is linked to our notion of self-love.

• Activist choice homophily operates on a group level and implies a certain solidarity facing shared structural barriers that arise from a common social group identity, and a desire to overcome such obstacles.

In order to focus on choice homophily, the researchers decided to conduct their studies in the context of crowdfunding. Indeed, induced homophily is reduced in two ways in this environment. Firstly, there is no formal gate keepers, unlike in venture capitals, and anyone can support a project thus making the opportunities for interaction heterogeneous. Secondly, Internet does not limit the environment in which people interact, which gives them broader choices in terms of who they wish to support.

Various elements motivate individuals to fund projects, such as a philanthropic behavior for donation-based crowdfunding. Nevertheless, it is important for individuals to be aware of their unconscious biases in order to make choices that they truly support. Why did I choose to fund this person’s project? Was it solely based on the project itself or did his gender impact my decision?

To find evidence for their newly defined activist choice homophily, Greenberg & Mollick (2017) conducted two tests. The first one was a laboratory experiment. 399 students at two private universities in the Northeastern US participated. A technology project that had been successfully funded in Kickstarter was chosen, as it is an area in which women are underrepresented and therefore portrays a group-level disadvantage appropriate to test for activist choice homophily. Furthermore, measures were taken to mimic real life circumstances. As such, key crowdfunding aspects such as a description, that are also antecedents for success, were added. The only two manipulated elements were the name and the picture of the founder as they portrayed his gender. The design of the experiment was a 2 (founder gender) × 2 (participant gender) design. That entails that half of the women participants were perceived the founder as the same gender as them and the other half saw him as a male. The same conditions applied for male participants. Several measures were taken to avoid possible cofounds when manipulating the picture and name. Different components measured the both types of choice homophily as well as one’s support for a funder. For instance, respondents were asked how much of $1 they were willing to donate to assess the latter.

A field experiment was then conducted with data on 1,250 Kickstarter projects from 5 categories:

• Two predominantly male (gaming and technology)

• Two predominately female (fashion and children’s books)

• One more balanced between both genders (film)

Restrictions were imposed on the sample, such as eliminating projects that sought less than $5,000. Control variables were defined to assess whether key Kickstarter aspects, such as whether a project was featured, biased the results. Robustness checks were also done to check the scales.

The main results of this study are the following.

• Results from both the laboratory and field experiments were consistent in showing that women were more likely to fund other women’s projects, as interpersonal choice homophily would suggest (results for men were inconclusive).

• Female founders in technology are more likely to reach their goal, as they face disadvantage because of their gender which is consistent with activist choice homophily

All in all, this research not only explored a subject that had not been properly researched (aka the underlying mechanisms of homophily), it did so in a very thorough manner. Thus, the results found in the laboratory experiments were confronted to those collected later in the field experiment. This approach guaranteed that the conclusions found were of high external validity and thus were applicable in real life. Moreover, control variables were used to make sure activist choice homophily was responsible for the effect that was observed on donations and funding decisions. In addition, robustness checks were conducted to test the scales.

In conclusion, this paper is not only relevant for consumers to better understand the unconscious motives behind their choice. It also concerns managers. Indeed, implications of this research go beyond a crowdfunding context. Even though the interaction that induced homophily would have with activist choice homophily is tough to assess, the latter also has a role to play in more traditional funding processes, such as a venture capital and even in a workplace in general. As such, to fight against women underrepresentation in technology, the solution has been to increase the proportion of women in related entrepreneurships and venture capitals. Nevertheless, with the concept of activist choice homophily, it appears that activism has an important role to play as well in breaking the constraints women face in such a field. As such, efforts in representation of women should be continued. However, it should be complemented with initiatives to increase identification, mutual support and activism for the individuals of unrepresented groups.

“One for all, all for one!” (Dumas, 2018)

• Cohen, L., Broschak, J., & Haveman, H. (1998). And Then There were More? The Effect of Organizational Sex Composition on the Hiring and Promotion of Managers. American Sociological Review, 63(5), 711. doi: 10.2307/2657335

• Dumas, A. (2018). Three Musketeers, The. La Vergne: Dreamscape Media.

