1. 0
‘Exploring Drivers of the Behavioral Intention to Solve IT
Problems Using IT Self-Service at SNS REAAL’
Jorrin Sebregts
SNS REAAL
17-01-2013
Master of Science in Marketing Management (MSc. MM)
Department Marketing
Faculty of Economics and Business
Tilburg University
Master thesis Supervisors: B.L.H. Schiffelers MSc (Tilburg University)
M.J. Gresnigt MSc (Co-reader Tilburg University)
P. Heslenfeld (SNS REAAL)
2. 1
Management Summary
Information technology user support is the single point of contact for all IT problems and IT
related questions that come from employees, which are the internal users of the IT
infrastructure at SNS REAAL. Employees are expected to be self-reliant with regard to
solving some of their IT problems. To aid them in doing this, SNS REAAL founded a
knowledgebase in January 2011, called IT self-service. However, the low extent to which IT
self-service is used up until now is disappointing to SNS REAAL, and in turn leads to an
inefficient use of resources. Therefore, SNS REAAL needs insight in what drives the
behavioral intention to use IT self-service in the context of using it to solve IT problems.
Previous research that used technology acceptance theories to explain drivers of
technologies that can be compared to IT self-service, serve as the theoretical backbone of this
research. Additionally, a qualitative research was conducted to make sure that the variables
that were identified in the literature review were recognized as drivers by employees. After
that, the relationships between the constructs were tested using regression analyses.
Perceived facilitating conditions, attitude towards technology and perceived enjoyment all
showed strong positive direct effects on the behavioral intention, while perceived
organizational support showed a small positive direct effect. Perceived enjoyment also had a
strong effect on attitude. Furthermore, attitude was positively influenced by the perceived
ease of use and social influence of referents. The social influence of referents is determined
by perceived voluntariness of the system on the one hand, which exerts a very strong negative
influence, and by perceived usefulness on the other hand, which exerts a moderate positive
effect on this construct. Furthermore, the perceived facilitating conditions are positively
influenced by both the perceived ease of use and the perceived speed of delivery.
Recommendations aim on decreasing the voluntariness of the system which will lead to an
increase of the social influence. Furthermore managers should exert their social influence on
their employees to use IT self-service when encountering IT problems. Also, SNS REAAL
should exert more messages that using IT self-service is important to them, which will
increase the behavioral intention due to enlarged perceived organizational support. Moreover,
the perceived speed of delivery should be updated by a communications campaign. Another
recommendation is that help desk employees should aid employees in using the
knowledgebase to find the right solution. To conclude, the perceived enjoyment and attitude
can be enlarged by perking up the online environment, and using multimedia to provide
solutions.
3. 2
Foreword
This master thesis that focuses on what drives users of the SNS REAAL IT infrastructure to
make use of the knowledgebase IT self-service when encountering an IT problem. It was
written to graduate for the Master of Science in Marketing Management program at Tilburg
University.
First of all, I would like to thank all that have took part in the research for their participation
and effort. Furthermore, I would like to thank my company supervisor, Paul Heslenfeld for
granting me the opportunity to graduate at SNS REAAL. Also, I would like to thank him for
his guidance during the project and helping me getting in contact with the right people.
To conclude, I would like to thank my university supervisor Bart Schiffelers for his
guidance during the entire project. His delivered clear and valuable feedback which enabled
me to finish the thesis successfully.
Jorrin Sebregts
Tilburg, January 2013
4. 3
Index
Management Summary .............................................................................................................. 1
Foreword .................................................................................................................................... 2
Chapter 1: Introduction .............................................................................................................. 5
§1.1. Short Introduction of the Company................................................................................... 5
§1.2. Problem Indication ............................................................................................................ 5
§1.3. Problem Statement ............................................................................................................ 6
§1.4. Research Method (Approach) ........................................................................................... 7
§1.5. Structure and Chapter Classification................................................................................. 7
§1.6. Scientific and Managerial Relevance................................................................................ 7
§1.6.1. Managerial Relevance:................................................................................................... 7
§1.6.2. Scientific Relevance:...................................................................................................... 8
Chapter 2: Theoretical Framework ............................................................................................ 9
§2.1 The Importance of Technologies in IT Help Desk Environments ..................................... 9
§2.2 Introduction to Knowledge Management Systems in General......................................... 10
§2.3 Classifying SNS REAAL’s IT self-service...................................................................... 11
§2.4 Behavioral Intention (BI) ................................................................................................. 13
§2.5 Social Influence (SI)......................................................................................................... 14
§2.6 Voluntariness (VOL)........................................................................................................ 15
§2.7 Attitude Towards Technology (ATT) .............................................................................. 16
§2.7.1 Perceived Enjoyment (PE) ............................................................................................ 17
§2.7.2 Perceived Usefulness (PU):........................................................................................... 17
§2.7.3 Perceived Ease of Use (PEOU)..................................................................................... 18
§2.8 Perceived Facilitating Conditions (PFC).......................................................................... 18
§2.9 General Computer Self-Efficacy (GCSE)........................................................................ 19
§2.10 Perceived Organizational Support (POS)....................................................................... 21
§2.11 Conclusion...................................................................................................................... 22
Chapter 3: Research Method.................................................................................................... 23
§3.1 Defining User Groups ...................................................................................................... 23
§3.2 Method of Qualitative Research....................................................................................... 24
§3.2.1 In-Depth Interview Design............................................................................................ 25
§3.2.2 In-Depth Interview Reportings...................................................................................... 25
§3.2.3 Perceived Speed of Delivery (PSOD) ........................................................................... 27
§3.2.4 Final Conceptual Model................................................................................................ 27
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Chapter 1: Introduction
The first chapter of this research provides an introduction to the company in general and to
the background of the problem situation. Also, the problem statement, research method, and
the managerial- and scientific relevance will be formulated in order to be able to give a clear
view on how this research is important to SNS REAAL, as well how it can contribute to the
literature in this specific research area.
§1.1. Short Introduction of the Company
SNS REAAL is a financial services company that is particularly active in the market of
small and medium companies and individuals, with products in the fields of saving, investing,
insurances and mortgages. SNS bank, REAAL insurances, Zwitserleven and SNS Property
Finance are the most well-known brands of the company. SNS is mostly active in the
Netherlands, and has approximately 7.200 internal employees, and around 1.900 external
employees. 1
SNS REAAL realized a turnover of 4.746 billion euro’s during the fiscal year of
2011.
§1.2. Problem Indication
Information technology user support is the single point of contact for all IT problems and IT
related questions that come from internal users at the SNS REAAL organization. In line with
the strategy for the company as a whole, the information technology help desk will need to
grow with the professionalization of the organization. Users are expected to be self-reliant
with regard to solving (some) IT problems, and information technology user support will
focus on specific, difficult IT problems where they can make a difference using their specific
expertise. With regard to letting users solve IT problems themselves without consulting an
information technology user support employee, an external knowledgebase was founded in
2011, called IT self-service. The articles and documents in IT self-service provide users with
information, insight and advice about the use of IT and how to solve IT problems that arise
within the organization.
Naturally, it will not be possible for employees to solve all IT problems on their own by
using the knowledgebase. However, the low extent to which IT self-service is used is
disappointing to SNS REAAL. Questions and situations that often arise are asked repeatedly
over the phone instead of using IT self-service, which leads to an inefficient use of resources.
Since information technology user support is controlled at the expense and judged by
1
Information obtained from the SNS REAAL intranet page ‘’ http://id/Pages/Default.aspx’’
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´customer´ satisfaction (where customers are the internal users of the IT infrastructure),
increased use by self-reliant users of the IT self-service will be (1) cheaper compared to
consulting information technology user support employees that need to be hired and paid to
answer (very often, very simple) questions, and (2) a faster method to solve their problems
compared to consulting user support employees.
§1.3. Problem Statement
In order to increase the usage of IT self-service, SNS REAAL needs insight what drives
users of the IT infrastructure at SNS REAAL to use the knowledgebase. Therefore the
problem statement is formulated as:
‘’What antecedents influence the behavioral intention of SNS REAAL’s employees to use IT
self-service in the context of using it to solve their own IT problems, and to what extent can
they be influenced in a way, that employees will make more use of IT self-service, and
therefore be more self-reliant?’’
Theoretical research questions:
1. Why are knowledge management systems important to companies and SNS REAAL
in particular?
2. What antecedents drive behavioral intentions to use information technology?
3. To what extent does voluntariness of use influence the relationship between behavioral
intention of information technology use and its antecedents?
Practical research questions:
4. To what extent can the model be applied to the context of the research setting at SNS
REAAL?
5. To what extent do the antecedents explain the behavioral intentions of SNS REAAL
employees to use IT self-service, and to what extent do(es) (the) moderator(s)
influence the relationship(s) between behavioral intention of technology use and its
antecedents?
6. To what extent can the behavioral intention to use IT self-service be enlarged by
altering one or multiple driving antecedents?
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§1.4. Research Method (Approach)
The research design will be based on a theoretical part and a practical part. The theoretical
part will need to provide enough support for antecedents that drive the behavioral intention of
system usage within organizations, so that a model can be composed. After that, the model
needs to be validated to the specific context of SNS REAAL in order to be able to apply it.
This will be done by in-depth interviews. Next, the practical research questions will be
answered by conducting quantitative research among a part of the total of 9.100 IT
infrastructure users at SNS REAAL. The conceptual model in this research will be tested
using regression analysis in order to see what direct, mediating and moderating relationships
are significant.
