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Crowdfarming: Platform-Enabled Investment in Nigerian Agriculture

20 November 2018 Leave a comment

Crowdfarming is fast becoming the easiest means of investing in agriculture in Nigeria. On one hand, we have smallholder farmers who have agricultural skills and farmland but lack sufficient finance.  On the other hand, there are individuals who have money to invest but lack agricultural skills and access to farmland. Intermediated by digital platforms (Figure 1), crowdfarming entails sourcing funds from several individuals (the crowd) to invest in smallholder agricultural enterprises. In some cases, investors receive returns in the form of agricultural produce, while in other cases returns are financial – that is, investors receive their initial investments plus profits [1].

Figure 1: Snapshot of a Nigerian digital platform-enabled crowdfarming webpage (source: Thrive Agric, 2018)

There are currently at least seven active (indigenous) digital platform-enabled crowdfarming agribusinesses in Nigeria. These are: Thrive Agric, Farmcrowdy, Growcropsonline, Growsel, Farmkart, eFarms and Agropartnerships. Drawing from research carried out with Thrive Agric, it is understood that investors (also called ‘farm subscribers’) are considered part-owners of farms they invest in. The contractual agreement between the crowdfarming platforms and farm subscribers provides details on the returns on investment per farm enterprise, length of the production/investment cycle (e.g. see Figure 1), insurance cover on funds invested, and secure online payments. Farm subscribers also receive regular information on the farm’s progress through email alerts and notification of final payments at the end of the production cycle. Subscribers can also apply to visit the farms they invest in.

In Nigeria, crowdfarming platforms are tapping into a large pool of financial investors who are mostly educated individuals, located in urban areas in Nigeria or in the diaspora. Thrive Agric’s model has attracted over 3500 investors, located in 10 countries (Figure 2), who have invested in nine agricultural value chains, directly supporting the livelihoods of over 12,000 farmers (Figure 3), since its inception in 2017.

Figure 2: Geographic spread of Thrive Agric’s crowdfarming subscribers investing in smallholder agricultural production across Nigeria (source: author’s field research, 2018)

Figure 3: Geographic spread of Nigerian states where crowdsourced funds are invested by Thrive Agric (source: author’s field research, 2018)

Despite its growing recognition as a means of investing in agriculture, some factors still constrain the scaling-out of the crowdfarming model beyond its current scope. These factors include:

  • Low level of awareness and trust issues: according to the Chief Technical Officer of Thrive Agric, not many people are aware of crowdfarming and its benefits to both investors and farmers in Nigeria. As such, there is still the potential for more people to invest but getting the word out there, cost effectively, remains a challenge.
  • Currency and bank transaction issues: currently, investing in Nigeria’s agriculture through crowdfarming can only be carried out in Nigeria’s currency (the Naira) due to fluctuations in foreign exchange rates. As a result, investors are required to have a Naira account to participate in this space.

Looking ahead: what does the future hold for Nigeria’s agricultural growth through crowdfarming?

Investing in Nigerian agriculture has been described as key to driving the growth of the sector and Nigeria’s economy in general [2][3]. However, the growth of Nigeria’s agricultural sector has been constrained by a myriad of factors especially those relating to low financial investments in infrastructure, agricultural research, high yielding inputs and information delivery [4]. As agricultural production in Nigeria is still largely rain-fed, the issue of timely access to finance, ahead of the rainy season, remains a reoccurring constraint to the socio-economic growth of farmers (ibid). Figure 2 shows that digital platforms are breaking down barriers to agricultural investments in Nigeria by bridging the gap between investors (both home- and diaspora-based) and smallholder farmers.

However, there is still a lot to understand in terms of the long-term impact of investing in agriculture through digital platform-enabled models like crowdfarming. Research is also needed to ascertain the nature of interaction between these platform models and the existing institutional forms that govern agricultural value chains. This will help broaden our understanding and the broader implications for the distribution of value among stakeholders along agricultural value chains that are platform-enabled.

References

[1] Flynn, P. (2015) What is Crowdfarming, Hazel Blog http://blog.hazeltechnologies.com/article-27-what-is-crowdfarming

[2] Izuchukwu, O. (2011) Analysis of the contribution of agricultural sector on the Nigerian economic development, World Review of Business Research, 1(1): 91-200

[3] Udoh, E. (2011) An examination of public expenditure, private investment and agricultural sector growth in Nigeria: bounds testing approach, International Journal of Business and Social Science, 2(13): 285-292

[4] Phillip, D., Nkonya, E., Pender, J. and Oni, O.A (2009) Constraints to Increasing Agricultural Productivity in Nigeria: A Review (Vol. 6). International Food Policy Research Institute, Washington, DC

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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

Social Media Analytics for Better Understanding of the Digital Gig Economy

27 April 2018 1 comment

Owing to the proliferation of digital platforms facilitating online freelance work such as Upwork, Fiverr and Amazon Mechanical Turk, the number of digital gig workers has been continuously increasing worldwide. In 2015, there were as many as 48 million digital gig workers [1]; between 2016 and 2017, a 25% increase in the number of such workers was reported [2].

