The Revolution 2.0 Index: Where Will The Next “Arab Spring” Occur?

In Networks of Outrage and Hope (NOH), Manuel Castells demonstrates the centrality of ICTs in the initiation, growth and mobilisation of recent mass protests: the Arab Spring, Iceland’s Kitchenware Revolution, the US Occupy Movement, and Spain’s Indignadas.

A central assertion is that cyberspace is a place of safety and autonomy for mass social movements, including those that grow into revolutions.  Looking worldwide, though, that clearly isn’t true – try organising your revolution online in China, for example, and see how far you get.

But can we measure “how far” you might get?  Of course, you can’t exactly but thanks to Freedom House’s Freedom on the Net scores, we can get an overview sense of how safe and autonomous cyberspace is around the world.  With higher numbers meaning less online freedom, this is an “e-Control Index”[1].  (If I was being snarky, I might say it’s a “NOH invalidity index”.)

But that’s just a measure of constraint.  Revolutions are driven forward by outrage.  As Castells points out, in the case of the Arab Spring, outrage had built up as earlier protests and alternative options were repressed by the state.

Therefore the driver to protest may be measured politically.  An example would be the Economist’s Democracy Index, though reversed into an “Outrage Index” so that higher scores mean more autocracy and repression, which could trigger more protest.

My first pass at a Revolution 2.0 Index (see chart below) is each country’s Outrage score minus its e-Control score.  A low overall score might derive from a country being relatively democratic, creating limited head of steam for regime change (e.g. India) or because cyberspace is so controlled (e.g. Cuba, China).  Conversely, high scores typically arise in countries where political freedoms are compromised but Internet freedoms are not (yet).

Hmmm . . . there’s a lot that’s cronky here: for example, this version of the Index has clearly not studied the recent history of Thai politics.

We could try some tinkering to develop the Index:

i.Taking account of ICT access rates, which would for instance push some of the African countries down the rankings. The version 2 diagram below does this, using the ITU’s ICT Development Index.  It then makes up the revised Revolution 2.0 Index from: one-half Net Freedom scores (though flipped so higher means more net freedom); one-quarter Outrage scores; one-quarter ITU scores.  Is that an improvement?

ii. Castells argues the mass protests have almost all had an economic foundation.  We could try to measure that in terms of GDP per capita, but that’s not much of a guide: all the examples he identifies are from mid- or high-income countries.  Or we could use the GINI measure of income inequality but checking that doesn’t show much correlation.  Unemployment rates (especially among graduates) or changes in income levels or prices (e.g. of staples like bread) might be better measures to use.

But there are some more foundational problems.  These could be in the statistics e.g. the component parts of the indexes; the different distribution patterns of scores within the indexes; the arbitrary weighting of the indexes within the Revolution 2.0 Index.

There is also unlikely to be a linear relation between index and real-world effects:

- For net freedom there might be a threshold effect e.g. that in countries scoring above, say, 70 there is not enough autonomy for a Revolution 2.0; whereas for those below there is enough and it is not particularly affected by variations in level of control.  So instead of a continuum we might only have a two-state model: either there is online autonomy or there is not.

- There’s no accounting for circumvention of net controls including use of non-ICT communication.

- There is no simple relation between political freedom and mass protest.  Spain and Iceland score well on political freedoms but both had a form of protest-induced regime change.  Moving to more repressive regimes, one sometimes sees more protest, but in highly-repressive regimes, protest can become less likely.  Protest and repression are also interlinked and change over time, the one feeding the other.

Finally, there are factors that matter but which are much harder to measure:

- Emotional effects: Castells argues that the key elements to enrolment and mobilisation are not rational and quantitative: they are fear, outrage, enthusiasm, hope.  Which don’t appear in common national surveys.

- Institutional and cultural factors, including history, are hard to quantify, but have been shown to mediate the relationship between polity and protest.

In sum, my sub-title was (c)heeky: the Revolution 2.0 Index will not identify the location for the next ICT-enabled regime change.  However, there is still a sound central point – that there are significant national differences in both the drivers to mass political protest, and the ability of such protest movements to freely organise themselves online.  Both of these combine to give us some sense of how likely “mass protest movements of the internet age” are to form in any given country.

Above all, the Revolution 2.0 Index may be useful in triggering discussion e.g. in class: about the value of its construction, about what it misses out, about its ranking of countries (will Iran bear out its low placing during and after the 2013 elections?).

For example, the first version suggests there is no particularly neat relation between offline and online freedoms.  That thought is developed via the following plot (of the flipped Democracy Index (here labelled ‘Repression’ rather than ‘Outrage’) vs. the e-Control (i.e. Freedom on the Net) Index)[2].  What does that tell us about e-politics?  Nigeria and Thailand both have combined scores just over 90: how do they differ, and where would you rather be as a modern-day political activist?