§1.5. Structure and Chapter Classification
After this introductory chapter, the theoretical framework will be composed as specific as
possible to the context of SNS REAAL. In chapter 3, the research method will be described.
First, a qualitative research will be set up in order to validate the theoretical conceptual
model. Once the constructs are validated, a survey will be set up to test the model among
users of the IT infrastructure of SNS REAAL. Chapter 4 will contain the analysis and results
of the survey. The results will be discussed in chapter 5, after which implications,
recommendations, limitations and direction for future research will be given.
§1.6. Scientific and Managerial Relevance
This research will contribute in both a practical and theoretical manner. Both the
contribution to SNS REAAL, as the contribution to the current state of literature will be
discussed below.
§1.6.1. Managerial Relevance:
With the recent investments into the external knowledgebase, which aides internal users of the
SNS IT infrastructure with their IT problems, it is necessary to get them to start making
(more) use of the IT self-service to solve (some of) their own IT problems. As mentioned
before, information technology user support is controlled at the expense and judged by user
satisfaction, and increased usage of the external knowledgebase will be a cheaper and faster
problem-solving solution.
Therefore it is important to SNS REAAL to find out how to improve the intentional use and
attendant usage of the IT self-service so that SNS REAAL can be more efficient using their IT
resources. This is supported by multiple researchers, who state that information technology,
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with its capacity to process, store and transmit information, has a significant potential impact
on organizational effectiveness and productivity (Igbaria & Iivari, 1995; Curley, 1984;
Fortune, 1993, 1993; Maglitta, 1991; Sullivan-Trainor, 1991).
Earlier research states that individuals are sometimes unwilling to accept and use available
systems and express less than enthusiastic response to new technology introduced by
companies, even if the system may increase their productivity (Igbaria & Iivari, 1995; Young,
1984; Bowen, 1986). The acceptance and use of computers systems by individuals appears to
be limited due to a number of reasons such as: fear of computers, confidence and ability,
resistance to new technology, perceived difficulty of use, not understanding the importance of
technology, and lack of motivation to adopt the usage of a new technology (Davis, Bagozzi &
Warshaw, 1989; Hill, Smith & Mann, 1987; Thompson, Higgins & Howell, 1991). A Fortune
article states that ‘’many workers are suspicious of new technology, even hostile to it’’
(Fortune, 1993, pp. 44). Therefore, it is of great importance to SNS REAAL to find out what
antecedents cause behavioral intention of the external knowledgebase, and how they can be
influenced in such a way so that the behavioral intention will increase.
§1.6.2. Scientific Relevance:
The scientific relevance of this research is ubiquitous. Employee self-service technology
(ESS) fits closely with the IT self-service technology at SNS REAAL. Within this area of
research, Marler & Dulebohn (2005) were the first to set up a theoretical framework for
possible important drivers of ESS technology acceptance. Later, Marler et al. (2009) tested a
modified conceptual model within a human resource information management context, which
provided empirical support for several drivers, such as subjective norm, attitude and perceived
resources on behavioral intention. To current knowledge, there is no literature that specifically
elaborated on an ESS technology with the purpose of aiding users of an IT infrastructure to
solve their own IT problems. Also, this is the first study that empirically tests it within a
commercial organization, as Marler et al. (2009) conducted his empirical research among
employees within a university context. Furthermore, self-service technologies are currently a
very popular research issue within a marketing context. For now, research has mainly focused
on business to consumer settings (Curran & Meuter, 2005; Liljander, Gillberg, Gummerus &
Van Riel, 2006; Leung & Matanda). This research might help better understand the constructs
influencing acceptance of service technologies in a business to business environment.
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Chapter 2: Theoretical Framework
In order to determine which antecedents influence the behavioral intention of individuals to
use IT self-service at SNS REAAL, a critical review of current literature will be conducted.
First, a general introduction is given to the importance of IT help desks in modern business
environments, and why there is an increasing need for knowledge management systems to
support help desks in business environments. Then, the type of knowledge management
system at SNS REAAL will be defined after which a conceptual model will composed be that,
according to the current state of literature, explains the drivers of behavioral intention to use a
system such as IT self-service.
§2.1 The Importance of Technologies in IT Help Desk Environments
Research emphasizes the important role that help desks serve at companies’ information
technology departments by providing the primary point of contact for clients to contact
analysts that can help them resolve (previous and new) problems with use of information
technology including hardware, software and networks (González, Giachetti & Ramirez,
2005). There are two types of help desks depending on whether the clients are internal or
external to the organization (González et al., 2005; Heckman & Guskey, 1998; Thomas,
1996). In this research, it is important to discuss the internal help desk, as the support that is
delivered by SNS REAAL´s information technology user support focuses on the employees
that work at SNS REAAL. It has been observed that the internal help desk has a great impact
on the productivity of the organization since it is resolves problems that may stop, delay or
otherwise impact the completion of daily business activities (González et al., 2005; Held,
1992). The faster the help desk can troubleshoot and resolve the problem, the better (González
et al., 2005; Lazarov & Shoval, 2002; Marcella & Middleton, 1996).
According to a study conducted by the Gartner Group2
, the average number of information
technologies supported by help desks has increased from 25 to 2,000 in the past 5 years
(Sandborn, 2001). One reason for the increase is the proliferation and distribution of
information technology such as different personal computers, software applications, printers,
and servers throughout the organization. Moreover, research suggests that the more
distributed the information technology, the more support the end users require (González et
al., 2005; Marcella & Middleton, 1996; Sharer, 1998). SNS REAAL employs approximately
9.100 people, who all use a variety of systems, applications, and all in different work
environments (individuals can work at home, at an office location, at a shop location, etc.).
2
See Sandborn (2001)
11. 10
Making sure SNS employees are able to work in a proper way, by solving IT problems that
occur in their specific working environment, causes a huge workload for the IT user support.
Remarkably, past research has shown that help desks spent about 60-70% of their time on
solving repeat problems (Sandborn, 2001; Simoudis, 1992). As was mentioned in the problem
statement, this is also the main concern to SNS REAAL, as they noticed that a lot of calls to
the IT help desk are frequently asked questions which can be easily solved by themselves.
Solving reported problems in a help desk environment requires specific information to be
transferred to the employee who needs it. Information and knowledge within organization can
reside in many disparate forms such as databases, files, people, electronic documents and
procedures (González et al., 2005), and much of this knowledge that is transferred, comes
from experiential learning (González et al., 2005; Marcella & Middleton, 1996; Taylor,
Gresty & Askwith, 2001). This corresponds to SNS REAAL, which also tries to reside the
information that is gained from experiential learning, managing this knowledge through its IT
self-service system. It is therefore important to give a short introduction to knowledge
management systems in general, after which the IT self-service will be categorized to a
specific technology type in order to be able to explore the drivers of the behavioral intention
to use the knowledgebase.
§2.2 Introduction to Knowledge Management Systems in General
Knowledge is a justified personal belief that increases an individual’s capacity to take
effective action (Alavi & Leidner, 1999; Nonaka, 1994; Huber, 1991). Nonaka & Takeuchie
(1995) distinguish two types of knowledge within organizations; (1) tacit knowledge, which
can be defined as personal, context-specific knowledge that is difficult to formalize and
communicate, and (2) explicit knowledge, which can be defined as factual and easily codified
so that it can be formally documented and transmitted. SNS REAAL’s strategy for IT
problems that require tacit knowledge will remain to be solved by help desk employees, since
these are the more technical and difficult problems where IT help desk personnel can make a
difference with their expertise. Explicit knowledge is the type of knowledge that can be
communicated through the articles and documents in the IT self-service knowledgebase, and
which should enable employees to solve their own IT problems.
Through knowledge management a company changes individual’s knowledge into
organizational knowledge (González et al., 2005; Sveiby, 1997). The organizational
knowledge is maintained in organizational knowledge resources which are operated by human
or computer processes that manipulate the knowledge to create value. Gray (2001) presents a
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framework that categorizes knowledge management according to a problem-solving
perspective. Along the horizontal axis there are new problems and previously occurred
problems; along the vertical axis there are problem recognition and problem solving. The
primary function of the help desk is solving both new and previously solved problems. When
solving new problems, Gray (2001) calls this knowledge creation. Solving previously solved
problems is called knowledge acquisition. Previous research stated that knowledge
management is about acquisition and storage of employees’ knowledge and making the
knowledge accessible to other employees within the organization (González et al., 2005;
Alavi & Leidner, 1999; Mertins, Heisig & Vorbeck, 2001; Meso & Smith, 2000; Satyadas &
Harigopal, 2001). Solving previous solved problems is the part that SNS REAAL wants to use
IT self-service for, while solving new problems (and more difficult problems previously
solved problems) is something where users can consult the help desk for.
In order to manage knowledge, technology is available to support the knowledge
management process (Nissen, Kamel & Sengupta, 2000). Alavi & Leidner (1999) defined
knowledge management systems (KMS) as systems that gather, organize, and disseminate an
organization’s knowledge. It is a discipline that provides strategy, process and technology to
share and leverage information and expertise that will increase the level of understanding to
more effectively solve problems (González et al., 2005; Satyadas & Harigopal, 2001). This
definition and underlying strategy is applicable to IT self-service.
Now that the importance of knowledge management systems in IT help desk environments
is made clear, it is important to see to how IT self-service can be classified. This way, specific
antecedents to behavioral intentions to use this particular technology which are described in
the literature can be defined in order to see what drivers can increase the behavioral intention
of IT self-service at SNS REAAL.
