Computers in Human Behavior 126 (2022) 106986

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Computers in Human Behavior

journal homepage: www.elsevier.com/locate/comphumbeh

Covid-19 sentiments in smart cities: The role of technology anxiety before

and during the pandemic
Orlando Troisi a, *, Giuseppe Fenza a, Mara Grimaldi a, Francesca Loia b
a
Department of Management & Innovation Systems, University of Salerno, Via Giovanni Paolo II, 132, Fisciano, Italy
b
Department of Management, University of Rome “La Sapienza”, Italy.Via del Castro Laurenziano, 9, Rome, Italy

A R T I C L E I N F O A B S T R A C T

Keywords: The spread of Covid-19 profoundly changed citizens’ daily lives due to the introduction of new modes of work
Covid-19 and access to services based on smart technologies. Although the relevance of new technologies as strategic
Smart cities levers for crisis resolution has been widely debated before the pandemic, especially in the smart cities’ context,
Pandemic
how individuals have agreed to include the technological changes dictated by the pandemic in their daily in­
Citizen behaviour
Technology anxiety
teractions remains an open question. This paper aims at detecting citizens’ sentiment toward technology before
Fuzzy formal concept analysis and after the emergence of the Covid-19 pandemic using Fuzzy Formal Concept Analysis (FFCA) to analyze a
large corpus of tweets. Specifically, citizens’ attitudes in five cities (Berlin, Dublin, London, Milan, and Madrid)
were explored to extract and classify the key topics related to the degree of confidence, familiarity and approval
of new technologies. The results shed light on the complex technology acceptance process and help managers
identify the potential negative effects of smart technologies. In this way, the study enhances scholars’ and
practitioners’ understanding of the strategies for enabling the use of technology within smart cities to manage the
transformations introduced by the health emergency and guide citizens’ behaviour.

1. Introduction allow the achievement of well-being and innovation (Lytras et al.,

2020). Adopting technology could be necessary but not a sufficient
The spread of the Covid-19 redefined organizational strategies, cit­ condition for the effective readaptation and redefinition of the organi­
izens’ daily lives, and the interactions between organizations and users zational models imposed by the desire to overcome the health emer­
by introducing new ways of working and providing (public, educational, gency. Human interaction with technology is mediated by the political
mobility) services. Thus, even if the effects of the global crisis cannot yet and institutional context in which the technologies are implemented
be assessed and measured definitively, smart technologies can be (Kummitha, 2020). The most recent contributions in literature highlight
considered one of the key factors in managing emergencies. the need to explore how humane smart cities can help manage critical
The relevance of new technologies as strategic levers for the devel­ issues in the administration of smart cities through entrepreneurship,
opment of urban areas and the improvement of city management governance, and citizens’ inclusion (Kitchin. 2015; Visvizi et al., 2018).
(Kunzmann, 2020; Costa & Peixoto, 2020) has been investigated in Moreover, individuals and organizations do not always own the right
smart cities context. Smart cities are instrumented, interconnected, and digital skills or the right propensity towards adopting new technologies
intelligent urban areas (Harrison et al., 2010) that pursue shared growth (Azoulay & Jones, 2020). Recent studies show that technology use may
through an integrated set of technologies that shape interactions be­ also have negative implications on users’ wellbeing by determining
tween actors (Nam & Pardo, 2011). In today’s complex scenario, the role stress toward applying ICT (information and communication technologies)
of the smart cities (and of their human resources and technology) as to daily lives (Hauk et al., 2019; Nimrod, 2018). To fully integrate
leading actors to face Covid-19 and future pandemics has been high­ technologies into their habits and routine, users should learn to manage
lighted to challenge the global emergency. Even before the pandemic, technological tools. In addition, they should refocus their cognitive
smart technologies contributed to redesign the configuration of urban strategies to accomplish the cultural and social requirements related to
spaces. However, despite the revolutionary role of technology in smart their use.
cities, it has been noticed that intelligent tools do not automatically The economic, relational, and social transformations determined by

* Corresponding author.
E-mail addresses: otroisi@unisa.it (O. Troisi), gfenza@unisa.it (G. Fenza), margrimaldi@unisa.it (M. Grimaldi), francesca.loia@uniroma1.it (F. Loia).

https://doi.org/10.1016/j.chb.2021.106986
Received 12 January 2021; Received in revised form 29 July 2021; Accepted 15 August 2021
Available online 17 August 2021
0747-5632/© 2021 Elsevier Ltd. All rights reserved.
O. Troisi et al. Computers in Human Behavior 126 (2022) 106986

the different strategies implemented to attain an active resolution of 2. Literature review and theoretical background of the study
Covid-19 can change, probably definitively, the nature of interactions
and collaborations between citizens and public organizations by The current section presents and critically debates the related works
emphasizing that the application of human intervention (e.g., attitude, that explore the relationship between technology and people in smart
adhesion, propensity, smart orientation and willingness to use technol­ cities by revealing the main criticalities in the adoption of ICT and ITs-
ogies of individuals-citizens) is the only way to use technology effec­ enhanced urban infrastructure. After the state of art analysis on smart
tively to manage unexpected phenomena (Kunzmann, 2020). For these cities and technology anxiety, identifying some gaps in extant research
reasons, there is the need to explore whether and how permits to derive the research objectives. Hence, the last paragraph
individuals-citizens have agreed to include the technological changes describes the need to detect users/citizens’ abilities to use, accept, and
dictated by the pandemic in their daily interactions by changing their integrate technology into their lives to reduce technological anxiety
habits and remodeling behaviours and attitudes. Detecting citizens’ sources and fully accept the technological, social, and cultural changes
sentiment toward technology can permit to clarify the current directions introduced by disruptive technologies.
of technology acceptance. Furthermore, it could support exploring the
(social, economic, psychological) barriers to using technology and 2.1. Smart cities and technology: origins and latest developments
removing them to tackle the pandemic or other similar future emer­
gencies by turning crises into opportunities for innovation and For about twenty years, the concept of “smart city” has received
improvement of public services. As an economical, social, and political increasing attention in urban planning and governance (Nam & Pardo,
global epidemic, Covid-19 should be studied to support healthcare 2011; Visvizi, & Lytras, 2018, 2019). As broadly discussed in the liter­
management and capture all the shades (e.g., distress, anxiety, fear) of ature, a smart city can be defined as complex sets of technology (in­
its psychological and behavioural consequences (Abd-Alrazaq et al., frastructures of hardware and software), people (creativity, diversity,
2020). Recent research emphasizes the urgency to define the drivers of a and education), and institutions (governance and policy) (Nam & Pardo,
digital mental health revolution that can support citizens in managing 2011). Smart cities should be explored according to an all-inclusive
pandemics through e-services platforms and mobile applications. perspective that does not overrate the technological dimension but
This study explores citizens’ attitudes toward technology (and the that considers the economic, social, governmental, and environmental
technological transformation determined by Covid-19) through public dimensions (Stratigea, 2012; Albino et al., 2015; Neirotti et al., 2014) as
sentiment analysis. In this way, the goal of the empirical research is to a set of integrated enabling factors for urban and service improvement.
assess the degree of the propensity to use technology employing the In such a scenario, smart cities can create a fertile environment to drive
variables and indicators of technology anxiety scale (Meuter et al., 2003; innovation from a technological, managerial, and organizational point
Tarafdar et al., 2007), which operationalizes the key factors and stimuli of view by fostering environmental and social wellbeing (Karvonen
that induce stress in the use of ICT. Thus, two research objectives are et al., 2018; Polese, 2021).
pursued: 1) to explore citizens’ sentiment towards the adoption of Therefore, creating effective and really “smart” cities can be
technologies to challenge Covid-19 by detecting their degree of tech­ considered a key lever for community welfare. Smart city networks can
nology anxiety; 2) to reveal the change of attitude and behaviour toward provide suitable instruments to empower data sharing in outbreaks or
technology by comparing the different variations of technology anxiety disasters, leading to better global understanding and management of
before and after the advent of the pandemic. The empirical research emergencies (Allam & Jones, 2020). The development of more efficient
analyses the different citizens’ sentiment in selected European smart and widespread smart city initiatives can improve the way critical data
cities, of which-following Brexit-four are European Union (EU) is retrieved, processed, stored, and disseminated, potentially improving
member-states (Berlin, Dublin, London, Milan, and Madrid). In detail, the detection and mitigation of outbreaks while reducing the execution
the attitude toward the changes induced by Covid-19 is analyzed by time when taking critical actions (Costa & Peixoto, 2020). Smart city
detecting citizens’ sentiment towards a series of topics related to the use approaches can drive individuals to use data and knowledge on
of the new digital tools, facilities, and services from an emotional and vulnerable groups and poor urban areas to support the social and eco­
linguistical point of view in the pre- and post-pandemic period. The five nomic crisis (Söderström, 2020). Accordingly, the increasing use of ICTs
cities have been selected based on their size, level of smartness, and has improved the internet of things applications in healthcare and citi­
strategic influence in Europe and, thus, based on their representative­ zen participation for epidemic detection during Covid-19 (Giffinger
ness of contemporary urban trends. et al., 2007; Abusaada et al., 2020). In this way, the multiple techno­
Tweets’ analysis is performed through the Fuzzy Formal Concept logical points and their real-time ability to collect and share data can
Analysis (FFCA) to build a fuzzy concept lattice identifying the critical significantly improve well-being and quality of life by strengthening
factors in using technology that receives most users’ comments. The citizens’ involvement in policymaking (Vanolo, 2016) and generating
methodology allows the detection of the most recurring topics related to added value and crisis response capacity in the urban context (Lytras,
users’ technology anxiety in different periods by comparing the public Visvizi, & Jussila, 2020).
sentiment of other cities worldwide. Then, regression analysis is realized In light of recent global events, implementing new technology in
to assess if the trends of technology anxiety can help understand the smart cities (Abusaada & Elshater, 2020) requires an integrated infra­
changes of attitude before-after the pandemic as a binary dependent structure to detect and prevent a public health emergency (Costa &
variable. The findings permit introducing a framework that classifies the Peixoto, 2020). Several digital solutions have been developed during the
different determinants of public sentiment related to the anxiety in using pandemic to implement a strategy to contain the virus spread, monitor
technology and the various opportunities and challenges for each area. human stress, and collective wellbeing, and collect complex space-time
The paper is structured as follows. Section 2 reviews previous works events in a smart city related to Covid-19 safety measures (Basmi et al.,
on the topic of technology use in smart cities and on technology anxiety. 2021). As happened in several smart cities, the proper combination of a
Section 3 describes the methodological approach followed (i) to identify contact-tracing app, robots, and digital thermal-gantries put in place by
the most recurring features of technology anxiety and (ii) to observe the the government to trace, track and mitigate the early first wave of the
variations in technology anxiety before and after the diffusion of Covid- pandemic along with the civil society involvements to manage the
19. The findings are debated (paragraph 4) and then synthesized to spread of the virus proved essential for containing the pandemic crisis
design a conceptual framework with the key determinants of technology (Söderström, 2020).
anxiety in the discussion (paragraph 5). Finally, in the last two para­ Hence, advanced technology can mitigate the negative effects due to
graphs, the theoretical and managerial implications and the conclusion the pandemic by permitting people to continue their lives while main­
of the study are discussed. taining social distancing (Jaiswal et al., 2020). However, citizens,

