The Puzzle of Digital Financial Inclusion: A Generation Game?

If we thought that financial inclusion and its digital variant are tightly correlated, we may be in for a surprise, judging from the Global Findex 2017 microdata released by the World Bank last month. Owning a bank account (financial inclusion) and owning a mobile money account (its digital variant) throw a puzzling pattern. I plot the averages of bank account ownership and mobile money account ownership in 144 countries across groups of low to high incomes economies, showing a clear separating trend. The thought is borne by 25 low income countries with the two measures of financial inclusion strongly correlated at 0.7. But as income level steps up (to middle and high incomes level) bank account shares increase while mobile money shares decrease. The final panel is flat at the bottom right: most of the 44 high income countries have more than 80% bank account shares with less than 20% mobile money account shares. The correlation? –0.2. One explanation for this negative correlation can be discounted. The digital variant is not yet a substitute for a bank account: savers cannot yet use their mobile money account on its own or as a substitute to secure property or business investment. As countries move up the economic ladder, the puzzle of separation insists on an explanation.

 

Figure 1. The puzzle of bank account ownership vs mobile money account ownership (number of countries in parentheses) Source: calculated from Global Findex 2017 microdataaccXmobXgroup

I explore an alternative here. In high income economies financial inclusion is nearly universal among adults. Not so in low and middle income economies; on the demand side lower average incomes as well as lack of trust in banks coupled with, on the supply side, weak financial infrastructures combine to leave many adults financially excluded. But the costs of financial services, such as sending and receiving money, have been pared down thanks to mobile technology, especially in low income economies. In Uganda, transfers can be made cheaply and directly from the south west to the north east without recourse to Kampala in the centre.

First in this exploration I show a map of the uneven financial inclusion around the world (https://globalfindex.worldbank.org/ accessed 31 October 2018). Map 1 shows that financial inclusion varies along levels of development. The high income economies of North America, Europe, Australia and New Zealand, are homes to adults with the majority of them having a bank account. Moreover a financial inclusion gradient is discernible with economies around the equator, where many lower and middle income economies are located, reporting lower percentages of account ownership. In particular, available data from African economies in the Global Findex and on the map show how financial inclusion is still a minority story on the continent.

 

Map 1 Financial inclusion around the world 2017, source: Global Findex 2017 report

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But has mobile technology made any difference to financial inclusion? It is increasingly so. A map of ownership of mobile money accounts (those who own an account and use a mobile phone to access it) tells how things have improved (Map 2). Over the last three years, some economies in East Africa such as Uganda or Kenya have accumulated owners of mobile money accounts; West African economies are treading the same path. Although it remains the case that the majority of African economies are home to the majority of adults without a mobile money account (60% or more without one).

 

Map 2 Digital financial inclusion in Sub-Saharan Africa, source: Global Findex 2017 report

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To explore further I build a non-linear multilevel model of accounts for each type of financial inclusion: in one the model explains owning a bank account, in the other owning a mobile money account. The model is non-linear because ownership is an indicator, as well as multilevel because 154,472 adults reside in 144 countries. The models account for country income groups, average national incomes, population, age, gender, education, employment, and personal incomes (quintiles). The most interesting findings relate to the associations with age and gender. I show marginal predictions of age and gender for financial inclusion below.

 

Figure 2. Marginal predictions of financial inclusion (own a bank account), calculated from the Global Findex 2017 microdata

accAgeFem

Figure 2 shows the age gradient of financial inclusion that is consistent with the life cycle effects of incomes and wealth. With age comes accumulation of wealth from earnings that needs to be stored for investment and consumption. So for both genders higher age groups have higher odds of owning a bank account (compared to the youngest age group) in a step-wise manner. The youngest (hollow point ○) and the oldest (solid point ●) form bookends to the predictions; both for men (left) and for women (right). There is also a clear gender inequality, although by age 25 women (diamond ◊, right) already have higher odds than the youngest male group. Thus financial inclusion reflects the life cycle effects of earning and saving.

 

Figure 3. Marginal predictions of digital financial inclusion (own a mobile money account), calculated from the Global Findex 2017

 

 

But the marginal predictions for digital financial inclusion do not conform at all to the life cycle effect (figure 3). Digital financial inclusion does not move lock-step with age. In contrast with traditional financial inclusion, the two oldest age groups have lower odds of owning a mobile money account; instead the highest predicted marginals are attained by the mid-30s. The solid point (● oldest group) for instance is furthest below the hollow point (○ youngest group). Here the two oldest–youngest groups do not form bookends. The gender digital divide is also sharper. For similar levels of other characteristics, no female groups have higher odds of owning a mobile money account than the youngest male group.

The strong age reversion effect (inclusion does not move in lock-step with age but reverts after age 40) suggests a generation effect. This is also consistent with the fact that many of the low income economies are still young while many of the high income economies are already ageing.

