Why do people use and abandon smartwatch-based activity tracking functionality?

The activity tracking function of wearable devices is becoming more and more popular. A report from Insight indicated that 13 million wearable devices were carrying the activity tracking function, from a total of 19 million such devic [1]. It is clear from this that almost 69% of devices have the activity tracking function, which also shows the huge market for wearable devices, including smartwatches. Users adopt this function to record their daily activities, track their actions, and monitor their sleep duration and quality. Users could use this function to improve their habits and customs or remind them to do some exercise. However, even though the function is popular, some issues still exist. One significant phenomenon is the rapid adoption of activity tracking devices, but with little sustainable and long-term use. For example, a 2017 report found that around 30% to 70% activity tracking products were abandoned after only a few months [2]. This phenomenon – the rapid adoption of activity tracking devices but subsequent limited use of functions – is of interest to study both academically and practically.

The ‘tracker’ was first invented by Dr Yoshiro Hatano in 1956 and aimed to combat obesity by counting users’ steps and thus encouraging them to take more exercise. This is the embryonic form of activity tracking [3]. Modern activity tracking then appeared, applied to various devices, including mechanical machines and wearable devices. In recent years, with the spread of innovation in advanced electronic technology as a new popular lifestyle, Levy notes the increasing interest in and adoption of these tools [4]. Most activity tracking functions are carried on wearable devices, such as smartwatches and smartphones, of which the smartwatch is one of the most notable and widely worn examples [5]. Hence, when investigating activity tracking on wearable devices, studying the smartwatch could be more representative and convenient. Moreover, the research of Harrion argues that participants have started to give up using the activity tracking function on different devices, including the smartwatch [6]. This illustrates that there are barriers to the users adopting the activity tracking on smartwatches. 

My own research investigates the adoption conditions of smartwatch-based activity tracking by identifying the facilitators and barriers. It employed a mixed-methods research approach that contains both quantitive and qualitative research, involving 10 semi-structured interviews and a questionnaire with 213 valid respondents. Through semi-structured interviews, data regarding personal usage from experience on the activity tracking function was gathered and analysed. We obtained key facilitators and barriers from the interview, and then used these as the main questions of the questionnaire, which was administered online with results being analysed using SPSS. 

The survey shows that 96.7% of the responders’ adoption frequency was decreasing. This indicates that most users reduce their usage frequency over time. Also, 47% of participants were not satisfied with the activity tracking function, while only 9% were satisfied. 59% of participants agreed there are barriers that exist to the adoption of smartwatch-based activity tracking.

After the analysis, the identified key facilitators and barriers are detailed in Figure 1. The key facilitators are activity tracking capabilities, design, smartwatch functionality, interaction and improvement of living habits. Among these factors, ‘activity tracking capabilities’ and ‘improving lifestyle’ are the two most important. The main barriers include five perspectives: data, technical, interaction and user-friendliness, design and social comparison. Each of the perspectives contains its own sub-barriers. 

Figure 1 Facilitators and barriers of smartwatch-based activity tracking adoption

Using ANOVA and T-test, we compared the different facilitators and different barriers. ‘Activity tracking capabilities’ and ‘improving living habits’ were regarded as the main points attractive to users, with 89.70% and 64.3% of participants supportive, respectively.

Table 1 Facilitating factors affecting activity tracking adoption.

As the table above indicates, during the long-term usage of smartwatch-based activity tracking, users consider ‘activity tracking capabilities’ as the most vital encouraging factor, while ‘smartwatch functionality’ was the least important. In addition, based on the different mean-values of the other three factors, their mean-value was equal to 4.16, 3.6 and 3.24, respectively (improve living habits > design and appearance > interaction and user-friendliness). In this case, among these five facilitators, ‘activity tracking capabilities’ and ‘improving lifestyle’ had more positive promotional effects of encouraging the users to adopt than the other three.

Table 2 The degree of influence of the barriers

Table 2 above provides evidence to explain the degree of influence of the five barriers. Thus, the mean of each factor shows the degree of influence compared to the others. The data indicate that ‘technology’ and ‘data’ were the most important barriers to users’ adoption of the smartwatch-based activity tracking function. However, according to participants, the barrier ‘social comparison’ had least impact on the use of this function.

