UTRECHT UNIVERSITY
Academia 2.0
Using Hofstede’s cultural dimensions to study the
relationship between ICTs and the Dutch academic
culture
Elisabeth Margot van Gent – 3183017
9/14/2014
Master Thesis:
New Media and Digital Culture
Tutor/first reader: Erik Huizer
Second reader:
Marianne van den Boomen
Contents
Abstract ................................................................................................................................................... 2
Chapter 1. Introduction ........................................................................................................................... 3
Chapter 2. Theoretical framework ........................................................................................................... 5
2.1 introduction ................................................................................................................................... 5
2.2 Power Distance Index in relation to ICTs: ...................................................................................... 5
2.3 Uncertainty Avoidance Index in relation to ICTs: ........................................................................... 6
2.4 Individualism vs collectivism in relation to ICTs: ............................................................................ 6
2.5 Masculinity vs Femininity in relation to ICTs: ................................................................................. 7
2.6 Critique on Hofstede’s theory ........................................................................................................ 8
2.7 Culture ........................................................................................................................................... 8
2.8 Subcultures .................................................................................................................................... 9
2.9 Digital immigrants versus digital natives ...................................................................................... 10
2.10 Digital humanities and the divide .............................................................................................. 11
2.11 Perceived ease of use ................................................................................................................ 12
Chapter 3. Method ................................................................................................................................ 12
3.1 Introduction ................................................................................................................................. 12
3.1 Participants .................................................................................................................................. 13
3.2 Justification of questionnaire ....................................................................................................... 14
3.3 Data collection ............................................................................................................................. 16
3.4 Measuring instrument ................................................................................................................. 16
3.5 Data analysis ................................................................................................................................ 16
Chapter 4. Results .................................................................................................................................. 17
4.1 Perceived ease of use .................................................................................................................. 17
4.2 Power Distance Index .................................................................................................................. 18
4.3 Uncertainty Avoidance Index ....................................................................................................... 19
4.4 Individualism versus Collectivism ................................................................................................. 20
4.5 Masculinity versus Femininity ...................................................................................................... 21
4.6 Comparing the disciplines ............................................................................................................ 22
Chapter 5. Findings ................................................................................................................................ 23
5.1 Perceived ease of use .................................................................................................................. 23
5.2 Power Distance Index .................................................................................................................. 24
5.3 Uncertainty Avoidance Index ....................................................................................................... 25
5.4 Individualism versus Collectivism ................................................................................................. 25
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5.5 Masculinity versus Femininity ...................................................................................................... 25
5.6 Comparing the disciplines ............................................................................................................ 27
Chapter 6. Conclusion ............................................................................................................................ 28
6.1 Limitations ................................................................................................................................... 28
6.2 Recommendations ....................................................................................................................... 28
6.3 Conclusion ................................................................................................................................... 29
8. Literature ........................................................................................................................................... 30
Appendix A. Questionnaire .................................................................................................................... 34
Appendix B. ............................................................................................................................................ 36
Confirmatory Factor Analiysis ............................................................................................................ 36
Independent Sample T-test ............................................................................................................... 38
Non-Parametric Mann-Withney U ..................................................................................................... 40
Tests of Between-Subjects Effects ..................................................................................................... 42
Abstract
This study compares two subcultures in the Dutch academic world in order to investigate the
relationship of mutual shaping between academia and Information and Communication Technologies
(ICTs). The two groups are divided according to year of birth, academics who were born before 1980
are considered digital immigrants, academics born after 1980 are considered digital natives. By doing
so, the influence of growing up with ubiquitous ICTs was measured. The data was collected in the form
of a cross-sectional survey. The results of this survey were analysed using Hofstede’s four dimensions
of culture (Power Distance Index, Uncertainty Avoidance Index, Individualism vs. Collectivism and
Masculinity vs. Femininity). The outcomes show significant difference in two of the four dimensions,
namely Power Distance index and Masculinity vs. Femininity. I suggest potential clarifications for these
outcomes and, due to the low significant difference between the groups, possible other ways of
studying the effects of ICTs on Dutch academia and vice versa.
Keywords: Academia, ICTs, (sub)-culture, digital immigrants, digital natives
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Chapter 1. Introduction
Over the past two decades, the use of Information and Communication Technologies (ICTs) in
academia has gone through an unprecedented growth. As such, there has been a large amount of
research conducted in the field of ICT use in companies and in home situations (Calhoun, et al., 2008;
Dewett & Jones, 2001; Fulk & boyd, 1; Stohl, 2001), yet there is still a hiatus in the literature when it
comes to ICTs and academia. This is of concern, considering the fact that these new ways of
communicating, collaborating, collecting, storing and distributing data are changing basic aspects of
conducting academic research. In addition, there has been a lot of attention for the way ICTs change
societies and groups, but how these societies and groups are changing (the use of) ICTs is still a
relatively unprecedented field. ICTs have become ubiquitous in academia and most research related
practices are now depended on these technologies.
The ICTs discussed are always being used in a social context therefore it is important to investigate the
way people from different cultures affect and are being affected by the ICTs they use. This is why this
study proposes to investigate the relationship of mutual shaping between academia and ICTs.
In 1996, Robin Williams and David Edge wrote an article about this phenomenon, named “The Social
Shaping of Technology”. This study critiques ‘the social shaping of technology’ (SST) theory as
proposed by MacKenzie and Wajcman (1985) and proposes to examine more than just the outcomes
of technological change, by incorporating ‘the content of technology and the particular processes
involved in innovation’ (Williams and Edge, 1996. P 865). In other words; the two propose to view the
matter of influence between culture and ICT as a circular motion, instead of a one-way street. Each of
the two elements can result in changes in the other because culture is a part of technology and the
other way around. In other words; cultures might influence ICT use and ICT use might affect cultures.
“Neither we nor ICTs are slaves to the other” (Slack & Wise, 2002. 486). This statement shows that
there is no principal force; ICTs are the focus of meaning construction, as well as the enablers of
meaning construction. Within the academic culture, as within most other cultures, “the ICTs and
communicative actions have become inseparable” (Silverstone & Haddon, 1996).
Because ICT and culture are both in constant flux, empirical research remains important to further
explain this symbiotic relationship of mutual shaping. The significance here is that once one
understands the different facets of the relationship between ICTs and academia, it is easier to adapt
the development of ICTs to a specific market, like academia, in a much more efficient way.
In the Netherlands, there has been a lack of research focussing on the use of ICTs among academics
and students, especially compared to countries like the United States and Great Britain (Betts, 2003;
La Velle et al., 2003; Osborne & Hennesy, 2003; Trindade, 2002; etc.). Therefore this thesis offers a
new way of investigating the relationship of mutual shaping by using Hofstede’s cultural dimensions.
This theory, developed by the Dutch psychologist Gerard Hofstede, is a framework for cross-cultural
communication. The use of this well-known theory was chosen because when one investigates two
groups with different backgrounds, it becomes clear what the differences are between those groups
and with that, what the influence of the different backgrounds is.
According to Hofstede, using this theory, one gains insight in the effects of a society’s ‘culture’ on the
values of its members (Hofstede 1991). As this study is focussed on the relationship of mutual shaping
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between academic culture and ICTs in the Netherlands, it was decided to classify two sub-cultures
within Dutch Academia, namely digital natives and digital immigrants. According to the Oxford English
Dictionary, a digital native is a person born or brought up during the age of digital technology and so
familiar with computers and the Internet from an early age.1 Prensky, who coined the terms in 2001,
explained that, in the history of education, it has been normal for students to differ from previous
generations. However, because of the arrival and the omnipresence of ICTs, he believes a really big
discontinuity has taken place;
“One might even call it a “singularity” – an event which changes things so fundamentally that
there is absolutely no going back. This so-called “singularity” is the arrival and rapid
dissemination of digital technology in the last decades of the 20th century”(Prensky, 2001a.
P1)
To underline this development, Prensky introduced the term digital immigrant. Digital immigrants can
be seen as the direct opposites of digital natives. As a disclaimer, he states that every Immigrant
adapts himself at his own pace.
“As Digital Immigrants learn – like all immigrants, some better than others – to adapt to their
environment, they always retain, to some degree, their "accent," that is, their foot in the past.
The “digital immigrant accent” can be seen in such things as turning to the Internet for
information second rather than first, or in reading the manual for a program rather than
assuming that the program itself will teach us to use it” (Prensky, 2001a. P2)
By using Hofstede’s theories on cultural dimensions several differences and similarities between the
two sub-cultures should emerge. The interesting part about Hofstede’s theory is that it focuses on the
specifics of the cultural dimensions of a culture, allowing the research to compare two groups,
whether they are completely different or (partly) similar. This research will show if and if so, how
digital natives and digital immigrants differ from each other. By doing that, an understanding of how
ICTs are shaping the scientific community and vice versa might emerge.
In order to define the mutual influence of academia and use of ICTs the following question was
composed:
To what extent is there a distinction in use of ICTs between digital natives and digital immigrants and
is this distinction measurable using Hofstede’s cultural dimensions?
In this study, the relationship of mutual shaping as described by Williams and Edge (1996) between
ICTs and (academic) culture is perceived as existing. Therefore the focus will not lie on the nature of
this relationship but on the influence of both elements. Secondly, this research will concentrate on
those elements in which the influence is most noticeable. Due to the fact that the method and the
theoretical framework are so intertwined, it was decided to first to set out the theoretical framework
in chapter 2. After that the methodology will be explained in full depth. In chapter 4 the results of the
analysis are set out and the findings of the analysis will be described in chapter 5. The conclusion is to
be found in chapter 6 and this study will be completed with future directions and limitations that are
discussed in chapter 7.
1
http://www.oxforddictionaries.com/definition/english/digital-native
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Chapter 2. Theoretical framework
2.1 introduction
How does one measure the mutual effects between ICTs and academia? This is not a question that is
easily answered, since these effects can vary widely and are often hard to identify because they
manifest themselves in processes. This makes it difficult to produce hard claims. As mentioned before,
to answer the main question, it has been decided to use a theory called Hofstede’s cultural
dimensions theory. It is, however, important to keep in mind that in this study, Hofstede’s cultural
dimensions will be used at a high level of abstraction. Due to time and space restrictions it is not
possible to fully employ the theory.
Hofstede developed the original model for cultural dimensions in the 1960s and 1970s to examine the
results of a world-wide survey of employee values by IBM. One of the most important aspects of this
particular theory is that by examining large sets of data, differences between cultures could be
explained and quantified.
