The seven use cases prioritized in Kenya’s Digital Agriculture Strategy

A small scale farmer’s field with Maize and darkening clouds in Lower Eastern Kenya / credits: John Kieti

A report titled Digitization and Coordination of Kenya’s Agricultural Sector Data was completed in July 2019. It is a guide for implementing the data and innovation flagship number 8 of the country’s Agricultural Sector Transformation and Growth Strategy (ASTGS). I got my hands on a copy which was recently publicly available on the Kilimo Open Data portal (download full report (7.9mb pdf)). In this article, I refer to the report as a digital agriculture strategy for Kenya’s Ministry of Agriculture, Livestock, Fisheries and Irrigation (MoALFI).  It is the closest I see to Kenya’s national strategy for harnessing the transformative power of digitalizing agriculture. The report, authored by global consulting giant McKinsey and funded by AGRA on behalf of MoALFI prioritizes seven (7) use cases to be implemented. It is a four year plan covering the fiscal year 2019/20 to 2022/23. I find no in-depth coverage of the seven prioritized use cases, so I feel it’s “better late than never” to write about them, especially considering typical delays in Government strategy implementation. In this post I provide my nuanced summary of what I see as the rationale, and design of the use cases. This is more so as the use cases (at least the second one) typify instances of what my earlier published research conceptualised as an aggregator platform for digital services in agriculture.

Use case 1: Improving farmer inputs subsidization

This use case is designed to improve input subsidization programs supported by the Government and other funders. The problems to be solved under this use case are that government subsidised inputs are not necessarily aligned to soil health management principles, and the unintended creation of a dual-pricing system aggravated through diversion of subsidized inputs by cartels. Other problems to be solved across the stages of the existing subsidy system include rent-seeking behavior hurting farmers, and fertilizer delivery being delayed. The key problem solving elements of this use case are, (a) A digital farmer registry managed by MoALF, (b) eVouchers sent by the national treasury directly to farmers, and (c) Payments to registered agro-dealers being made in a timely fashion while securing traceability of inputs to fight counterfeiting. The spending decision on the eVoucher is to be made by the farmer and is to be guided by the farmer’s uptake of registered agronomic advisors. Funding for the e-incentives is expected to come from a “basket fund”, pooling GoK and development partner finances, and with checks and balances for any release of funds. This use case aims to impact 1.4 million farming households and 2,300 agro-dealers by 2023. Its implementation is estimated to cost KES 1.2 Billion (Approximately USD 11 Million) by 2023. The digital platform implementing this use case is to be hosted by the Kenya Agricultural & Livestock Research Organization (KALRO). The prospects of this use case can be enhanced by harmonizing efforts with concurrent input subsidization programs such as those at the 47 counties and by national programs such as KCEP-CRAL

Use case 2: Improving farming practices through customised e-extension

This use case is designed to improve practices among farmers to enhance their productivity through enhanced agronomic advisory services. This is more so considering a grower-to-extension-officer ratio as poor as 1:5,000 whereas the FAO recommended ratio is 1:600. Data analytics on weather, pest and disease trends, production yields and pricing are envisaged to help amplify improvements in farming practices. The use case envisages farmers accessing expanded resources on farming practices through extension service providers vetted by KALRO. These extension service providers are in turn expected to tap into a searchable portal of digital agriculture solutions to support their consultations with farmers. The extension service providers may include the traditional extension officers, village-based advisors, private sector field officers, model farmers and members of the 4-H foundation. Under this use case, the consent of growers is required for a digital agriculture service to incorporate a grower to its userbase through the government-run digital platform. Growers and other users of the digital platform can be expected to rate and review the constituent digital services. This can boost platform-wide efficiency according to the research on sources of value creation in such digital platforms. Under this use case, the platform reserves the right to remove digital agriculture services whose ratings remain low in what could contribute to loyalty-centredness as a source of value creation. The extension service providers are also to be paid an incentive to log details of their consultations with farmers in the digital platform. This is so as to help generate rich new insights for KALRO and MoALFI. Such details logged may include geolocation, type of digital service used and type of problem solved. This use case targets the registration of 2,300 extension service providers by 2023. It also targets 500,000 farmers per year accessing services to improve their farming practices. The estimated annual cost of this use case is KES 50 million (USD 460 million).