Figure 2 shows the degree of influence of all sub-barriers on participants’ adoption of the activity tracking function on smartwatches using ANOVA and T-test. We set 1 to equal ‘strongly not influence’ and 5 to equal ‘strongly influence’.

Figure 2 Users’ sub-barrier scores

To conclude, in order to enhance users’ experience, application producers should develop the facilitators and pay attention to solving the issues of the main barriers. The key factors that encourage users’ long-term adoption of activity tracking are a) activity tracking capabilities, b) design, c) smartwatch functionality, d) interaction and e) improving the living habits. The ‘activity tracking capabilities’ was the best performing factor to motivate the users’ long-term usage. The second most important factor was ‘improving lifestyle’, which indicates that users pay attention to their habits and behaviours via the activity tracking function. Also, to the researcher’s surprise, ‘design and appearance’ and ‘interaction’ were far behind as facilitating factors. However, ‘smartwatch functionality’ was the least important factor that stimulated users’ long-term usage. Also, female users are attracted more by ‘smartwatch functionality’ and ‘interaction and user-friendliness’ factors than male users.

In terms of the research into barriers, ‘technology’ and ‘data’ have the largest influence on usage. Among ‘technology’, ‘battery issues’ and ‘pairing’ factors had quite a large impact on usage. In addition, the second most significant barrier to usage was ‘data’, specifically ‘data inaccuracy’ and ‘insufficient data categories’ being the two most influential factors. Moreover, the perspective of ‘interaction’ and ‘design’ was almost equally as important in preventing users’ adoption. However, ‘social comparison’ fell far behind, which was less than half as important as the most important perspective. This indicates that ‘social comparison’ has not hindered usage too much. Additionally, female users consider ‘data’ and ‘technology’ have more degree of preventing influence than male users. The user who goes to the gym seems to regard ‘data’ and ‘technology’ as the more serious barriers when compared to the users who do not go to the gym.

In practical terms, the product should increase the accuracy and integrity of the data produced by devices. Producers could add more abundant data categories for sports, such as tennis or basketball. The battery issues, including battery life, heating, and rechargeability, were shown to be vital by this study’s respondents. The producers and designers should provide more charging methods, such as solar charging, to increase convenient usage. Employing more smart voice control to replace Bluetooth is another method worthy of further enhancement given pairing issues. The use of holograms could also be seen as an ideal way to solve existent screen size or quality limitations. In improving interaction to enhance lifestyles, designers might, in future, focus on smart or customised feedback to enhance user experience. For example, calculating daily calorie intake and providing recipes or dividing data between aerobic and anaerobic exercise would represent novel developments. More generally, the long-term use of smartwatch-based activity tracking could be enhanced by strengthening the facilitators and addressing the barriers identified by this study.

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[1] Berg Insight. 2019. Shipments of connected wearables will reach 168 million in 2019. Berg Insight. Retrieved from: http://www.berginsight.com/News.aspx. 

[2] H. Lee, and Y. Lee, “A look at wearable abandonment. In MDM 2017: 18th IEEE International Conference on Mobile Data Management,” IEEE, pp. 392-393, 2017.

[3] Maurer, U., Smailagic, A., Siewiorek, D. P., & Deisher, M. (2006). Activity recognition and monitoring using multiple sensors on different body positions. International Workshop on Wearable and Implantable Body Sensor Networks (BSN’06), 4–7. 

[4] Levy, H. (2015). Wearable Technology Beyond Smartwatches. Retrieved from: https://www.gartner.com/smarterwithgartner/wearable-technology-beyond- smartwatches 3/ 

[5] Page, T. (2015). Barriers to the Adoption of Wearable Technology. Journal on Information Technology, 4(3), 1–13. 

[6] Harrion, D., Marshall, P., Bianchi-Berthouze, N., & Bird, J. (2015). Activity tracking. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing – UbiComp ’15, 617–621. 

