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Mora, L., Kummitha, R. K. R., & Esposito, G. (2021). Not everything is as it seems: Digital
technology affordance, pandemic control, and the mediating role of sociomaterial
arrangements. Government Information Quarterly, 101599.
https://doi.org/10.1016/j.giq.2021.101599
Not everything is as it seems: digital technology affordance,
pandemic control, and the mediating role of sociomaterial
arrangements
Luca Moraab*, Rama Krishna Reddy Kummithac, Giovanni Espositod
a The Business School, Edinburgh Napier University, Edinburgh, UK
b Academy of Architecture and Urban Studies, Tallinn University of Technology,
Tallinn, Estonia
c Newcastle Business School, Northumbria University, Newcastle, UK
d Smart City Institute, HEC Liège Management School, University of Liège, Liège,
Belgium
* Corresponding author: L.Mora@napier.ac.uk
Acknowledgements
We would like to thank the three anonymous reviewers and Co-Editor-in-Chief Marijn
Janssen for the very insightful and constructive feedback that they have offered during
the peer-review process. We also thank Martin De Jong for the useful comments that
he has shared with us after reading an early version of this article.
Funding
This work has been supported by the European Commission through the Horizon 2020
project FinEst Twins (Grant No. 856602)
1
Not everything is as it seems: digital technology affordance, pandemic control, and the
mediating role of sociomaterial arrangements
An overly favorable narrative has developed around the role played by digital technologies in
containing Covid-19, which oversimplifies the complexity of technology adoption. This
narrative takes sociomaterial arrangements for granted and conceptualizes technology
affordance - the problem-solving capability of a technology - as a standard built-in feature that
automatically activates during technology deployment, leading to undiversified and
predetermined collective benefits. This paper demonstrates that not everything is as it seems;
implementing a technology is a necessary but insufficient condition for triggering its potential
problem-solving capability. The potential affordance and effects of a technology are mediated
by the sociomaterial arrangements that users assemble to connect their goals to the materiality
of technological artifacts and socio-organizational context in which technology deployment
takes place. To substantiate this argument and illustrate the mediating role of sociomaterial
arrangements, we build on sociomateriality and technology affordance theory, and we present
the results of a systematic review of Covid-19 literature in which 2,187 documents are
examined. The review combines text data mining, co-occurrence pattern recognition, and
inductive coding, and it focuses on four digital technologies that public authorities have
deployed as virus containment measures: infrared temperature-sensing devices; ICT-based
surveillance and contact-tracing systems; bioinformatic tools and applications for laboratory
testing; and electronic mass communications media. Reporting on our findings, we add nuances
to the academic debate on sociomateriality, technology affordance, and the governance of
technology in public health crises. In addition, we provide public authorities with practical
recommendations on how to strengthen their approach to digital technology deployment for
pandemic control.
Keywords: digital technology; technology affordance; sociomaterial arrangements;
government; pandemic control; Covid-19
1. Introduction
During the fight against the novel coronavirus (Covid-19), digital technologies have proven
effective in helping to limit the spread of the infection and enhance resilience (Brem, Viardot,
& Nylund, 2021; Kumar, Gupta, & Srivastava, 2020; Steen & Brandsen, 2020; Ting, Carin,
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Dzau, & Wong, 2020). Covid-19 literature shows that public authorities
1
have heavily relied
upon digital technologies to intensify their virus containment efforts, in particular where
national infection curves have been flattened and mortality rates have been minimized
(Whitelaw, Mamas, Topol, & Van Spall, 2020). For example, in applauding South Korea for
its success in rapidly slowing down the infections, Sonn & Lee (2020) explain that public
authorities have achieved this result by combining different smart technologies - especially
digital solutions for surveillance, contact tracing, and testing purposes. Similar stories are also
shared in other studies, which examine the impact of Covid-19 containment measures in
different countries across the world - Australia, China, the United Kingdom (UK) and United
States of America (US), Italy, Brazil, and India, just to name a few (Guo, Ren, Yang et al.,
2020; Kummitha, 2020; Moloney & Moloney, 2020).
But embedded in this literature, we found an overly favorable narrative around the
effectiveness of digital solutions in the pandemic setting, which oversimplifies the complexity
of technology adoption. This narrative suggests that public authorities have contained Covid-
19 by easily benefitting from the problem-solving capability of digital technologies - which we
refer as technology affordance. The potential affordance of a technological object, according
to this reasoning, is a standard built-in feature, which activates by default during the technology
adoption process, leading to undiversified and predetermined collective benefits. However, not
everything is as it seems; as sociomateriality and technology affordance studies highlight, the
problem-solving capability of a technological device is not a static feature and is rooted in the
way users adopt it (Orlikowski, 2010; Parchoma, 2014). When a technology is brought into
action, users determine an actual affordance, which results from the connection between their
specific goals and the social and organizational context surrounding usage. The actual
affordance can deviate from the potential affordance, which is instead established by
technology designers during the ideation process (Conole & Dyke, 2004). Therefore, the
relationship between a technology, its users, and their approach to operationalization is pivotal
to achieve technology affordance, and it should not be considered as a predetermined condition.
Rather, this interrelation shapes during the interaction (Cecez-Kecmanovic, Galliers,
Henfridsson, Newell, & Vidgen, 2014; Leonardi, 2013). When identical technological
solutions are used in different contexts, different levels of affordance can materialize and the
1
In the framework of this study, the term ‘public authorities’ refers to governmental
organizations that carry out tasks in the public interest. For example, government departments
and agencies, legislative bodies, publicly funded healthcare systems, and the armed forces.
3
extent of the benefits can vary (Barley, 1986). Potential technology affordance and effects are
both mediated by the “sociomaterial arrangements” (Schraube & Sørensen, 2013, p. 7) that
users assemble to connect the social and material dimensions of technology deployment
(Strong, Volkoff, Johnson et al., 2014).
When discussing how public authorities use digital technologies in the context of the pandemic,
the current Covid-19 literature overlooks the importance of sociomaterial arrangements and
does not distinguish potential affordances, which are expected to materialize because
established by design, from actual affordances, which result from real-world applications.
Accordingly, by focusing on government-led digital technology deployment, this paper
illustrates how sociomaterial arrangements mediate the potential affordances and effects of
ICT-based virus containment measures. Understanding how public authorities can maximize
(or undermine) the potential affordance of technological solutions is critical to improve future
pandemic responses and can inform national and intergovernmental pandemic preparedness
and response plans.
To achieve our objective, we present the results of a systematic review of the Covid-19
literature that offers written accounts on how digital technology has been deployed for
pandemic control. This body of literature - gray and academic publications released between
January and April 2020 – is reviewed by combining techniques for data mining, co-occurrence
pattern recognition, and inductive coding. We begin with text data mining to extract the most
relevant words and phrases embedded in our selection of Covid-19 literature and to determine
their strength of association by using co-occurrence data. These textual components are then
organized in thematic clusters, in order for ICT-related expressions to light up and emerge from
the huge mass of unstructured qualitative data. We use these expressions to establish which
digital solutions have been deployed to contain the spread of Covid-19 during the four months
under investigation. By using inductive coding, we examine the textual data and uncover 39
technologies. Finally, we systematically analyze the Covid-19 literature that is associated to
each digital technology. The analysis makes it possible to extract qualitative data that illustrate
how sociomaterial arrangements mediate the potential affordances and effects of the digital
solutions that public authorities have introduced in their virus containment strategies. More
specifically, the available data point us in the direction of four technologies: infrared
temperature-sensing devices; ICT-based surveillance and contact-tracing systems;
bioinformatic tools and applications for laboratory testing; and electronic mass
communications media.
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Building on the findings of our review, we provide public authorities with practical
recommendations on how to strengthen their approach to digital technology deployment for
pandemic control. In addition, we add nuances to the academic debate on sociomateriality,
technology affordance, and the governance of technology in outbreaks of infectious diseases.
Our findings contribute to strengthening “the view that there is an inherent inseparability
between the technical and the social” (Orlikowski & Scott, 2008, p. 434). Introducing digital
technologies in pandemic response strategies require creating the necessary balance between
socio-organizational factors and the materiality of technological artifacts (López Peláez &
Kyriakou, 2008; Mora, Deakin, & Reid, 2019). Otherwise, technology adoption may result in
unmet expectations, which are likely to be rooted in the socio-organizational dimension of the
adoption process, rather than technical failure (Kane, Phillips, Copulsky, & Andrus, 2019;
Mora, Deakin, Zhang et al., 2021). For example, we uncover the undermining role of the
following factors: nonadherence to public health agencies’ recommendations, lack of training
and transparency, poor leadership, overcomplicated bureaucracy which hinders cross-sector
collaboration, logistical barriers, bureaucratic authoritarianism, unresolved privacy issues, and
inappropriate public sector values.
The paper is structured into four main sections. Initially, we provide a concise overview of
relevant literature on sociomateriality and technology affordance. This first section is
instrumental in setting the theoretical foundations of our study. We then report on the
methodology used to conduct the systematic review and offer a comprehensive account of the
results. Finally, in the last section of the paper, we describe the theoretical and practical
contributions of our findings. In addition, we detail the limitations of the study and advance
suggestions on future research directions.
2. Theoretical background
We define affordance as the problem-solving capability of a technology - a feature which is
determined by the “goal-oriented behavior” of its user (Bobsin, Petrini, & Pozzebon, 2019, p.