§2.3 Classifying SNS REAAL’s IT self-service
Fueled by the explosive growth in World Wide Web usage in the mid 1990’s, many
companies began to establish their own internet presence that ran entirely within an
organization which they called corporate portals or the intranet (Masrek, Karim & Hussein,
2006; Scheepers, 1999). According to Muller (2002), some of the reasons that companies
adopt intranet systems, or use technologies based on intranet environments, are to improve
internal communications, cost effectiveness, information distribution, and organizational
information and resource sharing. The IT self-service is part of SNS REAAL’s intranet, and
focuses on distributing IT information and resources across all users of the IT-infrastructure.
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Besides being based on intranet technology, IT self-service can also be classified as an e-
learning system. As Kosarzycki, Burke, Fiore & Stone (2002) suggested, e-learning generally
refers to the use of computer network technology, primarily over an intranet, to deliver
information and instruction to individuals. This fits with the purpose of IT self-service as it
emphasizes on delivering information and instructions to employees through the intranet,
which enables them to solve IT problems themselves.
Furthermore, IT self-service can also be theorized as a self-service technology (SST). SST’s
are defined as ‘’technological interfaces that enable customers to produce a service
independent of direct service employee involvement’’ (Meuter, Ostrom, Roundtree & Bitner,
2000; p. 50). In this research, the customers in the definition of Meuter et al. (2000) represent
the employees, whereas the service that is performed independently is solving a particular
problem instead of consulting an IT help desk employee.
Moreover, IT self-service can be seen as an employee self-service technology (ESS). ESS
technologies are currently a popular innovation that is of special interest to many researchers
because of anticipated cost savings and other efficiency related benefits (Marler, Fisher & Ke,
2009). ESS involves the use of internet-based technology to permit all employees, throughout
an organization, direct access to centralized information databases through the use of
computers that are connected to each other (Marler & Dulebohn, 2005). ESS deployment
assumes that the technology will be used by all employees, not just knowledge workers as is
the typical assumption underlying other IT implementations (cf. Harrison & Rainer, 1992). In
an HR context, it is considered to be a class of web-based technology that allows employees
and managers to conduct much of their own data management and transaction processes
rather than relying on human resource of administrative staff to perform these duties (Marler
et al., 2009; Marler & Dulebohn, 2005). The definition of an ESS technology can also be
applied to the context of IT self-service, as it is web-based (intranet), and allows employees
and managers to solve their own IT problems rather than relying on IT help desk personnel
resources to perform these duties. There are a couple significant aspects of ESS technology
that differ from typical IT contexts, and which are important to take into account. First, unlike
many IT applications, ESS technology functionality is typically not associated with the core
functions of employees’ jobs (Brown, 2003; Marler & Dulebohn, 2005). This matches with IT
self-service since most employees are hired to perform on core business related tasks, such as
banking, investments and insurances. Second, ESS technologies do not automatically involve
mandated use of the technology; much of it is voluntary, although clearly organizations want
their employees to use it, which also fits with IT self-service.
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In order to compose a conceptual model that is as specific as possible to the context of this
research, a critical review regarding drivers of the behavioral intention to use intranet, e-
learning and (employee) self-service technologies will be conducted. Furthermore, there are
three important constraining factors that need to be taken into account. First, IT self-service is
only partly mandatory, as it is part of the intranet. Actual use is mostly voluntary.
Furthermore, the IT self-service does not focus on improving an employee’s direct job
performance. To conclude, IT self-service is in a post-implementation phase as it has been
introduced in January 2011. Therefore, most users already have (some) experience with using
IT self-service. Although employees might still call a help desk employee for problems they
encounter, they do often use IT self-service to report the problem. So most do have some
experience due to this post implementation situation (user stats indicate that around 50% of all
users first consult IT self-service before calling the help desk) which might influence the
direction and strength of drivers of the behavioral intention. This is supported by Karahanna
et al. (1999) and Marler et al. (2009) who found that a post implementation phase might cause
an in- or decrease between drivers and behavioral intention, or even cause constructs to
become non-significant.
Relevant drivers of behavioral intention in this research will now be defined, and post-
implementation effects on each of the drivers will be taken into account when exploring the
relationships between different constructs.
§2.4 Behavioral Intention (BI)
Organizations cannot realize any return on their investments in information systems unless
the systems are actually used by their intended users. Despite their sizable cost, information
systems have been found underutilized or sometimes abandoned because of a lack of user
acceptance (McCarroll, 1991; King, 1994; Gillooly, 1998). The utilization of technology has
been a shared key concern between information system- (IS) (Kwon & Zmud, 1987; DeLone
& McLean, 1992) and human computer interaction researchers (Nickerson, 1981; Carrol &
Rosson, 1987). A strong indication of utilization of information systems is the BI by
individuals to use a specific technology. This posited relationship has been well established in
the social psychology literature (See Ajzen, 1991; Eagly & Chaiken, 1993, Pinder, 1998 for
reviews). Within the IS literature, the relationship between BI and actual use has also been
concluded in many studies and in a wide variety of settings (Davis, 1989, 1993; Davis et al.,
1989; Venkatesh & Davis, 1996; Venkatesh & Davis, 2000). BI represents the likelihood that
an individual will employ the technology (Ajzen & Fishbein, 1980). BI, originally used in the
15. 14
technology acceptance model (TAM) by Davis et al. (1989), has a high research significance
within the IS discipline (Straub, Keil & Brenner, 1997; Taylor & Todd, 1995).
More specifically, BI has been used as a dependent variable within an ESS technology
context (Marler, Fisher & Ke, 2009; Marler & Dulebohn, 2005), intranet context (Lederer,
Maupin, Sena & Zhuang, 2000) and e-learning context (Lee, Cheung & Chen, 2005; Yi &
Hwang, 2003; Lee, Hsie & Ma, 2011). Since IT self-service is closely related to these
technologies, BI will be used as the dependent variable in this research. In order to change the
behavior of SNS REAAL employees with regard to IT self-service, it is important to see how
their BI’s are currently formed. Therefore, antecedents described in the IS literature that are
applicable to the research context of IT self-service at SNS REAAL will now be discussed.
§2.5 Social Influence (SI)
SI in the IS literature is defined as the degree to which an individual perceives that
important others believe he or she should use the new system (Venkatesh et al., 2003; p. 451).
SI has an impact on individual’s behavior through the compliance mechanism, which causes
an individual to simply alter his or her intention to the social pressure (Venkatesh & Davis,
2000; Warshaw, 1980).
The construct of SI has, conceptually similar, different labels, and has been included in
many studies that focused on technology acceptance. For example, SI as a direct determinant
of BI to use a system is represented as subjective norm in theory of reasoned action, theory of
planned behavior, and in the technology acceptance model (Ajzen, 1991; Davis et al., 1989;
Fishbein and Ajzen, 1975; Mathieson, 1991; Taylor & Todd, 1995a; 1995b). In these studies,
subjective norm is described as the person’s perception that most people who are important to
him think he should or should not perform the behavior in question, and conceptually is a
function of both a referent’s normative belief about a behavior and the individual’s motivation
to comply with the referent (Ajzen, 1991) (for an overview, see Venkatesh et al., 2003).
While the subjective norm and SI constructs have different definitions, both contain the
explicit or implicit notion that the individual’s behavior is influenced by the way in which
they believe others will view them as a result of having used the technology (Venkatesh et al.,
2003). Empirical research has acknowledged the importance of SI on BI to use technology in
organizational settings (Karahanna, Straub & Chervany, 1999; Taylor & Todd, 1995).
In the IS literature, a variety of referents that have social influence within organizations
have been studied, including co-workers, supervisors, the IT department, close friends, top
management, IT instructors, and other IT specialists (Karahanna et al., 1999; Thompson et al.,
16. 15
2006; Venkatesh & Davis, 2000). According to Simon (1997), management can play a
significant role in shaping organizational values through providing positive signals about the
technology. Many researchers found that the support of management in innovation and
technology has been consistently associated with higher levels of success in the areas of
change, innovation, and the perceptions of technology (Bajwa, Rai & Brennan, 1998; Simon,
1997; Davis, 1989; Davis Bagozzi & Warshaw, 1989). Specific to IT self-service as a part of
SNS REAAL’s intranet, researchers consistently found that management support is a strong
determinant of intranet success (Al-Garbi & Al-Turki, 2001; Eder & Igbaria, 2001; Young,
2001; Zolla, 1998; Tang, 2000; Scheepers, 1999; Bajwa & Ross, 2000), and can be
operationalized by communication of top management to organizational members to use the
technology (Eder & Igbaria, 2001). Within an e-learning context, Lee, Hsieh & Ma (2011)
found that subjective norm directly influenced the BI to use a technology. Marler & Dulebohn
(2005) suggested that management influence has a direct influence on ESS acceptance, and
Marler, Fisher & Ke (2009) empirically showed that subjective norm has a direct influence on
the BI. With regard to the post-implementation phase, Marler et al. (2009) stated that ESS
technology users are less likely to respond to social pressures early in the implementation
process. Ajzen (2002) also stated that referents are able to form better judgments of the
perceived usefulness of a system as they gain more experience over time with the system,
increasing the SI. All theory described above make it very eligible for SI to play an important
role on the BI to use IT self-service. Therefore, it is expected that H1: susceptibility for SI
influences BI, so that high susceptibility leads to higher BI to use IT self-service.
As previously mentioned, another important condition that needs to be taken into account is
the fact that IT self-service is not a completely mandatory system. The effects of
voluntariness on the relationship between SI and BI will therefore now be discussed.
