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Understanding Inclusive Innovation

27 August 2013 5 comments

If you work on technology, you need to understand innovation.  If you work on technology and development, you need to understand inclusive innovation.

In simple terms, inclusive innovation is the means by which new goods and services are developed for and/or by those who have been excluded from the development mainstream; particularly the billions living on lowest incomes.  So new technologies for the base of the pyramid – mobile phones, mobile services, telecentres, better seed varieties, vaccines, etc – can all be included.

We can chart the rapid rise of interest in inclusive innovation in various spheres.  In the past few years, the World Bank, IDRC, GIZ, OECD and other development agencies have all launched inclusive innovation actions.  India, Thailand, China, South Africa, Indonesia and other national governments have added inclusive innovation elements into their policies.  And – as shown in Figure 1 below – academic publications related to the topic have been growing fast.

IncInnov GS Publications

Figure 1: Google Scholar Academic Publications for “Inclusive Innovation”

But what exactly is “inclusive innovation”?

The growth in publications means an increasing diversity of views, which now demand some overall conceptualisation.  This has two key aspects: firstly who, secondly what.

Inclusive innovation means someone is being included.  But who?  It must be some group that is typically marginalised within or excluded from mainstream processes of development.  Sometimes this may be women or youth or the disabled or the elderly.  But dominant attention has been on “the poor”; those on lowest incomes which may typically be defined as some small number of US dollars – US$1, US$1.25, US$2, US$2.50, etc – per day.  (There is also the issue of who, within this group, is then to be included via the innovation: will it be the whole group or just some part: perhaps the less-poor, or the men, or the adults?  This raises further questions about representation and heterogeneity and inequalities within the excluded group.)

And if (some of) this group are now being included in some way, in what are they being included?

It seems most helpful to understand the different views as a “ladder of inclusive innovation” (see Figure 2 below): a set of steps, with each succeeding step representing a greater notion of inclusivity in relation to innovation.  In more detail these are:

  • Level 1/Intention: an innovation is inclusive if the intention of that innovation is to address the needs or wants or problems of the excluded group.  This does not relate to any concrete activity but merely the abstract motivation behind the innovation.
  • Level 2/Consumption: an innovation is inclusive if it is adopted and used by the excluded group.  This requires that innovation be developed into concrete goods or services; that these can be accessed and afforded by the excluded group; and that the group has the motivation and capabilities to absorb the innovation.  All of those stages could be seen as sub-elements of this level of the inclusive innovation ladder, though all will be required for consumption so they are not hierarchical sub-steps (as appear in later levels).
  • Level 3/Impact: an innovation is inclusive if it has a positive impact on the livelihoods of the excluded group.  That positive impact may be understood in different ways.  More quantitative, economic perspectives would define this in terms of greater productivity and/or greater welfare/utility (e.g. greater ability to consume).  Other perspectives would define the impact of innovation in terms of well-being, livelihood assets, capabilities (in a Senian sense), or many other foundational understandings of what development is.  For those with concerns about inequality, this could include a condition that the benefits were restricted to the excluded group, or were greater than those achieved by ‘included’ groups using the innovation.  One can therefore differentiate an absolute vs. relative notion of inclusive impact of innovation, the latter being a sub-step above the former.
  • Level 4/Process: an innovation is inclusive if the excluded group is involved in the development of the innovation.  It is highly unlikely that the entire group could be involved so – as noted above – this immediately shrinks down to “members of the excluded group”.  This level needs to be broken down according to the sub-processes of innovation: invention, design, development, production, distribution.  These would create a set of sub-steps with, for example, an assumption of greater value of inclusion in the upstream elements than the downstream elements.  Further complicating matters, the extent of involvement is equated with different levels of inclusion.  Again, there would be sub-steps akin to those seen when discussing participation in development, with higher sub-steps representing deeper involvement.  Borrowing from Arnstein’s[1] ladder of participation, sub-steps can include: being informed, being consulted, collaborating, being empowered, controlling.
  • Level 5/Structure: an innovation is inclusive if it is created within a structure that is itself inclusive.  The argument here is that inclusive processes may be temporary or shallow in what they achieve.  Deep inclusion requires that the underlying institutions, organisations and relations that make up an innovation system are inclusive[2].  This might require either significant structural reform of existing innovation systems, or the creation of alternative innovation systems.
  • Level 6/Post-Structure: an innovation is inclusive if it is created within a frame of knowledge and discourse that is itself inclusive.  (Some) post-structuralists would argue that our underlying frames of knowledge – even our very language – are the foundations of power which determine societal outcomes.  Only if the framings of key actors involved in the innovation allow for inclusion of the excluded; only then can an innovation be truly inclusive.