15). This conceptualization builds on Gibson’s (1977) attempt to illustrate the complementarity
between human beings and the environment. In his research, Gibson focuses on environmental
cues - such as substances, surfaces, and places - and notes that human beings utilize identical
cues in multiple ways, generating different types of environmental affordances. According to
Gibson, these multiple approaches to usage result from the individual interpretations of
environmental cues that human beings develop, by pooling distinctive combinations of
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knowledge, skills, experiences, and expectations. Personal interpretations influence how
individuals engage with environmental cues and make them functional; subjectivity and
distinguishing attributes trigger different possibilities for action and interaction.
Building on this argument, Norman (1988) connects the environmental affordances perspective
to material objects and introduces the twofold origins of their affordances. According to
Norman, physical objects possess a potential affordance, which is defined by design, and actual
affordances, which users assemble during the practice in alignment with their goals (Conole &
Dyke, 2004). Through their actions, users can autonomously reinterpret the intentions of a
designer and actualize affordances that were not considered (Parchoma, 2014). Therefore, each
technology has a design mode and a user mode, where the latter does not always comply with
the former, producing different technology affordances (Orlikowski, 1992).
By following this logic, the sociomateriality perspective has shifted the focus of technological
affordance from technological determinism to volunteerism. From the standpoint of
technological determinism, the problem-solving capability of a technological device represents
a static feature, which is predetermined by its creator, whereas volunteerism proposes a
different frame of reference. It suggests that technology affordance is enabled by human-
technology interactions (Suchman, 2007) - and hence rooted in the way users deploy a
technology (Orlikowski, 2010). Technology and users symbolize “an ontology of separate
things that need to be joined together” (p. 257) in order for technology affordances to manifest.
In the study of technology affordance, sociomaterial research interprets human-technology
interactions by means of two different philosophical approaches: agential realism and critical
realism. Agential realism rejects subject-object dualism; it considers social and material as
inextricably related, to the point that “there is no social that is not also material, and no material
that is not also social” (Orlikowski, 2007, p. 1473). According to the agential realism view,
social and material do not hold any inherent properties and do not maintain their “ontological
separation” (Barad, 2003, p. 816). There is neither social nor material, but only the
sociomaterial, whose existence manifests during the enactment. However, without considering
social and material as independent entities, it remains unclear where the social ends and
material starts vis-à-vis (Leonardi, 2013). Therefore, as Orlikowski (2007) herself emphasizes,
when scholars adopt the agential realism approach, they experience severe difficulties in
operationalizing their research and in understanding the intertwining of humans and technology
in the practice. Conversely, critical realism considers social and material as inseparable but
independent entities, which are brought together by human activity. This interpretation allows
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to more easily pinpoint the elements that both users and technology introduce into the practice
and to determine what affordance is achieved via technology adoption (Mutch, 2013).
Reflecting upon this duality of approaches, Leonardi (2013) concludes that critical realism is
more suitable for studying technology affordance, because distinguishing material from social
is indispensable to examine how technologies, people, and their boundaries are enacted in the
practice (Cecez-Kecmanovic et al., 2014) and to analyze the social and organizational context
in which this interplay occurs. Technological objects and humans are not confined in a set of
“inherently determined boundaries and properties” (p. 811). Rather, they forge their
relationship by interacting with each other in real-world settings. The affordance of a
technological artifact is located in the boundaries that this interaction creates (Cecez-
Kecmanovic et al., 2014). The act of using a technological object triggers an ongoing double
dance of human and technology agency, in which the use and application context determine
technology affordance and technological effects (Rose & Jones, 2005).
This understanding advances a key point of sociomateriality theory: social dynamism shapes
technology affordance. When users belonging to different social contexts engage with identical
technological solutions, recognizably different approaches to utilization emerge, which can
result in varying technology affordances and technological effects (Taipale, 2019). For
example, Barley (1986) examined the use of computed tomography technology (CTT) in the
radiology departments of two community hospitals in Massachusetts. Although an identical
technological apparatus has been deployed in both hospitals to achieve the very same goal (to
perform standard radiological procedures), the comparative analysis shows that the radiologists
of each department have used CTT in different ways. This variation has generated diversified
results, which include unexpected effects, such as the alteration of power relations and
institutional interactions.
This example demonstrates that identical technologies and their potential affordances are
linked to multiple actual affordances (Leonardi & Barley, 2010). Enabling the potential
affordance of a technology during the practice and fully benefiting from its adoption require
users to take sociomaterial arrangements into account; their goals need to be correctly coupled
with the materiality of technological artifacts and socio-organizational factors influencing
technology adoption (Strong, Volkoff, Johnson et al., 2014). Given the neutrality of technology
and flexibility in usage, actual affordances cannot be established in advance, because they are
affected by the user during the interaction. The training, experience, skills, and knowledge of
users, for example, are well-known social aspects which modulate the affordance of a
technology (Goh, Gao, & Agarwal, 2011).
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“No matter what features are designed into a system, users mediate technological effects,
adapting systems to their needs, resisting them, or refusing to use them at all. The operative
technology is determined by patterns of appropriation and use by human beings” (Poole &
DeSanctis, 1990, pp. 176–177). Once technological devices are out in the market, social groups
develop and reinforce their understanding on how their goals can be achieved by deploying
such technologies (Leonardi, 2013). People use their agency to put technology into use and
fulfill specific goals. Based on their interests, groups of users can use the same technology, but
to reach different purposes and with different outcomes (Orlikowski & Scott, 2008). Based on
this rationale, identifying a standard outcome for a given technology tends to be inherently
problematic and leads to an oversimplistic view of technology deployment.
This issue is particularly evident in the overly positive perspective that we found in the Covid-
19 literature commenting on technology deployment for pandemic control. During the fight
against the novel coronavirus, we have witnessed a massive utilization of digital technologies
to limit the spread of the infection, with public authorities among the most-keen adopters. The
Covid-19 literature champions this digitally enhanced approach to containment, which has
produced undoubtable collective benefits (see Brem, Viardot, & Nylund, 2021; Guo, Ren,
Yang et al., 2020; Kumar et al., 2020; Sonn & Lee, 2020; Ting, Carin, Dzau, & Wong, 2020).
But this enthusiastic approval overlooks a relevant factor: the existence of a potential
affordance does not “guarantee its achievement or the accomplishment of the objective that
guides the relationship between action and technology” (Bobsin, Petrini, & Pozzebon, 2019, p.
19). This literature tends to take the potential affordance of technological solutions for granted.
As sociomateriality and affordance studies highlight, implementing a technology is a necessary
but insufficient condition for triggering its potential affordance and producing the benefits
which are associated to such affordance. The problem-solving capability of a technology is not
a built-in feature; it is mediated by sociomaterial arrangements. As a consequence, the very
same technology can have different levels of effectiveness and technology adoption can
produce different results, which cannot be predicted. And despite the expectations,
technological effects can even be negative. This line of thought functions as the theoretical
framework for our study (see Figure 1), which illustrates how sociomaterial arrangements
mediate the potential affordances and effects of ICT-based virus containment measures.
Figure 1 here
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3. Methodology
Our argument is substantiated with the results of a systematic review of academic and grey
publications that report on how digital technologies have been deployed in the fight against
Covid-19. This literature was examined by combining text data mining, co-occurrence pattern
recognition, and inductive coding, and it was identified by aggregating and filtering the results
of a series of keyword searches conducted in a number of online repositories. The peer-
reviewed literature on Covid-19 (letters, commentaries, journal articles, notes, and editorials)
was extracted from Scopus and Web of Science, whereas the grey literature was sourced from
the United Nations’ central archive of Covid-19 documents (reports, policy briefs, and
communications) and 97 repositories of newspapers, magazines, and news-based television
channels (articles and news). All keyword searches were performed in mid-April 2020 and
covered a 16-week timespan, going back to the beginning of January 2020. Prior to January,
no knowledge items on Covid-19 were found. By considering this period of time, the search
phase concluded with the identification of 2,187 records containing the following combination
of keywords: (tech* OR smart* OR digital* OR automation OR machiner* OR computer* OR
robotic* OR telecommunication* OR “applied science” OR “scientific knowledge” OR
“technical knowledge”) AND (coronavirus OR Covid OR SARS-CoV-2 OR pandemic).
After being organized in a single dataset, all the records were manually checked to determine
whether the selected combination of keywords was embedded in the body text. All documents
that failed to comply with this requirement were eliminated, because irrelevant to the analysis.
This verification process has proven very effective; a significant number of newspaper articles,
magazine articles, and news were removed because the keywords were located in the credit
sections - which provide details about the digital sources - or headlines and titles redirecting to
other online material. After completing this filtering process, we retained 515 documents for
text data mining, which has been instrumental in extracting the co-occurrence data required to
determine the most relevant textual elements and to measure their strength of association.
For the text data mining, we used the content analysis software WordStat (Version 8.0.21). But
before starting the analysis, all the source documents were converted to Rich Text Format
(RTF) files to facilitate automatic text recognition (see Mora, Xinyi, & Panori, 2020). WordStat
transformed the source documents into high-dimensional sets of unstructured textual data. This
transformation made it possible to semi-automatize data cleaning and data processing
operations. Through data cleaning, the dimensionality of the dataset was reduced while
preserving quality information. This process consisted in the removal of unimportant textual
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information. In the case of academic literature, for example, we filtered out the footers, headers,
and details about the authors and their institutions. Given their little semantic value, we also
removed stop words by relying on WordStat dictionaries. In addition, misspellings were
corrected, and variant forms of identical words were lemmatized. Following the data cleaning
phase, we extracted 13,894 textual items: 5,917 words and 7,977 phrases. Phrases are
conceptual units composed of minimum two and maximum four words.