• Greenberg, J., & Mollick, E. (2017). Activist Choice Homophily and the Crowdfunding of Female Founders. Administrative Science Quarterly, 62(2), 341-374. doi: 10.1177/0001839216678847

• Kossinets, G., & Watts, D. (2009). Origins of Homophily in an Evolving Social Network. American Journal Of Sociology, 115(2), 405-450. doi: 10.1086/599247

A review of the article “Personalized pricing and pricing fairness” by Timothy J. Richards, Jura Liaukonyte & Nadia A. Streletskaya (2016)

The development of high granular price appearance algorithms and the large amount of customer data that can be collected through online purchases, led to the increasing use of personalized pricing (Weisstein et al., 2013). In a transparent environment, personalized pricing may cause a feeling of distrust, unfairness and fears of price-gouging. These feelings can arise when customers notice that they pay a higher price for a product or service than others do. In the end, this feeling of betrayal could lead to reduced purchase intentions of customers. Therefore, it is of high importance for organizations to arrange their pricing system in a correct way, so that it does not affect customers’ purchase intentions in a negative way. The paper of Richards, Liaukonyte & Streletskaya (2016) investigates how interpersonal price differences could lead to customers’ perceptions of unfairness. Subsequently, the paper gives a mitigation strategy for companies to reduce the negative effects on sales because of personalized pricing.

During the course Customer-Centric Digital Commerce, we have discussed the phenomenon personalized pricing with the example of airline tickets and a bottle of Coca-Cola. Why do we feel betrayed if there would be interpersonal pricing differences for a bottle of Coca-Cola, but do we accept these differences when it comes to airplane tickets? This has to do with the product type and setting, but also with the customers’ price fairness perceptions. According to Richards et al. (2016)  the price perceptions of customers are mainly derived through social norms and transactional utility, which means that customers are more satisfied if they paid less than their reference price. A customer’s reference price is often based on what other people paid. Customer’s price fairness perception can be determined by comparing the customer’s reference price with the actual price. Perception of price unfairness is in this study shaped by self-interested inequity aversion, which entails that people are less likely to purchase when they value prices as unfair and people are more likely to purchase if prices are more fair or price inequity is in their favour. Based on these concepts, the article raises the following main hypotheses for the study:

• “There is self-centered inequity aversion in which agents experience negative marginal utility whether others pay less than themselves, or they pay less than others.” (Richards, Liaukonyte & Streletskaya, 2016: p143)
• “When we allow buyers to negotiate the final price, however, we expect to find fairness perceptions improve to the point where much larger differences in realized prices are acceptable, and discriminatory pricing equilibria are generally stable.” (Richards, Liaukonyte & Streletskaya, 2016: p139)

Methodology

The researchers have tested the hypotheses by an experiment in a lab setting with a survey among 278 students from a large Eastern US university. The students are randomly assigned between two treatment groups, the price-posted treatment or the price-discovery treatment. Before the treatment, the risk preferences of the students are measured by a multiple choice exercise. In the exercise, students are asked to choose between two lotteries (A or B) whereby lottery A is more safe in terms of risk compared to lottery B. This exercise is done, because the researchers expect that people’s risk seeking or risk avoiding behaviour can be an important factor in determining price inequity aversion. After this exercise, the experiment continues with the treatment procedure. First, the students had to practise with making purchase decisions though the board game Monopoly. Hereafter, the real experiment started, which consisted of multiple rounds where students were confronted with different price distributions for the same T-shirt.  In the price-posted treatment group, students had to accept or reject the T-shirt price without the chance to negotiate. In the price-discovery treatment group, the participants had the option to negotiate on the price by submitting their own bid, besides the option of accepting or rejecting the price just as in the price-posted treatment. In both groups, the students were asked to rate the fairness of the prices on a five-point Likert scale. By this way the researchers were able to measure the impact of inequity on purchase intentions.

Results

One of the most important results that were found by the researchers through the experiment, is about how fairness perceptions influence purchase behaviour. From the participants that rated the prices as unfair or slightly unfair, only 4.9% chose to buy the T-shirt. While from the participants that assessed the prices as fair or generally fair, more than 73% decided to buy the T-shirt. These results show that the feeling of inequity is expected to have an important effect on customer’s purchase behaviour. Another interesting finding is that the price-discovery treatment has a significant effect on the purchase intentions of customers. This means that customers are more likely to purchase consumer goods when they can participate in price determination through negotiation. Furthermore, the research indicates that in a transparent setting more risk averse people are more likely to purchase products even when there is inequity against them.

