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Big Data and Their Social Impact: Preliminary Study

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  • SGH Warsaw School of Economics | Deree College | Effat University

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Big data is the buzz-word of today, and yet their specific impact on individuals and societies remains assumed rather than fully understood. Clearly, big data and their use have already given rise to a number of questions, including those of how data can be collected and used in ethical and socially sensitive ways. Building on these points, the objective of this study was to explore how precisely big data and big data based services influence individuals and societies. This paper elaborates on individuals’ perceptions of data, especially on how they perceive the actual sharing of their data. In this way, this paper defines a value space for the social impact of big data relevant to three factors, namely the intention to share personal data, individual’s concerns, and social impact of big data.The main contribution of this study consists of the insights into the still nascent area of research that unfolds at the cross-section of social science and computer science. We expect that in the next years this area of research will gain prominence.
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sustainability
Article
Big Data and Their Social Impact: Preliminary Study
Miltiades D. Lytras 1,2,* and Anna Visvizi 1,3
1School of Business & Economics, Deree College, The American College of Greece, 153-42 Athens, Greece;
avisvizi@acg.edu
2Eat College of Engineering, Eat University, Jeddah P.O. Box 34689, Saudi Arabia
3Eat College of Business, Eat University, Jeddah P.O. Box 34689, Saudi Arabia
*Correspondence: mlytras@acg.edu; Tel.: +30-210-600-9800
Received: 26 July 2019; Accepted: 10 September 2019; Published: 17 September 2019
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Abstract:
Big data is the buzz-word of today, and yet their specific impact on individuals and societies
remains assumed rather than fully understood. Clearly, big data and their use have already given rise
to a number of questions, including those of how data can be collected and used in ethical and socially
sensitive ways. Building on these points, the objective of this study was to explore how precisely
big data and big data based services influence individuals and societies. This paper elaborates on
individuals’ perceptions of data, especially on how they perceive the actual sharing of their data. In
this way, this paper defines a value space for the social impact of big data relevant to three factors,
namely the intention to share personal data, individual’s concerns, and social impact of big data.The
main contribution of this study consists of the insights into the still nascent area of research that
unfolds at the cross-section of social science and computer science. We expect that in the next years
this area of research will gain prominence.
Keywords:
social impact; big data research; information systems; analytics; decision making;
social sciences
1. Introduction
Recent developments in data-driven information systems, set big data research and business
analytics at the core computer science and social science. In computer science research, there is
a consensus that big data and data analytics research will foster a new generation of information
systems capable of managing the collective wisdom in human decision making and smart machines [
1
].
Emerging research areas like cognitive computing [
2
] combined with artificial intelligence and machine
learning, permit advanced and sophisticated methods for processing data, including sentiment analysis,
image processing, natural speech recognition and text mining. In parallel emerging technologies,
including cloud computing, internet of things and virtual reality, the value proposition of application
and services that process data in dierent formats such as text, images, videos, microcontents in social
media is further enhanced [
3
,
4
]. The development of a huge data ecosystem around the globe, in which
providers and users of data promote business value in terms of data and decision making, is a key
development of our times. In this context, users of applications and services worldwide participate
consciously, or unintendedly, to an integrated data dissemination and aggregation process with critical
trust and privacy issues.
A great discussion on the real impact of big data research has been initiated. An interesting
study [
5
] sets the significance of the human decision maker at the center of any type of big data
information processing cycle. There is an agreement between dierent academics that big data can
make a big impact [1,6].
Big data research is aligned with the evolution in emerging information technologies research.
New information processing paradigms further promote the significance of big data, and have a great
Sustainability 2019,11, 5067; doi:10.3390/su11185067 www.mdpi.com/journal/sustainability
Sustainability 2019,11, 5067 2 of 18
impact on its volume and coverage. A number of application domains and industries already adopt
big data research with significant success. Consider social networks research and the contribution
of social networks to the big data ecosystem [
2
,
3
]. Other examples are artificial intelligence and
machine learning applications in various domains, such as customers/clients of big data repositories
for personalized and targeted services [3,4].
In the recent literature of big data research, an increasing section is dedicated to the capacity of big
data to support social sciences research. There is the anticipation that big data is potentially a social good
that must be secured and be used for the transparency of services, and for the evolution of a user-centric
new culture for sustainable computing. In parallel, several concerns have been documented, mostly
related to trust, privacy and the protection of personalities in the new technology-driven domain of
services and applications. In Figure 1, below, we provide our initial framework for the investigation of
the social impact of big data.
Sustainability 2018, 10, x FOR PEER REVIEW 3 of 19
Figure 1. An initial framework for understanding the social impact of big data research. Source: The
authors.
Figure 1 is used as a metaphor to communicate the overall idea of our research, that somehow
users, with their perceptions and intention to use big data applications, define their personal value
space and maybe also a societal value space. We understand that in our approach some key
assumptions are integrated. We do, however, believe that it is worthy to investigate this research
problem which has many psychological and social aspects. In the next section we provide a critical
review of the relevant literature towards the justification of our research model that will be presented
in Section 3 of this research study.
2. Literature ReviewUnderstanding the Debate on Big Data and their Social Impact
The agenda of big data research is quite wide and involved various multidisciplinary
communities. From a computer science and information systems perspective issues related to
standardization, data mining, aggregation of data, interoperability and recommendation systems are
at the top of research priorities. From a social science perspective, data as a social construct affecting
issues related to identity management, personality, privacy and security are the focus of social
research. Furthermore, the concept of the digital self, that combines personal, professional, social,
and other features of individuals is gaining more interest [1]. In an evolving way, big data that refer
to human entities and communities of people are established with convenient computational
methods that permit social analysis and reference.
The connection of big data research to social sciences as well as the big impact of data-intensive
applications and processing methods to societal challenges provides a very interesting research
challenge. From the one side we have the social actors, humans, decision makers that both provide
and consume data available in diverse, interconnected information systems [5]. The quest for impact
on big data platforms and big data [6] requires a detailed study of different factors and accordingly
new metrics like analytics or KPIs (key performance indicators) [6]. Humans, from this point of view,
realize a critical mental shift in their behavior. From data providers they are requested to perform a
Figure 1.
An initial framework for understanding the social impact of big data research. Source:
The authors.