You are welcome to propose further alterations or to point out better existing measures.  You can also mess around with the data, which can be found here: Revolution 2 Index Data.

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[1] The countries picked in this discussion are just those developing/transitional countries which appear in the 2012 Freedom on the Net report.

[2] With acknowledgement to Wagner Kamakura of Duke University for supplying the ability, which Microsoft can’t, to label a scatterplot.

Why M-Pesa Outperforms Other Developing Country Mobile Money Schemes

Why has M-Pesa been so successful in Kenya, yet mobile money initiatives in other developing countries much less so?  Recent Centre for Development Informatics research[1] can help provide a systematic response.

M-money services have two core functionalities.  Registered customers can convert between e-cash and real cash (typically at the physical premises of an m-money agent), and can transfer e-cash from their account to that of another account holder via SMS. They might use this to send money to family members or friends, or to pay a provider – anyone from a taxi driver to a local school – for goods and services.

M-Pesa was launched in Kenya in 2007.  It has grown spectacularly: in mid-2012, there were 19.5 million m-money users in Kenya (83% of the adult population), transferring nearly US$8 billion per year (equivalent to 24% of GDP) – M-Pesa is responsible for more than 90% of these transfers.  Transfers are growing at nearly 40% per year.

It’s not that m-money initiatives in other developing countries have failed: there are an estimated 250m users of m-money services in emerging markets.  Just that they have not – yet – succeeded on anything like the scale of M-Pesa, with Kenya accounting for 30% of all emerging market m-money transactions in 2011.  For example, a recent survey in South Africa found only 16% of respondents with a mobile money account.  In Nigeria, only 3% of adults use mobile money.  And Africa is the lead continent: outside the Phillipines, m-money has been very slow to catch on in Asia. In India, for example, Nokia quit the m-money business in 2012 after two years of failing to build a critical mass.

How do we explain the differences?  University of Manchester research, based on six months of primary fieldwork conducted by Chris Foster, analysed the reasons M-Pesa has grown so fast in Kenya; reasons summarised in the model shown below:

Ongoing support from government – liberalisation of the mobile market; investment in infrastructure; light-touch regulation; facilitation of the initial pilot, etc – combined with strong consumer demand across all strata of society (itself partly fed by the instability and disruption following the disputed 2007 elections).  These drove a virtuous circle:

• Competition between mobile sector firms pushed them to seek profits beyond the traditional middle-of-the-pyramid; answering the demand from the majority market of the country’s poor.
• The service was delivered via atomised distribution networks that reached right down into poor urban and rural communities; a network of nearly 50,000 agents by 2012.
• Those embedded intermediaries – essential in scaling any innovation to reach the base-of-the-pyramid – were given the flexibility to adapt business models, retailing patterns and service offerings so they met the specific and heterogeneous needs of their local customers.  Effective knowledge channels allowed these innovations to filter back up to the lead firms, which then scaled those they found most useful; fuelling yet further growth.

Armed with this model, we can analyse the m-money weaknesses in other emerging markets.  For example:

• Much lower levels of customer demand (put down to both culturo-institutional factors and more effective functioning of and access to existing financial services) combined with a more stringent regulatory regime are behind the slow growth rates in India.
• A much smaller number of intermediaries (agents) and a lack of innovation (e.g. to address cash float problems) is restricting growth of m-money in Uganda and Tanzania.
• Tighter regulation and the much small number of intermediaries has held back expansion of mobile money services in South Africa.

We are not the first to try to understand the different performance of M-Pesa vs. other countries (see e.g. Wolfgang Fengler, Amaka Okechukwu who both also note the value of Safaricom’s market domination).  However, we hope that our model provides a clear and transferable framework for comparison, that can be used alongside more in-depth evidence from other countries to help understand their relative success or failure in mobile money.

If you see ways in which you think the model should be modified – based either on experiences in Kenya or elsewhere; then let us know . . .

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[1] Foster, C. & Heeks, R (2012) Analysing policy for inclusive innovation: the mobile sector and base-of-the-pyramid markets in Kenya, paper presented at Globelics 2012, Hangzhou, 9-11 Nov [copy available on request: innov4dev@gmail.com]

Raspberry Pi: A Paradigm Shift for ICT4D?

Here at the Centre for Development Informatics we’ve spent years avoiding a techno-centric approach to ICT4D.  But . . . we are rather excited about Raspberry Pi.

If you don’t already know it, Raspberry Pi is not a low-cost computer.  It’s an ultra-low-cost computer (see photo below)[1].  And it was the subject of a recent demonstration and discussion workshop (see links for video) for CDI members in Manchester.  This focused on the development-related potential of Pi and its add-on interface ”Pi-Face”, which is being developed at the University of Manchester by Andrew Robinson.