Digital gig work is indeed attractive to many, with a number of benefits that such independent workers are perceived to enjoy, e.g., flexible working hours, reduced transportation costs, wide range of projects to choose from. However, there exist potentially distressing issues, e.g., lack of job security, tough competition, substandard wages, which are especially pronounced in developing country settings [3]. Whereas traditional media such as news were unable to pinpoint or bring attention to these concerns, social media analysis–done manually by Cision in 2017–provided a window to the thoughts of independent workers which led to the fine-grained identification of issues that they are faced with [4].

As part of the currently ongoing Social Media Analytics Research and Teaching @ Manchester (SMART@Manchester) project funded by the University of Manchester Research Institute (UMRI), we aim to automatically gain insight into people’s perceptions of digital gig work, based on their posts on social media platforms such as Twitter and Facebook, as well as on review sites such as Glassdoor.

Specifically, we wish to test the currently prevailing assumption that digital gig work is experienced differently in the Global South compared to the Global North. Workers tend to make comparisons with their local benchmarks (i.e., office-based work), and it is believed possible that in the Global North, digital gig work is worse than prevailing benchmarks, whereas in the Global South it is better.

The following are some of the research questions that will be addressed as part of this case study.

  1. How do digital gig workers feel about their jobs?
  2. Which topics pertaining to decent work standards do they frequently talk about?
  3. Are there any differences—in terms of sentiments and topics—across different geographic locations, or across genders?

The first question can be answered by opinion mining while the second is addressable by topic identification. To determine whether there are differences with respect to opinions and topics, between the Global North and South or between genders, results from opinion mining and topic identification need to be combined with social media content metadata (e.g., geographic locations). 

In the way of opinion mining, we are currently investigating the use of an automatic emotion identification tool called Illuemotion which was developed by University of Manchester final-year Computer Science student, Elitsa Dimova. The web-based tool, a screenshot of which is provided below, is underpinned by a neural network model that analyses tweets to determine the most dominant emotions expressed, which can be any of anger, fear, joy, love, sadness, surprise and thankfulness.

The image below shows one of the tweets directly fetched by the tool from Twitter (via their API) when supplied with “#upwork” as input query. The tweet, which speaks of hidden dangers of being a digital gig worker, was detected by Illuemotion as expressing sadness and fear. One of our next steps is to apply the tool on a collection of thousands of tweets to allow us to analyse them across different geographic regions as well as genders.

As we are analysing data that pertains to human emotion, ethical considerations are being taken into account, especially bearing in mind that we also do not wish to compromise any of the digital gig workers who are social media users. For example, many Twitter users are unaware that what they post publicly can be used to identify or (reverse) look them up. They also have a right to be forgotten (i.e., they can delete their posts as well as their accounts). Overall what this means for us researchers who make use of their data is that in scholarly publications, we should provide only aggregated results and ensure that we do not include any identifiable information. These and other ethical considerations were discussed in detail in the recently concluded symposium in the Academy of Management Specialised Conference on Big Data entitled, “Ethical and Methodological Considerations for Management Research in the Digital Economy” held at the University of Surrey from the 18-20th April.

As well as two other SMART@Manchester case studies, the above described research questions on perceptions of digital gig work and our proposed approaches will be presented in the upcoming 4th International Workshop on Social Media World Sensors (Sideways 2018) co-located with the 15th European Semantic Web Conference to be held in Heraklion, Crete, Greece from the 3rd-7th June.