After completing the extraction process, WordStat was tasked with measuring the strength of
association between each couple of words and phrases, by calculating their co-occurrence - an
indicator of semantic proximity (Lu, Liu, & Qian, 2016). Words, phrases, and their co-
occurrence data were then uploaded on the network analysis software Gephi, where textual
elements and their strength of association have been respectively represented as nodes and
edges of a co-occurrence network. In this network, the couples of words and phrases that occur
together in one or more source documents are connected by an edge, and each edge possess a
specific weight. This numerical value indicates the degree of semantic proximity between two
nodes. The more two items co-occur in the source documents, the higher their level of thematic
association. Given that the co-occurrence data were normalized, the weight ranges from 0 to 1,
where the former indicates a complete lack of similarity (the two textual items never occur
together in a source document). In alignment with research by Eck & Waltman (2009), to
normalize the data, the probabilistic affinity index Association Strength was preferred to set‐
theoretic measures.
To group words and phrases in clusters of thematically related textual components, we used
the Louvain community detection algorithm (Blondel, Guillaume, Lambiotte, & Lefebvre,
2008). The clustering process was conducted in Gephi (Panori, Mora, & Reid, 2019) by
adopting a trial-and-error approach (Sun & Sun, 2006). An initial attempt was made to join all
available expressions, which formed a 13,894x13,894 co-occurrence matrix. But this first step
did not produce significant results, because the co-occurrence data contained too much noise;
irrelevant network components generated defective clustering dynamics, which impacted
negatively on the accuracy of the results (Glenisson, Glänzel, Janssens, & De Moor, 2005).
When attempting to cluster the textual elements, the noise caused an excessive level of
fragmentation, which prevented us from obtained an organized representation of the data. The
ineffectiveness of the clustering process required reducing the extent of the co-occurrence
network by removing non-salient edges and nodes. To find an optimal partitioning scheme,
with compact and well-separated clusters, it is common practice to introduce a fixed cut-off
value (see Chi & Young, 2013; Ding, Chowdhury, & Foo, 2001; Liu, 2005; Shiau & Dwivedi,
10
2013). Following this approach, we progressively reduced the dimensionality of the matrix.
Three different settings were tested, with the threshold level of the strength of association
between couple of nodes at 0.1, 0.2, and 0.3, respectively. We obtained unambiguous results
only in the last two occasions. Therefore, to avoid excluding a higher number of network
elements, we pragmatically decided to only eliminate the edges with a co-occurrence measure
lower than 0.2 – and the nodes that this cut-off value left detached from others. Based on this
decision, the initial group of keywords was reduced to 4,733 expressions.
To uncover the technological solutions embedded in each thematic cluster, inductive coding
(Saldana, 2009) was deployed to tag all ICT-related expressions emerging from the huge mass
of unstructured qualitative data. These expressions represent the “seed keywords” (Sousa, De
Mello, Cedrim et al., 2018, p. 36) of this exploratory phase. They allowed to easily uncover
the technological solutions which have been deployed to contain the spread of Covid-19 during
the period under investigation, whereas the remaining textual data in the clusters helped
understand the context in which such solutions have been deployed. The thematic clusters with
no ICT-related words and phrases were excluded from the analysis because considered
irrelevant in the framework of this study.
In the final step of the review process, we systematically analyzed the Covid-19 literature
associated to each digital technology. The analysis aimed to source all the qualitative data
which could help illustrate how sociomaterial arrangements have mediated the potential
affordances and effects of the digital solutions that public authorities have introduced in their
Covid-19 containment strategies. The software Atlas.ti was used to support the extraction
process and to facilitate the organization of the qualitative data in concise written accounts
(Jack & Raturi, 2006), which are presented in the next section of the paper.
4. Findings
The analysis of the co-occurrence data has uncovered 50 thematic clusters. Eleven of these
clusters are non-technology-related, because no seed keywords can be found among their
textual components; on the contrary, the other 39 thematic clusters introduce a digital solution
each. These technologies are catalogued in Table 1, where they are accompanied by the list of
seed keywords extracted during the text mining process. For example, medical workers in
China have deployed remote-controlled robots equipped with ultraviolet light systems to
sanitize hospital rooms. Drones have assisted South Korean public authorities in disinfecting
outdoor spaces. Advanced computational tools for epidemiological modelling have been used
11
extensively across the world to model Covid-19 scenarios and inform decision-making via
predictive analytics. Using telepresence robots, medical staff and care workers in Asia, Europe,
and North America have been able to interact with Covid-vulnerable individuals without
endangering their health. Frontline workers in the UK have used online crowdfunding
technology to help overcome the shortage of medical devices and personal protective
equipment in healthcare facilities.
Table 1 here
The data sourced during the systematic review led us to consider the following technologies
(see Figure 2): (1) infrared temperature-sensing devices; (2) ICT-based surveillance and
contact-tracing systems - which include counterterrorism tracking systems using geolocation
data, facial recognition technology, unmanned aerial vehicles, and wearable GPS tracking
devices; (3) bioinformatic tools and applications for laboratory testing; and (4) electronic mass
communications media. Deployed by public authorities to contain the spread of Covid-19,
these digital solutions are best placed to showcase the mediating role of sociomaterial
arrangements on potential technology affordances and technological effects. This decision is
based on data availability reasons.
Figure 2 here
4.1. Infrared temperature-sensing devices
Because fever is among the most common symptoms of coronavirus infection (Tian, Hu, Lou
et al., 2020), many public authorities have introduced temperature checkpoints in public spaces,
where officials have started relying upon non-contact infrared thermometers (NCITs) and
infrared thermal image scanners to detect and isolate infected individuals during mass
screening operations. For example, NCITs have been introduced in a number of Chinese cities
and subway entrances (Dou, 2020; McFall-Johnsen, 2020; Normile, 2020a; Pietsch, 2020).
Military personnel in Kiev, the capital city of Ukraine, has been instructed to conduct
temperature checks with NCITs at the entrance of presidential office buildings (Rauhala, 2020).
The Civil Aviation Administration of China (CAAC) - the aviation authority of the People's
Republic of China - has required security personnel working at high-risk airports to check the
body temperature of all arriving and departing passengers with NCITs (CAAC, 2020b). In
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addition, since the beginning of March 2020, CAAC (2020a) guidelines for airlines have also
required cabin crews to take temperature measurements during flights. Between January and
February 2020, temperature checkpoints with NCITs or infrared thermal image scanners for
pre-boarding and after-landing testing were also introduced in several international airports
outside China (Baragona, 2020; Cripps, 2020; Gilbert, Pullano, Pinotti et al., 2020; WHO,
2020b). Examples include airports in the Philippines, United States of America (USA), United
Kingdom (UK), United Arab Emirates, Thailand, Japan, and South Korea (Drewett, 2020; Kim
& Talmazan, 2020; Ripley, 2020; The Guardian, 2020).
However, on several occasions, public authorities have acted against WHO recommendations,
by deploying infrared temperature-sensing devices as the sole testing measure for mass
screening operations. At the beginning of the Covid-19 outbreak, WHO (2020a, 2020d) has
urged public authorities to use temperature screening technologies only in combination with
other screening methods. These recommendations take into account that fever is but one
symptom of Covid-19 infection; although fever-free, individuals can still carry the virus. For
this reason, WHO has advised public authorities to always accompany temperature checks with
the dissemination of health communications - to properly inform individuals about the relevant
signs and symptoms that should be reported during mass screening – and to collect data on
symptoms by administering questionnaires.
In line with this advice, the Medicines and Healthcare products Regulatory Agency (MHRA)
of the UK Government urged caution in interpreting the results of temperature screening,
explaining that infrared temperature-sensing devices are not suitable for mass screening
operations when “used as the main method of testing. Infected individuals who do not develop
a fever […] would not be detected by a temperature reading and could be more likely to
unknowingly spread the virus” (MHRA, 2020, p. 8).
Infrared temperature-sensing devices are commonly used in healthcare facilities, where they
allow health professionals to obtain non-invasive and contact-free temperature readings of
patients. When deployed in healthcare settings, these digital devices have proven to be reliable
medical instruments. Conversely, when this technology has been positioned in the Covid-19
setting, many public authorities expected to achieve a technology affordance that did not fully
materialize in practice (Normile, 2020b). Against their expectations, infrared temperature-
sensing devices have a limited capability for virus spread reduction when deployed in isolation
from other testing measures; they struggle to detect infected individuals and offer false
assurance. Scientific evidence sourced during the review process confirms this assertion.
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During the evacuation of citizens from Wuhan to Frankfurt, two passengers with no fever have
been found positive for coronavirus with a throat culture test. However, the discovery was
made after the flight landed in Germany. The infected passengers were not detected during the
pre-boarding testing phase (Hoehl, Rabenau, Berger et al., 2020). In addition, at the end of
February 2020, a group of passengers travelling from Italy to Shanghai were tested for fever at
their arrival in China and passed undetected despite being infected (Normile, 2020b). On both
occasions, infrared temperature-sensing devices were deployed as the only screening measure.
These application cases cast doubt upon the use of infrared temperature-sensing devices as the
only measure for mass screening operations, and the modelling work presented by London
School of Hygiene and Tropical Medicine introduces additional skepticism. Their
mathematical model estimates that, during pre-flight screening, thermal scanning is likely to
miss up to 50% of coronavirus-infected individuals (Quilty, Clifford, Flasche, & Eggo, 2020).
