Web 3.0: a society scenario analysis
Author: Hendrik van der Kruka
Student number: 921023483210
Course: MCB 80436
Study: Management- Economics-, and consumer studies
Major: management studies
Supervisor: Dr. Arnout Fischer
Keywords:
Web 3.0
Semantic web
Internet of Things (IoT)
Big Data
Cloud
Artificial Intelligence (AI)
Scenario analysis
Abstract:
Web 3.0 (the semantic web) is the new phase in internet application. Web 1.0 is characterized by static web browsers.
Web 2.0 added social networks, enabling human-to-human
communication. And Web 3.0 evolves the web to human-toobject, object-to-human, and object-to-object communication. The previous Webs changed the societal status quo and
Web 3.0 will become even more disrupting.
This study adds to current literature in three ways: the first
contribution is that this paper stresses the interrelation of
Web 3.0 enabling technologies. These technologies are; the
Internet of Things (IoT), Cloud technology, Big Data, and Artificial Intelligence.
The second contribution is a scenario analysis based on literature. In these scenarios, Web 3.0 applications may opportunistically be exploited by various actors. The actors are able
to take advantage of current and future Web 3.0 technologies are; governments, communities, and ICT firms.
The last contribution is a survey among students that provides insight of favoritism of the scenario in which commons
are the leading actors. The survey indicates that even though
a world lead by commons is preferred, specific power distribution is still more leaning towards government regulation.
A: Marketing and Consumer behavior Group, School of Social Sciences, Wageningen University (The Netherlands)
Content
1.
Introduction ....................................................................................................................................................... 4
2.
Current practices.............................................................................................................................................. 6
2.1 Towards web 3.0 ........................................................................................................................................... 6
2.2 Current job-market ...................................................................................................................................... 7
3.
Defining core technologies of Web 3.0 .................................................................................................. 9
3.1 Internet of Things ......................................................................................................................................... 9
3.2 Cloud ................................................................................................................................................................. 9
3.3 Big data........................................................................................................................................................... 10
3.4 Artificial intelligence .................................................................................................................................. 11
3.5 The core technologies of Web 3.0 ....................................................................................................... 11
4.
Impact web 3.0 – Scenario analysis ........................................................................................................ 13
4.1 General impact Web 3.0 ........................................................................................................................... 14
4.2 The commons (1) ........................................................................................................................................ 14
4.3 Capitalism (2)................................................................................................................................................ 15
4.4 Government (3)............................................................................................................................................ 17
4.5 No dominant actor (4) .............................................................................................................................. 17
4.5.1 – balanced equilibrium (4.1) ................................................................................................ 18
4.5.2 – continuous power struggle / chaos (4.2) ........................................................................... 18
5.
Empirical study ............................................................................................................................................... 19
5.1 Participants.................................................................................................................................................... 19
5.2 Instrumentation ........................................................................................................................................... 19
6.
Results................................................................................................................................................................ 22
6.1 SVSS ................................................................................................................................................................. 22
6.2 ATTITUDE ....................................................................................................................................................... 22
6.3 POWER DISTRIBUTION ............................................................................................................................. 23
7.
Discussion......................................................................................................................................................... 26
7.1 General findings .......................................................................................................................................... 26
7.2 Added value of research .......................................................................................................................... 26
7.3 Limitations and further research ........................................................................................................... 28
References: ................................................................................................................................................................ 30
Appendix I – SVSS Questions ............................................................................................................................. 38
Appendix II – Scenario descriptions ................................................................................................................ 39
General introduction......................................................................................................................................... 39
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Scenario I............................................................................................................................................................... 39
Scenario II ............................................................................................................................................................. 39
Scenario III ............................................................................................................................................................ 40
Scenario IV ............................................................................................................................................................ 40
Appendix III – Questions attitude, power distribution, and preferred governance structure ... 42
Appendix IV – values derived from SVSS ...................................................................................................... 44
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1. Introduction
Web 3.0 (also called ‘the semantic web’) is the new phase in internet application. Web 1.0 is
characterized by static web browsers. Web 2.0 added social networks, enabling human-tohuman communication. And Web 3.0 evolves the web to human-to-object, object-to-human,
and object-to-object communication.
The game changing opportunity of Web 3.0 technologies, in which both people and objects
will generate data to provide services, gets widely acknowledged by businesses (Verizon
business, 2010; Manyika et al., 2013; ITU, 2015), governments (Schwab and Samans, 2016;
Linton and Jaokar, 2014; World Economic Forum, 2012), scientists (Rudman and Bruwer, 2016;
Kreps and Kimppa, 2015; Fuchs et al., 2010), and popular authors (e.g. Rifkin, 2014).
However, instead of directing and acting upon the new wave of technology evolvement,
most people just wait and see as they have become victims of ‘the “next new thing” fatigue’
(Manyika et al., 2013, p. iii).
The services created by the IoT will anticipate the user’s needs (Miorandi et al. 2012), which
will have profound impact. Current market disruptive developments are found in behavioral
interpretation systems, telemedicine, smart societal interaction, smart business management
and marketing, and automated public security (Alvi et al., 2015).
To make the previous statement more tangible, think of the newest technologies that enable
people to monitor and control their home appliances from their smart phones (domotics).
Homes can communicate with cars in order to know that the owner(s) arrives at home. Using
ambient intelligence (Alcaniz and Rey, 2005), the home can change the temperature, open
the curtains, start the coffee machine, put the lights on, and even turn up the oven.
Using the same line of reasoning, by automatically tracking everything, cups will be able to
measure your liquid intake (e.g. by myvessyl.com). This information can instantly be shared
with your doctor, dietician, gym, or even your mother, so they can act (e.g. nudge or demand) for you to change your (unhealthy) daily patterns by a certain threshold of intake (e.g.
automatically locking certain cabinets).
These new practices of technology will create ease and convenience at the cost of giving up
some personal independence. For businesses this is seen as a profitable and acceptable
tradeoff as it serves the overall mantra of the IoT to “improve the quality of life” (Botta et al.,
2016, p.14; Manyika et al., 2015, p. 8).
Current literature focuses on ICT technologies from a technological or application point-ofview in order to create flawless products and services, with perfect quality of service (QOS)
(e.g. no jitter, no delays, etc.), or perfect quality of experience (QOE) (Koreshoff et al., 2013;
Whitmore et al., 2015). For example, Yan et al. (2014) have conducted a comprehensive survey on trust management within the IoT and they deduct many issues that have yet to be
resolved.
This thesis approaches the technologies from a social point-of-view. This means that aggregated technologies will be discussed (Internet of Things, Big data, Clouds, artificial intelligence, and 5G), and not the underlying enabling technologies (e.g. Wireless sensor networks,
Radio frequency identification device, IPv6, Low Power wireless personal area networks, etc.).
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Different sources stemming from multiple disciplines are reviewed, as increased complexity
cannot be adequately understood from a single disciplinary perspective (Chang et al., 2014).1
According to Schwab and Samans (2016): “[t]he current technological revolution need not
become a race between humans and machines but rather an opportunity for work to truly
become a channel through which people recognize their full potential. To ensure that we
achieve this vision, we must become more specific and much faster in understanding the
changes underway and cognizant of our collective responsibility to lead our businesses and
communities through this transformative moment.” (p. vi).
However, to the best of the author’s knowledge no attempts have been made to holistically
assess the societal impact of Web 3.0 over an extended period of time.
In order to assess societal impact of Web 3.0, this thesis answers the following three questions:
- What are the enabling technologies of Web 3.0?
- What societal impact will Web 3.0 applications induce in the long run?
- How is this societal impact evaluated?
This thesis glimpses to the future to see how Web 3.0 could affect society. To do this, the rest
of this thesis is ordered the following. Section 2 focuses on Web 1.0, Web 2.0, and examines
its current impact on the job market. Section 3 motivates the core technologies of Web 3.0.
Section 4 provides four scenarios of the impact of Web 3.0 based on literature. Section 5
provides a method to assess the preferences of students towards the different scenarios using a survey. Section 6 provides the results of a survey among students. Section 7 concludes
and discusses the findings of this thesis.
1
Chang et al. (2014) mean the complexity of big data sets, but as the impact of current and upcoming
technologies will be that disruptive (as argued in this paper), that this could be interpreted as being a
wicked problem (Kreuter et al., 2004), therefore the same kind of reasoning counts here.
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2. Current practices
In order to assess the social impact of futuristic applications (products and services), it is important to comprehend the past. Section 2.1 describes the advent of Web 1.0 and Web 2.0.
Section 2.2 reviews the impact of the webs on the job market.
2.1 Towards web 3.0
“Despite the challenges and risks, the opportunities available to better serve individuals in
emerging markets should outweigh these risks.” (World economic Forum, 2012, p. 7, 8).
Web 1.0 and Web 2.0 indicate different transitional stages of the internet. Web 1.0 is the first
big expansion of publishing since the printing press, in the form of HTML-based web applications on static and passive read-only platforms (Kreps and Kimppa, 2015). The radical innovation resulted in a market bubble, which popped in 2001. The dot-com bubble-burst is said to
be the conceiver of a dynamic set of innovations that led to the revival of the web in its
transformed form; Web 2.0 (Darwish and Lakhtaria, 2011).
Web 2.0 transformed the web to a read-write environment with as most distinct feature usergenerated content (Kreps and Kimppa, 2015), due to communication (Fuchs et al., 2010;
Kreps and Kimppa, 2015; Manovich, 2009) and interactive participation (Barassi, and Treré,
2012; Kumbhar et al. 2014).
Web 2.0 can be described as applications integrated into Web 1.0 to enrich user experience
(Kumbhar et al., 2014; Fuchs et al., 2010), by using a range of new programming languages
(Rudman, 2010). Since the webs are built as integral parts on top of each other it is hard to
investigate its distinctive usage, as described by Barassi and Treré (2012).
Web 2.0 is often characterized by social network sites (SNS) (Hajli, 2014), in which people can
join virtual networks. These sites and groups are used to create loads of data, which can be
exploited by businesses in better directing the need of the consumer and as marketing channels (Newman et al., 2016). Network-profiles recommendation systems (either content or
collaborative) can be used to anticipate the users’ wants and demands (cf. Lops et al., 2011).
Web 2.0 is used for an abundance of different practices. Some use the web as tool for selfenrichment, like posting for attention, to convince people, instead of using the web as discussion platform (Barassi and Treré, 2012). Others use the discussion platforms (e.g. blogs) to
reaffirm their own opinion with likeminded people (Lawrence et al., 2010).
Social media has a strong effect on trust, which has a positive effect on engaging consumers
in social interaction (Hajli, 2014). The different webs did not only change inter-human interaction it also changed the way in which businesses do business. Almost every company has a
free access web presence (Manovich, 2009), and as SNS have become a part of our day-today life, overall communication has become more people-centric (Kreps and Kimppa, 2015),
which empowers customers (Hajli, 2014) and contributes to social capital (Ellison et al., 2014).
The networked communities of Web 2.0 enhance collective intelligence (Fuchs et al., 2010). A
major contributor to this growth is the smart phone (Manovich, 2009).
Web 2.0 has increased service-oriented-computing, in which the service is a business function implemented in software (Papazoglou et al., 2007). There are two main types of services,
simple and composite, of which the latter builds upon multiple sources (Papazoglou et al.,
2007). That the composite services currently rising fast can be seen in the vast rise of Applica-
6
tion Programming Interfaces (API’s) (The Economist, 2014).