In our approach three critical factors need further investigation:
User concerns/aordances: The first factor, namely user concerns or aordances is related to
all the psychological, social, personal or professional concerns of users in relevance to the use
of applications and services that generate and share personal data or other kinds of data from
individuals within the big data ecosystem
Intention to share data/informed consent: This aspect of our research problem is related to the
conscious agreement or the intrinsic motivation of users to share their data for the purposes of big
data application. In our research, we are interested in the connection of social challenges and social
problems to the intention of users to share their data. Furthermore, we wanted to understand if in
some scenarios, users of applications and services share their data without formal agreement due
to their interest in exploiting the added value of the service for themselves or for the society.
Sustainability 2019,11, 5067 3 of 18
Social impact of big data: The third critical factor also determines the value space in Figure 1. The
measurement of the social impact of big data seems to require interdisciplinary approaches and
metrics, thus we must deploy heuristics for the attachment of value contribution to the perception
of users for the impact of big data research to their lives and to our society. This is the ultimate
objective of our research, nevertheless, the requirements and the various research strategies we
deployed exceed the length and scope of this paper.
The value space of big data is defined as an aggregation of three factors/forces. At the X-axis, the
social impact of big data is presented in a spectrum of low to high value. Users of big data applications
develop perceptions and have their own interpretation mechanisms for the impact of big data. On the
Y-axis, concerns and fears of users of big data application develop an intrinsic motivation mechanism
for the use of such application. They deploy dierent ways for the use of big data applications and
they also express their concerns for various aspects of these applications. Furthermore, on the Z-axis in
Figure 1, it is shown that users also execute a dierent degree of willingness to share their data, for the
proper functioning of big data applications. Various studies in the literature mention these factors, and
our previous research has tried to investigate these factors. The value space that is defined by these
three axes, can be used as a model for discussing big data applications and services and for mapping
such services in wider contexts e.g., smart cities research. From a practical point of view, this model
can also be exploited by real users of big data applications for the customization of available services
or the personalization for added value of such applications. Also, from a policy making view, such a
model can guide public consultation and debate on how we protect the data and identity rights of
citizens against big data applications without compromise of social value and impact.
Figure 1is used as a metaphor to communicate the overall idea of our research, that somehow
users, with their perceptions and intention to use big data applications, define their personal value space
and maybe also a societal value space. We understand that in our approach some key assumptions are
integrated. We do, however, believe that it is worthy to investigate this research problem which has
many psychological and social aspects. In the next section we provide a critical review of the relevant
literature towards the justification of our research model that will be presented in Section 3of this
research study.
2. Literature Review—Understanding the Debate on Big Data and their Social Impact
The agenda of big data research is quite wide and involved various multidisciplinary communities.
From a computer science and information systems perspective issues related to standardization, data
mining, aggregation of data, interoperability and recommendation systems are at the top of research
priorities. From a social science perspective, data as a social construct aecting issues related to
identity management, personality, privacy and security are the focus of social research. Furthermore,
the concept of the digital self, that combines personal, professional, social, and other features of
individuals is gaining more interest [
1
]. In an evolving way, big data that refer to human entities and
communities of people are established with convenient computational methods that permit social
analysis and reference.
The connection of big data research to social sciences as well as the big impact of data-intensive
applications and processing methods to societal challenges provides a very interesting research
challenge. From the one side we have the social actors, humans, decision makers that both provide
and consume data available in diverse, interconnected information systems [
5
]. The quest for impact
on big data platforms and big data [
6
] requires a detailed study of dierent factors and accordingly
new metrics like analytics or KPIs (key performance indicators) [
6
]. Humans, from this point of view,
realize a critical mental shift in their behavior. From data providers they are requested to perform a
decision maker role, within the boundaries and across hi-tech socio-technical structures like smart
cities [7].
From a dierent angle, the big data ecosystem requires distribution and aggregation of information
in modes that were unforeseen in the past. The sophistication and the huge capacity of big data
Sustainability 2019,11, 5067 4 of 18
services to process significant volumes of data, automatically, without human intervention, sets critical
questions related to privacy, security and data protection [8].
Especially in the context of social networks and social media [
9
], the information diusion has
exceeded any prediction. The ease of sharing information as well as the increased openness of such data
warehouses permits advanced data processing that leads to critical insights about the data providers.
In this situation, big data applications serve as intermediaries, matching the gap between the providers
and the consumers of data, allowing several innovative business models to appear [
10
]. There is a
connection that needs further investigation. The power of big data applications as intermediaries and
as unique business models for adding value to raw data with data processing data, like sentiment
analysis and opinion mining [
11
]. The capacity of new information processing methods to conclude
about sentiments, attitudes or opinions is directly linked to some forms of social impact for such
applications [12].
Within this complex big data ecosystem, individuals, organizations as well as governments need to
develop frameworks to measure their readiness for the integration of big data research for measurable
individual and social objectives [
13
,
14
]. One direction for the exploitation of big data research is
analytics. The exploitation of value through huge volumes of data, requires the development of big
data analytics capabilities [
14
,
15
], aiming to provide visualizations and summaries of data that can
promote enhanced decision making. From a social science perspective, this connection directly leads to
a new era of smart urbanism, where human actors, e.g., citizens, exploit processed data in meaningful
visual forms for the improvement of the quality of their lives [16,17].
Another key aspect of big data literature is related to the big data hype. The utilization of big data
research for business or social purposes must identify opportunities, myths as well as risks [
18
]. It
is necessary for our societies and for policy making purposes to ask various provocative questions
related to the ownership, supervision, consumption and protection of big data [
19
]. Consider, for
example, a system for social rating based on microcontent contributions of citizens on social media,
capable of measuring sentiments, political beliefs etc. Smart cities and smart government research [
20
]
must take into consideration, a number of delicate issues related to privacy, security, safety and social
responsibility of individuals and groups. Without a focus on sustainability [
21
,
22
], social inclusive
economic growth and social justice, any isolated, monolithic big data application in the long term will
unfortunately fail to promote its social impact. Novel approaches are required in the management of
big data and their interoperability, as well as the annotation of data and services for improved social
services [23,24]
What seems to be less analyzed, is the social dimension, and the social dynamics of big data, that
refer to groups of people, businesses or social constructs. In both cases, the ultimate objective of big
data research is to provide useful insights for the personalization of services and the targeting of value
adding services.
A key challenge of big data research is to justify and to develop value reference layers to big
data. The usability of big data, for various purposes and targeted markets needs to be clarified. In
our research, our focus is on the social impact of big data. The key research question is related to the
capacity of big data to have a social impact, and to enable bold solutions and responsive actions related
to social problems.