§2.6 Voluntariness (VOL)
As Marler et al. (2009) suggested, ESS technologies like IT self-service are mandatory to
some degree, but the overall extent of ESS technology use is voluntary. Users can choose
(not) to use a wide range of ESS functions (of which most are not related to their core job
performance). In case of IT self-service, they are not forced to first look for a solution
themselves, as it neither is mandatory to categorize their type of problem (e.g. Microsoft
Outlook related, etc.). If they want they can dial the help desk service number right away, or
report their problem online.
17. 16
Marler et al., (2009) stated that, in order for organizations to benefit from ESS technologies,
a high level of adoption and use is needed. They reasoned that, although the technology is
voluntary, increasingly felt pressure from organizational agents could cause subjective norms
to play an important role in the adoption process, for which they found empirical support.
Hartwick and Barki (1994) found that the effect of subjective norms on BI varied depending
on whether the technology was mandatory or voluntary. The results suggested that subjective
norms are most powerful in situations where users must comply with a technology adoption
mandate. This implies that, although not as powerful, it also accounts for volitional situations.
Therefore it is proposed that H2: the perception of VOL in use of the system moderates the
relationship between SI and BI, so that higher perceived VOL leads to lower BI to use IT
self-service.
Now that the influence of SI on BI is clear, this research will now continue with the effect
of attitude towards a specific technology on BI.
§2.7 Attitude Towards Technology (ATT)
Research has consistently found that an individual’s ATT is a significant predictor of BI’s
to use the technology (Davis, 1989; Davis et al., 1989; Taylor & Todd, 1995 Igbaria,
Schiffman & Wieckowski, 1994, Teo, Lim & Lai, 1999; Venkatesh, 1999; Lee, Cheung &
Chen, 2005). ATT is the user’s evaluation of the desirability of employing a particular
information systems application (Ajzen & Fishbein, 1980). Existence of this relationship has
been supported in a variety of situations, including workplace using operation systems,
database programs and virtual community technology (Marler et al., 2009; Karahanna et al.,
1999; Venkatesh, Speier, & Morris, 2002). Therefore, it is expected that H3: a more
favorable ATT will result in a higher BI to use IT
self-service.
Attitudes in general are based on 2 types of motivators (Lee et al. 2005). Extrinsic
motivation pertains to behaviors that are engaged in response to something apart from its own
sake, such as a reward or recognition in the form of a positive outcome for itself. Intrinsic
motivation refers to the fact of doing an activity for its own sake: the activity itself is
interesting, engaging, or in some way satisfying. With regard to IT self-service, one intrinsic
motivator (perceived enjoyment) and two extrinsic motivators (perceived ease of use and
perceived usefulness) will be discussed, in order to see how they form an individual’s ATT.
18. 17
§2.7.1 Perceived Enjoyment (PE)
From an intrinsic motivational perspective, behavior is evoked from the feeling of pleasure,
joy, and fun. These outcomes occur immediately upon the performance of the acts that
produce them and therefore are self-administered rather than distributed by others (Pinder,
1998). Examples of intrinsic outcomes include positive feelings of accomplishment, a sense
of mastery, competence (Pinder, 1998) and playfulness (Venkatesh, 2000). It is about the
perceived enjoyment which in this light can be defined as ‘the extent to which the activity of
using the system is perceived to be enjoyable in its own right, apart from any performance
consequences that may be anticipated’ (Davis, Bagozzi & Warshaw, 1992; p. 1113). Within
the IS literature context, variables such as perceived enjoyment, anxiety and playfulness were
found to be conceptually similar, all tapping intrinsic motivation (Lee, Cheung & Chen,
2005). Empirical research by Venkatesh (2000) found that these constructs function as a distal
determinant of system use, achieving their effect indirectly through ATT. Within an ESS
technology context, Marler & Dulebohn (2005) showed that intrinsic benefits such as gaining
a sense of enjoyment or mastery over using a new internet technology might motivate them to
use the technology. Within an e-learning context, PE was found to be a powerful explaining
factor on ATT (Lee, Cheung & Chen, 2005). Weijters et al. (2005) showed that PE influences
attitudes towards SST’s, which was acknowledged by many researchers (Marzocchi &
Zammit, 2006; Liu & Zhou, 2006; Zhu, Nakata & Sivakumar, 2007). Therefore it is proposed
that H4: high PE results in a more favorable ATT.
§2.7.2 Perceived Usefulness (PU):
From an extrinsic motivational perspective, behavior is driven by its perceived values and
benefits derived. PU refers to the degree to which a person believes that a particular system
would enhance his or her performance with regard to performing specific tasks (Davis, 1989).
If a particular technology is able to enhance individuals’ job performance (PU), individuals
are more likely to have a positive ATT (Davis, 1989; Davis et al., 1989). In the case of IT
self-service, PU refers to the extent to which employees believe using the IT self-service will
help them solving their IT problems. Similar to PU is the construct of relative advantage
which can be defined as the degree to which using the IT system is perceived as being better
than using the practice it supersedes (Moore & Benbasat, 1996; Rogers, 1983). Tornatzky &
Klein (1982) meta-analysis showed that relative advantage is consistently related to
(intentional) utilization decisions. Furthermore, within an ESS technology environment,
Marler et al. (2009) empirically showed that PU is positively related to ATT. Lee et al. (2005)
19. 18
also incorporated PU as a predictor of ATT within an e-learning context, and found it to be a
key factor determining individuals’ ATT. Hernandez & Mazzon (2007) also identified PU as
an important determinant on ATT in a banking SST environment. If using the IT self-service
is being perceived as a better/faster/more useful way to solve an IT problem compared to
calling the help desk, then the PU will be high. Therefore, it is expected that H5: the high PU
results in a more favorable ATT.
§2.7.3 Perceived Ease of Use (PEOU)
In their widely accepted technology acceptance model, Davis et al. (1989) suggested that
ATT, next to PU, is also formed by PEOU, which they defined as ‘the degree to which a
person believes that using a particular system would be free of effort’ (p. 320). Effort is a
finite resource that a person may allocate to the various activities for which he or she is
responsible (Radner & Rothschild, 1975). Venkatesh et al. (2003) showed that the PEOU
construct was conceptually similar to the complexity construct (Thompson et al., 1991) and
ease of use construct (Moore & Benbasat, 1991), each proving to be a significant predictor of
ATT. Within an ESS context, Marler & Dulebohn (2005) defined effort expectancy as a
representation of an individual’s subjective assessment of how easy it will be to competently
operate an ESS technology. However, there is contradictive evidence regarding the PEOU
construct in the post-implementation phase. It is suggested that PEOU becomes non-
significant over periods of extended and sustained usage (Davis et al., 1989; Agarwal &
Prasad, 1997; Thompson et al., 1991; Karahanna et al. 1999; Thompson et al., 1994;
Venkatesh & Davis, 2000). However, Venkatesh & Davis (2000) showed that PEOU
remained significant, but the effect on ATT is just stronger for people with limited experience
(Venkatesh et al., 2003). Despite the contradictory results, PEOU is still theorized to have a
positive impact on ATT. Therefore it is expected that H6: high PEOU leads to a more
favorable ATT.
Furthermore, in the original TAM Davis et al.(1989) suggested that PEOU also influences
PU. However Marler et al. (2009) did not theorize that relationship in their ESS technology
acceptance model. Since their research context is most similar to that of this research, the
effect of PEOU on PU is not theorized.
§2.8 Perceived Facilitating Conditions (PFC)
In the IS literature, technology refers to computer systems (hardware, software and data)
and user support services (help lines, IT consultants, etc.). In this context, individuals view
20. 19
technology as a tool used for carrying out a task. It is important to see whether individuals
perceive barriers, preventing them from using a particular technology in order to perform the
specific task (Mathieson, Peacock & Chin, 2001; Marler & Dulebohn, 2005). Barriers exist if
individuals perceive the facilitating conditions or resources as being insufficient. This was
supported by Taylor & Todd (1995), who suggested that the extent to which users perceive
they are free from external constraints to use the technology, is important to BI.
Broadly, PFC are defined as ‘the degree to which an organizational and technical
infrastructure exists to support use of the system’ (Venkatesh et al., 2003, p. 453). It refers to
external resources or conditions that need to be facilitated by the organization in order to
support the user in using the system. Mathieson et al. (2001) empirically showed that
organizational resources were positively associated with intentions to use software. They
found that perceptions of accessibility of hardware and software are the most critical
predictors for how employees judge the PFC followed by knowledge. They also found that if
users do not believe that sufficient external resources such as necessary computer equipment,
documentation, or help functions exist, they are unlikely to even attempt to use the system.
The effect of PFC on BI in an ESS technology context are even stronger. Leonard-Barton &
Deschamps (1988) suggested that an ESS technology implementation is complex, requires
many members to use it in order to benefit the organization, and requires greater
technological resources than standalone software packages. Marler et al. (2009) found
empirical evidence between PFC and BI, and suggested that access to hardware and software
to perform ESS is essential, and having supporting consultants to assist and teach in quickly
learning the technology are important factors of an individual’s intention to use ESS.
Thompson et al. (2006) found that the relationship between perceived resources (similar to
PFC) and intentions was stronger for users who have more experience. This is due to better
awareness of the resources that are facilitated by the organization (Madden, Ellen & Ajzen,
1992), which enables them to better determine whether the available resources will actually
help them to use the technology. Therefore it is expected that H7: positively evaluated PFC
result in a higher BI to use IT self-service.