Use case 3: A food balance sheet monitoring national emergency reserves

This use case aims to maintain a reliably robust Food Balance Sheet (FBS) to help MoALFI and the Strategic Food Reserve Trust Fund (SFRTF). This is so as to reduce food shortages during emergencies. The use case is about digital inventory monitoring of national food reserves with accurate and real time data. It includes the use of satellite imagery to gather production data and to predict future stock needs. It also includes gathering intelligence from customs declarations and other trade records as proxy signals for fraudulent or anomalous market trends. Other features of this use case include gathering intelligence from consumption data and projecting future consumption patterns. With a robus FBS, the government agencies will be expected to make informed decisions that will also reduce the cost of procuring stocks under duress. This use case targets reducing volatility in stocks purchased for the SFRTF by 50%. It also targets boosted food resistance among close to 4 million high risk households during emergencies. The annual cost of this use case is estimated at KES 200 million (USD 1.84 million) by year three of the plan.

Use case 4: Early Warning System (EWS) for food price inflation 

This use case seeks to facilitate dynamic trade and price stability decisions using an Early Warning System (EWS) for food price inflation. The problems solved through this use case include the unreliably varying levels of accuracy and frequency of data collection on production which is manually done by enumerators and with limited validation. This use case integrates data with early warning elements to indicate likely changes in the prices of food commodities. Data on production, soil quality, pest and disease trends is expected to have early warning components. So is data on commodity trade from sources such as the Regional Agriculture Trade Intelligence Network (RATIN). The resulting early warning system is expected to be a single-source of information to forecast food price inflation. With such reliable information, MoALFI, the SFRTF and Cabinet can be expected to make timely, cost effective and targeted interventions with minimal distortion to market mechanisms. This use case targets to reduce volatility in food prices by 50% to match regional averages by 2023. The estimated annual cost of maintaining the system for this use case is KES 8 million by year 3 of the plan. I’d argue for additional consideration in this use case to incorporate market data from proven private sector driven price stabilization platforms such as Twiga Foods in the fruits and fresh vegetables space. This is especially with the demonstrated potential to use big data to power inclusive food markets. 

Use case 5: Optimal agricultural value chain selection 

This use case envisages selection and prioritization of focus value chains (crops, livestock, fishing etc) based on land use optimization models. The existing processes for selecting focus value chains by national and county level organs are faulted for having contradictory objectives and not being adequately evidence driven. Data to be fed into the optimization models may include aspects such as soil health and economic data including export markets. The land optimization models  may be tuned with parameters such as environment protection and yield optimization. Integrating the large datasets onto a single platform facilitating modeling under this use case anticipates support from enabling policies for data sharing, security and privacy potentially addressed under use case 7. This use case on value chain selection targets doubling the yields among small-scale farmers by 2023. It also targets boosting household food resilience for 1.3 million farming, pastoralist, and fishing households during drought in arid and semi-arid regions of Kenya. The use case estimates an annual cost of KES 120 million (USD 1.1 million) by year three of the plan’s implementation. It is notable that the implementation of this use case is envisaged to be county-based, allowing more local-level prioritizations that are more likely to resonate with growers on the ground. Grower-level solutions for value chain selection such as Waterwatch, CropIn are also acknowledged in  the plan and are considered complementary to the national and county level prioritizations. This begs the question of whether value chain selection should not be considered a grower-level decision made with the support of agronomic advisory under use case #2.