2.2 Current job-market
“Of all the grim statistics and stories accompanying the recent Great Recession and subsequent
recovery, those related to employment were the worst.” (Brynjolfsson and McAfee, 2012, p. 2).
With current technological progress a fourth wave of industrial revolution (Manyika et al.,
2013; Schwab and Saman, 2016) is upon us. The first revolution was based on steam, the second on electricity, the third on information technology (Datta, 2015), and upcoming revolution will be based on interconnectivity (Brynjolfsson and McAfee, 2012). This revolution will
manifest itself as Web 3.0. Web 3.0 will go beyond information technology practices of web
1.0 and 2.0 on user controlled devices such as laptops, tablets, and smartphones by interconnecting multiple devices to one another without required user intervention.
The current business environment of innovation and efficiency gains leads to practices of
continuously changing business models (Mitchell and Coles, 2004), which results in a pattern
of ongoing job losses to technology. Literature described in this section shows that the quantity of jobs is in decline and prospective work tells us that this trend will continue in the future.
Technology improvement is considered to benefit society, although there will always be
points of debate. For instance, average wealth and health have increased over the past 50
years, but relative inequality is rising (Deaton, 2013). Some previous technology developments raised discontinuity in the status quo (e.g. power of guilds, public concerns, unions),
resulting in riots and worse (extensive elaboration in Frey and Osborne, 2013).
Regarding the distribution of jobs, there is increasing evidence of job polarization. This
means that middle income jobs (routine/clerical jobs) are automated (Autor and Dorn, 2013;
Goos et al., 2008; Deaton, 2013; Jaimovich and Siu, 2012; Feng and Graetz, 2015; Manyika et
al., 2013). A survey of Cisco indicates that 50 percent of the existing manual operational processes can be automated (Noronha et al., 2014). The job exterminations during the last three
recessions (1991, 2001, and 2009) did not get replaced with the same amount in new jobs
(job-recovery), even though the recessions ended and the economy recovered (job-less recovery) (Jaimovich and Siu, 2012). Middle incomes suffer first due to relatively high labor and
training costs in relation to the investment in automatization (Feng and Graetz, 2015). This
results in more college degree educated people applying for labor intensive, low-educated
jobs (de-skilling), pushing the lowest-educated out of the market (Autor and Dorn, 2013).
Schwab and Samans (2016) describe that “workers in lower skilled roles, particularly in the
Office and Administrative and Manufacturing and Production job families, may find themselves caught up in a vicious cycle where low skills stability means they could face redundancy without significant re- and upskilling even while disruptive change may erode employers’
incentives and the business case for investing in such reskilling.” (p. 26). On top of that, it is
estimated that by 2020 more than a third of the desired core skill sets of most occupations
will consist of skills that are not yet seen as crucial to the job today (Schwab and Samans,
2016).
In the coming years, it is the prediction that computerization will mainly substitute the low-
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wage and low-skill jobs (Frey and Osborne, 2013). Autor (2015) finds that the high-wage jobs
(professional, technical and managerial occupations) are also slowing in growth. He does not
think that this is a direct effect of technological progress as there is no increase in software
investment. Reasons for stable or even decreasing high-wage jobs are (1) business cycle effects, such as the dot-com bubble and the collapse of the housing market, which effected
both the height of the wages and the investment in innovative capabilities, and (2) the rapid
globalization as it causes employment dislocation and import penetration (Autor, 2015). This
is, however, disputable as linear investment in technology does not indicate linear progress
of technology, as exemplified by the work of Nordhaus (2007). Hence, it is still possible that
in the future high-wage, high-educated jobs will become substituted by technology.
Sachs et al. (2015) estimate that technological progress will first increase output, but a decreasing demand in labor can reduce wages and consumption in the long run. Especially the
younger generation (new to labor market) is vulnerable to experience overall lower wellbeing as a result.
The most in-demand occupations or specialties in multiple industries did not exist ten or
even five years ago (Schwab and Samans, 2016) and current technological innovations have
the possibility to eliminate up to 47 percent of mainly low-wage jobs in the near future (Frey
and Osborne, 2013). With the emergence of Web 3.0 this 47 percent could be the beginning.
Overall, our current notion of work will change. And historically, with change in the work environment (e.g. industrial revolution, work participation by women) our societal structure will
change as well. The following section presents the technologies that can induce such change.
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3. Defining core technologies of Web 3.0
Web 3.0 and the Internet of Things (IoT) are often interchangeably used. This section deducts
the relation among the IoT, the Cloud, Big Data, and Artificial Intelligence (AI) of literature.
Moreover, it can reach its social potential (becoming Web 3.0 applications) by the implementation of the fifth generation (5G) infrastructure.
3.1 Internet of Things
The IoT is based on technological advances and visions of network ubiquity that are zealously being realized (ITU, 2005) and it has a global economic potential reaching the trillions
(Manyika et al. 2013; Noronha et al. 2014). Currently, many variations of the IoT are being
researched, such as; S(ocial)IoT (Atzori et al. 2012; Atzori et al. 2014b), IoM(ultimedia)T (Alvi et
al. 2015), S(ocial)/W(eb)oT (Mashal et al. 2015), IoI(ntelligent)T (Arsénio et al. 2014),
C(ognitive)IoT (Floris and Atzori, 2015), etc.. All of these variations, however, are used to distinguish among different aspects of the same technology. In this thesis the IoT (as being the
dominant terminology) is used to cover all of those variations.
The IoT will link the internet and the web to the physical world using objects with each a
unique IP. If everything is linked to the internet the IoT reaches its communication goal of
‘anytime, anywhere, anymedia’ (Zhou and Chao, 2011). The IoT will enable the expansion of
Web 2.0 social media applications by supporting it with additional services (Meerja and Almustafa, 2016). Many of these Web 2.0 social media application services will be aimed to
improve the functionality and satisfaction on current products, such as bikes, cars, chairs,
housing, or even leftovers. The services will provide platforms to either loan, lend, or sell the
products to people who are interested and nearby, as platforms to name or shame the products, events, services, etc..
The IoT consist of five layers, the (1) physical perception layer, (2) the network layer, (3) the
middleware layer, (4) the application layer, and (5) the business layer (Mashal et al., 2015).
Scholars (e.g. Sicari et al., 2016) are currently coping with issues regarding the five layers (e.g.
data integrity violation, packet sniffing, compromised keys, etc.), and how to use the five layers from a business perspective (e.g. Da Xu et al., 2014). Both topics are beyond the scope of
this thesis.
Data flowing from all kind of sensors through the five layers will provide the end-users (e.g.
businesses, scientists, consumers, etc.) with valuable information regarding the environment
of their interest, and enables them to (automatically) act (e.g. change current practices, activate actuators, go into action).
3.2 Cloud
The IoT cannot reach its full potential without the cloud (Alvi et al. 2015; Botta et al., 2016).
According to the national institute of standards and technology, “Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal
management effort or service provider interaction.” (Mell and Grance, 2011).
The Cloud consists of four layers; (1) the datacenter (hardware), (2) infrastructure, (3) platform, and (4) application (Botta et al., 2016). The combination of layers can be seen as a ma-
9
jor processor providing access to enormous amount of data and powerful distributed services to the end-user (Alvi et al. 2015). According to Newman et al. (2016), the reasons for the
late advent of the cloud was a slow integration of commoditization of virtualization technology, which is running the same software on multiple systems simultaneously. The second is
that due to a global improvement in bandwidth, the connections have become reliable
enough to ensure services (Newman et al., 2016).
Many businesses already adopted the Cloud by using a majority of software/services (e.g.
collaboration, email, payroll processing, HR applications, and customer relationship management) (Boggs et al. 2015). One of the most prominent examples that the Cloud is gaining
ground is the introduction of Windows 10, which encourages its users to adopt the Cloudbased office 365. This is in line with the anytime, anyplace, and anywhere ideal/mantra of the
IoT.
Important when combining the IoT with the Cloud, despite interoperability issues, is that the
Cloud is not a single entity. There are multiple types of Clouds, all owned and used by single,
or multiple stakeholders (Zhang et al. 2010; Botta et al., 2016). Of these types the smaller
data centers might be more cost effective (Zhang et al. 2010), which is currently known as the
Fog.
The Fog (or mini-Clouds, or Cloudlets) consists of smaller datacenters/processors located at
the edge of the internet (where the data is generated). The Fog is more adequate for direct
multimedia processing (Noronha et al., 2014), and reduces the necessity to store the data in
large centralized data centers (Cloud). Using the fog can empower the end-user to retain
more control over their data (Vaquero and Rodero-Merino, 2014).
The interest in the opportunities of the Fog is rising. However, since interconnectivity is the
core of both technologies (and the role of the Cloud in Web 3.0) we will use the common
term Cloud to address both the Cloud and the Fog .
3.3 Big data
The IoT will be one of the main sources of Big Data (Botta et al., 2016). “Big data is a term
that describes large volumes of high velocity, complex and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information.” (Mills et al., 2012).
Kaivo-oja et al. (2015) advocate the advent of Big Data as “the renaissance of knowledge in
decision-making” (p. 495), making Big Data analysis a cornerstone of many business opportunities. Current practices of Big Data in this regard are: predicting social behavior during
routine and chaotic situations, improving technology, to increase usefulness of product or
service to accommodate more users, and to prevent disasters and improve rescue procedures
(Meerja and Almustafa, 2016).
The idea of exploiting data is far from new and issues regarding the mining of it neither. In
1996 Fayyad et al. already stated the following: “Across a wide variety of fields, data are being collected and accumulated at a dramatic pace. There is an urgent need for a new generation of computational techniques and tools to assist humans in extracting useful information
(knowledge) from the rapidly growing volumes of data, These techniques and tools are the
subject of the emerging field of knowledge discovery in databases (KDD)” (p. 82).
In comparison to 1996, data is now collected at an even more dramatic pace. 90 percent of
10
the data is generated in the last two years (Bradshaw, 2013), and the prognosis towards 2020
is that data collection will continue to grow exponentially (Gantz and Reinsel, 2012). Current
Database management systems (DBMSs) are not able to meet the demands of Big Data (Che
et al., 2013; Hu et al., 2014), resulting in only a fraction of all the data being analyzed (Gantz
and Reinsel, 2012).
Hu et al. (2014) provide an extensive tutorial in which technologies are practiced in each
stage of the Big Data value chain (data- generation, acquisition, storage, and analytics). The
drive/urge to subtract and exploit value from Big Data will increase as data becomes more
available.
3.4 Artificial intelligence
Poor data quality, the so called veracity issue, becomes a serious problem for many cloud
service providers (Hashem et al. 2015). As more things become connected, it will be harder to
oversee control all sensors and actuators. The next step is to add the AI to IoT systems
(Poniszewska-Maranda and Kaczmarek, 2015).
The internet of intelligent things (IoIT) paradigm creates the experience of an omnipresent,
intelligent, and even a living, internet. It will fulfill the need to give commonplace objects the
ability to comprehend their surroundings and to make decisions autonomously (Arsénio et al.
2014).