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The Benefits of Mobile Phone Applications to Women Livestock Keepers in Zimbabwe

Pfavai Nyajeka and Richard Duncombe

Mobile phone applications have offered much value in the livelihoods of women in rural Zimbabwe.  Research conducted in resettlement areas during 2017 and 2018 used mixed methods to collect data on samples of women livestock keepers (Figure 1) who were household-heads (HHHs) or non-household heads (NHHs), providing an understanding of the unique forms of hardship that are imposed on married, single, divorced or widowed women in their pursuance of livelihoods.  The research investigated how women farmers used mobile phones to strengthen their position in livestock keeping and mitigate their vulnerability.

Figure 1. An Interview with a Woman Livestock Keeper in the Mashonaland East Province of Zimbabwe

Zimbabwe, in common with other sub-Saharan African countries, was experiencing a revolution in digital communications prior to and up until the end of the study period; but Zimbabweans, and particularly those in rural areas, remained disadvantaged due to poor electrical grid connections and digital connectivity compared with some other sub-Saharan African countries (Table 1).

Table 1. Digital Landscape: Selected Indicators for 2018

Country/Region	% of rural population with access to electricity	% of population using the Internet	Mobile cellular subscriptions (per 100 inhabitants) (a)	Secure internet servers (per 1 million inhabitants)
Botswana	24	58	150	134
Kenya	58	19.5	96	217
South Africa	67	62.4	160	12,032
Zimbabwe	19	25	89	47
sub-Saharan Africa	22	29	94	794

Sources: Human Development Report (2019) and ITU (2018) Indicators Database; see: World Bank Open Data | Data

(a) including accounts with mobile money service providers.

Women livestock keepers in resettlement areas (Mashonaland East and Midlands) pursued their livelihoods within a challenging vulnerability context, typified by adverse climatic conditions, volatile markets and lack of support services (Figure 2).  Their ability to participate in local economic development was also constrained by their position within the largely patriarchal social structures that govern livestock keeping in Zimbabwe.

Figure 2. Community Meeting Place for Women Livestock Keepers in the Midlands Province of Zimbabwe

Use of mobile phones enabled the women to resolve problems quickly, saving time that could be more profitably spent on other income generating activities.  One HHH commented… “no one likes to be constantly travelling distances to chase buyers or debtors, so you find that a lot of women livestock farmers in this area depend on their mobile phones to remind buyers or debtors about upcoming livestock sales and money owed.  A lot of the time constant mobile phone reminders are enough.  Even when the person on the other end does not answer the phone or respond to a message or post, seeing that missed call, or text, or post, is often enough to put pressure on debtors.  Some (women) will post a reminder on social media group forums such as WhatsApp.  You find that this is very effective and frees up time and money for them (women livestock keepers) to focus their energies on other things”.

WhatsApp was used for group messaging and exchanging of photos and short videos related to problems or threats to livestock.  WhatsApp was particularly useful in instances when livestock farmers used group chats to coordinate an emergency veterinary department’s visit.  One focus group participant in the Midlands province (Figure 3) stated… “we as women farmers can communicate quickly… this also allows us to get advice on livestock disease outbreaks.  Although some women do not have smart phones, due to the expense, everyone knows someone who has access to information through community WhatsApp groups… no one in the community is left out as the message can be spread quickly, meaning we are quickly able to manage disease and risks” (Respondent 49).

Figure 3. A Group Meeting with Woman Livestock Keepers in the Midlands Province of Zimbabwe

In addition to WhatsApp, locally designed applications such as Kurima Mari[1]provided farmers with information on livestock management, livestock market updates and information on crop production, with English, Shona and Ndebele language options.  Another platform service was EcoFarmer[2]– introduced in 2013 as a ‘weather indexed insurance business’ and EcoCash[3]– a mobile payment solution for Econet customers that let farmers carry out financial transactions and pay bills.

The survey suggested a high degree of independent information searching on behalf of married women.  Phones enabled women livestock keepers to enquire about market prices either directly or through the app, ascertain where livestock demand was, quantities, and agreed periods of payment, before travelling to market.  

The survey results also showed significant usage of mobile banking apps (such as EcoCash).  Many women moved to mobile banking due to the cash shortages, but most also viewed mobile money as the safest means of transferring money and conducting transactions.  Mobile banking fees were generally lower compared to bank charges, and some farmers were able to make and receive payments and gain access to credit more easily.  