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O. Troisi et al. Computers in Human Behavior 126 (2022) 106986

governments, and organizations do not always own the right digital et al., 2003; Washizu et al., 2019) can be assessed as a predictor of cit­
skills or the right propensity to adopt new technologies (Azoulay & izens’ behaviour and as a significant determinant of behavioural
Jones, 2020). In this scenario, further research efforts are needed to intention (Yang & Forney, 2013) in Covid-era. Technology anxiety is
understand how citizens and individuals have agreed to include the defined as a complex set of emotions such as nervousness, uncertainty,
technological changes dictated by the pandemic in their daily and fears associated with using and learning to use technology. This
interactions. concept is related to apprehension about the negative consequences of
using technology, such as losing important data or making mistakes
2.2. Public sentiment in smart cities as a predictor of citizen behaviour (Compeau & Higgins, 1995). It involves both the (objective) lack of
technological skills and the (subjective) low confidence in their abilities
The pace of technological, economic, and social changes dictated by to use specialized tools. In addition, it can be related to the user’s state of
the global emergency leads to rereading human-computer interactions mind about general technology tools (Meuter et al., 2003) or to hidden
and rethinking the rules that guide citizen behaviours in smart cities. social and psychological factors, such as cost concerns, dependency
However, the top-down adoption of technology in urban contexts cannot concerns, trust in technology providers and organizations adopt tech­
ensure growth and innovation and the effective and sustainable trans­ nology, privacy concerns.
formation of cities. Hence, city managers and policymakers should The need to explore technology anxiety in contemporary contexts
engage citizens in reframing spaces, habits, and routines in urban life stems from recognizing this variable as a determinant of resistance to
and should constantly assess their attitude toward technology, their technology and as a barrier to individuals’ involvement with technology
digital mindset, and their acceptance of the new solutions proposed (Thatcher & Perrewé, 2002). Moreover, anxiety can lead to rejection of
(Wnuk et al., 2020). technology and technophobia (Daruwala, 2020), a negative durable
It follows that the exploration of citizens’ perception and opinion emotional reaction towards ICT, and technostress (Ragu-Nathan et al.,
about the administration of public life, the introduction of new tech­ 2008), a general distressful state caused by technology (Nimrod, 2018).
nologies, and the general management of worldwide crisis (Chen et al., Technology anxiety (also known as TISA, Technology Induced State
2020) is a strategic lever to understand and predict people’s behaviours Anxiety) has been conceptualized in literature (Meuter et al., 2003) as a
and compliance with the new rules introduced and, consequently, to negative affective state toward technology that affects the relationship
evaluate the effectiveness of urban policies. Furthermore, the services between people and technology (Zhang, 2013). Hence, differently from
and the applications offered in smart cities should be aligned to users’ the concepts of technostress (Ayyagari et al., 2011; Tarafdar, Gupta, &
needs, expectations, and abilities to use these services and applications Turel, 2013) or general computer anxiety (Heinssen et al., 1987; Teki­
efficiently (Visvizi et al., 2020). narslan, 2008), this construct defines a temporary state deriving from
Coronavirus and global crisis, in general, can entail the development environmental turbulences (such as the advent of global emergencies)
of mass fear and panic accentuated by inaccurate information. There­ and permits to observe in-depth the individual psychological reactions
fore, there is the need to examine public sentiment in the Covid-era to to technology rather than analyzing a more “general” behavioural
constantly monitor the effects of government measures and regulations, aspect. For this reason, it seems to be a more easily generalizable
evaluate the degree of technology adoption, and undertake timely de­ concept that can also be used outside the business context. Moreover,
cisions and corrective policies in the management of pandemics (Samuel technology anxiety allows the exploration of the development of nega­
et al., 2020). tive emotions and fear as consequences of the introduction of a given
The use of textual data (Tweets) for sentiment analysis can fulfill the technology by evaluating the emotional state of individuals and not the
need to monitor the flow of information and the development of mass acceptance and use of technology per se (e.g., such as the technology
sentiment in a fast-changing setting characterized by the rapid and un­ acceptance model, Davis, 1989). Thus, investigating the degree of
controllable spread of Covid-19. The analysis of public opinion and the technology anxiety in contemporary cities can shed light on the different
identification of topics and trends (Hung et al., 2020) permit tracking emotional shades of public sentiment and citizens’ behaviors.
the progress of fear toward the virus itself and toward the use of tech­ The development of technology anxiety in smart cities can prevent
nology and to forecast future scenarios and the developments of the the inadequate usage of technology and play a vital role in adopting
crisis. smart services. Revealing how technology anxiety can take shape in the
Investigating public sentiment associated with the diffusion of smart cities of Covid-era can help policymakers assess the needs of
Covid-19 seems to be a priority in contemporary research. The explo­ stakeholders in an appropriate and relevant manner by understanding
ration of citizens’ discussion about Covid-19 can reveal unnoticed sen­ how crisis can be managed through the inclusion of citizens in the co-
timents and trends related to people’s acceptance and personal development of innovative solutions to address social change.
management of the changes imposed by the pandemic. For this reason, a
series of recent studies adopt sentiment analysis (Hung et al., 2020; 2.4. Background of the research
Samuel et al., 2020; Shah et al., 2019) to explore the textual data ob­
tained from the collection of the thoughts expressed through social Despite the increasing diffusion of the analysis of the opportunities
media posts to assess public opinion. and challenges in adopting technology to face pandemics in smart cities,
two main issues emerged from the brief overview conducted above.
2.3. Technology anxiety: assessing citizens’ attitudes and behaviour Firstly, there is the need to explore the weight of human capital in the
during global emergency use of ICT: citizens’ digital culture, their attitude toward technology
(Hollands, 2008; Mora et al., 2017), and their propensity to change their
The disrupting impact of Covid-19 on smart cities requires under­ lives through technology-mediated interactions (Kunzmann, 2020).
standing citizens’ sentiment and perceptions of governmental measures Secondly, future research must shed light on the negative effects that
and the estimation of the effects of a pandemic on individuals’ views technology can have on citizens’ wellbeing and identify the obstacles of
(Al-Hasan et al., 2020) and people’s degree of frustration and stress. As the acceptance and adhesion to smart technologies and of their advan­
discussed above, citizens’ behaviour (and their ability to adapt to tageous application (Hollands, 2008; Hauk et al., 2019; Mora et al.,
environmental changes) can be critical determinants for successfully 2017; Nimrod, 2018).
implementing services, applications, and new technological solutions in Therefore, the true turning point for the resolution of a pandemic
smart cities. would also concern citizens’ behaviour by referring to their acceptance
To explore the key drivers and obstacles to the acceptance of tech­ of technological changes, their willingness to use technology to reframe
nology in smart cities, technology anxiety (Compeau et al., 1999; Meuter their lives, and in the removal of psychological barriers (privacy

