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Innovation and scaling of ICT for the bottom-of-the-pyramid

  • Research Article
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Journal of Information Technology

Abstract

Scaling represents successful diffusion that ensures sizeable impact and earnings from information and communication technology (ICT) innovations in emerging markets. Practice can still be shaped by dualistic views – innovation vs diffusion, pilot vs scale-up, lead firm vs other actors, technical vs social. Synthesising the literature that challenges these dualities, this paper creates a systemic perspective that is particularly appropriate for scaling of ICT to bottom-of-the-pyramid (BoP) markets. That perspective is then instantiated through the case study of a successfully-scaled ICT innovation that has reached millions of poor consumers: the Kenyan m-money system, M-Pesa. It finds that scaling of this ICT system can be understood as a four-stage process of exploratory, incremental then aggressive growth, followed by (attempted) standardisation. Throughout these stages of scaling, ongoing adaptive innovations have been fundamental and have been both necessitated and shaped by the BoP context. These innovations have been more socio-technical than technical, and have emerged from a growing variety of actors and locations closer to poor consumers than the lead firm. The lead firm has buffered the unfamiliarity of BoP markets by approaching them through the ‘middle-of-the-pyramid’ and by intensive learning. At times, its planned ‘shifts’ in scaling strategy have triggered adaptive innovations. At other times, emergent innovations and learning lead to incremental ‘drifts’ in lead firm strategy. ICT firms wishing to scale goods and services for BoP markets must therefore recognise the multi-locational, continuous, and emergent nature of innovation, and develop processes to monitor and address those innovations.

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Notes

  1. Prahalad (2009) offers no specific definition of the BoP. He alludes to those who live on less than US $2 per day as being a starting point but argues that ‘there is no single universal definition of the Bottom of the Pyramid that can be useful’ (ibid: 7).

  2. Noting a division between development studies and business-for-development, which sometimes take opposing views on the BoP concept (e.g., Arora and Romijn, 2012).

  3. Three areas of literature on scaling were read but, by and large, have not been included here either because there were mainly descriptive, or because they dealt with issues unrelated to scaling ICT innovations for the BoP:

    • Literature on scaling of health and agro-forestry innovations in developing countries, for example, Uvin et al. (2000), Wambugu et al. (2011), which focusses largely on best practices for NGOs or state actors and/or on the factors that shape scaling but not the process of scaling itself or the role of innovation within scaling.

    • Literature on scaling of educational innovations (typically in US schools), for example, Blumenfeld et al. (2000), Elias et al. (2003), which consists mainly of descriptions of specific initiatives with guidance for educational reformers.

    • Literature on scaling ICT infrastructure, for example, Tomasic et al. (1995), Bolcskei et al. (2006), which focuses from a computer science perspective on technical issues.

  4. Note we are not including ‘roll-out’ within this discussion of scaling: the implementation of a large-scale IS, such as an enterprise system, within a single organisation (e.g., Holland and Light, 1999).

  5. These four components were selected because they were found as common elements relating to scaling ICT for the BoP that emerged from all three of the bodies of literature surveyed. We recognise that they represent a particular perspective on scaling and innovation, and that there are other aspects – the nature of communication and marketing, the role of trust and social capital, the ethics of serving BoP markets, the nature of different adopter cohorts, the role of power relations between innovation stakeholders, and so on – which could form the basis for research on this topic.

  6. Rogers associates this activity mainly with users but, as will be argued below, innovation during diffusion may be undertaken by a wide variety of actors.

  7. For further analysis of systems of innovation frameworks in relation to scaling of ICT innovations for the BoP, see Foster and Heeks (2013b).

  8. One can see this as equivalent to Prahalad’s (2012) notion of an ‘ecosystem’ of multiple actors that enables innovation and distribution for the BoP.

  9. Again, this is something that emerges in later BoP writing: ‘This shift in emphasis forces us to move from a product-centric approach to a focus on business model innovation, of which the product is but a subset. Systems thinking is a prerequisite for success in BOP markets’ (Prahalad, 2012: 11).

  10. Despite reticence to explicitly define the BoP, Prahalad (2009: 7) defines the MoP as those earning ‘[US]$2–13 [per day] at 2005 Purchasing Power Parity prices’.

  11. Mas and Radcliffe (2010), writing in late 2010, identify 72 m-banking deployments across 42 developing countries recorded by the GSMA’s ‘Deployment Tracker’. After 24 months, in late 2012, the Tracker showed 140 deployments in 68 countries, particularly in the developing and emerging economies of Africa and the Asia-Pacific region, with a further 104 deployments planned (GSMA, 2012).

  12. There are no universally-used definitions, but we can see applications of increasing scope: ‘m-transfers’ as use of mobiles to transfer money from one device to another; ‘m-money’ as use of mobiles for money payments and transfers including to and from bank accounts; and ‘m-banking’ as the use of mobiles to access a fuller range of banking services.