Main strength of the paper

In my opinion, a big strength of the article is that it brings some novel managerial implications to improve companies’ pricing strategy. The researchers found that customers are much more likely to buy a product when they can participate in the price formation. Companies can use this insight by including customers in the price determination to increase the sales of the company. Furthermore the paper indicates that customers are less likely to purchase products when they perceive the price as unfair. Companies should therefore ensure that customers feel like the prices are fair. Although these insights are very meaningful for companies, the paper does not give very concrete instructions of how companies should include consumers in the price formation process or how companies could ensure that their prices will be perceives as fair. Nevertheless, this paper brings some new, valuable insights in the personalized pricing issue and provides a foundation for future research.

References

Richards, T. J., Liaukonyte, J. & Streletskaya, N. A. (2016). Personalized pricing and price fairness. International Journal of Industrial Organization, 44, 138-153.

Weisstein, F.L., Monroe, K.B. & Kukar-Kinney, M. (2013). Effects of price framing on consumers’ perceptions of online dynamic pricing practices. J. Acad. Mark. Sci., 41 (5), 501–514.

The market

It is not a secret that the retail market has stepped into the ‘data science world’. Today, one of the main goals of retailers is to constantly find innovative ways to get useful insights of their customers through their goldmine of unstructured and structured data (Marr, 2015). One of the implications of these insights are personalized products. According to Mobasher, Cooley & Srivastava (2000), personalization is (any) action that tailors experience to a particular individual. In other words, the data is used to match the products as good as possible to the needs of every individual customer. The process of personalization goes as follows: first, retailers collect data about their customers and try to learn about their customers’ preferences and tastes. Second, they try to develop products or services that match these preferences and tastes. Finally, they evaluate the effectiveness of these personalized offerings, in order to optimize the personalization process (Tsekouras, 2019).

The business model and its opportunities

Since it becomes more generic for retailers to use data that they have about their customers, they have to differentiate themselves from their competitors in some other way than solely using data to obtain useful insights. Here, a new opportunity arises because of emerging technologies. In the present, retailers want to create superior customer experience (Verhoef, Lemon, Parasuraman, Roggeveen, Tsiros & Schlesinger, 2009). A superior customer experience could be delivered through accurate, personalized recommendations because customers like recommendations for the reason that it eases their selection process (Tsekouras, 2019). Next to personalized recommendations, a good in-store experience increases the customer experience (Phibbs, 2019). If you combine these two ways to deliver a superior customer experience, a new addition to a retailers’ business model arises, which could be turned into a competitive advantage. We’re talking about in-store artificial intelligence robots (AI robots).

Opportunities that come along with AI robots

The first opportunity is that the ‘real’ personnel of a retailer is able to do more high-level tasks. This is possible since the robots can replace them in general, repetitive interactions with customers (Ankeny, 2017).

Besides this, retailers find it hard to convert their goldmine of data into useful insights. Actually, the conversion rate from data to useful insights lies between 2- and 3% overall (Donaldson, 2018). By implementing AI robots, all the data is automatically processed through algorithms. This means that the robots are able to make the customer experience far more intimate, targeted and effective, while they are simultaneously improving over time (Donaldson, 2018).

Next to the data being processed better, the robots are able to collect more data, since all the ‘conversations’ with customers are stored into a database. Traditionally, conversations between employees and robots are not ‘recorded’ or anything. Justine Santa Cruz is the VP of partnerships at Satisfi labs, which is a company that makes AI robots. She states that by adding the robots in-store, retailers can learn about what their customers are actually thinking during the whole decision-making process.

One final opportunity is the fact that customers are curious and willing to use new sorts of technologies. Santa Cruz (2019) says that there are no generational challenges that have to be addressed, regarding the willingness to interact with the new robots. Apparently, as is observed in real-life situations, everybody wants to communicate with the robot (Nittle, 2017).

To strengthen these opportunities, a real-life example of the current applications of these kinds of robots is addressed in the next paragraph. This way, the business model becomes more tangible and the efficiency and value of this addition to a retailer’s business model will become clear.