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concerns, perception of inability, etc.) for employing technology suc­ Table 1

cessfully and solving the socio-economic crisis introduced by a public The identification of keywords for a tweet analysis on technology anxiety.
health emergency. For this reason, albeit the relevant literature high­ Authors Measurement Items Keywords
lighted the potential of smart technologies during the pandemic,
Meuter et al. I am confident I can learn technology- SKILLS
contemporary debate on smart cities should explore how relationships (2003) related skills. CONFIDENCE
between people and technologies can be redesigned to identify the most I have difficulty understanding most DIFFICULTY
adequate strategies to involve citizens in the active resolution of the technological matters. UNDERSTANDING
crisis and global emergency and to co-develop innovative solutions and I feel apprehensive about using APPREHENSION
technology.
social changes, which still represents an open question. To address the When given the opportunity to use FEAR
first gap identified in literature on smart cities (the lack of studies on the technology, I fear I might damage it in DAMAGE
key role of users’/citizens’ attitude as an enabling factor of wise use of some way.
technology, cf. paragraph 2.1), this study explores the public sentiment I am sure of my ability to interpret ABILITY
technological output.
of citizens during the public health emergency as a predictor of their
Technological terminology sounds like CONFUSION
potential behaviour and its possible variations. Despite the increasing confusing jargon to me.
diffusion of research that examines the public reaction to the contem­ I have avoided technology because it is UNFAMILIARITY
porary health emergency, a deep understanding of the most common unfamiliar to me.
themes, concerns, and sentiments related to the perception of Corona­ I am able to keep up with important DEAL WITH
technological advances.
virus has not been achieved yet. This study analyzes citizens’ sentiment I hesitate to use technology for fear of MISTAKE
shared through Twitter, one of the most popular microblogging plat­ making mistakes I cannot correct.
forms, with over 350 million users and 152 million daily users who Washizu et I feel apprehensive about using APPREHENSION
produce 500 million tweets a day (Statista, 2020). Microblogging is a technology.
al. (2019) Chen It scares me to think that I could cause SCARE
quick communication means that can capture tweeters’ perception at
et al. (2020) technology to destroy a large amount of
any moment and can help to catch insights into their attitudes and information by hitting the wrong key
opinion on the usefulness and usability of specific smart-city services I hesitate to use a computer for fear of MISTAKE
and applications (Oulasvirta et al., 2010). making mistakes I cannot correct
To bridge the second gap (the absence of studies that conceptualize Computers are somewhat intimidating INTIMIDATING
me
the critical obstacles to the acceptance of technology), technology anxiety
Lytras et al. Smart city services make me anxious ANXIOUS
(Compeau et al., 1999; Meuter et al., 2003; Washizu et al., 2019) can be (2021) about my ability to use technology ABILITY
assessed as a predictor of citizens’ behaviour and as a significant I think that a lot of money is spent on MONEY
determinant of behavioural intention (Yang & Forney, 2013) in smart city services without them offering SOCIETY
anything significant to the society and USEFULNESS
Covid-era.
individuals
This study explores the degree of acceptance and inclusion of smart I think that we lack the basic LACK
technologies into citizens’ daily lives by using technology anxiety as an infrastructure in the city and, so, smart INFRASTRUCTURE
indicator of adhesion to the measures imposed by the public health city services are a pointless luxury
emergency, participation, inclusion, and willingness to adopt new I feel that smart city services offer PERSONAL DATA
organizations a good excuse to manage PRIVACY
technologies.
my personal data, and I don’t like it
The measurement of this construct has been proposed in extant I think that, on average, people my age SKILLS
quantitative research through a re-adaptation of the computer anxiety lack the skills and nerve to use these
scale, developed by Ceyhan & Namlu (2000). As Table 1 shows, the key services
sub-dimensions and items of technology anxiety refer to (i) users’ con­
fidence in their capability to use technology; (iii) ICT pressure, lack of
information and learning capabilities (Visvizi et al., 2018). Lastly, the
technical support, and low usability of technological tools; (ii) economic
third sub-dimension considers the social impact of smart cities appli­
risk; (iii) perceived uselessness of technologies; (iii) privacy concerns
cations by investigating privacy, safety, and security issues (Chui et al.,
(Ayyagari et al., 2011; RaguNathan et al., 2008). Starting from the
2018) and users’ perception of transparency in the data processing
synthesis of the measurement items deriving from the scales introduced
methods used (Perez del Hoyo and Mora, 2019).
in extant research (Meuter et al., 2003; Washizu et al., 2019; Lytras
Two research objectives are pursued. In the first place, the themes
et al., 2021), some key indicators are obtained and employed as key­
(topics) connected with the mentions of the key indicators of technology
words to extract tweets, filter the analysis and guide the interpretation of
anxiety on Twitter are detected through sentiment analysis; in the sec­
results (see paragraph 4).
ond place, the potential change in the perception of the different sub-
The sub-dimensions related to self-confidence, user’s perception of
topics deriving from technology anxiety is revealed through regression
their ability to use technology, and fear have been borrowed from
analysis, by estimating the reduction or the increase in the degree of
Meuter et al. (2003) to explore the apprehension of citizens in the use of
technology anxiety before and after the advent of the pandemic. As a
technology and the potential lack of confidence in their capability that
result, the following research questions can be introduced:
can create concern about the coping ability to deal with new and
RQ1: Which are the most common topics associated with technology
demanding situations (Schwarzer et al., 1999).
anxiety in the public sentiment of citizens from five international smart
The items for the sub-dimensions of apprehension, scare, and
cities (Berlin, Dublin, London, Milan, and Madrid)?
mistake are re-adapted from Washizu et al. (2019) and Chen et al.
RQ2: How did citizens’ sentiment toward technologies change after
(2020) to explore citizen psychological concerns during the crisis predict
the advent of Covid-19?
their reactions to unexpected phenomena and their self-resilience in
response to disrupting events.
3. Methodology
The items re-adapted from Lytras et al. (2021) refer mainly to three
dimensions: economic, knowledge-based, and social. The first aims at
In this study, the research method develops a content analysis of
detecting users’ worries about the return on investment and the finan­
tweet stream by applying the Text-Mining process and conceptual data
cial sustainability of smart-cities services. The second refers to exploring
analysis techniques. The following subsections introduce the Fuzzy
one of the key levers of cities growth (and, consequently, one of the key
Formal Concept Analysis (briefly, Fuzzy FCA or FFCA, De Maio et al.,
obstacles in case of lack), knowledge development, and the sharing of

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2012) and Association Rules Mining. Finally, the description of the denoted by μI.
application of these techniques to the overall workflow for analysing Definition 2: Fuzzy Representation of Object. Each object O in a
tweets content underlying this study is given. fuzzy formal context K can be represented by a fuzzy set ɸ(O) as ɸ(O)=
The Fuzzy FCA is used to extract the hierarchy (the lattice) of Formal {A1(μ1), A2(μ2), …, Am(μm)}, where {A1, A2, …, Am} is the set of at­
Concepts, grouping tweets with the same main features. Finally, the tributes in K and μi is the membership of O with attribute Ai in K. ɸ(O) is
Association Rules Mining extracts the dependence among the concepts called the fuzzy representation of O. Unlike FCA that uses binary relation
intents (i.e., most co-occurring keywords and emotional features) and to represent formal context, Fuzzy Formal Context enables to model
the technology anxiety in the pre-/post- Covid contexts. relations among objects and attribute in a more smoothed way, ensuring
more precise representation and uncertainty management. Fuzzy
Formal Context (see Definition 1) is often represented as a cross-table, as
3.1. Fuzzy FCA shown in Fig. 1(a), where the rows represent the objects, while the
columns the attributes. After establishing a confidence threshold (e.g.,
Fuzzy FCA deals with fuzzy relations between objects (e.g., tweets, T=0.6), only the relationships with a membership value greater than it is
etc.) and their features (e.g., keywords, sentiment polarity, etc.) considered for the lattice construction (as the case in Fig. 1).
considering membership varying in (0, 1), instead of binary relation of Given Fuzzy Formal Context, the Fuzzy FCA algorithm can identify
traditional FCA (Ganter & Wille, 2012). So, it allows specifying more or Fuzzy Formal Concepts and subsumption relations among them. More
less relevant features to represent resources, enabling the granular formally, the definition of Fuzzy Formal Concept and order relation
representation. among them are given as follows:
Formal Concept Analysis (FCA) is a mathematical theory suitable for Given a fuzzy formal context K = (G,M, I) and a confidence threshold
several application domains, such as knowledge discovery, ontology T, for G’⫅ G and M’ ⫅ M, we define G* = {m ∈ M| ∀g ∈ G′ , μI (g,m) ≥ x}
learning, text mining, bioinformatics, etc. Its fuzzy extension provides and M* = {g ∈ G | ∀ m ∈ M’, μI (g,m) ≥ x}
more accurate data mining and data summarization to deal with the Definition 3: Fuzzy Formal Concept. A fuzzy formal concept (or
uncertainty of data representation, like in the content of the tweets. fuzzy concept) C of a fuzzy formal context K with a confidence threshold
Among other data mining techniques and machine learning tools, Fuzzy x, is C = (IG’, M′ ), where, for G’ ⫅ G, IG’ = (G′ , μ), M’ ⫅ M, G* = M′ and
Formal Concept Analysis (FFCA) is the most suitable in our case because M* = G’.
it provides transparent and intelligible results. Each object g has a membership μIG’ defined as
The resulting Fuzzy Concept Lattice is a hierarchical knowledge
structure that could be easily explored by filtering meaningful concepts μIG’ (g) = minm∈M’ (μI (g, m)) (1)
for answering some research questions. Moreover, it provides valuable
where μI is the fuzzy function of I.
measures evaluating the confidence of the extracted entails. Despite
Note that if M’ = ∅ then μI (g) = 1 for every g. G′ and M′ are the
other techniques, it also works if data are not so massive and relies on
extent and intent of the formal concept (IG’, M’), respectively.
the meaningful hierarchical knowledge structure instead of black-box-
Definition 4: Let (IG’, M′ ) and (IG’’, M’’) be two fuzzy concepts of a
based approaches more suitable for obtaining high performance not
Fuzzy Formal Context (G, M, I). (IG’, M′ ) is the subconcept of (IG’’, M’’),
required in our case. More in detail, the FFCA in this work is used to
denoted as (IG’, M′ ) ≤ (IG’’, M’’), if and only if IG’⊑ IG’’ (↔M’’ ⫅ M′ ).
extract the hierarchy (the lattice) of Formal Concepts, grouping tweets
Equivalently, (IG’’, M’’) is the super concept of (IG’, M’).
sharing the same main features. The Association Rules Mining carries
For instance, in Fig. 1(b), concept c7 is a subconcept of concept c3.
out the dependence among the concepts intents (i.e., most co-occurring
Equivalently the concept c3 is a super concept of concept c7. Let us note
keywords and emotional attributes) and the technology anxiety in the
that each node (i.e., a formal concept) is composed of the objects and the
pre-/post- Covid contexts. These results allow us to explore the depen­
associated set of attributes, emphasizing the object that is better repre­
dence among factors we are investigating.
sented by a set of attributes by means of fuzzy membership. In the figure,
Following, some definitions of Fuzzy FCA are given.
each node can be colored differently, according to its characteristics: a
Definition 1: A Fuzzy Formal Context is a triple K =(G, M, I), where
half-blue colored node represents a concept with its attributes; a half-
G is a set of objects, M is a set of attributes, and I =((G ⇥M), μ) is a fuzzy
black colored node instead outlines the presence of own objects in the
set. Recall that, being I a fuzzy set, each pair (g,m)∈ I has a membership
concept; finally, a half-white colored node can represent a concept with
value μ(g,m) in (0,1). In the following, the fuzzy set function μ will be

Fig. 1. Examples of (a)Fuzzy formal context and (b)Fuzzy formal concept lattice.