Autonomous decision making, for example by machine learning (Jordan and Mitchell, 2015),
will become a necessity for reaching the full potential of the IoT, especially with regard to
interpreting the heterogeneity of big data. A Sensory Processing system (Wolff, 2014) can be
one of the solutions, since it can be used as a process of finding good full and partial matches between patterns. Simultaneously, it uses data compression to decrease the volume of big
data (Wolff, 2014).
Combined with the cloud, as the brain, robots and smarthomes can become the same entity
that can provide seamless communication services (Arsénio et al. 2014).
3.5 The core technologies of Web 3.0
When assessing the social impact of each of the four technologies one should consider their
interrelationship. All four technologies singularly contribute to the upcoming changes in society, but combined the impact will be even greater as they can provide Web 3.0 applications.
The IoT has by nature a networking effect (Manyika et al. 2013; Mejtoft, 2011), meaning that
the more things are linked to the IoT, the more will get linked, creating an exponential
growth. The duration of the exponential growth or when the market will be saturated with
interconnected objects is impossible to forecast, but looking at literature of all four technologies gives a broader overview of both upcoming challenges/risks as well as solutions the
technologies can provide.
The linkage among the previously mentioned technologies can be explained in two ways, (1)
the strengthening effect of every technology (positive perspective), and (2) the essentiality of
every technology (negative perspective). The positive line of reasoning is as follows: As the
IoT grows big data will also grow exponentially, since the IoT is one of the main providers
(Botta et al., 2016). Simultaneously, the Cloud as processor/storage provider will gain momentum. With more data available and more sensors and actuators connected to the Cloud,
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the opportunity of exploiting AI techniques will increase. And using the same line of reasoning only negative: Without the implementation of the IoT 90 percent of future big data will
not be produced. Without big data the opportunities and benefits of the cloud, by linking
data together in a single everywhere available platform, will decline. Also AI needs data as
input to become more accurate and meaningful.
Essential is the network that links the technologies to each other. Currently the network is
still a bottleneck for extensive multimedia traffic integration (Alvi et al., 2015; Bell labs, 2013).
However, from 2020 onwards the fifth generation (5G) infrastructure will be rolled out which
will be capable of solving many of those technological constraints (Soldani and Manzalini,
2015; Osseiran et al., 2014). 5G (and generations to come) will address the challenge of allocating limited resources from users to connected devices (Thomas et al., 2015). “Think of future internet access as akin to the air we breathe – present everywhere and essential for the
very existence.” (Linton and Jaokar, 2014, p. 8).
This means that after 2020, the multiplying effect of the four technologies will reach its expectations and some important social issues need to be accounted for!
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4. Impact web 3.0 – Scenario analysis
According to Linton and Jaokar (2014), Web 3.0 will be responsible for a knowing society, a
society where tracking, tracing, analyzing, and reacting upon it all happens in real time. The
impact of a knowing society differs when different actors act opportunistically.
In this section four contradicting scenarios of the future societal structure are presented (see
figure 1). By envisioning such scenarios, decision makers can plan steps to make the most
ideal scenario happening, and prevent
steps to make the least ideal happening
(cf. Van den Hoven et al., 2013). The
scenarios described are written according to the ceteris paribus principle,
meaning that external unrelated factors/scenarios are not taken in consideration (e.g. smart terrorists bombing
databases instead of humans raising
global hysteria, a third (digital) world
war breaks out, natural disasters, pandemics, etc.) and the focus will be on
the impact of Web 3.0.
Figure 1: Power triangle: division of scenarios based on societal
structure. Digits indicate respective scenario.
Scenarios 1, 2, and 3 describe how different actors have the opportunity to claim currently distributed power. In the fourth scenario
the power will be distributed among a combination of these actors.
A shift in power distribution is not a new phenomenon, as elaborated by Michaels (2015) has
the separation of power evolved over time and will continue to do so. The framers separated
the power among a legislative, and executive, and a judicial branch, so no branch, on its own,
could dominate. Thus far, Michaels distinct the separation of power distribution of the USA in
three era’s: the constitutional era, the administrative era (as the complexity and diversity of
federal responsibilities in modern time is often too much for legislators to manage on dayto-day basis), and the privatized era (current mingling of private actors participating in public
policymaking and policy-implementing responsibilities) (Michaels, 2015). The era’s marked
societal change and distribution of power changed accordingly.
In some cases the actors in the scenarios will have to seize power in a limited timeframe, this
thesis assumes that if this might occur the actors taking over will succumb to article 43 of the
The Hague (1907) regulations stating that if: “[t]he authority of the legitimate power having
in fact passed into the hands of the occupant, the latter shall take all the measures in his
power to restore and ensure, as far as possible, public order and safety, while respecting,
unless absolutely prevented, the law in force in the country.”
In order to make the abstract impact of the scenarios clearer, governance structures have
been added to every scenario description. Governance structures have widely been covered
in literature; the simplest structure is the market structure (based on price mechanism). Over
the years several key structures have been added to cover more complex situations. These
are, the firm/hierarchy (Coase, 1937), the network (Powell, 1990), and the bazaar (Demil and
Lecocq, 2006).
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4.1 General impact Web 3.0
The implementation process of Web 3.0 in society will most likely be incremental, however it
will speed up the pace of innovation significantly, creating space for new disruptive opportunities to emerge (Verizon business, 2010).
The most promising application fields are based on the use of sensors (Botta et al., 2016) in;
(1) environmental monitoring, (2) smart cities, (3) smart business, inventory and product
management, (4) smart home and building management, (5) healthcare, and (6) security and
surveillance (Miorandi et al., 2012). In some of the application fields’ people will be the focal
point of the sensors, especially by the use of smartphones (Lane et al., 2010). This can be in
two ways, either opportunistic, or participatory (Campbell et al., 2008; Antonic et al., 2014).
The architecture and protocols should motivate people to contribute (participate) (Atzori et
al., 2014a), but the concept of invisibility is important for good integration of technology
(Mejtoft, 2011; ITU, 2005). The latter and AI make opportunistic sensing likely to increase.
A basic assumption for all scenarios will be that most basic necessities can be produced at
near zero marginal costs. Already the employment rate in agriculture has dropped from 41
(1900) to two percent (2000) (Autor, 2015), and the possibilities of current technologies will
make employment in agriculture even more redundant (e.g. autonomoustractor.com). This
trend of producing more with fewer people is becoming business as usual for many industries (e.g. banking, administration, packaging services, production halls, etc.).
The different scenarios provide insight in the distribution of welfare and power, but do not
assume more welfare or superior societal structure in either scenario. The scenarios are based
on the observation of Autor (2015) that if human labor is indeed rendered superfluous by
automation, then our chief economic problem will be one of distribution, not of scarcity.
4.2 The commons (1)
In this scenario nearly all data is freely accessible and exploitable by all (community owned).
The capitalist society as we know will no longer exist and we will enter a (near) zero marginal
cost society. This society marks the end-stage of capitalism in which efficiency gains have
become that prevalent that products and services are (nearly) freely available. This should not
come as a shock, as Rifkin (2014) states: “capitalism’s operating logic is designed to fail by
succeeding.” (p.3). Social capital will gain in value supported by the Collaborative Commons
(Rifkin, 2014). The changing job market, as described in section 2.2, can be used to underpin
this line of reasoning.
When everything is nearly free and always accessible the function of products will become
more important than the ownership/possession of it.
Already owning things is becoming of less value than it used to (de Bruin and Floridi, 2016),
while the service that comes with products rises in interest (Melin, 2015). This makes creative
talent more essential, creating room for an artisan economy to develop (Linton and Jaokar,
2014). First we will see an increase in Do-It-Yourself (DIY) platforms/boards, as is currently
happening for web 3.0 practices (e.g. Arduino) (Atzori et al., 2014a), but in the longer term
this will lead towards an economy of co-creation (do-it-with-others) (Fuchs et al., 2010;
Mejtoft, 2011; Zwass, 2010; Patrignani and Whitehouse, 2015). Zwick et al. (2008) explains
that co-creation is not more than an evolution of the consumer subject in marketing man-
14
agement, “because the co-creation paradigm deliberately posits consumers as knowledge
workers” (Zwick et al., 2008, p. 172). Further they “argue that the co-creation paradigm represents an attempt to establish a specific form of government … to bring about particular forms
of life in which consumers voluntarily provide unwaged and exploited, yet enjoyed labor.” (p.
176). Which? a big British consumer body, also sees a change from mere consuming by consumers to more producing by consumers (Which?, 2013).
A special report of The Economist (2014) about startups found the link between the new entrepreneurs and the late 19th century artisan economy. The introduction article describes the
crisis of 2008 as an important moment for many to start a startup. Many of these startups
have the aim to become micro-multinationals, which are businesses that operate globally
without becoming “large” (The Economist, 2014).
The governance of the commons has many similarities with the governance structure of the
bazaar. As the “open source communities do not rely on employment contracts and so are
unable to be governed by formal authority” (Demil and Lecocq, p. 1454). Another important
similarity is that acting “in open source communities, potential extrinsic rewards (improving
job and career prospects through prestige and visibility in projects) coexist with strong intrinsic motivations (fun, intellectual gratification, altruistic fulfilment) that can be more compelling” (Demil and Lecocq, p. 1454). Hence, the importance of networks and crowds increases in
relation to market institutions (Kaivo-oja et al., 2015).
The lack of hierarchy and formal authority makes this scenario directionless (chaotic), as people will most often contribute/participate voluntarily (not contracted or coerced) in projects/products they find worthwhile/fun doing.
4.3 Capitalism (2)
Building on Fuchs work, Lennerfors et al. (2015), find that the (Marxian) discourse of ICT and
sustainability as being a savior fails to meet its expectations, because “technology and economy is intrinsically related, which means that it is incorrect to separate technology and economy.” (Lennerfors et al., 2015, p. 770). Besides, Autor (2015) states that many overstate the
extent of machine substitution and that there are strong complementarities between labor
and automation, which makes a jobless future mere imaginative.
Capitalism is still the dominant economic paradigm and it is not likely to go extinct in the
near future. This can be supported by the predictions of Manyika et al. (2013), who estimate
that the technologies of Web 3.0 could have a global impact on society of over 49 trillion
dollar by 20252. Cisco estimates that by 2027, compared to 2011, 75 percent of the companies in the S&P500 will be new and mostly due to technology driven market disruption (Noronha et al., 2014).
Fact is that there is still a lot of money that will continue to flow and that there are still rules,
regulations, and a societal structure that demand people to go to work to provide themselves a livable income. In this capitalistic and opportunistic world, it is unlikely that the powerful will voluntarily resign. Hence, multinationals will do everything in their power to stay in
top of the game and to remain a leading player (The economist, 2014).
2
This impact estimation, however, includes cost-savings (efficiency gains), which distorts proper/easy
interpretation of the figure.
15
To stay leading, startups, as well as multinationals, will increasingly implement innovations in
society without government-covered regulation. As the world moves faster and faster towards a digital society (ITU, 2015), the new innovations can (relatively) quickly become
locked-in society, creating a ‘dilemma of control’ (1980). Being faster with implementing innovations than government can make regulation is therefore considered to be a main business opportunity.
Lower investment cost, due to cloud-based services and other Web 3.0 applications, make it
possible for small groups to compete with large firms on more equal footing (Verizon business, 2010; Newman et al., 2016). Exploiting this opportunity, the strategy of many (small) ICT
startups will be to become a micro-multinational (The economist, 2014). The difference between the small ICT startups and the commons scenario, is that these startups operate according to the current capitalistic paradigm, which makes them value profit over community.