Although credit card-sized, Pi is a fully-functioning computer.  Hook up a keyboard, mouse and monitor and away you can go with Linux and, for example, OpenOffice.  And, as noted, it is ultra-low-cost.  The actual production costs will depend on scale, with the economics catching even Raspberry Pi Foundation – the non-profit creators – by surprise.  Expecting they might eventually ship around 10,000 Pis, they have already shipped more than one million.

At those sorts of production scales, costs for Pi could be reduced to around the US$15-20 mark.  Adding a keyboard, mouse and Pi-Face will stack less than US$2 on top, and looking at similar products it is likely that a small screen can be produced for US$15.  Of course, cost is not the same as price but we are talking of a complete computer system that will likely cost less than US$35 to produce and perhaps US$50-60 to buy.  Just the Pi-plus-Pi-Face combination could be supplied to developing countries for as little as US$25.

In many ways, its key attributes are those of a mobile phone (not surprising since it runs with the same ARM chipset you’ll find in many mobiles):

• Very low cost puts it into the category of “semi-disposable” device, and a ready addition to many other innovations without breaking the bank.
• Its robustness and low maintenance requirements make it particularly suitable to harsh developing country environments.
• Its small size and portability make it suitable for applications that other computers can’t reach.
• It has very low power consumption, so can work more easily in electrical off-grid environrments.

But it’s not a mobile phone, and you can’t use it for calls and text.  What it does do is connect readily to a host of other devices.  And, unlike a mobile phone, it is easy to customise, using common open source software and “tinker-able” hardware components.  All run by a .org not a .com organisation.

Raspberry Pi may just fizzle and die, without much effect on international development.  But the potential is certainly there for it to paradigm shift ICT4D.  The mobile phone explosion has shifted ICT4D’s emphasis towards the “C”, with widespread acceptance that “m-development” models will dominate.  Raspberry Pi could shift us back towards the “I”; towards the computing and data processing and automation that were the origins of ICT4D in the 1970s and 1980s but which have fallen by the wayside.

At present, Pi is a solution looking for development problems, but three application areas spring to mind:

a)    Micro-enterprise and household computing: providing access to standard computing applications not for the community but for the individual enterprise and household.  Add an Internet connection and we might call it not OLPC (the One Laptop per Child initiative) but OTPH: a one telecentre per household approach that moves us beyond community computing models.

b)    Technical education: the prime motivation behind Pi was to reignite interest in computing as a subject among schoolchildren.  There’s a great thirst for IT education in schools, colleges and universities in developing countries but budgetary constraints are a major barrier (see earlier blog entry on revising computing curricula in Africa).  Pi can help to overcome those – the possibility is that it could do all the OLPC does at half the price, and allow kids to open the box and play about much more, learning how IT works.

c)     Data collection and automation applications: there’s a trickle of new electronic applications for development – smart motor controllers that save power and extend motor life, low-cost health monitors, water quality and climate change measurement devices, field-based agricultural sensors.  Raspberry Pi could turn that trickle into at least a stream if not a flood.

The promise of Pi, at root, is to enable a new ICT4D innovation paradigm: one in which Pis are widely used and understood within developing countries, and in which grassroots innovation is really possible for the first time in the ICT4D domain (see earlier blog entry on grassroots ICT4D innovation).  There’s no reason the same informal sector micro-entrepreneurs who now fix mobile phones can’t also work with Raspberry Pi.  But they can customise and adapt this technology much more than they can a mobile phone.  It can therefore be appropriated far more by the base of the pyramid.

Pi also allows a new model of collaborative innovation: that done working alongside base-of-the-pyramid consumers.  Large firms, university departments, social enterprises can now afford rapid, mass prototyping – trying out and iterating quickly through many different models until they find one that works.

As yet, of course, this is promise not reality, and one can foresee plenty of issues around everything from distribution through support and training to growth in e-waste.  But the international development impact of Raspberry Pi – good or bad, large or small, paradigm-shifting or incremental – is up for grabs.  Over to you.

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[1] Source: www.raspberrypi.org and www.blendernation.com

A Model for Assessing IT Impact Sourcing Relationships

What determines the success or failure of IT ‘impact sourcing’ relationships?

We have known for some time that various factors affect the outcome of IT outsourcing relationships.  But no-one has yet applied this to IT ‘impact sourcing’: outsourcing to bottom-of-the-pyramid employees with the aim of socio-economic development impact.  This was the focus for a study recently conducted within the Centre for Development Informatics at the University of Manchester by postgraduate researcher, Sheng Lu under the supervision of Dr Brian Nicholson.

As detailed in a previous blog entry (“The Research Agenda for IT Impact Sourcing”), Rockefeller/Monitor research estimates that impact sourcing is already a US$4.5 billion market employing 144,000 people and “has the potential to be a $20 billion market by 2015, directly employing 780,000 socio-economically disadvantaged individuals”. 