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O. Troisi et al. Computers in Human Behavior 126 (2022) 106986

no own objects (if the white-colored portion is the half below of the e., lattice) of concepts representing objects (i.e., tweets) and their at­
circle) or attributes (if the white half is up on the circle). Furthermore, tributes (i.e., words, emotional features, and sentiment). The Support
given Fuzzy Formal Concepts of Fuzzy Formal Context, it is easy to see associated with each lattice concept allows measuring how frequently
that the subconcept relation ≤ induces a Fuzzy Lattice of Fuzzy Formal the itemset (i.e., concepts’ intents) appears in the tweets collection.
Concepts. The lowest concept contains all attributes, and the uppermost Additionally, by browsing the resulting Fuzzy Lattice, we retrieve the
concept contains all objects of Fuzzy Formal Context. dependence degree among concepts’ intents (i.e., most co-occurring
keywords and emotional features) and the use of technology in pre-/
3.2. Association Rules Mining post- Covid contexts. Confidence of the Association Rules indicates how
often the linguistical and emotional features co-occurring when the
Association Rules aim to intercept co-occurrence implications among conditions of interest occur. The conditions (or condition) of interest
itemsets. Their most popular application regards the market basket may be represented by pre- or post-Covid context or/and by the smart
analysis that studies co-occurrences in transactions. The metrics, such as cities we are considering. By comparing citizens’ sentiment toward the
Support and Confidence, measures the strength of associations. Several different international smart cities before and after the emergence of the
algorithms are applied for Association Rules Mining (e.g., Apriori, FP- pandemic, the complex process of accepting the limitations dictated by
growth, etc.) in the literature. In this study, the algorithm for mining the health emergency and the potential improvement of fear and
Association Rules exploits the hierarchical relationships among concepts sentiment over time can be assessed.
in the lattice resulting from the application of Fuzzy FCA. Overall methodology (in Fig. 2) consists of the following activities:
Given a Formal Context K = (G, M, I) consisting of attributes M =
m1 , m2 , …, mm and objects G = g1 , g2 , …, gn , an association rule is an 1. Tweets collection about selected smart cities.
implication of the form X ⇒Y where X, Y ⊂G are sets of attributes and 2. Feature Extraction.
X ∩ Y = ∅. The algorithm extracts Association Rules where X, Y are 3. FFCA & Association Rule Mining.
the formal concepts intents in subsumption relation (see subconcept/ 4. Regression Analysis of the incidence of construct indicators.
super concept in the Definition 4).
The relevance of each Association Rule is measured by support and Subsequent sections describe in more detail each step.
confidence. The support of an association rule X ⇒Y is the percentage of
objects (i.e., tweets in this study) that contain X ∪ Y. The confidence of 3.3.1. Step 1: tweets collection about selected smart cities
X ⇒Y is the ratio of the number of objects containing X ∪ Y to the A web scraper allows collecting tweets responding to query param­
number of objects that contain X. More formally: eters by adopting the Twitter Advanced Search. It complies with Twit­
Definition 5. Being M is a set of attributes of a formal context K = (G, ter’s restrictions in terms of both adoption and privacy concerns. The
M, I). An association rule is a pair X ⇒Y with X, Y ⫅M. The support is query search for tweets responding to keywords “smart AND city” posted
defined as: from January to December 2019 (for the pre-Covid period) and from
′ January to November 2020 (for the post-Covid period, which includes
sup( X ⇒Y) =
|(X ∪ Y) | the first wave of lockdowns in Spring 2020 and the beginning of the
|G| second wave in October 2020).
About 41K tweets are retrieved. Through a filter based on indicators
where (.)’ is the derivation operator. The confidence is computed as: related to identified constructs (e.g., virus, money, infrastructure,
|(X ∪ Y) |
′
transport, etc.), only more relevant tweets are kept for a total of 32′ 334
conf( X ⇒Y) = ′ tweets by 22′ 202 users (with 17′ 579 replies and no retweets). The pre-
|(X) |
Covid period has 17′ 204 tweets while the post-Covid, 15′ 130. The dis­
In the lattice retrieved by Fuzzy FCA, the concepts frequently tribution among cities is as described in Table 2.
recurring in the collected tweets are measured using the Support indi­
cating how frequently the itemset (i.e., concepts’ intents) appears in the 3.3.2. Step 2: feature extraction
tweets collection. Confidence indicates how often the itemset (i.e., con­ The feature extraction activity applies a pipeline to descriptions of
cepts’ intents) of features characterizing the tweets occurs under interest collected tweets. The objective is to extract attributes characterizing
conditions. Indeed, confidence can be interpreted as an estimate of the tweets for the subsequent process of FFCA. More in detail, the Recepti­
conditional probability P(Y|X). viti API1 is adopted to extract emotional components from the text (e.g.,
joy, sadness, fear, etc.). Then, a Natural Language Processing pipeline
3.3. Overall workflow consisting of tokenization, POS tagging, lemmatization, stemming,
stopwords removal, and synonyms analysis is applied. Next, a sentiment
The main goal of the analysis is to represent tweets based on their analysis extracts the polarity of adopted tweets. Finally, keywords are
text contents and then assess words trend to understand citizens’ selected as attributes of the Formal Context in the next step.
sentiment and perception of the evaluated smart cities in the pre- and
post-Covid periods. The five cities included in the analysis (Berlin, 3.3.3. Step 3: FFCA & Association Rule Mining
Dublin, London, Madrid, and Milan) have been selected through emer­ Keywords extracted during the previous phase fill the formal context
gent sampling (Shakir, 2002; Teddlie & Yu, 2007), a case selection needed for constructing the Fuzzy Formal Concept Lattice. In particular,
procedure in which sampling decisions are undertaken during the pro­ for each selected smart city, the Formal Context contains a row for each
cess of data collection. As researchers gain more knowledge of a setting, tweet (i.e., objects of the context) mentioning it. Attributes are
sampling decisions that take advantage of events can be made. This composed of the most important terms (i.e., keywords), sentiment po­
flexible sampling design is used when little is known about a phenom­ larity, emotional components, and period (i.e., pre- or post- Covid). The
enon or a set, and a priori sampling decisions can be difficult. In this membership of each attribute is set to 1, except for emotional compo­
case, the number of citizen’s tweets on the key topics of technology nents for which the membership corresponds to the API’s value.
anxiety for the different international cities was not predictable before The fuzzy formal concept lattice generated by the formal context is
the analysis; thus, after a preliminary screening of the datasets, the cities then adopted to extract the frequent itemset consisting of combinations
with the highest number of tweets on the keywords selected have been
incorporated in the sample.
The Fuzzy Formal Concept Analysis allows extracting a hierarchy (i. 1
https://www.receptiviti.com/.

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Fig. 2. Overall methodology.