In addition, when examining how NCITs have been deployed during the Covid-19 pandemic,
technology experts have raised concerns in relation to the overall impact that an improper use
may have generated. On some occasions, NCITs were used in environments with extreme
temperatures, where they “tend to be unreliable” (Yaffe-Bellany, 2020). In these
circumstances, infrared thermometer measures are likely to identify false positive or false
negative cases. For example, misleading temperature readings have been reported in China by
a Financial Times journalist who was requested to undertake a temperature check in an outdoor
space - with freezing temperatures. The NCIT device provided an abnormal reading, which
turned out to be to a false positive. In addition, experts have reported of cases in which the
devices were managed by officials without the necessary medical training (Yaffe-Bellany,
2020; Yang, 2020). As a result, NCITs were hold inappropriately – for example, too far from
or too close to the subject - and incorrect usage is a well-known cause of wrong temperature
measurements (McFall-Johnsen, 2020).
4.2. ICT-based surveillance and contact-tracing systems
Tracking systems using geolocation data, facial recognition technology, unmanned aerial
vehicles, and GPS tracking devices are largely deployed in the fight against terrorism. But a
number of public authorities have decided to use these ICT-based surveillance and contact-
tracing systems to ensure that citizens adhere to social distancing restrictions, such as
lockdowns and home quarantine (Thompson, 2020). These drastic control measures have been
taken by appealing to the state of emergency that countries have declared to contain Covid-19,
14
and they have helped government officials to seize their power, suspend some conventional
constitutional rights, and control citizen behavior (Chandler, 2020).
For example, the Polish government has forced citizens undergoing a compulsory quarantine
to regularly share their location. Citizens have been requested to constantly provide police
officers with their real-time location data by using a purpose-built smartphone application that
combines facial recognition with geolocation (Bartoszko, 2020; Hamilton, 2020). Police forces
in the UK have used drones to keep citizens under surveillance, and this action stirred up a
debate on power abuse; the Derbyshire police posted uncensored drone footage on Twitter to
shame two individuals who were walking in an isolated outdoor environment - although
lockdown measures were in place - while being stealthily recorded (Castle, 2020). Drones have
also been extensively deployed as a spy tool for surveillance purposes in other European
countries, including Spain, Italy, Hungary, France, and Germany (Ball, 2020; Roth,
Kirchgaessner, Boffey, Holmes, & Davidson, 2020; Wood, 2020). Inspired by legislations
launched in South Korea and Taiwan, the Slovak parliament has authorized the national
government to use private-owned mobile phone data to track the movements of people who
have tested positive for Covid-19 and to ensure that they adhere to social distancing rules
(Fildes & Espinoza, 2020; Shotter, 2020). Similarly, the Israeli government has tasked its
counterterrorism unit with tracing the location history of coronavirus-infected individuals and
monitor their self-isolation by maintaining regular surveillance over their mobile phones. The
mobility patterns have been used to determine whether citizens have met up with infected
individuals and should therefore be forced to quarantine (Calvo, Deterding, & Ryan, 2020;
Gregory, 2020; Servick, 2020). Similar contact-tracing techniques have also been used in
Taiwan, China, and South Korea to enforce quarantine measures (Cho, Ippolito, & Yu, 2020).
Contact tracing in South Korea has been based on a combination of GPS, CCTV, and credit
card data (Jo, 2020; Kim, 2020), whereas China has used mobile phone apps - such as AliPay
and WeChat - as data sources to trace interactions (Ienca & Vayena, 2020). The Government
of South Korea has also collected GPS tracking information of a group of individuals who
contracted the virus and has shared detailed personal data via multiple smartphones apps –
including their recent movements - with users living in the vicinity (Servick, 2020; Zastrow,
2020). Since the data were not fully anonymized, this approach has been accused of
“unmasking and stigmatizing infected people and the businesses they frequent” (Zastrow,
2020, p. 10).
In all these cases, ICT systems for massive surveillance and contact-tracing may have helped
limit the spread of the infection during the Covid-19 crisis (Cho, Ippolito, & Yu, 2020; Ting,
15
Carin, Dzau, & Wong, 2020; Wheeler, 2019). But the approach to deployment has also severely
undermined privacy and public trust, by not taking ethical implications into account. Although
these authoritarian surveillance measures have been imposed to protect the public, they have
been enforced without robust and transparent accountability measures, leaving citizens and
their rights unprotected (Santow, 2020). As a result, insecurity and anxiety have increased
among local communities (Calvo, Deterding, & Ryan, 2020), leaving governments in dispute
with data privacy experts and human rights activists, who have called for a more responsible
use of digital surveillance tools and large-scale collection methods of location data (Ienca &
Vayena, 2020).
Concerns have been raised about the vast amounts of location data being used to track
individuals, which have triggered a growing lack of trust in the authorities who handle such
information (Beattie, 2020; Cho, Ippolito, & Yu, 2020; Ienca & Vayena, 2020; Santow, 2020;
Stein, 2020). Questions have surfaced in relation to how the data have been used, who has been
granted access to such data, and “how the data will be used once the crisis is over and whether
such datasets are ever truly anonymous” (Fildes & Espinoza, 2020, p. 7). These concerns
demonstrate that extreme monitoring tools can easily become a mean for generating unrest in
the society. In addition, governments may take advantage of this temporary control
mechanisms to abuse the system, by increasing mass surveillance through hype, criminalizing
citizens unnecessarily, interfering with their privacy, and jeopardizing personal freedoms,
especially in authoritarian governments (Kavanagh, 2020).
In response to these concerns, in March 2020, the UN Special Rapporteur on the right to
privacy has recommended countries to select more privacy-friendly alternatives and voluntary
data collection tools rather than authoritarian surveillance systems (Rossman, Keshet, Shilo et
al., 2020). The UN has also urged countries to set up independent monitoring agencies to
oversee such measures and ensure that responses are proportionate, absolutely necessary when
used, written in law, and strictly limited in time (Gregory, 2020). These recommendations are
also highlighted in the civil society statement released by Human Rights Watch (2020, p. 3),
which recognizes that “an increase in state digital surveillance powers […] threatens privacy,
freedom of expression, and freedom of association, in ways that could violate rights and
degrade trust in public authorities, undermining the effectiveness of any public health response.
Such measures also pose a risk of discrimination and may disproportionately harm already
marginalized communities”.
16
4.3. Bioinformatic tools and applications for laboratory testing
The use of computer-based approaches to the analysis of biological data has accelerated
biotechnological research. As a result of these technological advancements, the Covid-19 virus
genome was sequenced in only two weeks. During the 2002 Severe Acute Respiratory
Syndrome (SARS) pandemic, the genome sequencing process required two months (Callaway,
Cyranoski, Mallapaty, Stoye, & Tollefson, 2020). On 31 December 2019, the Chinese national
authorities informed WHO that a number of patients in Wuhan were diagnosed a pneumonia
of unknown cause. On 12 January 2020, Chinese researchers had already isolated the pathogen
causing the disease, discovered that it was a new type of coronavirus, determined its DNA
sequence, and shared their findings (WHO, 2020c). Accelerated by next-generation technology
platforms (Le, Andreadakis, Kumar et al., 2020), this knowledge production process allowed
the worldwide research community to start working on vaccines and to develop diagnostic tests
and computer-implementable instructions for laboratory testing at unprecedent speed
(Callaway et al., 2020). WHO-vetted diagnostic tests and testing protocols have been made
available at the beginning of February 2020, allowing governments across the globe for rapidly
collecting and testing appropriate clinical specimens from potentially infected patients (Cohen,
2020a; Sheridan, 2020).
Finding and isolating infected individuals is key to stop the spreading of highly contagious
viruses. Therefore, in January 2020, WHO recommended governments to prepare for Covid-
19 mass testing and started distributing a diagnostic test. In the meantime, China had already
marketed five additional tests, which made it possible for the country to massively increase its
coronavirus testing capacity. At the end of February, the Chinese government was already
processing 1.6 million tests a week (Cohen, 2020a; Maxmen, 2020).
Widescale testing efforts have become crucial to relax the social distancing measures that have
been imposed. This explains why a well-organized and fast-implemented nationwide testing
strategy has turned out to be one of the core components of the successful coronavirus response
that South Korea has adopted (Cohen, 2020b; Fleming, 2020). In collaboration with all regional
and city governments, the central government has designed a joined-up strategy which has
included the construction of an extensive network of drive-through testing stations. This
network made it possible for patients and medical staff to avoid risky contacts and process
5,200 tests per million inhabitants by 16 March. Meanwhile, the US was severely lagging
behind, with only 74 people tested per million inhabitants since the beginning of the outbreak
(Cohen & Kupferschmidt, 2020).
17
The enormous advantage generated by advancements in bioinformatics notwithstanding, USA
and UK have exposed serious difficulties in developing effective national testing programs
(Cohen & Kupferschmidt, 2020; Servick, 2020). The UK approach to massive infection
tracing, for example, has proved ineffective. Comparing government-declared testing
capacities in March 2020, we can report that the UK government was processing some 90,000
people a week, whereas Germany was performing almost 500,000 tests a week (Iacobucci,
2020; Pollock, Roderick, Cheng, & Pankhania, 2020). Data from the Department of Health and
Social Care show that only 218,577 UK citizens had been tested by 7 April (Schraer, 2020)
and the UK government opened its first mass coronavirus-testing facility very late, at the
beginning of April
2
.
The findings of an investigation conducted by the scientific journal Nature demonstrate that
the key factors contributing to the failure of the mass testing phase in the USA are the lack of
leadership at the federal government level, heavy bureaucratic environment, lack of control
over supply, and inaccurate decision-making. Everything started with the unclear decision of
the US Centers for Disease Control and Prevention (CDC) to replace the ready-to-use test and
computer-based protocol already vetted and distributed by the WHO (Dyer, 2020). Until the
end of February, no other tests than the one designed by CDC were permitted for testing Covid-
19 infections in the US. But health labs in the country started receiving the CDC test very late.