References:

[1] Kuek, S.C. et al. (2015) The Global Opportunity in Online Outsourcing. World Bank, Washington, DC. Available at: http://documents.worldbank.org/curated/en/138371468000900555/The-global-opportunity-in-online-outsourcing

[2] Lehdonvirta, V. (2017) The online gig economy grew 26% over the past year, The iLabour Project, Oxford Internet Institute. Available at: http://ilabour.oii.ox.ac.uk/the-online-gig-economy-grew-26-over-the-past-year/

[3] Heeks, R. (2017) Decent Work and the Digital Gig Economy: A Developing Country Perspective on Employment Impacts and Standards in Online Outsourcing, Crowdwork, etc, Centre for Development Informatics, Global Development Institute, University of Manchester. Available at: http://hummedia.manchester.ac.uk/institutes/gdi/publications/workingpapers/di/di_wp71.pdf

[4] Rubec, J. (2017) Study: The Dark Side of the Gig Economy, Cision. Available at: https://www.cision.com/us/2016/12/the-dark-side-of-the-gig-economy/

Industry 4.0 to Digital Industrialisation: When Digital Technologies meet Industrial Transformation

23 April 2018 1 comment

As digital technologies increasingly permeate all aspects of our physical world, many believe that we are moving into a hyper-connected, intelligent society and economy. One of the emerging concepts underpinning this potential transformation is the Fourth Industrial Revolution, or Industry 4.0.

What is Industry 4.0?

According to the proponents of Industry 4.0, each industrial revolution has shifted manufacturing opportunities and patterns of specialisation, enabled by key technological developments as illustrated in Figure 1.

industry 4.0 timeline2

Figure 1. Industry 4.0 trajectory (source: Author based on [1] and [2])

The vision of Industry 4.0 includes digitalising all elements of industrial activities to achieve a highly flexible, distributed production and service network. Through advancements of technologies such as Artificial Intelligence (AI), advanced automation and robotics, 3D printing, big data and Internet of Things, a tighter integration of digital and physical elements will allow machine-to-machine interactions and a mode of operation that provides more efficient production. In an absolute Industry 4.0 world, every object and all machinery in the factory will be interconnected to share data and operate without much human presence [2].

This of course, is only viable when an advanced level of technological, social and economic integration occurs. Given that technologies progress at an unpredictable rate, and that their real-world applications often lead to unexpected outcomes, it is difficult to know how (or whether) industry 4.0 will manifest. Nevertheless, recent studies warn us that this industrial change can drive uneven global development even further.

Shifting focus from manufacturing to “digital industrialisation”

AI and robotics may take 800 million jobs by 2030 in the world, and emerging economies such as China and India could be hit the hardest, losing 236 and 120 million jobs by 2030 respectively [3]. The costs of operating robots and 3D printers in furniture manufacturing in the US is predicted to be cheaper than Kenyan wages in 2033 [4], indicating that the lower labour cost may no longer be the main attribute ensuring competitiveness in a global market.

Given that industrialisation has long been considered to play a vital role in economic growth of developing countries, the development implications of this transformation have been mainly discussed in manufacturing, albeit with a negative perspective: changing patterns and geography of production [2] (such as re-shoring manufacturing back to high-income countries) and technological unemployment in labour-intensive manufacturing industry [4].

However, I would like to bring more attention to the development of the “digital” side of this industrial transformation – which I will refer to as digital industrialisation. This is a work-in-progress concept that encompasses not only the technological integration of digital technologies into manufacturing, but also the extensive re-organisation of an economy to digitalise production processes.

Some work on this has already been carried out within the DIODE (Development Implications of Digital Economies) network [5], but we need more research to build a better picture of the current and future landscape: for example, how digital industrialisation can take place in small-scale, localised production networks in the global South [6] and how the economic models emerging within the digital economy (such as platform economy and gig economy) may impact innovation and manufacturing processes globally [7].

I will further argue that this impending industrial transformation is best understood as a continuous process rather than a goal to reach – something that terms such as industry 4.0 tend to project. Rather than focusing on the potential winners and losers in this race, we need to elucidate how this transformation can take place in an inclusive and sustainable manner.


[1] Lasi, H., Fettke, P., Kemper, H. G., Feld, T. and Hoffmann, M. (2014) ‘Industry 4.0.’ Business and Information Systems Engineering, 6(4), pp. 239–242.

[2] Hallward-Driemeier, M. and Nayyar, G. (2018) Trouble in the making? The future of Manufacturing-led development. Washington, DC: The World Bank.

[3] Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., Ko, R., Sanghvi, S., (2017) Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute.

[4] Banga, K. and te Velde, D. (2018) Digitalisation and the future of manufacturing in Africa. London: Overseas Development Institute.

[5] Bukht, R. and Heeks, R. (2017) Defining, conceptualising and measuring the digital economy. GDI Development Informatics Working Paper 68. Centre for Development Informatics, University of Manchester, UK.

[6] Seo-Zindy, R., & Heeks, R. (2017) ‘Researching the emergence of 3D printing, makerspaces, hackerspaces and fablabs in the global south: A scoping review and research agenda on digital innovation and fabrication networks‘, Electronic Journal of Information Systems in Developing Countries, 80(1), pp. 1–24.

