How do you turn a relatively unsuccessful e-government (or ICT4D) project into a relatively successful one?
There’s not a lot of guidance on this question. Lists of success and failure factors are generic rather than specific to any one project, and need to be analysed before the project starts. Evaluation methodologies focus more on impact than implementation, and generally apply only after the project has ended.
What is needed is a “mid-implementation toolkit”: something that will both analyse where you’ve got to in the project, and recommend an improvement action plan for the future. Researchers working alongside an Ethiopian e-government project have recently published the results of testing just such a toolkit.
Using the “design-reality gap” framework, the researchers gathered data from four different stakeholder groups involved with the e-government project, which had introduced a land management information system into one of Ethiopia’s city administrations. The system was only partly operational and was not yet fully integrated into city administration procedures: it could therefore be described as a partial failure.
The design-reality gap framework helps measure any differences that exist between the project’s initial design expectations and current implementation realities. It does this along seven dimensions (see figure below).
Where large gaps are found, these highlight the key and specific problem areas for the project. In this particular e-government initiative, significant design-reality gaps were identified in relation to:
- Management systems and structures (a failure to set up an ICT department and to hire permanent IT staff).
- Staffing and skills (hiring only five of the required nine IT staff, and undershooting the necessary qualifications and experience).
- Project objectives and values (allowing some culture of corruption to remain among lower-level administrators).
- Information systems (absence of one core system module and of digitised documents).
These gaps demonstrated that the e-government system had not yet institutionalised within the city government. The gap analysis was therefore used as the basis for a discussion with senior managers. From the analysis and discussion emerged two things.
First, identification of small gaps that had lain behind the partial success of the system – the commitment of project champions, process re-design being conducted prior to introduction of new technology, and stability in the information that was digitised onto the e-government system.
Second, identification of an action plan that would close the main extant gaps between design and reality: creating the proposed new ICT deparment, hiring additional IT staff, and setting up permanent positions with clearly defined salary scales and promotional criteria. These, in turn, would provide the basis for implementing the missing module, and scanning the missing legal documentation.
Not all the gaps can readily be closed: it will take a much longer process of cultural change before the last vestiges of corruption can be eliminated. Nonetheless, design-reality gap analysis did prove itself to be a valuable mid-implementation tool. It is helping steer this e-government project from partial failure to greater success. And the authors recommend its use by e-government managers as they implement their projects: it has helped to focus management attention on key e-government project issues; it digs beyond just technical issues to address underlying human and organisational factors; and it offers a systematic and credible basis for project reporting and analysis.
Feel free to comment with your own experiences of design-reality gaps, or other mid-implementation techniques for e-government project analysis and improvement.Follow @CDIManchester
Often, IT companies sell e-government to politicians, and politicians sell e-government to citizens on the promise that it will save money. These claims regularly appear as “news” items, especially in IT- or government-related media. This has in part encouraged the huge expenditure on e-government: a ballpark figure would be US$3 trillion during the first decade of the 2000s.
So here’s my question: “If e-government is so great at cutting costs, how come my taxes haven’t gone down?”
Of course, taxes depend on far more variables than just e-government. But the simple answer to the question is “. . . because e-government does not save money, it costs money”. That seems likely the case in the global North where e-government seeks to cut costs by replacing expensive humans with cheap technology. It is most definitely going to be the case in the global South where the technology is more expensive and the humans are much cheaper.
Despite the obvious importance of the topic, good quality cost:benefit calculations are rare but can be found. Six years of e-government in UK local government saw £3.90 billion of investment release just £0.97 billion of savings. The aggregate cost:benefit ratio of e-government projects in Australia was 1.64:1.
Rarer still is good quality work from developing countries. However, a recently-published study of e-government in Bhutan by Mayumi Miyata provides a model for systematic and comprehensive evaluation of e-government costs and benefits. The case study focuses on the Road Safety and Transport Authority of Bhutan, which issues driving licences and vehicle registration documents. This was traditionally a paper-based process, and often slow; particularly for driving licences which had to be sent by post from regional offices to the head office in Thimphu. In the mid-2000s, an Internet-enabled database system was installed so the main information associated with these processes could be passed instantly between offices. (This was therefore an “e-administration” application for use by government staff rather than an “e-services” application for use by citizens.)
