All posts by joranbakema

Teqoia: the platform for experts, not stars

Work has changed dramatically through digitalization. New forms of organizing work are gaining more and more attention. The emergence of peer-to-peer platforms, collectively known as the “platform economy,” has enabled people to collaboratively connect with each other and thereby link the demand for labour with its supply. Consumers have so far enthusiastically adopted the services offered by firms such as Uber, Lyft, and TaskRabbit (Zervas et al. 2017). These business operate as gig-economy platforms, formally defined as digital, on-demand platforms that enable a flexible work arrangement (Burtch et al. 2018).

The challenges of gig platforms

Although there are a lot of advantages for these gig platforms, there are also a number of challenges that lie ahead. One of the biggest challenges is to keep the work offered on the platform relevant. For the three abovementioned platforms, this is not really the case, because the work that has to be done is not that complex. However, for platforms as UpWork, where experts can offer their work, this is becoming increasingly challenging. When these online crowd experts want to have a viable long-term career option, they must be able to grow and continually refresh their skills (Suzuki et al. 2016). 

The downside of stars

Traditional workplaces make use of on the job training and internships to enable employees to develop their skills while providing financial support. Crowd workers, however, are disincentivized from learning new skills, because the time they spent on learning they are not working, which reduces income. Even if a worker does spend time learning new skills, platforms do not make it easy for the investment to pay off, as it is difficult to get hired for new skills. This is caused by the fact that most platforms are based on review systems, as ratings and reviews (Gupta et al. 2015). Users of platforms increasingly rely on online opinions and experiences shared by fellow users when deciding what products to purchase, or who to hire for a job (Shen et al. 2015). Because gig economy platforms have no ratings for the workers in their new skill areas, the possibility to get hired decreases. As a result, the skills of many workers remain static, and workers today often view these platforms as places to seek temporary jobs for their already existing skills, rather than as marketplaces for long-term career development (Suzuki et al. 2016). With online work is capable of expanding many full-time jobs, new business opportunities arise that integrate crowd work and career development. 


Since last year this gap in the market has been filled by the platform Teqoia IT Solutions, which has the aim to match supply and demand of labor. Teqoia makes technical knowledge and capacity of highly trained and specialized IT staff accessible to (inter)national clients. It has a clear focus on local for local, learning & development, and entrepreneurship. In the right balance this approach results in an optimal result for all parties involved and there is a win-win situation in which the platforms, workers and suppliers reinforce each other (Teqoia 2019).

The platform doesn’t work with review systems but gives a guarantee that each individual on the platform does meet a certain standard. To realize that promise, they have the Teqoia academy, where different trainings are given to ensure that they keep up with the current changes in technology. Teqoia also offers the possibility to follow the teqoia masterclass to improve their services. This is a traineeship that, through various training courses and modules, ensures that the worker has the required skills within seven months. 

Business case

Like most gig economy platforms, the financial model of Teqoia is based on a commission fee for mediating between supply and demand. In terms of strategy, Teqoia is pretty unique. It has positioned itself between traditional employment agencies and purely digital gig platforms; the reasonably fixed group of workers, the training and the quality guarantee of a traditional business, but with self employed workers, as on many different gig platforms. Which ensures a more lean business, which is a main advantage over the traditional businesses (Aloisi 2015).

Downsides of Teqoia

So, the main strengths of Teqoia are their lean business model and their quality guarantee. However, the organization of a platform in this way also has its drawbacks. One of the downsides of this approach is that Teqoia can’t make use of network effects, as most platforms do, because all the workers must be tested and trained to meet the quality requirements. Other platforms have grown exponentially, partly because of the two-sided network effects. This implies that when the number of users one side of the platform increases, the other side will be attracted more as well. In the end, a greater number of users increases the value to each and thus the total value of the platform (Eisenmann et al. 2011).Another, more straightforward downside has to do with the cost of testing and training workers. Most gig platforms charge a commission fee of around the 15% (Aloisi 2015), which should therefore be higher at Teqoia to cover the costs of training and testing. 

Future of gig work

The use of review systems to measure quality of workers does not improve the expertise of gig workers on the long term. Therefore, other business models, as Teqoia, arise. However, Teqoia faces some challenges, the idea of not looking at reviews and star-ratings anymore but providing a quality label for workers seems plausible. So I think that the future of experts gig platforms no longer focusses on stars, but on expertise.

For those who are interested in the platform (unfortunately in Dutch only):


Aloisi, A. (2015). Commoditized workers: Case study research on labor law issues arising from a set of on-demand/gig economy platforms. Comp. Lab. L. & Pol’y J., 37, 653.

Burtch, G., Carnahan, S., & Greenwood, B. N. (2018). Can you gig it? An empirical examination of the gig economy and entrepreneurial activity. Management Science, 64(12), 5497-5520.

Eisenmann, T., Parker, G., & Van Alstyne, M. (2011). Platform envelopment. Strategic Management Journal, 32(12), 1270-1285.

Gupta, N., Martin, D., Hanrahan, B. V., & O’Neill, J. (2014). Turk-life in India. In Proceedings of the 18th International Conference on Supporting Group Work (pp. 1-11). 

Shen, W., Hu, Y. J., & Ulmer, J. R. (2015). Competing for Attention: An Empirical Study of Online Reviewers’ Strategic Behavior. Mis Quarterly, 39(3), 683-696.

Suzuki, R., Salehi, N., Lam, M. S., Marroquin, J. C., & Bernstein, M. S. (2016). Atelier: Repurposing expert crowdsourcing tasks as micro-internships. In Proceedings of the 2016 CHI conference on human factors in computing systems (pp. 2645-2656). 