The most common model of impact sourcing involves three main actors: the client, the BoP sub-contractor employees/enterprise, and an intermediary that sits between the other two and provides quality control.  This creates two relationships: client—intermediary; and intermediary—BoP sub-contractor of which the former will be the main focus here.

Numerous models have been used to help understand IT outsourcing relationships in general:

• Some have been more factor-oriented.  For example, Lee and Kim (1999)[i] argue that trust, business understanding, benefit and risk sharing, conflict, and commitment are factors that influence the relationship.
• Some have been more process-oriented.  For example, Kern and Willcocks (2002)[ii] take an interactional approach that focuses on different types of exchange that occur during an outsourcing relationship.

The model chosen to explain IT impact sourcing was Alborz et al’s (2003)[iii] IT outsourcing relationships model, shown in the figure below.

The model divides an IT outsourcing relationship into three stages: pre-contract, contract development, and post-contract implementation.  It identifies eight elements which operate during those three stages and which contribute to a successful relationship, some of which can be broken down into further sub-components:

- The initial strategy for outsourcing

- Due diligence through supplier selection, evaluation and development

- Development of the outsourcing contract

- Governance of the relationship through the role and support of senior management, the structure and style of the relationship, and the skills brought to bear upon it

- Monitoring and management of supplier performance

- Management of the contract

- Management of the working relationship between client and supplier

- Knowledge exchange and learning

Alborz et al’s model was selected because it integrates a number of earlier models, and combines both process- and factor-oriented approaches.

The model was tested through a review of IT impact sourcing case study secondary data, and through development of primary data from two interviews, providing a client- and intermediary-side perspective from one of the largest IT impact sourcing intermediaries, with operations in a number of developing countries.

Many of aspects of the client—intermediary impact sourcing relationship mirror those found in traditional IT outsourcing.  However, the social mission within impact sourcing was found to affect various elements including:

- Due diligence 1: clients select IT impact sourcing intermediaries on a quadruple-criterion basis of cost, delivery timescale, perceived quality of service, and social mission.  The latter may be perceived in terms of alignment with the client’s own mission statement (a number of clients themselves have, in part, a social mission).  The outsourcing activity may also be given a higher profile by the client than traditional IT outsourcing; for example, in annual reports or corporate social responsibility statements.  This pressurises impact sourcing intermediaries to maintain a strong public image of corporate and developmental responsibility.

- Due diligence 2: supplier development encompasses not just the intermediary, but also the terminal sub-contractors at the bottom of the pyramid.  These are selected from poor areas or communities, trained and screened for both service quality and social impact by the intermediary.

- Contract development: in some cases (though not all) social impact indicators may be written in to the supplier—intermediary contract.

- Governance 1: the social mission within IT impact sourcing may provide a hook that snags greater senior management support from the client than traditional outsourcing, but it may also create a gap in understanding and knowledge that must be bridged.

- Governance 2: the style of relationship management must, at least initially, be one that incorporates more patience and latitude than required within some traditional IT contracts.  Information and knowledge gaps are particularly significant between client and BoP sub-contractors, and all actors tend to be on a learning curve since impact sourcing is too new to contain significant repeat business.  But repeat business is growing and there seems to be a general assumption that clients, intermediaries, and BoP sub-contractors will be forming long-term contractual relationships.

- Performance management: as with contract development, this may include monitoring of the socio-economic impact of the contract within the developing country.

- Management of the working relationship: trust is a key factor.  But not just trust that the IT services will be delivered on time, on cost, on quality but that any explicit or implicit social impact will be delivered and – more importantly – than any implicit corporate reputational gains will also be delivered.  Once again, this emphasises the greater scrutiny that impact sourcing value chains may be subject to compared with traditional IT outsourcing, and the consequent need for intermediaries to exercise care over their reputation and image.

In sum, the limited base of data means this is only a proof of concept that indicates the potential relevance of the model, and points the way for future study.  Within those limits, it appears that Alborz et al’s model can be applied to help understand IT impact sourcing relationships.  While the core stages and elements work as specified, the context of IT impact sourcing and the influence of social mission mean that specific issues do arise.

We hope that the model will be used for further research, exploring in more detail the processes and factors that underpin success – or failure – of IT impact sourcing relationships.

The model can also be used by impact sourcing practitioners as an analytical tool to assess their own client—supplier relations either before or during contract implementation.  Analysis can investigate each of the components of the Alborz et al. framework, adding in the social mission modifications indicated above.

(This item is also available as a CDI Short Paper: http://www.sed.manchester.ac.uk/idpm/research/publications/wp/di/#sp)

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[i] Lee, J.N. and Kim, Y.J. (1999). Effect of Partnership Quality on IS Outsourcing Success: Conceptual Framework and Empirical Validation. Journal of Management Information Systems 15(4), 29-61.