4.1. Citizens’ sentiment of technology anxiety: key topics and emotional

Table 2
features
Tweets distribution among cities.
City Tweets Table 3 reports the most common topics and emotional features
London 18.086 related to technology anxiety detected by analyzing citizens’ sentiment
Milan 3.896 in the five international smart cities investigated (Berlin, Dublin, Lon­
Dublin 1.769
don, Milan, and Madrid). For each city (see column 1), the words that co-
Berlin 4.384
Madrid 4.199
occur most frequently with the key indicators of technology anxiety in
Total 32.334 users’ tweets are detected. The second and the third columns of the
Table show the most common topics associated with negative and pos­
itive sentiment toward using technology in the posts before the advent of
of cited attributes. In particular, using the support value, a measure of Covid-19. The fourth and the fifth columns reveal the most frequent
confidence is extracted for each “condition of interest”-items couple. words associated with negative and positive sentiment toward tech­
Thus, for example, assuming to be interesting in London city, if the nology in the tweets generated by users after the spread of Covid-19.
condition regards the pre-Covid period and negative sentiment, we can Only the attributes with the highest degree of confidence (greater than
assess the incidence of other attributes (e.g., keywords and emotional 0.6), the probability of co-occurrence of the linguistical and emotional
conditions). features under the conditions of interest, are considered valid for the
interpretation and provided in the Table.
3.3.4. Step 4: regression analysis of the incidence of construct indicators
Finally, the validity and incidence of identified indicators for each 4.1.1. The pre-covid period
construct are also verified by applying a logistic regression algorithm. By In the pre-Covid period, the prevalent negative sentiment that
considering indicators as independent variables and the period as the emerged from citizens’ tweets from Dublin, London, Madrid, and Milan
dependent variable, we try to identify the period basing on the keywords is anger. In contrast, the mention of anxiety toward technology is
used in the tweets’ contents by the citizens. Thus, for the significant associated most frequently with topics related to fear in Berliners’
variables (i.e., p < 0.05), the coefficient ϐ is evaluated. When ϐ is posi­ tweets.
tive, it indicates the log-odds probability (i.e., an incidence) of the pre- The analysis of the findings reveals that in Madrid, the anxiety is
Covid period and a decreasing probability for the post-Covid period. generated from the perception of the inability to use technology and
recognize the need to increase knowledge and learn how digital tools
4. Findings should be employed. Therefore, through the co-occurrence of words like
“lack”, “knowledge”, “learn”, a lack of self-confidence in technology
The empirical research investigates public sentiment (detected adoption can be noticed (Anger Knowledge Learn Lack Difficulty Want
through technology anxiety) towards the technological changes Care) and a negative user’s state of mind regarding the ability and
required to apply Covid-19 measures (distance learning, smart working, willingness to use technology-related tools can be observed. The Dub­
online public services) and the variation in the polarity of this sentiment liners in the sample also express the difficulty in using technology for
in the two-time frames considered (before the pandemic and after the private life and the perception of asymmetry and unbalance in the
advent of the pandemic). detection of technological power and skills (Anger Hard Business Work
In this way, it is possible to explore the attitude and propensity of Power). This finding is in line with citizens’ anxieties about the
citizens to change their lifestyles and their citizenship behaviour to centralization of the profits deriving from smart services and the lack of
challenge the public health emergency. Furthermore, it is possible to social usefulness (Lytras et al., 2021). The shortage of self-confidence to
evaluate the different evolution of the attitude towards the changes employ technologies is one of the key concerns identified in extant
introduced by Covid-19 in other international smart cities. empirical research. The perception of inability is an obstacle to adequate
To answer RQ1 (see paragraph 4.1), a fuzzy formal concept lattice for knowledge sharing and skills acquisition (Pérez-delHoyo & Mora, 2019),
each selected smart city is built to estimate the dependence between the which can be considered the crucial factors for the success of a smart city
most co-occurring keywords and emotional features and the tweets that (Visvizi & Lytras, 2018).
mention technology anxiety pre-/post-Covid contexts. The analysis of Londoners’ tweets reveals that the anger toward the
To address RQ2 (see paragraph 4.2), the key topics detected through economic exploitation of the advantages deriving from the use of tech­
FFCA are used as the independent variables, and the time-lapse of nologies (“money”) and the general unsatisfaction toward the use of
Tweets is treated as a dependent variable in a logistic regression to technology for the fulfilment of personal success (Money Work Anger
predict the period (before and after the spread of Covid-19) as the binary Business One Sadness). Thus, technological anxiety is related to an in­
dependent variable. dividual dimension before the advent of Covid-19; this negative attitude

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Table 3
Findings for RQ1: the key topics obtained through FFCA.

can lead citizens to conceive smart technologies as a “luxury”, or a Manage Solutions).

means to enrich the powerful men. The positive attitude toward technology in Berliner’s tweets before
The rage against technology expressed by the Italian citizens is the advent of Covid-19 is not related to the adequacy of the techno­
related to the distrust toward society and public management of tech­ logical infrastructure itself (characterized by a negative sentiment) but
nologies and the non-acceptance of changes (Anger Care Public People to users’ trust in the ability of citizens to build a network of connections
Change Milan). Resistance to technology and the inability to accept new to create a better future through technology-mediated interaction based
technologies (and the modification they bring in users’ lives) are among on sharing and mutual support (Future Build Citizen Sustain Connect
the most assessed barriers to technology acceptance (Davis, 1989; Together Share).
Bhattacherjee et al., 2007). In previous studies, resistance towards The positive mentions of technology in the tweets posted by citizens
technology is considered a key determinant of technology anxiety. from Madrid occur in topics related to general satisfaction and trust
Moreover, it can predict potential users’ perception and behaviour toward the use of personal data, seen as an opportunity for innovation
(Lytras et al., 2021) and discard technology. rather than as a threat to personal privacy (Care Data Desire Network
Lastly, the co-occurrence of words such as “fear” and “personal data” Innovation Want). In contrast, in the tweets posted by citizens from
in Berliners’ tweets seems to reveal a lack of trust in the transparency of Milan, the positive mentions refer to satisfaction in using technology for
data collection. Thus, it can be noticed that privacy and security issues, work and personal life (Good Work Gratitude Want Love Like Joy).
key challenges in the implementation of smart cities applications (Zhang Table 4 reports the key findings obtained from the analysis of tweets
et al., 2017), are the main concerns for Berliners’ tweets in the sample. published before the advent of Covid-19.
In the pre-Covid period, the positive attitude toward the technology
of Londoners and Dubliners is characterized by trust in the structural 4.1.2. The post-covid period
adequacy of technological infrastructure. Before the diffusion of Coro­ In the post-Covid period, the negative sentiment and anger toward
navirus, Londoners in the sample show a general satisfaction toward the technology expressed in the tweets by citizens from Dublin, London,
technological architecture of their city and a positive mindset toward Madrid, and Milan turn into fear.
the use of data, seen as an opportunity for sharing and help (Cities In detail, Dubliners’ tweets reveal the co-occurrence of topics such as
Infrastructure personal Data Help Joy Want). Dubliners seem to consider “fear”, “life”, “home”, “office”, by showing a negative opinion of the new
technology as a means to improve cities, pursue innovative solutions and technologies imposed by the pandemic and the potential inability to
enhance well-being by creating a system and integrated architecture accept the changes that Covid-19 brought into daily lives, work, and
based on multiple touchpoints and devices, as shown by the co- personal spaces.
occurrence of words such as “Future”, “Internet of Things”, “Manage”, The anxiety toward the technology of Londoners turns into a “col­
“Solution” (New Data Infrastructure Future Citizen Collaboration Iot lective” fear toward the public use of the technologies required to

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Table 4
A comparison of citizens sentiment toward technology before Covid-19.
Cities

Berlin Dublin London Madrid Milan

Negative Fear Anger Anger Anger Anger

Sentiment Privacy and security risks Inability to use technology Economic unbalances in the Lack of self-confidence Resistance to technology
use of technology
Positive Trust in technology- Structural adequacy of Structural adequacy of Trust toward the use of Effectiveness of technology for
Sentiment mediated interactions technological infrastructure technological infrastructure personal data personal life and success

manage the pandemic (Fear People Public Government Think Time Start technology (Community Desire Sustain Want Partner Team System).
Sadness). The unsatisfaction toward both the individual and collective In the tweets of citizens from Madrid in the post-Covid period, the
use of technology does not reveal the lack of trust in technology per se positive sentiment toward using technology is associated with words
(the tools to employ new technological solutions). Still, it shows mistrust like “share” and “sustain”. Therefore, it can be assumed that after the
in policymakers (people who apply and support citizens in using tech­ advent of the pandemic, collaboration and sharing are intended as
nologies). The fear toward the technology of citizens from Madrid (Fear valuable means to increase collective well-being and mutual support
Think Sadness Job Time One Stop) is related to the individual dimension (Share- Best- Think- Sustain Want- Safe- Calmness) and to control the
of isolation (“stop”, “one”) and of withdrawal into oneself (sadness and negative impact of the emergency by avoiding panic (calmness).
worry about time). It can be hypothesized that, as Covid-19 spreads, the In the tweets published by citizens from Milan, the optimistic atti­
general technology anxiety can be transformed into individual fear since tude to technology is associated with trust in people’s use of personal
social distancing makes people feel more and more isolated. The lack of data (Care Love People Power Think Sustain Joy Share knowledge) to
sense of belonging toward the city and its managers confirms the exis­ increase knowledge and improve the sense of control over the
tence of a misbelief of citizens in smart cities as a social and economic emergency.
phenomenon (Simonofski et al., 2019; Visvizi & Lytras, 2019; Lytras, In the post- Covid period, the trust in data expressed in Londoners’
Visvizi, & Sarirete, 2019). tweets is associated with a positive attitude toward the issue of privacy,
In the tweets of citizens from Milan, the fear toward technology is which reveals that potentially the twitters do not worry about data
related to the lack of self-confidence in personal ability to use technol­ manipulation (Infrastructure Network Personal Data Privacy Want).
ogy (Fear Person Inability One Stop). It can be noticed that the citizens’ In short, as Table 5 shows, the mentions of technology anxiety in
technology anxiety shifts from a generic-collective dimension of Dubliners’ tweets reveal the heightening of the difficulties in the use of
dissatisfaction (before Covid-19) to individual fear (after Covid-19). In smart technologies, determined by the dramatic redefinition of daily
contrast, Londoners’ anxiety translates into a collective fear after the lives that occurred after the diffusion of Coronavirus. However, despite
advent of the pandemic. In the first case, the increase of social distancing the lack of self-confidence in using technologies, citizens show a positive
and the fear of contagion foster the isolation of citizens; in the second digital attitude, characterized by a great sense of belonging to the
case, the perception of inadequacy and the sense of helplessness leads community and a high degree of trust in the opportunities offered from
citizens to transfer their anxiety to society. shared use of smart tools.
On the contrary, in Berliners’ tweets, the fear toward technology is The degree of technology anxiety of Berliners discloses a coping
transformed into anger, and the distrust is turned into suspicion toward behaviour in the acceptance of the opportunities offered from a
government and the absence of technological support to citizens. In collaborative and bottom-up approach to smart technologies (before and
addition, the unsatisfaction toward the degree of democracy in the during the pandemic), associated with the unsatisfaction toward the
process of digitalization can be observed in the co-occurrence of words ability of government and policymakers in the management of tech­
like “power”, “interest”, “support”, which can be considered as signals of nologies (emphasized after the spread of Covid-19).
a top-down power administration and of an incapability to align with Londoners show general compliance toward adopting a digital
citizens’ needs (Anger Everyone Power Government Data Interest Sup­ mindset and a high degree of trust in the potential of data and the
port Citizen Listen). appropriateness of the technologies employed in the city. This agree­
In the post-Covid period, the positive sentiment expressed by Twitter ment toward the technological dimension is associated with a high de­
from the five cities shifts from an individual dimension to a collective gree of anxiety toward the adoption of technology for personal success
and social dimension. and toward the improper management and support of the government in
Dubliners express a positive sentiment toward technology that the use of technology.
translates into an increased openness to data use by confirming the Citizens of Madrid show a lack of confidence in their digital skills,
general trust in the technological infrastructure of the city for the and their attitude toward technology seems to worsen after the spread of
development of innovation (Data Infrastructure Sustain Share Innova­ Covid-19. However, these negative features are balanced with a high
tion Open People Manage). It can be noticed that after the advent of degree of trust in data sharing and people’s collaboration to limit the
Covid-19, the confidence towards technology is boosted towards the threats of the pandemic. The tweeters from Milan in the sample show a
enlargement of trust to a more “collective” sphere in which the human tendency to resist change before the advent of the pandemic. Then, after
intervention of people that share their contribution is considered as a the spread of the Coronavirus, their degree of technology acceptance is
lever to foster the proposition of innovative solutions and to support the reduced further. The uncertainty and anxiety related to technological
management of health emergency. dimensions are associated with a general positive mindset toward the
In Berliners’ tweets, the positive sentiment toward a collaborative use of data and toward collaboration between people to manage the
approach to the use of technology (revealed in the pre-Covid era) is negative effects of the pandemic.
confirmed after the advent of the pandemic and strengthened through
the increase in the sense of belonging to the community. Furthermore,
4.2. Changes in technology anxiety before and after Covid-19
the co-occurrence of words such as “partner”, “system”, “sustain” can be
considered as a signal of the confidence of citizens’ ability to collaborate
To assess if the trends in citizens’ sentiment identified above can
for the creation of a technological system activated by “people” as a real
predict changes in their attitude toward the technologies after the
solution to compensate the lack of governmental support in the use of
advent of the pandemic, Table 6 identifies the predictive performance of

