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Criteria-

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1. Use of peer reviewed source

2. Focuses on a relevant case study.

3. Demonstrates understanding of the ways in which the Internet is changing economic practices, with reference to relevant concepts from the unit material.

4. Analyses rather than merely describes, specific economic events to explain the nature of the digital economy.

5. Presents a consistent and coherent analysis, demonstrating effective collaboration.

6. Presents analysis using effective communication skills.

Economic trend- ride sharing

Principles and characteristics- Network effects

Case study organization- Uber

Guiding statement

“Being paid to drive someone with your own car and paying someone to use their car to drive you has become a new norm in this past decade. Network effects are fundamental to this norm and can be demonstrated by the ride sharing organization Uber.”

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What is network effects?

The network effects are the bigger your network is, the more valuable it is (Kelly, 1997).

The more people are using a network, the better (Kelly, 1997).

The four key aspects of network effects that Kelly (1997) uses in his research to explain his predictions about the new economy is:

1) Growing your network quickly is important.

2) Value comes from abundance/abundance is valuable.

3) Success often has a ‘tipping point’; and

4) Networks are ‘decentralised’.

Why do networks effects matter to ride sharing?

Networks according to Kelly (1997) and Flanagan (2019) connect things with things, platforms with platforms, people with people, etc.

Network effects matter to ride sharing because the more connections there are with passengers and drivers and drivers with passengers, the bigger the effect of the network (more people looking for rides and more people who can drive them) (Kelly, 1997).

In order for the ride sharing trend to run smoothly, there needs to be a lot of people connected to the network (Kelly, 1997).

This is because, a ride sharing service cannot provide its service if there are not enough drivers for passengers or passengers for drivers (a small network).

Whereas, if the network is big, passengers will have more drivers available to drive them and passengers for drivers to take. This is an example of network effects.

Kelly (1997) also states that when choices/options are cheap or free, it will grow the network faster and see a further abundance of passengers and drivers (as well as attention, information and data). This is an example of trying to grow a network quickly.

Also, networks effects are important because when ride sharing services reach a certain amount of users, network effects (the effectiveness of that network) will attract more people to use that service or take ideas from that service (Kelly, 1997). This is an example of a ‘tipping point’ of success.

Example 1- new ride sharing platforms taking ideas from Uber and Lyft.

Example 2- Uber users communicating to others about Uber and convincing them to use it.

Looking further, the flexibility that network effects brings by decentralising who controls what drivers and passengers can do, ride sharing becomes flexible, providing services at any location, for anyone at any time-date (Kelly, 1997). This is one example of network decentralisation.

What predictions are made about network effects?

1) Growing your network quickly

Kelly (1997) states that if you want to grow your network fast you need to offer something low-cost or free that appeals to people and can compete with others.

By doing so, he believes you should be able to grow your network to the point where its success has a ‘tipping point’ (Kelly, 1997).

Kelly (1997) states that a service is more valuable if there is plenitude (abundance) and the costs of that service are less, as he thinks this will continue to facilitate the growth of the network of a service/organisation.

He further argues that to grow networks and facilitate continual growth, it is the most valuable things that must be given away (Kelly, 1997).

2) Abundance as valuable

Kelly (1997) says the power (or effectiveness) of a network will come from an abundance rather than scarcity of resources (or connections, people).

In the case of ride sharing, an abundance of connections will mean more passengers can get rides and drivers can get passengers (work).

The more drivers = the more locations, times and dates can be covered for passengers = more passengers (Kelly, 1997; Jordon, 2017).

The more passengers = the more work for drivers = more drivers (Jordon, 2017).

3) ‘Tipping Point’

Success often has a tipping point where if enough people are using it, it attracts others to use it or take ideas from it (Kelly, 1997).

Kelly (1997) also mentions that low-costs seem to make a service more ‘contagious’ and potent, which may help to better explain the impact of the ‘tipping point’.

If people are more swayed to cheap things of value (like ride sharing) and many are already using them, this would explain why riders would attract drivers (to drive in exchange for work), which then attracts more riders, which attracts more drivers, etc. (Jordon, 2017; Kelly, 1997).