The benefit of being a small startup-like organization is to be more versatile and less important/more difficult to control by single nations.
The strategy of (big) ICT multinationals will be to become too big to control. Already companies as Microsoft and Apple are so interwoven in society, that they could undermine or even
neglect the authority of national governments.
Example: if a government puts a ban on the products of Microsoft this would have such economic impact (e.g. investment costs, loss of knowledge, lack of proper substitutable product)
that such a thing is unlikely to occur, since most Microsoft users (industry, home-users, and
governments internally) will fiercely resist.
Exploiting their position, dominant digital service-providers (small and big) will become
powerful international monopolistic players. Already it is hard to compete with (substitute)
large players due to current networking effects (e.g. Facebook). Also big public cloud owners
(Amazon, Google, etc.) will gain authority in this regard. In order to gain networking effect,
companies can rely on network relations, which have the following basic assumption: “one
party is dependent the resources controlled by another, and that there are no gains to be
had by the pooling of resources. In essence, the parties to a network agree to forego the
right to pursue their own interest in the expense of others.” (Powell, 1990, p. 303).
Businesses can anticipate the needed change in the education system (Schwab and Samans,
2016; Datta, 2015), by setting up corporate funded schools in which they provide (and control) a significant portion of the taught knowledge. Businesses can do this because the power
of the (local) governments keeps lagging behind as economics and society becomes more
international focused (globalization). Doing so, we slowly enter a borderless society, a society
in which big companies set international rules and standards instead of governments.
In this scenario data will not be freely accessible to all and ICT capitalists (top management)
will exploit (monopolize) the value of their gathered and mined data for their own personal
(business) gains.
Easy-substitutable services (e.g. lacking networking effects) will, due to initial investment
costs, not reach (near) zero marginal cost, making them relatively expensive for the masses.
16
ICT capitalists will become more and more oligopolists/monopolists, and in those nonsubstitutable roles they will be able to spread a large portion of their desired future vision of
society in society.
4.4 Government (3)
In this scenario governments acknowledge and act on their need to change (Autor, 2015;
Schwab and Samans, 2016; Linton and Jaokar, 2014; Which?, 2013). Concurrently egovernment is trying to incorporate Web 2.0 technologies in the public sector, however the
development and use of this technology is still at its early stages (Sivaraja et al., 2014). With
the dawn of Web 3.0 and its game changing potential, the big and slow bodies of the public
sector can act in extreme ways as they see their national sovereignty being at stake (previous
scenarios). The uncertainty about their own grip on political power in the years ahead will
prompt them to favor structures that insulates their achievements from politics (Moe, 1989).
Governments will anticipate the loss of power by strengthening rules and regulation. New
rules and regulation will give them more legal power with regard to standards, decision making, and overall control over individuals and organizations (e.g. accessibility to data, ownership over strategic industries). An instrument that can be exploited in this regard are intelligence agencies. Intelligence agencies are “mandated to collect, refine, analyze, and/or distribute information potentially relevant to national security” (O’Connell, 2006, p.1661), of
which the latter is a broad and debatable topic.
Another important leverage in this scenario is the advent of smart cities, which opportunities
and technological architecture is elaborated by Zanella et al. (2014), as these will be institutionalized in favor of local governments.
Due to the changing job environment, governments have to undertake redistributive policies.
If they do not do this it will decrease the well-being of future generations as calculated by
Sachs et al. (2015). Web 3.0 has both the opportunity to play a pivotal role in addressing societal challenges (Koreshoff et al., 2013), and to create new societal challenges. Governments
will extract value from Big Data for informed policy reasons in order to address public concerns or social issues (Amankwah-Amoah, 2015), but at the same time they will exploit and
monopolize certain data for national security reasons. Doing so they can keep track, control,
and penalize parties who have their own opportunistic agenda.
Governments function due to their hierarchical structure in which everybody is assigned to
specific tasks. This ‘master and servant’ relationship is typical for the firm governance structure (Coase, 1937). In this scenario data will not be freely accessible to all as governments will
use it to control companies and the (virtual) communities/commons.
4.5 No dominant actor (4)
The final scenario lacks a dominant actor; instead a combination of the previous scenarios
will rule the world. This can lead to two extreme scenarios. In the first extreme scenario the
three actors are all peacefully integrated in society leading to a balanced equilibrium. In the
second extreme scenario the different actors cannot come to a peaceful understanding, leading to a continuous power struggle.
17
4.5.1 – balanced equilibrium (4.1)
In a balanced equilibrium no actor will be able to successfully make the first bold move (as
people will condemn the action unitedly). As result, no actor can force a clear future vision in
the world.
Governments will not act because (1) they want to leave space for companies to be innovative and to contribute to overall progress, and (2) they cannot upset entire communities of
commons as these groups have elected them. Businesses will not act because they lack proper scope, vision, and feeling of responsibility to do so. The communities/commons will not
act as they lack enforcement power, or broadly accepted internal vision.
Hence, there will be no consensus on the vision and every actor deals with its own individual
(short-term) problems.
In a balanced equilibrium, however, various actors will take over functions of the other actors.
For example, businesses and big established communities can become part of the government. This could increase the response time to new innovations, since governments will be
better informed.
Another example can be that interested/specialized communities of commons can become
stage-keepers of companies’ innovation processes (in stages before implementation). Governments might impose such regulation as it will make innovation processes more transparent and the commons will have more legal influence.
4.5.2 – continuous power struggle / chaos (4.2)
In the second scenario without a dominant actor, all the actors actively strive to become
dominant. The actors will not cooperate, since they have the conviction that they can either
become the dominant actor, or become independent of the other actors.
In this scenario governments will impose hard regulation on the communities and businesses
(scenario 3). Businesses in turn, will not obey the regulation, but instead use, act, and innovate illegal products and services, based on the exploitation of data (scenario 2). And communities will create independent platforms in which they can operate without the license of
governments or businesses (scenario 1).
Locked-in services/products are hard to ban for governments, as they can be of paramount
importance for the functioning of society (and thus for the commons as well). Businesses will
individually not have enough power to overrule other businesses, governments, or communities, so they need legislation for a fair level playing field.
If all actors act opportunistic without consent of the others, society will retrogress. Governments will have too local authority; businesses will not have enough scope, expertise, vision,
and authority; the commons will lack the proper resources, authority, and shared vision. A
combination of these will lead to a struggle with a disputable winner.
Especially in this, but as main consideration point for all previous scenarios holds the observation of Moe (1989), that: “[t]he hitch is that those in positions of power are not necessarily
motivated by the national interests. They have their own interests to pursue in politics … and
they exercise their power in ways conducive to those interests.” (P. 268).
18
5. Empirical study
Each scenario described can only become reality if the different actors (communities, capitalists, or governments) gain sufficient authority or are able to exert power over the public. Authority can make binding decisions accepted by the masses in favor of the masses, where
power can be exerted in the favor of a single entity (Grimes, 1978).
An exploratory survey was conducted to estimate which scenario is most likely to gain public
support. Individual values were used to explain preferences to the different scenarios. How
authority can best be exercised was questioned by preferred types of governance systems.
Knowing the most preferable scenario will make it possible for the society to act towards it
(e.g. by innovation, legislation, research direction, public debates, etc.) (cf. Van der Hoven,
2013). This can make the transition towards that scenario less radical/disruptive.
The survey shed light on whether different types of power (based on the trias politica) are
most desired (based on attitude) to authoritatively be exerted by which actor (different scenarios) in the future. The trias politica has been used as “The Framers of the Federal Constitution similarly viewed the principle of separation of powers as the absolutely central guarantee of a just government.” (Morrisson v. Olson, 1988).
This study used a one factor between participant design to assess the attitude of different
future-oriented scenarios among a sample of students. The purpose of the design was to
correlate personal values with the attitude of the different scenarios.
5.1 Participants
To assess which scenario has most preference in the future, young educated people were
surveyed. This specific segment of the population was chosen for the following reasons; first,
the described scenarios will most likely not fully develop in the next decade, making the
young educated people of today the leaders of the described societies. Second, young educated people of today are the first digital natives and will have more knowhow of how to
exploit the possibilities of the Web 3.0. Finally, alongside the first two reasons, it is likely that
young educated people will (somehow) exert their values throughout their lives. This implies
that the most preferred scenario(s) is more likely to develop anyhow.
The sample of N = 104 students was obtained through a online survey. Students from multiple studies have been approached to diminish the already existing homogeneousness among
the participants.
5.2 Instrumentation
19
The questionnaire was held among college students who were approached by e-mail, social
media, or a flyer to participate in the online survey. The survey was accessible for several
weeks during July till September in 2016.
Figure 2: Design of questionnaire
Procedure (see figure 2). Upon starting, participants were thanked for participating in the
survey after which a short introduction of the purpose of the survey was given.
Second, personal [static] values were assessed based on the Schwartz Value Survey (SVS)
(Schwartz, 1992). SVS was used to profile the participants universal values into one of the ten
value types based on 56 values. As values are only a relatively small part of the survey, to
keep the survey manageable for participants the Short Schwarz Value Survey (SSVS) is used
instead of the full scale (Lindeman and Verkasalo, 2005). The SSVS has only ten items, using
the related original value items as descriptors. SSVS has good internal consistency, temporal
consistency, and correlates highly with the original SVS (Lindeman and Verkasalo, 2005), but
it takes six times less time to fill in the SSVS in contrast to SVS (Lindeman and Verkasalo,
2005, study 4). This makes the use of SSVS a good alternative for the original SVS.
The participants were asked to rate the importance as a life guiding principle of (for example)
“power, that is social power, authority, and wealth.” on a seven point scale ranging from -1
(against my principles) to 5 (of supreme importance). The rest of the questions can be found
in (Appendix I)
Third, background information on web 3.0 was given (based on chapter 2 and 3), after which
one of the scenarios was introduced (based on chapter 4) (See Appendix II for the full texts).
Fourth, the participants were asked to make judgments on their attitude towards of the scenarios. The first questions were about the attitude of the participants toward the scenarios.
These questions were based on the power distribution in the given scenario using the direct
measures for attitude of the Theory of planned behavior as used by Francis et al. (2004). The
participants rated their attitude towards the scenarios using a seven point scale 1: low - 7:
high. In the following questions the participants rated the desirability of power distribution in
20
the prescribed scenario. In the last set of questions in this block the participants were asked
to assess the favorability of the different governance structures in the prescribed scenario
(see Appendix III).
Fifth, the participants were asked to fill in some personal information, such as gender, age,
field of study, etc. This information was used to assess the heterogeneity, or homogeneity, in
the different samples, in order to make judgment on the validity of the results.
Finally, the participants were thanked for conducting the survey.
21
6. Results
Of the 123 people who started the survey, 104 participants filled enough of the survey to
make their data interpretable. The division of valid surveys and scenarios is as follows: (1)
Commons [n=26], (2) Capitalists [n=27], (3) Government [n=26], (4) No dominant actor
[n=25].
The survey measured several valuations of people towards the different scenarios. The different valuations were attitude, preferred power distribution and preferred governance structure. Also personal values where measured which are used as covariant to measure if attitude
and values are correlated to certain scenarios. The outcome of these valuations is covered in
this section.
