My name is John S. Seberger, and I will be present our study on the perceptions and attitudes
in India regarding pandemic-tracking apps.
In this short video, I will provide a brief overview of the findings.
For the rest, we encourage you to read the full paper.
The study is a replication of the work on the attitudes and perceptions of pandemic-tracking
apps that we presented at the CHI 2021 conference.
That work is based on a sample composed of workers from Amazon Mechanical Turk in the
United States.
We used the same method and analyses to replicate the study with a sample drawn from Amazon
Mechanical Turk workers in India.
The study was grounded in two research questions: understanding the factors associated with
the perceived benefit and expected use of pandemic-tracking apps in India and understanding
the relationship between the perceptions and expectations in India with privacy concerns.
As in the original study we replicated, we
used a scenario involving a fictional smart-thermometer pandemic-tracking app to answer these questions.
Please refer to the paper for specifics of the scenario we used in the study.
The goal of the replication was to compare the results obtained in India with those from
the United States reported in the original study.
We used 7-point Likert-type responses for
questions connected to the scenario to measure our dependent variables.
Perceived benefit of pandemic-tracking apps, as indicated by the responses to the item
shown, and Expected Use, split into three facets:
Expected Honest Use of pandemic-tracking apps
Expected Recommended Use of pandemic-tracking apps
and Expected Continued Use of pandemic-tracking apps.
We refined the original study by including additional measures for prior use of health-related
technologies, specifically health Web sites, telehealth services, and smart health devices.
We deployed the study as a questionnaire on Amazon Mechanical Turk available to those
from India above the age of 18 who had completed at least 50 tasks with an approval rate of
95% of higher.
Those who passed the screening criteria and consented to participate proceeded to the
study in which we randomly assigned them to either the control or the treatment conditions.
The between-subjects treatments varied the social framing of the scenario by describing
the pandemic-tracking app using either individualist or collectivist framing.
As mentioned earlier, we asked participants
several questions about the app as measures of our dependent variables.
In addition, participants indicated their prior use of health-related technologies.
Next, we collected responses to the Mobile
Users’ Information Privacy Concern, i.e., MUIPC, and the Individualism-Collectivism,
or IND-COL, scales as measures of privacy concerns and social orientation, respectively.
We ended the study by collecting standard demographics.
The analyses in this paper are based on the data from the 236 participants in the sample
who were predominantly individualists or predominantly collectivists, according to a median split
based on the responses of the sample to the IND-COL scale.
Of these, 128 were individualists, and the remaining 108 were collectivists.
The sample covers a wide age range and includes
participants residing in urban, suburban, as well as rural localities.
More than half of the participants reported having children under the age of 18.
We explored the answers to our first research question by building Cumulative Link Models,
i.e., CLMs, corresponding to each of the dependent variables mentioned earlier.
Each CLM included the outcome variables: social orientation of the participant, social framing
of the scenario, interaction between social orientation and social framing, reported use
of health technologies, locality of resident, parental status, gender, and age.
Since privacy concerns were not normally distributed, we examined the relationships between the
dependent variables and privacy concerns with Spearman correlations.
In this video, we visually highlight the major
findings of these analyses.
Please refer to the paper for a detailed discussion.
We found that perceived benefit of pandemic-tracking apps in India is predicted by social orientation,
social framing of the app description
prior use of smart health devices, and
being a parent of a child under the age of 18 years.
The CLM for the expectation to use pandemic-tracking apps honestly showed that it is statistically
significantly related to social orientation
all three measures of prior use of health technologies, and
whether one is younger or older than 35 years of age.
We found that the expectation to take app-recommended infection-prevention actions is predicted
by social orientation
all three measures of prior use of health technologies, and
whether one resides in an urban, surburban, or rural area
In contrast, the expectation to continue using pandemic-tracking apps after the pandemic
was unrelated to any of the factors we examined, with the exception of prior use of Health
Web sites.
When comparing these results with those of the original study from the United States,
we see that the associations between social orientation and perceived benefit and expected
use of pandemic-tracking apps in the United States and India are similar, with the exception
of the expectation to continue using such apps after the pandemic.
Similarly, we found that the relationships between perceived benefit and expected use
of pandemic-tracking apps with privacy concerns in the United States were similar to those
in India , with the exception of continued use after the pandemic.
Notably, the difference makes the use of such apps in India completely uncorrelated with
privacy concerns, unlike the United States where privacy concerns do exhibit a negative
correlation with the expectations for longer-term adoption.
The mostly similar results regarding the relationships between social orientation and the perceived
benefits and expected use of pandemic-tracking apps across samples drawn from two culturally
distinct regions indicates that social orientation might be a meaningful and somewhat stable
predictor of people’s expectations about pandemic-tracking apps across regions.
However, the absence of a relationship between privacy concerns and the perceptions and use
of pandemic-tracking apps in India indicates that privacy concerns exist within a greater
ecology of factors that contribute to the perceptions and adoption of such apps.
In conclusion, our replication confirmed, refined, and extended the results of the original
study from the United States by studying a sample from India, highlighting the merit
of conducting replication studies.