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Table 5
A comparison of citizens sentiment toward technology after Covid-19.

their mentions, the log-odds probability of being cited in the pre-Covid

Table 6
period increases (the probability of being cited in the post-Covid period
Findings for RQ2: regression analysis.
decreases) respectively by 54% and 37%. As discussed in paragraph 4.1,
Technology Anxiety money and the concerns about the economic exploitation of new tech­
Accuracy = 0.752
nologies are the most recurring topics in pre-Covid sentiment, which
Indicators Coefficient ϐ “disappears” after the advent of the pandemic that determines a shift of
LACK − 0.180*
the focus to psychological and social concerns (fear, mistrust, isolation).
INFRASTRUCTURE − 0.344*
MONEY 0.542***
Moreover, anger is a common negative sentiment that expresses a high
PERSONAL DATA − 2.080* degree of technology anxiety before the spread of Coronavirus in 4 out of
SELF-CONFIDENCE − 0.413*** the 5 cities (Dublin, London, Madrid, and Milan). The perceived lack of
MISTAKE − 0.430* digital skills and the inability to use technologies has a strong signifi­
ABILITY − 2.840***
cance (0.0) but a low coefficient, which shows a low probability of being
SKILLS 0.029***
FEAR − 0.026* cited in the pre-Covid period.
ANGER 0.372** The topics with the most negative log-odds coefficients are “ability”,
DIFFICULTY − 0.724* “difficulty”, and “personal data”. For every unit increase in their men­
Note: ***p < 0.001. **p < 0.01. *p < 0.05. tions, the log-odds probability of being cited in the post-Covid period
increases (whereas the probability of being cited in the pre-Covid period
the most recurring topics of technology anxiety (see column 1), taken as decreases) more than 70%. Ability and difficulty can be considered valid
independent indicators of post-Covid sentiment. One of the key concerns of citizens in
variables, to classify sentiment after and before the Covid-19. Dublin and Madrid is the lack of confidence in their digital abilities that
In detail, the first column of the Table reports the value of prediction reveals several difficulties in using new technologies. What is more,
accuracy (0.752), a performance measurement that specifies the ratio of personal data is a commonly cited issue in the post-Covid period. It is
correctly classified observations in the dataset. For example, a value of related mainly to adopting a positive mindset toward digital culture
0.752 shows that the proportion of correct predictions exceeds over total (especially in Madrid, London, and Dublin).
predictions in the association between the different indicators (inde­ Moreover, “lack”, “self-confidence”, “mistake”, “fear”, and “infra­
pendent variables) and the tweets in the pre-Covid period or post-Covid structure” show lower ϐ coefficients. For every unit increase in their
period (a dichotomous variable that ranges from 0 to 1). mentions, the probability of being cited in tweets of the post-Covid
In the second column, the coefficients from the logistic regression period increases by around 18% and 40%. The motivation of the low
model predicting the dependent variable from the independent variables coefficients can be found in the presence of these topics also in the pre-
are provided in log-odds units. The coefficients (ϐ) reveal the positive or Covid period. Therefore, in the research sample, there is not a unique
negative incidence (proportionality) of the independent variables trend related to the perception of ability and self-confidence in the use of
(anxiety indicators) on the dependent variables (period): a positive co­ technology in each period. In one city, the issue is an indicator of post-
efficient means that the attribute affects the pre-Covid sentiment, a Covid (Milan). In contrast, in two cities, it is an indicator of the tech­
negative coefficient means that the attribute affects post-Covid senti­ nology anxiety before the diffusion of Coronavirus (Dublin and Madrid).
ment. A positive ϐ shows the log-odds probability (incidence) of the At the same time, cities infrastructure is a commonly mentioned topic,
occurrence of the given attribute in the pre-Covid period and a which is debated both before and after the pandemic. For this reason, the
decreasing probability for the occurrence in the post-Covid period. low significance associated with its coefficient can be explained through
Moreover, p-values are reported to evaluate the degree of interest the presence of this issue in the two times periods, which determines the
(significance) of the coefficients. The different values of significance are almost total independence from the dependent variable (period).
sub-divided into three classes: p < 0.001; p < 0.01; p < 0.05. As Table 6
shows, all the variables are significant.
The topics with the most positive and statistically significant log-
odds coefficients are “money”, and “anger”. For every unit increase in

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5. Discussion: identifying the determinants of technology Table 7

anxiety The identification of the determinants of technology anxiety in 5 smart cities in
Covid-era.
The analysis of the public sentiment of citizens from the five cities Related topics emerged from the Macro- areas/
investigated permits the identification of variations and unanticipated Indicators analysis determinants of
shades of meaning in the phenomenon of technology anxiety, which can confirmed technology anxiety