IncInnov Ladder Model

Figure 2: Understanding the Different Levels of Inclusive Innovation

The levels are akin to steps on a ladder because each level involves a gradual deepening and/or broadening of the extent of inclusion of the excluded group in relation to innovation.  In general each level accepts the inclusion of the levels below, but pushes the extent of inclusion further.  Thus, for example, those concerned with inclusion of impact accept – necessarily – the value and actuality of inclusivity of intention and consumption, but feel this is not sufficient to fully justify the label of ‘inclusive innovation’.

The corollary is that a commentator standing at any particular step of the ladder would not regard views or practice at lower levels to represent true inclusive innovation.  Taking the example of those at the base-of-the-pyramid as the excluded group, commentators at Level 4 would feel innovation is only inclusive if those on low incomes somehow participate in the innovation process; perhaps typically in the development of the new good or service.  A new good or service which benefited the poor without this (i.e. an innovation at Level 3 developed non-participatively by a large firm or by government) would not be regarded as an inclusive innovation.

One may also detect a move from the positive towards the normative in ascending the ladder, with a decreasing number of real-world examples as one ascends.  Thus there are many examples of new goods and services which are developed and consumed by excluded groups, some of which have a beneficial impact.  Involvement of excluded groups in innovation processes is not frequent but it does occur.  However, one may be harder-pressed to find examples of structures let alone widely-shared knowledge frames in practice: these levels may represent aspirations more than realities at present.

Armed with the ladder model, we will find that dialogue, research, policy-making, practice, etc. are easier to achieve because all parties have the basis for framing their own understanding of inclusive innovation, and that of others.

However, this is just a first attempt.  So comments or pointers to other conceptualisations of inclusive innovation are welcome.

(This model and related text are extracted from “Inclusive Innovation: Definition, Conceptualisation and Future Research Priorities” by Richard Heeks, Mirta Amalia, Robert Kintu & Nishant Shah; a conference paper for AIE 2013 which can be found at: http://bit.ly/IncInnov)


[1] Arnstein, S.R. (1969) A ladder of citizen participation, Journal of the American Institute of Planners, 35(4), 216-224

[2] For further details on the relation between innovation systems and inclusive innovation, see: Foster, C. & Heeks, R. (2013) Conceptualising inclusive innovation: modifying systems of innovation frameworks to understand diffusion of new technology to low-income consumers, European Journal of Development Research, 25(3), 333-355 [see also: https://www.escholar.manchester.ac.uk/uk-ac-man-scw:198318]

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Development Informatics Research Must Stop Ignoring ICT’s Downsides

The dominant narrative within ICT4D associates digital technologies with positive impacts, and has tended to underplay negative impacts.  What are the implications for development informatics research?

Jessops Amazon

There has been a recent cluster of global evidence about negative impacts:

We can begin to understand this via the ICT impact/cause perspectives diagram shown below.

ICT Impact Cause Diagram

Unless we adopt an extreme perspective, we can recognise that in terms of impacts, it would have been equally easy to pull out a set of positive evidence about ICT.  But it is positive and negative together that tell the whole story.  And in terms of causes, there is no simple relationship between the technology and the impacts identified above but, instead, a socio-technical foundation.

This leads to a number of implications for the academic field of development informatics:

Balance: are we balanced enough in terms of the impacts we associate with ICTs in our work?  Pushing a largely positive narrative can have the effect of making our work seem like hype; a relentless monotone buzz to which those working in development become habituated, and start to ignore.