The theory on cultural dimensions is originally based on four dimensions of culture; Power Distance
Index (PDI), Uncertainty Avoidance Index (UAI), Individualism versus collectivism (IDV) and Masculinity
versus femininity (MAS). These dimensions were identified through analysing group-level data
collected from IBM employees in 40 countries around the world. More recently a fifth and a sixth
dimension have been added: Indulgence vs Restraint (IND) and Pragmatic vs Normative (PRA). For this
study, however, I chose to use only the first four dimensions, due to restrictions in time and space.
The two dimensions that have been added later are mainly based on findings in Asia, and therefore
have no place in this study.
Hofstede argues that his framework “can serve to explain and understand observed similarities and
differences between matched phenomena in different countries” (Hofstede, 1991. P 14). The
differences between countries are, according to Hofstede, attributed to the national culture in those
countries. To clarify the dimensions Hofstede constructed, they are explained below with regards to
ICTs.
2.2 Power Distance Index in relation to ICTs:
“The Power Distance Index […] can be defined as the extent to which the less powerful members of
institutions and organizations within a country expect and accept that power is distributed unequally”
(Hofstede et al. 2010. P 61). The lower the score in this index - countries like Norway, the Netherlands
and the USA score quite low - the more limited the acceptation of power inequality and less
dependence of subordinates on bosses. Low scores also show a preference for consultation and
cooperation, in other words; there is more interdependence between higher and lower ranked
people. Along the same lines, contrary to high PDI cultures, where emphasis is put on showing one’s
identity and thereby revealing one’s status, in low PDI cultures people are more likely to mute their
identity since it has less bearing on the communication process and the outcome (Watson, Ho &
Raman, 1994).
The PDI for the Netherlands is relatively low, which would mean that the results of this study will
probably show that cooperation with the help of ICTs between lower and higher ranked academics is
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accepted and widely spread. Think for example of how social media influence the way subordinates
contact their boss or even their boss’s boss. ICTs make it easier for academics to discuss topics among
higher and lower ranked colleagues in an efficient and accessible manner. Similarly, where before
discussions took place in journals, now there are possibilities to do so online. The influence of ICT on
academia in this case is that it lowers the Power Distance Index, therefore lowering barriers between
high and low ranked academics.
The second element of the Power Distance Index focusses on conveying ones status. ICTs facilitate
myriad ways of conveying ones status, for example in the form of accomplishments and successes on
profile page. The hypothesis for this element is that due to the use of ICTs, the digital natives that
participate in this study are more prone to showcase their identity than digital immigrants, because of
the myriad ways to do so and the peer pressure that is created by the platforms or social networks.
This would mean that the overall PDI score would be increased, as the need of conveying ones statues
is a sign of high PDI cultures.
When looking at both elements of this index, the expected outcome is that there is no difference
between the groups, because the first hypothesis claims that the Power Distance Index for natives will
decrease and the second hypothesis claims that this index would be increased. When studying the
individual questions, however, it is expected that there is a difference between the two groups.
2.3 Uncertainty Avoidance Index in relation to ICTs:
Hofstede defines this concept as: the extent to which the members of a culture feel threatened by
uncertain or unknown situations (Hofstede, 1991. P13). How do users of the ICTs accommodate
ambiguity and uncertainty in the workplace? The Netherlands has a relative low score on the UAI. In
cultures like the Dutch culture, there is less need for predictability and written and unwritten rules to
guide tasks. Due to less rule dependency, these cultures are more trusting than their counterparts (De
Mooij, 2000). This may lead to early experimentation with, and adoption of, new ICTs and the use of
multiple technologies in their working tasks (Maitland and Bauer, 2001; Veiga et al., 2001). Hofstede
states that the level of uncertainty and ambiguity found in a culture profoundly affects how
institutions are organized and managed (Hofstede, 1983).
Hofstede listed specific characteristics of low UAI cultures, saying that managers often depend on
expert opinions of workers from lower down in the hierarchy. This will result in the fact that managers
don’t have to be experts in the field they manage (Hofstede, 1991). This works similarly in
interdisciplinary research in academia; with the use of ICT it is easier to collaborate, and assign specific
jobs to experts. Consistent with that logic, low UAI will therefore likely affect how individuals choose
media for their communication tasks (Straub, 1994).
The first hypothesis for this pillar is that digital immigrants are less prone to try new platforms and
therefore have a higher uncertainty avoidance index than natives. Subsequently, the second
hypothesis for this pillar is that natives are more likely to use multiple platforms, whereas immigrants
like to limit the amount of ICTs they use.
2.4 Individualism vs collectivism in relation to ICTs:
The third dimension focusses on the relationship between the individual and the group. This
dimension, as well as the next, uses a scale measurement and for this particular dimension cultures
are either labelled ‘individualistic’ or ‘collective’. On the one side of this dimension, called
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individualism, individual ties are loose and everyone is expected to look after themselves. In collective
societies, people are formed in strongly cohesive groups. The position of a (sub-) culture within this
dimension is mirrored in whether the self-image of the members of said cultures is defined in terms if
“we” or “I”.
In cultures that score highly on individualism, personal accomplishments and productivity are
emphasized. The time and effort required to establish or maintain relationships is often compromised
to get the job done. In short, in individualistic societies, the task will normally prevail over any personal
relationships (Hall, 1976). What is interesting to question about this subject with regards to ICT is for
example; do social networks influence individualism and collectivism? This pillar is especially thoughtprovoking, because it is arguable that the use of ICT can both influence individualism and collectivism.
In societies that score highly on collectivism, people feel that they belong in groups that take care of
them, paying these groups back in loyalty.
The first hypothesis here is that natives will score higher on individualism than immigrants with regards
to personal accomplishments; this is related to a phenomenon that was discussed in the chapter about
the Power Distance Index. ICTs challenge users to put more and more emphasis on personal features,
for example by providing extensive personal pages on LinkedIn or other social media, and therefore
seem to be enhancing individualism. In other words; natives will show more individualism on the
questions about personal accomplishments. On the other hand, the second hypothesis is that ICTs also
strengthen cohesiveness within groups. ICTs offer new, faster ways of forming groups and enhance
ways to maintain membership within these groups. Social media play in to the fact that human beings
love and need to be part of a group. This hypothesis focusses on the questions about cohesiveness
within groups.
This would mean that ICTs both play in to the individualistic as well as to the collective nature of
humans and with that, of academics. If this would be true, it would mean that the aspects of the
Collective versus Individualistic pillar in the case of academic culture may need to be revised.
2.5 Masculinity vs Femininity in relation to ICTs:
In Hofstede’s view, cultures are either masculine or feminine; masculinity stands for societies where
social gender roles are clearly distinct, feminine societies have blurred boundaries when it comes to
gender roles. Besides that, masculine cultures value assertiveness, and focus on success, while
feminine cultures value modesty, tenderness and quality of life (Hofstede, 1991). Given the value
placed on modesty in more “feminine” cultures, Triandis (1995) asserts that individuals from such
cultures don’t like to stand out – that is, be unique or conspicuous —unlike the more assertive and
career-seeking individuals found in countries that are more masculine.
Think about skills and endorsements on LinkedIn and other platforms for example. Research shows
that femininity crosses over from the space of home to the work space (e Silva & Sutko, 2009). In
feminine cultures, for example, it is more accepted to watch TV at work, or to check the news or your
personal online networks. Masculine societies have a stricter task orientation, in other words, rules
are stricter and people are expected to follow them (Hofstede, 1991).
The first hypothesis in this pillar is that the boundaries between work and home are less clear with
natives and thus natives are more feminine than immigrants. As the natives grew up surrounded by
ICTs, they might be better at dividing their attention between ICTs for personal and professional use.
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The second hypothesis is that ICTs give users more insight into others’ accomplishments, and therefore
focus on success and assertiveness. This is based on the assumption that natives are more sensitive to
success and assertiveness than immigrants because of this insight, which would make the natives
more masculine. Again this would mean that the aspects of Hofstede’s pillar of MAS might need to be
revised for academic culture.
2.6 Critique on Hofstede’s theory
Hofstede’s theory has been an important model for cultural research, but it has also endured quite
some criticism, which is typical for any pioneer model. This part of the theoretical framework will
focus on two themes of critique; individual stereotyping and the complexity of culture versus the
simplicity of the model.
One of the main problems with Hofstede’s model is that the derivation of individual level information
from group level data could possibly lead to incorrect stereotyping. Hofstede cautions his readers
about this mind set, but as Imwalle and Schillo demonstrated with their paper, Masculinity and
Femininity: The taboo dimension of national cultures, it is not easy to resist the temptation and
Hofstede does not provide the readers with suggestions to avoid this way of thinking (Imwalle and
Schillo, 2004).
Peter B. Smith, who wrote a paper about Culture’s Consequences in 2002, offers a viable solution to
this problem; the separate testing of hypotheses of both group-level and individual-level data. Even
though Hofstede recognizes studies like that conducted by Smith, he continues to focus exclusively on
group-level findings.
Others, like McSweeney (2002) comment on Hofstede’s model because of its simplicity. McSweeney
expresses his concern about the four dimensions by stating that they do not deal with the ultimate
complexity of culture. He also states that other dimensions should be considered when one wants to
research and make solid statements about culture. Hofstede reacted to McSweeney’s critique, by
declaring that the dimensions he constructed are universal but do not exclude other dimensions that
differentiate one culture from the other (Hofstede, 2001). Therefore it is important to keep in mind
that Hofstede’s dimensions are a framework that can be developed and adapted in order to fit into a
study. Williamson (2002) agrees with this and suggests that although the dimensions seem simplistic,
they offer a practical method for quantitative analysis.
2.7 Culture
In order to be able to define digital natives and digital immigrants as sub-cultures, it is important to
define the term culture. According to Hofstede et al. culture in the narrow sense mostly refers to the
training or refining of the mind (Hofstede et al. 2010. P516). We often think of culture as only this; for
example in the form of education, arts, literature etc. Among social anthropologists, this narrow
version of culture is often referred to as ‘Culture One’. Culture, or civilization, taken in its broad,
ethnographic sense, is that complex whole which includes knowledge, belief, art, morals, law, custom,
and any other capabilities and habits acquired by man as a member of society (Tylor, 1871. P1).