  13. Statistics suggest that around 75% of Kenya’s adult population are registered with M-Pesa, with Ksh56 billion ($650 million, £410 million) moving in the system on a monthly basis by mid-2012 (equivalent to one quarter of total GDP); a figure growing by c. 40% per annum (CCK, 2012).

  14. Further details about the role of policy in scaling of M-Pesa can be found in Foster and Heeks (2013a).

  15. Number of agents: later growth spikes occurred during scale-out as Safaricom came to agreement with various banks and service providers to use their networks (e.g., January 2010 spike arose from over 500 Equity Bank ATMs becoming ‘agents’). Percentge revenue: graph draws on 6-monthly Safaricom revenue results.

  16. Emergent activity revolved around unexpected applications of the service among the 600 trial users including trial users converting cash into e-cash during travel for security (DFID, 2006), increasing numbers of non-trial users receiving e-cash (Kwama, 2006) and trial users becoming involved in small-scale entrepreneurial selling of airtime (Hughes and Lonie, 2007).

  17. Zap initially took custom from M-Pesa but, thanks in part to Safaricom’s reaction and first-mover advantage, it did not attain critical mass.

  18. This is supported by a comparative study of M-Pesa in Kenya and Tanzania. Camner and Sjöblom (2009) argue that the sub-agent model, not established in Tanzania, can be seen as one of the principal factors that has pushed the growth of the service in Kenya, particularly into poorer areas.

  19. It is an open question whether this model was officially sanctioned at a regulatory level. Our interviews suggested that the sub-agent model’s existence was an open secret, but it only came to be officially acknowledged later when the aggregator model emerged (see next section).

  20. Revenue per agent can be seen as an indicator of the average volume of transactions – and hence, levels of commission – for the agents. In 2008/2009, the average agent earned $125 per month (c. Ksh9200) as personal income from M-Pesa (which might be one of multiple products offered by the agent); which can be compared with average monthly GDP per capita in Kenya at the time of $75 (Pickens et al., 2009). Registration (showing proof of ID and providing personal details) is the most profitable commission activity for agents. In terms of the new registrations the general trend is of a curve peaking in mid-2008. The massive spike in 2010 relates to government-mandated SIM registration which was undertaken at this time, with all existing Safaricom customers who came forward automatically being registered as new M-Pesa customers.

  21. Larger slums in Kenya are inhabited by a large demographic of the population that runs from lower middle class, to working poor, to poor – often in different parts of the same slum.

  22. This interview data matches interview accounts from other dealers, who increasingly ‘micro-franchised’ sub-agents. (This is also documented in Eijkman et al.’s (2009) account of a master agent with 106 sub-agents, and Haas et al.’s (2010) account of a dealer in another Nairobi slum with 20 sub-agents.)

  23. Such a change was possible for smaller sub-agents located in poorer areas because of the dramatic expansion of banks focussed on poor communities in Kenya, particularly the Equity, Co-operative and Family Banks (FSD Kenya, 2009) who became super agents, and were increasingly locating in trading areas in poor communities resulting in complementarity between these two services.

  24. Only Safaricom subscribers can actively use M-Pesa. As of February 2011, 78% of all Safaricom users had M-Pesa and 68% of all mobile users in Kenya used Safaricom (Safaricom, 2011). Thus, in a highly competitive market with many other providers, M-Pesa can be seen to be moving towards saturation of the number of users it could likely recruit.

  25. These are not for the very poorest but include Kalima Salamo, a crop insurance service for small farmers, and the Jua Kali ‘Mbao’ pensions plan (for informal traders), both of which link into M-Pesa payments.

  26. Indeed, in 2012, Safaricom announced some new lower limits for financial transfer that will begin to enhance the potential for micro-payments within lower-income groups (Safaricom, 2012b)

  27. The first phase of m-money can be seen to have occurred chronologically alongside Phases [2]–[4] of the scale-out of m-transfers, rather than being a separate pilot: ‘exploratory scoping’ may therefore a better term for this stage. As this model was emergent from fieldwork and has not been the subject of secondary literature to date, this m-money phase is shown as a dotted box. As outlined above, the second dotted box corresponds to suggestions that m-money services may expand into BoP provision as part of a future strategy.

  28. Itself deriving from the notion of ‘co-production’ in which firms and customers both contribute to innovation (Ramirez, 1999).

  29. End users have continued to adapt and innovate – see Omwansa (2009). Although their role was not the focus during the later stages of scaling, they can be seen to re-emerge in the discussion of functional expansion.

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Foster, C., Heeks, R. Innovation and scaling of ICT for the bottom-of-the-pyramid. J Inf Technol 28, 296–315 (2013). https://doi.org/10.1057/jit.2013.19

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