Exploring the mismatch between mobile phone adoption and use through survey data from rural India and China

Abstract

Persistent disciplinary and methodological divides between technology diffusion and adoption studies and the study of use and engagement with technology raise obstacles to understanding the development implications of mobile technology diffusion, for example in the area of healthcare access. As quantitative assessments in the area of health and technology almost exclusively rely on binary indicators of mobile phone adoption, it is not clear whether this is indeed a reasonable proxy that does not obscure the distributional implications of mobile phone use. This paper therefore compares patterns of mobile phone adoption and utilisation using original survey data from rural India and China. "Utilisation" here is assessed through a simple yet novel multidimensional index. The paper further assesses the role of these concepts as determinants of locally emerging forms of mobile-phone-aided healthcare-seeking behavior ("health action"). The investigation uses descriptive statistical analysis and multilevel logistic regression analysis, which provide evidence in support of the claims that (a) patterns of mobile phone diffusion and utilisation are related yet incongruent, that (b) mobile phones facilitate health action in both field sites to a notable extent, and that (c) the mobile phone utilisation index is a better predictor for phone-aided health action than mobile phone adoption. In light of the superiority of the utilisation index vis-à-vis binary measures of mobile phone adoption, other researchers can apply the survey instrument and technology utilisation concept developed in this paper to support the analysis of the social implications of technology diffusion.

The struggle for digital inclusion: Phones, healthcare, and marginalisation in rural India

“…This variable is calculated as the populationweighted percentage of households who own a mobile phone. In addition, the IDHS data does not include patients' healthcare-related mobile phone use, but previous research has found that the absence of household mobile phones predicts the absence of phone-aided healthcare-seeking better than the absence personal phone ownership (Haenssgen, 2015a). I therefore use household-level mobile phone ownership to approximate the likelihood of household members to engage in health-related phone use.…”

Manifestations, Drivers, and Frictions of Mobile Phone Use in Low- and Middle-Income Settings: A Mixed Methods Analysis of Rural India and China

“…Thirdly, my phone utilisation index was only a partial representation of a multidimensional concept of "adopting" mobile technology. The index focused on general yet basic functional engagement with mobile phones, which ignores specific uses like social, economic, or healthcare applications of the phone (for examples of healthcare uses, see Haenssgen, 2015aHaenssgen, , 2018Haenssgen & Ariana, 2017), and it did not include symbolic forms of engagement that could be of interest in sociological research (Lee et al, 2012). The quantitative findings were therefore shaped by my construction of the utilisation variables, which exceeded variation contained in common binary indicators of mobile phone adoption.…”

“…In this model, Utilisationi is the respondent's mobile phone use, measured through a multidimensional and decomposable utilisation index that goes beyond conventional adoption measures and represents different manifestations of mobile phone use (Haenssgen, 2015a). As described in Appendix Table 2, the aggregate index ranges from 0 to 1 and measures the extent to which six different mobile phone functions were used directly or indirectly by the respondent in the past year (0 corresponding to less than monthly use of any function or "minimal utilisation"; 1 corresponding to daily use of all six functions or "full utilisation").…”

Precarity and clinical determinants of healthcare-seeking behaviour and antibiotic use in rural Laos and Thailand

“… 25 30 The social dimension of precarity was evaluated through the (3) absence of other adults in the households and (4) the lack of a health-related social network (in terms of regular exchange of health advice 38 64 65 ). Lastly, logistical indicators comprised the absence of solutions to flexibly address health problems, namely (5) no household mobile phone (as consistent predictor of individual non-use of mobile phones during an illness 66 ) and (6) no motor transport option within the household (including motorcycles/tricycles, cars or tractors).…”