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This broad view is often referred to as ‘culture two’. “Culture two points to culture as mental
software” (Hofstede, 1991. P5). This kind of culture contains much more (mundane) acts and habits,
like eating, the way feelings are shown, but also how members of a certain culture interact with others
in general. “In social anthropology, ‘culture’ is a catchword for all those patterns of thinking, feeling,
and acting […]. Culture (two) is always a collective phenomenon, because it is at least partly shared
with people who live or lived within the same social environment, which is where [culture] was
learned” (Hofstede, 1991. P5). “Culture is the collective programming of the mind that distinguishes
the members of one group or category of people from another” (Hofstede, 2001. P9). Hofstede
defines groups and categories as follows; “A group means a number of people in contact with each
other. A category consists of people who, without necessarily having any contact, have something in
common (e.g. all women managers or all people born before 1940)” (Hofstede et al., 2010. P 6).
In this study, two categories are researched. These categories consist of people who live within a
certain culture, namely the Dutch culture. Even though not all participants have the Dutch nationality,
they all live in the Netherlands and experience the Dutch morals and values, rituals and practices.
Therefore they have ‘learned’ the Dutch culture. Hofstede explains the word ‘learning’ as follows;
“[Learning] means modified by the influence of collective programming (culture) as well as by unique
personal experiences” (Hofstede et al., 2010. P7).
This collective programming does not only take place by living in a country; it is also set in motion by
‘living’ in organizational cultures within this country. As culture can be seen as the collective
programming of the mind, according to Hofstede organisational culture can be defined as “the
collective programming of the mind that distinguishes the members of one organisation from others”
(Hofstede, 1991. P347). Therefore the Dutch academic scene can be addressed as a culture within a
culture.
2.8 Subcultures
In a broad sense, and in this study, a subculture is a group of people within a larger culture that
distinguishes itself from the rest by having different values and norms. Among some researchers, like
Nanda (1994) and Samovar, Porter and Stefan (1998), the consensus is that the term co-culture is
more fitting. This derives from the fact that the different co-cultures co-exist. In order to avoid
confusion, only the term subculture will be used in this study.
In order to classify the two categories within the culture of academia, one needs to see the academic
world in the Netherlands as overarching culture and the two generations discussed as subcultures.
When using Prensky’s theory of digital natives and digital immigrants, this is possible because within
this culture, different values and norms are accepted than outside of this group. This fits in with
Hofstede’s theory because the cultural dimensions compare these values and norms in order to show
similarities and differences.
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2.9 Digital immigrants versus digital natives
Over the past decade – the introduction of the term digital native took place in the early 2000s – quite
a few studies have been conducted on the subject. The term was first coined by Marc Prensky, in 2001
in his paper Digital Natives, Digital Immigrants. This paper caused some uproar in the academic
community and soon there were both adversaries and proponents of Prensky's theory. This section
will serve as a theoretical framework of the developments and theories in the field of digital natives
and digital immigrants.
In order to make the distinction between immigrants and natives more tangible, Prensky listed a few
characteristics which he assigned to digital natives. For example; natives are used to receiving
information really fast and they like to parallel process and multi-task. They prefer random access (like
hypertext) and work best when networked (Prensky, 2001a). According to Prensky, the assumption
that students learn the same way as the have always done, is no longer valid. In the second part of
Prensky’s paper, he explains that human brains wire themselves. He bases this on the latest research
in neurobiology in 2001, which shows that ‘there is no longer any question that simulation of various
kinds actually changes brain structures and affects the way people think, and that these
transformations go on throughout life’(Prensky, 2001b. P1).
According to Prensky, digital natives are immersed in and surrounded by technology, and because of
that, they are more productive, more open minded, and possess a certain savviness that the
immigrants lack (Prensky, 2001b). He also explains very clearly the differences between immigrants
and natives. The most striking metaphor he uses to explain the difficulties encountered by immigrants
is the ‘accent’ that remains. He states that even though immigrants are able to learn and adapt to the
new environment, they still retain an accent. With regards to the educational system, he identifies this
as the most influential problem; digital immigrants speak an outdated language (pre-digital age) and
are thus struggling to teach a population that speaks an entirely new language.
When Prensky wrote his papers in 2001, the digital skills described were very new, maybe even
foreign to the Digital Immigrant. It seemed necessary to change the way students learn, communicate
and collaborate. It is safe to say that, over a decade, things have changed. But not only for natives;
immigrants have been developing themselves as well.
More than 10 years after the publishing of Prensky’s paper, Davies, Halford and Gibbins (2012)
question the original work of Prensky. They propose that polarized stereotypes are unhelpful in
understanding young people’s relationships with the Web. They also state that “it could be potentially
counterproductive in driving policy interventions founded on inadequate evidence”(Davies et al.,
2012. P1). The paper points out that it is important to research background (education, family, peers,
etc.) rather than whether they are natives or immigrants.
Davies, Halford and Gibbins were not the only critics of Prenksy’s work; in 2009, Neil Selwyn wrote his
view of the matter in his paper “The Digital Native – Myth and Reality”. He proposes to keep in mind
but also look beyond the divide, ‘whilst [remaining] mindful of the changing information and
technological “life worlds” of […] young people’ (Selwyn, 2009. P375). He stresses the importance of
enhancing the understandings of the realities of technology use in contemporary society. Interesting
10
for this research is the fact that Selwyn does see the positive parts of the divide, where other critics
often fail to look beyond.
One of the biggest problems with texts like the one by Selwyn but also ‘Digital Natives: Ten Years
After’ by Apostolos Koutropoulos (2011) and ‘Digital Natives? Investigating young people’s critical skills
in evaluating web based information’ by Davies, Halford and Gibbins, is that they focus on the younger
generation, in other words, the newest generation that uses the internet and ICTs. Even though these
texts are all relatively new, the authors do not take into account the fact that the digital natives
described by Prensky, are now in the age group 24 to 34 years. They are ready to enter, or have
already entered the scientific landscape.
It is important to keep in mind that there have been conducted numerous empirical studies into the
Prensky’s claims and many of them could not support these claims. Helsper and Eynon (2010), for
example, state that it is also important to look at “the extent to which the differences between digital
natives and digital immigrants can be explained by generational differences” (Helsper and Eynon,
2010. P 4). This means that it is not exactly clear whether the differences between generations stems
from growing up with ICTs versus not growing up with ICTs or it simply derives from differences in
generations and therefore influenced by a myriad of other factors.
Even Prensky has conveyed reservations about the legitimacy of the concept of digital natives and
digital immigrants he coined (Prensky, 2009). He, however, also state that the categories are still
useful, as it can be helpful to categorize learners into types in order to analyse their behaviour. It can
be helpful to create categories in order to be able to make general statements. The problem however
with general statements, as Prensky also admits, is that the types in these categories are often
inflexible. The result of this inflexibility is that often people who do not entirely fit into a category are
placed anyway, without taking into account other (conflicting) data.
Despite of the critique on Prensky’s theory, it was decided that for this study, his divide could be
useful to investigate potential differences and similarities.
2.10 Digital humanities and the divide
As explained above, Prensky states that young people today learn differently from their predecessors,
due to having grown up in the digital age. According to him, this results in the fact that educational
and pedagogical tools and methods that were once used to teach immigrants are no longer suitable
for the digital natives. Instead of learning the same way previous generations have done, more and
more educating is done using ICTs.
In sciences, ICTs have been used for decades for purposes ranging from methods, tools, teaching, and
even as study objects. In humanities however, ICTs are only now rising to their potential. Over the past
decade, Digital Humanities has been developing as a field of research as well as an area of education
and construction focussing on the node between computing and humanities. Digital humanities is
basically a field of humanistic research where ICT or ICTs serve as a vital element of the methodology.
This can mean ICTs are used in order to create or to process data, but it can also mean a combination
of the two.
“I consider “Digital Humanities” to be an umbrella term for a wide array of practices for creating,
applying, interpreting, interrogating, and hacking both new and old information technologies.
11
These practices—whether conservative, subversive, or somewhere in between—are not limited to
conventional humanities departments and disciplines, but affect every humanistic field at the
university and transform the ways in which humanistic knowledge reaches and engages with
communities outside the university” (Presner, 2010)
The digital humanities debate is roughly divided into two camps. The one side consists of total
believers, who are of the opinion that large-scale deployment of ICT is essential in the humanities to
give new impulses to the entire field. The other side is made up of a group of critics who point out that
ICTs are never neutral instruments and that therefore the influence of ICTs should be taken into
consideration. They state that it is important to keep in mind that the use of ICTs requires thorough
knowledge of and reflection on the instrument. The two groups, however, reached consent about one
subject; they both believe that ICTs have established themselves in the academic world, that ICTs are
made indispensable but that there is still room for improvement for ICTs as research tools for the
humanities.
One of the questions in the questionnaires used for this study focusses on the field of expertise of the
participant. By adding this question, it is possible to divide the participants according to discipline (i.e.
Alpha: humanities; Beta: science, medicine, veterinarian medicine, geoscience; Gamma: social and
behavioural sciences, law, economics and governance), and with that, it is possible to compare the
outcomes of the different disciplines. This will hopefully help gain insight in the way the different
disciplines interact with ICTs and therefore shine a light on the issue of the digital humanities and the
divide between digital immigrants and digital natives.
2.11 Perceived ease of use
In order to interpret the outcomes of this study, it is important to gain insights into the perceived ease
of use of ICTs among academics. In this paper, the perceived ease of use is defined as “the degree to
which a person believes that using a particular system would be free of effort” (Davis, 1986. P 320). In
2011, Al-Hujran et al. conducted an extensive study into the perceived ease of use of ICTs and the
actual use of ICTs, such as websites and services. They concluded that among other aspects, “the
perceived ease of use was a significant predictor of the [users’] attitude toward using [ICTs]” (AlHujran et al., 2011. P 102). Because digital natives have been working with ICTs their whole lives, the
hypothesis for this element of testing is that digital natives will have a relatively higher perceived ease
of use than digital Immigrants. This derives from the assumption that it is easier for natives to learn to
operate new ICTs and that they know better how to operate devices.
Chapter 3. Method
3.1 Introduction
In the elaboration of the central research question, nine hypotheses have been formulated, based on
Hofstede’s four dimensions and the perceived ease of use in general. In order to investigate the nine
hypotheses and with that to answer the main question, a quantitative research method was used.
Surveys are often used to transform human experiences into numerical values. Researchers claim that
“quantitative research is useful for giving intricate details of phenomena that are difficult to convey
12
with quantitative methods”(Strauss & Corbin, 1990. P19). Aliaga and Gunderson (2000) describe
qualitative research as explaining phenomena by collecting numerical data that is analysed using
mathematically based methods (in particular statistics). Therefore a quantitative method is justified
for this study. As opposed to qualitative research, quantitative research is useful in an explorative
study. These studies often require a large body of participants. Due to time and space limitations, it
was not possible to perform a qualitative research on a large scale. This chapter will discuss the
participants, the measuring instruments and the overall process of the study.