Preparation: are the policy makers and practitioners who use our work prepared for what’s coming?  Development informatics research needs to engage with the negative impacts, providing research users with an understanding of those impacts and, where possible, some strategies for amelioration.

Analytical Tools: do we understand what is behind these ICT trajectories?  ICTs are not the direct cause of the impacts outlined above; they are an enabler of particular economic and political interests.  Development informatics needs to ask the age-old question: cui bono?  Who benefits when high street shops close?  Who benefits from cyber-repression?  Who benefits from printed guns?  Who benefits from pornography?  Cui bono is answered by the analytical tools of political economy.  We need to be answering those questions and using these tools a whole lot more in development informatics.

Advocacy: how do we engage with ICT4D innovation trajectories?  Even as it becomes more open and more decentralised, the trajectory of innovation can still be shaped by debate, by advocacy and by activism.  Development informatics has always been an engaged area of academic endeavour, not stuck in the ivory tower.  We have often worked with those seeking to deliver the positive impacts of ICT4D.  The challenge now is to work more with those seeking to avoid the negative impacts of ICT4D.

If you see other implications, then let us know . . .

From Digital Divide to Digital Provide: Spillover Benefits to ICT4D Non-Users

31 August 2011 5 comments

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[1] 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.


[1] 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

Using Actor-Network Theory in ICT4D Research

30 July 2011 14 comments

Actor-network theory (ANT) has been around since the 1980s, and significantly utilised in some disciplines, such as information systems.  But – oddly – it has hardly been applied at all in development studies, including within ICT4D research.  That is recently starting to change but to give some further impetus, we organised an international workshop in June 2011: “Understanding Development Through Actor-Network Theory”.  You can find online a working paper series derived from the workshop.

Actor-network theory began as a means to explain how science works, such as the operation of scientific laboratories and projects.  However, it has subsequently grown to be seen as a full-blown social theory.  In particular, ANT says three things.

First, it says, “Hey, sociologists, you’ve been so obsessed with humans that you’ve been ignoring all the objects in the world.  But those objects – documents, mobile phones, plants, websites, etc – play an important role; just like humans they shape the people and other objects around them. So ANT is going to treat them the same as people, and call them both ‘actors’.”

Second, it says, “Hey, sociologists, because you’ve been so obsessed with humans, you think that society and social contexts or social factors are what explains everything in life.  But you’re wrong.  In fact you’re so wrong you’ve got your basic equation of life the wrong way around.  You think that society explains what goes on in the world.  Nope.  What goes on in the world is what explains society.  So ANT is going to focus on the mechanics of life: the ways in which people and objects interact with each other.”

Third, it says, “Hey, more recent French-type sociologists, you’ve been so obsessed with breaking things apart to understand the bits of grammar and bits of history that made them that your idea of researching a clock would be to smash it to pieces with a hammer.  That is not how to research a clock.  To research a clock you need to understand how all the pieces got put together, following the network of people and objects that interacted in order to make that clock.  So ANT is going to focus on how networks are assembled.”

Much ANT writing is horribly obscure, so full of hideously complex sentences and words that the writers must surely have done this deliberately in the hope of avoiding Oscar Wilde’s dictum, “to be intelligible is to be found out”.  But, done well, ANT can tell a good story and even occasionally give you the sense that you are suddenly seeing the world in a whole new light.  A whole new light that – because it’s about dynamics and innovations and technology and networks – seems especially relevant to ICT4D.

A couple of good entry points – good because they each provide a fairly clear and portable conceptual framework that you can re-use in your own research – are:

–         Callon, M. (1986) Some elements of a sociology of translation: domestication of scallops and the fishermen of St Brieuc Bay, in: Power, Action and Belief, J. Law (ed.), Routledge & Kegan Paul, London, 196-233

–         Law, J. & Callon, M. (1992) The life and death of an aircraft: a network analysis of technical change, in: W.E. Bijker & J. Law (eds), Shaping Technology/Building Society, MIT Press, Cambridge, MA, 21-52

Also not too unreadable is Latour’s Reassembling the Social, though had Latour been shot half-way through the dialogue with a PhD student that is reported in the book, I can’t help feeling a verdict of justifiable homicide would have been returned.