The puzzle that digital financial inclusion parts ways with financial inclusion may be driven by the generation effect. But there is no reason to expect that the life cycle effect should disappear soon. Thus the need for financial accounts around the world is likely to grow as adults age, leading to some reconciliation in paths of financial inclusion.

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Using Big Data to Learn from Positive Outliers

29 October 2018 Leave a comment

Why do a few individuals, communities or organisations achieve significantly better results than their peers?  The positive deviance approach tries to answer this question.

The story began in 1990, the Vietnamese government invited Save the Children (SCF) to help overcome the problem of child malnutrition.  Jerry Sternin, the SCF Programme Director, was asked to demonstrate impact within six months and decided to try the idea of positive deviance.  Building on past work[1]he undertook a village survey of child height and weight, looking for positive deviants: children from poor families, living among high malnutrition rates, who were nonetheless well-nourished.

In the pilot survey, he found six such families and began to study them intensively (see Figure 1).  By observing the food preparation, cooking and serving behaviours of these families, he found three consistent yet rare behaviours. Mothers of positive deviants:

  1. washed their children’s hands every time they came in contact with anything unclean;
  2. added to their children’s diet tiny shrimps from the rice paddies, and the greens from sweet potato tops; and
  3. fed their children less per meal but more often: four to five times per day compared to two times in non-positive deviant families.

Sternin and his team then scaled out those simple, affordable, community-inspired practices and, within two years, this had reduced malnutrition by 80% in 250 communities, rehabilitating an estimated 50,000 malnourished children[2].

Figure 1: Jerry Sternin speaking to mothers in a village in Vietnam

The simple power of the positive deviance (PD) approach has led to its successful application in more than 60 countries across the globe[3].  Yet PD still faces a number of challenges to its diffusion and implementation.  As a result, we decided to investigate whether big data might help address those challenges, via a systematic review, published in the Electronic Journal of Information Systems in Developing Countries.

A priori, big data provides opportunities in relation to two main PD challenges.

1. Time, Cost and Sample Size. Relying on in-depth primary data collection, the PD approach is time- and labour-intensive with costs proportional to sample size[4]. As a result, PD sample sizes are traditionally small.  Statistically and practically, this can make it hard to identify positive deviants, given their relative rarity (see Figure 2)[5].  By contrast, cost of gathering big data tends to be very low since it often makes use of already existing “data exhaust” from digital processes.  With big data thus covering large – often very large – sample sizes, greater numbers of PDs can be identified, and generalisation to even-larger populations is easier.

Figure 2: Positive deviants in a normal distribution

2. Domain and Geographic Scope. To date, most applications of PD have been highly concentrated. In a recent systematic literature review[6], 89% of applications in developing countries were in public health, 83% were in rural communities, and just four countries had hosted roughly half of all PD implementations.  A simultaneous review of big data in developing countries, on the other hand, showed datasets and demonstrated value across a much wider set of domains and locations.  As a result, big data could help positive deviance to break from its current path dependency.

To assess these and other benefits that big data may bring to the PD approach – relating to behaviour identification, methodological risk, and scalability – a “big data-based positive deviance” research project has been designed and is underway.  The project is currently identifying positive deviants from large-scale datasets in the education and agriculture domains, with results planned to emerge in 2019.

For further details on the challenges of positive deviance and the opportunities offered by big data, please refer to the review article.

REFERENCES

[1]Wishik, S. M. & Van Der Vynckt, S. (1976) The use of nutritional “positive deviants” to identify approaches for modification of dietary practices, American Journal of Public Health, 66(1), 38–42. Zeitlin, M. F. et al.(1990) Positive Deviance in Child Nutrition: With Emphasis on Psychosocial and Behavioural Aspects and Amplications for Development. Tokyo: United Nations University.
[2]Sternin, J. (2002) Positive deviance: a new paradigm for addressing today’s problems today, The Journal of Corporate Citizenship, 57–63.
[3]Felt, L. J. (2011) Present Promise, Future Potential: Positive Deviance and Complementary Theory.  Lapping, K. et al.(2002) The positive deviance approach: challenges and opportunities for the future., Food and Nutrition Bulletin, 23(4 Suppl), 130–7.  Marsh, D. R., Schroeder, D. G., Dearden, K. A., Sternin, J. & Sternin, M. (2004) The power of positive deviance, BMJ, 329(7475), 1177–1179.
[4]Marsh et al. (ibid.).
[5]Springer, A., Nielsen, C. & Johansen, I. (2016) Positive Deviance by the NumbersPositive Deviance Initiative. Available at: https://positivedeviance.org/background/.
[6]Albanna, B. & Heeks, R. (2018) Positive deviance, big data and development: a systematic literature review, Electronic Journal of Information Systems in Developing Countries.