3.1 Participants
As described earlier, a clear distinction has been made between digital natives and digital immigrants;
therefore the sample mainly consisted of Bachelor students, Master students and PhD candidates
(born after 1980) on the one hand, and scientific workers, professors, teachers and students (born
before 1980) on the other hand. The native group has always worked with ICTs within their scientific
career, whereas the immigrants have also used other ways of communicating, collaborating and
networking. The only criterion participants from both groups have to meet is that they, in their daily
lives, make use of ICTs when conducting academic research. The composition of participants came
about at random, in other words, backgrounds, location and other factors were not taken into
consideration.
The distinction between natives and immigrants are solely based on age. Respondents born before
1980 are considered as digital immigrant, whereas digital natives are respondents born after 1980. It
is therefore hard to state whether or not the sample was as random as possible. It is, for example,
possible that the group of digital immigrants in this case is more tech-savvy than their peers, because
of the way participants were approached; using ICTs.
Table 1. Overview questionnaire
Total questionnaires filled out
Empty questionnaires
Unusable questionnaires
Total usable questionnaires
Number
122
27
10
85
Percentage
100
22.1
8.2
69.7
Due to incomplete or empty questionnaires, 22.1 percent of the questionnaires were unusable.
Another 8.2 percent were not usable due to irregular answers. The questions in these ten
questionnaires were all answered with five stars. Due to the fact that these questionnaires were all
coming from the same IP-address, a decision was made not to use them.
Table 2. Survey respondents per group
Digital immigrants
Digital natives
Number of participants
47
38
Percentage
55.3
44.7
13
Table 3. Inventory of the digital immigrants according to function
Professor
PhD student
Post doc
Other
Total
Number
31
2
7
7
47
Percentage
66.0
14.9
4.2
14.9
100
Table 4. Inventory of the digital natives according to function
Bachelor student
Master student
PhD student
Post doc
Professor
Other
Total
Number
5
18
5
2
2
6
38
Percentage
13.2
47.4
13.2
5.2
5.2
15.8
100
The differences between the two groups will be measured by the outcomes of the questionnaire. In
cross-cultural research, it is common to compare groups with very different backgrounds, but for this
study, it was decided to compare two rather similar ‘sub cultures’ in order to uncover the small
details.
3.2 Justification of questionnaire
The questions in the questionnaire have been drawn up using the four cultural dimensions developed
by Hofstede and a general set of questions concerning the overall perceived ease of use. In this
section of the method the decisions to use the particular questions are discussed.
Perceived ease of use.
The questions for this part of the questionnaire were developed for a study that was which was set up
to investigate the acceptance and use of ICTs for teachers in higher education institutions. The main
advantage of using existing – and with that tested – questions is that there is no doubt about the fact
that the questions will measure what they convey to measure.
The decision to use these questions was based on the idea that it is important to know how the
participants feel about ICTs before testing the participants on the four cultural dimensions. This
provides the study with a basis to go off from.
Power Distance Index
The four questions of the Power Distance Index were based on the two hypotheses drawn for this
construct. PDI1 (The use of ICTs makes it easy to contact high ranked academics in my field) and PDI4
(ICTs enable me to engage in discussions with academics from different ranks) questions the extent to
14
which participants use ICT to contact higher ranked academics. Establishing and maintaining contact
with higher ranked academics is an important aspect of the Power Distance Index.
PDI2 (: ICTs help me express my academic identity) and PDI3 (: When using ICTs, conveying my status
is very important to me) focus on the extent to which participants use ICTs to express status and
identity. By asking these two questions, one gains insight in the significance or insignificance of
conveying status and identity using ICTs.
Uncertainty Avoidance Index
The questions asked in the UAI focus on two subjects, namely the use of multiple ICTs (UAI1: When it
comes to ICTs, I am an early adopter; UAI2: In my daily activities, I use a lot of different ICTs; UAI4: I
use a lot of different ICTs to be able to communicate others) and rules and regulations (UAI3: It is
important to have clear rules and regulations about ICT use in research).
These two subjects originate from two important aspects of the uncertainty avoidance index; the
extent to which users are afraid or prone to use new ICTs and the extent to which users feel the need
for rules and regulations in order to avoid uncertainty.
Individualism versus Collectivism
In the IND pillar of Hofstede’s cultural dimensions theory, there are again two important elements; the
dissemination of one’s personal accomplishments and groups success versus personal gain. These two
elements show to what extent cultures are either individualistic or collectivistic.
Personal accomplishments were questioned using IC1 (it is important to me to showcase my personal
accomplishments using ICTs) and IC3 (having insight in other’s accomplishments through ICTs forces
me into disseminating my own). The second element was tested using IC2 (Group success is more
important than individual gain) and IC4 (I use ICTs to look after and take care of my environment
(special interest group, work group, class mates, colleagues etc.).
Masculinity versus Femininity
The questions for the MAS pillar are as follows; MF1: It is important to me to be able to mix ICTs for
personal and professional goals when working or studying; MF2: I use ICT that are officially meant for
work for personal purposes; MF3: ICT enables me to engage in discussions with my peers worldwide
and thus achieve better results; MF4: I use ICTs in order to get recognition for a job well done.
Question MF1 and MF2 were constructed to test the extent to which the participants use ICTs for
leisure and work purposes because using ICTs for different purposes is a ‘feminine’ action whereas
maintaining strict distinction between the purposes for ICTs is a masculine aspect of culture. The final
two questions, MF3 and MF4, focus on achieving and recognition, which both are masculine aspects
of culture according to Hofstede.
15
3.3 Data collection
Both groups filled out the questionnaire online. This questionnaire covered sixteen questions about
specific features of Hofstede’s four dimensions of culture alongside four questions about the overall
perceived ease of use. The latter part was inserted because it is important to know the participants’
general perception of ICTs. The data for this research was collected over a period of 10 days, from the
11th June 2014 until the 21st June 2014.
The data collection was conducted using an online survey tool called KwikSurveys.com. This tool offers
custom branding, useful and efficient ways to collect responses (through social media for example). To
approach the participants (social) network sites, like Facebook, Twitter and LinkedIn, have been used,
and large groups of academics were contacted by sending 400 emails. This group of academic
consisted of Alpha, Bèta and Gamma academics, affiliated with several universities (among others;
Utrecht, Eindhoven, Amsterdam). For ethical considerations the questionnaires were filled out
anonymously.
3.4 Measuring instrument
The study consists of a cross-sectional survey in which a single measurement took place by using a
questionnaire consisting of two parts (see appendix A). The first part of the survey consisted of general
questions about the participants’ background, such as age, affiliation with current faculty and whether
the participant is a male of a female. The answers to these questions were of a nominal measurement
level.
Part two consists of five constructs each in turn consisting of four questions. These questions were
answered with a five-point Likert scale, where one star is “I completely disagree” and five stars is “I
completely agree”. The first set focussed on the perceived ease of use (question one through four),
covering the questions about learning how to operate devices and the way ICTs are used to
communicate with others. The second construct centred on the Power Distance Index (question five
through eight). In this construct the questions posed focussed on contact with high ranked academic
colleagues and expressing ones academic status. The third construct tested the individualism versus
collectivism of the two groups by asking questions about group and individual success and how the
participants stay up to date about their peers. The last construct focussed on Masculinity versus
Femininity by asking questions about the blurred lines between personal and professional ICTs,
alongside the aim for success and better results.
The answers were coded and processed using the statistical application SPSS. During the test, use was
made of a nominal scale for the fixed factors and an ordinal measurement level for the scale
questions.
3.5 Data analysis
In order to analyse the survey outcomes, four different analyses in SPSS were conducted. First a
confirmatory factor analysis (CFA) was conducted. This is used to test the hypothesis that the items
are associated with specific factors (Polit and Beck, 2008). In other words; “CFA allows the researcher
to test the hypothesis that a relationship between observed variables and their underlying latent
constructs exists […] the researcher uses knowledge of the theory, empirical research, or both,
postulates the relationship pattern a priori and then tests the hypothesis statistically” (Suhr, 2006. P1)
16
After the CFA shows a relationship between the observed variables and the underlying construct, a
Cronbach’s alpha reliability test will show whether the results contain internal consistency – or how
closely related a set of items are as a group. Cronbach's alpha can be written as a function of the
number of test items and the average inter-correlation among the items2. If the outcome of the
Cronbach’s alpha is higher than .7, the construct possesses enough internal consistency. However,
there are academics, like Moss et al. (1998) and Hair et al. (2006) who deem a Cronbach’s alpha
between .6 and .7 as acceptable when the sample size is small, as is the case in this study.
When the Cronbach’s alpha is considered sufficient, a Mann-Withney U test will be performed. This
non-parametric test tests the null hypothesis which states that two groups are the same against the
alternative hypothesis (two groups differ from each other in any way). This test shows whether or not
there is a significant difference between the two groups.
In order to test the difference between the individual questions, a T-test is used. A T-test is used
when there is one independent variable (in this case immigrants and natives) and one dependent
variable (in this case each of the questions on itself). If there were more than two independent
variables, an ANOVA test would have been used, but since this was not the case, a T-test was
performed. This test produces a T-value, which represents the number of standard units the means of
the groups are apart. With this test, it is possible to state with a certain degree of confidence that the
difference showed by the results of the sample group is too great to be a coincidence and therefore
should also exist in the population pool out of which the participants were drawn.
Chapter 4. Results
4.1 Perceived ease of use
As previously mentioned, the first test conducted was a confirmatory factor analysis. The results of
this analysis show that the Cronbach’s Alpha for this set of questions scores .866, which is higher than
.7. This means that the consistency is more than high enough to conclude that the outcomes are
reliable.
The second test conducted was the Non-Parametric test, using the option Mann-Withney U test. The
results here show that there is no significant difference between natives and immigrants when looking
at the full set of questions (.993>.05). When examining the individual questions posed in the
questionnaire, it becomes clear why the outcome of the Mann-Witney U test is so high; on all four
questions both groups score exactly the same.
This means that the hypothesis for this construct of the survey is disproved.