§2.9 General Computer Self-Efficacy (GCSE)
Stajkovic & Luthans (1998) defined self-efficacy as ‘an individual’s convictions about his
or her abilities to mobilize, motivation, cognitive resources, and courses of action needed to
successfully execute a specific task within a given context’ (p. 66). It reflects a future-oriented
belief about what one can accomplish. Since IT self-service offers employees help to solve
21. 20
their own IT problems, it is important to see how individuals perceive themselves capable of
solving problems that are computer related. More specifically, computer self-efficacy (CSE)
is defined as ‘’an individual’s judgment of one’s capability to use a computer’’ (Compeau &
Higgins, 1995; p. 192). Passed research has suggested that an individual’s perceived ability to
adopt a computer technology is a major factor affecting the BI to use the system (Ellen,
Bearden & Sharma, 1991; Hill, Smith & Mann, 1987; Agarwal, Sambamurthy & Stair, 2000;
Venkatesh, 2000; Hill, Smith & Mann, 1987). Compeau, Haggerty & Kelley (2006) suggested
that the degree of confidence possessed by an individual regarding some aspect of computing
behavior exerts a strong influence on his or her ultimate choice to undertake these behaviors.
This relationship has been established within a variety of domains within the IS literature
(Hill et al., 1987; Venkatesh et al., 2003).
A variety of views and measures of CSE exist in the IS literature. They include both general
computer self-efficacy (GCSE), focusing on ability to use computers overall, and more
specific computer self-efficacy measures (SCSE), tailored to the context of a particular
computer system. IT self-service can help employees in solving their own problems, which
they may encounter with any of the applications that they use in doing their job (e.g. outlook
to send mail, RES manager to synchronize their files, etc.). So the CSE construct is not
specific to using IT self-service, as the type of problem can relate to any specific application.
IT self-service just aids by providing instructions to solve the problem that is encountered
with the specific application, and therefore the BI to use IT self-service will depend on the
CSE level that is associated with the application where the problem occurs. Since there are
many applications that are used within the working environment, GCSE will be included as a
construct influencing BI. Marakas, Yi & Johnson (1998) suggested that GCSE can be thought
of as a weighted average of a collection of SCSE judgments. So the higher the level of GCSE,
the higher the average SCSE per application will be, and the higher the BI will be.
Furthermore, individuals’ their GCSE judgments change over time as new information and
experience is acquired, sometimes even during actual task performance (cf. Bandura, 1988a;
Bandura & Wood, 1989; Wood & Bandura, 1989). When employees encounter the same
problem multiple times, they do acquire experience and information on how to solve it, which
causes attendant increases in performance (Gist, 1989; Gist, Schwoerer & Rosen, 1989). As
employees perform their jobs by mostly using computer applications, it is expected that H8:
positively evaluated perceptions of one’s GCSE level results in a higher BI to use IT self-
service.
22. 21
§2.10 Perceived Organizational Support (POS)
An important consideration in fostering participation in voluntary learning and development
activities, such as voluntary ESS acceptance for non-job related tasks, is the extent to which
the organization provides an environment to encourage these conditions (Marler & Dulebohn,
2005). As usage of the IT self-service is not required for core job performance, these activities
will more immediately benefit the organization rather than the employee. In particular to IT
self-service, the employee is supposed to use their cognitive resources to look for an answer
as opposed to that he or she would just report the problem to a help desk employee, while
SNS REAAL will benefit by cost effectiveness as discussed earlier.
POS is defined as the ‘extent to which top and middle management allocates adequate
resources to help employees achieve organizational goals, including purposive instructions
and guidance in using computer applications’ (Konradt, Christophersen & Schäffer-Külz,
2006; p. 1143). Based on the norm of reciprocity (Marler et al., 2009; Gouldner, 1960),
greater POS is expected to result in a perceived obligation to engage in behaviors or to adopt
attitudes that reciprocate how employees perceive the organization treats them. High levels of
POS are associated with greater affection to the organization (Marler et al., 2009;
Eisenberger, Fasolo & Davis-LaMastro, 1990), which increases a person’s tendency to
interpret the organization’s gains and losses as one’s own, creates positive biases in judging
the organization’s actions and characteristics, and increases the internalization of the
organization’s values and norms (Marler et al., 2009; Eisenberger, Huntington, Hutchison &
Sowa, 1986; Marler et al., 2009; Eisenberger, Armeli, Rexwinkel, Lynch & Rhoades, 2001).
This is particularly effective in ESS contexts (Eisenberger et al., 2001).
POS is theorized to be important, as at the time of the introduction of the IT self-service, it
did not have a big introduction among employees, nor did employees participate in any
training sessions on how to effectively use the IT self-service. Also, over time, there was little
communication regarding IT self-service, which means that employees are not being made
aware of how important it is to SNS REAAL that employees make use of IT self-service.
POS is particularly instrumental in situations where an employee’s actions result in
outcomes, which benefit others, such as the employee’s supervisor or the organization
generally, rather than the employee personally (Marler & Dulebohn, 2005).Within an ESS
research setting, individuals who perceive the organization cares about their goals and values
are likely to be more motivated to comply with managerial pressure (a form of social
influence), because there is a trust that the response will likely be appropriately rewarded
(Marler et al., 2009). Also, according to Kim, Park & Lee (2007) and McFarland & Hamilton
23. 22
(2006), POS is associated with subjective norm, which was empirically supported by Lee,
Hsieh & Ma (2011), who proved that POS indirectly influences BI to use a technology
through subjective norms. Therefore, it is expected that H9a: high POS increases the
susceptibility for SI.
Furthermore, if POS is high, this nurtures a favorable attitude towards behavior benefitting
the organization (Marler et al., 2005). Davis (1989), Igbaria, Parasuraman & Baroudi (1996)
and Igbaria, Pavri & Huff (1989) found that POS positively affects system adoption through
beliefs and behaviors. Several ESS context studies suggested that when a user perceives a
high level of POS, he or she will evaluate the adoption of ESS technology in a favorable way
(Aryee & Chay, 2001; Coyle-Shapiro & Conway, 2005; Mahmood et al., 2000) because it
represents reciprocal behavior that positively benefits the organization. This effect of POS is
particularly significant in volitional technology contexts (Eisenberger et al., 2001). Therefore,
it is expected that H9b: high POS results in a more favorable ATT.
§2.11 Conclusion
In this chapter the drivers of the behavioral intention to use of the knowledgebase have been
identified according to theories such as Technology Acceptance Model (1 and 2), Theory of
Reasoned Action, Motivational Model, Theory of Planned Behavior and Social Cognitive
Theory. Previous research that used these theories and focused on e-learning system usage,
intranet usage, self-service technology usage and employee self-service technology usage
served as the backbone of this research. The preliminary conceptual model can be found in
figure 1 below.
H4
H5
H7
H8
H6
H9b
H3
H2
H1
POS
PEOU
PU
PE
SI
ATT
GCSE
PFC
VOL
BI
Negative impact
Positive impact
H9a
Figure 1: Preliminary conceptual model for IT Self-Service at SNS Reaal
24. 23
Chapter 3: Research Method
In this research, both a qualitative and quantitative analysis will be conducted .In order to be
able to apply the model as described in the previous chapter, a number of in-depth interviews
will be held first in order to see whether the model is specific enough to the research context.
Also, the qualitative research should lead to more insight in how user groups differ so that
more specific implications can be given. It is also checked whether there are additional
constructs which were not found in the literature review, but do apply in the research context
of SNS REAAL. After that, the conceptual model will be tested organization wide using a
survey among a random sample of users of the IT infrastructure.
§3.1 Defining User Groups
Before the interviews with users of the IT infrastructure were held, two interviews with
stakeholders of this project were conducted. This was done in order to see what their thoughts
were on how to define user groups of the IT infrastructure. The results of these interviews can
be found in appendix 1 (interviews 1 and 2).
The first way they indicated that users might be defined was based on function classification
according to a similar project named ‘’Het Nieuwe Werken’’ (HNW) (see the interview with
Hiddo Born in appendix 1 for a brief description of HNW). The function classification
consists out of 3 types; itinerant workers, knowledge workers and back office workers.
Itinerant workers can be classified as employees who travel a lot for their jobs, and have job
titles such as sales advisor, account manager, consultant, etc. Back office workers are
employees who use a specific application a lot to enter new or change existing data. These are
for example policy employees, or mortgage workers. The knowledge workers are employees
with some kind of specialism, and can work very independent at anytime, anywhere. For a
more extensive description, see appendix 1.
The second possibility that was suggested in defining user groups was based on
generation/age differences. Both managers indicated that they expected differences between
generations. Literature suggests that within the current working environment, there are three
generations (Smola & Sutton, 2002). The first generation is called baby boomers because of
the boom in their births between 1946 and 1964. This generation grew up embracing the
psychology of entitlement, expecting the best from life. Their positive work abilities, or
strengths, include consensus building, mentoring, and effecting change (Kupperschmidt,
2000). The second generation is called generation X, and consists of people who grew up with
financial, family, and societal insecurity; rapid change; great diversity; and a lack of solid
25. 24
traditions. They bring well-honed, practical approaches to problem solving. They are
technically competent, and very comfortable with diversity, change, multi-tasking, and
competition (Kupperschmidt, 2000). The third generation that can be defined is generation Y
or ‘’the Millennials’’(generation Y will be the term used to define this group in this research).
This generation is said to be the first born into a wired world; they are ‘connected’ 24 hours a
day. They distrust institutions and voice their opinions (Ryan, 2000).