The kits were in short supply and, in addition, they were not fit to be used, because distributed
with a faulty reagent (Cohen, 2020a; Maxmen, 2020).
The issues that caused low testing supply availability have also forced US university labs to
work at limited capacity. Additionally, despite being certified and equipped with the
technology needed for increasing the national testing capacity, universities have been
significantly slowed down by the impossibility to work independently. Processing the tests
required to activating a collaboration with the healthcare system. But developing this
relationship has proved very challenging due to overcomplicated administrative procedures
and software interoperability issues (Cohen, 2020a; Maxmen, 2020).
2
See the coronavirus coverage webpage of the scientific journal Nature:
https://www.nature.com
18
4.4. Electronic mass communications media
During the Covid-19 emergency, electronic mass communications media, such as social media
platforms, instant messaging apps, online video-sharing services, digital media, and websites
of governmental organizations have played a twofold role. On the one hand, they allowed
public authorities to provide citizens with a one-stop shop for sourcing up-to-date and reliable
information about the outbreak. On the other hand, these digital communication channels have
become the main supporting tool for online users interested to fuel one of the largest infodemic
of misinformation in the human history (Zarocostas, 2020).
Fake online news tends to spread faster than information from trusted sources (Vosoughi, Roy,
& Aral, 2018) and can put public health at risk, in particular when the use of unproven medical
products for curing the infection is suggested or evidence-based medical advice is falsely
contradicted (Ioannidis, 2020; Tasnim, Hossain, & Mazumder, 2020). Recognizing the gravity
of the situation, governmental authorities, intergovernmental organizations, and tech and media
companies have reacted with a coordinate response, by proposing multiple ICT-based
countermeasures (Cellan-Jones, 2020; Holmes, 2020). For example, factchecking systems have
been deployed to scrub off the fast-growing number of false claims hosted on the web
(Brennen, Simon, Howard, & Nielsen, 2020). Facebook have sent alerts to users who have
engaged with posts containing harmful coronavirus-related misinformation (Wong, 2020).
WHO and BBC have opened myth-buster webpages, which have been used to crack down
some of the most common false beliefs that Internet users have disseminated
3
.
But despite joining the front-line fight against fake news, even government officials have
sometimes become the carrier of misleading coronavirus information. For example, social
media misconduct is visible in Brazil and the US. A video circulating on Facebook and Twitter
shows the Brazilian President Bolsonaro who publicly endorses the anti-viral drug
hydroxychloroquine as a Covid-19 therapy (Ricard & Medeiros, 2020). The same message was
also backed by US President Donald Trump. However, the CDC immediately confirmed that
these claims were made only by considering anecdotal evidence rather than the findings of
empirical studies (Mahase, 2020; Milman, 2020). In addition, during the live streaming of a
daily briefing of the coronavirus task force - whose recording has circulated across several
digital communication platforms - the US President has also suggested administering
3
The myth-buster webpages of BBC and WHO can be found at https://www.bbc.co.uk and
https://www.who.int.
19
disinfectant products into the human body as a possible treatment (Cookson & Sevastopulo,
2020; Yamey & Gonsalves, 2020).
Additional criticisms against the approach of government institutions to social media
communication can also be raised in relation to the restrictions that the Chinese state censorship
has imposed to cover up the Covid-19 outbreak. Evidence presented by Fu & Zhu (2020, p. 4)
demonstrates that “Chinese authorities censored online discussion on the coronavirus and
restricted the public access to early warning on social media”. A report released by Citizen Lab
– a Canada-based Internet censorship research organization - confirms these findings. The
report shows that, in December 2019, the Chinese government introduced an initial list of
keywords to filter information in online discussions reporting on the novel coronavirus
infection. According to the report, to control the narrative surrounding the pandemic, the scope
of censorship was also broadened in February 2020, when the government selected 516
coronavirus-related keyword combinations and blocked them on the instant messaging and
social media app WeChat. The research notes that, at the initial stage of the outbreak, this
censorship curbed alerts to the public on the threat of the then-unknown virus. Later, the
censored contents were broadened to include any reference to Li Wenliang (Ruan, Knockel, &
Crete-Nishihata, 2020). In late December 2019, Wenliang was among the first Chinese doctors
who warned medical officials and the public about the outbreak in Wuhan (Fu & Zhu, 2020).
But after sharing this early warning in a number of WeChat groups, Chinese police forces
accused him of spreading false statements which were endangering the public (Green, 2020).
Although we cannot estimate the overall economic and social impact that the Internet
censorship has generated, this lack of transparency has certainly prevented citizens from
becoming aware of the outbreak in its early stages (Zhu, Fu, Grépin, Liang, & Fung, 2020).
Communication on the outbreak on Chinese social media platforms was almost inexistent
before the Chinese Government publicly revealed the existence of human-to-human
transmissions of a novel coronavirus in Wuhan. But this only happened the 20th of January.
5. Discussion and conclusion
One out of five WHO Member States faces a public health crisis every year
4
. Most of these
crises remain confined within national borders and impact upon a small and localized group of
individuals. But when a disease spread to larger populations within a short period of time, what
4
Data from WHO: https://www.who.int/hac/about/threeyearplan_focus/en/.
20
started as a local health emergency can escalate into devastating epidemics or pandemics. For
example, after reaching Europe in 1347, the Black Death - a multi-century pandemic of bubonic
plague – killed an estimated one third of the continent’s population in a few years (Ziegler,
1969). During a 1576 epidemic caused by a malignant form of hemorrhagic fever, almost half
of the population of Mexico was killed. Hemorrhagic fevers started in 1545 and have remained
in the Mexican territory for three centuries (Acuna-Soto, Romero, & Maguire, 2000). The 1889
influenza pandemic – also known as Russian flu - started with some earliest cases in Russia,
but it only took six weeks for the virus to spread throughout Europe and four months to circulate
around the globe. Unfortunately, we know little about the mortality impact (Ramiro, Garcia,
Casado, Cilek, & Chowell, 2018). Other three influenza pandemics followed in 1918, 1957,
and 1968, which are known as Spanish Flu, Asian Flu, and Hong Kong Flu, respectively. The
Spanish Flu caused an estimated number of deaths which ranges between 20 and more than 50
million (Trilla, Trilla, & Daer, 2008), whereas the Asian Flu and Hong Kong Flu ended about
one million lives (Mylius, Hagenaars, Lugnér, & Wallinga, 2008). A few years later, Acquired
Immune Deficiency Syndrome (AIDS) made its first appearance. Due to this life-threatening
condition, millions have died over the last fifty years and a cure is yet to be discovered (Gallo
& Montagnier, 2003; UNAIDS, 2020). The number of influenza pandemics grew again in
2009, when a novel strain of swine influenza hit the world (Butler, 2009); during the first year,
the Swine Flu pandemic claimed between 152,700 and 575,400 lives worldwide. The 2014
West African Ebola epidemic - the largest in history - severely damaged Guinea, Liberia, and
Sierra Leone, with a total number of 29,616 cases and 11,310 deaths in two years
5
. Finally,
previously unknown coronaviruses associated with SARS - a highly contagious respiratory
disease - have triggered the first two large-scale outbreaks of the twenty-first century: the
SARS and Covid-19 pandemics. With 8,422 cases and 916 fatalities between 2002 and 2004,
the SARS pandemic was the less severe (Cherry & Krogstad, 2004). As of April 2021, WHO
data show that Covid-19 has reached all continents, infected 135,000,000 individuals and
caused 3,000,000 deaths in approximately 18 months
6
.
5
The data on the 2009 Swine Flu pandemic (https://www.cdc.gov/flu/pandemic-
resources/2009-h1n1-pandemic.html) and 2014 West African Ebola epidemic
(https://www.cdc.gov/vhf/ebola/history/2014-2016-outbreak/index.html) are provided by
CDC.
6
Data sourced from the WHO Coronavirus (COVID-19) Dashboard: https://covid19.who.int.
21
The ravages of infectious diseases have loomed large over the years, challenging the existence
of humanity. In response to this threat, governments and health organizations have been
investing considerable resources in contingency planning, while academic and industrial
research has been releasing improved vaccines, faster production methods, and new
technological developments for minimizing health risks and socio-economic effects. Digital
technologies are among these advancements, which have been extensively deployed during the
fight against Covid-19. The impact of the pandemic has pushed public authorities towards
experimenting with ICT-based virus containment measures in an attempt to more quickly
identify, isolate, and monitor infected individuals and to prevent infection rates from rising
(Islam, Marinakis, Majadillas, Fink, & Walsh, 2020).
Evidence from recent studies confirm that digital technologies have proven effective in helping
public authorities to limit the spread of the infection and enhance resilience. But when reporting
on how these technologies have been deployed, the current Covid-19 literature tends to
oversimplify the complexity of technology adoption; it conceptualizes technology affordance
as a standard built-in feature that automatically activates during technology deployment. Based
on this rationale, no matter who is using a technology, how, and in what circumstances, the
affordance that such technology is attributed by design will always materialize in full, leading
to undiversified and predetermined benefits. Potential and actual affordances are not examined
in detail, and the mediating role of sociomaterial arrangements is not taken into account.