Growers often prioritise for themselves multiple value chains / photo credits: John Kieti

Use Case 6: M&E dashboard for data collection, verification and visualization

This use case is expected to streamline the data collection, verification and visualization of the outcomes targeted in use cases 1-5 (above). It builds on the observation that there are more than 10 visualization efforts capturing Kenya’s agricultural sector data from 200+ data sets. Some of these efforts are observed to be inactive or outdated, ostensibly due to their scope-related cost constraints. Examples of such visualization efforts include the upcoming KCSAP big data platform funded by the World Bank, the National Crop bulleting, the AfDB’s Africa Information highway, and the defunct Kenya National Bureau of Statistics visualization portal. For simplicity, the focus of the dashboard in this use case is limited to approximately 10 KPIs considered most relevant to the senior leadership at MoALFI. Upon success with the simplified, focused dashboard, the cascading of the same is envisaged within MoALFI and among the 47 counties. More granular project tracking tools are then expected to emerge, automating the build-up of insights for the macro-level dashboard. The estimated annual cost of this module is KES 20 million (USD 184,000)

Use case 7: Standards and protocols for a national data sharing platform

This use case seeks to establish standards and protocols for a national shared-access platform for agriculture data. The idea is to start with data handled by GoK and to progressively incorporate data handled by private sector players. Under this use case, platform users are expected to build on the high volume, highly interoperable data to create new insights for their interventions in agriculture. The problem to be solved by this use case is typified by the existence of multiple siloed farmer registries profiling more than two million farmers. These registries include those maintained by MOA-Info, Digifarm and OneAcre Fund. Under this use case, The Zambia Agriculture Management Information System – ZIAMIS, a FAO supported platform, is indicated as a candidate for adapting and scaling out in Kenya. The estimated annual cost of this use case is KES 20 million which is indicated as already funded by the World Bank under the Kenya Climate Smart Agriculture Project (KCSAP).

Caveat: Government prioritized value chains and intervention areas

It is worth noting the intentional prioritization of digital interventions that the government is well placed to champion and implement among the seven use cases. As such, digital agriculture services deemed best implemented by the private sector and players other than Government agencies were not prioritized in the strategy. For instance, my favourite topics of overall market efficiency and demand driven production did not feature among the digital agriculture use cases. This is ostensibly because such issues are arguably best addressed by the private sector and are arguably related to market linkage concerns being addressed in the warehouse receipt system law enacted in 2019. Furthermore, the use cases have inherent prioritization of selected value chains, especially the grains sub sector including beans, maize, rice, wheat. In any case, I encourage readers keen on the full context and finer details about the planned implementation of these use cases to read the full report. Moreover, the full report has additional insights about implications for data ecosystem requirements in the use cases that should be of interest to the digital agriculture ecosystem actors, especially the providers of digital services.

Appreciation: For this post, I am grateful to Nixon Gecheo, AGRA’s Senior Program Officer for Digital Systems and Solutions for Agriculture. He brought this report to my attention, I have been otherwise clueless.


Loyalty-centredness in digital platforms – What really is that?

My co-authors and I have proposed a two-factor structure to explain the underlying structure of the sources of value creation in an aggregator platform for digital services in agriculture (AP4DSA). This is in a recently published research paper in the Digital Business Journal. We termed the two concepts represented in the two-factor structure as platform-wide efficiency and loyalty centredness. I wrote a brief summary of the issues in the research here. It’s has been some weeks since the publication was out. As I further explore the prospects of AP4DSAs, I ask myself – what really does loyalty centredness mean? In the paper’s discussion section, we attempted to explain the concept in line with where our evidence pointed us. I am obliged to look further into the term and to perhaps solicit more insights from all you fellow thinkers and readers.

Loyalty centredness according to the research is an attribute arising in the viewpoint of likely digital platform users, meshing together the ideas of loyalty and innovativeness. It evokes the notion of altruism and engagement with a digital platform according to the paper. This is as the users or stakeholders feel an increased sense of ownership and affiliation to the platform. The research paper further relates loyalty-centredness with an accountability arrangement among participating actors to generate win-win scenarios for both providers and consumers of services on a platform. According to the paper, the concept suggests the need for a platform to safeguard its ecosystem-wide integrity whereby, “genuineness and legitimacy of actors as well as the information, goods and services accessed through the platform can be guaranteed”. You may read further on these and other explanations of the concept in the research paper which is freely downloadable under a creative commons licence on the link below.