Thus, the success of a ride sharing service would get to a point where lots of people attract more people to use its service or take an idea from it (Kelly, 1997; Jordon, 2017).

4) Decentralization

Kelly (1997) believes network effects must be decentralized for organizations to be successful.

This is because, as Kelly (1997) predicts, the less restrictions a service has on location, who can access/use it (users), and what ideas can be included, the more people may use it and attract others to use it (or take ideas from it).

How does Uber use network effects?

See sources from Uber website (Refer to Bibliography).

See ‘Why do networks matter to ride sharing’.

2) Abundance as valuable

To Uber, having an abundance of connections is what made the service well-known to start with.

And its abundance of connections continues to keep the organization moving.

As stated by Scholz (2016), Uber lost half its drivers in 2016 due to underpaying them and violating their privacy as Jordon (2017) mentions in their article.

Passengers/riders privacy has also reportedly been violated many times by Uber as well (Jordon, 2017).

However, despite the significant loss, Uber continues to attract more new users (Jordon, 2017).

Jordon (2017) points out that because there were a lot of riders/passengers and this managed to attract people to become drivers, as there are a number of ‘clients’ drivers can drive (and earn money from).

Furthermore, this attraction of drivers manages to attract more riders and leads to even more drivers (Jordon, 2017).

So, despite losses in growth in 2016, Uber manages to keep growing its network and maintaining even though its poor pricing model may make many drivers leave (Scholz, 2016; Jordon, 2017).

Although, while the underpayments for drivers does not work in Uber’s favour, it still manages to retain its passengers/riders thanks to its valuable, low-cost services (Scholz, 2016; Jordon 2017; Kelly 1997).

3) ‘Tipping Point’

As stated in ‘Abundance as valuable’, Uber has managed to maintain and continue to grow its network and service.

We also see that Uber has gotten to a point of success where its current users attract more users (Jordon, 2017) and that its loss of users while significant has not led to the downfall of ride sharing services like Uber yet.

In a sense, because of its ‘tipping point’, no matter the significant losses it incurred, it continues to gain significant growth (Kelly, 1997).

Bibliography (Includes in-text references):

References

Flanagan, F. (2019). Theorising the gig economy and home-based service work. Journal of Industrial Relations, 61(1). doi: 

https://doi.org/10.1177/0022185618800518

Jordan, J. M. (2017). Challenges to large-scale digital organization: the case of Uber. Journal of Organization Design, 6(1), 11. doi: 

https://doi.org/10.1186/s41469-017-0021-2

Kelly, K. (1997, September). New Rules for the New Economy: Twelve Dependable Principles for Thriving in a Turbulent World. Wired, 5(9). Retrieved from 

http://www.wired.com/wired/archive/5.09/newrules_pr.html

Kohler, T. (2018). How to Scale Crowdsourcing Platforms. California Management Review, 60(2), 98–121. doi: 

https://doi.org/10.1177/0008125617738261

Scholz, T. (2016). Platform cooperativism: challenging the corporate sharing economy. New York: Rosa-Luxemburg-Stiftung. Retrieved from 

http://www.rosalux-nyc.org/wp-content/files_mf/scholz_platformcoop_5.9.2016

Bibliographic sources

About Us. (n.d.). Uber. Retrieved from

https://www.uber.com/au/en/about/

Emergency Data Sharing. (n.d.). Uber. Retrieved from

https://www.uber.com/legal/safety-and-security/sos-share/en/

Opportunity is everywhere. Why drive with us. (n.d.) Uber. Retrieved from

https://www.uber.com/au/en/drive/

Our commitment to safety. (n.d.). Uber. Retrieved from

https://www.uber.com/au/en/safety/

Ready to hit the road? (n.d.). Uber. Retrieved from

https://www.uber.com/au/en/drive/how-it-works/

Uber’s technology offerings. (n.d.). Uber. Retrieved from

https://www.uber.com/au/en/about/uber-offerings/

Uber Community Guidelines Australia and New Zealand. (2019). Uber. Retrieved from

https://www.uber.com/legal/community-guidelines/anz-en/

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