6.1 SVSS
One-way Anova’s with scenario as dependent variable and the values as factor presented no
significant differences of the values of the participants among the scenarios (Appendix IV).
This indicates successful randomisation of the participants across the scenarios.
6.2 ATTITUDE
An One-way Anova of attitudes by scenarios indicated a significant difference of attitude of
the participants among the scenarios [F(3,100) = 7.554, p < 0.00]. The highest attitude score
towards the commons scenario indicates this scenario to be preferred over the other scenarios.
Means and standard errors of attitudes towards the scenarios are summarized in Table 1.
Table 1: general attitude towards scenario
Scenario:
Mean:
Commons
4.44a
Capitalist
3.43b
Government
3.75b
Mix of all
3.55b
SE:
.166
.163
.166
.169
Means with different superscripts differ within each norm formulation within each dependent variable at p < 0.05 for Tukey significant difference comparison.
The results of an Unianova of attitudes by scenarios with the different powers as covariate
and an alpha of 0.05 is provided in table 2. Table therefore shows the relative importance of
the value towards the scenario.
Table 2: impact of values on attitude towards scenarios
Significance of values
Commons Capitalist
Government Mix of all
Power
F(3,96) = 10.49, p = 0.00
-.447
.182
-.028
.604
Achievement
F(3,96) = 9.56, p = 0.00
0.092
.685
.733
1.116
Hedonism
F(3,96) = 0.87, p = 0.460
.045
.073
.380
.260
Stimulation
F(3,96) = 0.71, p = 0.546
-.217
.197
.094
.103
Self-Direction
F(3,96) = 0.14, p = 0.936
0.19
.106
.221
.244
22
Universalism
F(3,96) = 3.48, p = 0.019
.283
-.229
-.117
-.344
Benevolence
F(3,96) = 2.94, p = 0.037
.032
-.293
.347
-.174
Conformity
F(3,96) = 1.77, p = 0.159
-.088
-0.190
.245
-.195
Tradition
F(3,96) = 1.82, p = 0.148
-.025
-.167
.007
-.406
Security
F(3,96) = 0.03, p = 0.992
-.135
-.207
-.184
-.186
For every value the scenario without dominant actor was the baseline for comparison, as this
is the least extreme scenario that covers aspects of all others.
The results indicate that participants with high values for power, achievement, universalism,
and benevolence have a significant impact on the attitudes towards the scenarios. Interpretation of the data can be done in the following way: As the ‘no dominant actor’ scenario is a
combination of the three extreme scenarios this can be considered the ground scenario (basis). Then, dependent on peoples values, people are attracted to one scenario over another.
This means that for the value:
Power: high power value makes people more negative of the government, commons, and
capitalist scenario over the scenario without dominant actor.
Achievement: high achievement value makes people more negative of the government,
commons, and capitalist scenario over the scenario without dominant actor.
Universalism: high universalism value makes people more positive of the government, commons, and capitalist scenario over the scenario without dominant actor.
Benevolence: high benevolence value makes people more negative of the capitalist scenario,
but more positive towards the government and commons scenario in relation to the scenario
without dominant actor.
6.3 POWER DISTRIBUTION
MANOVA is used to test differences of preference in the distribution of power among the
different scenarios.
The power to create
The MANOVA indicated significant differences in the preference of participants who should
have the authority to create rules and regulation among certain scenarios. There was a statistically significant interaction for the governments to create the rules and regulation [F(3,100)
= 5.155, p < 0.002], for the Capitalist [F(3,100) = 6.618, p < 0.00], and for the Commons
[F(3,100) = 3.191, p < 0.027]. There was no significant interaction for the mix of all [F(3,100) =
1.598, p < 0.195]. The latter indicates that people favouring the mix of all to create rules and
regulation do not have a preference towards any scenario.
Means preferred creating power towards the scenarios are summarized in Table 3.
23
Table 3: preferred creating power distribution among scenarios.
Preferred actor
Government
Capitalist
Commons
to create
regulation
Scenario
Governments
Capitalists
Commons
Mix of all
Cumulative
5.69a
5.63a
4.85b
4.84b
21.01
2.19b
2.55b
2.69b
3.64a
11.07
3.85ab
4.26ab
4.58a
3.40b
16.09
Mix of all
3.85
3.96
3.58
3.16
14.55
Means with different superscripts differ within each norm formulation within each dependent variable (= preferred
actors) at p < 0.05 for Tukey significant difference comparison.
The data in table 3 indicates that the preference of which actor to have creating power differs
significantly. Among all scenarios governments are most valued to create regulation as indicated by the cumulative value.
The data indicates governments to have the highest preferred power, which is currently the
status quo. A probable reason is that it could be difficult for people to imagine, let alone accepting, other actors to have this power.
The power to maintain
The MANOVA indicated significant differences in the preference of participants who should
have the authority to maintain rules and regulation among certain scenarios. There was a
statistically significant interaction for the Capitalist [F(3,100) = 3.767, p < 0.013] and the
Commons [F(3,100) = 3.152, p < 0.028] to maintain rules and regulation. There was no significant interaction for the governments [F(3,100) = 1.866, p < 0.140] and for the mix of all
[F(3,100) = 2.110, p < 0.104].
Means preferred creating power towards the scenarios are summarized in Table 4.
Table 4: preferred maintaining power distribution among scenarios.
Preferred actor
Government
Capitalist
Commons
to maintain
Regulation
Scenario
Governments
Capitalists
Commons
Mix of all
Cumulative
5.269
5.037
4.769
4.880
19.955
3.846ab
4.259ab
3.385b
4.640a
16.13
4.308ab
4.519ab
5.000a
3.840b
17.667
Mix of all
4.192
4.593
4.000
3.640
16.425
Means with different superscripts differ within each norm formulation within each dependent variable (= preferred
actor) at p < 0.05 for Tukey significant difference comparison.
The data in table 4 indicates that the preference of which actor to have maintaining power
differs significantly. Governments are still attributed to a large portion of the power, but in
the commons scenario the commons have the highest score to maintain the power.
The power to control
The MANOVA indicated significant differences in the preference of participants who should
have the authority to control rules and regulation among certain scenarios. There was a statistically significant interaction for the Capitalist to control rules and regulation [F(3,100) =
3.133, p < 0.029]. There was no significant interaction for the governments [F(3,100) = 1.111,
24
p < 0.348], for the Commons [F(3,100) = 1.711, p < 0.170], and for the mix of all [F(3,100) =
0.519, p < 0.670].
Means preferred creating power towards the scenarios are summarized in Table 5.
Table 5: preferred controlling power distribution among scenarios.
Preferred actor to
Government
Capitalist
Commons
control
regulation
Scenario
Governments
5.192
2.962b
ab
Mix of all
4.231
3.923
Capitalists
Commons
Mix of all
5.259
4.923
5.000
3.519
3.519ab
4.080a
3.963
4.692
4.520
4.148
4.308
4.320
Cumulative
20.374
14.080
17.406
16.699
Means with different superscripts differ within each norm formulation within each dependent variable (= preferred actor)at p < 0.05 for Tukey significant difference comparison.
As the previous tables, the preferred power to control is attributed to governments. The impact of current status quo towards this trend is unknown.
GOVERNANCE STRUCTURE
The MANOVA indicated significant differences in the preferred governance structure among
certain scenarios. There was a statistically significant preference differences for the firm structure [F(3,100) = 3.940, p < 0.0211] and the network structure [F(3,100) = 10.000, p < 0.000]
depending on the scenario. There were no significant differences among the preference of
the market structure [F(3,100) = 1.277, p < 0.287] and the bazaar structure [F(3,100) = 1.302,
p < 0.278] in relation to the scenarios.
Means preferred creating power towards the scenarios are summarized in Table 6.
Table 6: preferred governance structure among scenarios.
Preferred
The market
The firm
governance
structure
Scenario
Governments
Capitalists
Commons
Mix of all
Cumulative
3.577
3.741
3.962
4.240
15.520
5.462a
4.852ab
4.192b
4.840ab
19.346
Network
4.846b
5.703a
5.769a
4.680b
20.998
Bazaar
3.558
4.019
4.000
4.140
15.717
Means with different superscripts differ within each norm formulation within each dependent variable (= preferred
governance structure) at p < 0.05 for Tukey significant difference comparison.
Table 6 indicates the firm and the network governance structures to be preferred among the
scenarios.
25
7. Discussion
7.1 General findings
The goal of this thesis was to provide a basis for future policy and development. This thesis
can help “[a]s technology continues to transform our world, business leaders, policy makers,
and citizens must look ahead and plan.” (Manyika et al., 2013, p. 2).
This thesis provides insight on the impact of Web 3.0. Added value to existing literature is the
division of multiple scenarios which are all theoretically possible to occur.
Based on the results of the survey, there is indication towards favorability of certain scenarios
and power distribution among actors within those scenarios. The commons scenario is most
preferred by the participants of the survey, while the governments should remain in control
of the powers of the trias politica.
The first question (section 1) was to find the enabling technologies of Web 3.0. Literature
indicates these technologies to be the Internet of Things, Cloud, Big Data, and Artificial intelligence. The combination of the four technologies have the possibility to cumulate into
something (either a device, a service, or a random object) that is connected, is able to subtract data from the environment, has the capacity of storing and interpreting data, and can
make adjustments in current patterns based on the data. This will enable the vision of ‘anytime, anywhere, anymedia’ (Zhou and Chao, 2011).
The second question was to identify the impact Web 3.0 applications can have over a longer
period of time. To assess this impact, four scenarios were written. In three of the scenarios
different actors (governments, capitalists, and the commons) opportunistically seize the advantages occurring from Web 3.0. In the fourth scenario, a combination of the aforementioned actors will rise.
The third question answered how the scenarios are evaluated. A survey among students of
Wageningen indicated personal values about power, achievement, universalism, and benevolence have a significant effect on the preferences of certain scenario over the others. The survey also revealed that depending on the scenario, different actors are preferred to exercise
the powers of the trias politica. This insinuates that there is not a general preferred actor to
exercise the powers, but that it depends on the given environment.
7.2 Added value of research
The commons scenario was significantly preferred over the other scenarios among the participants. This favorability can partly be explained by the participants of the survey, as they have
some specific characteristics; they are young, high educated, and study in a left-idealistic
oriented university. As values among the participants are the same the outcome of the impact of each value towards the scenario is valid. Other segments of people, would merely
favor different values, and therefore different scenarios. However, as the segment is rather
small, other research should be conducted for validation of current findings.
The survey indicates four values which are of significantly important in the different scenarios. The value of power is most important in the ‘mix of all’ scenario and has a negative impact on the commons scenario. Reasons behind the weight of certain values over others in
the respective scenario is not part of this study. However, logically they can be explained.
26
That power and achievement are important in the ‘mix of all’ scenario can be explained by
the fact that there is no actor with all powers, where the other scenarios have strict division.
This makes having power, or at least the ambition to gain power an important value.
Universalism is considered to be an important value in the commons scenario and least in
the ‘mix of all’ scenario. This is also in line of expectation as the commons should favor
equality and the wish for everyone to be able to succeed in life, while in the other scenarios
this is of less importance.