Table 5: Perceived ease of use per group per question (0 = Immigrants; 1 = Natives)
PEU-Q1
2
Group
0
N
45
Mean
Std. Deviation
4,09
,821
http://www.ats.ucla.edu/stat/spss/faq/alpha.html
17
Std. Error Mean
,122
PEU-Q1
PEU-Q2
PEU-Q2
PEU-Q3
PEU-Q3
PEU-Q4
PEU-Q4
1
0
1
0
1
0
1
38
4,00
,870
,141
45
3,80
1,014
,151
38
3,84
1,001
,162
45
3,73
1,009
,150
38
4,00
,870
,141
45
4,11
,832
,124
38
4,00
,959
,156
Table 6. Computed PEU per group
Computed PEU
Computed PEU
Group
N
Mean
Std. Deviation
Std. Error Mean
0
46
15,6739
3,23903
,47757
1
38
15,8421
2,99122
,48524
4.2 Power Distance Index
The primary hypothesis for the first pillar of Hofstede’s cultural dimensions is; the influence of ICT on
academia in this case is that it lowers the Power Distance Index, therefore lowering barriers between
high and low ranked academics resulting in higher scores for immigrants. The second hypothesis
focusses on conveying ones status; due to the use of ICTs, the digital natives that participate in this
study are more prone to showcase their identity than digital immigrants, because of the myriad ways
to do so and the peer pressure that is created by the platforms or social networks.
First a confirmatory factor analysis was conducted to find out if the questions asked actually measure
the intended construct (which is the Power Distance Index). Then the Cronbach’s Alpha analysis was
conducted. The outcome of this analysis is .689, which is close enough to a .7 outcome. This means
that there is enough consistency in the answering of these four questions, that the answers to these
questions are sufficiently useful.
When a Non-Parametric test was conducted, using the Mann-Withney U test, the outcomes show that
there is a significant difference between digital natives and digital immigrants (p=<.05; .024). The
mean for the immigrant group was 12.79, the standard deviation was 3.45 (Df = 82.94; T = 2.38; P =
0,020.). For the digital native group the following outcomes were shown; mean = 11.16; Standard
deviation; 2.86 (Df = 82,94; T = 2,38; P = 0,020.). This means that the group consisting of digital
immigrants scores higher than the group consisting of digital natives. The implication of this outcome
is that digital immigrants have a lower Power Distance Index than digital natives.
The independent T-test points out where the significance manifests itself; in three out of the four
questions there is no significant difference, but the second question (ICTs help me to express my
academic identity) shows a major difference between natives and immigrants. The immigrants have a
considerably higher score here. This is a reverse outcome and therefore a contradiction with the
second hypothesis.
The second hypothesis is disproved, due to the lack of significant different between the groups in the
outcome of the questions.
18
Table 7. Power Distance Index per group
PDI-Q1
PDI-Q1
PDI-Q2
PDI-Q2
PDI-Q3
PDI-Q3
PDI-Q4
PDI-Q4
Group
0
1
0
1
0
1
0
1
N
Mean
Std. Deviation
Std. Error Mean
47
3,96
1,021
,152
38
3,47
1,246
,202
47
3,51
1,199
,179
38
2,87
1,095
,178
47
2,27
,963
,144
38
2,11
1,034
,168
47
3,16
1,331
,198
38
2,71
1,063
,172
Table 8. Computed PDI per group
Computed PDI
Computed PDI
Group
N
Mean
Std. Deviation
Std. Error Mean
0
47
12,7872
3,45113
,50340
1
38
11,1579
2,86192
,46427
4.3 Uncertainty Avoidance Index
The first hypothesis for this pillar is that digital immigrants are less prone to try new platforms and
therefore have a higher uncertainty avoidance index than natives. The second hypothesis for the
Uncertainty Avoidance Index is that natives are more likely to use multiple platforms, whereas
immigrants like to limit the amount of ICTs they use.
To find out whether or not the four questions concerning this dimension of Hofstede actually measure
the intended construct, again a confirmatory factor analysis was conducted. The Cronbach’s Alpha
analysis shows that three out of the four questions measure really well, scoring .832.
To get this high score, it was necessary to leave out question three (“It is important to have clear rules
and regulations about ICT use in research”).
This results in the fact that it is possible to say that there is no difference between the two groups
concerning the Uncertainty Avoidance index. The outcome of the Non-Parametric Mann-Withney U
test is .254, which is relatively high. It is therefore justified to say that both groups, in contradiction
with the hypotheses, behave the same when it comes to uncertainty avoidance with regards to ICTs.
Therefore the two hypotheses for this construct have to be rejected.
Table 9. Uncertainty Avoidance Index per group (0 = immigrants; 1 = natives)
UAI-Q1
UAI-Q1
UAI-Q2
UAI-Q2
Group
0
1
0
1
N
Mean
Std. Deviation
Std. Error Mean
47
3,02
1,485
,221
38
3,39
1,264
,205
47
3,40
1,338
,199
38
3,84
1,128
,183
19
UAI-Q3
UAI-Q3
UAI-Q4
UAI-Q4
0
1
0
1
47
3,40
1,195
,178
38
3,16
1,197
,194
47
3,13
1,408
,210
38
3,26
1,155
,187
Table 10. Computed UAI per group (0 = immigrants; 1 = natives)
Computed UAI
Computed UAI
Group
N
Mean
Std. Deviation
Std. Error Mean
0
46
9,5435
3,72801
,54967
1
38
10,5000
2,92935
,47520
4.4 Individualism versus Collectivism
The first hypothesis for the IDV dimension is that natives to score higher on individualism than
immigrants with regards to personal accomplishments. The second, contradicting in terms of
Hofstede’s theory, is that ICTs strengthen cohesiveness within groups and that this will influence the
outcome of the questions concerning this construct.
The main problem with this pillar is that the confirmatory analysis shows that there was little ground
to say that the questions actually measure the construct (IDV). When conducting the Cronbach’s Alpha
analysis, the outcome confirms this (.7>.512). When the lowest score (question 2) was left out, the
outcome was still too low (.591). This means that it is not safe to address the outcome of the
questions as being representative for the construct.
That being said, it is possible to make statements about the individual questions even though the
Cronbach’s Alpha is too low. Most questions of this pillar show no significant difference between the
groups, except for the second question (Group success is more important than individual gain). Digital
immigrants score significantly higher on this question (Mean = 3.72; Standard deviation = 0.994; Df =
83; T = 2.17; P = 0.033) than digital natives (Mean = 3.26; Standard deviation: 0.950; Df = 83; T = 2.17;
P = 0.033). Because this question was left out of the Cronbach’s Alpha analysis, it is important to keep
in mind that statements about this question can only be made when one is not considering the rest of
the construct.
Because of the low Cronbach’s Alpha score, and the lack of significant differences between de
different groups, both hypotheses have to be rejected.
Table 11. Individualism versus Collectivism per group ( 0 = immigrants; 1 = native)
IDV-Q1
IDV-Q1
IDV-Q2
IDV-Q2
IDV-Q3
IDV-Q3
IDV-Q4
Group
0
1
0
1
0
1
0
N
Mean
Std. Deviation
Std. Error Mean
47
2,60
1,268
,189
38
2,24
1,195
,194
47
3,76
1,004
,150
38
3,26
,950
,154
47
2,98
1,055
,157
38
2,71
1,037
,168
47
2,91
1,184
,176
20
IDV-Q4
1
38
3,21
1,119
,181
Table 12. Computed IDV per group
Computed IDV
Computed IDV
Group
N
Mean
Std. Deviation
Std. Error Mean
0
46
8,4783
2,68112
,39531
1
38
8,1579
2,41086
,39109
4.5 Masculinity versus Femininity
The first hypothesis in this dimension is that the boundaries between work and home are less clear
with natives and thus natives are more feminine than immigrants. The second hypothesis is that ICTs
give users more insight into other’s accomplishments, and therefore focus on success and
assertiveness.
The first test that was conducted was the confirmatory factor analysis. The outcomes show that there
is little ground to state something about the intended construct. When the first question was left out
of the equation, the Cronbach’s Alpha analysis still only showed an outcome of .604. This is on the low
side, but according to, among others, Moss et al. (1998) and Hair et al. (2006) this is just high enough
to consider these three questions as being a correct measurement of the construct.
The Non-Parametric Mann-Withney U test shows that when question 1 is left out, there is a significant
difference between the digital native group and the digital immigrant group (P = <.05; .017). The
mean for the immigrant group was 8,7234 the standard deviation was 2,75604. For the digital native
group the following outcomes were showed; mean = 7,2895 and the standard deviation; 2,30050. The
2-tailed significance between the groups was .012, which is lower than .05 and therefore acceptable.
Because there is a significant difference between the two groups, it is important to see where these
differences come from. The Independent T-test shows that there are three out of the four questions
where difference manifests itself; question 1, 2 and 3.
The first question (It is important to me to be able to mix ICTs for personal and professional goals) was
left out of the construct, but still has significance. The outcomes namely show that digital natives
score relatively higher (Mean = 3.50; Standard deviation = 1.109) than the digital immigrants (Mean =
2.91; Standard deviation = 1.297). These outcomes are confirmative for the first hypothesis.
The second question (I use ICTs that are officially meant for work for personal purposes) contributes
to the construct and shows significant difference between the groups. In this case the digital
immigrants score higher than the digital natives (0: Mean = 2.82; Standard deviation = 1.141; 1: Mean
= 2.16; Standard deviation = 1.220). These outcomes do not add to the conformation of the first
hypothesis.
The third question (ICTs enable me to engage in discussions with my peers worldwide and thus help to
achieve better results) again shows significant differences between the groups. Here the immigrants
(Mean = 3.59; Standard deviation = 1.257) also score higher than the natives (Mean = 2.97; Standard
21
deviation = 1.127). The second hypothesis can therefore be rejected, because this study shows that
Immigrants more often use ICTs to contact others in order to achieve better results.
Table 13. Masculinity versus Femininity (0 = immigrants; 1 = natives)
MAS-Q1
MAS-Q1
MAS-Q2
MAS-Q2
MAS-Q3
MAS-Q3
MAS-Q4
MAS-Q4
Group
0
1
0
1
0
1
0
1
N
Mean
Std. Deviation
Std. Error Mean
47
2,91
1,311
,195
38
3,50
1,109
,180
47
2,82
1,154
,172
38
2,16
1,220
,198
47
3,58
1,270
,189
38
2,97
1,127
,183
47
2,40
1,136
,169
38
2,16
1,053
,171
Table 14. Computed MAS per group
Computed MAS
Computed MAS
Group
N
Mean
Std. Deviation
Std. Error Mean
0
47
8,7234
2,75604
,40201
1
38
7,2895
2,30050
,37319
4.6 Comparing the disciplines
In order to compare the outcomes of the three disciplines, the data was divided into two groups of
independent variables, namely digital natives and digital immigrants and alpha, beta, gamma. This is
necessary if one wants to perform a 2-way Anova test. With this test the influence of the interaction
between the two groups of independent variables on the dependent variable (the five constructs) was
measured
This test only showed a significant difference between the disciplines in the perceived ease of use (F:
(2,62) 3,511; P: .036). These results were then further analysed with a post-hoc Tukey test to find
where this difference could be found, however a direct influence between two different disciplines
was not found. Therefore, it can be stated that the outcome showed a general difference on the
perceived ease of use between the different disciplines, but no specific difference between for
instance alpha and beta or gamma and alpha.