Current applications of the business model and its value

Mall of America has adjusted its business model in a way that they implemented AI robots in their physical stores, called Pepper. Pepper is a robot that can guide customers to specific locations through the mall, talk to the customers about (personalized) promotions and deals and is able to connect customers to other employees. The process of people that interact with Pepper is described by Brandli (2018). But to address this process clearly, I created a figure that illustrates this process, which is presented below.

One stage that might need clarification is the ‘Pepper is unable to help with the customer’s query’. It might be that the AI-robot will not be able to respond to certain specific questions that the robot has not seen before. When this is the case, Pepper is able to connect the customer with a real person that works in the mall. This way, the customer is still being helped at his expectations (Brandli, 2018).

The implementation of Pepper in Mall of America could have resulted in negative feedback, but this has not been the case since its trial (Kumar, 2018). The Ave, a retailer who implemented the robots as well, published their tangible results. They give credits to Pepper for driving a 98% increase in the interactions with customers, a 20% increase in foot traffic and a 3x revenue jump (Ankeny, 2017). In addition, Nestlé implemented Pepper in its Nescafé stores. This resulted in their sales increasing double digits. Concluding, the results up until today are real (Ankeny, 2017).

Potential drawbacks

First, I would like to mention the additional costs for retailers that have to be incurred if you want to implement the AI-robots. If you want to add Pepper to your stores, you will have to pay up $1700 up front, $134/month for maintenance and $89/month for insurance (both for a minimum of 36 months). This means that your costs for Pepper in 3 years will be at least $9.728. This could be a large investment for retailers since they still need to staff other employees (Faw, 2016).

Next to the additional costs, Pepper is a robot that will cost some peoples’ jobs according to some. McCormick (2016) argues that cognitive technology, including artificial intelligence, will replace 7% of U.S. jobs by 2025. However, the tasks are replaced, not the jobs. The robots are not meant to replace employees, but rather assist them in simple tasks. So actually, the robots are a blessing for the employees, since they are able to work on more meaningful tasks that are not repetitive in nature (Donaldson, 2018).

Reference list

Ankeny, J. (2017). How robots in stores could revolutionize the customer experience. Retrieved from https://www.retaildive.com/news/how-robots-in-stores-could-revolutionize-the-customer-experience/434056/

Brandli, A. (2018). CX trendsetters: Is the future of malls a robot named Pepper?. Retrieved from https://blog.centriam.com/is-the-future-of-malls-a-robot-named-pepper-cx-trendsetters

Donaldson, S. (2018). How AI Is Changing the Retail Industry. Retrieved from https://www.business.com/articles/artificial-intelligence-changing-retail/

Faw, L. (2016). Pepper The Robot Will Be Your Companion (For A Price). Retrieved from https://www.mediapost.com/publications/article/276387/pepper-the-robot-will-be-your-companion-for-a-pri.html

Kumar, K. (2018). Mall of America tests robots, chatbot as it looks to improve visitor experience. Retrieved from http://www.startribune.com/mall-of-america-tests-robots-chatbot-as-it-looks-to-improve-experience/474997543/

Marr, B. (2015). Big Data: A Game Changer In The Retail Sector. Retrieved from https://www.forbes.com/sites/bernardmarr/2015/11/10/big-data-a-game-changer-in-the-retail-sector/#5e47c5209f37

McCormick, J. (2016). Predictions 2017: Artificial Intelligence Will Drive The Insights Revolution. Forrester. Retrieved from https://go.forrester.com/wp-content/uploads/Forrester_Predictions_2017_-Artificial_Intelligence_Will_Drive_The_Insights_Revolution.pdf

Mobasher, B., Cooley, R. and Srivastava, J., 2000. Automatic personalization based on web usage mining. Communications of the ACM, 43(8), pp.142-151.

Nittle, N. (2017). Mall of America Gets High-Tech With Chatbot and Humanoid Robots. Retrieved from https://www.racked.com/2017/12/18/16781234/mall-of-america-chatbot-humanoid-robots-pepper

Phibbs, B. (2019). 4 Ways To Improve Your Retail Customer Experience and Sales. Retrieved from https://www.retaildoc.com/blog/improve-retail-customer-experience-sales-follow

Santa Cruz, J. (2019). VIRTUAL RETAIL – THE FUTURE IS NOW. Retrieved from https://mr-mag.com/virtual-retail-the-future-is-now/

Tsekouras, D., 2019. Customer Centric Digital Commerce, Lecture 2, slide 20.

Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009). Customer experience creation: Determinants, dynamics and management strategies. Journal of retailing, 85(1), 31-41.

“When people think of Airbnb, they visualize accommodation. However, Airbnb itself is nothing more than a tech company, cleverly using software that translates into more than a million rooms across the globe” (Eventige.com, 2019).

Whilst Airbnb was founded 11 years ago, it has become one of the leading companies in the hospitality industry. Contrarily to traditional firms in the hospitality industry, Airbnb does not own any property, but offers a platform on which users can rent their own houses and property to each other (Guttentag, 2015). Between 2016 and 2018, the estimated value of the company has skyrocketed to approximately 31 billion dollars, worth more than multi-billion traditional hotel chains Marriott and Hilton (Smith, 2018).

Sharing economy
The phenomenon of the sharing economy refers to the “sharing of both physical and nonphysical goods and services through the availability of various information systems on the Internet” (Hamari, Sjöklint and Ukonnen, 2016). Airbnb is seen by most as the precursor of the sharing economy, being an inspiration for other firms which have built their business model around the concept of the sharing economy (Proserpio and Tellis, 2011). As the hospitality industry was disrupted by the innovative tech company Airbnb, the transport industry has been disrupted by several companies with similar business models. Uber and Lyft are examples of such companies, both to have disrupted the taxi industry with peer-to-peer platforms (Cramer and Krueger, 2016). Along with the disruption of traditional industries, new concepts came to life as well. An example of such a concept would be the carsharing industry. Car2go is one of the leading companies in the carsharing industry. Originally founded as a subsidiary of automobile producer Daimler AG in the late 2000s, Car2Go enables a user to commandeer a shared vehicle and only charges a fee for the time driven (Martucci, 2018). Car2Go has experienced tremendous growth and reached 3.6 million members in 2018 (Martucci, 2018). After noticing the success of Car2go and the rise of the sharing economy, two Dutch entrepreneurs thought off a similar concept which resulted into the successful start-up Felyx.

Felyx profits by charging a 30 cent per minute fee for the time the scooters are used. Furthermore, in case of incidents, large fees are charged to the users responsible. By the end of 2018, Felyx has grown to 50 thousand members and has expanded its business to Rotterdam and The Hague (Van Raemdonck, 2018). Furthermore, Felyx is planning an expansion to cities as Utrecht, Den Bosch and Eindhoven, as well as a move to outside of the Netherlands, where it has to compete with E-Scooter sharing companies as Cityscoot (France) and Emmy (Germany)(Govecsgroup.com, 2018).

Felyx
Felyx was founded in 2017 by Quinten Selhorst and Maarten Poot and is one of the pioneers in the e-scooter sharing industry (Govecsgroup.com, 2018). By collecting a starting capital of 700 thousand euro via a crowdfunding campaign, Selhorst and Poot were able to place the first 108 e-scooters in Amsterdam (Boerop, 2018). Felyx’s concept is as follows: within the service area users can rent a scooter for 30 cent per minute. Through the mobile app users can find the location of the parked scooters, unlock them and thereafter they can drive the scooter wherever they would like. Parking the scooters and ending the drive, is allowed anywhere at any time. If the drive has ended, the scooter will appear once again on the mobile app as available with its renewed location (Van Raemdonck, 2018).

Being similar to the concept of Car2Go (however with scooters instead of cars), Felyx offers a new way of transport, forming an alternative to public transport and the traditional taxi industry, as well as forming an alternative to disruptive companies as Uber. Felyx is unique in the Netherlands, as it is the first company in the Netherlands to offer the possibility to make use of shared scooters (Govecsgroup.com, 2018). Whereas most companies in the transport business which have built their business model around the concept of the sharing economy offer services involving the use of cars (e.g. Uber, Car2Go), Felyx offers a service involving scooters, which are perceived by many to be more convenient for transport within a city (Brustein and Lanxon, 2018).Furthermore, whereas companies like Uber had the regulatory struggle with entrenched taxi industries about unfair advantages, Felyx does not oppose an incumbent industry which could sue them about similar issues (Brustein and Lanxon, 2018). This offers Felyx an advantage in gaining market share.