Benevolence is a favored value for the government scenario. The reason can be that value of
responsibility and helpfulness is the idea of how a government should function. And this is
therefore least in the capitalist scenario.
Although the participants mention the commons scenario to be most appealing, they prefer
the government to have most influence with regard to power based on the trias politica. This
controversy is well defined by Franck (2008), who stated: “We want the right to be close to
governance and yet we also want governance that can get the result we need. Those results
sometimes can be got only when national sovereignty defers to some other level of governance.” (p. 23). To translate this to the two preferred actors; on the one hand the participants
of the survey prefer the commons scenario to occur, but on the other hand, they do not believe the leading actors in that scenario able to provide the right results.
Interesting to notice in this regards is that power already shifts as “[n]ew legislation is increasingly agreed on at a global (UN) or at least supranational level (e.g. European Union)
before being formally codified by national legislative, interpreted by judiciary and enforced
by executive powers in each country.” (Malsch, 2013, p. 161). And that the trias politica is also
in ongoing evolution as exemplified by Michaels (2015) in relation to US practices.
If, whether, or how the given preferred scenarios will clash in the future is not to foresee. But
current status quo will have much influence in the development of the scenarios. If in the
future idealists (those favoring the commons scenario) will become more progressive, they
will find much resistant from them not who are not that fanatic, those who are more conservative (i.e. Frey and Osborne, 2013). Depending on the intensity of the two sides this could
even result in a form of scenario 4 in which there is no dominant actor.
Secondly, the results show least interest among the participants in the capitalism scenario.
However, as the scenario describes, the capitalists will not instantly change, let alone in consent with the people. As described by Moe (1998) “An interest group may be powerful
enough to exercise public authority today, but tomorrow its power may ebb, and its right to
exercise public authority may then be usurped by its political opponents. Should this occur,
they would become the new “owners” of whatever the group had created, and they could use
their authority to destroy –quite legitimately– everything the group had worked for.” (p.
274).
Usurped does not (necessarily) imply ‘in consent with the people’. Businesses can gradually
extend their global market share in order to become non-substitutable. From that position
they can increase their influence over other aspects of society than those directly related to
their own product or service.
Lastly, if people favor certain actors to have power they should not necessarily favor the re-
27
spective scenario. Most prominent example is when people favor the capitalists to govern. As
the capitalist scenario is least favored among the participant in general, they have most
chance for the capitalist scenario to develop when supporting the ‘mix of all’ scenario. This
contradiction can be explained by the following. If the capitalist scenario happens, many
people will resist them from having the powers of the trias politica. This is probably as there
is the least interest among all participants for the capitalist scenario to occur. However, when
the mix of all scenario occurs, people are more willing to provide the power to the capitalists.
So, if people want the capitalists wants to gain power, they have most chances when they
prefer the mix of all scenario to develop.
7.3 Limitations and further research
Everything mentioned above is speculative and forecasting actual future is not part of this
thesis. This thesis does provide a foundation for future research regarding the likelihood of
future scenarios to occur. The survey is a good first step for further research to test the validity of this research.
For this thesis, as being an explorative study, the scope of the survey is enough to give some
indications to build upon. The participants were mainly young students from Wageningen
university. This university attracts a crowd that is interested in resolving global issues, such as
sustainability and eradicating hunger (Kropff, 2007). Further research should expand the current survey to a broader, more diversified, audience.
Other interesting segments that other research can include are: early adopters, if they favor a
certain scenario over other this could lead to profound insights. The rulers of today (politicians, industry leaders, etc.), the participants in this thesis might provide insight in the more
favorable future scenario, but the rulers of today have the means (and interest) to build the
path towards it.
Many unsearched issues can have a great deal of influence in determining the future ahead
of us. Some of the more influential ones are: speed of future innovations; adoption rates regarding Web 3.0 technologies; new, yet unknown, innovations stemming from Web 3.0; the
development of current globalization combined with polarization; new operating software
developments; or the preference of the scenarios to happen from the perspective of each
individual actor. Further research should find out which of those (and other) issues are important, and to what extend they will influence the likelihood of the scenarios to occur (innovations like Linux might favor the commons scenario, where innovations as Microsoft might
favor the capitalist scenario).
New products/services can have the ability to enable certain scenarios to develop over others. This can be done by selecting different product categories, select certain products/services within that category, and see who benefits. What is the aim of the product/service (steer or ease repetitive actions, or completely unburden the consumer). Which
data is collected, and who owns that data (governments, businesses, or public). Knowing
which technologies enable certain scenarios over others could guide investors and other actors insight to which technology developments they should support.
Certain factors which are not covered in this thesis could be of big influence on certain sce-
28
narios to develop over others. Many factors can be of influence in both favorability for certain
scenarios among the population as well the scenario likelihood in general. Factors to consider are political situation, demographics, culture, technology adoption rate, and others.
As mentioned before in the introduction of the scenarios. The scenarios are written using a
ceterus paribus principle. Hence, the scenarios are probably too extreme or specific to actually have the possibility to occur. Nevertheless the most likely situation will be somewhere between the current scenarios and exploring the extremes can help us understand how society
will respond on such intermediate situations.
Obviously, chance exists that time, new innovation, and other research could lead to completely new scenarios which are not covered in this thesis. Hence, new research should always
include the newest trends and developments.
To sum up the thesis: it is a first step towards a better understanding of the impact of the
actions of today on future society.
To see the forest for its trees,
this thesis uses specific literature,
searched for by a specific methodology,
to provide a clear portrayal of a few specific trees,
leaving a multitude of unique other trees covered in a fog-like cloud.
29
References:
Alcaniz, M., Rey, B., (2005). New technologies for ambient intelligence. In: Riva, G., Vatalaro, F.,
Davide, F., Alcaniz, M. (Eds.), Ambient Intelligence: The Evolution of Technology, Communication and Cognition Towards the Future of Human–Computer Interaction. IOS Press, Amsterdam, 3–15.
Alvi, S. A., Afzal, B., Shah, G. A., Atzori, L., & Mahmood, W. (2015). Internet of multimedia
things: Vision and challenges. Ad Hoc Networks, 33, 87-111.
Amankwah-Amoah, J. (2015). Safety or no safety in numbers? Governments, big data and
public policy formulation. Industrial Management & Data Systems, 115(9), 1596-1603.
Antonic, A., Roankovic, K., Marjanovic, M., & Pripuic, K. (2014). A mobile crowdsensing ecosystem enabled by a cloud-based publish/subscribe middleware. In Future Internet of Things
and Cloud (FiCloud), 2014 International Conference on (pp. 107-114). IEEE.
Arsénio, A., Serra, H., Francisco, R., Nabais, F., Andrade, J., & Serrano, E. (2014). Internet of
intelligent things: Bringing artificial intelligence into things and communication networks. In
Inter-cooperative Collective Intelligence: Techniques and Applications (pp. 1-37). Springer
Berlin Heidelberg.
Atzori, L., Carboni, D., & Iera, A. (2014a). Smart things in the social loop: Paradigms, technologies, and potentials. Ad Hoc Networks, 18, 121-132.
Atzori, L., Iera, A., Morabito, G., & Nitti, M. (2012). The Social Internet of Things (SIoT) – When
social networks meet the Internet of Things: Concept, architecture and network characterization. Computer networks, 56, 3594–3608.
Atzori, L., Iera, A., & Morabito, G. (2014b). From" smart objects" to" social objects": The next
evolutionary step of the internet of things. Communications Magazine, IEEE, 52(1), 97-105.
Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace
automation. The Journal of Economic Perspectives, 29(3), 3-30.
Autor, D., H., & Dorn, D. (2013). The growth of low-skill service jobs and the polarization of
the US labor market. The American Economic Review, 103(5), 1553-1597.
Barassi, V., & Treré, E. (2012). Does Web 3.0 come after Web 2.0? Deconstructing theoretical
assumptions through practice. New media & society, 14(8), 1269-1285.
Bell labs. (2013). Metro network traffic growth: an architecture impact study. Alcatel-Lucent.
Strategic white paper.
Boggs, R., Chute, C., & DuBois, L. (2015). The Growth Opportunity for SMB Cloud and Hybrid
30
Business Continuity, IDC, white paper.
Botta, A., de Donato, W., Persico, V., & Pescapé, A. (2016). Integration of Cloud computing
and Internet of Things: A survey. Future Generation Computer Systems, 56, 684-700.
Bradshaw, L. (2013). Big data and what it means. US Chamber of Commerce Foundation.
Brynjolfsson, E., & McAfee, A. (2012). Race against the machine: How the digital revolution is
accelerating innovation, driving productivity, and irreversibly transforming employment and
the economy. MIT Sloan, Research brief.
Che, D., Safran, M., & Peng, Z. (2013). From big data to big data mining: challenges, issues,
and opportunities. In Database Systems for Advanced Applications (pp. 1-15). Springer Berlin
Heidelberg.
Coase, R. H. (1937). The nature of the firm. economica, 4(16), 386-405.
Collingridge, D. (1980). The social control of technology. London: Pinter.
Darwish, A., & Lakhtaria, K. I. (2011). The impact of the new Web 2.0 technologies in communication, development, and revolutions of societies. Journal of Advances in Information Technology, 2(4), 204-216.
Datta, S. P. A. (2015). Dynamic socio-economic disequilibrium catalyzed by the Internet of
Things. Journal of Innovation Management, 3(3), 4-9.
Da Xu, L., He, W., & Li, S. (2014). Internet of things in industries: A survey. IEEE Transactions
on Industrial Informatics, 10(4), 2233-2243.
de Bruin, B., & Floridi, L. (2016). The Ethics of Cloud Computing. Science and engineering
ethics, 1-19.
Deaton, A. (2013). The great escape: health, wealth, and the origins of inequality. Princeton
University Press.
Demil, B., & Lecocq, X. (2006). Neither market nor hierarchy nor network: The emergence of
bazaar governance. Organization studies, 27(10), 1447-1466.
Ellison, N. B., Vitak, J., Gray, R., & Lampe, C. (2014). Cultivating social resources on social network sites: Facebook relationship maintenance behaviors and their role in social capital processes. Journal of Computer‐Mediated Communication, 19(4), 855-870.
Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996). Knowledge Discovery and Data Mining: Towards a Unifying Framework. Proc. 2nd Int. Conf. on Knowledge Discovery and Data
Mining, Portland, OR, 96, 82-88.
31
Feng, A., & Graetz, G. (2015). Rise of the machines: the effects of labor-saving innovations on
jobs and wages. Centre for Economic Performance.
Floris, A., & Atzori, L. (2015). Quality of Experience in the Multimedia Internet of Things: Definition and practical use-cases. In Communication Workshop (ICCW), 2015 IEEE International
Conference on, IEEE, 1747-1752.
Francis, J. J., Eccles, M. P., Johnston, M., Walker, A., Grimshaw, J., Foy, R., ... & Bonetti, D. (2004).
Constructing questionnaires based on the theory of planned behaviour. A manual for health
services researchers, 2010, 2-12.
Franck, T. M. (2008). The Centipede and the Centrifuge: Principles for the Centralisation and
Decentralisation of Governance, in: Broude, T., & Shany, Y. (Eds.). (2008). The shifting allocation of authority in international law: considering sovereignty, supremacy and subsidiarity.
Bloomsbury Publishing.