Although these definitions of the different generation groups are commonly used in the
current literature, the birth years dividing people into different generations do vary a little. To
classify the different generations in this research, the distribution of birth years of Smola &
Sutton (2002) is used, which is the most widely cited paper regarding generational differences
within a working environment. This defines the distribution as follows: Baby Boomers (1946-
1964), Generation X-ers (1965-1977) and Generation Y-ers (1978-1995).
The third way that was mentioned in defining user groups, was based on the organizational
chart. Both expected that there will be differences between managers and non-managers.
During the actual in-depth interviews held with Mark Nierop and Suze Krijnen, they were
also asked for ways to define user groups within SNS REAAL due to their positions in similar
projects (see appendix 1). They both acknowledged that HNW and generation differences as
ways to define user groups.
§3.2 Method of Qualitative Research
Based on the three proposed definitions of user groups, a mixture of interviewees was
selected to cover up all types of employees. This was done to make sure that the results can be
generalized to every type of employee, and there would be no selection bias. Six interviewees
were selected among members of the IT user panel which is often used for a wide range of IT
related matters. The IT user panel consists out of nineteen people, differing in generation,
function classification and the level in which they are regarding the organization chart. Next
to that, two other employees were selected to complement the qualitative research. Based on
generation type, three baby boomers, three generation X-ers and two generation Y-ers were
interviewed; based on organizational chart, three managers and five non-managerial
employees were interviewed; based on the type of user group, five knowledge workers, two
back office workers and one itinerant worker were questioned. Based on this distribution, the
model can be validated among all possible user groups. For a complete overview of the
employees that were interviewed, please see appendix 2.
26. 25
§3.2.1 In-Depth Interview Design
To validate the model, qualitative semi-structured interviews with the intention to allow
new viewpoints to emerge freely were used. A loose interview schedule was designed based
on the constructs that are discussed in chapter two of this research (Kvale, 1996; Aira,
Kauhanen, Pekka Larivaara & Rautio, 2003). The purpose of the interview was explained to
the participants twice. At first they were sent an invitation by email, which included a small
explanation what the research was about, and why specifically they were asked to take part in
the interview. To see the email sent to the employees, see appendix 3. At the beginning of the
interview the problem indication and problem statement (as described in chapter one of this
research) were briefly discussed so that the purpose of this project is completely clear to them.
The interviews were carried out in the workplaces of the interviewees at the office locations
in Utrecht, Den Bosch and Amstelveen. All the interviews were initiated with the same
question; what are factors that might influence your BI to use IT self-service to solve an IT
problem when you encounter it, compared to calling the help desk to solve your problem. The
interviews resembled a general conversation between two professionals within the
organization, where the interviewer did not attempt to take any leading position. The
interviewer just listened, gently directing the conversation to make sure all the main
themes/constructs were discussed (Aira et al., 2003).
The interviews were at first recorded to make sure no relevant information could be
forgotten. Then, the interviews were transcribed from oral speech to written text, after which
they could be analyzed. Every sentence was carefully interpreted to see if questioned
constructs could be verified, or additional constructs were mentioned. For the complete
transcript and analysis of all interviews, please see appendix 4.
§3.2.2 In-Depth Interview Reportings
The results of the interviews are displayed in table 1. They strongly support the constructs
in the conceptual model of this research. Every construct was verified by all eight
interviewees except for POS. POS was only verified by seven out of eight interviewees. A
weight measure was introduced regarding the way the construct was acknowledged. If the
construct was mentioned very clearly by the employee himself, without the interviewer first
having to ask him whether this would be a factor, then it was awarded with a category one
status, scoring three points. If something was mentioned closely related to one of the
theorized constructs, a follow-up question was asked whether this is what he meant. If
acknowledged, this was awarded a category two status, scoring two points. All other
27. 26
constructs were identified by just asking whether the construct played part in the decision
making process of using the knowledgebase. If acknowledged, this was awarded with a
category three status, scoring one point. In the end, all points were added up in order to see
how good the construct was represented according to this method. For an overview of the
transcripts of the interviews, see appendix 4. For a summary of the interview results, see
appendix 2. The interviewee who did not acknowledge POS to be a factor explained that ‘’it
remains an affair that is decided by an employee himself, if it is useful to him. Not whether
the organization wants him to use the system’’ (see appendix 4 - page 30). The construct
validation based on the weight measures that were used is displayed below in table 1.
Construct PEOU PU PE POS SI ATT PFC GCSE VOL
Total points out of 24 17 12 9 7 9 14 19 15 10
Confirmation % 70,8 50 37,5 28,1 37,5 58,3 79,1 62,5 41,7
Table 1: Construct validation based on in-depth interviews
Furthermore, in two interviews the respondents mentioned an additional driver that
influenced their BI. One of the interviewees stated ‘If an employee still has to finish a pile of
work before he can go home, then he will be inclined to let his IT problem be solved by an
help desk employee’ (appendix 4 – page 39). Another interviewee explained ‘What is the type
of the problem that occurs and how high is the urgence to solve it?’ (appendix 4, page 36)
and ‘’Do I have the time available to solve it myself?’’ (appendix 4 – page 41). He elaborated
more about it when he said ‘Sometimes I just have ten minutes before I have to attend a
meeting. If I have to solve the IT problem before that meeting, and I estimate that as being too
difficult to do it myself, then I will just call the helpdesk’ (appendix 4 – page 41). Another
interviewee stated ‘To what extent is it necessary that the problem is being solved at once’
(appendix 4 – page 25). He furthermore indicated ‘But when I feel I have been searching for
too long using IT self-service, and my problem is urgent, then I will call the helpdesk’
(appendix 4 – page 26). On the occasion of these statements made by the two interviewees,
additional literature research will be conducted in order to see whether an extra construct can
be implemented into the conceptual model to gain extra predictive power when testing it
organization wide.
28. 27
§3.2.3 Perceived Speed of Delivery (PSOD)
The statements of the two interviewees above can be summarized as perceived speed of
delivery. People estimate whether they will have to wait for a significantly longer time using
a particular service delivery option compared to using an alternative option (Dabholkar &
Bagozzi, 2002). Mathieson et al. (2001) empirically showed that availability of time was
positively associated with intentions to use software and consequent system usage within a
working environment. In a business to consumer research context, there is evidence that when
customers are in a hurry, this can have a strong influence on the use of self-service
technologies (Silpakit & Fisk, 1985). Time pressure can affect a consumer who must
complete a task quickly to meet a deadline (Berry, Seiders & Grewal, 2002). Therefore, if the
PSOD of a service delivery option is low, they are more likely to pursue an alternative option.
However, if the PSOD is high, it will be likely that they will pursue that option. Dabholkar &
Bagozzi (2002) stated that perceived waiting time (which is equivalent to the construct of
PSOD), is a situational factor that moderates the relationship between ATT and BI. Therefore,
it is assumed that H10: PSOD has a moderating effect on the relationship between ATT and
BI, so that faster perceived service deliveries lead to higher BI to use the system.
§3.2.4 Final Conceptual Model
With the additional identified construct of PSOD implemented in the conceptual model, the
final model that will be tested among members of the IT infrastructure at SNS REAAL is
displayed below in figure 2. Accordingly, the method of quantitative research will now be
described.
H10H4
H5 H7
H8
H6
H9b
H3
H2
H1
POS
PEOU
PU
PE
SI
ATT
GCSE
PFC
VOL
BI
Negative impact
Positive impactPSOD
H9a
Figure 2: Final Conceptual Technology Acceptance model for IT Self-Service at SNS Reaal
29. 28
§3.3 Method of Quantitative Research
All theoretical constructs were operationalized using measures that had been previously
validated and used in similar research contexts. Items specifically addressing the ESS
technology at SNS REAAL were edited slightly to include the formal name of the specific
ESS that is implemented in the organization (Marler et al., 2009). All measures were used
with a seven-point Likert scale with response options ranging from 1 = strongly disagree to 7
= strongly agree (except for PSOD which was measured using a seven-point semantic scale).
Likert scales were used due to the fact that they contain more variance, have a bigger chance
of a normal distribution due to the wider scale, and the original items that are used also use a
7-point scale. See table 2 on this page for sources which were used to operationalize the
constructs, in which research context they have been used, the number of items they contain,
and the reported Cronbach Alpha level in that research. For the operationalization of the
constructs towards the specific research of SNS REAAL, see appendix 5. For the actual
survey as it was submitted to the respondents, see appendix 6.
Construct Scale Research context # items C.A. Level
PEOU Davis et al.
(1989)
Marler et al. (2009) used the scale in a voluntary post-
implementation ESS technology acceptance study
4 .88
PU Davis et al.
(1989)
Marler et al. (2009) used the scale in a voluntary post-
implementation ESS technology acceptance study
6 .92
PE
Davis et al.
(1992)
Venkatesh (2000) used the scale in a voluntary interactive
help desk system acceptance study
3 .92
SI
Thompson et al.
(1991)
Venkatesh (2003) used the scale in a voluntary post
implementation technology acceptance study
4 .92/.94
ATT
Agarwal & Prasad
(1999)
Marler et al. (2009) used the scale in a voluntary post-
implementation ESS technology acceptance study
4 .93
PFC
Mathieson et al.
(2001)
Marler et al. (2009) used the scale in a voluntary post-
implementation ESS technology acceptance study
5 .83
GCSE
Compeau &
Higgins (1995)
Venkatesh (2000) used the scale in a voluntary use of the
technology setting
10 .80/ .90
VOL
Moore &
Benbasat (1991)
Venkatesh & Davis (2003) used the scale in voluntary
technology acceptance study
4 .82/ .91
PSOD Dabholkar (1996) Dabholkar (1996) used the scale in a SST acceptance study 2 .81
BI
Davis et al.