Although, as noted, use of ANT in ICT4D research has been limited there have been enough examples, at least from developing country cases within the information systems field, that we get a sense of the questions ANT is good at answering:

–         How do you explain the trajectory of an ICT4D project?

–         What role does technology play in an ICT4D project?

–         How does power manifest itself in an ICT4D project?  How were apparently powerless actors able to influence the direction of an ICT4D project?  How was it that apparently powerful actors didn’t get their way on an ICT4D project?

–         How does a particular ICT4D innovation (be it a new technology or business model or idea) diffuse or scale-up or sink without trace?

–         How did a particular ICT4D impact or ICT4D policy come about?

If you’ve identified other ICT4D questions that are especially suitable for an ANT lens, then do contribute them.

If you want an example of applying ANT in ICT4D that also includes a reflection on the pros and cons of the theory, and some thoughts on applying it in your research, I can recommend:

–         Stanforth, C. (2007) Using actor-network theory to analyze e-government implementation in developing countries, Information Technology and International Development, 3(3), 35-60

There is also a discussion of the relation between ICT4D and ANT in:

–         Rubinoff, D.D. (2008) Towards an ICT4D geometry of empowerment: using actor-network theory to understand and improve ICT4D, in: Developing Successful ICT Strategies, M.H. Rahman (ed.), Information Science Reference, Hershey, PA, 133-154

And feel free to comment on other ICT4D literature that makes use of ANT.

If you would like to participate in discussions about ANT, you can join our online forum on LinkedIn at: http://www.linkedin.com/groups/ActorNetwork-Theory-in-Development-Studies-3995328

We are also populating a group on Mendeley with reference details, and welcome contributions: http://www.mendeley.com/groups/1255941/actor-network-theory-in-development-studies/

Finally, the first of our working paper series delves into some of these issues in greater detail: “Development Studies Research and Actor-Network Theory

The First e-Government Research Paper

30 April 2011 4 comments

Who wrote the first research paper about e-government?

I’m going to nominate W. Howard Gammon writing in Public Administration Review in 1954.  Please comment with earlier nominations, but otherwise, W. Howard Gammon becomes the godfather of e-government research.

Of course Gammon’s review article: “The Automatic Handling of Office Paper Work” doesn’t mention e-government: according to Heeks & Bailur’s “Analyzing e-Government Research”, “The term ‘electronic government’ seems to have first come to prominence when used in the 1993 U.S. National Performance Review, whereas ‘e-government’ seems to have first come to prominence in 1997.”

However, Gammon is writing about the use of ICTs in the public sector: which is a common definition of e-government.  Hence, his is an article about e-government, even though computing was just in its infancy with, as he notes, some technical literature available but very little written for a management audience and nothing – until his review article – for a public management audience.

In some ways things were very different then.  Even by around 1990, there were more than 1 million computers in use across the US federal government.  Back in 1954, there were roughly forty computers installed in total, half “large-scale” such as the UNIVAC I (weight 13 tonnes, c.2,000 operations per second, memory <1kb; cost c.US$10m in today’s terms) and half punch-card-based “baby computers” such as the IBM-604 (c.100 cards per minute, program of up to 40 steps, monthly rental cost c.US$5,000 in today’s terms plus a shift team of 2-10 supervisors and operators).  Most were in the Department of Defense with a few in the Atomic Energy Commission, Census Bureau and Bureau of Standards.  There was a pilot application to automate selection of optimum procurement bids, and plans to apply computers for use in air traffic control, taxation and weather forecasting.  These applications were part of a broader expenditure (in 1952) of more than US$1.5billion (c.US$12billlion in today’s terms) on “adding, accounting and other business machines” within US public and private sectors combined; by 2008, total spending on ICTs in the US was roughly US$1.2trillion annually – a one hundred-fold increase in spending on ICTs.