In Figure 2, below, we organized in a simple way, some complementary aspects of the big data
literature from an information systems/computer science perspective. The list of topics is definitely not
exhaustive, but rather representative of significant aspects of the research.
Sustainability 2019,11, 5067 5 of 18
Sustainability 2018, 10, x FOR PEER REVIEW 5 of 19
methods like sentiment analysis to understand opinions, to analyze social behavior and the attitudes
of individuals can be used extensively for sophisticated social sciences research.
Dynamic big data service composition and selection is also another very interesting area of
research and literature. Big data, per se, have limited value without some services (clients) that
consume these data for well-defined purposes. The design of socially aware value adding services
that consume big data, will soon be a key trend in social sciences. From this perspective, we are going
to realize a convergence of social sciences and computer science. Consider real time platforms that
provide analysis and collective intelligence, over social media micro-contents (e.g., analysis of sexual
harassment, bullying, anti-terrorism detection etc.).
Advanced user profiling [1,5] is also critical for the launch and management of social sensitive
applications powered by big data research. The standardization of profiles is the first step toward
interoperability of applications and social services. To this direction, latest developments in computer
science as well as in policy-awareness frameworks, provide significant contributions. From a social
impact perspective, a key question is how, within governmental institutions, and regulations, can we
envision trustable, participatory and democratic platforms that exploit big data profiles for social
good. Social rating systems, or social filtering platforms are key examples for this emerging area of
research. Furthermore, from a social perspective, another key concern is about the ownership of the
big data. However, this topic goes beyond the scope of our research articulated in this research study.
Smart cities research is another example of critical integration for social sciences and computer
sciences research [68]. In all these cases several research questions link big data research to critical
social impact [1117].
The availability of big data ecosystems offers numerous options for sophisticated services [68].
With the evolution of artificial intelligence e.g., machine learning approaches, we can have systems
that are trained by the availability of big data. For special social problems, like poverty, exclusion,
migration, we have a brand-new era of information services and recommender systems.
All the previous complementary aspects of research move the quest of our time forward for the
provision of sophisticated social insights over individual’s data or communities’ data. Several ethical
issues are involved, but it seems that the next thread of big data services and applications will
materialize some of the aspects mentioned in this compact literature review.
Figure 2. An overview of the key aspects of big data research literature for social sciences. Source: The
authors.
Big Data
Research
Data
Annotation
& Data
Mining
Methods
Services
Composition
Profiling
and
Matching
Algorithms
AI Enabled
Recommen
ders
Individual/
Social
Insights
Social
Rating
Systems
Figure 2.
An overview of the key aspects of big data research literature for social sciences. Source:
The authors.
Data annotation and packaging, as well as the emerging data mining methods such as sentiment
analysis or social mining, set a new domain of research. Especially for social sciences, the capacity of
methods like sentiment analysis to understand opinions, to analyze social behavior and the attitudes
of individuals can be used extensively for sophisticated social sciences research.
Dynamic big data service composition and selection is also another very interesting area of
research and literature. Big data, per se, have limited value without some services (clients) that
consume these data for well-defined purposes. The design of socially aware value adding services
that consume big data, will soon be a key trend in social sciences. From this perspective, we are going
to realize a convergence of social sciences and computer science. Consider real time platforms that
provide analysis and collective intelligence, over social media micro-contents (e.g., analysis of sexual
harassment, bullying, anti-terrorism detection etc.).
Advanced user profiling [
1
,
5
] is also critical for the launch and management of social sensitive
applications powered by big data research. The standardization of profiles is the first step toward
interoperability of applications and social services. To this direction, latest developments in computer
science as well as in policy-awareness frameworks, provide significant contributions. From a social
impact perspective, a key question is how, within governmental institutions, and regulations, can
we envision trustable, participatory and democratic platforms that exploit big data profiles for social
good. Social rating systems, or social filtering platforms are key examples for this emerging area of
research. Furthermore, from a social perspective, another key concern is about the ownership of the
big data. However, this topic goes beyond the scope of our research articulated in this research study.
Smart cities research is another example of critical integration for social sciences and computer sciences
research [
6
8
]. In all these cases several research questions link big data research to critical social
impact [1117].
The availability of big data ecosystems oers numerous options for sophisticated services [
6
8
].
With the evolution of artificial intelligence e.g., machine learning approaches, we can have systems
that are trained by the availability of big data. For special social problems, like poverty, exclusion,
migration, we have a brand-new era of information services and recommender systems.
Sustainability 2019,11, 5067 6 of 18
All the previous complementary aspects of research move the quest of our time forward for
the provision of sophisticated social insights over individual’s data or communities’ data. Several
ethical issues are involved, but it seems that the next thread of big data services and applications will
materialize some of the aspects mentioned in this compact literature review.
In our approach the big data social impact research problem is part of a greater smart cities research
approach [
25
32
]. In the next section, we provide our research methodology for this challenging
research problem. From the beginning we have to communicate the limitations of our study and also
the complexity of the research phenomenon. This study is based on a pilot survey, in which participants
are academics and researchers from computer science and social sciences. The generalization of
findings and conclusions should be analyzed within this context.
3. Research Methodology
In our research study we emphasize on an interpretive qualitative research, together with
quantitative research. The understanding for the social impact of big data, is a very complex research
problem because:
The technical aspects of big data are complicated and continuously evolving.
The social aspects of big data research involve human actors with complicated profiles and social
references, and thus it is hard to generalize conclusions that are derived from a sample population.
In Figure 3we provide a synopsis of the research model of our study. There are six critical research
objectives:
Research Objective 1.
To understand the degree of awareness of big data research and the actual use
of big data applications by our responders.
Research Objective 2.
To analyze, the perceived value of big data as it is realized by our responders.
Research Objective 3.
To understand the main concerns of users of big data services, as well as their
aordances towards greater deployment of such applications.
Research Objective 4.
To clarify the impact of big data to individual’s life and perceptions.
Research Objective 5.
To understand the determinants of social impact of big data in our societies in
relevance to critical social problems and challenges.
Research Objective 6.
To develop guidelines for socially aware big data-enabled services.