What is the value created?
Felyx creates value for users in several ways. A first example, as mentioned above, would be that they introduce a new kind of shared transport, which is perceived as more convenient within cities than their competitors as Car2Go (Brustein and Lanxon, 2018). Second, as Felyx’s scooters can be rented per minute, they offer a cheaper alternative to rental companies which usually rent per whole day (Martucci, 2018). Third, as all of Felyx’s scooters are electric scooters, they are better for the environment than most of their competitors. This ensures a sustainable advantage for Felyx.

What are the challenges?
Besides Felyx’s strengths, the company faces several challenges as well. The main challenge Felyx faces, would be the regulatory struggle it has with municipalities. In Amsterdam, Felyx was ordered to remove all its scooters from the streets in line with the removal of four shared bike services in a battle with nuisance caused by the shared transport services (Kruyswijk, 2017). Even though Felyx was able to fight the above-mentioned decision (Boerop, 2018), if in the future similar decisions will be made by municipalities or the government, Felyx will face a gloomy future.

Additionally, another challenge for Felyx would be that the expansion possibilities might not be too big. The concept is expected to work only in the bigger cities of the Netherlands (Keunen, 2018) and the competition in the rest of Europe is fierce (Govecsgroup.com, 2018). Moreover, outside of Europe, riding a bike is not considered as mature way of transport, let alone driving by scooter (Brustein and Lanxon, 2018). These factors might limit Felyx’s potential growth.

References:

Boerop, L. (2018). Hoe twee 29-jarigen met elektrische deelscooters Amsterdam een beetje schoner willen maken. Retrieved from https://www.businessinsider.nl/hoe-felyx-met-elektrische-deelscooters-amsterdam-een-beetje-schoner-wil-maken/

Brustein, J., & Lanxon, N. (2018). How Electric Scooters Are Reshaping Cities. Retrieved from https://www.bloomberg.com/news/articles/2018-09-07/are-electric-scooters-the-future-of-urban-transport-quicktake

Cramer, J., & Krueger, A. B. (2016). Disruptive change in the taxi business: The case of Uber. American Economic Review, 106(5), 177-82.

Eventige.com. (2019). How AirBnB Became the Largest Hotel Chain. Retrieved from https://www.eventige.com/blog/airbnb-largest-hotel-chain

Govecsgroup.com. (2018). GOVECS boosts e-scooter fleet of Paris-based sharing provider Cityscoot. Retrieved from https://www.govecsgroup.com/presse/detail/news/govecs-boosts-e-scooter-fleet-of-paris-based-sharing-provider-cityscoot/

Guttentag, D. (2015). Airbnb: disruptive innovation and the rise of an informal tourism accommodation sector. Current issues in Tourism, 18(12), 1192-1217.

Hamari, J., Sjöklint, M., & Ukkonen, A. (2016). The sharing economy: Why people participate in collaborative consumption. Journal of the association for information science and technology, 67(9), 2047-2059.

Keunen, Y. (2018). Rotterdam krijgt ook een deelscooter. Retrieved from https://www.ad.nl/rotterdam/rotterdam-krijgt-ook-een-deelscooter~a273ca1b/

Kruyswijk, M. (2017). Ook deelscooters Felyx moeten van de straat. Retrieved from https://www.parool.nl/amsterdam/ook-deelscooters-felyx-moeten-van-de-straat~a4522009/

Martucci, B. (2018). Car2Go Review & Promo Code – Flexible Self-Serve Carsharing. Retrieved from https://www.moneycrashers.com/car2go-review/

Proserpio, D., & Tellis, G. J. (2017). Baring the Sharing Economy: Concepts, Classification, Findings, and Future Directions. Classification, Findings, and Future Directions (December 28, 2017).

Smith, J. (2018). From broke to billionaires: The rise of Airbnb founders. Retrieved from https://www.dailymail.co.uk/news/article-5918039/From-broke-billionaires-rise-Airbnb-founders.html

Van Raemdonck, H. (2018). De deelscooter rukt op – newmobility.news. Retrieved from https://newmobility.news/2018/12/12/de-deelscooter-rukt-op/

The founders of Handy claim that the platform is the Uber for cleaners and handymen. The platform connects service providers with households who need services (Kerr, 2016). By doing so, Handy provides value for both its providers and customers. Regarding the former, Handy enables them to take on many jobs and therefore offers them the possibility to earn additional income. Considering the latter, the platform makes the search for cleaners and handymen simple by automatically assigning a service-provider to the customer (Kletzing, 2015).