Frey, C. B., & Osborne, M. A. (2013). The Future of Employment: How Susceptible are Jobs to
Computerization? Oxford Martin School.
Fuchs, C., Hofkirchner, W., Schafranek, M., Raffl, C., Sandoval, M., & Bichler, R. (2010). Theoretical foundations of the web: cognition, communication, and co-operation. Towards an understanding of Web 1.0, 2.0, 3.0. Future Internet, 2(1), 41-59.
Gantz, J., & Reinsel, D. (2012). The digital universe in 2020: Big data, bigger digital shadows,
and biggest growth in the far east. IDC iView: IDC Analyze the Future, 2007, 1-16.
Goos, M., Manning, A., & Salomons, A. (2008). Recent changes in the European employment
structure: the roles of technology, globalization and institutions (No. urn: hdl:
123456789/208133). Katholieke Universiteit Leuven.
Grimes, A. J. (1978). Authority, power, influence and social control: A theoretical synthesis.
Academy of Management Review, 3(4), 724-735.
Hajli, M. (2014). A study of the impact of social media on consumers. International Journal of
Market Research, 56(3), 387-404.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of
“big data” on cloud computing: review and open research issues. Information Systems, 47,
98-115.
Hu, H., Wen, Y., Chua, T. S., & Li, X. (2014). Toward scalable systems for big data analytics: A
technology tutorial. IEEE Access, 2, 652-687.
ITU. (2005). The internet of Things. International Telecommunication Union. Executive sum-
32
mary.
ITU. (2015). ICT Facts and Figures. International Telecommunication Union.
Jaimovich, N. and Siu, H.E. (2012). The trend is the cycle: Job polarization and jobless recoveries. Tech. Rep., NBER Working Paper No. 18334, National Bureau of Economic Research.
Jordan, M., I., & Mitchell, T., M. (2015). Machine learning: Trends, perspectives, and prospects.
Science, 349, 255-261.
Kaivo-oja, J., Virtanen, P., Jalonen, H., & Stenvall, J. (2015). The effects of the internet of
Things and big data to organizations and their knowledge management practices. In International Conference on Knowledge Management in Organizations (pp. 495-513). Springer International Publishing.
Koreshoff, T. L., Robertson, T., & Leong, T. W. (2013). Internet of things: a review of literature
and products. In Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration, ACM, 335-344
Kreps, D., & Kimppa, K. (2015). Theorising Web 3.0: ICTs in a changing society. Information
Technology & People, 28(4), 726-741.
Kreuter, M. W., De Rosa, C., Howze, E. H., & Baldwin, G. T. (2004). Understanding wicked problems: a key to advancing environmental health promotion. Health Education & Behavior,
31(4), 441-454.
Kropff, M. J. (2007). Science for Impact; Impact for Science Academic values in use. Dies
Natalis speech.
Kumbhar, H., Kulkarni, R., & Limkar, S. (2014, January). Legacy to Web 2.0. In Proceedings of
the International Conference on Frontiers of Intelligent Computing: Theory and Applications
(FICTA) 2013 (pp. 517-525). Springer International Publishing.
Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. T. (2010). A survey of
mobile phone sensing. Communications Magazine, IEEE, 48(9), 140-150.
Lawrence, E. Sides, J. and Farrell, H. (2010). Self-Segregation or Deliberation? Blog Readership, Participation, and Polarization in American Politics. Perspectives on Politics, 8(1). 141157.
Lennerfors, T. T., Fors, P., & van Rooijen, J. (2015). ICT and environmental sustainability in a
changing society: The view of ecological World Systems Theory. Information Technology &
People, 28(4), 758-774.
Lindeman, M., & Verkasalo, M. (2005). Measuring values with the short Schwartz's value sur-
33
vey. Journal of Personality Assessment, 85(2), 170-178.
Linton, P., & Jaokar A. (2014). The digital world in 2030 what place for Europe? European internet foundation. Report.
Malsch, I. (2013). Governing nanotechnology in a multi-stakeholder world. Nanoethics, 7(2),
161-172.
Manovich, L. (2009). The practice of everyday (media) life: from mass consumption to mass
cultural production?. Critical Inquiry, 35(2), 319-331.
Manyika, J., Chui, M., Bughin, J., Dobbs, R., Bisson, P., & Marrs, A. (2013). Disruptive technologies: Advances that will transform life, business, and the global economy. McKinsey Global
Institute. San Francisco, CA, 180, 1-162.
Manyika, J., Chui M., Bisson, P., Woetzel, J., Dobbs, R., Bughin, J., & Aharon, D. (2015). The Internet of Things: mapping the value beyond the hype. Executive summary. McKinsey Global
Institute.
Mashal, I., Alsaryrah, O., Chung, T., W., Yang, C., Z., Kuo, W., H., & Agrawal, D., P. (2015).
Choices for interaction with things on Internet and underlying issues. Ad hoc Networks, 28,
68–90.
Meerja, K. A., & Almustafa, K. (2016). Big Data Outburst due to Wireless Internet of Things.
International Journal of Computer Science and Information Security, 14(5), 31-41.
Mejtoft, T. (2011). Internet of Things and Co-creation of Value. In Internet of Things (iTh-
ings/CPSCom), 2011 International Conference on and 4th International Conference on Cyber,
Physical and Social Computing (pp. 672-677). IEEE.
Melin, H. (2015). Consumer empowerment in the Internet of Things: A silent unfolding of a
“new normal” where code trumphs rights? International In-house Counsel Journal, 8(30), 111.
Mell, P. & Grance, T. (2011). The NIST definition of cloud computing. National Institute of
Standards and Technology, Special publication 800-145.
Michaels, J. D. (2015). An Enduring, Evolving Separation of Powers. Columbia Law Review,
515-597.
Mills, S., Lucas, S., Irakliotis, L., Rappa, M., Carlson, T., & Perlowitz, B. (2012). Demystifying big
data: a practical guide to transforming the business of government. TechAmerica Foundation,
Washington.
Miorandi, D., Sicari, S., De Pellegrini, F., & Chlamtac, I. (2012). Internet of things: Vision, appli-
34
cations and research challenges. Ad Hoc Networks, 10(7), 1497-1516.
Mitchel, D., W., & Coles, C., B., (2004). Establishing a continuing business model innovation
process. Journal of business strategy, 25(3), 39-49.
Moe, T. M. (1989). The politics of bureaucratic structure. Can the government govern, 267,
285-323.
Morrison v. Olson, 487 U.S. 654, 697–98 (1988) (Scalia, J., dissenting). Retrieved from:
https://supreme.justia.com/cases/federal/us/487/654/case.html Retrieved at: 17-02-2017
Newman, R., Chang, V., Walters, R. J., & Wills, G. B. (2016). Web 2.0—The past and the future.
International Journal of Information Management, 36(4), 591-598.
Nordhaus, W. D. (2007). Two centuries of productivity growth in computing. The Journal of
Economic History, 67(1), 128-159.
Noronha, A., Moriarty, R., O’Connell, K., & Villa, N. (2014). Attaining IoT Value: How To Move
from Connecting Things to Capturing Insights. Cisco.
O'Connell, A. J. (2006). The architecture of smart intelligence: structuring and overseeing
agencies in the post-9/11 world. California Law Review, 94(6), 1655-1744.
Osseiran, A., Boccardi, F., Braun, V., Kusume, K., Marsch, P., Maternia, M., ... & Fallgren, M.
(2014). Scenarios for 5G mobile and wireless communications: the vision of the METIS project.
Communications Magazine, IEEE, 52(5), 26-35.
Papazoglou, M. P., Traverso, P., Dustdar, S., & Leymann, F. (2007). Service-oriented computing: State of the art and research challenges. Computer, (11), 38-45.
Patrignani, N., & Whitehouse D., (2015). Slow tech: bridging computer ethics and business
ethics. Information Technology & People. 28(4). 775-789.
Poniszewska-Maranda, A., & Kaczmarek, D. (2015). Selected methods of artificial intelligence
for Internet of Things conception. In Computer Science and Information Systems (FedCSIS),
2015 Federated Conference on (pp. 1343-1348). IEEE.
Powell, W., W., (1990). Neither market nor hierarchy: network forms of organization. Research
in organizational behavior. 12. 295-336.
Rifkin, J. (2014). The zero marginal cost society: The internet of things, the collaborative
commons, and the eclipse of capitalism. Palgrave Macmillan.
Rudman, R. J. (2010). Incremental risks in Web 2.0 applications. The Electronic Library, 28(2),
210-230.
35
Rudman, R., & Bruwer, R. (2016). Defining Web 3.0: opportunities and challenges. The Electronic Library, 34(1), 132-154.
Sachs, J. D., Benzell, S. G., & LaGarda, G. (2015). Robots: Curse or Blessing? A Basic Framework
(No. w21091). National Bureau of Economic Research.
Schwab, K., & Samans, R., (2016). The Future of Jobs. World economic forum, Global Challenge Insight Report.
Sicari, S., Rizzardi, A., Miorandi, D., Cappiello, C., & Coen-Porisini, A. (2016). A secure and
quality-aware prototypical architecture for the Internet of Things. Information Systems, 58,
43-55.
Sivarajah, U., Irani, Z., & Jones, S. (2014, January). Application of Web 2.0 technologies in EGovernment: A United Kingdom case study. In 2014 47th Hawaii International Conference on
System Sciences (pp. 2221-2230). IEEE.
Soldani, D., & Manzalini, A. (2015). Horizon 2020 and Beyond: On the 5G Operating System
for a True Digital Society. Vehicular Technology Magazine, IEEE, 10(1), 32-42.
The Economist. (2014). A Cambrian moment. Special report tech startups.
The Hague. (1907). Convention (IV) respecting the Laws and Customs of War on Land and its
annex: Regulations concerning the Laws and Customs of War on Land.
Thomas, D., Paul, G., & Irvine, J. (2015). Going Beyond the User—The Challenges of Universal
Connectivity in IoT. Wireless world research forum. 1-4
Van den Hoven, J. (Ed.). (2013). Options for Strengthening Responsible Research and Innovation. Report of the Expert Group on the State of Art in Europe on Responsible Research and
Innovation. Publications Office of the European Union.
Vaquero, L. M., & Rodero-Merino, L. (2014). Finding your way in the fog: Towards a comprehensive definition of fog computing. ACM SIGCOMM Computer Communication Review,
44(5), 27-32.
Verizon Business. (2010). Web 3.0: Its Promise and Implications for Consumers and Business.
White paper.
Retrieved from: http://www.verizonbusiness.com/resources/whitepapers/wp_web‐3‐0‐
promise‐and‐implications_en_xg.pdf retrieved at: 01-06-2016
Which? (2013). Consumers in 2030 Forecasts and projections for life in 2030. Demography,
Urbanization and Migration. UK
36
Whitmore, A., Agarwal, A., & Da Xu, L. (2015). The Internet of Things—A survey of topics and
trends. Information Systems Frontiers, 17(2), 261-274.
Wolff, J. G. (2014). Big data and the SP theory of intelligence. Access, IEEE, 2, 301-315.
World Economic Forum, (2012). Big Data, Big Impact: New Possibilities for International Development, World Economic Forum Report
Yan, Z., Zhang, P., & Vasilakos, A. V. (2014). A survey on trust management for Internet of
Things. Journal of network and computer applications, 42, 120-134.
Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of things for smart
cities. IEEE Internet of Things Journal, 1(1), 22-32.
Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: state-of-the-art and research
challenges. Journal of internet services and applications, 1(1), 7-18.
Zhou, L., & Chao, H. C. (2011). Multimedia traffic security architecture for the internet of
things. IEEE Network, 25(3), 35-40.
Zwass, V. (2010). Co-creation: Toward a taxonomy and an integrated research perspective.
International Journal of Electronic Commerce, 15(1), 11-48.
Zwick, D., Bonsu, S. K., & Darmody, A. (2008). Putting Consumers to Work Co-creation and
new marketing govern-mentality. Journal of consumer culture, 8(2), 163-196.
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Appendix I – SVSS Questions
The students were asked to rate the importance as a life guiding principle of the following
ten items, using a seven point scale from -1 (against my principles) to 5 (of supreme importance), based on the research of Lindeman and Verkasalo (2005):










“Power, that is, social power, authority, wealth.”
“Achievement, that is, success, capability, ambition, influence on people and events.”
“Hedonism, that is, gratification of desires, enjoyment in life, an exciting life, selfindulgence.”
“Stimulation, that is, daring, a varied and challenging life, an exciting life.”
“Self-Direction, that is, creativity, freedom, curiosity, independence, choosing one’s
own goals.”
“Universalism, that is, broadmindedness, beauty of nature and arts, social justice, a
world at peace, equality, wisdom, unity with nature, environmental protection.”
“Benevolence, that is, helpfulness, honesty, forgiveness, loyalty, responsibility.”
“Tradition, that is, respect for tradition, humbleness, accepting one’s portion in life,
devotion, modesty.”
“Conformity, that is, obedience, honoring parents and elders, self-discipline, politeness.”
“Security, that is, national security, family security, social order, cleanliness, reciprocation of favors.”
38
Appendix II – Scenario descriptions
General introduction
This general introduction was used to introduce each scenario.
“After Web 1.0, the static web browsers, Web 2.0, a web in which users participate, Web 3.0 is
on its upsurge. Web 3.0 will make the internet pervasive throughout every aspect of life, using both object-to-object and object-to-human communication (including tracking, tracing,
and analyzing).
Web 3.0 enabling technologies are the Internet of Things, Big Data, the Cloud, Artificial Intelligence, and a proper infrastructure (5G). The networking and inter-enhancing effects of the
combined technologies will increase digitization and foster an ever growing serviceeconomy. A main impact of Web 3.0 will be the polarization of work (a growing reduction of
middle wage jobs).
On the next page one futuristic scenario based on the current developments is described.
Read it carefully and try to imagine its impact on the world.”
Scenario I
“Web 3.0 will have such impact on the amount of jobs available, that there will not be
enough jobs to employ everyone. Products will become cheaper and cheaper, with result that
most basic necessities can be produced at nearly zero marginal costs. Thereby, most products will be sold as services (e.g. light vs. light bulbs; usage vs. ownership), which is possible
because tracking product specific (usage) data can be analyzed in real-time. As result purchasing costs for products will decrease as does the value of personal ownership.
To be more specific: cars can communicate via the internet, so they can tell when, where, or
how they are in use. If a car is linked to a network (function of Web 2.0) then people in the
network can request its usage as a transportation service. This function decreases the need
for personal ownership and increases the value of the community (network).
In this scenario, having a job is not a necessity for survival anymore. Jobs get substituted by
digitization which is why people will invent new ways to be productive. People will add value
to their lives by being creative using network linked platforms (e.g. by designing new features/creations/innovations/etc.).
When most objects are linked to the internet, it will become custom to be part of communities around products, services, neighborhoods, and practically of anything of a person’s interest. Communities will be filled with people who will jointly change, add, improve, their
products, services, neighborhoods, creating a (mass) crowd-based regulatory innovationsystem based on cooperation.”
Scenario II
“Digitization in combination with a service economy will increase productivity, but it will not
substitute demand for all manual labor, it rather will be complementary. The current societal
economic structure will prohibit the opportunistic leaders of the ever more digitized multinationals to give up their influence. In fact, in this scenario ICT related businesses will do everything in their power to become, or remain, a leading global player.
New and radical Web 3.0 innovations will be quickly introduced and adopted. This will hap-
39
pen so fast that governments are not able to anticipate anymore, and they will become responders. This creates the opportunity for companies to enter unregulated territories/markets (e.g. drones, Uberpop).
Two main types of businesses will take advantage of this opportunity, (1) the small business
entrepreneur that will enter an unregulated/new market with the aim to become a dominant
player, and (2) the big established ICT companies that will try to become cornerstones of an
increasingly borderless global society.
In this scenario ICT-capitalists will become oligo-/monopolists with respect to data ownership. The data will be gathered and mined for current business practices, but will also be
used as leverage to suppress regional governments.
As an example of the last statement, think of the societal impact if Microsoft would demand
any European country to pass a specific bill or they will stop delivering/securing its products/services to that specific country.”
Scenario III
“Governments have to cope with many global challenges (e.g. urbanization, globalization,
global warming, an ageing society, job polarization, etc.). For many of these issues Web 3.0
can be part of the solution. There are already some examples tested in practice, like:
(1) Using new innovations patient characteristics can better be tracked and traced, making
visits (health checks) to hospitals redundant and reducing costs, or
(2) Using new innovations governments can built smart cities in which traffic, pollution, and
noise gets mapped and taken care of (e.g. smart parking spots can show drivers where parking space is available).
In this scenario governments see the accelerating speed of new product introductions as a
threat. New innovations are accepted, or demanded, by society before governments have
time to create the necessary legislation (e.g. Uberpop, drones). This generates a legislativevoid (opportunity) to be exploited by companies.
In order to maintain and strengthen their position, governments will harden rules and regulation regarding data control and ownership. These new regulations will be in their own favor,
making the governments monopolists of valuable data streams.
In this scenario the governments will not only use the captured data for informed policy reasons, but also to track, trace, and penalize companies/businesses/communities/individuals
that have their own opportunistic agenda.”
Scenario IV
“In this scenario three main types of actors will rule the world. The first actors are the governments that will set rules and regulation on regional (country) level. The second actors are
big ICT multinationals who provide the infrastructure of the (global) digital society, and own
and exploit massive amounts of data. The third actors are communities; communities can
either be active locally (e.g. neighborhood control), or globally (e.g. anonymous).
Web 3.0 will increase the polarization of jobs, which means that a fraction of the people will
end up as executives of the multinationals. Most people, however, will be stuck in a low-wage
40
job, or will not be able to get a job at all. The latter groups will form communities around
products/projects/services of their interest (e.g. Wikipedians).
As the current world is fragmented, the three actors will act different depending on their respective region. Two main types of societal structure will occur.
(1) The three different actors keep themselves in a balanced equilibrium in which every actor
has enough capability to not get overpowered by another actor.
(2) The different actors end up in a continuous struggle, in which every actor acts as opportunistic as possible (e.g. disregarding the authority of the other actors), leading to overall
chaos (e.g. Microsoft will neglect national laws, because governments do not have the power
and means to stop/forbid/ban Microsoft).
Predictions about which region will most likely end up with which type of societal structure is
beyond the scope of this survey.”
41
Appendix III – Questions attitude, power distribution, and
preferred governance structure
The first questions were used to assess the attitude of the allocation of the powers in the
scenario. The measure on attitude was constructed using the direct measures of Francis et al.
(2004) as guideline.
Within every scenario different actors possess the three powers described by Montesquieu,
the law-making-, the executive-, and the controlling power, whether this is perceived as acceptable is measured in the second set of questions.
The third set of questions was used to measure which governance structure fits best by the
dominant actor of the scenario. It is also interesting to know whether, regardless of the scenarios, different types of governance structures are preferred over others.
First the attitude was tested. Francis et al. (2004) uses four bipolar adjectives on a seven-point
Likert scale. These four bipolar adjectives have been transcribed to eight unipolar adjectives,
since some scenarios can be perceived as having both positive and negative aspects at the
same time. The following questions were asked:
“This scenario is:
Low
High
 Bad
(1)
…
(7)
 Worthless
(1)
…
(7)
 Unpleasant
(1)
…
(7)
 Harmful
(1)
…
(7)
 Useful
(1)
…
(7)
 Good
(1)
…
(7)
 Beneficial
(1)
…
(7)
 Pleasant
(1)
…
(7)”
Second, the students were asked whether the powers in a given scenario are allocated to the
desired actor, using a seven point scale.
“In order to improve the given scenario, indicate how much influence each actor should have
with regard to the following aspects: (1 = low; 7 = high)
o In the given scenario, rules and regulation would best be created by:
Government
(1)
…
(7)
Corporations
(1)
…
(7)
The commons/communities
(1)
…
(7)
A combination of the above
(1)
…
(7)
o In the given scenario, rules and regulation would best be maintained by:
Government
(1)
…
(7)
Corporations
(1)
…
(7)
The commons/communities
(1)
…
(7)
A combination of the above
(1)
…
(7)
o
In the given scenario, the maintaining of the rules and regulation would best be in-
42
spected by:
Government
Corporations
The commons/communities
A combination of the above
(1)
(1)
(1)
(1)
…
…
…
…
(7)
(7)
(7)
(7)”
Third, the students were asked how favorable the different governance structures are in the
given scenarios, using a seven point scale.
 The following governance structure is favorable as dominant in the prescribed scenario:
[Market:] the dominant actor(s) will act when there is enough demand to do so
(1) Strongly disagree
…
(7) Strongly agree
[The Firm:] the dominant actor(s) will act according to a structure/protocol via a line
of hierarchy
(1) Strongly disagree
…
(7) Strongly agree
[The network:] the dominant actor(s) will act in collaboration with the actions of
other actors
(1) Strongly disagree
…
(7) Strongly agree
[The bazaar:] the dominant actor(s) will act when there is enough positive attitude to
do so
(1) Strongly disagree
…
(7) Strongly agree
The dominant actor(s) will act when there is enough negative attitude to do so
(1) Strongly disagree
…
(7) Strongly agree
43
Appendix IV – values derived from SVSS
ANOVA
Sum of
Squares
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Between Groups
df
Mean Square
,587
3
,196
Within Groups
130,759
100
1,308
Total
131,346
103
4,809
3
1,603
Within Groups
104,730
100
1,047
Total
109,538
103
1,599
3
,533
Within Groups
88,391
100
,884
Total
89,990
103
,341
3
,114
Within Groups
54,649
100
,546
Total
54,990
103
,887
3
,296
Within Groups
108,873
100
1,089
Total
109,760
103
4,901
3
1,634
Within Groups
147,561
100
1,476
Total
152,462
103
,525
3
,175
Within Groups
115,234
100
1,152
Total
115,760
103
,183
3
,061
Within Groups
124,701
100
1,247
Total
124,885
103
,948
3
,316
Within Groups
147,052
100
1,471
Total
148,000
103
1,105
3
,368
Within Groups
82,433
100
,824
Total
83,538
103
Between Groups
Between Groups
Between Groups
Between Groups
Between Groups
Between Groups
Between Groups
Between Groups
Between Groups
F
Sig.
,150
,930
1,531
,211
,603
,615
,208
,891
,271
,846
1,107
,350
,152
,928
,049
,986
,215
,886
,447
,720
44