Sustainability 2018, 10, x FOR PEER REVIEW 6 of 19
In our approach the big data social impact research problem is part of a greater smart cities
research approach [2532]. In the next section, we provide our research methodology for this
challenging research problem. From the beginning we have to communicate the limitations of our
study and also the complexity of the research phenomenon. This study is based on a pilot survey, in
which participants are academics and researchers from computer science and social sciences. The
generalization of findings and conclusions should be analyzed within this context.
3. Research Methodology
In our research study we emphasize on an interpretive qualitative research, together with
quantitative research. The understanding for the social impact of big data, is a very complex research
problem because:
The technical aspects of big data are complicated and continuously evolving.
The social aspects of big data research involve human actors with complicated profiles and social
references, and thus it is hard to generalize conclusions that are derived from a sample
population.
In Figure 3 we provide a synopsis of the research model of our study. There are six critical
research objectives:
Research Objective 1. To understand the degree of awareness of big data research and the actual use
of big data applications by our responders.
Research Objective 2. To analyze, the perceived value of big data as it is realized by our responders.
Research Objective 3. To understand the main concerns of users of big data services, as well as their
affordances towards greater deployment of such applications.
Research Objective 4. To clarify the impact of big data to individual’s life and perceptions.
Research Objective 5. To understand the determinants of social impact of big data in our societies in
relevance to critical social problems and challenges.
Research Objective 6. To develop guidelines for socially aware big data-enabled services.
Figure 3. The research model for the study of social impact of big data. Source: The authors.
Two different directions of research have been deployed:
a. A quantitative research based on a sample of responders for some initial insights related to
research objectives 14. In the next section we present the results of this analysis. Briefly, we
designed a research tool for the collection of responses based on a survey, and we collected 108
questionnaires in total from academics, students and researchers.
Figure 3. The research model for the study of social impact of big data. Source: The authors.
Two dierent directions of research have been deployed:
Sustainability 2019,11, 5067 7 of 18
a.
A quantitative research based on a sample of responders for some initial insights related to
research objectives 1–4. In the next section we present the results of this analysis. Briefly, we
designed a research tool for the collection of responses based on a survey, and we collected 108
questionnaires in total from academics, students and researchers.
b.
A qualitative research design focused on targeted interviews with five heavy-users of big data
applications and five non-users. The aim was to gain an in depth understanding of their mental
models for the use and value of big data.
In this research paper we present the main findings of the first research direction, the quantitative
analysis. From the beginning of our analysis we communicate the limitations of our study that refer to
the limited sample and the complicated aspects of human behavior. We have completed the collection
of the questionnaires and we are also currently designing the structured interviews for the heavy
and light users of big data applications. Furthermore, we must state that this research on the social
impact of big data is part of a greater research project related to smart cities research. In the latter, the
objective is to explore end-users perceptions of smart cities’ applications, especially as seen from the
perspective of personal data sharing. We also understand that this is a complex research problem that
needs further investigation and various contributions from dierent disciplines. Indeed, it is necessary
to initiate a sound dialogue on these matters among researchers and decision-makers to create and
explore synergies.
4. Analysis and Main Findings
Our eort to elaborate on the social impact of big data research is by default a very challenging
research. The critical psychological and personal factors, aecting the adoption of big data applications
goes far beyond traditional information systems or computer science research. From the other side,
the technical sophistication of advanced applications and services also poses critical challenges to the
protection of privacy, identity management and safety on the internet. From the beginning of this
analysis, we have to declare the following facts:
All the findings presented in this section refer to the “biased” population sample of our survey:
Academics and researchers that are probably not the average users of big data applications.
The generalization of findings and their interpretation must consider the previous fact.
Beyond the previous two statements, the contribution of our study remains important: It is one of
the first studies, that integrates social sciences and information systems research in the context of
measuring the social impact of big data.
We do not intend to discuss advanced statistics in this survey, but only descriptive statistics. Our
intention is to provide interpretations of the main findings and to use these for the development
of a global social impact of big data research study.
In this section we will present the main findings and their interpretation for the context of our
research. As it was communicated in the previous section, we focus on the quantitative analysis of our
survey. The presentation of key facts of our research will be accompanied by interpretations relevant
to the key research objectives, clarified in Section 3. In Section 5, we provide an additional discussion.
We start with an overview of the demographics of our survey.
4.1. Demographics
Our survey has a critical objective to understand the social aspects of big data and to interpret
and measure the integration of social sciences and big data research.
The questionnaire used is available in Appendix A. We deployed SurveyMonkey software and
we targeted users of applications, also familiar with the use of social networks. This survey is used
as a pilot survey, since we are planning a global big data and social impact research to run in 2020.
We circulated our survey to students and academics in universities of our scientific networks and we
Sustainability 2019,11, 5067 8 of 18
received, within three months from July to October 2018, 108 responses. In Tables 13we summarize
the main demographics data from our study. In total 108 respondents; academics, researchers and
students in management, international business, social sciences and information systems, in age
clusters from 18 to 70 years old (Table 2). We admit that the sample is biased, since it is constituted by
academics and researchers which are familiar with big data and analytics research. In the preliminary
study, this special feature of responders was needed. We also have to declare that the generalization of
the findings of this preliminary research study, should be done within the context of this limitation.
Our purpose is to use the key findings of this research in order to populate a new research tool that
will target broader clusters of users of big data applications.
Table 1. Age of responders.
Answer Choices. Responses Numerical
Under 18 0.00% 0
18–24 3.74% 4
25–34 18.69% 20
35–44 38.32% 41
45–54 26.17% 28
55–64 11.21% 12
65+1.87% 2
Table 2. Discipline of responders.
Answer Choices Responses Numerical
Social Sciences 32.67% 33
Sciences (including Computer Science, Engineering) 67.33% 68
Total 101
Table 3. Awareness of big data concept by responders.
Answer Choices Responses Numerical
Yes 96.26% 103
No 3.74% 4
Total 107
Two thirds of the respondents were from a science discipline and one third from the social sciences.
The balance is achieved since some respondents have joint expertise. Most of them were junior or
experienced researchers in domains related to management, international business, social sciences and
information systems. In Table 2we summarize the relevant information.
Another key characteristic of our sample is that the vast majority or participants expressed their
awareness about big data. Almost 97% of the responders claimed that they were aware of the big
data phenomenon. This is important, also as a finding of our research, because big data for several
communities is considered as an information system, or computer science research domain, but it
seems that also social scientists are quite aware of it (Table 3).