Some key findings from the study include…

• A largely positive picture of the use of mobile phones amongst women livestock keepers.  Everyday use of mobile phones and applications has brought considerable benefits associated with better overall communications, helping to meet rural women farmers’ information needs in a timely manner.
• A divergence of the results according to whether the woman livestock keeper is a HHH or NHH.  HHHs tend to be more active in relation to income generation due to not having to defer to the waged husband in the household.  The use of the phone tends to reinforce and strengthen this income earning activity for HHHs, both in relation to livestock keeping and other income earning opportunities.
• Various limits and social pressures are placed on the NHHs in the use of their phones, thus restricting the ability of NHHs to accrue the full benefits of phone use.  The ability of NHHs to link with new social networks and other livestock intermediaries is limited.
• Despite the potential benefits, the cost of accessing information with mobile phones could be prohibitive, even when considering the relatively low initial cost of buying (mostly) second-hand phones.  In part this is dealt with by opting for cheaper phone data bundles that facilitate use of web-based applications such as WhatsApp.

The results of this research will be presented at the International Conference on Information and Communication Technologies and Development (ICTD 2022) in Seattle between June 27^th – 29^th and published in the Conference proceedings.  International Conference on Information & Communication Technologies and Development (ictd.org)

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[1] Kurima Mari is a family farming Knowledge Platform which gathers and digitized quality information on family farming from all over the world; including national laws and regulations, public policies, best practices, relevant data and statistics, researches, articles and publications. Kurima Mari – Apps on Google Play

[2] EcoFarmer provides farmers, government, contracting companies, NGOs and farmer unions a range of digital solutions to assist productivity across the agriculture value chain. Launched in 2013 as a weather indexed insurance and micro insurance product with an SMS based advisory service it has evolved to offering diversified services like Vaya Tractor, logistics, warehousing, cold chain, Hay Bailing, combine harvesting and soil testing. Farmers register to access the application by paying a small charge.  Services for Farmers – EcoFarmer

[3] EcoCash is a mobile payment solution for Econet customers in Zimbabwe. It facilitates financial transactions, like sending money, the purchase of prepaid airtime or data and payments for goods and services, using a mobile phone. http://www.ecocash.co.zw/about

 

How Widespread are Digital Water Payments in Ghana?

Digital systems are seen as important elements in the governance and management of the water sector. For instance, systems such as digital meters, IoT applications, digital payments, etc can significantly improve aspects of water service delivery and access. But are these new technologies widely adopted as yet, particularly in the global South context?

The open access paper “Diffusion of Electronic Water Payment Innovations in Urban Ghana. Evidence from Tema Metropolis” explores aspects of this question; looking specifically at uptake of electronic water payments (EWP) in Ghana. Drawing on data from water utility customers and the utility’s own database, three main conclusions emerged.

i. EWP adoption is very low (below 3%) though many utility customers were aware of these payment options. 

ii. The growth of EWP uptake in urban Ghana is rapid (annual growth rate of 41% from 2017-2018), but from a low base.

iii. Awareness and potential uptake of these payment options were significantly associated with customers’ age, employment status, income, and means of receiving monthly water bills. EWP awareness was higher among elderly customers perhaps since they constitute a larger portion of people with utility pipeline connections from the study. Also, awareness was higher among utility customers with higher income, those employed and those who receive their water bills through electronic channels i.e. SMS or email. 

Explanations of why adoption rates are low range from behavioural to transaction fees to technological challenges. However, mobile phone ownership and mobile money usage may not be significant predictors or barriers to EWP uptake given universal mobile phone ownership by customers, and widespread use of mobile money.

Some actions to take to improve adoption include:

• Developing specific guidelines and engagements that target unaware sections of the population, particularly low-income customers through advertising of payment solutions etc. 
• Understanding prevailing baseline characteristics of targeted customers before rollout of these innovations. Also, these innovations should be piloted before upscale.