Against this backdrop of overstated optimism, we show that potential technology affordances
and collective benefits are mediated by the sociomaterial arrangements that users assemble to
connect their goals to the materiality of technological artifacts and the socio-organizational
context in which technology deployment is embedded. To substantiate this argument, we
present the results of a systematic review of Covid-19 literature that reports on how digital
technologies have been deployed to fight the pandemic. The review illustrates the mediating
role of sociomaterial arrangements by focusing on the virus containment efforts in which public
authorities have introduced the following digital technologies: infrared temperature-sensing
devices; ICT-based surveillance and contact-tracing systems; bioinformatic tools and
applications for laboratory testing; and electronic mass communications media. Table 2
presents a summary of the findings; for each technology, we provide a concise description of
how sociomaterial arrangements can mediate potential affordances by using the data collected
during the review process and lessons learned from the application cases that such data relate
to.
22
Table 2 here
5.1. Theoretical contribution
Our findings offer a three-fold contribution to the current academic debate on sociomateriality,
technology affordance, and the governance of technology in public health crises.
First, the review confirms that the problem-solving potential of digital technologies is
determined by the way in which actors position their use in a given context. This evidence
reinforces the assertation that affordance is a context-specific feature. Therefore, we argue that
a clearer distinction should be made between potential and actual affordances when the impact
of digital technology deployment in pandemic settings is examined (Conole & Dyke, 2004).
Our findings align with theorizing in sociomateriality and technology affordance literature
(Iden, Methlie, & Christensen, 2017; Salancik & Pfeffer, 1978; Tyre & Orlikowski, 1994);
when adopting a technological artifact, the potential affordance can be undermined by
sociomaterial factors. Statements on the effectiveness of digital technology adoptions in
pandemic settings, therefore, need to take technology usage into account. Technology
affordances in a given application context depend upon the interaction between technological
features and socio-organizational arrangements (Picazo-Vela, Fernandez-Haddad, & Luna-
Reyes, 2016). Looking beyond technological built-in affordance is pivotal when facing a large-
scale outbreak, and more efforts should be oriented towards understanding the role that actors
and institutional setups play in technology adoption – and their diversified effects. As
Orlikowski (Orlikowski, 2000) notes, despite technologies have a preferable operationalization
mode (which is more likely to materialize potential affordances), users may be unaware, and
they also “have the option, at any moment and within existing conditions and materials, to
choose to do otherwise” (p. 412). But “in such possibilities [lie] the potential for innovation,
learning, and change” (p. 412) that comparative studies on the effects of different technology
adoption processes can help trigger.
Second, introducing digital technologies in the response to the pandemic represents an effort
of public authorities to enhance public sector performance and resilience in times of crisis. This
decision is in line with recent developments in the field of public administration, which
suggests governance can be more efficient and objective when public authorities rely on digital
technologies (Nograšek & Vintar, 2014; Weerakkody, Janssen, & Dwivedi, 2011). But the
results of our study show that, although deploying digital solutions can uplift the capacity to
protect the public from infections like Covid-19, technology deployment may not result in
23
objective governance (Kummitha, 2020); once again, the outcome depends upon the approach
to usage. We argue that technologies offer “a vector of options for decision-makers to choose,
based on their own judgment” (Kummitha, 2020, p. 8). Public authorities can use digital
solutions to reinforce their power, manipulate public information, and act against collective
interests, by screening and controlling information flows based on their own interests. For
example, our study shows that the role of human agency in attempts to enhance objective
governance via social media should not be underplayed (Mohajerani, Baptista, &
Nandhakumar, 2015), and technologies used for surveillance purposes are neither autonomous
nor capable to objectively shape society (Bierwisch, Kayser, & Shala, 2015).
Third, the notion of sociomateriality builds on the practice-based perspective of science and
technology studies (Moura & Bispo, 2020) and suggests examining affordances by “exploring
technology at work” (Orlikowski, 2007, p. 1435). But the practical utility of sociomateriality
theory has been criticized, because research in this knowledge area is more oriented towards
theorizing rather than empirical applications. This explains why the concept of sociomateriality
remains “extremely theoretical” (Leonardi, 2013, p. 59). As Moura & Bispo (2020) have noted,
limited efforts have been made to determine how the sociomateriality concept can be
operationalized and what methodological possibilities should be considered for conducting
empirical research that captures an understanding guided by the observation of the practice. In
addition, methodological issues are still to be clarified. Due to these critical gaps, researchers
continue to experience difficulties when attempting to operationalize a sociomaterial
perspective, which represents a complex and resource intensive task (Mutch, 2013).
Our research supports the dialogue on the practical contribution of sociomateriality and brings
new insights into the discussion on methodological possibilities and constraints. Although
limited in scope, this study contributes to demonstrating the theoretical and - most important -
analytical support that sociomateriality can offer to the study of technology affordances. In
addition, it showcases how content analysis of academic literature and media-generated
contents can be used as a method to acquire and organize empirical evidence for sociomaterial
enquiries. These data sources function similarly to archives (see Johri, 2011): they can help
capture “events that occurred in the past and can uncover a diversity of heterogeneous
interactions” (Moura & Bispo, 2020, p. 360).
5.2. Practical contribution
24
Reflecting on the moderating factors captured during the review process, we have formulated
a set of practical recommendations that can help public authorities to strengthen their approach
to digital technology deployment for pandemic control.
First, we encourage public authorities to take notice of pre-existing knowledge and
recommendations of public health agencies on digital technology adoption and to ensure that
technology users receive the necessary training. Public authorities have operated infrared
temperature-sensing devices during mass screening operations, to detect and isolate Covid-19
infected individuals in public spaces. But our review shows that the potential technology
affordance has been reduced in situations where infrared temperature-sensing devices were
deployed as the only screening measure - acting against WHO's Covid-19 technical guidance
- and by officials without the necessary training. The WHO Interim Guidance for Ebola Virus
Disease (EVD) released during the 2014 West African epidemic had already exposed the
limitations of mass screening operations in which temperature checks were used as the only
screening method. As a result of these limitations - exactly as it happened during the Covid-19
pandemic - WHO recommended public authorities to screen large groups only by combining,
“at a minimum, a questionnaire, a temperature measurement and, if there [was] a fever, an
assessment of the risk that the fever [was] caused by EVD” (WHO, 2014, p. 3). Similar
concerns on the usability of infrared temperature-sensing devices as mass screening tools for
Ebola were also expressed by the European Centre for Disease Prevention and Control
(ECDC), which decided to assess the performance of NCITs. Their study concludes that NCITs
can perform relatively well in pandemic settings, because they are sufficiently accurate and
low cost. But to reach an appropriate level of performance, infrared temperature-sensing
devices should be complemented with visual reviews and health questionnaires. In addition, to
ensure that accurate temperature readings are collected, the ECDC has highlighted that infrared
temperature-sensing devices should be handled by trained staff only (ECDC, 2014).
Second, the right to privacy of citizens should be protected as part of any attempt to use ICT-
based contact tracing and surveillance systems. In addition, alternative measures should be
considered to replace authoritarian monitoring tools, whose usage – if unavoidable – requires
cautious planning, a clear rationale, and independent monitoring systems. A key aspect in this
process is the design of appropriate strategies that define – in a transparent manner – who
oversee such measures, how the data are collected and stored, who can access such data, and
how the data will be erased after the crisis ends. For example, in response to the call for
solutions that mediate between privacy concerns, data protection requirements, and the need to
increased surveillance, the Singapore Government and Australian Government have
25
respectively launched TraceTogether and Covidsafe. These privacy-preserving mobile
applications for instant contact-tracing have helped break the chain of transmission by
combining community-based voluntary action and Bluetooth technology, while ensuring
privacy protection for their users. They record when users are in close proximity and, if one of
them is found positive to Covid-19, the applications automatically alert all users who have been
in direct contact with the infected person, suggesting self-quarantine measures and immediate
testing (Cho, Ippolito, & Yu, 2020; Greenleaf & Kemp, 2020). Similar mobile applications
have also been developed by a number of European countries - such as Italy and the United
Kingdom – and introduced in the post-lockdown phases. But despite the potential affordance
of privacy-preserving mobile applications for instant contact-tracing, it is important to note that
some under-investigated challenges remain, which may constrain the implementation of this
technology. For example, more research is required to assess the willingness of citizens to use
bottom-up contact tracing functionalities and clarify what strategic approaches are more likely
to stimulate public participation (Gerli, Arakpogun, Elsahn, Olan, & Prime, 2021).
Third, public authorities should also ensure that strategic preparedness and response plans are
ready to implement, strong leadership is available in time of emergency, and stringent
bureaucratic protocols can become more agile and flexible when subject to the intense pressure
of public health crises. The pandemic has undoubtably posed public leaders in front of very
unusual challenges, and they may have struggled to handle the emergency due to a lack of
experience (Ahern & Loh, 2020). Although the cause is not clear, the limited capability of
some leaders to stand at times of uncertainty, to build and sustain trust, and to boost efficiency
and resiliency, has contributed to undermining potential technology affordances – for example,
in the cases of electronic mass communications media, bioinformatic tools, and laboratory
testing applications. More preparatory and planning work for emergencies is needed in the light
of the leadership gap that the current pandemic has exposed, which includes a review of those
response plans where key activities - such as national mass testing operations - have proven to
be inefficient. Flexibility is also required to ensure that stringent bureaucratic arrangements
can be loosen up when technological advancements need to be adopted quickly in line with
local needs, as in the case of US university labs, which have been prevented from working at
full capacity during mass testing operations.