[7] UNCTAD (2017) The ‘new’ digital economy and development, UNCTAD Technical Notes on ICT for Development no.8. Geneva: United Nations Conference on Trade And Development.

Do Outsourcing Clients Want Decent Digital Work?

22 December 2017 Leave a comment

There are growing concerns that digital gig work – supplied by platforms like Mechanical Turk, Upwork, Freelancer, etc – falls short of decent work standards.  (For further details see the working paper, “Decent Work and the Digital Gig Economy”.)  To address this, and as discussed previously in this blog, there are plans to encourage new ethical standards.

But almost all evidence on this to date comes from workers.  The voices of only a few platforms have been heard, and there seems to be no evidence from clients.  Yet clients are central to decent digital work standards: if they create incentives for platforms to improve, that will be a powerful motivation.  Conversely, if clients don’t care, it removes a key driving force from the gig economy ecosystem.

So, what evidence can be found?

Here, I summarise Babin, R., & Myers, P. (2015) Social responsibility trends and perceptions in global IT outsourcing, Proceedings of the Conference on Information Systems Applied Research, v8, n3663.  This in turn summarises results from surveys conducted during 2009-2014 by the International Association of Outsourcing Professionals.

The survey was specifically about corporate social responsibility (CSR) in IT outsourcing.  So: a) it is not exactly about digital gig work but a broader category of outsourcing; b) the survey may encourage some level of “virtue signalling”: respondents wanting to appear more socially-responsible than they are in reality.  Nonetheless, it offers some relevant guidance about client attitudes to decent digital work.

In general terms, half the respondents were US-based; half were non-US; a fair reflection of gig work clients.  They ranged from SMEs to multinationals and just over half had a written CSR policy.  They are thus larger and more formally-CSR-inclined than the modal micro-enterprise client for digital gig work, but important given the increasing involvement of firms in gig outsourcing.

Key findings include the following:

– Nearly half “often” or “always” gave preference to outsourcing providers who had demonstrable CSR capability.

– Nearly two-thirds expected CSR consideration to become “more” or “much more” important in their future IT outsourcing.

– The largest factor in evaluating CSR capabilities of an outsourcing provider was its labour practices (see figure below).

Figure: Key factors in evaluating the CSR capabilities of an outsourcing provider, survey median (IAOP, 2009-14)

At least for this group of clients, then, the type of labour practices covered by proposed decent digital work standards were the top CSR issue; and CSR was quite widespread as a determinant in digital-related outsourcing (only 5% said they never used CSR as a determinant).

This gives some basis for believing – at least among larger clients for digital gig work – that an appetite exists for better employment and working conditions; an appetite that can encourage platforms to change.

Decent Digital Work and the FairWork Foundation

31 October 2017 1 comment

How can we improve standards for digital gig workers: those undertaking micro-work and online freelancing via platforms like Upwork and Mechanical Turk?

The recent research paper – “Decent Work and the Digital Gig Economy” – explains why such standards are needed.  With up to 70m workers worldwide registered for online work and growth rates of 20-30% per year, this is already a sizeable activity.  It is especially popular with the c.80% of workers based in middle- and low-income countries, who often see online work as better than local alternatives.  However, this ignores the chronic precarity and structural inequality associated with such work: damaging outcomes that will only spread if nothing is done.

But what should be done?

The paper develops an inventory of “Decent Digital Work” standards.  This is a comprehensive set of guidelines that integrates two things: first, the global decent work standards set by the ILO; second, the actions needed to address specific digital gig economy problems.

A key value for this inventory is as a comparator with other decent work initiatives.  For example, the paper analyses the way in which two major initiatives – SA8000, and the Ethical Trading Initiative – do and do not cover the requirements for decent digital work.

Below, a further comparison is undertaken, between the Decent Digital Work standards, and the criteria adopted by the FairWork Foundation; an initiative aiming to rate and certify gig economy platforms.  The table indicates those elements which are the same in both standards; those where a completely-different element is included; and those where there is some variation in the element.

From this, three things can be concluded:

a) A number of Decent Digital Work standards are absent in the FairWork Foundation certification criteria. Several of these relate to the broader context for work, would be outwith the scope of an individual platform, and therefore are not relevant to platform certification. However, those identified under “Employment” and “Work Conditions” can form part of a further discussion to consider their relevance to certification.

b) Some elements (e.g. around access to digital work opportunities, and accounting for worker costs other than unpaid time) speak to the particular conditions of gig workers from the global South. This is the location for the great majority of gig workers: already for digital gig workers; increasingly for physical gig workers. As such, the FairWork Foundation must ensure its global North origins do not skew its focus.

c) The FairWork Foundation should review how prevalent the non-competition and non-disclosure agreement problems are, and whether they are worth including. (Human review of task instructions – something unlikely to be practicable for most platforms – appears to have been dropped from later versions of the certification criteria; hence, its inclusion in brackets.)