Notwithstanding the barriers that currently exist, it can be seen from this example that digital innovations in the water sector are on the rise. Beyond understanding adoption issues, we will increasingly need better evidence on the impact of such innovations in the global South: not just digital payments but also applications across the water value chain, from water sourcing to end-use. I look forward to examining the experiences and impacts of these innovations in an ongoing project.

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How Whatsapp Strengthens Livelihoods of Women Farmers in Rural Zimbabwe

Whatsapp [1] (icon shown in Figure 1) is improving women farmer’s social capital – facilitating effective social networks in rural Zimbabwe.  We know that mobile technology use leads to information sharing – with the possibility of building social capital and leading to asset creation.  Some even argue that ultimately this can lead to better and sustainable livelihoods strategies.  There is talk, however, to suggest that many rural women in sub-Saharan Africa have not realised the benefits of mobile technology, despite widespread positive outcomes of mobile phone uptake in agricultural settings [2].  This is concerning, so exploring Zimbabwe’s situation is perhaps relevant and enlightening.

Figure 1: Whatsapp Icon

Whatsapp is facilitating access to support networks which better allow rural women farmers to pursue sustainable livelihoods in Zimbabwe.  Support networks (for example informal farming groups, church and savings’ clubs, as well as formal support from local NGOs and extension workers) are prevalent here.  In fact these links are particularly valuable in an environment (vulnerability context) which is typified by four factors.  First there are complex market trends (like flooded livestock markets and price fluctuations).  Then there are confounding financial shocks (like the lack of capital and abundant cash shortages).  Third are the challenging and extreme climatic shocks.  Fourth is the threat of disease (which is usually high and persistent throughout the year)[3].

Such characteristics are compounded by multiple role expectations on these rural women, and multifaceted, often contradictory structural relations.  Inequalities of access and women’s multiple competing roles limit opportunities [4], and so it seems reasonable to argue that social networks are central to mitigating vulnerability, which in turn enhance sustainable livelihoods prospects for Zimbabwean rural women livestock farmers.  In this sense, social media application Whatsapp is being used to [3] (see Figure 2):

a) Solve livestock problems, for example rural women are able to post/ send photos and videos of livestock to Whatsapp group members with common livestock interests, local vets and extension workers.

b) Help out in emergencies, allowing quick access to Whatsapp group forums to warn community members when livestock is stolen/ when disease threats arise, thereby efficiently coordinating emergency visits.

c) Build and strengthen women’s networks whereby women chat to each other and seek advice/ information through Whatsapp group forums.

Figure 2: A Zimbabwean Woman Using Whatsapp [3]

Essentially effective support is garnered through creating Whatsapp chat groups to openly communicate livestock issues.  Granted, some women do not have smart phones (largely due to cost), but it seems normal that an informed connection is never far off [3].  Also, in true Zimbabwean style, more experienced women farmers share experiences and knowledge with younger women farmers, serving as mentoring platforms where strong bonds are often formed through vulnerabilities and hardships.  A strong sense of togetherness and willingness to assist each other through these open channels ensues.

Whatsapp is accepted as a cheaper, useful and effective way of coordinating meetings (see Figure 3 – a photo with a group of livestock farmers brought together using Whatsapp).  A preferred form of communication, it enables rural women to inform each other, keep records of, and forward important (livestock) information.  It is perceived as being revolutionary in transforming communication amongst community members [3].

Figure 3: A Group Meeting in the Mashonaland East Province of Zimbabwe [3]

Given its apparent prominence in allowing economical flow of useful information, it is permissible to suggest that social networks accessed through Whatsapp are facilitating rural women’s pursuit of (diversified) livelihoods in an otherwise complex and challenging vulnerability context.  It would be useful to explore how the same/ similar mobile phone applications can be used to provide equal/ further access to key influential social and political networks [5] in order to abate the apparent perceived complex and contradictory structural relations and gender differences in such contexts. 

References 

[1]  Whatsapp is a cross-platform messaging and voice over IP service that allows users to send text messages, documents, images, and other media.  It also allows users to make voice and video calls.  Chat groups can also be formed on the application.  Whatsapp (2018). Simple. Secure. Reliable messaging. Whatsapp [Online]. Available at: https://www.whatsapp.com/ [Accessed 28 November 2018].