Organisational Use of Social Media: A Perspective on International Development NGOs

28 September 2018 Leave a comment

Social media have become a ubiquitous phenomenon; no-one would argue nowadays. From sharing personal experiences to connecting with other people the implications of these technologies are beyond the eye can reach. Social media have entered the international development area too. The ICT-for-development field is exploring and trying to understand the potential of social media and emerging technologies such as cloud. The impact of social media for development purposes is still an ongoing research process [2]. The potential of social media in the context of international development covers four broad areas, which are: connecting with others; collaborating with other people; creating and sharing content; and finding, using, organising and reusing content [1].

How organisations make sense of technology use for their goals is an ongoing research topic. This also applies to non-governmental organisations (NGOs). NGOs are active players in the field of international development, both as providers of aid and services to underprivileged communities as well as policy advocates [3, 4]. Despite the enormous diversity of NGOs, a general characterisation of NGOs is nonetheless possible within the context of this study. NGOs have the following five characteristics: institutionalised organisation, separate from the government (non-state), non-profit, self-governing and often some degree of voluntary participation in its activities [5-7].

Table 1. A classification of Northern development NGOs

Table 1 is a classification of Northern development NGOs. “North” in development discourse often means the OECD countries are considered, whereas “South” depicts non-OECD countries [8]. The first generation of NGOs focused on emergency relief and welfare. As a development strategy relief and welfare are mostly a temporary alleviation of the signs of underdevelopment. The second generation brought more attention to small-scale and self-reliant local community development. However, NGOs soon realised the limited impact of this approach, and this led to the third generation, aiming at sustainable systems development, in local public and private organisations that are linked into a supportive national development system. These NGOs are moving from a service delivery role to a facilitating one, where they facilitate other organisations to create capacities, relationships and responsibilities required to address designated needs in a sustainable way [9]. Korten [5] and De Senillosa [10] go even further suggesting the need for a fourth-generation category, which will facilitate the coming together of loosely-defined networks of people and organisations to transform the institutions of global society [5]. Fowler [11] speaks of civic innovation for creating innovative solutions to old and new social problems based on action and support from the citizen base.  A fifth scenario is that development NGOs are beginning to stimulate the role of international and local businesses in the social sustainability of the South [12] or even to take up that role themselves as social entrepreneurs using commercial undertakings to cross-subsidise social interventions [13].

Some NGOs tend to focus solely on emergency aid but nowadays development NGOs often have activities that cover a mixture of these so-called generations or development mindset goals, thus showing a combination of roles the NGOs take within the same organisational entity. NGOs are not static throughout their lifespan and some of their activities may be dropped or evolve to others that fit better with a different NGO role in another column as shown in Table 1.

Organisational Social Media Use for Development NGOs 

More and more development NGOs are harnessing the power of social media to affect change [14]. Social media have been used for activities such as organising community activism, for empowering citizens, and for coordinating emergency or disaster relief efforts [15]. Examples of mapping disaster-struck regions using social media after earthquakes or after hurricanes have shown the potential of crowdsourcing for NGOs involved with relief activities [16-18]. Table 2 illustrates the specific uses of social media for the various roles and accompanying dominant mind-set the development NGOs have as found in research I have undertaken.

NGO’s development mindset Example
Social Media Use in Relation to NGO’s Activities for Emergency Assistance A Dutch NGO developed an emergency app that mapped the needs of communities in need in a disaster struck area. Local communities can relay information on what’s needed via mobile phones (even via text messages), or via Internet-connected devices.
Social Media Use in Relation with Development Activities An NGO created an online community mainly of villages in the global South who share indigenous knowledge and experience, mainly on agricultural practices. The community connected villages from Africa, Asia and Latin America.
Social Media Use when Development Becomes a Self-Reliant Political Process An NGO has created online resources to inform citizens on digital activism. They have also established emergency response capacity and support for bloggers, cyber activists, journalists, human rights defenders, and other civil society activists, that are under threat. 
Social Media Use for Human and Sustainable Development An example of this case is a network of young practitioners from various development NGOs who organised an online (and offline) community to address prejudices in international development and particularly reframing the message and perception of the global South. They have set up an annual online contest to showcase good and bad examples from social media campaigns by Dutch development NGOs.
Social Media Use when Development Goes Beyond Aid

 

An example is one NGO that is transforming from being a crowdsourcing platform for small-scale private initiatives for development projects, toward a social enterprise that will work increasingly with businesses and cities by offering them a “do good” platform for their employees and citizens. This has also changed a North-South dichotomy as the projects are both in the global North as well as in the global South.

