Addressing Data Collection Challenges in ICT for Development Projects

Addressing Data Collection Challenges in ICT for Development Projects

Devendra Dilip Potnis (School of Information Sciences, University of Tennessee, Knoxville, TN, USA)
DOI: 10.4018/IJICTHD.2015070104
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This paper equips researchers for addressing a wide range of data collection challenges experienced when interacting with marginalized communities as part of ICT4D projects in developing countries. This secondary research categorizes data collection challenges reported in multiple disciplines, and summarizes the guidance from the past literature to deal with the challenges. The open, axial, and selective coding of data collection challenges reported by the past literature suggests that it is necessary to manage scope, time, cost, quality, human resources, communication, and risks for addressing the data collection challenges. This paper illustrates the ways to manage these seven dimensions using (a) the success stories of data collection in the past, (b) the lessons learned by researchers during data collection as documented by the past literature, and (c) the advice they offer for collection data from marginalized communities in developing countries.
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Problem Statement

A significant number of projects using information and communication technologies for the development of marginalized communities (ICT4D) fail every year to meet their goals (Avgerou & Walsham, 2000; Bailur, 2007; Bhatnagar & Odedra, 1992; Heeks, 2002). For instance, during the fiscal period 2003-10, the World Bank invested around $4.2 billion to support its ICT4D projects that promoted access to and adoption of ICT across all sectors in the developing world. In 2011, the Independent Evaluation Group of the World Bank revealed that 70% of its ICT4D projects failed to achieve their goals (World Bank, 2011).

Studies show that data collection with marginalized communities is one of the main reasons for the failure of ICT4D projects in developing countries (Krishna & Walsham, 2005; Mamba & Isabirye, 2014; Qureshi, 2008). For instance, due to the inability of researchers to address data collection challenges, a majority of ICT4D projects in developing countries fail to sustain (i.e. remain continuously operational), scale (i.e. spread, enhanced, scoped, and enlarged heterogeneous networks of technology, people, processes, and institutional contexts), or benefit marginalized communities without undesired outcomes (Heeks, 2002; Krishna & Walsham, 2005; Sahay & Walsham, 2006).

Data collection serves as a medium to understand and interpret various dimensions of the relationship between ICT and marginalized communities. It requires researchers to collect and make sense of the needs and experiences of marginalized communities along with other contextual factors for: (a) designing ICT solutions to address the issues related to the development of marginalized communities (Parikh et al., 2003; Sahay & Walsham, 2006), (b) testing prototypes of ICT solutions with marginalized communities (Heimerl et al., 2010), (c) deploying ICT solutions in marginalized communities (Brewer et al., 2006; McCallum & Papandrea, 2009), or (d) assessing the impact of ICT solutions on the social, economic, and human development of marginalized communities (Mohan et al., 2013; Souter et al., 2005). Since data collection attempts to understand someone else’s experience, researchers’ outsider status from marginalized communities’ point of view may challenge researchers’ effort to understand and interpret the relationship between marginalized communities and ICT solutions (Potnis, 2014).

There is a lot of useful guidance available to address data collection challenges in ICT4D projects but it is scattered across multiple disciplines. As a result, for instance, researchers from technology-related disciplines engaged in ICT4D projects may not benefit from the experience and advice of researchers from non-technology disciplines, and vice-a-versa. It is necessary to systematically organize and summarize the scattered guidance so that researchers from all disciplines could avoid unexpected challenges and manage risks during data collection in ICT4D projects.

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