Finally, more consistent collaborative efforts are required to improve the public response to
large scale infections. A pandemic generates complex challenges that public authorities may
struggle to tackle without tapping into the know-how and resources of other stakeholders
(Budd, Miller, Manning et al., 2020). For example, complementary measures linking
26
quadruple-helix actors – government organizations, industry, academia, and civil society – are
needed to accelerate the large-scale rollout of privacy-preserving mobile applications for
instant contact-tracing and to improve their functionality. In addition, cross-sector
collaboration is also indispensable to ensure that misinformation and unjustified censorship do
not prevent the public from receiving reliable data on public health crises. Factchecking
systems and myth-buster webpages can be deployed to fight fake news, but these counter
measures are insufficient; as we witnessed during the Covid-19 pandemic, false claims can
reach the public before being invalidated. Teaching the public to recognize misinformation is
a complementary action that can generate additional resilience, and the education sector is
already moving in this direction. For example, some secondary and primary schools in Finland
have recently introduced multi-platform information literacy in their national curriculum
(Charlton, 2019); the objective is to teach children understand how to spot fake news.
Similarly, universities have started offering courses which aim to increase public awareness of
false information and to improve media literacy skills
7
. These examples support the OECD’s
(2021) call for stronger national and international collaborative efforts between public sector
organizations and science and technology actors, whose cooperation is indispensable to address
grand challenges, such as pandemics.
5.3. Limitations and future research directions
Despite its rigor, we recognize that there are limitations in our study, which open up future
research opportunities.
Our review focuses on the digital solutions that have been introduced during the first four
months after Covid-19 was discovered. Considering that the pandemic was still unfolding
while we were completing our analysis, additional technologies may have emerged in the fight
against the newly discovered virus. Therefore, the list of digital solutions presented in Table 1
is extensive but should not be considered as exhaustive. Future research is required to
understand whether additional technologies have been adopted.
In addition, data availability has represented a limiting condition, which we faced by examining
only four digital technologies out of the initial 39 that we mapped. We assembled a
7
For example, the University of Michigan has launched the short online course “Fake News,
Facts, and Alternative Facts”: https://online.umich.edu/teach-outs/fake-news-facts-and-
alternative-facts-teach-out/.
27
methodological approach whose design combines the research requirements associated to our
objective with the limitations imposed by the pandemic, which has made it difficult to conduct
field research based on interviews, observations, and ethnographic methods. Our analysis has
captured sufficient evidence to reach our goal and demonstrate that studies commenting on
digital technology affordances in a pandemic scenario should more clearly distinguish potential
from actual. But we overlooked how users have perceived technological affordances and what
decision-making processes have framed the sociotechnical arrangements that we discovered.
Although positioned beyond the scope of our analysis, these lines of enquiry can offer
additional insight into the relationship between public health crises and the governance of
digital technologies. In addition, further moderating factors related to the technologies that we
have analyzed - or others that were not included in our study - may surface as more data become
available. For these reasons, we encourage future research to build on our results and expand
the investigation, in particular through fieldwork.
Future research is also encouraged to bring a focus on a broader range of actors. During the
pandemic, public actors have become but one user group of digital technologies; it would be
interesting to compare how different users implement digital technologies in time of crises by
using quadruple-helix innovation as a theoretical lens.
Finally, new research questions related to digital technology adoption have surfaced from our
analysis. For example, what are the consequences that the misuse of electronic mass
communications media has generated? The societal impact of misinformation in a global public
health crisis is worthy of future study, so is a more in-depth understanding of the real benefits
that authoritarian surveillance measures have produced and how governments in different
geographic regions have coped with personal data collection processes. Comparative studies
should also be encouraged in order to identify best practices.
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Figure 1. Theoretical framework: technology affordances, technological effects, and the
mediating role of sociomaterial arrangements
Figure 2. Co-occurrence network. Textual elements (words and phrases) are presented as nodes. The
edges represent the strength of connection between couples of nodes, which was measured by means of
co-occurrence data. The diameter of each node is directly proportional to its co-occurrence frequency.
CLUSTER
DIGITAL TECHNOLOGY
KEYWORDS
CL01
AI-based predictive critical care
systems
Biofourmis; Data Include; Remotely Monitor; Sensor
CL02
AI-enabled early warning systems for
infectious disease outbreak
AI Algorithm; AI Enable; AI Model; AI Power; AI Technology; Bluedot; Data Analytics; Data Science; Deep Learning; Diagnostic Tool; Early Warning;
HealthMap; Metabiota; Natural Language Processing; Online News; Social Media Data
CL03
AI-powered diagnosis algorithms
AI System; AI Driven; Algorithm; Alibaba; Artificial Intelligence; Baidu; Big Data; Develop an Algorithm; Huawei; Scan; Tencent
CL04
AI-powered online platforms for risk-
assessment and triage
Access to Data; CloudMedx; Computational; Conspiracy Video; Data Collected; Data Collection; Data Sharing; Digital Technology; Early Detection; Learning
Platform; Posted Online; Predictive Model; Screening Tool; Telemedicine Support; Bright.md; Buoy Health; Digital Platform; Internet Giant
CL05
Counterterrorism tracking systems
using geolocation data
Cellphone; Digital Tool; Geolocation; Radar; Satellite; Surveillance Technology; Text Message; Tracking Technology; User Data; Counter Terrorism
Technology; Tracking Patients
CL06
Automated fact-checking
Banner; Circulate Online; Facebook and Youtube; Facebook Post; Facebook User; Fact Checker; Google and Twitter; Hashtag; Instagram; Photo and Video;
Private Facebook Group; Quickly Remove Video; Social Network Site; Source of Information; Spread Online; Third-Party Fact Checker; Twitter and Google;
Twitter Hashtag; User Search; Video Feature; Youtube Video; Automate System; Blog; Blog Post; Include Posts; Including Google; Information Flow;
LinkedIn; Social Media Giant; Technology Firm; Amazon; Apple; Facebook; Facebook and Twitter; Fact Checking; Google; Social Media; Social Media
Company; Social Media Platform; Tech Company; Tweet; Twitter; WhatsApp; Youtube; Audio Message; Facebook and Google; Infodemic; International Fact-
Checking Network; Smartphone Location Data; Twitter and Youtube; Google Search; Tackle Misinformation; Tweeted; Twitter Account; Twitter and
Facebook; Work with Google; Work with Twitter
CL07
Bioinformatic tools and applications
for laboratory testing
Biotech; Biotech Company; Biotechnology; Kaiser Permanente; Moderna; Clinical Lab; Commercial Lab; Develop Tests; Diagnostic Test; Mammoth
Bioscience; PCR Test; PCT Technology; RNA Extraction Kit; Test Kit; Testing Kit; Bioinformatics; Computer Program
CL08
Blockchain-based anti-censorship
systems
App WeChat; Apple and Google; Blockchain; Browser; Facebook and Instagram; Firewall; Internet Platform; Online Misinformation; Podcast; Quality Video;
WeChat Account; WeiBlocked; Wikimedia
CL09
Cloud computing technology
Cloud Computing; Computing; Computing Resource; Supercomputer
CL10
Cloud robots
CloudMinds; Online Health Platform
CL11
Computed tomography scanning
Chest Radiograph; Computed Tomography; CT Change; CT Finding; CT Scan; Radiology; Tomography
CL12
Contactless technology
App and Website; Cashierless; Cashless; Digital Payment
CL13
Counter-cybercrime technology
Chinese Hacker; Computer System; Cyber; Cyberattack; Cyberattack Response; Cybercriminal; Cybercriminal Activity; Cybercriminal Prevention;
Cybersecurity; Cyberthreat; Dark Web; Digital Shadow; DomainTools; Email; Email Address; Email Attachment; Email Fraud; Email Phishing; Email Scam;
Emotet; Fake Email; Falsely Claimed; Fearware; Hack; Hack Group; Hacker; Hackers Exploit; Interactive Map; Internet User; Kaspersky; Malicious Software;
Malware; Message Platform; Mimecast; Password; Phishing; Phishing Email; Phishing Scam; Ransomware; Remote Access; Scammer; Send Emails; Service
Provider; Spam; Virtual Private Network; VPN Server; VPN User; Web App; Website; Collect Information; Connectivity; Cybersecurity System; Information
System; Sequencing Data; Teleconference
CL14
Disease situation dashboard
Collect Data; Dashboard; Real Time Update; Tracker; Visualization
CL15
Driverless vehicles
Apollo; Autonomous Vehicle; Driverless; Driverless Delivery; Driving Technology; Neolix; Food Delivery Platform
CL16
Ecommerce platforms
Airpods; Ebay; InstaCart; Meituan; Ocado; Online Marketplace; Online Shopping; Ecommerce
CL17
Electronic mass communications
media
Artificial Intelligence Powered; Broadcast; Broadcaster; Broadcaster CCTV; ByteDance; Camera; CCTV; Chinese Internet; Chinese Internet User; Chinese
Social Media; Computerized; Cyberspace; Digital Health; Facebook Group; Hand Over Data; Internet Police; Mobile Phone; Online Platform; Phone Call;
Retweet; Short Video; Smartphone App; Social Media Account; Social Media Site; Social Media User; Surveillance Camera; Tencent And Alibaba; Tencent
WeChat; TikTok; TikTok User; Tracking App; WeChat; WeChat Group; Weibo; App; Data; Digital; Internet; Online; Platform; Post; Real Time;
