The User-Usage Model of Technology Adoption by the Emergent Users

[PDF of the PhD thesis is embedded below]

Santo lives in Haripur, a village 25 kilometres away from Morena in Madhya Pradesh, India. She is 40 years old and educated till class 5th. She owns a small grocery shop in the village and supplements the income of her husband who works as a mason. Her elder son, who is 20 years old, works as a driver in the city. Her daughter is 18 years and studies in a school.

Recently, her son gifted her a feature phone. He was not happy with the older basic phone she had been using. He wanted to gift her one which could play songs (S. likes regional songs in Bundelkhandi language). The new phone, a Chinese made, cannot only play songs but has a camera too. One can also record voice and use Bluetooth to transfer songs.

What would Santo do with the new phone? Would she continue doing the simplest of tasks— she used the older one only to make calls (through the last-call notifications) and accept them—on the new phone too? Would she learn to play songs by herself or ask for assistance every time she wanted to? If she learns, then in what manner would she play the songs? Would she use the camera? Would she be able to view the saved pictures?

The Model


The PhD Thesis (PDF)


The Summary

The Emergent Users

The Emergent Users are the users who have been significantly disadvantaged in terms of capabilities to (1) access, (2) learn and (3) use ICT. The word ‘Emergent’ signifies this hope which is not conveyed through the terms such as ‘poor', ‘low-literate' or ‘bottom of the pyramid’.

It is well appreciated that ICTs could benefit the EUs in many ways, such as improving access to and efficiency of education, healthcare and governance; and helping in the livelihood-activities. Though the barriers of cost and infrastructure have been largely addressed by the innovations in technology, the effectiveness of ICT for the EUs may still be compromised owing to unsuccessful Technology Adoption. To put it simply, an ICT- intervention for the EUs may be unsuccessful if the intended beneficiaries cannot initiate and sustain the usage in an effective manner. That implies, if we aim to design ICT artefacts that are meaningful for the EUs, we need to investigate and model Technology Adoption by the EUs. This is the objective of this research.

‘Usage' is central to our conceptualisation of Technology Adoption and we have situated it in the actual contexts of the EUs. This contrasts with other research, such as Technology Acceptance model by Davis [1985] and Diffusion of Innovation by Rogers [1962], where Technology Adoption was studied without focussing on usage or was studied it in the contexts of non-emergent users (educated, urban and white collar).

User, Usage and Design

Usage denotes the relationship between the artefact and the user (within a context). Usage is affected negatively whenever there is a mismatch between the user and the artefact. A particular and important type of mismatch happens between a user's (in)capabilities and an artefact's design. 

If the type and level of complexity offered by a given design of an artefact could not be handled by a user, the artefact is unlikely to be adopted. By understanding how a user’s (in)capabilities help or bar a user in the usage of ICT artefacts within a design landscape, we could arrive at designs that could ‘match' the user and the artefact. This, in turn, may enhance the chances of successful Technology Adoption.

Typical human (in)capabilities are the inability to read, lack of adequate mental models, inability to remember instructions, inability to abstract concepts et cetera. These are difficult to manage. If a user cannot read, it would take years to become literate. On the other hand, artefactual-complexity could be managed, in response to the user’s (in)capabilities, through design. Text-free interfaces (by Medhi [2006]), designed for low-literate persons is a good example where design responds to the (in)capabilities in a positive way.

The (in)capabilities arise due to user-related constructs such as Age, Gender, Income and Education. For example, a person who could not buy a mobile phone for many years due to a low-income is less likely to have adequate mental models of its functions. Through literature and contextual investigation, we have identified many constructs that are relevant, and in many cases unique, to the EUs in the context of rural/semi-urban India. We have also studied how these constructs affect the usage. 

The usage itself was defined in terms of various classes (of usage). That means, instead of simply informing that an ICT artefact would be adopted/not adopted, we are able to inform the extent and the manner in which adoption would take place. In other words, given a value of a construct, we are able to predict the usage patterns and behaviour of a user with respect to ICT usage. The User-Usage model embodies the various classes (of usage pattern and behaviour) of Technology Adoption.

The Model

The User-Usage model is a two-dimensional ordinal matrix (Figure 1). Depending on user-related factors such as Age, Gender et cetera (which make up the input), ‘placement’ of a user in the matrix (the output) could be decided. A given placement corresponds to two dimensions informing about the extent and manner of (potential) Technology Adoption by the user.  