[ii] Kern, T. and Willcocks, L.P. (2002). Exploring relationships in information technology outsourcing: the interaction approach. European Journal of Information Systems 11(1), 3-19.

[iii] Alborz, S., Seddon, P.B., and Scheepers, R. (2003). A Model for Studying IT Outsourcing Relationships. 7th Pacific Asia Conference on Information Systems, Adelaide, Australia. 10th-13th July, 1297-1313.

Steering e-Government Projects from Failure to Success

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.

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The Research Agenda for IT Impact Sourcing

So, what is “impact sourcing” and why is it important?

It is part of a continuum of approaches that clients can take when they outsource IT-related work to bottom-of-the-pyramid suppliers, summarised in Figure 1, adapted from a previous blog entry on IT sourcing from the BoP:

• Exploitative outsourcing seeks to bear down on wages and working conditions in order to minimise costs and maximise profits.
• Commercial outsourcing is a mainstream approach that reflects the steady diffusion of outsourcing from cities to large towns to small towns and beyond.
• 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.
• 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 supplier.

 

Figure 1: BoPsourcing Approaches

 

As shown in the diagram, “impact sourcing” is a rather loose agglomeration of a number of these models, defined as “employing people at the base of the pyramid, with limited opportunity for sustainable employment, as principal workers in outsourcing … to provide high-quality, information-based services to domestic and international clients” in order “to create sustainable jobs that can generate step-function income improvement”.

Impact sourcing received a significant fillip in 2011 when the Rockefeller Foundation released its report on “Job Creation Through Building the Field of Impact Sourcing” which suggested that this activity was already well established in countries like India, South Africa and Kenya.  (The definitional quotes above are taken from p2 of that report.)

Report authors Monitor estimated that impact sourcing was already a US$4.5 billion market employing 144,000 people and “has the potential to be a $20 billion market by 2015, directly employing 780,000 socio-economically disadvantaged individuals”.  Rockefeller has subsequently set about funding and encouraging significant growth in this market.

The various terminologies can be confusing and, personally, I prefer the more immediately-meaningful “BoPsourcing”.  However, this new model is clearly already sizeable, and likely to be growing fast in future.  It also – despite the absence from the name – has IT as a foundation: all these types of outsourcing are IT-based and IT-focused whether they involve data entry, digitisation, back-office processing, search engine optimisation support, etc.

In that case, where is the research on impact/BoP sourcing?  The answer is: almost entirely absent as yet.  The journal article on “Social Outsourcing as a Development Tool” is a rare exception, which traces the developmental impact of one initiative using this new model.

In that case, what research should we be doing: what is the impact sourcing research agenda?

A helpful guide comes from two articles recently published in the Journal of Information Technology by Mary Lacity and colleagues: “A Review of the IT Outsourcing Empirical Literature and Future Research Directions” and “Business Process Outsourcing Studies: A Critical Review and Research Directions“.  These papers review the literature to date on IT outsourcing overall, and on BPO specifically, summarise that literature in an overview model, and propose a future research agenda.

Figure 2 – from the first article – summarises the review of IT outsourcing research (including overlaps with BPO research), which boils down to the factors which affect outsourcing decisions by client firms (e.g. whether to outsource or not; or what type of contract to use), and the factors which affect the outcomes of outsourcing (typically the outcomes for the client firm or its relationship with the supplier).

Figure 2: Review of IT Outsourcing Research

 

Given the lack of existing work on impact sourcing, all these relations are yet to be investigated, so Figure 2 already sets a sizeable research agenda.  However, we can tease out more in three ways.

First, because Lacity et al lump rather a lot together into the “outcomes” category.  The nature of the client-supplier relationship is better understood as part of the process by which outcomes are achieved.  From this, we can identify a set of outsourcing process research that could be applied to impact sourcing – from the “COCPIT” approach to maximising client-supplier relations in IT outsourcing, to work on the development of intermediaries in IT outsourcing relations.  Treating decisions as key inputs, the research agenda can be shaped around an Input – Process – Outcome structure.

Second, because the Lacity et al map is of past research.  Their papers also identify generic IT outsourcing research priorities for the future that will apply equally to impact sourcing, including the effect of broader environmental factors on client decisions, such as public attitudes; the capabilities required within suppliers; and the different financial and business models being used.

Third, because impact sourcing is different from mainstream outsourcing: it involves different suppliers, often different intermediaries, different business models, different objectives, etc.  This adds a set of additional research agenda items not previously identified, such as:

• Needs and means for building capabilities within BoP suppliers.
• A broader typology of business models that spans the boundaries of traditional business and traditional development.
• The requirement to judge business models in terms of their accessibility (to lower-income groups), ethicality (e.g. providing a decent income for the suppliers involved) and sustainability (for BoP suppliers, their clients and the intermediaries).
• Understanding that clients may want more than just a financial bottom-line outcome from impact sourcing.
• Analysing the developmental outcomes of impact sourcing, including the effect on the livelihoods of individual suppliers.