The two-way ANOVA also indicated that there is no overall significant different between the
group*discipline interaction and the influence on perceived ease of use. However, when observing the
plot (see: figure 1) a fairly high mean difference between the groups can be found at the gamma
discipline. To further test this difference, the gamma discipline was tested on its own using a NonParametric Mann-Whitney and an Independent Samples T-test. These results yielded a significant
difference between digital natives and digital immigrants in the gamma discipline, namely: the digital
immigrants score significantly lower on the perceived ease of use than the digital natives.
22
Figure 1: Plot Perceived Ease of Use
Table 15: Computed PEU for Gamma
Group
N
Mean
Std. Deviation
Std. Error Mean
Computed PEU
0
6
12,429
3,32666
1,35810
Computed PEU
1
12
15,500
3,37100
,97312
Chapter 5. Findings
5.1 Perceived ease of use
The results of the survey show that both digital immigrants as well as digital natives score the same on
all of the questions posed in this construct. The mean for this set of questions was 15.67 for digital
immigrants and 15.84 for digital natives. This means that participants of both groups consider ICTs
relatively easy to use.
On the one hand this can be considered as a desirable outcome, because in this case, it seems like the
perceived ease of use does not influence the rest of results. On the other hand these results do not
coincide with the hypothesis, which stated that digital natives have a higher perceived ease of use and
thus this needs to be rejected.
23
5.2 Power Distance Index
The most striking aspect of the Power Distance Index is that there seems to be an overall significant
difference between immigrants and natives. In this case this results in the fact that immigrants have a
lower Power Distance Index than natives, which is not in line with the hypotheses for this construct
(the influence of ICT on academia in this case is that it lowers the Power Distance Index, therefore
lowering barriers between high and low ranked academics resulting in higher scores for immigrants.
And due to the use of ICTs, the digital natives that participate in this study are more prone to showcase
their identity than digital immigrants). As PDI is defined as the extent to which less powerful members
of institutions and organisations […] expect and accept that power is distributed unequally3, the
results can be interpreted in two ways.
1. Immigrants are older and therefore are more likely to have a higher rank than digital
natives which might change the point of view of the individual participants.
2. Immigrants are older and therefore are more likely to have a job in academia, where
the group of the natives mostly consists of students, which also might influence how
ICT users view the dissemination of status.
The first statement is closely related to two of the questions asked in this construct, namely question 1
and question 4 (“the use of ICTs makes it easy for me to contact high ranked academics in my field”
and “ICTs enable me to engage with academics from different ranks”). This statement can be
explained as follows; the immigrants mostly have high ranked functions (at least 68.2 % has a function
as high as a post doc or higher, see table 3), where the natives who participated in this study are
mostly bachelor, master or PhD students (at least 73.8% is student, see table 4), and are therefore
generally lower in rank.
Research that is done by academic workers has other rules and regulations than research done by
students, as students are still learning how to conduct research. The studies conducted by students
are not likely to be published, where studies conducted by higher ranked academics are almost always
written with the aim to publish, as “publishing has been the business model for academia for
hundreds of years” (Nielsen, 2012. P160). Besides that, students often work with other students and
their mentoring professors or other academic workers when conducting research. The need to reach
out to academics in the field might be smaller than when one is already conducting research for
publication.
The second statement is related to the other two questions in this construct; “ICTs help me to express
my academic identity” and “When using ICTs, conveying my status is very important”. The statistics
show that this difference mainly derives from the answers to the question about expressing an
academic identity; immigrants more often seem to express their academic identity using ICTs. This can
be explained as follows; the digital immigrants in this study already work in the academic world, where
students are still studying in the field and might chose not to pursue an academic career. In 2013
36.441 master students graduated opposed to 945 PhD students.4 This might influence the
importance of expressing and conveying one’s academic status using ICTs and therefore explain the
relatively low score of the digital natives in this study.
3
http://geert-hofstede.com/netherlands.html
http://statline.cbs.nl/StatWeb/publication/?VW=T&DM=SLNL&PA=70962NED&D1=13&D2=a&D3=a&D4=0&D5=0&D6=0&D7=l&HD=110627-1140&HDR=G3,G4,G5,G6,T,G2&STB=G1
4
24
5.3 Uncertainty Avoidance Index
The results of the Uncertainty Avoidance Index construct show no significant difference, however it is
interesting to see that there is an overall relatively high difference when observing the means of the
two groups.
These outcomes show that the digital native group scores slightly higher on this construct, but due to
the fact that the questions are inversed, this means that natives score lower on the uncertainty
avoidance index. This confirms the hypotheses (digital immigrants are less prone to try new platforms
and therefore have a higher uncertainty avoidance index than natives and natives are more likely to
use multiple platforms, whereas immigrants like to limit the amount of ICTs they use) for this
construct, but due to the fact that there is no significant difference, further research might be of
importance for this construct.
5.4 Individualism versus Collectivism
The factor analysis showed that question two did not fit into the same dimension as the other three
questions. However, the reliability of the other three questions together had a Cronbach’s Alpha of
.591, which is too low to make statements about the construct as a whole. The independent Samples
T-test showed that there is no significant difference when these three questions are analysed as a
group. It is, however, possible to analyse the questions individually.
The interesting thing about the second question, although left out of the construct in the analysis, is
that it has proven to be the only question out of four that were set up to measure IDV, that shows a
significant difference. One could argue that the group of three questions were not correctly set up to
measure the construct, but it is also possible that the second question is indeed an outlier in the
construct. This is up for discussion. This problem should have been eliminated beforehand but due to
limitations, it was not possible to do so.
The outcome of the independent sample T-test of the second question (“Group success is more
important than individual gain”) shows that immigrants value group success over individual gain (See
table 11). This is not in line with the first hypothesis that natives score higher on collectivism due to
ICTs. This outcome is interesting but due to the fact that the construct as a whole is not reliably
measured, it is not possible to draw a generalized conclusion on the differences between individualism
and collectivism between digital natives and digital immigrants. The only statement that can be made
about this construct is that the differences between natives and immigrants as stated by Prensky
cannot be confirmed, which, once again, underlines the fact that Prensky’s ideas about natives and
immigrants need to be refined or overall dismissed.
5.5 Masculinity versus Femininity
The Masculinity versus Femininity dimension is the construct with the most significant difference, both
between individual questions as between the overall means of the two groups. The overall difference
shows that the digital immigrants have a higher score than digital natives, but due to the fact that this
dimension is of the scale variant, this does not mean that immigrants are more feminine or masculine.
To further explore this construct, it is important to investigate the three questions that show
significant difference (question one, two and three).
The first question (It is important for me to be able to mix ICTs for personal and professional goals)
focusses on the possibility to mix the use of ICTs for different goals. The results show that natives have
25
a significant higher score on this question. This means that natives are more feminine about mixing
ICTs than immigrants, as it is considered more feminine to blur the lines between work and pleasure
(e Silva & Sutko, 2009). This outcome confirms the hypothesis that natives are in a sense more
feminine in their ICT use than immigrants.
Question two resembles question one, in order to gain a more well-rounded insight into this element
of behaviour. Remarkable is that in this case, the immigrants show a more feminine outcome, by
having a higher score. The exact question refers to the use of ICTs that are officially meant for work,
which might have caused the significant difference. This can be caused by a difference in semantics –
the digital native group consists mostly of students, who might not consider their research for their
study as being work. It might also derive from different relationships with or point of views on ICTs.
Young people might consider their ICTs as being mostly for personal purposes and might therefore
consider work as a side issue. However, concrete claims cannot be made about this so further
research needs to be conducted to indicate what the reasons are for this outcome. This could be done
by developing an extensive questionnaire that solely focusses on the different aspects of masculinity
and femininity. Another way of studying this construct would be a qualitative one, for example by
conducting in-depth interviews or focus groups.
The final significant difference reveals itself in question three, which focuses on cooperation and the
achievement of better results using ICTs. The results for this question show that digital immigrants
score higher, and are thus more masculine. Immigrants seem to attach more value to better results,
and with that success, than natives. The outcome of this part of the survey can be explained in two
ways;
1. Immigrants more often work together with peers worldwide to achieve better
results and therefore have a higher score
2. Immigrants are in this case academics, who tend to be more result-driven than
their native counterparts.
The first option can be explained like one of the statements in the Power Distance Index; where many
senior academics already work in the academic field and therefore collaborate with other academics,
many students only work with other students and their supervisors.
The second option focusses on the result-drive of the participants. Students have different goals from
academics. To pass their exams and courses, they need to meet standards set for their particular
studies. This might cause them to be less (or differently) result-driven than digital immigrants, who
mostly write to publish. In hindsight, result-drivenness should have been measured in a different way,
in order to ensure neutrality. This could have been done by asking questions that focus solely on
result-drivenness in various situations.
Having said that, the findings show that natives have more feminine features when using ICTs where
immigrants show more masculine characteristics. This is in line with the first hypothesis (the
boundaries between work and home are less clear with natives and thus natives are more feminine
than immigrants) but as the second hypothesis (ICTs give users more insight into other’s
accomplishments, and therefore focus on success and assertiveness) is rejected, the mutual influence
of ICTs and academia in the field of masculinity versus femininity could and should be further
investigated. This could be done by further explicating the entire pillar of masculinity versus
26
femininity. By doing so, more in-depth questions could be developed, focussing on every detail in the
pillar.
5.6 Comparing the disciplines
As previously discussed, some significant difference was detected between alpha, beta and gamma
academics. This difference, however, only manifests itself in the section about perceived ease of use.
The plot and the overall data show that gamma academics have a lower score in the perceived ease of
use of ICTs. Based on the results, it is possible to make two assumptions;
1. Gamma academics lag behind in the field of perceived ease of use of ICTs
2. Alpha and beta academics have a well-nigh score when it comes to perceived ease of use
of ICTs.
To explain the first assumption, it is important to look further into the results of the gamma academics
because they show that there is a significant difference between immigrants and natives. The mean
for this construct for gamma immigrants is 12.492, where the mean for gamma natives is 15.50. This
means that natives find it significantly easier to use ICTs. This could mean that growing up with ICTs
has influenced this particular group of participants.