The platform of Handy is a typical example of a sharing economy platform. A sharing economy platform is a fee-based internet-platform which doesn’t have assets themselves but facilitates asset-sharing among participants. The established personal interactions are facilitated by the platform but paid by peer-to-peer transaction. To create trust and generate reputation among participants, a reputation mechanism is a requirement of a sharing economy platform (Proserpio & Tellis, 2017). 

The rising interest in using a sharing economy platform, like Handy for home cleaning and handymen services, is due to the increasing reliance on the internet and the underutilization of goods and services (Cullen & Farronato, 2018). The peer-to-peer market balances employees with unstable jobs or income and consumers with non-sufficient resources to compensate for some wanted services (Proserpio & Tellis, 2017).

The success of Handy’s business model is coherent with the rising interest in the sharing economy platforms. In order to keep growing, the company shifted their focus from cleaning services towards home repair services (Dotan, 2018).

The business model relies on the brokering of a transaction between customers and providers and thereby charging a 20% fee of each booking price. Therefore, the fee is paid by the customers who are looking for the services offered through Handy’s website. The pricing of the home cleaning services differs from the handymen services in the function of the pricing variables (Kletzing, 2015). This makes both prices varying but handymen services overall more expensive. Therefore, these services generate the biggest revenue for Handy (Handy, 2019). Most of Handy’s bookings are done by repeat customers, which shows the loyalty towards the firm’s brand. The platform takes care of this loyalty by providing excellent and trustworthy services (Cleverism, 2019). The repeat customers that use Handy’s subscription model are of huge importance for the company because they are yielding the highest profit margins for Handy. Namely, the company will no longer have to pay acquisition costs for these bookings (Kletzing, 2015).

Joint Profitability Efficiency

The value system design of Handy is efficient because of the joint payoffs for providers, customers and the firm itself. The obtained system welfare consists of the total of consumer valuation minus the costs of both the consumers and the firm (Carson et al., 1999). The customer experiences for both users within the sharing economy platform of Handy are higher than in the traditional market for cleaning and handymen services. Considering consumers of the services, the platform allows for convenience and a higher level of satisfaction. The use of the platform is convenient for customers because provider selection is removed from their booking process and executed by the firm’s technology (Kletzing, 2015). Also, customer satisfaction in terms of performance and trustworthiness is realized by their extensive feedback system (Handy, 2019). Delleart (2018) emphasizes the importance of developing mechanisms to build reputations for co-producers in order to exclude poorly performing providers. The costs for consumers are the fees paid by them. However, already in 2017, the convenience and satisfaction outweigh these costs for half a million customers (Rosenbush, 2017).

In order to generate more profit, Handy has to satisfy providers to make customers of the services pay. For them platform is advantageous because it reduces the costs of customer acquisition and results in more job offering with flexible working hours. The costs of customer acquisition are relatively low because the platform sources the jobs for them (Kletzing, 2015). For providers, the ease of connecting to consumers when participating on the platform offers them the possibility to take on more jobs while having flexible working hours (Kletzing, 2015). According to Dellaert (2018), this is strengthened through the mechanism in which more activity will be beneficial for the collective of the consumers in the platform-network. Namely, the needs of this consumers will be better matched which subsequently results in enhanced customer experiences and more job-offerings (Dellaert, 2018).

When considering the firm itself, the positive customer experience gives them greater returns. The charged fee of 20% results in a wide margin for Handy, which means that the existence of the platform generates enough value to continue with their business model.

The Feasibility of Required Reallocations

When taking the research of Dellaert (2019) into consideration, the institutional arrangement of Handy results in the feasibility of required reallocations. Regarding Handy, this institutional arrangement compensates for the agents who would not want to make the transition to their new marketing value system design. The feasibility, which indicates efficiency, is reached through Handy’s contracting, ownership and social elements (Carson et al., 1999). First of all, the contracting of Handy is covering the commission for the service providers, which are relatively high compared to the industry’s average. A cleaner at Handy earns on average $17,55 per hour (indeed, 2019). The industry’s average for a cleaner in the US is $11,24 (payscale, 2019). Second, the ownership arrangements are in hands of Handy because the platform possesses the customer base and data (Connolly, 2015). This is beneficial for platform-users because it lowers the costs of customer acquisition and solves trust issues (Kletzing, 2015). Finally, the social components consisting of relational and reputational factors are advantages of Handy’s business model. The huge focus on user satisfactory and the implementation of an extensive feedback system show these advantages (Handy, 2019).

