- Visualisations help them to clearly see and analyse the connections in a way that is difficult or impossible with interviews
- They could tell which networks were “open” or “closed”
- They obtained a sense of who was “brokering” the gaps in networks
- As SNA is a statistical technique, the networks could be examined in terms of size, number of ties, and other parameters
Social network analysis has limitations as well as benefits. Like any model, it simplifies reality, collapsing a lot of information about family ties and obligations. People send money for a variety of reasons, including deep kinship ties, social obligation, or as a debt, but these differences are not generally visible in social network models (unless survey data are well-complimented by interview data). Whereas social network modelling shows what people do, in-depth interviews demonstrate why they do it.
Methodologically, networks drawn from interview data need to be treated as samples. People forget or intentionally omit their connections for various reasons. Like any other kind of ethnographic information, information needs to be verified wherever possible by talking to the people who an interviewee says they have sent money to or received money from. This sometimes yields contradictory information, but can also improve certainty as to the accuracy of data if different interviewees’ accounts agree.
The team's combination of qualitative and quantitative analysis of social networks resulted in a wide array of discoveries. Many of the findings contradict common assumptions about how mobile money operates as a social and economic tool:
- The assumption that primarily “individuals” use mobile money to conduct person-to-person transfers or for their own particular purposes, such as saving money. In contrast, the study argues that it is better to conceptualize mobile money as created by collectives and groups.
- Promoters of mobile money for development often represent the service as a tool that empowers people both socially and economically. Sending money via a mobile phone can present a significant reduction in economic and transaction costs compared to other kinds of financial services. However, most people use mobile money to reach out to their traditional networks, not to create new ones or invent entirely new practices. Moreover, its functions and uses are sufficiently different from those of mainstream banking that it does not act as a close supplement.
- Mobile money is often seen to benefit women because it provides a way to make transactions privately, and this can help women gain some autonomy from their husbands and other men. But while women tend to receive a large share of remittances, they often view mobile money as something that helps them cope rather than empowering them. This is because productive wealth is tied up in land and stock, which are predominantly controlled by men.
SNA has many potential applications in consumer finance, as it can be used to track all kinds of mobility, and through any conceivable actors. As well as showing how money circulates (product / service mobility), it could easily be used to show how information moves through a network, which would be highly useful for demonstrating how word-of-mouth contributes to service uptake (e.g., friends and family recommending services to each other).
On the supply side, SNA could be used to map out business relationships and show how they change over time, helping us to understand how business collaborations are changing, and how different sized businesses work together (e.g., start-ups, medium-sized enterprises and large enterprises). It may also be used to track how talent flows around the market, which may help solve the chronic labour shortage problems that plague technology in particular.
In short, when implemented robustly, SNA can be used to show how just about anything moves around and why. SNA is perhaps our most useful predictive tool due to the way it combines qualitative and quantitative information.
PUTTING THE PIECES TOGETHER
Mobility is at the heart of money itself and many of the practices that drive consumer behaviours, business models, technological development, and the dissemination of information. Most importantly, mobility is changing the structure of consumer finance markets. More diversity among providers and products means a greater range of problems to solve. By definition, this entails a flattening of the bell curve, such that fewer problems are falling within a normal distribution, and instead are tending more towards the long tails. This means that we can no longer rely on our standard ways of thinking and research methods to work.
For many years now, people have been urging us to “think outside the box,” but what people mean by this is rather unclear and unhelpful. We generally associate out-of-the-box thinking with being creative, coming up with new ideas, and innovating. But we actually need a far more robust approach than this haphazard means of generating ideas and solutions. We should not rely on chance to guide our research, but instead on a solid, comprehensive knowledge of different frameworks and research methods. Otherwise we will continue to look hopefully, yet hopelessly, towards normative sources of information—the media, self-styled thought leaders, and large research consultancies—which cannot provide us with the information we need. Research reports by the likes of Deloitte, Capgemini, and PWC provide useful analysis, but they depend upon in-house surveys and normalised data sets to make their claims. Their reports largely address problems that fall within the bell curve, not in the long tails. This means they miss critical information for businesses facing highly specific problems.