Putting all this together, we get the research agenda summary shown in Figure 3.

Figure 3: Impact Sourcing Research Agenda

 

If this research is to be done well, in a way that adds lasting knowledge, it must be well-theorised.  Dealing fully with this issue would require pages of text, but we can identify some examples:

• Inputs and Processes: transaction cost economics can provide a quantitative basis for exploring decisions and business models; resource-/capabilities-based perspectives on organisations offer a more qualitative route (see Mahnke et al 2005).
• Outcomes: the livelihoods framework or Sen’s capability approach can be used to assess the developmental effects of impact sourcing.

Beyond these initial pointers, though, there are many other theoretical foundations waiting to be used.

If you identify some gaps here – i.e. some other priority research issues that need to be addressed, or some other theoretical models that will be appropriate to apply to impact sourcing – then do add your thoughts.

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Understanding Mobiles and Livelihoods

How can we understand the impact that mobiles are having on the livelihoods of the poor?

We all know that mobile phone use has grown exponentially in developing countries.  And that phones are having an increasing impact on the livelihoods of the poor by providing market prices, by supplying health information, by enabling financial transfers, etc.

But we know a lot less about how to conceptualise all this.  Can we just pull some development studies ideas off-the-shelf?  Or do we need to do more than this?

A new working paper in the Development Informatics series – “Understanding Mobile Phone Impact on Livelihoods in Developing Countries: A New Research Framework” – argues the livelihoods approach is a good starting point.  But that it needs modification.

The livelihoods approach suggests four potential impacts of mobiles on the assets that underpin all livelihoods:

−        Asset substitution: saving time and costs for journeys, but adding costs for mobile expenditure.

−        Asset enhancement: greater efficiency in use of other assets e.g. for agricultural production or relationship-building.

−        Asset disembodiment: the conversion of assets to digital form e.g. the codification of social contacts, or digitisation of money.

−        Asset exchange/combination: e.g. the exchange of airtime or m-cash.

Important intermediaries – mobile operators, their agents, community-based organisations and NGOs, family and friends – help shape the extent and distribution of these impacts.  These are also shaped by the three livelihood strategies to which the poor apply mobiles:

−        Maintaining existing livelihoods and mitigating vulnerability: e.g. use of mobiles to maintain social networks that can assist in an emergency.

−        Expanding and enhancing existing activities: e.g. using mobiles to obtain greater earnings from existing produce, to save more effectively, or to obtain greater remittances from existing social contacts.

−        Diversifying into new activities: e.g. employment in the mobile sector, or use of mobiles to complete micro-work tasks.

These components of the livelihoods approach – assets, intermediating organisations and institutions, strategies – are therefore very useful in understanding the role of mobiles in development.  But the approach also has four shortcomings.

i. Reconceiving assets.  The assets pentagon was developed within the context of traditional agriculture, and it underplays recent understandings of the importance of networks, agency and capabilities in development.  It would be better replaced by a three-way categorisation of assets:

−        resource-based assets (RBA) that are tangible (physical, financial, natural capital);

−        network-based assets (NBA) that derive from connections (social, political, cultural capital);

−        cognitive-based assets (CBA) comprising human and psychological capital including competencies (knowledge, skills, attitudes).

ii. Incorporating information.  Mobiles expose a truth that information is the lifeblood of development, and yet it is essentially ignored within the livelihoods framework.  Information is essential to individuals’ awareness of, and ability to utilise, all assets; and the use of information requires other assets to turn it into decisions and livelihood strategies.  Those processes need to be recognised within any understanding of livelihoods.

iii. Recognising bottom-up processes.  The livelihoods framework tends to see intermediating processes and structures in macro-terms (government, laws, policies, culture).  But diffusion and use of mobile has equally been shaped by more bottom-up processes including the functioning of specific market transactions, and user appropriations and adaptations within poor communities.  The latter need to be recognised.

iv. Categorising impacts.  If the core interest is impact of mobiles, the homogenising of that impact into a single “livelihood outcomes” box is not particularly helpful.  Better to borrow from the ICT4D value chain and differentiate a broadening scale: from direct changes in behaviour, through process-level outcomes, to broader impacts on development goals.

Adapting the livelihoods framework on the basis of these four points, we arrive at the revised framework shown below, for use in conceiving and researching the impact of mobiles on livelihoods in developing countries: 

The framework immediately helps to identify possible research questions:

−        What is the effect of contextual factors – processes of globalisation, processes of technological innovation, population migration, etc – on the livelihoods impact of mobiles?

−        How are markets and market processes shaping the impact of mobiles, including the tension between seeking to make markets more inclusive, and markets’ tendency towards exclusion and inequality?