reframe the key dimensions of the construct identified in extant Money Power; Interest; Business; Work Utilitarian Dimension
research. Berliners’ anxiety toward technology is determined mostly by Lack Abilities; Confidence; Knowledge; Psychological Dimension
Learn
an unsatisfaction toward government’s management of technology and
Ability Fear; Inability; Person; Stop;
power distribution (cultural gap), balanced with a great sense of Skills; Confidence
belonging to the community (social dimension) and a high degree of Fear Sadness; One; Time; Stop; Life;
trust in the bottom-up use of technology. Home; Office
Dubliners’ resistance to technology is caused mainly by the fear Difficulty Learn; Knowledge
Personal Data Privacy
deriving from a lack of self-confidence in using technologies and Want
adapting rapidly to new technology (psychological factors). This psy­ Infrastructure New; Data; Collaboration; Social Dimension
chological gap is compensated with a great sense of belonging to the Network
community and a positive attitude toward a shared use of smart tools Community; Sustain; Sharing;
Internet of Things; Manage;
(social trust).
Solutions
The key obstacles in using technology for Londoners are related to Data System; Open; People; Build;
the mistrust in the economic exploitation of smart technology and the Future; Innovation
perceived uselessness of smart technologies for personal achievement Anger Government; Public; Support; Cultural Dimension
(psychological inadequacy and low utilitarianism). However, psycho­ Meuter et al. Citizen
(2003) Listen; Change
logical resistance is associated with a positive attitude toward the shared
use of data (digital culture).
The anxiety toward technology perceived by the citizens of Madrid 5.1. Utilitarian dimension
stems from a lack of self-confidence that, after the diffusion of the
pandemic, turns into fear (psychological factors). This psychological The utilitarian dimension is referred to the rational and cognitive
inadequacy to adapt to new technologies is compensated with a general evaluations that lead citizens to assess technology based on the indi­
trust in data sharing and collaboration to manage the negative effects of vidual and social benefits provided.
Covid-19 (social trust). In the results obtained from tweet analysis, three sub-dimensions can
Citizens from Milan show high resistance to change due to the be identified: 1) perceived social usefulness; 2) ability of government to
perceived inability to use technology (individual and psychological allocate resources and distribute technological power; 3) perceived
level) and distrust in public management (cultural and context- personal fulfilment.
dependent level). As in the other cities, there is a high degree of social The first sub-dimension concerns the worries about the economic
acceptance of technologies and the general trust in people’s use of exploitation of technology implementation in smart cities and about the
technology through collaboration (social trust). management of profit and the allocation of resources on the part of the
The different degrees of compliance toward technology and the government, associated with a low degree of perceived social usefulness.
different obstacles that prevent the full acceptance of technology Citizens’ evaluation of the appropriateness of the money spent on smart
revealed in the analysis are related in some tweets to social motivations, city services influences the perception of smart technologies as useful
in others to psychological, cultural, or economic motivations. Thus, the means for society. If people believe that smart technologies are a luxury
different compliant and non-compliant behaviours detected in the and do not compose the basic infrastructure of a city, they will consider
sample of tweets can shed light on the varied spheres involved in the technologies useless, and this will increase their resistance to use them.
complex building of technology anxiety, which includes rational, Users who perceive advanced information systems as levers for inclusive
cognitive, and psychological processes and can be affected by social, and sustainable socio-economic growth will be more predisposed to
contextual, and cultural influence. adopt technologies and to accept the changes brought by their adoption.
Starting from the commonalities and the discrepancies in the senti­ The second sub-dimension refers to the appropriateness of gover­
ment of citizens and, consequently, in their compliant and non- nance and technology and human capital, as one of the major contrib­
compliant behaviours toward technology, four dimensions that can utors to citizens’ participation and smart city development. Different
foster the development of the multi-level process of technology anxiety governance models for the distribution of technological power and
can be identified: 1) utilitarian; 2) psychological; 3) social; 4) cultural. different kinds of support to the access and use of technology can enable
As Table 7 shows, the four dimensions are obtained from the indicators a different degree of technology acceptance in citizens’ perception. In
of anxiety derived from literature, confirmed and enriched through the this sense, Helsinki smart city is one of the European best practices in
results, and reframed and re-elaborated to identify the key determinants developing a bottom-up approach (Anttiroiko, 2016) based on the res­
of technology anxiety (See Table 7). olution of urban problems through triple helix collaboration
The identification of four interdependent dimensions can enable the (Hämäläinen, 2020). Through a series of smart projects and innovation
categorization of some determinants of technology anxiety. These di­ platforms (such as Forum Virium Helsinki), citizens are engaged in the
mensions can be synthesized in a framework that introduces an inte­ co-design of the smart city as the most precious resource and the
grated and holistic understanding of users’ perceptions about beneficial owners of the benefits generated within the city. Moreover,
technology in smart cities by conceptualizing the multi-levelled psy­ non-profit associations, local businesses, citizens, and students are
chological and social beliefs, cultural habits, and rational factors involved in the co-creation of new ideas and innovative services (such as
engaged in the complex acceptance of technologies and technological healthy neighbourhoods, mobile services tests, waste collection sys­
changes. The different determinants of technology anxiety, depicted in tems) through innovation communities, collaborative urban design,
Fig. 3, are discussed in the following sub-paragraphs. joint investments, living labs, open data.
Lastly, the utilitarian dimension can involve the creation of obstacles
or enablers of technology acceptance. In the sample of tweets, people

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Fig. 3. A multi-levelled framework for the determinants of technology anxiety.

with a high degree of dissatisfaction toward the use of technology for the the digital era. Fear and uncertainty are other recognized enabling
fulfillment of personal success can develop more probably a negative factors of technology anxiety. If anxiety is related to the lack of control,
sentiment toward technology. Thus, users with low anxiety perceive fear stems from an emotional state of fright that leads users to perceive a
higher facilitating conditions than users with a high level of anxiety. threat in their lives.
Trust is one of the most effective sensations to reduce uncertainty
and to generate a sense of safety related to the use of technology (Lin,
5.2. Psychological dimension
2011). Users’ and citizens’ trust in technologies and service providers
plays a crucial role in the intention to adopt the technology. Trust has a
As Fig. 3 shows, the findings of the empirical research reveal that the
key role in reducing perceived risk, enhancing usage behavior, behav­
psychological factors that can prevent a full acceptance of new tech­
ioural intention (Yoon, 2002), and perceiving the usefulness of tech­
nologies are: 1) the lack of self-confidence and the perception of
nology (Ha & Stoel, 2009). In the sample of tweets, citizens do not show
inability to use technologies; 2) fear and uncertainty, which determine
a lack of trust in technology per se but reveal a mistrust in policymakers’
resistance to change; 3) the mistrust in technology, in government’s and
application of smart technologies and in governmental support to use
people’s management of technology; 4) the privacy concerns. Psycho­
technology (Hartanto & Siregar, 2021).
logical factors can motivate users to adopt smart-cities services and help
Lastly, researchers identified the security and privacy issues as the
individuals achieve their personal and professional tasks (Macke et al.,
major challenges to adopting and accepting technology (Hancke et al.,
2019). The psychological impact of technology is widely debated in the
2010). From the individual users’ perspective, the use of smart tech­
literature. For example, perceived self-efficacy, self-confidence, and
nologies involves a series of concerns about privacy, security, and who
self-esteem are considered threatening factors for developing technol­
has access to data collected by governments and companies (Weber,
ogy anxiety (Bandura, 1986) and the decrease of the sense of control
2010) and in smart cities (Van Zoonen, 2016). Privacy is considered as
over daily activities. Therefore, reducing anxiety can increase the sense
one of the most common inhibitors of technology adoption (Venkatesh
of control and make the events more predictable by decreasing the
et al., 2003). It can be defined as the individual believes that make users
feeling of risk. One of the effective strategies to reduce citizens’
sceptic about releasing their personal information to others (Xu &
perception of the inability to use technology is to increase digital skills
Gupta, 2009).
and culture. For this reason, the development of digital competencies
(for children, practitioners, urban managers, and citizens as a whole) has
been settled as one of the priorities of the European Digital Agenda 5.3. Social dimension
2020. Digital citizenship, as the exertion of active engagement in the
development of cities and communities, has been promoted over the last The social determinants of technology anxiety (the social features
years in international smart cities to address the lack of digital skills in associated more frequently with negative sentiment toward technology
students, citizens, and public administration workers. For instance, UK in the tweet analysis) are: 1) social inclusion and citizens’ engagement in
Government is encouraging the implementation of digital citizenship as technology use and management; 2) sense of belonging to a community;
a mandatory subject in schools. Similarly, Common sense, a British 3) adoption of a collaborative mindset based on the trust in data sharing
non-profit organization, is devoted to providing services for the and people’s mutual support.
improvement of kids’ and families’ education by offering them trust­ Technology anxiety in the tweets analyzed is associated with the
worthy information and the right competencies and attitude to thrive in development of social exclusion, which can stem from the lack of skills