Take, for example, a widespread issue in the insurance industry. Insurance professionals complain that they struggle to sell insurance products because people don't want to think about their own death. Yet historical and contemporary evidence from social science research indicates that this assumption is wrong. Geographically and historically, life insurance has been one of the most popular insurance products. Could it be that there is no ‘natural’ fear of either insurance products or death that is deterring people? Perhaps people's aversion to life insurance is due to something else; for example, distrust of insurance companies, a dislike of the way product information is presented, or perceptions of choice.
The only way that an incorrect assumption such as this can be overturned is by adopting a rigorous approach to asking questions, and being prepared to look to a wide array of knowledge sources for clues. Many academic disciplines have critical things to say about human behaviour and our changing world, and a wide range of methods with which to generate, ask, and answer questions. For example, there are many disciplines that already produce research that can quickly challenge our insurance salesman's assumptions, including sociology, anthropology, political science, computer science, psychology, and behavioural economics. But we cannot blame the salesman—finding this research is not his job. Businesses working in consumer finance do not usually have in-house expertise in methods and frameworks, and even if they do, trawling through this research and learning about methods accrues far too many transaction costs to make it a viable proposition.
In contrast, research professionals armed with a broad and robust knowledge of contemporary consumer research and a wide variety of methods are in a strong position to ask pertinent questions regarding how consumer finance markets and consumers are behaving, why, and what likely directions markets are headed. There are many ways this can be achieved. The mobility framework I have presented in this paper is one tool to generate questions relevant to understanding chances in consumer finance.
As the case studies show, most methods geared towards understanding mobility produce data that covers different kinds of mobility: technological, informational, human, and product / service mobility. Social network analysis can be used to track the movement of virtually anything, and is especially valuable because it can show what flows (whether tangible or intangible), where it flows to, its direction, frequency, and quantities, and the relationships between people sending things to each other. Its potential value for consumer finance research is immense, since so many business problems are affected by mobility issues.
Like social network analysis, financial diaries can collect both qualitative and quantitative data and link them together. Their main value stems from the fact that they are used to collect data about people's entire financial portfolios and toolkits, and can track how these change over time (and why). They could also be adapted to investigate informational mobility, if questions are included that ask people what they know about different products, services, companies, trends, and so on. Compared with SNA they are less adept at examining human mobility, but they could easily be coupled with SNA to produce similar network data. Similarly, when used alone they do not generally do not produce much information about user experience, but they can be adapted to include more in-depth and object-centred interview methods.
Whereas SNA and financial diaries focus more on flows of information and money, object-centred interviews focus on material things. As such, they are especially useful for investigating how people use certain financial products and services, or manage documents and information relating to those services (e.g, receipts, bills, etc.). They are also useful for examining how people use financial tools while on the move, such as using phones, cards, and cash in public places.
We still have little idea how the shift from computer to “portable kit” (usually phone, cards, and cash) is affecting financial management. What happens when people transact in public? Do they make different purchasing decisions when they can manage their finances while in a shop? Are they more or less aware of what they spend when they use contactless payment versus ordinary bank card or cash? Are people exposed to greater or lesser risk of fraud or theft when their payments are digital? There is unlikely to be one universal answer to any of these questions, since people's preferences and personalities are diverse. But object-centred interviews can demonstrate the range of ways in which people respond, and help us think about what products to develop for whom, and how to build safety features into products and services.
Ignorance drives our creativity, argues neuroscientist Stuart Firestein, but it is our professional knowledge that helps us frame good questions. The very mobility that is reshaping consumer finance practices also makes it possible for us to access the information we need to expand our professional capacities as knowledge experts.
Erin B. Taylor is an economic anthropologist specializing in research on financial behaviour. She is the author of Materializing Poverty: How the Poor Transform Their Lives (2013, AltaMira). Erin holds the positions of Principal Consultant at Canela Consulting and Senior Researcher at Holland FinTech. Email: email@example.com
2017 Ethnographic Praxis in Industry Conference Proceedings, ISSN 1559-8918, epicpeople.org/intelligences
Acknowledgments – This paper is based on a collaboration between Canela Consulting (with Gawain Lynch) and the Institute for Money, Technology, and Financial Inclusion, and partially sponsored by Holland FinTech. Sibel Kusimba, Jofish Kaye, and Alexandra Mack generously provided the case studies discussed in the toolkit and this article. Thanks to Simon Lelieveldt for giving me access to his papers, which influenced my use of the concept of the dissatisfier. Finally, many thanks to Gawain Lynch for his comments on this paper.
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