The vertical dimension of the model represents the User Type—archetypical categories of users based on their usage-patterns. There are five User Types. Basic Users only do tasks that require one or two pushes of hardware buttons. Navigators can navigate menu hierarchies. Text-Inputters can type text. Savers can follow, design and manage directory structure to save files. Account Holders can manage account based applications, such as e-mail. Transactors have enough trust online to commit their money. The User Types are arranged in the order of increasing ability to deal with the task-complexity (for example, Savers deal with more complex tasks than Basic Users) 

The horizontal dimension of the model comprises of Stages of Usage—typical interaction-behaviour. The Stages are acquired as part of a users’ journey in acquiring adeptness at using an artefact. The journeys, which could range from minutes to years, are interspersed with barriers which a user may face. These are four in number—the barrier of non-exposure to ICT, the barrier of task-complexity avoidance, the barrier of low-frequency of usage and the barrier of inability to form adequate mental models. The five Stages resulting from the barriers are Unexposed, Novices (cannot deal with task complexity), Rote-Learners (memorise tasks as recipe or routine), Fluent (same as Rote Learner but have done tasks a large number of times, so exhibit better command) and Competent (have adequate mental models, so can put their knowledge to new situations, which others could not.) The Stages are also arranged in the increasing order of the difficulty of barriers.

The placement on the User-Usage model would inform about Technology Adoption potential of a user both quantitatively and qualitatively. As both the dimensions are ordinal, two users could be compared. At the same time, every User Type and Stage of Usage would also tell about how a user would interact with ICTs of various kinds. For example, Savita, the 40 years old class 5th educated female user, is likely to be a Basic User in Novice Stage. If provided with a new phone, she is likely to perform tasks with minimal cognitive load—receiving calls or starting a music player from a desktop shortcut. There is a likelihood of error even in tasks of low complexity. She is likely to be reluctant to do anything else.


We explored the issues related to Technology Adoption in the contexts of the Emergent Users. We conducted contextual studies, which provided us with constructs for the model. The study involved 50 Emergent Users in rural, semi-urban and urban India. 

The rules of placement of a user on the User-Usage model had been arrived through the operationalisation of 13 of the constructs. Some like Age, Education and Gender had been found easy to operationalise, others like Prevalence (of ICT users in a person’s social context), difficult. The data needed for it was based on a quantitative study involving 85 rural and semi-urban Emergent Users across India. The study was done in users’ contexts and involved mediators who spoke the local language. The instrument prepared for the study involved verbal questions and observation of the tasks. Ordinal Logistic Regression was employed to determine the nature of the relationship between the independent variables (the operationalised 13 constructs) and the dependent variables (the classes of User Types and Stages of Usage). 

The application of the model was done in two ways:

We predicted the different number of User Types and Stages of Usages in a part of the Indian population using the census data.During the quantitative data analysis, it was found that Age, Education levels, Gender, Proactiveness (motivation to use technology for its own sake) and Time (total time since first use of ICT) were significant factors in determining both User Types and Stages of Usage. Given that three of them were demographic variables, and were readily available for the population in the form of census data, contributed to the validity of prediction. 

We have also used User-Usage model as an analytical lens to understand the widespread adoption of WhatsApp among the EUs in India. We have found that WhatsApp has helped the Navigators among the EUs accomplish complex task of operating `Accounts’. We have realised that WhatsApp `breaks the model’, in the sense, that model would posit that Navigators cannot operate `Accounts’. However, in a true sense, the model has been validated because the barriers posed by complex tasks would not have been circumvented for the EUs without an innovative design. Through this exercise, we were able to identify design principles which could help the EUs to overcome the barriers to Technology Adoption. 


The primary contribution of this work is the User-Usage model which has been described above. Another important contribution is the term ‘Emergent User’ which may be used to inform about the users who are likely to have low incomes, low education, live further from urban centres or have less social and political power. These users differ from the ones for whom the ICTs have been traditionally designed. The term embodies the fact that traditional approaches of designing may fail for the Emergent Users because they are disadvantaged on many accounts such as literacy and income, which negatively affects their ability to initiate, learn and use ICTs. The important aspect of this definition is that it plays its role without affecting the dignity and hope, absent in terms like ‘digital divide’ and ‘bottom of the pyramid’, that should drive a design thought. Moreover, the definition is flexible to contain a large number of scenarios such as urban poor and women 

The other contribution is the research methodology (described above) which, in its generic form, would involve: (1) Identifying the constructs from a given context; (2) Describing Technology Adoption in terms of classes based on usage patterns and behaviours, with respect to a given technology landscape; (3) Building and quantifying a model, which involves establishing relationships between the constructs and the classes of Technology Adoption; and (4) Validating the model through analysing a design or through act of designing. The methodology is based on the premise that users differ from each other in terms of their capabilities needed to deal with ICT artefacts. The capabilities arise due to user-related constructs. The relative importance of these constructs may be different according to context. Some constructs may acquire new forms. Moreover, many of the constructs may be unique to a context. Therefore, in order to design meaningful artefacts for users in a given context, it is important to identify the constructs for the context 


• Medhi, Indrani, Sagar, Aman and Toyama, Kentaro. "Text-free user interfaces for illiterate and semi-literate users." Information and Communication Technologies and Development, 2006. ICTD'06. International Conference on. IEEE, 2006.

• Davis, Fred D. A technology acceptance model for empirically testing new end-user information systems: Theory and results. Diss. Massachusetts Institute of Technology, 1985.

• Rogers, Everett M. Diffusion of innovations. Free Press of Glencoe, 1962.