In the next section of our survey, and in our relevant research model, we are interested in
understanding the exposure of our responders to big data applications as well as their concerns and
perceived value. A key motivation in our research is to understand the degree to which users of big
data applications have concerns or feel ambiguity or danger in terms of trust or privacy.
Sustainability 2019,11, 5067 9 of 18
4.2. Use of Big Data Applications and Perceived Value
The transparency of big data applications and their ubiquitous nature means that several times
users of applications, or services that deploy big data, use them as black boxes. They do not care about
the computational aspects of the information model of the application, but rather they want to enjoy
the service. In our survey we discovered that 55% of applications users, have a deep knowledge of the
big data applications that they use, that collect and aggregate data, which are stored in a distant server
(Table 4). Also 45% of participants said that they are not users of big data applications, which also
proves that currently, there are many people that do not intend to use advanced big data services. The
question for sure is, how many of them dislike the use of smart cities services because they’re afraid of
the violation of their privacy or for other kinds of personal concerns. As a next question, we also focus
on this.
Table 4. Do responders use big data services?
Answer Choices Responses Numerical
Yes 55.24% 58
No 44.76% 47
Total 105
Given the key findings that most of the participants of our survey are aware of big data applications
and phenomenon, and also that more than half of the respondents are using big data applications
extensively in their lives for various purposes, it is quite challenging to investigate, the perceived
value they attach to the use of these applications and if it is relevant to their “quality of life” or just
a “contribution” to their expectations and perceptions. While it is hard, even from a scientific way,
or a statistical “correct” approach, to measure this value, we deployed a heuristic rule/approach. We
asked our participants on a scale from 0–100 to attach a “numerical value” to the value of big data in
their lives.
The result is summarized in Table 5. The average rating of all the responders for the value of big
data in their lives is 66 out of 100. This numerical value seems to be overall “positive” in the sense that
most responders attach a value greater than the average in the spectrum of low (0) to high (100) values.
It is also evident from this value that responders seem to be skeptical about some aspects of big data.
Thus, we need to understand the main concerns of users, specifically the features of big data that make
them worried or concerned.
Table 5. Value of big data in the lives of the responders.
Answer Choices Average Number Total Number
66 6.964
Total Respondents 106
Table 6, below, shows one of the most interesting findings of our survey, and deserves a more
detailed analysis. Given the overall, rather high score (66 out of 100), for the perceived value of big data
as provided by our respondents, we tried to understand some qualitative aspects of this positive eect.
Sustainability 2019,11, 5067 10 of 18
Table 6.
How responders perceive the value of big data. Personal ratings on the following statements.
1 (Totally
Disagree)
2 (Rather
Disagree) 3 (Neutral) 4 (Somehow
Agree)
5 (Fully
Agree)
Weighted
Average
Big Data allow me to enjoy
personalized services 2.83% 2.83% 21.7% 49.06% 23.58% 3.88
Big Data oer me access to
unique services 0.94% 3.77% 29.25% 47.17% 18.87% 3.79
Big Data enabled services, save
time for me and eort 1.90% 2.86% 20.00% 51.43% 23.81% 3.92
Big Data is about social security 13.46% 29.81% 30.77% 17.31% 8.65% 2.78
Big data protect my privacy 27.18% 41.75% 25.24% 4.85% 0.97% 2.11
Big Data promote the collective
intelligence and this has an
impact in my daily life
3.81% 2.86% 18.10% 53.33% 21.90% 3.87
Big Data promote interoperability
of services worldwide—thus i
enjoy integrated services
1.90% 6.67% 25.71% 48.57% 17.14% 3.72
From the answers of our sample, we found that one of the key features of big data applications, is
that they save time and eort for their users. Thus, developers of big data applications or designers of
smart cities services must know that users would be happier to use their applications if they realized
that they would be saving time. The next most important value components, according to our findings
are the interoperability that big data applications oer to users, irrelevant of country or place. Users
like to enjoy the same services, worldwide, with the same quality and transparency. This is also one
more extremely interesting finding. If users, want to enjoy mobility, and in parallel to have access to
the same services, then the big data research community and industry must promote this ecosystem of
services worldwide. From the other side, this request and wish of users, needs to comply with several
local and/or global policy making requirements.
Uniqueness of services is another critical value component of big data applications. Users understand
that several services that are big data enabled, are unique, so they are somehow happy to use them.
From this perspective, another key characteristic of big data applications is their innovative nature.
Users are happy to use innovative services, that save them time in their lives, and can be enjoyed
locally or globally with the same, high quality standard. This also means that big data industry should
be always in a progressive, evolutionary process for the launch of novel services and innovations to
the market.
Personalization, also seems to be valued by our responders. Users understand that most big data
applications exploit their data for the enhancement of their personal experience. For this finding, there
is also a side eect. Users recognize that big data applications challenge the protection of their personal
data. Somehow a compromise between user experience and privacy is understood by our responders.
Another important finding of our survey is also that users recognize that big data applications
somehow aggregate the collective intelligence of humans and potentially this could improve the quality
of life. A direct interpretation of this finding is that some big data applications must certainly promote
collective intelligence, but at the same time they must secure the trust, and the protection of privacy.
Finally, in Table 6, one more user perception is recorded. There is a rather neutral understanding
that big data research can potentially promote also social security. With the bold debate on social
media, fake news, fake profiles, social networks, analytics scandals, it seems that social security is a big
theme for social security and currently users are not convinced for big data contribution.
These key findings permit several interpretations for further investigation in future studies:
If citizens and users of big data application recognize that several big data applications save time
and eort for them, then the next research question is what is the cost they are willing to pay
Sustainability 2019,11, 5067 11 of 18
for their use, in terms of money or indirect costs, for example partial loss of their privacy, or
agreement from their side to oer their personal data under specific conditions.
Also, if users are interested in personalization of services, the next research question is which are
the clusters that categorize dierent users to dierent clusters, and how happy would they be for
such personalization if greater openness and access to their personal data is required.
The finding related to the uniqueness of services is also critical. If users recognize that some
big data are unique, then the next question is how can they resist to the necessity to share their
personal data with such applications. For the big data industry, how easy it is to keep a limit to
the penetration of sensitive personal data for the parametrization of their services to dierent
users features?
The anticipation of interoperability of big data services across cultures and nations also needs to
be understood in a social context. Recent examples of cool applications like FaceApp, prove the
capacity of big data applications to generate spontaneously huge data bases of critical personal
data e.g., faces.