[2]  Baird, T.D., and Hartter, J., (2017). Livelihood diversification, mobile phones and information diversity in Northern Tanzania. Land Use Policy, 67, pp.460-471.

[3]  Author’s Zimbabwean fieldwork data, August – September 2017.

[4]  Wyche, S., and Olson, J., (2018). Gender, Mobile, and Mobile Internet Kenyan Women’s Rural Realities, Mobile Internet Access, and “Africa Rising”. Information Technologies & International Development, 14, p.15.

[5]  Ruswa, G., (2007). The Golden Era?: Reflections on the First Phase of Land Reform in Zimbabwe. African Institute for Agrarian Studies.

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Why M-Pesa Outperforms Other Developing Country Mobile Money Schemes

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

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

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

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

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

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

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

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

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

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

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

Follow @CDIManchester

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

Understanding Mobiles and Livelihoods

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The framework immediately helps to identify possible research questions:

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

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

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

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

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

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

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

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

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.

 Follow @CDIManchester

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[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

ICT and Economic Growth: Evidence from Kenya

Do ICTs contribute to economic growth in developing countries?

In the 1980s, Robert Solow triggered the idea of a productivity paradox, saying “You can see the computer age everywhere but in the productivity statistics.”  And for many years there was a similar developing country growth paradox: that you could increasingly see ICTs in developing countries except in the economic growth data.

That is still largely true of computers and to some extent the Internet, but much less true overall as mobiles have become the dominant form of ICTs in development.  In particular key studies such as those by Waverman et al (2005), Lee et al (2009), and Qiang (2009) have demonstrated a clear connection between mobiles and economic growth and/or between telecoms more generally and economic growth.  They all address the “endogeneity” problem: that a correlation between telecoms (indeed, all ICTs) and economic growth is readily demonstrable; but that you then have to tease out the direction of causality: economic growth of course causes increased levels of ICTs in a country (we buy more tech as we get richer); you need to try to control for that, and separate out the interesting bit: the extent to which the technology causes economic growth. 

The studies try to do this and show ICT investments cause economic growth, but they are all multi-country and provide no specific insights into the experiences of a particular developing nation.  If you know of such data, do please contribute.  Meanwhile, a recent edition of “Kenya Economic Update” provides an example.  Some overall points:

• The ICT sector grew at an average of nearly 20% per year from 1999-2009 (by contrast, Kenya’s largest economic sector – agriculture – shrank by an annual average of nearly 2% per year).
• The number of phone subscriptions has grown from the equivalent of one per 1,000 adults in 1999 to the equivalent of nearly one per adult in 2010; Internet usage rates for 2010 were around four per ten adults.
• Person-to-person mobile money transactions at the end of 2010 were equivalent to around 20% of GDP with two of every three Kenyan adults being users.

But the report’s strongest claim is this: “ICT has been the main driver of Kenya’s economic growth over the last decade. … Since 2000, Kenya’s economy grew at an average of 3.7 percent. Without ICT, growth would have been a lackluster 2.8 percent—similar to the populaton growth rate—and income per capita would have stagnated”.  So ICTs were responsible for 0.9 of the 3.7% annual GDP growth, and for all of Kenya’s GDP per capita growth.  Put another way, ICTs were responsible for roughly one-quarter of Kenya’s GDP growth during the first decade of the 21^st century.

Other nuggets from the report and from original World Bank data underlying the report:

• The “ICT sector” is actually the “posts and telecommunications” sector.  Comparing figures from Research ICT Africa for mobile + fixed line + Internet/data services with those for the overall sector suggests that ICTs form by far the majority (likely greater than 90%) of that sector.  For the ICT part of the sector, latest figures for 08/09 show mobile takes a 54.8% share, fixed line takes 39.5%, with 1.8% for Internet services and 3.8% for data services (not 100% due to rounding).
• The ICT sector in 2009 still represented only 5% of total Kenyan GDP (compared to 21% for agriculture/forestry), and growth has been volatile, at least as based on the recorded figures, ranging from 3.5% per year up to 66% per year during the first part of the decade, and from 7.9% to over 30% during the second part of the decade.  Only tourism (hotels/restaurants) was more volatile.  In six of the ten years of the 2000-2009 decade, though, ICT was Kenya’s fastest growing sector.
• In the first half of the decade, annual investments in mobile were higher than annual revenues; but the ratio has subsequently slipped to investment averaging around half of revenue.  Investments in mobile during 2001/02 to 2009/10 are estimated at US$3.2bn (c.KSh250bn) and US$3bn in fixed phone services, with broadband, Internet and BPO investments adding perhaps another US$1bn.
• The ICT sector provided a more than six-times-greater contribution to Kenyan GDP in 2009 compared to 1999.  Directly, the ICT sector contributed to 14% of the country’s GDP growth between 2000 and 2009 (at constant (i.e. not actual/current but accounting for inflation) prices, it grew from KSh13.7bn in 2000 to KSh71.8bn in 2009; GDP overall grew from KSh976bn to KSh1.382tn).  So the World Bank’s calculation that ICTs contributed a quarter of GDP growth during the decade also include a specific, quantified assumption about ICTs triggering growth in other sectors, in particular the financial sector.
• Employment in the ICT sector is estimated to be around 100,000 in 2011 (c. 0.7% of the estimated 14m overall labour force).  But ICT punches above its weight in other ways: changes in mobile prices at the start of 2011 were credited with both causing the Kenyan inflation rate to drop and with potentially derailing government constitutional talks due to the substantial knock-on effects in causing tax revenues to drop since phone companies now contribute such a significant proportion of government income.

So, overall, what do we have here?  Some fairly solid evidence that ICT sector growth (predominantly due to mobiles) is making an important direct contribution to economic growth in this developing country.  And some less clear evidence that the indirect GDP growth effect of ICTs may nearly double this.  Thanks to mobile money, Kenya has seen a particularly strong take-up and economic role for ICTs, but it is fairly typical in terms of mobile investment, revenues, subscriber base, employment, etc.  In that case, it’s not too much of an extrapolation to expect that ICTs will have contributed something like one quarter of GDP growth in many developing countries during the first decade of the 21^st century.  Evidence of ICT impact that development strategists and practitioners should be more aware of.

Follow @CDIManchester

Mobile Phone Use in West Africa: Gambian Statistics

This entry reports findings from a survey of nearly 400 mobile phone users in The Gambia conducted by Fatim Badjie, who recently participated in Manchester’s MSc in ICTs for Development.

Its findings fall into six main areas:

Ownership and Costs: 83% of phone users owned their mobile; roughly 70% said that it was cheap to use a mobile.

Mobile Usage: 82% said the most-used facility on their phone was calls; 12% said it was texting; 3% said it was Internet browsing.  Overall, 38% said the service they enjoyed most was texting; 15% said Internet browsing; 8% said conference calls; 5% said video calls.  47% share their mobile with other people, sharing with an average of 3.1 other people.  That means, overall, the average mobile is used by 2.5 people (i.e. shared with 1.5 other people).  On average, users said they used their mobiles 28 times per day, and two-thirds use their mobile at least 10 times per day.

Availability and Issues: roughly 60% of users said they always had a signal and that services were available even in “inconvenient” locations (though of course Gambia is a small country).  Only 30% reported the mobile was always effective for communication and roughly one third reported they felt mobile use had become a burden to them – mostly financially but also socially or personally.  For the 55% of users who wanted improvements, these almost all related to getting 100% network coverage in the country, or wanting cheaper prices.

Impacts and Benefits: 78% felt they benefited from having a mobile particularly due to low cost of calls.  31% felt having a mobile helped them to make or get money, for example through calls from customers to go and collect money owing or, more often, calling family/friends for money (“money calls”).  58% thus felt they had come to depend on their mobile, and 78% said they could not see themselves living without one.

In terms of male-female differences:

– No real difference in rates of ownership, rates and scope of phone sharing, difficulties experienced, or dependency on mobiles.

– Slight tendency for women to have been using fixed lines rather than telecentres as a prior means of communication.

– Women use mobiles a little more than men on average per day (28.6 vs. 26.6 times).