To further illuminate the concept beyond the inherent space limitations of a published scientific article, I have hived off the loyalty-centreness half from the two-factor structure in the figure below :-

Loyalty centredness as a source of value creation in an AP4DSA (adapted from Kieti et al. 2021)

In the diagram, the rectangular boxes illustrate the indicators measured to have strong influences on the loyalty centredness concept. For instance, creating and moderating a virtual community – L4 for users to interact on such a digital platform is influential in value creation as an indicator of loyalty centredness. Likewise, introducing new to the world products, services or information – N1 on the digital platform can strongly influence loyalty centredness as a source of value creation. As such, an increase in any of the 10 indicators results in an increase in loyalty centredness as a collective notion and a source of value creation in the platform.

Essentially, we used the term Loyalty centredness to represent a collective of the 10 indicators in the rectangular boxes. The notion captured in these 10 indicators is certainly not about user-human centred design as might be tempting to broaden out to. It is also more distinct than the wider concept of user centricity. The indicators in yellow(ish) boxes were initially from the concept of loyalty while the items in pink(ish) boxes were from the concept of innovativeness. In the research, the two indicators with the highest influence on loyalty centredness were from the loyalty concept. These most influential indicators were, (a) guarantees for reliability and quality – L6 and (b) upholding trust – L2 which includes ensuring data protection, safety and security guidelines are adhered to. For more on these influences and their strengths, see their factor loadings in Figure 4 of the published paper. These stronger influences and the higher number of indicators from loyalty than innovativeness informed our conceptualization of the collective notion as loyalty centredness.

In the published paper, we argued that, “a new innovation may be onboarded and showcased on a platform yet not be ready to exhibit the kind of reliability and quality guarantees expected by the loyal platform users“. Among the Kenyan participants, it would be that aspects of innovativeness foster the sense of loyalty and pride in affiliating with the platform. Arguably, the indicators from innovativeness might have proved to be more impactful on the resulting collective concept if the research was conducted in a country where the history of innovations in digital agriculture was not as long as Kenya’s. Likely users in those countries may as well have rated the innovativeness indicators more highly, being more easily impressed by such nascent efforts. As such, it may be that innovativeness aspects can be more impactful than the loyalty aspects in those countries.

I am thinking that loyalty-centredness might have acquired a slightly different name if the research was conducted in a country where the excitement and buzz about digital innovations for agriculture are still fresh. This thinking may be worth validation with further investigation among likely AP4DSA users in other sub-Saharan African countries. It may also be argued that in due course, such sub-Saharan African countries would still evolve their digital agriculture ecosystems to the slightly more advanced situation in Kenya. Therefore they would still have to face the concept of loyalty-centredness as a source of value creation in an AP4DSA as their ecosystems evolve.

I am curious to hear what others interested in the value creation mechanisms of digital platforms think about this notion of loyalty centredness.

How could value be created in a one-stop-shop platform aggregating digital solutions for agriculture?

Reports by GSMA, CTA, and Disrupt Africa consistently position Kenya as a global leader in the number of tech ventures and digital solutions for agriculture (DSAs). By GSMA’s 2020 report which tracked 713 active DSAs, 437 (61%) were in sub-Saharan Africa. In the report focusing on low and middle income countries, Kenya led with 95 instances and Nigeria was second with about half of Kenya’s number. The trends suggest fragmentation in digital agriculture ecosystems among other pitfalls. Studies have questioned the ability of these DSAs to scale out so as to significantly impact a sector that is the mainstay of most sub-Saharan African economies (including Kenya).

My co-authors and I sought to diversify the thinking about efforts to unlock the promise of digitalization and digital transformation of agriculture in sub-Saharan Africa as a contribution to literature. We conceptualized an aggregator platform for digital services in agriculture (AP4DSA). This is a special type of digital platform for agriculture whose characteristics can partially be observed in nascent platforms such as Safaricom’s DigiFarm, EcoFarmer in Zimbabwe, Bayer’s Climate FieldView, and an “imaginary instance of Google play store for agriculture”. Such a platform can be expected to address DSA discoverability challenges including fragmentation of the digital agriculture ecosystem, absence of a one-stop-shop, and an unmet desire for comprehensiveness. My co-authors were Prof. Timothy Waema, Prof. Bitange Ndemo, Dr. Tonny Omwansa and Dr. Heike Baumüller

We proceeded to examine the underlying structure of value creation sources in such a digital platform as perceived by likely users in Kenya. Our findings suggest that sources of value creations can be explained in three themes, namely (a) platform-wide efficiency (b) loyalty-centredness (c) platform inclusivity. We have recently published the findings in the peer-reviewed Digital Business Journal via Elsevier. Click on the link or graphical abstract below to access the full article. 