In the next section we present a third level of analysis for our survey with an emphasis on the
potential social impact of big data. One of the key assumptions of our research model is the following:
If citizens use big data applications and if they attach a positive value to their behavior, then the
next step is to understand if the individual behavior also have some social contracts and positive
implications for the society. In our approach this is defined as the social impact of big data.
4.3. Social Value of Big Data
In our modern, complicated social environment, in order to respond to social challenges, we have
to understand them. In our survey, with its given limitations and limited reach, we made a first eort
to record the key societal challenges of our times. Given the focus of our research on the social impact
of the big data, the direct connection between these concepts is the capacity of big data applications to
promote bold actions or solutions to societal challenges or problems. In Table 7, we summarize the
outcome of our eort. According to our survey, the top five societal challenges of our time are:
Security
Socially inclusive economic growth
Access to education/quality of education
Equal opportunities for all
Job opportunities
Table 7. The main societal challenges of our times based on the own beliefs of responders.
Answer Choices Responses Numerical
security 74.77% 80
social inclusive economic growth 48.60% 52
equal opportunities to all 42.99% 46
fair justice 34.58% 37
poverty 31.78% 34
depression 14.02% 15
education quality 54.21% 58
Job opportunities 39.25% 42
happiness 27.10% 29
Total 107
Sustainability 2019,11, 5067 12 of 18
If we accept, as a working hypothesis that our responders reflect a greater population, then a key
point for our future research is to analyze the social impact of big data research in terms of its capacity
to promote sustainable goals related to the societal challenges reflected in Table 7. For example, a direct
interpretation could be the following:
Can big data applications enhance the capacity of people to have access to high quality open
education? (Can we develop a big data learning platform to oer free, open, personalized training
modules to individuals that will enhance their skills and competencies?)
Can we design big data enabled, advanced, sophisticated services that promote the feeling of
security in modern societies? (e.g., can we build big data enabled antiterrorist detection systems
over social media?)
Is there a way to exploit big data research in order to promote socially inclusive economic growth
by defining, for example, new markets, or new data-intensive industries or innovations?
Can we deploy big data research in order to promote new and better jobs in our societies? Is there,
for example, any possibility to “measure talents”, to codify skills and competencies and to match
job profiles with candidates etc.?
Can we integrate big data research with sophisticated computational methods like artificial
intelligence and machine learning in order to investigate “personalities” and personal habits that
are linked to critical social challenges e.g., antiterrorism detection, harassment etc.?
We understand, and we admit that this is an extremely significant objective that goes beyond the
scope and the depth of this limited survey. Nevertheless, it oers a very good starting point for further
analysis and integration to a forthcoming greater research in terms of scope and coverage. It is also a
good context for skepticism and interdisciplinary understanding. Somehow, big data research needs
new contributions from the social sciences that have for years developed research tools and theories
for understanding human behavior and personality. In our understanding in the context of the virtual
world, internet and social media, there are many more things to be done in this direction.
In a next step, we tried to cross-check and to integrate the perception of our responders with one
more question, related to the value and the concerns of users about big data use. Our intention, from a
research point of view, is to build a theoretical framework to be tested in a future research about the
connection between the social impact of big data, concerns and intention of use. This will be presented
in Section 5of our paper.
In Table 8, the findings reflect key aspects of users concerns about big data use and their associated
added value.
Table 8. How responders perceive the value of big data.
1 (Totally
Disagree)
2 (Rather
Disagree) 3 (Neutral) 4 (Somehow
Agree)
5 (Fully
Agree)
Weighted
Average
Big data applications violate my
privacy 3.74% 11.21% 26.17% 45.79% 13.08% 3.53
Big data services require
advanced computer knowledge 0.93% 21.50% 12.15% 46.73% 18.69% 3.61
Big data services promote the
gap between computer literated
and non computer litarated
0.94% 18.87% 25.47% 39.62% 15.09% 3.49
I somehow feel that my data are
used for uknown purposes 1.87% 5.61% 8.41% 37.38% 46.73% 4.21
Big data services do not use
transparent methods for
processing my data
1.87% 6.54% 14.02% 41.12% 36.45% 4.04
I do not like that companies use
my data for getting customer
insights about me
0.93% 10.28% 19.63% 28.97% 40.19% 3.97
Sustainability 2019,11, 5067 13 of 18
The main concern is related to uncertainty. Users of big data applications have a fear that their data
will be used for unknown purposes. From a policy making point of view it is an absolute requirement
that regulatory and legislative frameworks provide protection to users. The feeling of our responders
is that currently there is a significant gap in this area. In close relevance to this finding, users also
believe that big data applications do not use transparent methods for data processing. Some initiatives,
like the General Data Protection Regulation (GDPR) are headed in the right direction. Users must have
the right to be informed about who uses their data, for which purposes, and under which methods.
One more bold finding of our survey is that our responders are also not happy that companies use big
data research in order to gain better customer insights about them. This is also another huge theme for
further research, on which we will elaborate further in the conclusion of our research study.
Some interpretations of these research findings include the following:
What is the role of governmental authorities and supervising bodies towards the design,
implementation and well-functioning of big data awareness policies related to privacy and
data protection?
How can users and citizens have an increased awareness about the processing methods of their
data. Is it possible for them to have access to an IT-service where all the “users” of their personal
data appear and are analyzed further?
Concerning the social impact of the big data research the key question is, is there a fair-justice
approach in which social bodies or organizations can supervise and rate “behavioral” oriented big
data of individuals or groups. Answers to these questions are not obvious. They need significant
social agreement and consultation.
Concerning the compromise of sharing personal data for the use of unique big data applications,
another critical question is how can users resist in using unique services without letting third
party organizations gain significant insights into their personalities?
Also, if we promote it as socially-fair to oer supervising organizations access to personal data,
then does the individual level of decision refer to the degree of declining such services? We have
to investigate a rather increasing population of users that deny to use big data applications due to
this fear.
The respondents answers to our questions related to their reluctance to oer customer insights to
companies about themselves guided the next part of our survey. We need to understand how people
and users of big data applications interpret the new trend in informatics and social computing about
analytics research. For this purpose, we attached 3 more questions that are summarized in Tables 911.
Table 9. Are responders aware about the concept of data analytics?