Data for Miyata’s research was gathered both before and after the introduction of this e-government system including detailed observation and timing of work processes, a breakdown of departmental accounts, and a survey of citizens. The “after” component was undertaken in 2007; two years after implementation of the system, allowing plenty of bedding-in time.
Activity-based costing showed that the direct labour cost for issuing licences and registrations fell 24% following introduction of e-government; from US$15,080 to US$11,530 per year. For example, the direct cost of issuing one driving licence fell from US$1.57 to US$1.17. This was achieved largely through a significant redesign and decentralisation of internal decision-making and workflow.
However, introduction of e-government brought additional costs – hardware, software, internet connectivity and the cost of IT staff – totalling US$11,080 per year (set-up costs being amortised over 10 years). The only indirect saving was in reduced postal cost (US$720). Thus, overall costs were US$15,800 per year before e-government; US$22,610 after e-government. A rise of 43%.
We need to recognise some specific features of this case that make it typical of a least developed country:
- the particularly low labour costs and high IT costs;
- the relatively low volumes of transactions across which costs can be spread (the case is more akin to a local than national government in size);
- the use of e-administration rather than a web-based self-service system which, while still requiring human back-office intervention, would automate some processes.
Miyata’s research thus provides a model that should be replicated for a broader set of examples.
On the other hand, Miyata’s work misses out three additional reasons why e-government globally fails to deliver cost savings:
- the relatively high rate of e-gov project failure, the costs of which must be included in any overall cost:benefit accounting;
- the learning curve – often of some years – that must be traversed before e-government applications can be used efficiently and effectively;
- the need for government e-services to be run in parallel with existing face-to-face, phone and postal service channels in order to bridge the digital divide and avoid excluding large sections of the population from access to government services; public e-services thus being a supplement to, not substitute for, other channels.
Does this mean e-government is a waste of money, and we should ask for our US$3 trillion back? No. What it means is that e-government is not going to save money for government and help bring taxes down. The benefits of e-government lie elsewhere. Again, Miyata’s paper is a good illustration:
- External savings: the lead time from application to receipt was reduced by minutes, weeks, even months for outlying offices. Wait times in offices may also have come down. Other studies report shorter waits and fewer journeys. Saving of journeys can be monetised, and saving of citizen time might be (it depends how that saving is spent). The key cost savings of e-government may thus be external not internal: for service users not administrators.
- Internal control and accountability: e-government provided managers with greater oversight of work processes and staff.
- Service quality and equity: citizens reported the quality of service and the fairness of treatment improved after introduction of e-government.
Other research shows further qualitative and external benefits delivered by e-government including: greater transparency of public services; greater accountability of public servants and politicians; reduced corruption; lower costs for business; greater attraction of foreign investment. Please comment to add your own examples of evidence.
So e-government may not bring your taxes down, but – if properly designed and implemented – it will bring a positive economic and social return on investment.
 Heeks, R.B. (2006) Managing and Implementing eGovernment, Sage, London http://books.google.com/books?id=hRzAnMulatUC&dq; WITSA (2008) Digital Planet 2008, World IT Services Association, Kuala Lumpur, Malaysia; see: http://www.witsa.org/KL08/DigitalPlanet2008_ReportTables.pdf
 Kable (2005) Implementing Electronic Government 4, Kable, London
 Foley, P. & Ghani, S. (2007) The Business Case for e-Government, paper prepared for High-Level Seminar on Measuring and Evaluating E-Government, Dubai, 12-13 March http://www.oecd.org/dataoecd/44/42/38404094.pdf
 These are costs for issuing just over 31,000 documents. Note this excludes the cost of materials for licences/registrations, which was the same before and after e-government.
