The uncertainty depositaries

Most people would consider uncertainty a bad thing. You don’t know what will happen, you can’t control for the outcomes you would prefer. I’m not talking risk here, which can technically be insured against. I’m talking uncertainty, where there is no objective way to determine what the future state of things will be; no means to end framework by which to go.

However, not all people experience uncertainty in the same way. There is a great theory developed by the economist Frank H. Knight about uncertainty and entrepreneurship. Knight claims entrepreneurs have lower thresholds to apply their subjective decision making under highly uncertain circumstances. Whether positive outcomes are ascribable to competent judgment or sheer luck, is not really discernable, but at least it’s important to know that there are people who aren’t afraid to apply their own judgment to make decisions under deeply uncertain circumstances (with all the consequences of being wrong, and pretty much alone in your beliefs at many points).

Now consider two practical examples of this. First one is about someone whom we all know, Steve Jobs. Now at the time of developing the first iMac and the iPod, Jobs made a judgment call on the future state of the internet, where he envisioned that the ordinary consumer would have wide spread access, and could ship and receive much larger bundles of data. This was in a time that competitors will still banking on diskette drives… Job’s judgment resulted in the built-in modem and LAN port on the iMac, as well as the online distribution model for the  iTunes store that fed your iPod. The difference it made for Apple is nicely explained in this quote from this Forbes article:

The iPod took off after earlier MP3 players hadn’t not only because of its simplicity and ease of use but also because Jobs waited until broadband technologies were ready to support the music data transfers it would rely on.  

Another such example of uncertainty can be found in the informal economy, which is by definition a very uncertain operating environment. This one was discovered by Niti Bhan during field work in Kenya. She found somebody that had wired their house completely, without having access, nor guarantee of access to the grid: “it would come” was the home owner’s prediction.

Wired house before the grid came

Wired house before the grid was even available in the area (Photo credit: Niti Bhan)

This very much triggers my thoughts. Both examples are from widely different environments, but show the same type of judgment call about a very uncertain outcome. Could we be looking at the same thing here? Would that mean that we could thus better understand entrepreneurship and people who live in the operating environment of the informal economy, by relating the effect of uncertainty to decision making?

The only article which comes remotely close to this question, is a psychology experiment set up by Chip Heath, and Amos Tversky. A gem of an article, but very little used since publication, so I’ve learned by Chip himself. The article indicates that competence and aspiration seem to lower people’s thresholds to actively engage and invest, under conditions of uncertainty.

Would it be worthwhile to define personas on such basis? To inform accelerator programs on the people they’re funding? To engage with specific farmers in development programs in the informal economies in developing countries? To find the early adopters of the internet in emerging markets? I’d love to hear your thoughts!

Reaching rural communities in emerging markets: Ecosystems, people, and pipelines

In my previous post I’ve written about the limitations we encountered in applying the value chain approach when my team I wanted to understand how information and knowledge adoption and exchange works with farmers within their agricultural market system. What we were in effect mapping out appeared not to be a chain, but rather a network, or a web, for sharing information and knowledge. I’ve received some interesting responses on this post in the “Value Chain Thinking” group on Linkedin (requires administrator permission to join), as well as offline. The prevailing question posed to me was: “what have you observed that might work as an alternative for value chain mapping then?”

I don’t have this alternative ready. But, I won’t leave it at that. In this post I would like to share some observations on the patterns on information exchange my team and I saw in this web, and on its “components”. This in attempt to help improve the process of realizing understanding when conducting market system mapping exercises.

Patterns in the web
We started off with an attempt to map out the value chain, both in the context of Maharashtra in India, and in several areas of rural Kenya. The webs that resulted were too complex to enlighten us in our research. In search for an alternative approach to better illuminate how the system works, we took things back to the basics. We went through the conversations we had with several of the actors within the value chains in each of the countries (particular those conversations where we interacted with farmers). We then compiled profiles of these people to help in seeking patterns on information exchange within the complex maps we sketched out earlier.