Sources of value creation in an aggregator platform for digital services in agriculture – source: Kieti et al. (2021)

The article is readable online and can also be downloaded freely as a pdf on the basis of the creative commons (CCBY4.0) license. Our findings should be of interest to practitioners such as tech entrepreneurs, accelerator program managers, and large tech corporations endeavouring to actualize the digital transformation of agriculture. Policymakers seeking to unlock the transformative power of digital platforms for agriculture through may also find our findings useful. I look forward to feedback on our findings that could further inform my ongoing research on the prospects of AP4DSAs in sub-Saharan Africa.

Of Mobile Money Reliability and a Case for Multi-SIM phones

Mobile Money in Kenya, and East Africa in general has become part and parcel of everyday activities. Often times users of M-Pesa in Kenya are seen complaining about how their mobile money payments (or transfers) did not go through in good time to much their hurry. Sometimes users complain about a complete failure of their attempted transactions. One would think then, \”if M-PESA is that unreliable, why not Use alternative mobile money transfer platforms?\” The reality is that the alternatives – the likes of Airtel Money, Yucash, Orange Money and Mobikash may not always be better. For instance, the alternatives might imply one driving for a long while looking for an agent to deposit money for the transaction as their agent networks are largely under-developed.

Critical minutes of need

At a critical moment this evening, I run out of power in my house (still learning to match KPLC pre-payments and usage). So having got used to the convenience of recharging the pre-paid meter account using M-PESA, I reached out for my phone telling people around that I would get power back in just a few minutes. I went ahead and used M-PESA\’s pay bill option for some units of power (business number 888880). After twenty minutes in darkness and not  having received a recharge token, I began ranting about unreliable M-PESA and slow delivery of SMS messages by Safaricom.

Emergency funds on Airtel Money

In the moment of impatience and grit (in darkness of course), I remembered I had recently loaded my Airtel Money account with some emergency funds.  Emergency it was, so I checked my Airtel 
Sim card option and used Airtel Money’s relatively simpler pay-bill service to order some units of power for the same KPLC meter. After about 1 minute, interestingly, I got SMS notifications on both Airtel and Safaricom Sim cards of the requested recharge tokens.

Simplistic conclusion

I waited 21 minutes to receive prepaid KPLC power units from Safaricom’s M-Pesa and 1 minute to receive the same from Airtel Money. There is probably an easy conclusion on the relative speeds of these two mobile money platforms. Those with more scientific minds might however wish for more “sample readings” for the same experiment. Regardless of the ultimate conclusion for such a rigorous approach, it is obvious that the speed of transactions is not the only factor for the choice of a mobile money platform.

While M-PESA may have an impressive national spread of agents in Kenya, the service is often down for various reasons (including congestion).  Orange Money, Airtel Money and others may be feature rich and perform better on transaction speeds. They may even boast of better international agent networks. However their local agent networks often fail would-be customers miserably.

Indeed there are ideal situations for instance where one has easy access to Airtel’s customer care center for money top up and Pesanet ATMs for withdrawals. In that case one might not really feel the pinch of a poor agent network. That would be more so if all they do is use pay pill options for utility payments and other integrated remittances. In that case the receiving party as well is not directly affected by the agent network’s extensiveness.

Enjoy from all sides

With mobile number portability efforts having failed to fulfill much of their promise, people may wish to try a time tested approach – getting the best from all sides. By keeping multiple SIMs – active or inactive, one can subscribe to services from all mobile network operators and enjoy the benefits from all directions. They can then invoke the service from the most ideal mobile carrier in their immediate context. With my little KPLC pay bill experiment (or accident) above am thinking I shall continue keeping at least 2 mobile money services with some emergency money for such situations. When more people begin to get smart in this suggested way, then multi-SIM devices become a necessity. The argument can be extended with much ease for voice and data services where pricing and service quality can vary significant across mobile carriers, taking into account usage at different times and geographical spaces.