 Heeks, R.B. (2006) Managing and Implementing eGovernment, Sage, London http://books.google.com/books?id=hRzAnMulatUC&dq; Gauld, R. & Goldfinch, S. (2006) Dangerous Enthusiasms: E-Government, Computer Failure and Information System Development, University of Otago Press, Dunedin, New Zealand
 Poostchi, M. (2003) Implementing E-government, MBA dissertation, Carleton University, Ottawa, ON
 Helbig, N., Gil-Garcia, J.R. & Ferro, E. (2009) Understanding the complexity of electronic government, Government Information Quarterly, 26(1), 89-97
 Accenture (2004) eGovernment Leadership: High Performance, Maximum Value, Accenture, Dublin; Bhatnagar, S. & Singh, N. (2010) Assessing the impact of e-government: a study of projects in India, Information Technologies and International Development, 6(2), 109-127 http://itidjournal.org/itid/article/viewFile/523/231Follow @CDIManchester
ICTs bring benefits to those who have them and not to those who don’t. They therefore increase inequality. Right? Well . . . let’s see.
First question: what do you mean by “those who don’t have ICTs”?
We need something a bit more nuanced than a simple, binary digital divide, and can use instead a digital divide stack of four categories (see figure below):
- Non-Users: those who have no access to either ICTs or ICT-based information and services.
- Indirect Users: those who do not get hands-on themselves, but gain access to digital information and services via those who are direct users.
- Shared Users: those who do not own the technology, but who directly use ICT owned by someone else (a friend, workplace, ICT business, community, etc).
- Owner-Users: those who own and use the technology
Of course we would need to make transverse slices through the figure; potentially, one slice for each different type of ICT, but particularly noting many in developing countries would be in a different category level for mobiles compared to the Internet.
Second question: what’s the evidence on inequality?
It is relatively limited and often bad at differentiating which digital divide categories it’s talking about. However, we can find three types of evidence.
The Rich Get Richer; The Poor Get Poorer: situations in which some category of user gains a benefit from ICT while non-users suffer a disbenefit. For example, micro-producers of cloth in Nigeria who owned or had use of a mobile phone found they were gaining orders and income; micro-producers without mobile phone access found they were losing orders and income (to those who had phones). (See also work on growing costs of network exclusion.)
Development vs. Stasis: situations in which some category of user gains a benefit from ICT while non-users do not gain that benefit. For example, farmers in rural Peru who used a local telecentre were able to introduce improved agricultural practices and new crops, which increased their incomes. Those who did not use the telecentre just continued farming in the same way as previously.
Spillover Benefits: situations in which some category of user gains a benefit from ICT while non-users also gain a (lesser) benefit. One rather less-publicised outcome from the case of Keralan fishermen using mobile phones to check market prices is an example. Those fishermen without mobile phones saw their profit rise by an average Rs.97 (c.US$2) per day as a result of the general improvements in market efficiency and reduced wastage which phones introduced. This was about half the profit increase seen by phone owners and meant, even allowing for the additional costs, that returns to phone ownership were greater than those for non-ownership. However, it was a spillover benefit to non-ICT-users.
ICT4D research on spillovers to non-users specifically has been rare, with the main interests in non-users being to understand why they are non-users; and most spillover work being done between sectors or enterprises and/or focusing on the spillover of encouraging ICT adoption rather than more immediate benefits.
This does seem to be changing, perhaps because of the growth of mobile and related to earlier work on the externalities to non-users of arrival of rural telecommunications. Rob Jensen’s Kerala study found a second digital spillover: while fishermen’s revenues rose, the price per kg fell due to the increase in supply arising from less waste. Fish consumers (many likely non-users) now paid less than previously thanks to the mobile-induced efficiency gains. More directly, a study of M-PESA’s community effects in Kenya found its use providing positive financial, employment, security and capital accumulation externalities that affected both users and non-users within the community.