Table 2. Specific uses of social media for the various roles and accompanying dominant mindset of development NGOs

If we take the five aforementioned NGO strategic activities and cross-reference these with the four potential activity areas of social media use in the context of aid and development [1], we arrive at an applicability framework as shown in Table 3. The social media activities are sorted along the four areas for each NGO’s strategic developmental activity. This work-in-progress framework provides NGOs with a practical instrument for assessing the use of social media for international development purposes.

Table 3. Classification of social media activities related to development objectives of NGOs

This work-in-progress artefact provides a useful and nuanced starting point for development NGOs to explore their organisational use of social media and align these to the NGO activities as mentioned in the columns of the table. Based on the NGO’s activities one or more columns are relevant for assessing the use of social media. The cells in the table that are found when intersecting the column with the rows provide information on how social media acts for that specific development purpose and social media activity in the context of development in mind. The arrows indicate that these cells are similar to the cell on the left of them.

Social media have the potential of transforming patterns of work and interactions of organisations [19]. For the changing role that NGOs take when development goes beyond aid, this aspect of social media may prove to be very useful.

The framework as shown in Table 3 is being discussed with practitioners from development NGOs to assess its practical contribution. Some are cautious about development outcome of ICT and social media [20]. “Social media for development is a contested process that might amplify rather than dissipate powerful voices, and transform a fairly open online space as a proxy for mediated participation in support of the status quo”, they argue. The table is hoped to help explore such issues further.

References

  1. Zuniga, L. and N. White, Module Web 2.0 and Social Media for Development, in Information Management Resource Kit (IMARK). 2009, FAO.
  2. Heeks, R., ICT4D 2016: New Priorities for ICT4D Policy, Practice and WSIS in a Post-2015 World, in Development Informatics Working Paper Series. 2014, Centre for Development Informatics, Institute for Development Policy and Management, SEED, University of Mancheste: Manchester.
  3. Clarke, G., Non-Governmental Organizations (NGOs) and Politics in the Developing World. Political Studies, 1998. 46(1): p. 36-52.
  4. Atack, I., Four Criteria of Development NGO Legitimacy. World Development, 1999. 27(5): p. 855-864.
  5. Korten, D.C., Getting to the 21st century: voluntary action and the global agenda. 1990: Kumarian Press.
  6. Salamon, L.M. and H.K. Anheier, In Search of the Nonprofit Sector I: The Question of Definitions. 1992: Johns Hopkins Institute for Policy Studies.
  7. Lewis, D. and N. Kanji, Non-Governmental Organizations and Development. 2009: Taylor & Francis.
  8. Mitlin, D.C., A study of relations between Northern and Southern NGOs in Kenya. 2003, London School of Economics and Political Science (United Kingdom).
  9. Korten, D.C., Third generation NGO strategies: A key to people-centered development. World Development, 1987. 15, Supplement 1(0): p. 145-159.
  10. De Senillosa, I., A new age of social movements: A fifth generation of non-governmental development organizations in the making? Development in Practice, 1998. 8(1): p. 40-53.
  11. Fowler, A., NGDOs as a moment in history: Beyond aid to social entrepreneurship or civic innovation? Third World Quarterly, 2000. 21(4): p. 637-654.
  12. Bendell, J. and D.F. Murphy, Partners in Time? Business, NGOs and Sustainable Development. 1999, The United Nations Research Institute for Social Development (UNRISD): Geneva.
  13. Fowler, A., NGO futures: Beyond aid: NGDO values and the fourth position. Third World Quarterly, 2000. 21(4): p. 589-603.
  14. Ørecomm, Social Media in Development Cooperation, ed. R.S. Braskov. 2012, Malmö University and Roskilde University: Ørecomm – Centre for Communication and Glocal Change.
  15. Bresciani, S. and A. Schmeil. Social media platforms for social good. in Digital Ecosystems Technologies (DEST), 2012 6th IEEE International Conference on. 2012.
  16. Crowley, J. and J. Chan, Disaster relief 2.0: the future of information sharing in humanitarian emergencies, in Harvard Humanitarian Initiative. 2011, iRevolution – From Innovation to Revolution: Washington D.C. and Berkshire, UK.
  17. Livingston, S. and G. Walter-Drop, Bits and Atoms: Information and Communication Technology in Areas of Limited Statehood. 2014: OUP USA.
  18. Meier, P. Using AIDR to Collect and Analyze Tweets from Chile Earthquake. iRevolution Blog: From innovation to Revolution 2014 3 April 2014 [cited 2014 3 May]; Available from: http://irevolution.net/2014/04/03/using-aidr-to-collect-and-analyze-tweets-from-chile-earthquake/.
  19. Suarez, D.F., Nonprofit Advocacy and Civic Engagement on the Internet. Administration Society, 2009. 41(3): p. 267-289.
  20. McLennan, S.J., Techno-optimism or Information Imperialism: Paradoxes in Online Networking, Social Media and Development. Information Technology for Development, 2015: p. 1-20.