Of Samsung (Wyre’s) Duos and others

Incidentally Samsung seem to have discovered the promise of Dual SIM markets in emerging economies well before other manufacturers. Samsung dual sim phones are the only ones that have worked for me over time (4 years now). Of late I have been trying Samsung’s Duos (C3222?) and I think it is as convincing as its predecessors in handling dual SIM cards. Sadly no manufacturer yet has dual SIM smart phone yet (forget the Chinese counterfeits).

In summary, multi SIM mobile phones increasingly have a way of saving consumers from unhealthy emotional attachment to their mobile networks. Gladly I think I recently convinced a friend – @techweez  to take this trend seriously. It is perhaps by recognizing the need for such mobile phones that manufacturers might endear themselves better to the peculiar consumer market in East Africa.

Tribulations of the M-PESA Agent

M-PESA is the mobile money transfer platform introduced in Kenya by Safaricom – Kenya\’s arguably dominant mobile network operator. It is a fact that M-PESA has revolutionized lifestyles of Kenyans in the last three years. To-date there are about 20,000 M-PESA agents in Kenya according to statistics from Safaricom. It is the extensive network of M-PESA outlets that Michael Joseph – former CEO of Safaricom attributes to the phenomenal success of the M-PESA money transfer system (see my notes on ‘reflections with MJ’ in October 2010). Although the former CEO\’s assertion remains arguable, the significance of the agents and their outlets cannot be overstated in analyzing the money transfer system\’s success.

Recently I engaged a couple of M-PESA agents in some discourse to try and understand their contribution to the platform\’s success. Perhaps it is because of our age old tendency to complain over everything that I caught a few concerns that would qualify to be the \’Miseries of the M-PESA agent\’. Here are some :-

1. SMS trickery
M-PESA in Kenya is revolves around SMS texts exchanged between individuals, agents and suppliers as \’promissory notes\’. The promissory notes  in form of text messages are guaranteed by M-PESA agents deposits with Safaricom (also known as float) through a trust deed (read more on this and an early 2009 systems audit report). With the maturity of M-PESA as part and parcel of our society, even the fraudsters have jumped on board to make their contribution in the diverse society. Agents have in the recent months fallen prey to these \’cleverer\’ citizens who send fake system withdrawal messages at outlets. Unsuspecting attendants failing to scan the entire SMS message for authenticity dish out money only to discover the trickery when the fraudsters has  vanished.

For readers wondering just how that can happen see an earlier post on this blog of a \’transcript\’ detailing a real example of such an incident. Of greater concern to the agent is the fact that their contract with Safaricom pushes liability for such losses to the hapless agent.

It might be easy to say that the amounts stolen by the fraudsters using this method is little and theoretically limited to Ksh.35,000 (Approx USD 440) per instance. However those who are privy to operational details of  small enterprises like M-PESA outlets might know that once an outlet is hit with theft of such an amount, it could take months to recover. A complete closure is also possible for such affected outlets.

2. Service Outages
Earlier in the week of  8th November 2010, Safaricom put out advertisement in traditional press and social media notifying M-PESA users of a scheduled downtime that would last most of the weekend from Saturday 13th 9pm to Monday 15th 6am. The scheduled outage would be due to a planned upgrade of the money transfer platform. A service outage for a whole day meant loss of a day\’s worth of revenue (commissions) by agents. The planned upgrade was later suspended on 11th November 2010, supposedly due to other unrelated outages of the Safaricom data network, that had to be brought under control first. I was curiously shocked to learn that Safaricom had a reason to bring down a service so critical to Kenya\’s economy for over 32 hours. For a moment then I thought service availability was not an important service quality metric to Safaricom. I defer my curiosity for now until they announce the new upgrade schedule.

Scheduled outages not withstanding, it is not rare for M-PESA agents to be found helpless by customers who cannot be served because \’the network is down\’. The same is experienced by customers themselves from their phone when thet occasionally try to transfer money to others only to get a message that their transfer was not successful (to try again after ten minutes). Worse cases of service reliability affect M-PESA agents when a customer deposits money and there is a delay in the receiving the deposit confirmation text (on the customer\’s phone). The  agent is left in a precarious position of mistrust with an impatient customer who might not believe that their confirmation message will eventually come (perhaps after 20 minutes).