As noted in the Decent Work and the Digital Gig Economy paper’s action research agenda, next steps here would be:

– Survey of worker, client and platform views about identified standards.

– A multi-stakeholder dialogue to agree a minimum set of certification standards and evaluation methods.

– Parallel research on the impact of standards and certifications in the gig economy, and analysing the costs and benefits of interventions such as standards and certifications at micro- and macro-level.

This is just one example of the application of the Decent Digital Work standards.  We hope you can identify other uses . . .

The Demographics of Digital Development

13 April 2017 2 comments

Any emergent digital development paradigm will be shaped by three changing demographics of ICT usage: geographical, maturational and experiential.

Geographically, we have already moved from domination of the old Internet world (the US and Europe) to domination of the new Internet world (emerging nations of the global East and South), as summarised in the table below[1].  Use of digital technology in developing countries[2] now represents the majority not minority global experience.

 

Region % Share in 2001 % Share in 2017
RISING SHARE
Africa 1% 9%
Middle East 1% 4%
Latin America/Caribbean 5% 10%
Asia 32% 50%
FALLING SHARE
North America 30% 9%
Oceania 2% 1%
Europe 29% 17%

Regional Share of Global Internet Users (2001, 2017)

 

Maturationally, there are growing numbers of digital natives: defined as those 15-24 year olds with five or more years of online experience[3].  While only around one-fifth of the youth cohort in developing countries are digital natives (compared to four-fifths in the global North), youth in the global South as twice as likely to be digital natives as the total population, and so they have a disproportionate role which might be worth specific encouragement.  Given they see ICTs as more important and more beneficial than others do, and given they make proportionately greater use of digital technologies and of social networks, then engagement of digital natives – for example in education or politics – may be enhanced by ensuring there are effective digital channels in these sectors.

Experientially, ICT users are experiencing changes that include[4]:

  • Time-space compression: a shortening of timespans for activities moving towards Castells’ notion of “timeless time” in which biological and clock time are replaced by compressed, desequenced notions of time; and a new geography that replaces physical distance with virtual space so that individual experience moves from a “space of places” to a “space of flows”[5].
  • Public to private: moving from shared-use to individual-use models of ICT interaction. Voice communication is moving from public payphones to shared mobile phones to individually-owned mobile phones.  Internet access is moving from public access telecentres and cybercafés to semi-public home or work computers to personal mobile devices.  The digital experience thus becomes increasingly private and personal.
  • Fixed to mobile: as mobile devices become the dominant means of access to digital infrastructure and content.
  • Text/audio to audio-visual: while it may be premature to call the emergence of a post-literate society, increasing bandwidth and technical capabilities mean digital experiences can increasingly resemble rich, natural real-life experiences rather than the artificial restrictions of just text or just audio.

One can argue that all four cases, represent an increasing presence yet decreasing visibility of the digital as its mediation merges more seamlessly into everyday life and activities.  This growth-but-disappearance of mediation thus represents a final experiential trend – that digital technologies more-and-more intercede between us and our experiences, and yet we notice them doing this less-and-less.  If the medium is the message, our conscious awareness of the message may be diminishing.

All three of these trends – geographical, maturational and experiential – form the emerging background underlying digital development, which is the subject of a Development Informatics working paper: “Examining “Digital Development”: The Shape of Things to Come?”, and will be the topic for future blog entries.

[1] IWS (2017) Internet Usage Statistics, Internet World Stats http://www.internetworldstats.com/stats.htm

[2] http://www.oecd.org/dac/stats/daclist.htm

[3] ITU (2013) Measuring the Information Society 2013, International Telecommunication Union, Geneva http://www.itu.int/en/ITU-D/Statistics/Pages/publications/mis2013.aspx

[4] Barney, D. (2004) The Network Society, Polity Press, Cambridge, UK; Boettiger, S., Toyama, K. & Abed, R. (2012) Natural obsolescence of Village Phone, in: ICTD’12, ACM, New York, NY, 221-229; Molony, T. (2012) ICT and human mobility: cases from developing countries and beyond, Information Technology for Development, 18(2), 87-90; Ridley, M. (2009) Beyond literacy, in: Pushing the Edge, D.M. Mueller (ed), American Library Association, Chicago, IL, 210-213

[5] Castells, M. (2000) Materials for an exploratory theory of the network society, British Journal of Sociology, 51(1), 5-24

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