However, the more striking thing that echoes across the decades is not how different but how similar the issues in the 1950s were to those we still face today.  The following examples illustrate:

a) Skill Set: E-Gov Needs Systems Skills More Than Technical Skills: “…it is not necessary to know how to make, or even to repair, these machines in order to make use of them.  For the public administrator … the emphasis needs to be placed on how and when to use these new devices” (p63).  Just so, for those learning today about e-government, understanding technical aspects is of relatively limited importance; much more important is to understand the application of the technology.  Put another way, e-government must be approached from an information systems not an information technology perspective: “it is a systems job which depends more on knowledge of what must be done, and why, than on knowledge of what makes electronic computers tick.” (p73).

b) Skill Set: E-Gov Needs Hybrids: a socio-technical approach is required that combines understanding of the ‘business’ of government with knowledge about the application of technology.  Such a combination could be undertaken within a team: a “joint effort between the business managers and the engineers, so that engineers may learn enough about the businessman’s problem to translate the requirements of the job into machine procedure and so that management staff may learn enough about the capabilities and limitations of electronic machines to allow management staff to visualize how the new devices can be applied and how the … organization must be changed to take full advantage of the capabilities of the new equipment.” (p67)  Such a combination might also be effected within a single person to create a socio-technical “hybrid” individual.  But in that case, it will be far easier to hybridise a mainstream manager than an IT person: “As the Metropolitan Life Insurance Company found in its study, it is far easier to teach company management specialists what they need to know about the possibilities and limitations of electronic data processing than it is to teach electronic engineers about the internal operating problems of the life insurance business.” (p73).  The exact same findings were reported in the 21st Century for e-government in Chapter 12 of Heeks’ book “Implementing and Managing eGovernment”.

c) Implementation: E-Gov Needs Re-Engineering Not Just Automation: More than thirty-five years before Hammer’s exhortations to stop “paving the cowpaths” and stop “automating the mess”, Gammon had already identified the limited gains to be made from automation, and the need to start improvements by re-engineering the business processes of govenrment: “One quick generalization may be made: the introduction of an electronic information processing system is not like buying a new adding machine which can be plugged in as part of an existing established clerical routine. It would be foolish and wasteful to make the large investment required to install electronic methods without first conducting a careful study which begins with considering the basic objective of the operation” (p73) … “The effective application of electronic methods in a given organization requires a rethinking of its organization and procedures. When electronic methods are applied, many of the intermediate reports and steps in the transmission of information become unnecessary and should be eliminated.” (p72)

d) Implementation: E-Gov Needs Top Management Support: “Rapid progress can be made during such an investigation only if the management representatives are high enough in the organization to make the broad decisions regarding the methods of operation” (p67).  In the same way, more recently, top management support is still identified as a key necessity in successful e-government projects and its absence as a key cause of e-government project failure.

e) Implementation: Politics Matters in E-Gov: “there are organizational, procedural, economic, and social problems which must be resolved before automatic operation of … an office can be realized.” (p63).  Some of these problems relate to internal politics given the danger that ICTs in government will cause “the disturbance of established bureaucratic empires” (p72), thus making political factors an important cause of e-government failure.  This further explains why top management support is needed in implementation of e-government: “It also requires a broad point of view which looks to the good of the organization as a whole without being too much concerned about the effects of changes in methods on particular vested interests in the agency.” (p73).

f) Impact: E-Gov Affects Clerical Not Professional and Managerial Jobs: for lower-level clerical jobs, ICT brought the threat of “lowered prestige, relative decrease in real income, threat of unemployment, and routinization of many office skills” (p66).  That has come to pass: around 25% of federal white-collar employees were in clerical/typing work in 1952.  By the mid-2000s that had fallen to roughly 7%, largely as a result of new technology[1].  Meanwhile, skilled professionals would either be upskilled: “our accountants will then be free to do the more important job of analyzing and interpreting financial reports for management.” (p66) or unaffected: “there is no real possibility that the executive or the top administrator will become obsolete as the result of foreseeable advances in the use of electronic equipment.” (p73).  And there were already signs that shortages of ICT professionals would slow the rate of e-government: “the shortage of qualified experts to design, build, program, and service these electronic data processing systems will keep this possible revolution from taking place rapidly.” (p67).  More than fifty years later, Heeks (2006:101) still writes “The dearth of competencies is a major brake on the spread of e-government”.