Big Data and Healthcare in the Global South

The global healthcare landscape is changing. Healthcare services are becoming ever more digitised with the adoption of new technologies and electronic health records. This development typically generates enormous amounts of data which, if utilised effectively, have the potential to improve healthcare services and reduce costs.

The potential of big data in healthcare

Decision making in medicine relies heavily on data from different sources, such as research and clinical data, rather than only based on individuals’ training and professional knowledge. Historically, healthcare organisations have often based their understanding of information on an incomplete grasp of reality on the ground, which could lead to poor health outcomes. This issue has recently become more manageable with the advent of big data technologies.

Big data comprises unstructured and structured data from clinical, financial and operational systems, and data from public health records and social media that goes beyond the health organisations’ walls. Big data, therefore, can support more insightful analysis and enable evidence-based medicine by making data transparent and usable at much broader verities, much larger volumes and higher velocities than was ever available to healthcare organisations [1].

Using big data, healthcare providers can, for example, manage population health by identifying patients at high-risk during disease outbreaks and then take preventive actions. In one case, Google used data from user search histories to track the spread of influenza around the world in near real time (see figure below).

Google Flu Trends correlated with influenza outbreak[2]

Big data can also be used for identifying procedures and treatments that are costly or delivering insignificant benefits. For example, one healthcare centre in the USA has been using clinical data to bring to light costly procedures and other treatments. This helped it to reduce and identify unnecessary procedures and duplicate tests. In essence, big data not only helped to improve high standards of patient care but also helped to reduce the costs of healthcare [3].

Medical big data in the global south

The potential healthcare benefits of big data are exciting. However, it can offer the most significant potential rewards for developing countries. While global healthcare is facing challenges to improve health outcomes and to reduce costs, these issues can be severe in developing countries.

Lack of sufficient resources, poor use of existing funds, poverty, and lack of managerial and related capabilities are the main differences between developing and developed countries. This means health inequality is more pronounced in the global south. Equally, mortality and birth rates are relatively high in developing countries as compared to developed countries, which have better-resourced facilities [4].

Given improvements in the quality and quantity of clinical data, the quality of care can be improved. In the global south in particular, where health is more a question of access to primary healthcare than a question of individual lifestyle, big data can play a prominent role in improving the use of scarce resources.

How is medical big data utilised in the global south?

To investigate this key question, I analysed the introduction of Electronic Health Records (EHR), known as SEPAS, in Iranian hospitals. SEPAS is a large-scale project which aims to build a nationally integrated system of EHR for Iranian citizens. Over the last decade, Iran has progressed from having no EHR to 82% EHR coverage for its citizens [5].

EHR is one of the most widespread applications of medical big data in healthcare. In effect, SEPAS is built with the aim to harness data and extract value from it and to make real-time and patient-centred information available to authorised users.

However, the analysis of SEPAS revealed that medical big data is not utilised to its full potential in the Iranian healthcare industry. If the big data system is to be successful, the harnessed data should inform decision-making processes and drive actionable results.

Currently, data is gathered effectively in Iranian public hospitals, meaning that the raw and unstructured data is mined and classified to create a clean set of data ready for analysis. This data is also transferred into summarised and digestible information and reports, confirming that real potential value can be extracted from the data.

In spite of this, the benefit of big data is not yet realised in guiding clinical decisions and actions in Iranian healthcare. SEPAS is only being used in hospitals by IT staff and health information managers who work with data and see the reports from the system. However, the reports and insights are not often sent to clinicians and little effort is made by management to extract lessons from some potentially important streams of big data.

Limited utilisation of medical big data in developing countries has also been reported in other studies. For example, a recent study in Saudi Arabia [6] reported the low number of e-health initiatives. This suggests the utilisation of big data faces more challenges in these countries.

Although this study cannot claim to have given a complete picture of the utilisation of medical big data in the global south, some light has been shed on the topic. While there is no doubt that medical big data could have a significant impact on the improvement of healthcare in the global south, there is still much work to be done. Healthcare policymakers in developing countries, and in Iran in particular, need to reinforce the importance of medical big data in hospitals and ensure that it is embedded in practice. To do this, the barriers to effective datafication should be first investigated in this context.

References

[1] Kuo, M.H., Sahama, T., Kushniruk, A.W., Borycki, E.M. and Grunwell, D.K. (2014). Health big data analytics: current perspectives, challenges and potential solutions. International Journal of Big Data Intelligence, 1(1-2), 114-126.

[2] Dugas, A.F., Hsieh, Y.H., Levin, S.R., Pines, J.M., Mareiniss, D.P., Mohareb, A., Gaydos, C.A., Perl, T.M. and Rothman, R.E. (2012). Google Flu Trends: correlation with emergency department influenza rates and crowding metrics. Clinical infectious diseases, 54(4), 463-469.