The second assumption can be explained as follows; the humanities (or alpha) academics are catching
up with the sciences (or beta) academics when it comes to perceived ease of use of ICTs. In 1959, C.P.
Snow gave a lecture about ‘the two cultures’, in which he considers humanities and sciences as being
complete opposites. This outcome, however, shows that, with regards to the perceived ease of use of
ICTs, alpha and beta sciences are more alike. This could be further explained if more researched was
conducted in this field.
But what does this lack of difference in perceived ease of use between alpha and beta academics
mean for the digital humanities debate? Is there still ground to speak of digital humanities as if it is
something completely different than ‘digital beta sciences’ would be? It might mean that it is time to
look at what ICTs do with academia in general, instead of just investigating what ICTs do to humanities
and stimulating only these developments. It might also mean that there has been a big shift in
humanities because of ICTs but because it has not been measured before, it is impossible to make
hard claims. It would be an interesting new study if this was measured in regular intervals to study the
influence of mutual shaping over time.
Oh the other hand, there seems to be a difference between the way alpha academics and gamma
academics perceive the ease of use of ICTs. This difference is not significant, which means that it is not
possible to make statements about it, but it would be a remarkable subject to further investigate with
a larger group of participants.
When looking at Hofstede’s four pillars, there seems to be no significant difference between the three
disciplines, which points out that in academic culture aspect of the study, there is no difference in the
relationship of mutual shaping between the different disciplines in academia and ICTs.
27
Chapter 6. Conclusion
6.1 Limitations
In further research on this topic, one needs to keep in mind the limitations that have emerged during
this study. First of all, future research should be more extensive. Although it is possible to make
statements based on the results of 85 filled out questionnaires, it is not ideal. When the body of
participants was divided into the three disciplines, the smallest group consisted of only 18 academics.
Even though the study showed interesting results, this is very small and it is questionable if it is
enough to make statements about these results.
Besides the relatively small amount of respondents, there was another limitation concerning
respondents. The group consisted of respondents who all voluntarily took part which in itself is not a
big problem. The problem lies in the fact that the respondents were approached digitally, which could
mean that the respondents are overall more tech savvy than their peers. Therefore it would be good
to also approach participants in other, preferably non-digital ways.
The recruitment of participants is also questionable, as it was digitally done. This means that digital
refugees (digital immigrants who refuse to use ICTs) or less tech-savvy participants were not reached.
In future research, this could be avoided by using both digital as manual ways of recruiting and
executing of questionnaires.
Furthermore, the questionnaire used contained four questions per construct, which might be
considered (too) low as Hofstede’s theory is much more extensive than the extracted hypotheses
show. Therefore, if more research was to be done on this topic, Hofstede’s theory should be deployed
even further in light of this research subject, using every aspect of the model. This would give the
reseach more ground to make more concrete conclusions.
The next limitation of this research is the fact that one of the five constructs (Individualism versus
Collectivism) did not measure the construct as was shown by the confirmatory analysis. To avoid this,
it is advised to perform a pre-test. This does not only apply to this construct, but for all questions in
the questionnaire. Pre-testing ensures that this kind of problem does not arise and the entire data set
is usable.
The final limitation is the fact that it will always be hard to find two ‘identical’ groups for this study as
the people who are born before 1980 will most likely always have a higher rank than people who are
born after. This limitation is unavoidable but one has to keep in mind that it does influence the
outcome of the study.
6.2 Recommendations
The implications of this research are that the divide between digital immigrants and digital natives is
not as static as claimed by Prensky. However it also proves that there is a difference in how these two
groups interact with ICTs and with that the effects of ICT use on the academic culture. Therefore I
propose to further investigate the subject, in order to gain more insights. However, the results in this
study show that the difference between natives and immigrants is not as strong as Prenksy stated in
2001. Therefore it might necessary to leave the discussion about immigrants and natives and solely
focus on the relationship between culture and ICT use. This relationship requires new terms and new
forms
of
operationalization
in
order
to
fully
understand
it.
28
This will ultimately lead to a better mutual understanding of each other and with that more valuable
collaborations and ways of communicating. One way of conducting more research could be, like Smith
(2002) proposed, looking into a combination of group-level data and individual-level data. By doing so,
more well-rounded results could emerge as it would also leave room for specific insights of individuals.
When one wants to research the immigrant/native divide despite of the findings, the two constructs in
which significant differences have emerged should be examined in depth, as these outcomes
generated a range of new questions. For example an entire research devoted to how academics react
to certain rules and regulations of ICT use in research might be an interesting angle.
It would also be interesting to gain more insight in the masculine versus feminine construct, as it can
be argued that ICT use makes academics both more feminine as more masculine. This means that
Hofstede’s categories do not apply as they did previously. Therefore it might be interesting to
hypothesise and measure masculinity and femininity in a (completely) different way. This could be
done, for example, by deciding to not make femininity and masculinity opponents, but using this pillar
more as a sliding scale.
Finally, a whole new study into the differences and similarities in ICT use between the different
disciplines could give future generations insight into the mutual shaping of ICTs and academia. This
would not only be beneficial for movements like digital humanities, but also for beta and gamma
research and education.
6.3 Conclusion
The goal of this study was to find an answer to the main question, by testing hypotheses using a
quantitative research method. The research question was To what extent is there a distinction in use
of ICTs between digital natives and digital immigrants and is this distinction measurable using
Hofstede’s cultural dimensions?
An answer to this question was found comparing the two groups, one having to learn to operate ICTs
at a later age and one having grown up the ubiquity of ICTs. By using this method, the outcome
showed differences on several aspects of Hofstede’s theory. It is however not clear what caused those
differences; it can be growing up with ICTs but it can also be factors that do not have anything to do
with being a digital native or a digital immigrant, like the hierarchical constructions within academia.
The analysis and the findings in this study clearly show two elements of Hofstede’s cultural dimensions
where digital natives and digital immigrants differ from one another. These two elements were the
Power Distance Index and Masculinity versus Femininity.
The difference measured in the PDI can be explained more than one way, so further research about
this construct is advised. It is, however, interesting to see that digital immigrants have a lower power
distance index than digital natives. As a product of the nature of the relationship of mutual shaping
between ICTs and academic culture, it is not possible to say what influenced the Power Distance
Index; the culture (i.e. having a higher rank influences PDI) or the ICTs (i.e. having access to and willing
to use specific ICTs that influence PDI), but it is that this derived from the expression of identity using
ICTs. It is, however, not possible to make hard claims about this.
The Masculinity versus Femininity dimensions showed the most significant difference between the
two groups. This led to the conclusion that digital natives have more feminine values than digital
29
immigrants when it comes to the use of ICTs. Growing up using various ICTs seems to have caused a
shift in how users deal with ICTs and the desire to be able to mix ICTs for different goals. The results
show that the lines between work and leisure are more blurred in the native group than in the
immigrant group. It also showed a difference in the desire to achieve better results, by using ICTs to
interact with peers worldwide.
In general, there was less significant difference measured than expected. On the one hand this
outcome indicates that the divide between digital natives and digital immigrants might not be as static
as Prensky conveyed it to be. If the divide was as extreme as Prensky conveyed it to be and if it was
still intact, it would show in the results. On the other hand, there was a significant difference in
individual questions the other constructs, what results in the fact that further research into this topic
may also be desirable. This could be done by posing new questions that relate to the questions that
have shown difference, in order to further investigate these specific areas. By further exploring these
specific subjects, it might be possible to adapt Hofstede’s theory into new theories in order to make it
more fitting to investigate the habits and behaviours of academics and the differences and similarities
between younger and older generations. This might be helpful for further development in ICTs in
academia and therefore help to accelerate academic research.
Because of the small difference between the two groups, and the lack of ground to make concrete
statements about the relationship and its effects on both academia and ICTs, it might be necessary to
study the phenomenon in a different way. This could be done by further investigating the difference in
perceived ease of use between alpha, beta and gamma academics. This might show how the mutual
shaping by establishing baseline for development, ensuring better comparisons between the three
disciplines and with that more insight in the ways ICTs are influencing academic culture and vice versa.
Overall this study shows that the divide between digital natives and digital immigrant is not extremely
helpful because the differences between the groups are not as extant as Prensky conveyed it to be.
Therefore it is not the ideal manner to investigate the relationship between ICTs and academia.
Hofstede’s cultural dimensions can uncover interesting aspects of academia, but it might be more
useful to use this theory in a different way. This could, for example, be done by comparing academic
cultures of different countries, both developed in using ICTs as not as developed, instead of applying it
to one country.
30
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Appendix A. Questionnaire
Dear participant,
First of all; thank you for helping me graduate! The following questionnaire is developed for academics
who are currently active (working or studying) in the Dutch academic world and are using Information
and Communication Technologies (ICTs) when conducting research. By ICTs I mean hardware and
software, but also social networks and other platforms. The questions I will ask are mainly focussed on
conducting research and should be interpreted as such.
The survey will take 5-10 minutes to fill out. The data will be handled with care and will be processed
anonymously.
Thank you in advance!
With kind regards,
Lisa van Gent (Master student at the University of Utrecht, Ma New Media and Digital Culture)
1. I was born in: (1940-1949; 1950-1959; 1960-1969;1970-1979; 1980-1989; 1990-1999)
2. I am currently: a bachelor student
a master student
a PhD student
a Post-doc fellow
a professor
other
3. I am affiliated with the following faculty of faculties:
Geosciences
Humanities
Law, Economics and Governance
Medicine
Science
Social and Behavioural Sciences
Veterinary Medicine
Other
4. I am:
a male
a female
5. To what extent do you agree with the following statements
(1 star = I do not agree, 5 stars = I totally agree)
I find ICTs easy to use
34
I find it easy to get a device to do what I want it to do
Learning to operate new ICTs is easy for me
The use of ICTs makes it easy to contact high ranked academics in my field
ICTs help me express my academic identity
6. You are halfway there. To what extent do you agree with these statements:
(1 star = I do not agree, 5 stars = I totally agree)
When using ICTs, conveying my status is very important to me
ICTs enable me to engage in discussions with academics from different ranks
When it comes to ICTs, I am an early adopter
In my daily activities, I use a lot of different ICTs
It is important to have clear rules and regulations about ICT use in research
I use a lot of different ICTs to be able to communicate others
It is important to me to showcase my personal accomplishments using ICTs
7. Only seven questions left. Again, to what extent do you agree with the following questions:
(1 star = I do not agree, 5 stars = I totally agree)
Group success is more important than individual gain
Having insight in other’s accomplishments through ICTs forces me into
disseminating my own
I use ICTs to look after and take care of my environment (special interest
group, work group, class mates, colleagues etc.)