– Less use of mobiles for Internet browsing by women than men; more use of phones for texting.

– More men (38%) than women (24%) said the mobile helped them get money and resources, though women used phones proportionately more for “money calls” than men.

My commentary would be that, overall, this is a reminder of how mature the mobile market is getting in Africa with very high rates of ownership, very high rates of usage, and signs of movement beyond basic calls/SMS: at least 15% going online via their mobiles, at least 13% using video/conference calls.  With roughly one-third saying they use mobiles to make or get money, it looks like quite a valuable financial tool: so embedded that nearly fourth-fifths of users couldn’t imagine life without it, including some who see mobiles as a “necessary burden”.

ITU estimates for 2009 (the year prior to the survey) there were 84 mobile subscriptions per 100 population in The Gambia.  Even allowing for calculations to convert from subscription data to actual ownership and use (see earlier blog entry), this means phone users were by far the bulk of the Gambian population during this survey (so skews compared to the overall population will be present but probably limited).  Given the rates of sharing reported it means that access to a mobile is virtually universal (though it must also mean that many people share their phone with others who already have one).

Noting exclusion from the survey of women (and men) who don’t use mobiles, there was relatively little difference in ownership and usage patterns between men and women.  Is that, too, a sign of market maturity?

Finally, a reminder that, even in a small country there can be significant locational differences and that “market maturity” has a rural—urban axis.  Users were surveyed in seven different parts of The Gambia but the table below compares some of the key findings for those surveyed in the capital, Banjul, and those surveyed in Bansang, a small town three-quarters of the way up-country.

	Banjul (urban)	Bansang (rural)
Ownership	100%	32%
Cheap to use?	66%	84%
Access Internet via mobile	17%	0%
Use SMS texting	69%	4%
Share your mobile?	24%	86%
Average uses per day	39.8	6.7
Available in inconvenient locations?	75%	12%
Main problem (of those reporting a problem)	Cost (87%)	Network availability (98%)
Help you to get money/resources?	28%	40%
Calls for money	14%	44%
Live without mobile?	52%	20%

The data show some not unexpected differences.  In the rural location, there was much less ownership of mobiles and much more sharing; much less use of non-call services and generally much less daily use of the mobile.  Network availability is more of an issue in the rural area, but the mobile seems to be more useful for getting money and far fewer users in the rural area can imagine life without it.

You can access the results of the survey by clicking here: they also include more Gambia-specific questions about operators, services, and awareness of institutions.  Note the breakdown-by-location is very lengthy, and not provided in this document.

Mobile Phone Penetration: Google Motion Chart Data Visualisation

I’ve entered the ITU data on mobile phone penetration for all countries from 1998-2008 into a Google Docs spreadsheet, and then added the Motion Chart visualiser (the same engine made famous by Hans Rosling and TED, though they use the Gapminder Trendalyzer version).

Unfortunately, WordPress scripting rules mean I can’t post the active chart here. To access the spreadsheet data and Google Motion chart, you need to go to:

http://spreadsheets.google.com/pub?key=tUzZsw5SoG_jXRDl6p8tRCg&single=true&gid=0&output=html

Screenshots below give an indicator of how you can visualise the data. The chart offers three main means to visualise (bubble, bar chart, and line graph) via tabs at the top right. You can change the axes and element colouring/size, and highlight individual countries. For bubble and bar, the main point of the chart is that you can click play (bottom left) and show how things change over time. (Note playback speed variation control, and also the ability to drag over and zoom in on parts of the chart.)

Not sure it adds a lot of analytic value but it’s engaging, helps give a sense of some overall trends, and identifies some interesting outliers. (Some older PCs and low-bandwidth connections will struggle to display.) I’ll repeat for other ITU data in later posts (e.g. broadband data visualisation here). You can find similar visualisation for mobile, Internet and a host of other development data at: http://devdata.worldbank.org/DataVisualizer (though currently up to 2007 only, no obvious access to underlying data, and the mobile data display doesn’t seem to work properly).  And, finally, on a separate blog entry you can find a set of rough converters to change mobile phone subscription data to data on ownership, access, use and non-use.