g) Impact: E-Gov Impact Assessment Fails to Account for Total Cost of Ownership: there have always been ambitious claims for ICT in government e.g. that it “can make substantial savings and render better service” (p63).  But on the savings side, e-government impact calculations often focus just on cost savings (e.g. of labour) but fail to include the costs of ICT.  When the latter are taken into account, overall gains can disappear.  Gammon’s paper is suggestive of this: he reports a case where preparation time for monthly reports fell from forty person-days to six/eight hours.  Given clerical costs (in 1954 prices) of around US$200 per month, that would represent savings of about US$400 per month.  Yet according to P.B. Hansen in “Classic Operating Systems” the IBM Card Programmed Calculator on which this saving was achieved cost US$1,800 per month in rental to which would have to be added the costs of calculator operations staff.  Government’s tendency for lavish spending on ICTs was also already in evidence with reference to a Navy-organised symposium on “moderately-priced” computers; that criterion being defined as those costing (in today’s terms) less than US$1million.

Gammon’s paper is as much a review of then-current ideas about computing, drawn largely from the private sector, for a public sector audience as it is about computing in the public sector.  However, this focus means it still stands eligible for recognition as the first e-government journal article.

How, overall, should we read it?  I invite you to choose from its reflecting:

– “La plus ça change, la plus c’est la même chose”

– The failure of e-government practitioners to take note of key lessons known right from the start of IT in the public sector, given the continuing absence in e-government projects of many of the skill and implementation factors identified all those years ago.

– The failure of e-government researchers to find much new to say: you can see these same issues still in the conclusions of many of today’s e-gov journal articles.

Click here to link to a blog entry on the first application of e-government in a developing country.


[1] Though total US federal employment in 2009 – just under 2.1 million – was almost exactly what it was in 1952; albeit with a near-halving in DoD numbers.

The ICT4D Value Chain

28 December 2010 7 comments

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.

Development Studies Journal Ranking Table

17 June 2010 17 comments

The following represents a first attempt at a “league table” for development studies journals.

Rank Journal Citation Score
1 World Development 6.04
2 Journal of Development Studies 4.90
3 Oxford Development Studies 4.06
4 Development Policy Review 3.20
5 Studies in Comparative International Development 2.40
6 Sustainable Development 2.39
7 European Journal of Development Research 1.90
8 Development and Change  1.89
9 Information Technology for Development 1.58
10 Information Technologies and International Development 1.55
11 Journal of International Development 1.46
12 Development 1.33
13 Third World Quarterly 1.30
14 Public Administration and Development 1.21
15 Development in Practice 1.03
16 Progress in Development Studies 0.88
17 Electronic Journal of Information Systems in Developing Countries 0.81
18 African Development Review 0.79
19 Gender and Development 0.58
20 Enterprise Development and Microfinance 0.45
21 Canadian Journal of Development Studies 0.45
22 IDS Bulletin 0.40
23 Information Development 0.37
24 Forum for Development Studies 0.17
25 Journal of Third World Studies 0.11
     
  Comparator Journals  
  Journal of Development Economics 10.90
  Human-Computer Interaction 4.06
  Environment and Planning D 3.42
  Information Systems Journal 2.89
  The Information Society 1.64
  Mountain Research and Development 0.91

Basis

– Selection was on the basis of development studies journals that appear in various other tables or lists.  However, development economics journals (inc. Economic Development and Cultural Change, Journal of Development Economics, Review of Development Economics, and The Developing Economies) were not included.  If you have suggestions for additions (or deletions), then let me know.

– Citation score is calculated by taking papers published in each journal in 2008 and identifying how many times each paper is cited in Google Scholar.  The average number of cites per paper was then divided by the average number of years since publication.  Very roughly, then, the score equates to average number of GS citations per paper per year.

– All papers published in 2008 were used if less than 20 were published; a sample of at least 20 building outwards from the mid-year issues was used if more than 20 were published.

– One anomalous paper, with over 10 times the citations of any other (a pattern not seen in any other journal), was omitted from African Development Review.  Had this been included, ADR would place seventh.