We also have a little evidence of spillover benefits from owner-users to indirect users:
- Follow-up work with Keralan fishermen found fish workers who will only get into a boat with a mobile phone-owner due to safety concerns, with these indirect users able to benefit from the owner should the boat get into difficulties. That paper’s author (personal email) also gives the example of an indirect user citing as a benefit being informed of – and able to curtail – his daughter’s illicit elopement via his boat owner’s phone.
- Research on farmers in Northern Ghana found those who did not themselves own or use mobiles benefitting from information passed on from phone owners, including more frequent meetings with agricultural extension officers; meetings that were coordinated by phone owners.
In all these cases, owner-users are benefitting more than the lower-category users to whom benefits spill over. That means – if you’ll forgive the pun – that in these cases ICTs are causing all boats to rise but the ICT-using boats to rise somewhat faster. Inequality may still grow; perhaps absolutely but not relatively.
I look forward to what appears to be forthcoming work by the Global Impact Study on non-user spillovers. However, this remains a poorly-understood and little-researched issue; one that needs a greater focus since it is central to understanding the digital divide and digital inequalities. It also has implications for practice; suggesting ICT4D projects should promote non-user spillovers as much as they promote ICT usage. As ever, your pointers to spillover research and practice are welcome.
 Smith, M. (2010) A Technology of Poverty Reduction for Non-Commercial Farmers? Mobile Phones in Rural North Ghana, BA dissertation, unpublished, University of Oxford, UK
Do ICTs contribute to economic growth in developing countries?
In the 1980s, Robert Solow triggered the idea of a productivity paradox, saying “You can see the computer age everywhere but in the productivity statistics.” And for many years there was a similar developing country growth paradox: that you could increasingly see ICTs in developing countries except in the economic growth data.
That is still largely true of computers and to some extent the Internet, but much less true overall as mobiles have become the dominant form of ICTs in development. In particular key studies such as those by Waverman et al (2005), Lee et al (2009), and Qiang (2009) have demonstrated a clear connection between mobiles and economic growth and/or between telecoms more generally and economic growth. They all address the “endogeneity” problem: that a correlation between telecoms (indeed, all ICTs) and economic growth is readily demonstrable; but that you then have to tease out the direction of causality: economic growth of course causes increased levels of ICTs in a country (we buy more tech as we get richer); you need to try to control for that, and separate out the interesting bit: the extent to which the technology causes economic growth.
The studies try to do this and show ICT investments cause economic growth, but they are all multi-country and provide no specific insights into the experiences of a particular developing nation. If you know of such data, do please contribute. Meanwhile, a recent edition of “Kenya Economic Update” provides an example. Some overall points:
- The ICT sector grew at an average of nearly 20% per year from 1999-2009 (by contrast, Kenya’s largest economic sector – agriculture – shrank by an annual average of nearly 2% per year).
- The number of phone subscriptions has grown from the equivalent of one per 1,000 adults in 1999 to the equivalent of nearly one per adult in 2010; Internet usage rates for 2010 were around four per ten adults.
- Person-to-person mobile money transactions at the end of 2010 were equivalent to around 20% of GDP with two of every three Kenyan adults being users.
But the report’s strongest claim is this: “ICT has been the main driver of Kenya’s economic growth over the last decade. … Since 2000, Kenya’s economy grew at an average of 3.7 percent. Without ICT, growth would have been a lackluster 2.8 percent—similar to the populaton growth rate—and income per capita would have stagnated”. So ICTs were responsible for 0.9 of the 3.7% annual GDP growth, and for all of Kenya’s GDP per capita growth. Put another way, ICTs were responsible for roughly one-quarter of Kenya’s GDP growth during the first decade of the 21st century.
Other nuggets from the report and from original World Bank data underlying the report:
- The “ICT sector” is actually the “posts and telecommunications” sector. Comparing figures from Research ICT Africa for mobile + fixed line + Internet/data services with those for the overall sector suggests that ICTs form by far the majority (likely greater than 90%) of that sector. For the ICT part of the sector, latest figures for 08/09 show mobile takes a 54.8% share, fixed line takes 39.5%, with 1.8% for Internet services and 3.8% for data services (not 100% due to rounding).