[3] Allouche G. (2013). Can Big Data Save Health Care? Available at: https://www.techopedia.com/2/29792/trends/big-data/can-big-data-save-health-care (Accessed: August 2018).

[4] Shah A. (2011). Healthcare around the World. Global Issues. Available at: http://www.globalissues.org/article/774/health-care-around-the-world (Accessed: August 2018).

[5] Financial Tribune (2017). E-Health File for 66m Iranians. Available at: https://financialtribune.com/articles/people/64502/e-health-files-for-66m-iranians (Accessed: August 2018).

[6] Alsulame K, Khalifa M, Househ M. (2016). E-Health Status in Saudi Arabia: A Review of Current Literature. Health Policy and Technology, 5(2), 204-210.

Is Digital Transformation in Nigerian Agriculture a Myth or Reality?

31 July 2018 1 comment

There is a lot of hype about digital agriculture as the ‘next big thing’ after crude oil in Nigeria. Currently, there is hardly any debate on agricultural development in the Nigerian news and on social media platforms without the use of buzz words such as ‘digital disruption’ or ‘digital transformation’ in describing the future of Nigerian agriculture. But what are these digital innovations causing all the hype? They are digital platforms, developed over the past five years by start-ups, established by young Nigerian entrepreneurs. While some start-ups are self-funded, most have benefited from international funding and incubation programmes provided by the growing number of tech-hubs across Nigeria (see Figure 1) [1] [2].

Figure 1: Number of active tech hubs in West Africa. Source: GSMA (2018a)

The digital platforms currently mainstreamed into the Nigerian agricultural sector mainly utilise mobile applications, web applications and short messaging service (SMS). These platforms are used to provide a range of transactional and information services which can be grouped into four main business models:

  1. Crowdfarming: A venture capital model that sources investment capital to fund several farm enterprises [3].
  2. Agricultural advisory service: This model uses mobile apps, SMS and Unstructured Supplementary Service Data (USSD) to provide tailored information to farmers in all stages of the value chain.
  3. Online farm management information system: This offers a platform for farm owners to provide data about their farms and receive location-specific recommendations.
  4. Online agro-trading: These platforms serve as an avenue for farmers and other value chain actors to advertise their agricultural products to potential buyers.

As research on agro-digital platforms in Nigeria is still at a nascent stage, the magnitude of impact relative to platform usage is still unclear. However, some assumptions are currently driving the perception that these innovations would digitally transform Nigerian agriculture. Two of these assumptions are:

Assumption 1: With the widespread adoption of mobile devices in Nigeria the rural population, who make up the largest share of stakeholders in agriculture, can now participate in the emerging digital agricultural platform economy. In reality, mobile network infrastructures in rural Nigeria are weak or non-existent in some cases. Actively engaging with these platforms requires strong mobile network and reliable internet connection to download apps or access web platforms. 2G remains the predominant mobile network broadband in Nigeria while 3G coverage is centralised in big cities, especially Abuja, Lagos and Port Harcourt (see Figure 2) [3]. Also, the signal strength of both 2G and 3G networks ranges from medium to weak as we move from the urban centres to rural areas – where most farmers are located. This discrimination in mobile network coverage further reinforces the digital divide between the rural and urban population [4], and also shows what groups are more likely to benefit from the growing platform economy in Nigeria.

Figure 2: Mobile network coverage maps (Nigeria) for 2G and 3G respectively. Source: GSMA (2018b)

Assumption 2:  If we assume that farmers have reliable mobile networks and internet access which allows them to download mobile apps or access web platforms, the second assumption is that farmers have the technical skills to use these platforms. Yet most farmers are not ‘tech-savvy’ and some of these digital platforms tend to be different from the conventional voice and SMS platforms with which farmers are more familiar. Not only does this serve as a constraint to fully actualise the affordances of these platforms, it has also resulted in the emergence of ‘digital intermediaries’. These digital intermediaries either help farmers to gain access to digital platforms by performing more skill-intensive tasks such as downloading apps and creating user profiles, or they perform the functions of traditional agricultural intermediaries such as: aggregating produce, standardising and marketing produce on digital platforms, independent of farmers’ involvement on the platform itself. While this is not a bad thing, it is important to understand the role and impact of digital intermediaries in influencing value capture and value sharing along digitally-enabled agricultural value chains.

Transformation is a process and there is great potential for digital transformation in Nigerian agriculture. However, it is said that the apps won’t plough the field [4]; neither would the apps build roads to connect farmers to markets. While the digital tools to facilitate the transformation process already exist, poor infrastructure and digital skill gaps still serve as constraints to actualising the transformational potential of digital innovations in agriculture [5]. To move from ‘potential’ to ‘actual’ transformation requires investment in ICT infrastructures, road networks, electricity, and digital literacy; as well as an enabling policy environment which supports upcoming agro-digital entrepreneurs [6].