Technological; Technology; Video; Website User; File Photo; Group Chat; Whatsapp Group
CL18
Facial recognition technology
Facial Recognition Company; Facial Recognition System; Facial Recognition Technology; Mass Surveillance System; Mobile Network; Mobile Payment;
Sensetime; Software Company; Surveillance Tech; Surveillance Tool
CL19
Health QR codes
Alibaba and Tencent; Alipay; Alipay Health Code; Alphabet; Bluetooth; Close Contact Detector; Computing Power; Data Security; Google Account; Id
Number; Meituan Dianping; Messaging App WeChat; Pin; QR Code; Scan QR Code; Server; Share Information; Social Media Post; Travel Data; Webpage
CL20
Infrared temperature-sensing devices
AI Champion; Data Tracking; Deploying Drone; Fever Detection System; Infrared Camera; Infrared Sensor; Infrared Thermometer; Robotic; Screening System;
Technology to Track; Temperature Gun; Temperature Screening System; Thermometer; Thermometer Gun
CL21
Live streaming and online event
management technology
Google and Facebook; Move Online; Online Event; Online Format; Online Tool; Livestream; Livestream Event; Livestreaming; Post Video; Programming;
Video Platform; Webcam
CL22
Advanced computational tools for
mathematical modelling of infectious
diseases
Mathematical Modelling; Real Time Information; Surveillance System; Epidemiological Model; Historical Data; Online Activity; Google Map; Location
Information; Location Tracking; Modeller; Modelling; Palantir
CL23
Privacy-preserving mobile
applications for instant contact-tracing
Aggregate Data; Amount of Data; Anonymized Data; Anonymized Mobile Data; Anonymizing Personal Data; Automatic; Carrier; Contact Tracing; Customer
Data; Data Driven; Data Privacy; Data Protection; Data Protection Law; Deutsche Telekom; Digital Strategy; Facial Recognition; General Data Protection
Regulation; Gmail; Health App; Health Data; Identify Information; Location Data; Mobile Advertising; Mobile Carrier; Mobile Company; Mobile Data; Mobile
Operator; Mobile Phone Data; Mobile Technology; Personal Data; Phone App; Provide Data; Share Data; Share Location Data; Telecom Data; Telecoms;
Telecoms Company; Telecoms Operator; Track Case; Track Individual; Track Infectious Disease; Track the Movement; Vodafone; Wireless
CL24
Online crowdfunding technology
Online Crowdfunding
CL25
Online crowdsourcing technology
AI Tool; BioRxiv; Data Analysis; Data Mining; Data Scientist; Dataset; Free Online; IBM; Information Technology; Kaggle; Machine Learning; Machine
Readable; Major Online Platform; Open Dataset; Open Research Dataset; Repository; Database
CL26
Online education tools
Bandwidth; Broadband; China Mobile; Class Online; Cloud Learning Platform; Digital Divide; Distance Education; Distance Learning; Douyin; Edtech;
Education Platform; Home Computer; Home Learning; iPad; Laptop; Learn from Home; Offer Online; Online Class; Online Education; Online Education
Market; Online Learning; Online Teaching; Remote Instruction; Remote Learning; Teaching Remotely; Video App; Virtual Classroom; Virtual Reality; Virtual
Reality Training; Work Online; Zoom Meeting; Augmented Reality; Coursera; Digital Trend; Google Classroom; Internet Access; Kaltura; Mobile Application;
Stream Video; Videoconferencing Software; Virtual Reality Technology
CL27
Online health care platform for
consultation
Good Doctor; Online Consultation; Online Health Care; Ping An Good Doctor; Qihoo; Taobao; Video Consultation
CL28
Online web-based systems for
interviewing
Online Interview; Virtual Open House; WhatsApp Message
CL29
Remote controlled disinfection robots
Deploying Robot; Disinfection Robot; Robot; Robotics
CL30
Rule-based chatbots
Babylon; Chatbot; Medical Chatbot; Recording their Response
CL31
Teleconferencing and telework
technology
Chat App; Cisco; Conference Call; Desktop; DocClocker; Facebook Messenger; Facetime; Gaming; Google and Microsoft; Google Cloud; Google Drive;
Google Hangout; Internet Connection; Live Streaming; LogMeIn; Making Video; Microphone; Online Service; Platforms like Facebook; Record Meetings;
Remote Meeting; Screen Share; Skype; Snapchat; Social Platform; Technological Development; Teleconferencing; Telework; Video Call; Video Chat; Video
Conferencing; Video Conferencing Feature; Video Conferencing Service; Video Meeting; Videogame; Virtual Meeting; Webex; WIFI; WIFI Connection; App
Store; Business App; Digital Room; DingTalk; Google Hangout Meeting; Interact with Digitized; Make Remote Work; Microsoft Team; Rumii; Teleconference
App; Video Conferencing App; WeChat Work; Zoom Video Communication; Group Video Call; Microsoft; Remote Work; Trello; Virtual; Work from Home;
Work Remotely; Zoom; Antivirus; Daily Download; Emailed; iPhone; Lark; Remote Employee; Remote Work Software; Remote Worker; Remote Workforce;
Telecommuter; Telecommuting; Microsoft and Facebook; Online Post; Remote Work Policy; Tech Leader; Transition to Online
CL32
Telepresence robots
Digital Health Tool; Humanoid; Intouch; Medicaid; Medicaid Service; Robot Design; Robotic Arm; Tech Platform; Telehealth; Telehealth Company;
Telehealth Service; Telehealth Visit; Telemedical; Telemedicine; Telemedicine Service; Telemedicine Visit; Virtual Visit; Virtually
CL33
Thermal-imaging cameras
Big data analytics; Contact information; Data processing; Facial Recognition Camera; Megvii; Online Report; Scanner; Short Message Service; Thermal
Camera; Thermal Scanner
CL34
Unmanned aerial vehicles for
disinfection
Agricultural Drone; Drone and Robot; High Tech; Internet of Things; Unmanned Vehicle; Xag
CL35
Unmanned aerial vehicles for
surveillance
Drone; Phone Track
CL36
UVD robots
Suning; Ultraviolet Light; UVD Robot; Xenex; Disinfecting Robot
CL37
Virtual town hall technology
Online Chat; Virtual Campaign; Virtual Event
CL38
Voice-activated intelligent virtual
assistants
Alexa; Chatbot System; Google Assistant; Search Query; Siri; Voice App; Voice Assistant; Wikipedia
CL39
Wearable GPS tracking devices
Mobile Device; People-tracking wristband
Table 1. Digital Technologies deployed during the Covid-19 pandemic.
DIGITAL TECHNOLOGY
AFFORDANCE AND EFFECTS
SOCIOMATERIAL ARRANGEMENTS
AFFORDANCE AND EFFECTS
Potential
Description
Moderating factors
Actual
Infrared temperature-sensing
devices
Infrared temperature-sensing devices
can be introduced in mass screening
operations when they are
complemented with other testing
measures that contribute in detecting
and isolating infected individuals.
Infrared temperature-sensing devices
are deployed as the only screening
measure and managed by officials
without the necessary medical training.
• Nonadherence to WHO’s technical
guidance on Covid-19 mass
screening operations
• Lack of training
Infrared temperature-sensing devices
have a limited capability to identify
infected individuals when deployed in
isolation from other testing measures.
Therefore, they offer false assurance.
Infected individuals who do not
develop a fever are not detected by
temperature readings and help the virus
to spread, because no additional
screening tools searching for
alternative symptoms are deployed. In
addition, the lack of training can lead
to improper usage and is more likely to
generate misleading temperature
measurements (false positive or false
negative cases).
ICT-based contact-tracing
and surveillance systems
Tracking systems using geolocation
data, facial recognition technology,
unmanned aerial vehicles, and GPS
tracking devices can be used to ensure
that citizens adhere to social distancing
restrictions - such as lockdowns and
home quarantine.
ICT-based contact-tracing and
surveillance systems are enforced as
authoritarian surveillance measures;
they are imposed without robust and
transparent accountability mechanisms
and without taking into account
privacy and ethical implications.
• Bureaucratic authoritarianism
ICT systems for massive surveillance
and contact-tracing may help limit the
spread of the infection. But this
approach to deployment increases
insecurity and anxiety among local
communities, leaving governments in
dispute with data privacy experts and
human rights activists, who call for a
more responsible use of this
technology. In these circumstances,
public authorities may take advantage
of this temporary control mechanisms
to abuse the system by increasing mass
surveillance through hype,
criminalizing citizens unnecessarily,
interfering with their privacy, and
jeopardizing personal freedoms.
Bioinformatic tools and
applications for laboratory
testing
Bioinformatic tools and applications
for laboratory testing can help to
quickly sequence the genome of the
novel coronavirus, use the sequence
data to produce diagnostic kits and
computer-implementable instructions
for laboratory testing, and accelerate
large-scale testing operations.
National governments fail to develop
effective testing programmes.
• Poor leadership
• Overcomplicated bureaucracy
• Logistical barriers
Accelerated by next-generation
technology platforms, diagnostic kits
and computer-implementable
instructions for laboratory can be made
available at unprecedent speed. But
despite the advantage generated by the
technological advancements, the
national testing capacity remains low.
As a result, mass testing is significantly
delayed, so is the relaxation of social-
distancing measures.
Electronic mass
communications media
Electronic mass communications
media - such as social media
platforms, instant messaging apps,
online video-sharing services, digital
media, and websites of governmental
organizations - can provide the public
with a one-stop shop for sourcing up-
to-date and reliable information about
the outbreak.
Restrictions are imposed to cover up
the existence of the outbreak in
electronic mass communications
media and government officials use
this technology to spread misleading
information on unverified treatments
for curing the infection.
• Lack of transparency
• Inappropriate public sector values
The censorship prevents the public
from receiving relevant alerts on the
outbreak, while inaccurate information
on unproven medical products reaches
a large number of electronic mass
communications media users. Both
actions significantly put public health
at risk.
Table 2. Summary of findings: lessons on digital technology deployment for large-scale virus containment purposes and the moderating role of
sociomaterial arrangements.