- The ICT sector in 2009 still represented only 5% of total Kenyan GDP (compared to 21% for agriculture/forestry), and growth has been volatile, at least as based on the recorded figures, ranging from 3.5% per year up to 66% per year during the first part of the decade, and from 7.9% to over 30% during the second part of the decade. Only tourism (hotels/restaurants) was more volatile. In six of the ten years of the 2000-2009 decade, though, ICT was Kenya’s fastest growing sector.
- In the first half of the decade, annual investments in mobile were higher than annual revenues; but the ratio has subsequently slipped to investment averaging around half of revenue. Investments in mobile during 2001/02 to 2009/10 are estimated at US$3.2bn (c.KSh250bn) and US$3bn in fixed phone services, with broadband, Internet and BPO investments adding perhaps another US$1bn.
- The ICT sector provided a more than six-times-greater contribution to Kenyan GDP in 2009 compared to 1999. Directly, the ICT sector contributed to 14% of the country’s GDP growth between 2000 and 2009 (at constant (i.e. not actual/current but accounting for inflation) prices, it grew from KSh13.7bn in 2000 to KSh71.8bn in 2009; GDP overall grew from KSh976bn to KSh1.382tn). So the World Bank’s calculation that ICTs contributed a quarter of GDP growth during the decade also include a specific, quantified assumption about ICTs triggering growth in other sectors, in particular the financial sector.
- Employment in the ICT sector is estimated to be around 100,000 in 2011 (c. 0.7% of the estimated 14m overall labour force). But ICT punches above its weight in other ways: changes in mobile prices at the start of 2011 were credited with both causing the Kenyan inflation rate to drop and with potentially derailing government constitutional talks due to the substantial knock-on effects in causing tax revenues to drop since phone companies now contribute such a significant proportion of government income.
So, overall, what do we have here? Some fairly solid evidence that ICT sector growth (predominantly due to mobiles) is making an important direct contribution to economic growth in this developing country. And some less clear evidence that the indirect GDP growth effect of ICTs may nearly double this. Thanks to mobile money, Kenya has seen a particularly strong take-up and economic role for ICTs, but it is fairly typical in terms of mobile investment, revenues, subscriber base, employment, etc. In that case, it’s not too much of an extrapolation to expect that ICTs will have contributed something like one quarter of GDP growth in many developing countries during the first decade of the 21st century. Evidence of ICT impact that development strategists and practitioners should be more aware of.Follow @CDIManchester
ICT4D projects and policies can best be understood through a value chain model. As shown in Figure 1 below, this builds on a standard input—process—output model to create a sequence of linked ICT-for-development resources and processes. The model can be used for projects and policies in various ways: to trace their history; to analyse their content; to assess and evaluate.
The ICT4D value chain offers four main domains that can be the focus for historical or content analysis or evaluation:
- Readiness: the systemic prerequisites for any ICT4D initiative; both the foundational precursors that we might conceptualise mainly at the national level such as ICT infrastructure, skills and policy; and the more specific inputs (both ‘hard’ and ‘soft’) that feed into any individual initiative. Assessment could focus on the presence/absence of these resources and capabilities, or the strategy that converts precursors into inputs.
- Availability: implementation of an ICT4D initiative turns the inputs into a set of tangible ICT deliverables; typical among which might be a telecentre or mobile phones. Again, assessment can focus on either the delivered resources and/or the delivery process.
- Uptake: the processes by which access to the technology is turned into actual usage; also noting that key concerns around this process and its ability to contribute to development have related to the sustainability of this use over time, and – for various innovations that are prototyped – the potential or actuality of scaling-up. In practice, usage indicators are more often assessed than the various uptake processes.
- Impact: which can be divided into three sub-elements:
- Outputs: the micro-level behavioural changes associated with technology use.
- Outcomes: the wider costs and benefits associated with ICT.
- Development Impacts: the contribution of the ICT to broader development goals.
Figure 1: The ICT4D Value Chain
How has interest in these four domains changed over time?