Based on the research of Carson et al. (1999), the three dimensions of the institutional environment of Handy is are considered macrolevel aspects for the feasibility of the required reallocation through the company’s institutional arrangement. First of all, the judiciary dimension of the US-based Handy is named ‘De Jure’, which means that it relies on democracies with greater independence (Carson et al., 1999). It supports Handy’s institutional arrangements by making contract enforcement easier. Therefore, the platform would always have to meet their promises, like 100% money-back guarantees, to the platform users. Second, the polity dimension for Handy is federal because the company is based in the US. This is not efficient for supporting the business model of Handy. Namely, not all states of the US are supportive towards their business model which hinders potential users in taking advantage of the platform (Kessler, 2018). Lastly, trust covers the dimension of social norms. The clear norms and rules of cooperative behavior in the platform-economy of the US are positively affecting the social components of Handy. This generates efficiency.

All things considered, Handy has implemented an efficient sharing economy business model which has feasible institutional arrangement and generally a supportive environmental arrangement, except for the inefficiency of the federal policy which hinders the use of the platform in multiple states.

REFERENCES

Carson, S., Devinney, T., Dowling, G., & John, G. (1999). Understanding Institutional Designs Within Marketing Value Systems. Geraadpleegd op 24 februari 2019, van https://www.jstor.org/stable/pdf/1252106.pdf

Cleverism. (z.d.). Handy. Geraadpleegd op 24 februari 2019, van https://www.cleverism.com/company/handy/

Connolly, A. (2015, 5 november). How Handy‒s founder Oisin Hanrahan is scaling his startup into an on-demand powerhouse. Geraadpleegd op 24 februari 2019, van https://thenextweb.com/insider/2015/11/05/how-handys-founder-oisin-hanrahan-is-scaling-his-startup-into-an-on-demand-powerhouse/

Dellaert, B. (2019, 19 juli). The consumer production journey: marketing to consumers as co-producers in the sharing economy. Geraadpleegd op 20 februari 2019, van https://link.springer.com/content/pdf/10.1007%2Fs11747-018-0607-4.pdf

Dotan, T. (2018, 27 februari). Handy’™s Lesson for Gig Economy Startups: Profit vs. Growth. Geraadpleegd op 22 februari 2019, van https://www.theinformation.com/articles/handys-lesson-for-gig-economy-startups-profit-vs-growth?

Handy. (z.d.). House Cleaning & Handyman Services | Handy. Geraadpleegd op 23 februari 2019, van https://www.handy.com

Kerr, R. (2016, 3 juni). How does Handy make money? Geraadpleegd op 22 februari 2019, van https://vator.tv/news/2016-06-03-how-does-handy-make-money

Kessler, S. (2018, 30 maart). Handy is quietly lobbying state lawmakers to declare its workers aren’t employees. Geraadpleegd op 22 februari 2019, van https://qz.com/work/1240997/handy-is-trying-to-change-labor-law-in-eight-states/

Kletzing, A. (2015, 6 maart). Handy: an online broker for commoditized home services. Geraadpleegd op 20 februari 2019, van https://www.hbs.edu/openforum/openforum.hbs.org/goto/challenge/understand-digital-transformation-of-business/handy-an-online-broker-for-commoditized-in-home-services.html

Proserpio, D., & Tellis, G. (2017, 28 december). Baring the Sharing Economy: Concepts, Classification, Findings, and Future Directions by Davide Proserpio, Gerard J. Tellis :: SSRN. Geraadpleegd op 21 februari 2019, van https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3084329

Rosenbush, S. (2017, 9 mei). Handy CEO Oisin Hanrahan Says Data Science Key to Startup’s Model. Geraadpleegd op 22 februari 2019, van https://blogs.wsj.com/cio/2017/05/09/handy-ceo-oisin-hanrahan-says-data-science-key-to-startups-model/+/