It is important to me to be able to mix ICTs for personal and professional goals
when working or studying
I use ICTs that are officially meant for work for personal purposes
ICTs enable me to engage in discussions with my peers worldwide and thus
achieve better results
I use ICTs in order to get recognition for a job well done
35
Appendix B.
SPSS statistics
Confirmatory Factor Analiysis
Total Variance Explained
Component
Initial Eigenvalues
Rotation Sums of Squared Loadings
Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative %
1
2
3
4
5
6,923
2,416
1,731
1,258
1,128
34,616
12,082
8,655
6,288
5,641
34,616
46,697
55,352
61,640
67,281
3,359
3,345
3,328
2,123
1,301
16,795
16,725
16,638
10,617
6,506
16,795
33,520
50,158
60,775
67,281
6
,839
4,196
71,477
7
,767
3,834
75,311
8
,743
3,715
79,026
9
,680
3,401
82,428
10
,512
2,561
84,989
11
,485
2,426
87,414
12
,432
2,162
89,576
13
,382
1,911
91,487
14
,362
1,811
93,298
15
,308
1,541
94,839
16
,263
1,316
96,155
17
,243
1,216
97,371
18
,214
1,072
98,443
19
,172
,858
99,301
20
,140
,699
100,000
Extraction Method: Principal Component Analysis.
36
Rotated Component Matrixa
Component
1
Q5-1
Q5-2
Q5-3
Q5-4
Q5-5
Q5-6
Q6-1
Q6-2
Q6-3
Q6-4
Q6-5
Q6-6
Q6-7
Q7-1
Q7-2
Q7-3
Q7-4
Q7-5
Q7-6
Q7-7
2
,447
,558
,674
,591
,350
3
4
,821
,721
,630
,816
,688
,397
,324
,301
,623
,722
,336
,343
,647
,357
,323
5
,664
,398
,729
,686
,748
,667
,319
,863
,385
,626
,338
,713
,411
,722
,594
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 9 iterations.
37
Independent Sample T-test
Group Statistics
Q5-1
Q5-2
Q5-3
Q5-4
Q5-5
Q5-6
Q6-1
Q6-2
Q6-3
Q6-4
Q6-5
Q6-6
Q6-7
Q7-1
Q7-2
Q7-3
Q7-4
Q7-5
Q7-6
Q7-7
group
N
Mean
Std. Deviation
Std. Error Mean
0
47
4,09
,821
,122
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
38
47
38
47
38
47
38
47
38
47
38
47
38
47
38
47
38
47
38
47
38
47
38
47
38
47
38
47
38
47
38
47
38
47
38
47
38
47
4,00
3,80
3,84
3,73
4,00
4,11
4,00
3,96
3,47
3,51
2,87
2,27
2,11
3,16
2,71
3,02
3,39
3,40
3,84
3,40
3,16
3,13
3,26
2,60
2,24
3,76
3,26
2,98
2,71
2,91
3,21
2,91
3,50
2,82
2,16
3,58
2,97
2,40
,870
1,014
1,001
1,009
,870
,832
,959
1,021
1,246
1,199
1,095
,963
1,034
1,331
1,063
1,485
1,264
1,338
1,128
1,195
1,197
1,408
1,155
1,268
1,195
1,004
,950
1,055
1,037
1,184
1,119
1,311
1,109
1,154
1,220
1,270
1,127
1,136
,141
,151
,162
,150
,141
,124
,156
,152
,202
,179
,178
,144
,168
,198
,172
,221
,205
,199
,183
,178
,194
,210
,187
,189
,194
,150
,154
,157
,168
,176
,181
,195
,180
,172
,198
,189
,183
,169
1
38
2,16
1,053
,171
Independent Samples Test
(E.v. = Equal variances)
Levene's Test for t-test for Equality of Means
Equality of Variances
F
Sig.
t
Df
Sig. (2- Mean
tailed) Difference
Std.
Error 95% Confidence Interval
Difference
of the Difference
Lower
38
Upper
E.v. assumed
Q5-1
Q5-2
Q5-3
Q5-4
Q5-5
Q5-6
Q6-1
Q6-2
Q6-3
Q6-4
Q6-5
Q6-6
Q6-7
Q7-1
Q7-2
Q7-3
Q7-4
Q7-5
Q7-6
,828
,095
,758
E.v not assumed
E.v assumed
E.v not assumed
E.v assumed
,989
,323
E.v not assumed
E.v assumed
1,240
,269
E.v not assumed
E.v assumed
5,757
,019
E.v not assumed
E.v assumed
,868
,354
,243
,624
E.v not assumed
E.v assumed
E.v not assumed
E.v assumed
2,478
,119
E.v not assumed
E.v assumed
1,292
,259
1,976
,164
E.v not assumed
E.v assumed
E.v not assumed
E.v assumed
,009
,924
E.v not assumed
E.v assumed
1,455
,231
E.v not assumed
E.v assumed
,650
,423
E.v not assumed
E.v assumed
,248
,620
,173
,679
E.v not assumed
E.v assumed
E.v not assumed
E.v assumed
,003
,958
E.v not assumed
E.v assumed
1,760
,188
,938
,336
E.v not assumed
E.v assumed
E.v not assumed
E.v assumed
2,032
,158
E.v not assumed
E.v assumed
Q7-7
,048
E.v not assumed
,276
,601
,478
81
,634
,089
,186
-,281
,459
,476
76,965
,635
,089
,187
-,283
,461
-,190
81
,850
-,042
,222
-,484
,400
-,190
79,010
,850
-,042
,222
-,484
,399
-1,277
81
,205
-,267
,209
-,682
,149
-1,293
80,958
,200
-,267
,206
-,677
,144
,565
81
,573
,111
,197
-,280
,502
,559
73,890
,578
,111
,199
-,285
,507
1,936
81
,056
,482
,249
-,013
,977
1,904
71,528
,061
,482
,253
-,023
,986
2,531
81
,013
,643
,254
,137
1,148
2,551
80,480
,013
,643
,252
,141
1,144
,735
81
,464
,161
,219
-,275
,598
,731
76,517
,467
,161
,221
-,278
,601
1,661
81
,100
,445
,268
-,088
,978
1,693
80,774
,094
,445
,263
-,078
,968
-1,218
81
,227
-,373
,306
-,981
,236
-1,235
80,992
,220
-,373
,302
-,973
,228
-1,610
81
,111
-,442
,275
-,989
,104
-1,633
81,000
,106
-,442
,271
-,981
,096
,919
81
,361
,242
,263
-,282
,766
,919
78,628
,361
,242
,264
-,282
,767
-,454
81
,651
-,130
,286
-,699
,439
-,461
80,943
,646
-,130
,281
-,690
,430
1,334
81
,186
,363
,272
-,178
,905
1,341
79,997
,184
,363
,271
-,176
,902
2,282
81
,025
,492
,216
,063
,922
2,293
79,921
,024
,492
,215
,065
,920
1,159
81
,250
,267
,231
-,192
,726
1,160
79,111
,249
,267
,230
-,191
,726
-1,177
81
,243
-,299
,254
-,805
,207
-1,183
79,942
,240
-,299
,253
-,803
,204
-2,186
81
,032
-,589
,269
-1,125
-,053
-2,217
80,999
,029
-,589
,266
-1,117
-,060
2,546
81
,013
,664
,261
,145
1,184
2,534
77,047
,013
,664
,262
,142
1,186
2,272
81
,026
,604
,266
,075
1,133
2,295
80,786
,024
,604
,263
,080
1,128
1,000
81
,320
,242
,242
-,240
,724
1,006
80,266
,317
,242
,241
-,237
,721
39
Non-Parametric Mann-Withney U
40
41
Tests of Between-Subjects Effects
Tests of Between-Subjects Effects (Different disciplines)
Source
Dependent Variable
Type III Sum of df
Mean Square
F
Sig.
Squares
Corrected Model
Intercept
group
Discipline
Computed_Ease_of_Use
101,639a
5
20,328
2,190
,067
Computed_PDI
100,364b
5
20,073
1,791
,128
Computed_UAI
51,775c
5
10,355
,866
,509
Computed_IND
15,918d
5
3,184
,464
,801
Computed_MAS
44,690e
5
8,938
1,305
,273
Computed_Ease_of_Use
15035,915
1
15035,915
1619,584
,000
Computed_PDI
8870,255
1
8870,255
791,650
,000
Computed_UAI
6038,919
1
6038,919
504,807
,000
Computed_IND
4233,043
1
4233,043
617,087
,000
Computed_MAS
3728,273
1
3728,273
544,490
,000
Computed_Ease_of_Use
12,055
1
12,055
1,299
,259
Computed_PDI
16,808
1
16,808
1,500
,225
Computed_UAI
26,046
1
26,046
2,177
,145
Computed_IND
,095
1
,095
,014
,907
Computed_MAS
26,174
1
26,174
3,823
,055
Computed_Ease_of_Use
65,194
2
32,597
3,511
,036
Computed_PDI
29,778
2
14,889
1,329
,272
42
group * Discipline
Error
Total
Corrected Total
Computed_UAI
18,202
2
9,101
,761
,472
Computed_IND
2,827
2
1,414
,206
,814
Computed_MAS
1,836
2
,918
,134
,875
Computed_Ease_of_Use
42,413
2
21,206
2,284
,110
Computed_PDI
39,667
2
19,833
1,770
,179
Computed_UAI
14,303
2
7,152
,598
,553
Computed_IND
13,781
2
6,890
1,004
,372
Computed_MAS
9,596
2
4,798
,701
,500
Computed_Ease_of_Use
575,596
62
9,284
Computed_PDI
694,695
62
11,205
Computed_UAI
741,695
62
11,963
Computed_IND
425,302
62
6,860
Computed_MAS
424,531
62
6,847
Computed_Ease_of_Use
17514,000
68
Computed_PDI
10780,000
68
Computed_UAI
7474,000
68
Computed_IND
5169,000
68
Computed_MAS
4647,000
68
Computed_Ease_of_Use
677,235
67
Computed_PDI
795,059
67
Computed_UAI
793,471
67
Computed_IND
441,221
67
Computed_MAS
469,221
67
a. R Squared = ,150 (Adjusted R Squared = ,082)
b. R Squared = ,126 (Adjusted R Squared = ,056)
c. R Squared = ,065 (Adjusted R Squared = -,010)
d. R Squared = ,036 (Adjusted R Squared = -,042)
e. R Squared = ,095 (Adjusted R Squared = ,022)
43