The following farmer-types emerged from the data we were working with:

  • innovator farmers: people with a very broad worldview, looking outside their local context for interesting things to try. They are usually not (active) members of their immediate community
  • business oriented farmers: a farmer who is on top of the farming community, using it to pool supply for the market, someone who acts as a node in information gathering and exchange, and also can be selling inputs to the community.
  • bourgeois farmers (the large majority of the farming community); people who have been able to make a step forward, usually due to improvement in infrastructure, sometimes even moving there from the “stuck” situation. Despite their relative advancement they are still conservative in their aspirations.
  • Stuck farmers: farmers that are subject to slow decay of their land holding. They just about make ends meet enough to sustain their livelihoods. They’re main goal is to allow their children to advance, not so much focusing on opportunity for themselves
  • Drop-out farmers:  farmers who have no chance of sustaining their livelihoods and are likely to abandon their holding in the short to medium term

Consequent to this profiling, we looked at the interaction between these farmer types, to understand how the web of knowledge exchange and adoption between them would work. We observed that interactions between these farmer types could be differentiated and layered according to who the influencer is amongst them, who is being influenced, and what peoples’ aspirations are to achieve within the system.

In other words by taking a simplification of peoples’ conceptual worldviews of influence and aspiration, a mapping emerged of an information and communication exchange ecosystem amongst farmers. We have tried to capture our perspective on this ecosystem and its different layers in the sketch below.

The information exchange ecosystemAn information exchange ecosystem (visual by Jeroen Meijer of JAM Visual thinking)

Looking at the sketch in more detail it generally indicates that exchange doesn’t flow fully and freely within the ecosystem. There are people that subscribe to a certain part of the ecosystem, and information flows more easily and directly between them. This due to connecting worldviews that people in these groups have. There are also people who aren’t part of this section of the system (like the innovator), and information flows differently. Here, unconnecting worldviews are cause for a barrier to exchange.

People in the web
As a second step to understanding exchange, we attempted to identify key determinants that could show how farmers operate in their part of the ecosystem, what sources they would use for information, and what they could consequently do with that information and how they would apply it. The sketch below shows our thinking on these determinants.

Farmer persona

A farmer persona template in relation to information exchange

A farmer in each part of the ecosystem could be characterized according to 2 dimensions:

  1. a static dimension of information on her holding, categorized according to timing of cash flow (and the uncertainty thereof) generated from various crops, animals, and alternative non-farming sources of income.
  2. a dynamic dimension consisting of the farmer’s dreams, and aspirations on the one hand, and networks that the farmer belongs to on the other.

In this model changes in aspirations and networks, influence decisions made in planning out farming cash flows, and thus how the land is to be tilled and what investments will be made. By influencing aspirations and information that trickles through the network, this model would predict changes in management of the farm holding.

A different perspective
Taken together, what we have created is a conceptual model for understanding information adoption and exchange in the rural context of India and Kenya. By no means is this a complete method, nor the information validated as fully representative. More research is needed (*cough*). But by separating the communities of information exchange in an ecosystem, and by looking at the different members within these communities we have broken down a theory that could inform how information:

  • enters an ecosystem and is collected
  • is aggregated
  • interaction and dissemination takes place.

In other words we have compiled a theory of what an existing ecosystem of information and knowledge exchange looks like, and linked it to a behavioral model of how people work with, and respond to that system. Perhaps such a composition could be used as a premise for further research that will inform the design of appropriate ecosystem “grapevine pipelines”, which can provide contextually relevant and timely information to a targeted group of farmers who voice the need for it. After all, progress by technology comes from emulating the structures of human interaction that already exist. Our work continues…

PS. I came across a super informative video through Ken Banks’ twitter handle @kiwanja. It’s made by Dr. Clint Rogers, collating a couple of invaluable lessons on failure in ICT4D projects. Some points mentioned above resonate with the points delivered in the video. Do have a look at it, highly recommended!

——————

This is the seventh piece in a continuing series of posts (starting here) on what the role of human-centered design could be in development work. I’m working on this together with Niti Bhan, who will also be posting her observations at her Perspective blog. Posts are categorized as VCD