Teqoia (2019). Jouw toekomst. Via:

Zervas, G., Proserpio, D., & Byers, J. W. (2017). The rise of the sharing economy: Estimating the impact of Airbnb on the hotel industry. Journal of marketing research, 54(5), 687-705.

What’s in the name?

Firms have tracked consumers’ shopping behavior in their stores for decades. Back in the days, most businesses were neighborhood stores. Employees greeted each customer by name and knew what each customer liked. These employees used consumers’ information to tailor interactions on an individual level across sales, marketing, and customer service. Nowadays, customer interaction often takes place online so firms rely on online customer data. Therefore, customers benefit by receiving products and services that match their personal preferences. Firms, however, can benefit as well, by charging higher prices for the recommended products as they provide better service (Chen et al. 2001). Therefore, the use of information for personalization sounds like a win-win proposition for firms and customers. However, customers can see it as a double-edged sword, which on one hand enhances consumer utility and at the same time cause privacy violation (Malhotra et al. 2004).
Recently, customers are becoming increasingly aware of the amount of data that firms collect and what risks are involved. We call this phenomenon overpersonalization (Bleier and Eisenbeiss 2015). Because of the trade-off between enhanced consumer utility and privacy concerns, Wattal, Telang, Mukhopadhyay and Boatwright try to find out how consumers respond to firms’ use of two types of information: product preferences and name.

These two types of information can for instance be used in an e-mail campaign targeting customers. Firms can send you such recommendations via e-mail in two ways: explicitly and implicitly. Where explicitly means that the company discloses that they based the recommendation on your preferences, whereas implicitly means they do not. Imagine that you receive an e-mail of a company recommending you the product that you have always wanted. You visited the website the day before for the first time and the sites’ algorithm already learned about your preferences (Johan, Mookerjee & Sarkar 2014). The e-mail does not explicitly state that the recommendation was based on your browsing behaviour. So, this is an example of implicit personalization.
These two ways of giving recommendations can lead to different levels of effectiveness. Imagine that a week later you wake up and the firm took their personalization a step further and begins the recommendation e-mail with a personalized greeting. You are not really familiar with the website as you only visited it for the first time last week. At this point you might start to become wary of the e-mail, as in recent years your awareness of the data you provide to retailers on the internet has increased. As the recommendation helps saving time, the data usage might lead to increasing concerns by customers (Tsekouras 2019). Companies start to take the negative effects of explicitly mentioning the use of personal information into account. Therefore, it might be more interesting for firms not mentioning the recommendations explicitly, but implicitly.

To find out how customers react to these different forms of personalization and how familiarity moderates this effect, the researchers collected data of approximately 20.000 customers from a web-based firm that is a distributor for many products varying from phone services to mortgage lending.
To research implicit personalization, the researchers studied the product-based personalization emails that the firm sent to customers and the customer’s reactions. The customers were divided into pools and when a customer in the pool “long-distance” received an email about long-distance phone services, it was classified as product-based personalization. When a non-related pool of customers received the email about long-distance phone services, it was classified as non-personalized. To research the explicit personalization, the researchers looked at personalized greetings that the firm used. The firm used these personalized greeting randomly, so some customers were greeted with their name while others were not and therefore the researchers could measure the differences between them.
To assess whether a consumer was familiar with a company, the researchers looked at prior purchases. If a consumer already bought something at the firm, the customer was deemed familiar with the firm.
The customers went through two decision phases. Imagine when you receive an email. First, you need to decide whether you open the email. Once you choose to open the email, you can choose several actions: unsubscribe, do nothing, click through but buy nothing and purchase the product. When the customers decided click through but buy nothing or purchase the product, their reaction was branded positive.

The researchers found that consumers respond positively when product-based personalization is used in the email. Contrary to this, they discovered that consumers respond negatively when a personalized greeting was used in the email. Furthermore, they found that familiarity moderates the negative effect of a personalized greeting. Customers who already made a purchase at the firm responded less negatively to the firm using their name.

A main strength of this study is that this study examines personalized emails that are directly sent by the merchant to consumers, whereas prior work only examined the personalized content made available on merchants’ websites or in controlled experiments. The biggest advantage of this research design is that it resembles real world answers the most; it incorporates people’s real reactions as they have to respond to personalized offers with real monetary risks. Controlled experiments can cause various biases. For instance, knowing the nature of a study can make consumers behave differently and subconsciously give response that they think that the researcher wants to hear, also known as the research bias. Using real world data can to a great extent omit these biases.

An important implication that you could take away from this article as a business owner is that personalization of an e-mail to your consumers might not always yield the positive responses you hoped for. It rather depends on the familiarity customers experience with your firm and the type of personalization you choose to use. Like Top marketing speaker David Meerman Scott once said “Instead of one-way interruption, personalized marketing is about delivering value at just the right moment that a user needs it”. So firms need to carefully consider how they use personalization because these good intentions might have the opposite effect.


Bleier, A., & Eisenbeiss, M. (2015). Personalized online advertising effectiveness: The interplay of what, when, and where. Marketing Science, 34(5), 669-688.

Chen, Y., Narasimhan, C., & Zhang, Z. J. (2001). Individual marketing with imperfect targetability. Marketing Science, 20(1), 23-41.

Johar, M., Mookerjee, V. and Sarkar, S., 2014. Selling vs. Profiling: Optimizing the Offer Set in Web-Based Personalization. Information Systems Research, 25(2), pp.285-306.

Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users’ information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information systems research, 15(4), 336-355.

Tsekouras, D. (2019) Customer Centric Digital Commerce Lecture 2 [Lecture 2]