– This exercise will be repeated and expanded in future years.  What is presented here should only be seen as a first, fairly rough-and-ready set of figures.  The original data used for the calculations can be found here.

Notes

– The raw figures shown here should not be compared with the impact factor scores under Planning and Development provided in ISI’s Journal Citation Reports.  The rankings can be compared.

– Different disciplines have different citation habits and norms.  Specifically, if economists cite more highly, then those development studies journals that include a greater proportion of development economics papers may gain a greater overall citation score.

– Conversely – and requiring further investigation – in compiling the figures, I got some sense that papers in special issues tend to receive fewer citations.  Journals that have a lot of special issues may receive a lower overall citation score.

Reflections

– These average figures provide no guidance on whether your individual paper would be cited more highly if published in one journal or another.  However, the rankings could be used to provide guidance or evidence on the general impact of a selected journal.  (Of course recognising that overall impact is about more than just citations.)

– The figures suggest that, beyond the obvious top two of JDS and World Development, there may be some mismatch between previous subjective ratings and actual impact.  For example, Oxford Development Studies and Development Policy Review rank 3rd and 4th here, yet are unrated by most other journal rating schemes.

– There is a moderate mismatch with the ISI JCR 2008 impact factor ranking.  Most notably, four of the top ten journals here do not appear at all in the ISI list including the two top-cited ICT-for-development journals.

Other Data

– The table below gives details of other ranking and rating data on development studies and some development economics journals.

High->Low Aston 2008 (4->0) CNRS 2008 (1*->4) Ideas 2010 (/731) SJR 2010 (/118) WoK 2010 (/43) ABDC 2010 (A*->C) ABS 2010 (4->1) SoM 2010 (4->1) Heeks 2010 (/25)
African Development Review       65 43     2 18
Canadian Journal of Development Studies       78 42       21
Development     666 28         12
Development and Change 2 2   15 19 B 2   8
Development in Practice       32         15
Development Policy Review     270 10 8       4
Economic Development and Cultural Change     117   24 A 3 4  
Electronic Journal of Information Systems in Developing Countries                 17
Enterprise Development and Microfinance                 20
European Journal of Development Research     438 48         7
Forum for Development Studies                 24
Gender and Development       73         19
IDS Bulletin       70 37       22
Information Development                 23
Information Technologies and International Development                 10
Information Technology for Development                 9
Journal of Development Economics     43   36 A* 3 4  
Journal of Development Studies 2 2 152 2 26 A 3 4 2
Journal of International Development 1 3 292 22   B 1 1 11
Journal of Third World Studies       86         25
Oxford Development Studies     192 58       1 3
Progress in Development Studies       30         16
Public Administration and Development       62 39 A 2 2 14
Review of Development Economics     129 26 32     1  
Studies in Comparative International Development       23 31 A     5
Sustainable Development       9 11       6
The Developing Economies     474   35 B      
Third World Quarterly   2   29 30 A 2   13
World Development 3 1 134   9 A 3 3 1
High->Low Aston 2008 (4->0) CNRS 2008 (1*->4) Ideas 2010 (/731) SJR 2010 (/118) WoK 2010 (/43) ABDC 2010 (A*->C) ABS 2010 (4->1) SoM 2010 (4->1) Heeks 2010 (/25)


Key
 
– ABS – UK Association of Business Schools: http://www.the-abs.org.uk/?id=257

– Ideas – citation data from RePEc project of paper downloads: http://ideas.repec.org/top/top.journals.simple.html (economics and finance research)

– SJR – Scopus-based citation ranking: http://www.scimagojr.com/journalrank.php?category=3303&area=0&year=2008&country=&order=sjr&min=0&min_type=cd (development journals)

– SoM – Cranfield School of Management: https://www.som.cranfield.ac.uk/som/dinamic-content/media/SOM%20Journal%20Rankings%202010%20-%20alphabetical.pdf

– WoK – 2008 impact factor in ISI Journal Citation Reports under Planning and Development

– All other data from Harzing’s Journal Quality List: http://www.harzing.com/jql.htm

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