Reference

[1] GSMA (2018a) The Mobile Economy: West Africa. GSMA, London https://www.gsmaintelligence.com/research/?file=e568fe9e710ec776d82c04e9f6760adb&download

[2] David-West, O., Umukoro, I.O. and Onuoha, R.O. (2018) Platforms in Sub-Saharan Africa: startup models and the role of business incubation, Journal of Intellectual Capital, 19 (3): 581-616 https://doi.org/10.1108/JIC-12-2016-0134

[3] GSMA (2018b) Mobile Coverage Maps. GSMA, London https://www.mobilecoveragemaps.com/africa

[4] Naruka, P.S., Verma, S., Sarangdevot, S.S., Pachauri, C.P., Kerketta, S. and Singh, J.P. (2017) A study on role of WhatsApp in agriculture value chains, Asian Journal of Agricultural Extension, Economics & Sociology 20 (1): 1-11

[5] Deichmann, U., Goyal, A. and Mishra, D., 2016. Will Digital Technologies Transform Agriculture in Developing Countries? The World Bank, Washington, DC

[6] Akanbi, B.E. and Akanbi, C.O. (2012) Bridging the digital divide and the impact on poverty in Nigeria, Computing, Information Systems & Development Informatics, 3 (4): 83-85

Cloud Computing in China

29 June 2018 Leave a comment

In the last decade, cloud computing has become an important information support technology in different countries. According to research by Synergy Research Group shown in the following figure, the global cloud computing market has been dominated by a few big players, and most of them are from developed countries especially the US. The Chinese firm Alibaba Cloud is catching up and competing with these international technology giants. We explain this phenomenon from the following three aspects.

Cloud Infrastructure Services – Market Share Trend
(IaaS, PaaS, Hosted Private Cloud)

Market Drivers

Since the global economic crisis in 2008, the business sector has been looking for ways to reduce costs. Cloud computing can assist this. It enables the provision of services with five essential characteristics: on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service [1]. Using cloud computing technology, companies do not need to invest heavily in infrastructure when starting a business or offering new services. They only need to pay cloud service providers for what they will use, and do not need to buy expansive IT equipment, which normally involves a high fixed cost plus operational costs related to maintaining, monitoring, and managing IT systems.

Simultaneously, in recent years we have witnessed the rapid development of (mobile) Internet and wide application of Internet-based technologies. In China there exists a huge online and mobile user population with tremendous purchasing power. The business sector calls for technologies like cloud computing to process ubiquitous and intellectual information [2]. Furthermore, cloud computing is characterized by low cost, high efficiency, and environmental benefits, which satisfies the sustainable development principle.

Technology Innovation and Accumulation

Cloud computing enables the integrative use of different existing technologies, including broadband communications, Internet, distributed computing, distributed database, virtualisation technology and distributed processing technologies. These technologies form the basis of cloud computing as an innovative mode of computing (for more information, refer to [3] and [4]). Obviously, dominant technological giants from developed countries have advantages in this field for their accumulated know-how in different technologies. To compete with them, Alibaba as a latecomer has had to innovate in technologies, and build up experience in providing services.

In February 2009 Alibaba Cloud developed its own computing operating system called Apsara, and Alibaba Cloud was the first company to be able to provide 5K cloud computing capability in the world. By November 2016, Alibaba Cloud was a global leading player in cloud computing, and Apsara was selected as one of the 15 most representative scientific and technological innovations of the World Internet. To make these achievements, Alibaba Cloud has engaged in years of collaboration with private companies e.g. Taobao, a Chinese online shopping platform, and the public sector e.g. the Chinese state-controlled railway sector in operating its online ticketing service.

Policy Support

Scholars have confirmed that governments could play an important role supporting companies in developing countries catch up technologically [5][6]. The Chinese government has taken measures to promote the Chinese cloud computing industry. In 2010, the government backed the formation of industrial alliance China Cloud Computing Technology and Industry Alliance (CCCTIA). Significant financial support has been provided. For example in October 2011, a grant of 1.5 billion RMB (c.US$225m) was given to cloud computing pilot demonstration projects. Also a tax allowance was provided for cloud-related R&D initiatives. To set an example for other organisations, different government sectors purchased cloud services from private companies like Alibaba Cloud.

When the cloud computing industry entered into the expansion stage, the government began to get involved in standardisation of technologies and services. The Decree No. 05 issued by the State Council in 2015 was set to regulate the market and ensure its development. At present the cloud computing industry in China has entered into the mature stage, and Alibaba Cloud has been recognized for its high-quality, low-cost services. The Chinese government is pushing hard for the integration of cloud computing and other emerging industries like big data and artificial intelligence. This signals that Chinese companies will be supported to take a lead in these fields in the future.