It is these planned and unplanned system outages (or degraded performance) that occasionally make the M-PESA agent a helpless businessman. Their supplier is also so powerful that they have no chance of negotiating favorable service level agreements to protect their small businesses from effects of such diminished service quality. Safaricom deserve a little more credit though. From the planned upgrade, it appears they have realized a need to improve the quality of their service (including increasing maximum transaction throughput from 70 to 200 transactions per second). Although the planned platform improvement might alleviate some of the recurrent outages, the little bargaining power of the agents will remain a matter of concern.

3. Employee theft
A blog post by @coldtusker sometime last year once attempted to highlight the culture of dishonesty among other costs of doing business in Kenya. Dishonesty can be argued to be prevalent among employees in Kenya.  Arguably, the desired combination of reliability and honesty among our workforce remains quite elusive. M-PESA attendants are not aliens to the purported culture of dishonesty. It should be correct to say that mobile money transfer systems include elaborate mandatory record keeping – some of which are electronically hosted by the money transfer platform. However many people forget that for as long as attendants must handle real money at some point, a temptation to steal or divert money meant for their outlet\’s operations exists.

Dishonest employees combined with an inept law enforcement system means that the M-PESA agent has to pray every day for their attendants not to yield to stealing temptations. The current police and justice system is such that it may be obvious who stole but nothing beyond knowing the thief is doable. It is this ever present fear of losing an outlet\’s cash that can permanently keep the M-PESA agent crossing their fingers. Some insurance companies I am told offer insurance cover against such losses but with 20,000 shillings \’excess\’ fees for any theft instance claimed. The insurance cover then rarely to makes sense to M-PESA agents since typically lost amounts are about the same as the \’excess\’.

4. Fake Currency
A couple of weeks back I was listening in to one of our morning radio shows. Then there was this exasperated caller who was narrating how someone had deposited fake 20,000 shillings notes at their M-PESA outlet. The outlet\’s attendant had discovered the fake notes and alerted the local police before the conman had left. The police arrived at the scene, confiscated the fake notes, and left with the conman in \’custody\’. To the astonishment of the agent, the police did nothing to assist agent who had already \’received\’ the fake deposit – hence deducted from their float. According to the caller, the conman eventually went scot free. With the current arrangement, no form of assistance was to be expected from Safaricom for mitigating such risks since the \’nonnegotiable\’ liability remains the agent\’s.

Long Conclusion
There are many other experiences that add up to bad ordeals for M-PESA agents ranging from general risks in the external environment to business risks directly related to the nature of outlets operations. It should not surprise many that the much touted 20,000+ M-PESA agents are really not having sustainable businesses. It might also be that the extensive network of agents is the single biggest success factor of Safaricom\’s M-PESA platform for money transfer. In that case, with the above sentiments of M-PESA agents, it is the same factor that Safaricom has not quite controlled to their favor. Some of the M-PESA agent\’s troubles appear to be way out of reach in Safaricom\’s external environment. It is however the same environment that an entity of their size and might could work with the government to influence – for their favor. Some of the mitigation measures are as basic as additional agent capacity building.

Incidentally, Telkom Kenya have recently launched their feature rich OrangeMoney platforms. Essar Communication\’s Yu is inducing M-PESA agents to become YuCash agents. Bharti Airtel is also sustaining their onslaught on Safaricom\’s dominance on various fronts including propping up its lower priced ZAP platform. The long term success of these competing mobile network operators in the money transfer market might just as well be pegged on how aptly they handle their value chain – including their dealers and agents.

All that said, I shall try not to contradict my earlier post on value proposition to Kenyans as the ultimate success factor and suggest that “The most significant success factor for mobile money transfer operators working in Kenya will be their value proposition to Kenyan stakeholders including their customers, agents, and shareholders”. In my opinion, patriotic sentiments and feel good aspects such as corporate social responsibility will take a back seat and value drivers (including market forces) will determine future growth paths for the competing mobile network operators.