One way to trace this is through key staging posts for the ICT4D community:
- The Digital Opportunity Taskforce (DOTForce) arose from the 2000 G8 summit in Okinawa. In 2001, it produced its “Digital Opportunities for All” report which encompassed four focal areas. Three – readiness, connectivity and human capacity – were related only to the Readiness domain; and one – participation in e-networks – looked mainly at Readiness and Availability issues.
- In 2003, the first World Summit on the Information Society was held in Geneva. Its main report was, tellingly, entitled “Building the Information Society” and not surprisingly the main focus was on building ICT connection and access; again looking mostly at the Readiness and Availability domains.
- The second World Summit on the Information Society was held in Tunis in 2005. Unlike its predecessor, its agenda did start to talk about impact. It still had a strong focus on precursors like financing and governance, but it included additional discussion about the application of ICTs, thus starting to encompass the Uptake and Impact domains.
- The largest subsequent meeting was the GK3 event in Kuala Lumpur at the end of 2007. It was shaped by twelve main sub-themes. Analysing these shows a fairly even spread across the four domains, though with Impact by now the largest single focus, followed by Availability.
There has been no subsequent comparable single event in the area drawing together many thousands of participants as these staging posts did; rather, a growing number of smaller events drawing several hundreds. However, a useful bellwether is the Information and Communications for Development Report produced by the World Bank. In its 2009 edition, the ratio of mentions of ‘readiness’ to ‘impact’ was 1:35.
Such evidence is best seen as straws in the wind rather than definitive, but it does suggest a similar pattern to that seen in other areas of ICT application, and summarised in Figure 2.
Figure 2: Changing Focus of ICT4D Priorities Over Time
Whatever the exact shape of the graph, it reflects the relative lack of attention that has been paid to ICTs’ contribution to development until quite recently. That is problematic because, as you move from left to right along the value chain, assessment becomes more difficult, more costly but also more valuable. Of course there has been literature assessing the connection to development including the summary Compendium on Impact Assessment of ICT4D Projects, and the 2010 Journal of International Development policy arena: “Do Information and Communication Technologies (ICTs) Contribute to Development?“.
However, donor agencies, governments, academic departments and others must still do more to shift the focus of attention along the ICT4D value chain; and to demonstrate ICTs’ development impact.
BoPsourcing – the outsourcing of work to bottom-of-the-pyramid communities – is on the rise. Outsourcing used to mean sub-contracting work from one big firm to another nearby. Then, with offshoring, the contracts crossed borders. With BoPsourcing, the contracts cross several income strata as well.
BoPsourcing initiatives can be found that are run by:
- Governments: such as the IT component of Kerala State’s Kudumbashree initiative which has created more than 2,500 jobs for women from below-poverty-line urban households
- Social Enterprises: such as Anudip Foundation which set up its first ‘MERIT’ (mass employment through rural IT) Centre in rural West Bengal in 2010. (There are suggestions that some 200 BoPsourcing initiatives are currently being run by social enterprises and NGOs.)
- Private Firms: such SourcePilani which operates a 50-person business process outsourcing centre in Rajasthan
All these, and many other examples from India, are onshore BoPsourcing. There are also offshore varieties, such as Digital Divide Data, which outsources from large US organisations to telecentres in Cambodian small towns and villages.
BoPsourcing has no necessary connection with ICTs. Indeed, many agricultural value chains are arguably examples. Here, though, my focus is on ICT-related outsourcing. Examples of contracts include data entry, transcription, digitisation, call centres and ICT training.
One of the key concerns about outsourcing and developing countries has been the potential to fuel income and other inequalities. I could already see this studying software offshoring to India in the 1980s: an economic shearing in which those involved saw their incomes pull far ahead of the bulk of the population. More recent evidence suggests offshoring increases overall wage growth but also increases inequality.
BoPsourcing presents an obvious solution. Channelling the benefits of outsourcing down to the poor can drive wealth creation for those on the lowest incomes, and serve to reduce inequalities.
