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. 

Teqoia

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):

References

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: https://teqoia.com/jouw-toekomst/het-begint-bij-jou/

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.

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