Authors: Jiaying Li, Ping Gao

Reference

[1] Fortiş, T.-F., Munteanu, V. I., & Negru, V., 2012. Towards a service friendly cloud ecosystem. In Proceedings of the 11th IEEE International Symposium on Parallel and Distributed Computing (ISPDC), pp. 172-179.

[2] Mell, P. & Grance, T. 2010. The NIST definition of cloud computing. Communications of the ACM, 53, 50.

[3] Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I., 2009. Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 25, 599-616.

[4] Zhang, Q., Cheng, L., & Boutaba, R., 2010. Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications, 1, 7-18.

[5] Gao, P., & Yu, J., 2014. Has China caught up in IT? Communications of the ACM, 53 (8), 30-32.

[6] Landini, F. & Malerba, F., 2017. Public policy and catching up by developing countries in global industries: a simulation model. Cambridge Journal of Economics, 41, 927-960.

Measuring the Big Data Knowledge Divide Using Wikipedia

Big data is of increasing importance; yet – like all digital technologies – it is affected by a digital divide of multiple dimensions. We set out to understand one dimension: the big data ‘knowledge divide’; meaning the way in which different groups have different levels of knowledge about big data [1,2].

To do this, we analysed Wikipedia – as a global repository of knowledge – and asked: how does people’s knowledge of big data differ by language?

An exploratory analysis of Wikipedia to understand the knowledge divide looked at differences across ten languages in production and consumption of the specific Wikipedia article entitled ‘Big Data’ in each of the languages. The figure below shows initial results:

  • The Knowledge-Awareness Indicator (KAI) measures the total number of views of the ‘Big Data’ article divided by total number of views of all articles for each language (multiplied by 100,000 to produce an easier-to-grasp number). This relates specifically to the time period 1 February – 30 April 2018.
  • ‘Total Articles’ measures the overall number of articles on all topics that were available for each language at the end of April 2018, to give a sense of the volume of language-specific material available on Wikipedia.

‘Big Data’ article knowledge-awareness, top-ten languages*

ko=Korean; zh=Chinese; fr=French; pt=Portuguese; es=Spanish; de=German; it=Italian; ru=Russian; en=English; ja=Japanese.
Note: Data analysed for 46 languages, 1 February to 30 April 2018.
* Figure shows the top-ten languages with the most views of the ‘Big Data’ article in this period.
Source: Author using data from the Wikimedia Toolforge team [3]

 

Production. Considering that Wikipedia is built as a collaborative project, the production of content and its evolution can be used as a proxy for knowledge. A divide relating to the creation of content for the ‘Big Data’ article can be measured using two indicators. First, article size in bytes: longer articles would tend to represent the curation of more knowledge. Second, number of edits: seen as representing the pace at which knowledge is changing. Larger article size and higher number of edits may allow readers to have greater and more current knowledge about big data. On this basis, we see English far ahead of other languages: articles are significantly longer and significantly more edited.

Consumption. The KAI provides a measure of the level of relative interest in accessing the ‘Big Data’ article which will also relate to level of awareness of big data. Where English was the production outlier, Korean and to a lesser extent Chinese are the consumption outliers: there appears to be significantly more relative accessing of the article on ‘Big Data’ in those languages than in others. This suggests a greater interest in and awareness of big data among readers using those languages. Assuming that accessed articles are read and understood, the KAI might also be a proxy for the readers’ level of knowledge about big data.

We can draw two types of conclusion from this work.

First, and addressing the specific research question, we see important differences between language groups; reflecting an important knowledge divide around big data. On the production side, much more is being written and updated in English about big data than in other languages; potentially hampering non-English speakers from engaging with big data; at least in relative terms. This suggests value in encouraging not just more non-English Wikipedia writing on big data, but also non-English research (and/or translation of English research) given research feeds Wikipedia writing. This value may be especially notable in relation to East Asian languages given that, on the consumption side, we found much greater relative interest and awareness of big data among Wikipedia readers.

Second, and methodologically, we can see the value of using Wikipedia to analyse knowledge divide questions. It provides a reliable source of openly-accessible, large-scale data that can be used to generate indicators that are replicable and stable over time.

This research project will continue exploring the use of Wikipedia at the country level to measure and understand the digital divide in the production and consumption of knowledge, focusing specifically on materials in Spanish.

References

[1] Andrejevic, M. (2014). ‘Big Data, Big Questions |The Big Data Divide.’ International Journal of Communication, 8.

[2] Michael, M., & Lupton, D. (2015). ‘Toward a Manifesto for the “Public Understanding of Big Data”.’ Public Understanding of Science, 25(1), 104–116. doi: 10.1177/0963662515609005

[3] Wikimedia Toolforge (2018). Available at: https://tools.wmflabs.org/

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