Answer Choices Responses Numerical
Not at all familiar 1.87% 2
Not so familiar 9.35% 10
Somewhat familiar 30.84% 33
Very familiar 39.25% 42
Extremely familiar 18.69% 20
Total 107
Nine out of ten respondents in our sample are familiar with the analytics concept and consider
analytics to be potentially beneficial for users. In a similar approach, as we did for big data research,
our responders attach a value of 76 out of 100 to the value of analytics. Given the limitation of our
numerical approach to value measuring, this is an indication that users consider analytics to be of
greater value by ten units, than big data (76 versus 66 out of 100). This indicates that users indirectly
attach and associate increased value to advance decision-making capabilities.
Sustainability 2019,11, 5067 14 of 18
Table 10. What responders think about data analytics.
Answer Choices Responses Numerical
Not at all important 0% 0
Not so important 0.93% 1
Somewhat important 16.82% 18
Very important 50.47% 54
Extremely important 31.76% 34
Total 107
Table 11.
How responders rate the value of data analytics for social purposes (related to the capacity of
data analytics to provide insights to social problems or challenges).
Answer Choices Average Number Total Number
76 8.172
Total Respondents 107
Given the rather high value attached by our responders, to the impact of data analytics research
for social purposes, we asked our participants to clarify the key aspects of analytics research that
have increased social impact. In Table 12, we present these responses. The most interesting finding is
that our sample states that analytics enhance social aware services. According to our responders, the
analysis and organized presentation of analytics (e.g., key performance indicators, or visual overviews
of big data or advanced data mining methods) can lead significant socially aware responses to social
problems. An interpretation of this finding is that designers of IT/IS services and social scientists have
to collaborate to deliver fully functional big data and analytics platforms for social issues and problems.
Table 12. How responders perceive the value of data analytics for social impact.
1 (Totally
Disagree)
2 (Rather
Disagree) 3 (Neutral) 4 (Somehow
Agree)
5 (Fully
Agree)
Weighted
Average
Data Analytics provide usefull
insights for understanding
critical social
problems/challenges
0.00% 1.89% 10.38% 58.49% 29.25% 4.15
Data Analytics techniques enable
social-aware services 0.00% 2.83% 14.15% 59.43% 23.58% 4.04
Data Analytics promote solutions
to social problems 0.95% 4.76% 28.57% 53.33% 12.38% 3.71
Data Analytics enhance social
responsibility 1.89% 7.55% 41.51% 38.68% 10.38% 3.48
Data Analytics support civic
engagement 0.00% 7.55% 40.57% 42.45% 9.43% 3.54
Data Analytics help the
recognition and monitoring of
new social problems
0.94% 3.77% 14.15% 55.66% 25.47% 4.01
Data analytics promote Social
coherence 0.00% 10.38% 43.40% 37.74% 8.49% 3.44
In close relevance, our survey also concludes that based on the responses of participants, analytics
can also help to clarify novel social problems and issues, not easily diagnosed with other methods.
This finding can initiate, for sure, a social dialogue and a policy-driven open participatory procedure.
While our responders recognize the potential social impact of analytics research, more than one
third (36%) of them still state that they are still not ready for sharing their data for social aware services
Sustainability 2019,11, 5067 15 of 18
(Table 13). This proves that there is a great distance to be covered until a new era of social aware
services commences. From the other side, the combined big data and analytics research and integration
for social purposes increases the perceived value to 70 points (Table 14). Finally, 98% of the responders
are confident that in the next few years social aware big data services will be a key trend (Table 15).
This proves that most users nowadays feel that somehow their data are processed for such purposes
and that soon they will be forced, or they will be happier to use such services.
Table 13.
Would respondents be happy to share their data in order to promote social aware big
data services?
Answer Choices Responses Numerical
Yes 63.21% 67
No 36.79% 39
Total 106
Table 14.
Responders rating of the impact of big data and analytics research to society (negative to
positive).
Answer Choices Average Number Total Number
70 7.360
Total Respondents 105
Table 15.
Do respondents think that big data driven services for social impact will be a trend in the
near future?
Answer Choices Responses Numerical
Yes 96.11% 104
No 1.89% 2
Total 106
5. Key Implications and Future Research Directions
The research problem under study, is complicated. The contribution of our research, within the
given limitations, is significant: It provides interesting insights for the perception of big data users for
the added value of these services to their lives and the potential social impact. We summarize, in the
next paragraphs, the key response of our study to the research objectives we set out to address. We
also elaborate on future research directions and policy making implications related to sustainability.
Research objective 1:
To understand the degree of awareness of big data research and the actual
use of big data applications by our responders.
Key finding 1: Based on the qualitative analysis and interpretation of respondents’ answers, we
conclude that most responders, irrelevant if they are from a sciences or social sciences background,
are aware of big data research. Of them, 55% also consciously use big data services due to
unique features.
Further research direction 1: It is interesting to investigate the people that do not use big data
services, and under which conditions they would use big data applications for social purposes.
Policy making and sustainability implication: The development of a policy aware regulation
framework to deal with aordances and concerns of users of big data services.
Research objective 2:
To analyze, the perceived value of big data as it is realized by our responders.
Key finding 2: The majority of responders recognize the added value of big data services to their
lives. They understand and value that applications save them time, and improve their life in some
Sustainability 2019,11, 5067 16 of 18
respects. Furthermore, they recognize that most big data services oer unique services that can be
enjoyed with the compromise of sharing personal data. Additionally, they feel that somehow big
data applications aggregate collective wisdom and thus they improve the quality of their lives.
Further research direction 2: The perceptions of individuals about the added value of big data
services and big data research needs to be codified. An ontological approach to value components
of big data should be promoted further. This will lead to a new measurement approach capable of
codifying various aspects of value related to personality, ethics, social responsibility, social justice,
open democracy etc.
Policy making and sustainability implication 2: The evolution of big data research, from a policy
making perspective needs to set all the required regulations about trust, transparency, security,
anonymity as well as to protect the digital self of individuals. From a social perspective, it is
nevessary to integrate social insights in a well-defined context for socially inclusive economic and
social growth. We need policies that will enhance the reusability and exploitation of big data for
social purposes.
Research objective 3:
To understand the main concerns of users of big data services, in regards to
their use, as well as their aordances towards greater deployment of such applications.
Key finding 3: Most of the responders recognize that there are three critical obstacles to big data
applications: The unknown information processing methods of their data, and their purposes,
as well as the fact that most of the time they consider that the ultimate destination of big data
research is to gain more customer insights about them.