−        What exactly is the impact of mobiles on the substitution, enhancement/diminution, disembodiment, exchange and combination of livelihood assets at the household level?

−        Are mobiles forging new forms of connection to the intermediating structures and processes that govern the enactment of livelihood strategies?

−        What new livelihood strategies are mobiles enabling; how do they come into being and come to sustain; and what impact are they having?

−        What factors mediate the conversion of mobile behavioural outputs into broader outcomes and development impacts?

No doubt there are many other questions that the framework can be used to identify and conceptualise.

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Can a Process Approach Improve ICT4D Project Success?

Many ICT4D projects fail[1].  There are various mooted reasons for this, of which I will highlight five here:

• Failure to involve beneficiaries and users: those who can ensure that project designs are well-matched to local realities.
• Rigidity in project delivery: following a pre-planned approach such as that mandated by methods like Structured Systems Analysis and Design Methodology, or narrow use of LogFrames.
• Failure to learn: not incorporating lessons from experience that arises either before or during the ICT4D project.
• Ignoring local institutional capacities: not making use of good local institutions where they already exist or not strengthening those which could form a viable support base.
• Ineffective project leadership: that is unable to direct and control the ICT4D project.

This does not represent an exhaustive list of causes but one can find one or more of them in many failed ICT4D projects.  And they are deliberately selected because – if we turn them around to their mirror-image project enablers – they become the five key components of the “process approach” to development projects: beneficiary participation; flexible and phased implementation; learning from experience; local institutional support; and sound project leadership.

The process approach arose during the 1980s and 1990s as a reaction to the top-down, “blueprint” approach[2].  The blueprint approach was particularly associated with use of foreign technologies in rural development projects.  Perhaps, then, it is no surprise that it has filtered through into ICT4D practice.

Equally, though, one can see elements of the process approach in action in successful ICT4D projects:

• Beneficiary participation: the M-PESA mobile finance project in Kenya incorporated the views of users into project design through user trials and volunteer focus groups.
• Flexible and phased implementation: India’s agricultural information kiosk project, e-Choupal, used a pilot approach for all new services; introducing them one-by-one and planning designs and scale-up on the basis of those pilots.
• Learning from experience: Grameen incorporated the lessons from its microfinance projects into the design and delivery of its Grameen Phone programme of rural mobile telephony.
• Local institutional support: Brazil’s community computing project, the Committee to Democratise Informatics, is founded on the development of local institutional capacity through each of the schools it creates.
• Sound project leadership: returning to M-PESA again, Vodafone put skilled project managers in place in Kenya in order to make the project work.

Each one of these projects – and one can no doubt find many others within the ICT4D field – demonstrates more than one of these five elements.  This is not unexpected since the process approach can be understood not as five rather arbitrarily-categorised, separate components but as an integrated whole.  It can be conceived like a wheel (see figure below[3]): flexible, phased implementation being the tyre that absorbs the bumps as the project goes along, feeding contextual information to learning from experience: the central axle from which the spokes of participation, local institutions and leadership radiate, giving strength to the whole.

 Figure 1: The ICT4D Process Approach Wheel

The process approach also reconceives the notion of success in ICT4D projects.  Instead of seeing either success or failure as cross-sectional, final judgements on a project, instead – like a point on the rolling wheel – any judgement must be seen as contingent and passing.  Instead of success and failure, we would therefore talk of multiple “successes” and “failures” as the project proceeds.  Any overall judgement would rest on relevance of the ICT4D solution, opportunities for capacity building, and sustainability.  A process approach contributes to each of these.

And for ICT4D practitioners, a process approach can help pose questions:

• What is the role of beneficiaries throughout the project’s stages?
• What is the mechanism for changing direction on the project when something unforeseen occurs?
• What is the basis for learning on the project?
• What local institutions can be used for project support?
• What is the nature of project leadership?

And so forth – these and other questions can lead to concrete plans, schedules and roles which incorporate the lessons of the process approach into future ICT4D activities.

This blog entry is a summary of the online working paper “Can a Process Approach Improve ICT4D Project Success?“, published in the University of Manchester’s Development Informatics series.

If you have experiences of ICT4D project failure or success to share, please do so via comments.

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[1] Good data on success/failure of ICT4D projects is embarrassingly limited, and more historical than recent.  See: “Information Systems and Developing Countries: Failure, Success and Local Improvisation“

[2] A foundational paper is David Korten’s article “Community Organization and Rural Development: A Learning Process Approach“

[3] Source: Bond, R. & Hulme, D. (1999). Process Approaches to Development: Theory and Sri Lankan Practice. World Development, 27(8), 1339-1358

Evaluating Computer Science Curriculum Change in African Universities

Effective use of ICTs in Africa requires a step change in local skill levels, including a step change in ICT-related university education.  Part of that process must be an updating of university computer science degree curricula – broadening them to include ICT and information systems subjects, moving them from the theoretical to the applied, and introducing modern teaching and assessment methods.