So. Job done. We can add BoPsourcing to our list of great development solutions.
Well, not quite yet. First because the evidence base is very weak. Second because BoPsourcing comes in nearly as many varieties as Heinz. Figure 1 summarises.
Figure 1: Continuum of BoPsourcing Approaches
There are at least four conceivable models that form the continuum (but feel free to add your own evidence and ideas):
- Exploitative outsourcing seeks to bear down on wages and working conditions in order to minimise costs and maximise profits. The result is an ICT sweatshop that does little to grow incomes, to deliver empowerment, or to reduce inequality. At present this seems more of a bogeyman brandished by those at the other end of the continuum, than it is an evidence-based reality. The potential, though, is certainly present with so many outsourcing firms seeking to drive down costs.
- Commercial outsourcing reflects, for example, the steady diffusion of outsourcing in India and other nations, from cities to large towns to small towns and beyond. Whether this can yet be called BoPsourcing (e.g., forgive the pun, whether BPO is BoP) is unclear. Quite likely commercial operators have to date only got as far as large towns. But the migration trend is clear, and it will reach poorer towns and even villages soon enough. As for inequality, the effect is likely to be as arguable as it is for outsourcing generally: evidence is contested and, unfortunately, fought more by economists pitting ever-more complex models against each other, than on the basis of field data.
- Ethical outsourcing (also known as socially-responsible outsourcing) takes commercial outsourcing and requires that it meets certain minimum standards; typically relating to labour practices but also starting to include environmental issues. The International Association of Outsourcing Professionals has taken a lead on this. This is likely to have some impact on inequality but, again, the extent to which such work really reaches the BoP as yet is questionable.
- Social outsourcing (also known as developmental outsourcing) differs from ethical outsourcing as fair trade differs from ethical trade. Ethical outsourcing involves existing commercial players with either a commitment to or measurement of adherence to standards. Social outsourcing involves new non-market intermediaries who sit between the client and the BoP sub-contractor. Social outsourcing most definitely does reach the BoP; indeed that is its raison d’être. It has already been shown to increase incomes, increase asset holdings, increase skills, and increase empowerment. It is therefore likely to reduce inequality.
True ICT BoPsourcing is on the increase – you only have to monitor the growing number of initiatives to see that. Much of the more commercial end of outsourcing has yet to get this far. It’s more like MoPsourcing (i.e. middle of the pyramid) just now, but cost pressures, supply-demand gaps, ICT diffusion, and growing awareness of BoPsourcing mean this is changing.
The impact of these trends on inequality will depend on which outsourcing model comes to dominate the BoPsourcing business. If social outsourcing wins, then so too will the poor. If exploitative outsourcing wins, the opposite will be true.
In practice we may well see some messy combination of models, as social and commercial approaches intersect. For the social outsourcing intermediaries, the lure of big clients, contracts and growth may pull them into bed with commercial operators. Conversely, the operators will be attracted by the contacts, expertise and CSR-window dressing that social intermediaries provide. The poor themselves will get jobs, skills and income. Whether they will see the structural transformation necessary to really clobber inequality, only time will tell.
Mountain regions are home to one-tenth of the world’s population. Yet they are also among the poorest, most-remote and most-excluded areas. Can ICTs address these issues?
Maybe. But, to date, there has been very little research on this: partly because mountain areas are the last places on earth to get connected; partly due to the lack of conceptual frameworks attuned to the specific conditions of these areas.
Manchester’s Centre for Development Informatics has published a working paper – Remoteness, Exclusion and Telecentres in Mountain Regions: http://bit.ly/Hvkk4 – which develops two simple frameworks. One looks at the positive and negative impacts that ICTs have on resources moving into and out of mountain communities. The other looks at the “information chain” (see below): the set of actions and complementary inputs required for information to have a resultant development impact.
Using these frameworks to analyse the impact of a telecentre set up within a poor community in the high Andes, we found ICTs enabling new and positive resource flows for the two key user groups: teenaged school students and young farmers. These flows help to maintain social networks. They also support information searches that have improved agricultural practice so long as other information chain resources have been available. But non-use and ineffective use of the telecentre are found where information chain resources are lacking.