Further research direction 3: Given the high degree of concerns of individuals about the use of
their data, it is required to understand, through further research, the compromise between the
shift of their willingness to share their data and to enjoy better services that improve the quality of
their lives.
Policy making and sustainability implication: One of the most significant directions for the future
would be to develop policies that will protect individuals from being the victims of extreme
third-party insights about their habits, preferences and social behavior. In a next step, there must
be a sustainability regulation, through which all big data services should comply with general
requirements and this must also be included in inform consent statements of user agreements.
Research objective 4:
To clarify the impact of big data in the lives of individuals and to
their perceptions.
Key finding 4: The overall understanding of this survey is, that users have a positive impression
of the value of big data research in their lives, and a rather positive perception. There is, however,
a critical mass of users that are not willing to use big data services due to their concerns and fear
that their data will be used for unknown purposes. The social impact of big data is also perceived
as a potential under specific requirements.
Further research direction 4: The measurement of the individual and social impact of big data will
require advanced heuristic methods, and a lot of support is needed from the social sciences to
adopt psychological, personality, mental, cognitive and other models to incorporate metrics and
key performance indicators. It is also important to adopt studies and social research about the
impact of big data applications in addiction or other psychometric directions.
Policy making and sustainability implication: We need to develop more advanced social policies
and regulations for the interconnection of information and communication technology with
social sciences. We must recognize that all the modern big data applications are setting a huge
socio-technical system where humans are actors and their interactions are more complicated
than ever.
Sustainability 2019,11, 5067 17 of 18
6. Conclusions
This paper calls for a revisit into the insights related to the use of big data research from individuals.
The main point of departure is that users have an intrinsic motivation to protect their privacy and
ownership of their data, but from the other side they are also happy to use unique services that improve
the quality of their lives. In this required compromise, the contribution of our research is that it
provides evidence from an empirical survey with objective interpretation of findings.
The research, research findings and discussion presented in this paper seek to initiate a debate on
the diusion and impact of big data on our lives. The ultimate contribution of this research work is the
agreement of participants that the added value of big data towards responsive social aware services to
critical social problems, is just the beginning of the journey and not its end. In the next few years this
line of research will have a great impact on the development of a brand new research area related to
social aware big data enabled social services and analytics.
Author Contributions:
M.D.L. and A.V. contributed equally to the design, implementation, research and analysis
of data and the delivery of the main findings. Both coauthors contributed equally to all phases of this research.
Acknowledgments:
The authors would like to thank Eat University in Jeddah, Saudi Arabia for funding the
research reported in this paper through the Research and Consultancy Institute.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A. The Pilot Survey—Questionnaire
The research tool-questionnaire for our survey is available through this web link: https://www.
surveymonkey.com/r/LY2R3XV.
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... It has been argued that organizations cannot rely solely on the open market as a source of sustainable competitive advantage (Barney, 1986(Barney, , 1991. However, previous studies suggest that if organizations neglect the sustainability of Big Data Analytics and Capabilities (BDAC), the social impact will be temporary (Lytras & Visvizi, 2019). Instead, according to the resourcebased view, organizations must create such an advantage from their resources, which should be rare, imperfectly imitable, and non-substitutable. ...
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Purpose The purpose of this paper is to rethink the focus of the smart cities debate and to open it to policymaking and strategy considerations. To this end, the origins of what is termed normative bias in smart cities research are identified and a case made for a holistic, scalable and human-centred smart cities research agenda. Applicable across the micro, mezzo and macro levels of the context in which smart cities develop, this research agenda remains sensitive to the limitations and enablers inherent in these contexts. Policymaking and strategy consideration are incorporated in the agenda this paper advances, thus creating the prospect of bridging the normative and the empirical in smart cities research. Design/methodology/approach This paper queries the smart cities debate and, by reference to megacities research, argues that the smart city remains an overly normatively laden concept frequently discussed in separation from the broader socio-political and economic contexts in which it is embedded. By focusing on what is termed the normative bias of smart cities research, this paper introduces the nested clusters model. By advocating the inclusion of policymaking and strategy considerations in the smart cities debate, a case is made for a holistic, scalable and human-centred smart cities agenda focused, on the one hand, on individuals and citizens inhabiting smart cities and, on the other hand, on interdependencies that unfold between a given smart city and the context in which it is embedded. Findings This paper delineates the research focus and scope of the megacities and smart cities debates respectively. It locates the origins of normative bias inherent in smart cities research and, by making a case for holistic, scalable and human-centred smart cities research, suggests ways of bypassing that bias. It is argued that smart cities research has the potential of contributing to research on megacities (smart megacities and clusters), cities (smart cities) and villages (smart villages). The notions of policymaking and strategy, and ultimately of governance, are brought into the spotlight. Against this backdrop, it is argued that smart cities research needs to be based on real tangible experiences of individuals inhabiting rural and urban space and that it also needs to mirror and feed into policy-design and policymaking processes. Research limitations/implications The paper stresses the need to explore the question of how the specific contexts in which cities/urban areas are located influence those cities/urban areas’ growth and development strategies. It also postulates new avenues of inter and multidisciplinary research geared toward building bridges between the normative and the empirical in the smart cities debate. More research is needed to advance these imperatives at the micro, mezzo and macro levels. Practical implications By highlighting the connection, relatively under-represented in the literature, between the normative and the empirical in smart cities research, this paper encourages a more structured debate between academia and policymakers focused on the sustainable development of cities/urban areas. In doing so, it also advocates policies and strategies conducive to strengthening individuals’/citizens’ ability to benefit from and contribute to smart cities development, thereby making them sustainable. Social implications This paper makes a case for pragmatic and demand-driven smart cities research, i.e. based on the frequently very basic needs of individuals and citizens inhabiting not only urban but also rural areas. It highlights the role of basic infrastructure as the key enabler/inhibitor of information and communication technology-enhanced services. The nested clusters model introduced in this paper suggests that an intimate connection exists between individuals’ well-being, their active civic engagement and smart cities sustainability. Originality/value This paper delineates the relationship between megacities and smart cities research. It identifies the sources of what is termed normative bias in smart cities research. To address the implications of that bias, a nested clusters model for smart cities is introduced, i.e. a conceptual framework that allows us to redraw the debate on smart cities and establish a functional connection between the array of normatively laden ideas of what a smart city could be and what is feasible, and under which conditions at the policymaking level.