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Trust Issues and Ride-Hailing Platforms in Lagos, Nigeria.

The idea of building trust is often central to the adoption and use of technology platforms in general such that the processes and governance of these platforms ought to align with the realities of user-groups which are essential for a seamless service. Since 2013, the entry of ride-hailing platforms in Nigeria has increased because of an overall technology awareness in Lagos and continuous successes of existing ride-hailing companies such as Uber and Taxify (see Table 1). Ease of access, trip predictability and ease of fare calculations and payment, amongst other things have improved.

Despite its growing impact on urban transport in Nigeria, the industry has suffered several challenges such as insecurity and lack of safety for user-groups. Prior to ride-hailing platforms, the notion of trust has been integral for taxi businesses or technologies to thrive. For instance, a passenger who builds a bond with a local taxi driver such that the driver runs personal errands such as dropping off school kids.

Trust in simple terms is the belief in the ability of someone or something. There has been increasing interest in the concept of trust in online transactions since the development of the internet and e-commerce in the early 1990s (1). The concept of ‘trust’ encapsulates both offline environments and online environments such that the difference lies in the varying characteristics of these environments as well as the context in which trust is formed and maintained. In technology, “it is a belief that a specific technology has the attributes necessary to perform as expected in a given situation in which negative consequences are possible” (2).Risks and uncertainties are exacerbated because users lack total control of the processes governing ride-hailing apps.

Table 1: Ride-hailing companies in Lagos
Source: Author’s fieldwork

In the ride-hailing industry in Lagos, both drivers and passengers are aware of the risk in engaging with a complete stranger via an app which is monitored by platform companies through data analytics and algorithms. Unlike the conventional taxi industry, user-groups often build trust in platform companies based on the efficiency and reliability of their apps over time. For example, Mr Ayo, the Taxify driver has just accepted his first trip for the day, but later declines because the rider would only pay via an ‘online bank transfer’ and from experience, the driver does not trust this process because it is often a fraudulent tactic used by riders without money. Using a third-party banking app to make a transfer to the driver’s account gives the rider more power in this situation because the payment could be reversed in 24 hours if reported by the rider. If it were a card-paid trip, the driver would feel safer because the ride-hailing app acts as an intermediary between both parties such that if a conflict occurs, it can be resolved amicably.

One of the many instances where the rider loses trust is through trip manipulations by drivers.  Since Uber slashed the base fare of trips by 40% in Lagos, drivers have reacted with strategies for increasing the fare of trips through manipulative techniques (3). In 2017, Lockito, designed for testing geofencing-based apps, was being used in inflating fares by manipulating the distance of a trip.  For example, a trip that should be about 5.9km would be double the distance when the Lockito app is being used (see Figure 1).

Although drivers are responsible for altering the GPS function in the Uber app, riders become aware that the app is also vulnerable to fraudulent activities. Riders frequently monitor the Uber app, drivers’ behaviour and prefer cash payments to card payments to avoid being defrauded during trips. Although there are other factors involved such as low smartphone and card penetration overall (4), the psychological construct of trust remains central to the reliability and predictability of drivers, riders, and the algorithms behind ride-hailing apps.

Figure 1: Incorrect GPS reading vs correct GPS reading
Source: BrandSpurNG (2017)

Regardless of ride-hailing platforms’ success in Nigeria, trust issues surrounding usability and culture remain a stumbling block especially for indigenous start-ups like Oga-Taxi. More research would be needed to understand the implications on user behaviour and what coping strategies are needed to thrive in an increasingly ‘networked’ environment as well as how these strategies may create new realities in the global South.

References.

  1. Li, F., Pieńkowski, D., van Moorsel, A. & Smith, C. (2012). A Holistic Framework for Trust in Online Transactions. International Journal of Management Reviews, 14(1), pp. 85-103
  2. McKnight, D. G., Carter, M., Thatcher, J.B., & Clay, P.F. (2011). Trust in a specific technology: An investigation of its components and measures. ACM Transactions on Management Information Systems, 2(2), pp. 12 – 32.
  3. Adegoke, Y. (2017). Uber drivers in Lagos are using a fake GPS app to inflate rider fares, Quartz Africa, 13 Nov
  4. appsafrica (2015). Can Uber really work in Lagos, Nigeria? appsafrica, 2 Jun
  5. BrandSpurNG (2017). Uber Drivers In Lagos Using Fake GPS App To Inflate Fares – Report, Nairaland Forum, 14 Nov
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Data, Platforms and Power

19 February 2019 Leave a comment

We know that digital platforms can be very powerful, but how does their use of data relate to power?

In three ways[1] that derive from the datafication and digitisation affordances of platforms:

  1. Addressing Information Failure. Platforms succeed in part by finding ways to overcome information failures in existing markets. These failures may be sources of power for incumbents. For example, estate agents (realtors) hold power in real estate markets due to information asymmetries; such as knowledge of house sale prices.  Real estate platforms put such data into the public domain, thus undermining the power of incumbents.  Information failures may also be a source of weakness in existing markets.  For example, riders with traditional taxi firms don’t know exactly when their cab will arrive.  Platforms provide such data and so, again, undermine incumbents.

 

  1. Mashing Up. As they deal with digitised data, platforms can gain power by integrating different data streams onto the platform. Real estate platforms integrate online information about neighbourhoods.  Ride-hailing platforms integrate online maps to show cab location and routes to riders and drivers.

 

  1. Controlling New Data. By digitising transactions and associated processes, platforms create, capture and control new data. This bolsters their power; typically by creating new information asymmetries: the platforms know things that others don’t.  Real estate platforms can monitor search behaviours of buyers to understand more about which features of house listings they value most.  Ride-hailing platforms understand spatio-temporal patterns of supply and demand alongside many other behavioural characteristics of riders and drivers.

 

This simple framework can usefully be applied in order to analyse the role of data in platforms, and its contribution to power.

 

[1] Categorisation and examples developed from Drouillard, M. (2017) Addressing voids: how digital start-ups in Kenya create market infrastructure. In: Digital Kenya, B. Ndemo and T. Weiss (eds). London: Palgrave Macmillan, 97–131

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