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to use smart technology, the inability of the government to involve cities growth and innovation has been revealed. A data-driven culture is
citizens in the use of technologies by removing the obstacles to access defined in the literature (McAfee & Brynjolffson 2012) as an approach to
infrastructure. These findings reveal the need to understand the smart decision-making based on the relevance of data (and of the insights
city as a social and political phenomenon (Visvizi & Lytras, 2018) and as extracted from it) as a strategic asset to undertake more effective de­
a leading actor to build an inclusive society and boost participation. The cisions. Extant research shows that the enhancement of digital literacy
digital divide and the gap in the technological skills of managers and (Axelsson et al., 2010; Wiig, 2016) can enable citizens’ engagement in
citizens can be addressed through the engagement of stakeholders and a cities decision-making by implementing a series of digital strategies and
diffused decision-making (Nurmandi et al., 2017). smart projects that can create an ecosystem of citizen-centric services.
The sense of belonging to the community revealed in the tweets For instance, the activity “Smart Polis2020”, launched by the Puglia
highlights the weight of social influence on technology anxiety. As Region in 2019, aims at creating a new version of smart cities that in­
confirmed in the literature, users with a high level of technology anxiety volves citizens in the design and implementation of policies. Smart polis
tend to be more influenced by the opinion of other people and by society can be intended as a physical and geographical place built on a network
(Yang & Forney, 2013). Extant research shows that a high level of of new technologies, but also as a relational space “delimited” by cul­
technology anxiety is related to a lack of confidence; hence, users with a tural, social, political, and economic connections which, if exploited
high level of anxiety rely more on family’s and friend’s beliefs and appropriately, can permit users to co-create innovative services and
follow referent group norms to use technology (Kulvivat et al., 2009). satisfy individual and community’s needs.
The degree of trust toward the community and enhancing the sense The general familiarity and unfamiliarity of citizens with technology
of belonging can be strengthened through social initiatives such as civic and the potential adoption of a digital culture stem from the different
crowdfunding. For instance, in Milan, new spaces for collaboration and management of cities realized in the different national contexts, in
co-design have been created to reduce the gap between civil society and which government can adopt a top-down or bottom-up diffusion of
decision-making. The project started in 2020, involved non-profit or­ technological tools and can prevent the spreading of digital skills with a
ganizations, citizens, and third sector organizations (social enterprises, low degree of support to the use of technology. The national culture and
associations, foundations, etc.) that proposed over one hundred projects. the political context can influence citizens’ attitudes and behaviours
As a result, the municipality has selected the best twenty bottom-up toward technology and can prevent the acceptance of technological
projects to realize a series of smart activities for urban revitalization changes (Harris & Davison, 1999). Extant research shows that technol­
and technological accessibility by engaging users in the decision-making ogy anxiety does not depend only on an individual’s unfamiliarity with
of the projects. technology but also on situational and context-dependent factors that
In all the cities included in the sample, the most common positive enrich users’ experiences and perceptions of technology use.
sentiment toward the use of technology is trust in technological infra­ As the findings of the tweet analysis reveal, government support and
structure and data sharing, intended as useful means to increase col­ diffused decision-making can be associated with citizens’ positive per­
lective well-being and mutual support and to control the negative ceptions of technology. This result is confirmed by Ramanathan et al.
impact of the emergency by avoiding panic. The sharing and the (2014), who hypothesize that a higher level of government support can
collection of big data in smart cities are viewed in the sample of tweets as help the strengthening of usability and adoption. Different kinds of
a new method to enhance growth and address social issues. Data sharing support are identified in the literature: financial, project, training, and
can ensure the real-time collection of epidemiological data and can regulatory approval (Lin & Ho, 2009). Other scholars analyze the rele­
strengthen risk-assessment, decision-making processes, and the design vance of citizens’ engagement in the decision-making of cities and in the
of public policies (Allam & Jones, 2020). Moreover, the adoption of a community’s life as an enabler of technology acceptance in city space
collaborative mindset based on data can be fostered through the con­ that must also be considered (Nurmandi et al., 2017). Citizen partici­
stant sharing of information, technical experiences, and knowledge be­ pation and engagement are crucial drivers of citizens’ successful
tween experts and civil society to involve citizens in the active use of deployment of ICT (Olphert & Damodaran, 2007).
technology and the proposition of innovative policies for the territory.
Open data projects and hackathons seem to be useful practices to raise 6. Theoretical and managerial implications
citizens’ involvement in a better and more acknowledged use of tech­
nology and the co-development of innovative solutions. For instance, the The key theoretical contribution of the study is the building of a
open data hackathon at the municipality of Livorno held in 2018 aimed framework that detects the main psychological, rational, social, and
at increasing citizens’ digital culture and simplifying the access and use cultural determinants that can foster or prevent the acceptance of the
of data (geographic, geo-referenced, or geo-referenced data) to make changes forced by the pandemic, the adhesion to digitalization, and the
every actor understand how to extract relevant content from data. Then, transactional distance processes launched in the public sectors.
after the diffusion of technological capabilities, the data analyzed was The recognition of the strategic drivers for optimal exploitation of
used to create cultural or environmental activities in the city or the technologies in managing health emergencies can enrich policymakers’
territory by supporting mart actions or projects (e.g., the and public managers’ understanding of the assessment, forecasting, and
recovery-revitalization of underused or degraded areas). management of crises and emerging events. The main managerial
contribution of the study is the proposal of a tool to improve the
5.4. Cultural dimension decision-making process by detecting the criticalities in citizens’ adop­
tion of technology through a classification of the main factors that
The cultural determinants (obstacles or enablers) of technology hinder or enable efficient use of technologies and redefine the daily lives
anxiety identified in tweet analysis are: 1) digital culture in the com­ of individuals and human-machine interactions. Some of the critical
munity/city; 2) familiarity with technology in the cultural context; 3) factors identified, such as the lack of self-confidence in digital skills or
public management of technology. the perceived lack of governmental support in the use of technology, can
Developing a cohesive culture is considered in the literature as a help managers discover the strategic levers that can be employed to
critical lever for adequate exploitation of technologies and data analysis align with the needs and expectations of end-users.
opportunities in companies, organizations, and cities. It has been The results of the empirical research reveal that individuals with a
demonstrated that the primary cause of failure in technology imple­ high level of technology anxiety are less disposed to make use of it
mentation is the absence of a totalizing digital culture rather than the (Meuter et al., 2003). Thus, exploring and detecting the degree of anx­
structural characteristics of technology (LaValle et al., 2011; Ross et al., iety toward technology plays a vital role in successfully adopting smart
2013). In the sample of tweets, a positive attitude to the use of data for services.

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Moreover, shedding light on the most common topics shared on so­ 2019) and smart villages in which, according to extant research (Tran
cial media platforms related to Covid-19 and the management of public et al., 2017; Yu et al., 2017), there is the need to reveal some strategies to
health emergencies can provide policymakers with relevant suggestions support underprivileged people or individuals that do not have the right
to challenge the negative implications of a pandemic, assess the needs of skills to use technology. By clarifying the means to reduce technology
stakeholders, and address them appropriately (Abd-Alrazaq et al., anxiety, the study can suggest how to enhance citizens’ perceived use­
2020). Anxiety plays a key role in shaping behavioural responses to the fulness of technology to challenge pandemics and foster the restarting of
public health emergency; hence, it is critical that decision-makers the economy and social activities. Policy-makers should highlight the
recognize the multiple individual psychological responses to the cur­ potential benefits of technology, such as improved efficiency and per­
rent crisis (Wahbeh et al., 2020). formance of healthcare, mobility, and public services in general. In the
The classification of the different emotional shades of public senti­ EU, as discussed above, several attempts are made to coordinate the
ment/technology anxiety can foster smart cities management. The rapid policies on smart cities through a series of initiatives on sustainability
spread of Coronavirus and Covid-19 infections created a strong need for and digital culture (Agenda 2020; SDGs, etc.); however, the power of
discovering efficient analytics methods for understanding the flow of decision remains at the city level, and this can prevent a full harmoni­
information and the development of mass sentiment in pandemic sce­ zation of the strategies across the different nations. The discussion of
narios. While numerous initiatives analyze healthcare, economic, and some international cities that can be considered best practices in
network data, there has been relatively little emphasis on analyzing the implementing smart projects advances the first step for applying the
aggregate personal level and social media communications. framework to other urban contexts by confirming the generalizability of
Sentiment analysis using social media data will thus provide valuable the four dimensions. In this way, it can be noticed that citizens’ inclusion
insights on attitudes, perceptions, and behaviors for critical decision- and digital literacy are relevant issues that do not apply only to urban
making for business and political leaders and societal representatives. contexts and should be addressed to solve societal, economic, techno­
As a global pandemic, Covid-19 is adversely affecting people and logical, and political problems worldwide. Thus, a new mindset for ed­
countries. Besides necessary healthcare and medical treatments, it is ucation that goes beyond the urban context is spreading to pursue the
critical to protect people and societies from psychological shocks (e.g., objectives of technological access and digital literacy for children, stu­
distress, anxiety, fear, mental illness) (Hung et al., 2020). In this context, dents, teachers, policy-makers, practitioners, and managers to build a
automated machine learning-driven sentiment analysis could help shared digital world (Johnson et al., 2021). The categorization of the
health professionals, policymakers, and state and federal governments main determinants of the technology anxiety developed after the advent
to understand and identify rapidly changing psychological risks in the of the pandemic can be intended as a starting point for further qualita­
population. Identifying public sentiment can detect the strategies that tive and quantitative research to explore the drivers of the change in
can reduce people’s uncertainty and detect the factors that can prevent citizen’s sentiment before and after the spread of Covid-19. First, a
engagement and compromise the diffused decision-making in the digital mixed-method approach can enrich and extend the framework proposed
ecosystems (Samuel et al., 2020). in the study through observations and the administration of
In the era of digitalization, the exploration of online activities is one semi-structured interviews to a sample of citizens to identify a most
of the most useful means to understand the motivation of real-life ac­ detailed classification of the key indicators of technology anxiety. Then,
tivities and behaviours. Consequently, to detect a possible response to by transforming the indicators into items, a measurement scale of
public health crisis, the analysis of users’ opinion on social media can technology anxiety can be tested and validated through quantitative
provide policymakers with relevant insights on the people’s reactions to analysis based on regression and structural equation modelling. A lim­
the state of emergency (Ordun et al., 2020). In addition, it can offer an itation of the study can be found in the discrepancy between online
opportunity to communicate directly with public opinion. Monitoring behaviour and offline behaviour and the difficulty in exploring psy­
users’ activities on social media can permit organizations, public in­ chological characteristics through social media analysis. However, it is
stitutions, and companies to be more proactive, challenge the spread of acknowledged that users currently perceive their online profiles as an
fake news, and limit the propagation of the negative psychological ef­ extension of the self rather than a separate entity, revealing their real
fects of pandemics (Chakraborty et al., 2020). psychological features. Moreover, the technique employed (and actual
data mining and machine learning techniques) can ensure a great level
7. Conclusions of accuracy to predict characteristics based on online data (Gouda &
Hasan, 2019).
The impact of Covid-19 on people’s lives, organizational practices,
urban policymaking, and decision-making entails the need to capture Credit authors statement
how individuals react to public health emergencies (and to the man­
agement of this emergency) and reveal their concerns. For this reason, Orlando Troisi; Conceptualization, Methodology, Formal analysis,
this paper aims at investigating citizens’ sentiment and concerns about Investigation, Resources, Data curation, Writing – original draft, Writing
the Coronavirus pandemic and at identifying the sources of these con­ – review & editing, Visualization, Supervision, Giuseppe Fenza;
cerns by exploring tweet’s posts to discover people’s reactions to social Conceptualization, Methodology, Formal analysis, Investigation, Re­
issues. sources, Data curation, Writing – original draft, Writing – review &
The framework proposed helps enrich the debate on the de­ editing, Visualization, Supervision, Mara Grimaldi: Conceptualization,
terminants of technology anxiety and identifies the different criticalities Methodology, Formal analysis, Investigation, Resources, Data curation,
that influence citizens’ behavior and attitude concerning tools and in­ Writing – original draft, Writing – review & editing, Visualization, Su­
struments used to digitize the relationships between individuals and pervision, Francesca Loia: Conceptualization, Methodology, Formal
organizations. analysis, Investigation, Resources, Data curation, Writing – original
The classification of some cultural, social, and psychological drivers draft, Writing – review & editing, Visualization, Supervision.
can help urban policy-makers in the identification of the most proper
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