(1989)
Venkatesh (2003) used the scale in a post-implementation
voluntary technology working environment
3 .90
POS
Eigenberger et al.
(1997)
Marler et al. (2009) used the scale in a post-
implementation ESS technology acceptance study.
4 .91
Table 2: Operationalization of constructs
30. 29
§3.3.1 Sample Size and Procedure
In order to perform a proper regression analysis, different theories exist regarding the
sample size. Although there are more complex formulae, the general rule of thumb is ‘’no less
than fifty participants for a correlation or regression with the number increasing with larger
numbers of independent variables’’ (Van Voorhis & Morgan, 2001; p. 140). Green (1991)
provides a comprehensive overview of the procedures used to determine regression sample
sizes. He suggests N > 104 + m for testing individual predictors (assuming a medium‐sized
relationship). Although Greenʹs (1991) formula is more comprehensive, there are two other
rules of thumb that could be used. Harris (1985) suggested that for regression equations using
six or more predictors, a minimum of ten participants per predictor is necessary. Cohen
(1988) used calculations to determine sample size based on a power analysis. To achieve a
medium effect size with ten predictors, a sample of 117 is required.
Based on the literature above the aim was to get at least 100 respondents, but preferably 130
so that there is some margin with respect to outliers. The response was estimated to be around
25%. This is due to the fact that the invitation to the survey was sent using the name of the
senior manager who is responsible for IT user support. Earlier it was already indicated that
around 50% of the users of the IT infrastructure at SNS REAAL used the knowledgebase.
Therefore the first question that was asked was whether they used the knowledgebase in the
past. Hence, only approximately 50% of the total response that was generated is of interest to
this research. Regarding the wanted response of N=130, it was calculated that 130
(respondents) / 0,5 (only 50% has ever used it) = 260 respondents that needed to fill in the
questionnaire. Also, it was taken into account that there will be some participants who will
not complete the survey. In the end, if 300 employees initially take part in the survey, it
should be enough to generate enough response. Taking into account the expected response
rate of approximately 25%, a list of 1300 randomly selected users of the IT infrastructure was
generated.
Before the email was sent to the selected respondents, the accompanying email, introduction
to the survey and the survey itself were pre-tested by five employees. One of the employees
was a person that works at the corporate communication department of SNS REAAL. Some
small adjustments were made so that it was made sure the purpose of the survey and all
questions were clear. The actual invitation email was sent to the randomly selected
participants on Tuesday 11th
of December. It was sent on Tuesday due to the fact that most
employees receive a lot of emails during the weekend which means they have to update their
mailbox on Monday. It was imagined that this will cause people to ignore an invitation to
31. 30
voluntarily take part in an email more easily. 3
The email was sent at approximately 13:00
hours. This was done so that this was one of the first emails they read after lunch. It was
devised that this way, it would be one of the first and few emails that employees see after they
come back from lunch. Furthermore, it was devised that when people come back from lunch,
filling in a survey is something that does not require a lot of mental effort compared to
straight going back to their actual work. See appendix 7 for the accompanying email to the
respondents. A reminder was sent on Thursday at the same time. The response was gathered
during three and a half days in total, from Tuesday afternoon until Friday evening.
3.4 Conclusion
In this chapter, a qualitative research was performed to make sure that the variables that
were identified in the literature review were recognized as possible factors by employees.
After that, the final conceptual model was derived. In order to be able to test the conceptual
model using quantitative analysis, the constructs were operationalized using scales of
previous research in similar settings obtaining high Cronbach Alpha reliability values. After
that, the data was collected using Thesistools4
, a survey website. In the next chapter, this
research will continue with analyzing the results.
3
http://www.peoplepulse.com.au/Invite-Timing-Tips.htm
4
http://thesistools.nl/
32. 31
Chapter 4: Analysis & Results
At first, descriptives of the respondents and variables are given after which it will be
checked whether the data meets all the assumptions in order to perform a regression analysis.
After that, results of the regression analysis will be reported as well as other relationships
among variables. To conclude, the data will be tested for differences between groups.
§4.1 Data Preparation
The total number of participants was N=330. The first question that was asked was whether
they have ever used the knowledgebase or not, since in order to be able to give a proper
judgment on some of the constructs, it was necessary for them to have at least some
experience with the knowledgebase. As expected, approximately 50% (51.51% to be exact,
n=171) did ever use the knowledgebase compared to 50% who did not (48.49%, n=161).
Checking for errors and blanks was firstly done using the filter function in Excel, since the
dataset was generated by Thesistools.nl. After that, the dataset was exported to SPSS, where it
was checked for errors on both categorical and continuous variables again. No out-of-range
values were noticed on any of the continuous or categorical variables.
Before the actual analysis of the data could be started, several items still needed to be
recoded. First of all questions 20 (ATT item 3), 26 (PFC item 5) and 35 (VOL item 1) were
recoded due to a reverse item scale (see appendix 5 and 6). Furthermore, the PSOD items
were measured using 7-point semantic scales with 1 being ‘’really fast’’ in item 39, ‘’really
short’’ in item 40 and 7 being ‘’really slow’’ in item 39 and ‘’really short’’ in item 40. So the
lower scores represent a higher PSOD, which means that they have to be reversed in SPSS.
After that, the full variables were computed by adding up the item scores per construct.
Furthermore, an additional variable for age was created with the categories Baby Boomers
(1946-1964), Generation X-ers (1965-1977) and Generation Y-ers (1978-1995) (Smola &
Sutton, 2002).
§4.2 Descriptives Respondents
A lot of the 171 respondents who initially participated in filling in the complete survey,
did unfortunately not complete it. Only 117 out of the total 171 did complete the survey,
which is still enough to conduct a proper regression analysis (Green, 1991; Harris, 1985; Van
Voornis & Morgan, 2001). The data showed that 62,4% (n=73) was male versus 37,6%
(n=44) female. Age varied between 24 years old and 61 years old. As the age was divided in
groups, 29.9% (n=35) of the sample consisted of Baby Boomers, 40.2% (n=47) consisted of
Generation X and 29.2% (n=35) consisted of Generation Y. User groups according to the
33. 32
definition of HNW showed an approximate even distribution between itinerant workers
(24,8%, n=29), back office workers (27,4%, n=32), knowledge workers (23,1%, n=27). The
other 24,8% (n=29) were employees to which none of the profiles are applicable to since they
are not yet transferred to an HNW IT profile. The distribution of managers versus non-
managers turned out to be 12% (n=14) versus 88% (n=103). The brands that the respondents
worked for were SNS REAAL (34.2%, n=40), SNS Bank (27.4%, n=32), REAAL (18.8%,
n=22), Zwitserleven (6.8%, n=8) and BLG Wonen (5.1%, n=6). Furthermore, employees
indicated to work for ASN Bank, Property Finance, Regiobank, Proteq and KBS, all varying
between 2.6% (n=3) and 0.9% (n=1).
§4.3 Descriptives Variables
As stated earlier, the total scores of all items per construct were added up for further
analysis. To interpret the descriptives of the variables more easily, the constructs were divided
by the number of items that the construct contained. All analyses conducted further in this
chapter, used the original construct scores that were added up. The descriptives are shown in
table 3 below.
Construct N Minimum Maximum Mean Std. Deviation
PEOU 117 1 6.5 3.98 1.32
PU 117 1 6 3.19 1.26
PE 117 1 6.67 3.44 1.47
SI 117 1 7 2.90 1.42
ATT 117 1 6.75 3.42 1.37
PFC 117 1 6.5 3.77 1.16
GCSE 117 1.5 7 4.20 1.00
VOL 117 2 7 5.09 1.32
PSOD 117 1 7 3.49 1.34
BI 117 1 7 4.17 1.67
POS 117 1 7 4.59 1.09
The reliability of the constructs is represented in table 4 on the next page. Most authors
assume that reliability estimates (Cronbach Alpha values) of .7 or .8 are acceptable (e.g.,
Nunnaly, 1978). All scales showed good internal consistency, obtaining high Cronbach Alpha
(C.A.) values except for PFC. This initially showed a Cronbach Alpha value of .634. After
deleting item 26 (PFC5), the Cronbach Alpha value scored .828. Furthermore, the correlations
table shows three relatively high significant correlations, achieving values higher than .7.
Table 3: Minimum, maximum, mean and standard deviation of all variables
34. 33
Correlation Coefficients
Construct # items C.A. BI PEOU PU PE SI ATT PFC GCSE VOL PSOD POS
BI 3 0.95 1,000
PEOU 4 0.90 ,528 1,000
PU 6 0.93 ,442 ,602 1,000
PE 3 0.95 ,419 ,672 ,726 1,000
SI 4 0.94 ,314 ,311 ,464 ,394 1,000
ATT 4 0.86 ,576 ,709 ,684 ,847 ,439 1,000
PFC* 4* 0.83 ,561 ,672 ,576 ,575 ,364 ,636 1,000
GCSE 8 0.81 ,129 ,176 ,176 ,104 -,015 ,051 ,159 1,000
VOL 4 0.77 -,238 -,090 -,138 -,138 -,507 -,181 -,203 ,228 1,000
PSOD 2 0.89 ,341 ,351 ,411 ,376 ,223 ,461 ,490 -,113 -,382 1,000
POS 3 0.91 ,289 ,238 ,171 ,191 ,282 ,128 ,246 ,182 -,182 ,049 1,000
§4.4 Assumptions Regression
In order to be able to perform a regression analysis on the data collected, a number of
assumptions need to be met. These assumptions will now be discussed.