International curricula – such as those provided by organisations like the IEEE and the ACM – offer an off-the-shelf template for this updating.  But African universities are going to face challenges in implementing these curricula, which were designed for Western (typically US) rather than African realities.  And when curriculum change is introduced, African universities and Education Ministries need a systematic means to evaluate progress, to highlight both successes and shortcomings, and to prescribe future directions.

A recently-published case study – “Changing Computing Curricula in African Universities: Evaluating Progress and Challenges via Design-Reality Gap Analysis” – investigates these issues, selecting the case example of Ethiopian higher education.  In 2008, Ethiopia decided to adopt a new IEEE/ACM-inspired computing curriculum.  It moved from three-year to four-year degrees, introduced a new focus on skills acquisition, more formative assessment, greater diversity in teaching approaches, and a more practical engagement with the subject matter.

Most literature and most advice about changes to ICT-related curricula has tended to focus on content rather than process.  As a result, there has been a lack of systematic guidance around the implementation of curriculum change; particularly in relation to evaluation of change.

In the Ethiopian case, the design-reality gap model was brought into play since it has a track record of helping evaluate ICT-related projects in developing countries.  The explicit objectives and implicit expectations built into curriculum design were compared with the reality found after implementation.  This enabled assessment of the extent of success or failure of the change project, and also identification of those areas in which further change was required.

The gaps between design and reality were assessed along eight dimensions – summarised by the OPTIMISM acronym, and as shown in the figure below.

 

Using field visits to nine universities and interviews with 20 staff based around the OPTIMISM checklist, the evaluation process charted the extent to which the reality – some 18 months after the curriculum change guidance was issued by the Ministry of Education – matched the design objectives and expectations.

The evaluation found a significant variation among the different checklist dimensions, as shown in the figure below. 

For example, the new curriculum expected a combination of:

• Specialist computer classrooms to support advanced topics within the subject area, and
• General-purpose computer classrooms to teach computer use and standard office applications to the wider student body.

Yet in most universities, there were no specialist computing labs, and ICT-related degrees had to share relatively basic equipment with all other degree programmes.

Similarly, the spotlight focus of curriculum change on new student skills had tended to throw into shadow the new university staff skills that were an implicit design requirement for change to be effective.  The evaluated reality was one in which a largely dedicated and committed teaching community was hampered by the limitations of their own prior educational experience and a lack of computing qualifications and experience.

But progress in other areas had been much better.  The national-level environment (milieu) had changed to one conducive to curriculum change.  Formally, two new Educational Proclamations had been issued, supporting new teaching methods and new learning processes; and two new public agencies had been created to facilitate wider modernisation in university teaching.  Informally, Ministry of Education officials were fully behind the process of change.

Similarly, university management systems and structures had been able to change; assisted by the flexible approach to structures that was particularly found in Ethiopia’s new universities, and by a parallel programme of business process re-engineering within all universities.

Evaluation using the design-reality gap model was therefore a means of measuring progress, but it was also a means of identifying those gaps that continued to exist and which needed further action.  It thus, for example, led to recommendations of ring-fencing a capital fund for technology-related investments; some redirection of resources from undergraduate to postgraduate in order to deliver the necessary staffing infrastructure; and a reconsideration of some curriculum content to make it more Ethiopia-specific (in other words, changing the design to bring it closer to local realities).

There were challenges in using the design-reality gap model for evaluation of curriculum change: allocation of issues to particular OPTIMISM dimensions, and drawing out the objectives and expectations along all eight dimensions.  Overall, though, the model provided a systematic basis for evaluation, one that was assuredly comprehensive, and one through which findings could be readily summarised and communicated.

The full case study can be found here.  Other pointers are welcome to materials on computer science curriculum change in developing countries, including specific materials on the evaluation of such changes.

 

e-Government Benefits And Costs: Why e-Gov Raises Not Lowers Your Taxes

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[1].

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[2].  The aggregate cost:benefit ratio of e-government projects in Australia was 1.64:1[3].

Rarer still is good quality work from developing countries.[4]  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[5].  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[6];
• the learning curve – often of some years – that must be traversed before e-government applications can be used efficiently and effectively[7];
• 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[8].

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[9].  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.

 

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[1] 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

[2] Kable (2005) Implementing Electronic Government 4, Kable, London

[3] 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

[4] There is a good study of e-government projects in India but it was unable to capture cost data, so focuses only on benefits.

[5] 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.

[6] 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

[7] Poostchi, M. (2003) Implementing E-government, MBA dissertation, Carleton University, Ottawa, ON

[8] Helbig, N., Gil-Garcia, J.R. & Ferro, E. (2009) Understanding the complexity of electronic government, Government Information Quarterly, 26(1), 89-97

[9] 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/523/231

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