ICTs have some impact on intangible elements of remoteness. In this particular example, they also offer access to some previously-excluded resources. But they have not really addressed the systemic exclusions faced by mountain communities. And they so far appear to be a technology of inequality; favouring those residents who begin with better resource endowments.
On this basis, we recommend that mountain ICT projects need to be:
- “Info-centric“: focusing less on the technology and more on the data that technology carries.
- “Chain-centric“: attending to the additional information chain resources – over and above technology and data – that are required in order to turn digital data into development results.
- “Socio-centric“: recognising that new information chain resources are mainly provided by individuals’ social contact networks.
- “Econo-centric“: being especially mindful of ICT uses that enable new or more productive income-generating activities.
But this work is just a small start: we need much more research to be done as ICTs diffuse into mountain communities; work that takes account of the specific geographies of those communities.
Enterprise resource planning (ERP) systems are increasingly being used in business organisations in developing countries; also in the public and NGO sectors. ERP promises to integrate data systems – financials, logistics, HR, etc – across the organisation; thus saving money and improving decision-making. But the failure rate for ERP implementations is high, with particular problems found in developing country organisations.
A new research paper from the University of Manchester’s Centre for Development Informatics analyses why ERP systems in developing countries fail: http://www.sed.manchester.ac.uk/idpm/research/publications/wp/di/di_wp45.htm
It draws evidence from an in-depth Middle East case study, and first uses an analytical model based on DeLone & McLean’s work. This gathers evidence on the success or failure of any ICT project against five evaluation criteria: system quality, information quality, use and user satisfaction, individual impact, and organisational impact. This provides an objective basis for identifying the case study ERP system as an almost-complete failure.
A second analytical model – the design—reality gap framework – was then used to explain why this ERP implementation failed. Using rating scale evidence gathered on seven ‘ITPOSMO’ dimensions, this shows there was a large gap between ERP system design expectations, and case organisation realities prior to implementation.
This is often true of ERP systems since they seek to make significant changes within client organisations. However, the design—reality gap analysis was repeated later on, showing that gaps did not close during implementation, as they need to do for a successful system.
Practical recommendations for risk identification and mitigation are outlined based on closure of both specific design—reality gaps during ERP implementation, and also on a set of generic gap closure techniques such as development and use of ‘hybrid’ professionals.
In research terms, the case demonstrates the value of the DeLone/McLean model for categorisation of ERP and other information system project outcomes, and the value of the design—reality gap model for analysing project implementation, and in explaining why project outcomes occur.
A revised version of the paper has been published in the Journal of Enterprise Information Management: http://www.emeraldinsight.com/10.1108/17410391011019741
Other experiences of ERP or similar enterprise system implementations in developing countries would be welcome as comments.
What have we got to show for the billions invested in ICT4D projects?
By and large, we’re not sure because relatively little impact assessment of ICT4D projects has been undertaken; and what has been undertaken often lacks clear framing and rigour.
Impact assessment is therefore pushing its way up the ICT4D agenda. For example, a number of ICT4D agencies have IA programmes; perhaps the biggest being the joint Gates Foundation/IDRC IPAI programme.
As a feed-in to that programme, staff with the University of Manchester’s Centre for Development Informatics created a “Compendium on Impact Assessment of ICT-for-Development Projects”. IDRC – the sponsor for its creation – has given permission for this Compendium to be shared, and it is attached here (2MB .doc file): idrc-ia-for-ict4d-compendium
The Compendium is arranged into three parts:
· Overview – explains the basis for understanding impact assessment of ICT4D projects (including the ICT4D Value Chain), and the different assessment frameworks that can be used.
· Frameworks – summarises a series of impact assessment frameworks, each one drawing from a different perspective.
· Bibliography – a tabular summary of real-world examples of ICT4D impact assessment.
This is an ongoing work, and comments or pointers to similar resources are welcome.