§4.4.1 Multicollinearity
The first assumption to be discussed is to check for multicollinearity among the independent
variables. First correlations between independent variables were checked by checking
correlations between the variables. Table 3 shows that the correlations between PEOU and
ATT (.709), PE and ATT (.847), and PE and PU (.726) are relatively high. However, looking
at correlations among pairs of predictors only is limiting. It is possible that the pairwise
correlations are small, and yet a linear dependence exists among three or even more variables
or vice versa. Therefore the variance inflation factors (VIF) were checked next.
VIF values indicate the degree to which each independent variable is explained by other
independent variables in the model. Large VIF values denote high multicollinearity (Hair,
Anderson, Tatham & Black, 1995). A common cutoff threshold for VIF values is 10. An
initial regression analysis was run including all independent variables as predictors of BI.
The VIF values can be found in table 5 below. For the SPSS output, see appendix 8.
Construct PEOU PU PE SI ATT PFC GCSE VOL PSOD POS
VIF values 2.603 2.661 4.314 1.917 4.772 2.354 1.207 1.709 1.173 1.216
Table 4: Correlation coefficients and Cronbach Alpha internal reliability values
Table 5: VIF values of constructs on dependent variable BI
*Original construct contained 5 items
35. 34
According to the results that are presented in table 5, the constructs are safe to include in the
rest of the analysis. This means that the theorized relationships as they are presented in the
conceptual model can be tested. To be sure, the Durbin-Watson statistic was also checked.
Normally its value should lie between 0 and 4. A value close to 2 suggests no correlation; one
close to 0 negative. The Durbin-Watson statistic of the model obtained a value of 2.218,
indicating no signs of autocorrelation.
§4.4.2 Normality and Outliers
The computed variables were tested for normality and outliers. The normal probability plots
for all the variables showed a reasonably straight line. The histograms of PEOU, PU, PE,
ATT, PFC, GCSE, PSOD, POS, and BI showed reasonably normal distributions. The
distributions of SI and VOL were doubted to be normal. However, the mean and median were
very alike, indicating a proper distribution on both sides of the mean. For output on normality,
skewness, and kurtosis, see appendix 9.
The boxplot of GCSE showed five outliers. Therefore the actual mean was compared with
the 5% trimmed mean, and showed hardly any difference: 33.60 for the actual mean versus
33.62 for the 5% trimmed mean. The boxplot of POS indicated that it had one outlier, but
again the actual mean (18,38) did not differ greatly from the 5% trimmed mean (18,60). Also
the scatterplot of the standardized residuals (see figure 3 on the next page for output) was
checked for outliers. Tabachnick and Fidell (1996) defined outliers as cases that have a
standardized residual of more than 3.3, or less than -3.3. None of the cases exceeds these
values. Therefore no cases were excluded for further analysis..
§4.4.3 Linearity, Homoscedasticity and Independence of Residuals
To check for these assumptions, the scatterplot of the standardized residuals has to be
interpreted. The residuals that are shown in the scatterplot (see appendix 8) are roughly
rectangularly distributed with most scores concentrated in the center. This means that the
assumptions of linearity, homoscedasticity and independence of residuals are not violated,
and therefore the data is appropriate to perform the regression analysis. This research will
now continue with performing the actual regression analysis to see how the independent
variables affect BI.
§4.5 Regression Analysis
Due to the complexity of the model it is not possible to run the entire model within a single
regression analysis. To test all relationships, including mediation and moderation, the SPSS
36. 35
Macro called PROCESS, which was developed by Andrew Hayes, was used. Two statistical
models included in PROCESS were used to assess the hypotheses (see appendix 10).
PROCESS makes use of bootstrapping, which is “a nonparametric resampling procedure
advocated for testing mediation that does not impose the assumption of normality of the
sampling distribution” (Preacher & Hayes, 2008, p. 880). By using this method, a large
number of mini-samples of equal size cases were drawn (with replacement) from the original
data set and the indirect effects in the resamples were calculated. This way an empirical
sampling distribution for the indirect effects was created and used to construct confidence
intervals (CI) for the indirect effects, which was more appropriate for a small sample that was
a subject of this study. Following the recommendation of Preacher and Hayes (2008), point
estimates of indirect effects were considered to be significant when the confidence intervals
(CI) did not contain zero. CI’s were included for both 90% and 95% significance levels.
Furthermore, PROCESS is only capable to include one X at a time. However, according to
Preacher & Hayes (2008) all other variables can be included as covariates, which will
minimize the likelihood of parameter bias caused by omitted variable. Covariates are
mathematically treated exactly like independent variables in the estimation, with paths to all
mediators and the outcome. Therefore, when testing the hypotheses, all remaining variables
were included as covariates. A summary of the results of all conducted tests can be found in
table 6 on page 36 (see appendix 11 for SPSS output).
The R-square values of the models differ between .4776 (F=9.6897) and .4824 (F=8.8958)
due to the fact that the regression analysis was conducted multiple times. However, it can be
concluded that approximately 48% of the variance can be explained by the independent
variables. First, hypothesis 6, PEOU on ATT is supported (b= .2034, t(107) = 2.85, p < .01).
Also hypothesis 4, PE on ATT (b= .7308, t(107) = 8.23, p < .001), hypothesis 3, ATT on BI
(b= .5683, t(106) = 4.04, p < .001), and hypothesis 7, PFC on BI (b= .0400, t(106) = , p < .05)
showed significant results. No significant results were found for the other hypotheses.
Additional tests were completed to see if any other significant relationships among the
variables existed. Since ATT and PFC were the only significant direct relationships, these
were used as mediators in the additional tests. All remaining variables were included as X to
see whether these variables have a significant indirect effect through either ATT or PFC.
Furthermore, a regression analysis was run with all variables included as an X to see if any
other variables achieved a direct effect on BI. These results can be found in table 7 on page 37
(see appendix 12 for SPSS output).
37. 36
Variables
analyzed
(othervar.
Includedas
covariates)
Statistical
method
R-square
Modelsig.
F-value
Relationship
between
constructs
P-Value
Unstandard-
ized
Coefficient
T-value
CI with 95%
Effect
strengthwith
95%
Indirect
effectwith
95%
CI with 90%
Effect
strengthwith
90%
Indirect
effectwith
90%
Moderating
effect
Output
Lower
limit
CI
Upper
limit
CI
Lower
limit
CI
Upper
limit
CI
Y=BI
Process
model4
.4776
.0000
9.6897
POS on ATT .0886 -.1035 -1.7184 -.1603 .0147 -.0588 No -.1404 -.0060 -.0588 Yes
- 1ME=ATT
X=POS ATT on BI .0001 .5683 4.0424 - - - - - - - -
Y=BI
Process
model4
.4776
.0000
9.6897
POS on SI .0738 .1783 1.8055 -.0711 0.164 -.0121 No -.0586 .0114 -.0121 No
- 2ME=SI
X=POS SI on BI .4279 -.0681 -.7959 - - - - - - - -
Y=BI
Process
model4
.4776
.0000
9.6897
PU on ATT .9535 .0031 .0584 -.0698 .0728 .0017 No -.0628 .0543 .0017 No
- 3ME=ATT
X=PU ATT on BI .0001 .5683 4.0424 - - - - - - - -
Y=BI
Process
model4
.4776
.0000
9.6897
PEOU on
ATT
.0052 .2034 2.8523 .0311 .2501 .1156 Yes .0464 .2395 .1156 Yes
- 4ME=ATT
X=PEOU ATT on BI .0001 .5683 4.0424 - - - - - - - -
Y=BI
Process
model4
.4776
.0000
9.6897
PE on ATT .0000 .7308 8.2293 .2261 .6445 .4153 Yes .2520 .5989 .4153 Yes
- 5ME=ATT
X=PE ATT on BI .0001 .5683 4.0424 - - - - - - - -
Y=BI
Process
model1
.4824
.0000
8.8958
SI on BI .5316 .1464 .6276 - - - - - - - -
No 6
MO=VOL
X=SI VOL on BI .9895 -.0021 -.0132 - - - - - - - -
Int. effect on
BI
.3251 -.0114 -.9887 - - - - - - - -
Y=BI
Process
model1
.4780
.0000
8.7260
ATT on BI .0129 .5751 2.5286 - - - - - - - -
No 7
MO=PSOD PSOD on BI .9325 -.0290 -.0849 - - - - - - - -
X=ATT Int. effect on
BI
.9695 -.0009 -.0383 - - - - - - - -
Y=BI
Standard
linearregr.
.4780
.0000
9.690
GCSE on BI .4130 .0400 .821 - - - - - - - -
- 8
X= GCSE,
PFC
PFC on BI .0450 .2360 2.027 - - - - - - - -
Table6–Resultsoftestinghypotheses
Seeappendix11forSPSSOutput
38. 37
Variables
analyzed
(othervar.
includedas
covariates)
Statistical
method
R-square
Modelsig.
F-Value
Relationship
between
constructs
P-Value
Unstandard-
ized
Coefficient
T-Value
CI with 95%
Effect
strengthwith
95%
Indirect
effectwith
95%
CI with 90%
Effect
strengthwith
90%
Indirect
effectwith
90%
Output
Lower
limit
CI
Upper
limit
CI
Lower
limit
CI
Upper
limit
CI
Y=BI
Processmodel4
.4776
.0000
9.6897
SI on
ATT
.0405 .1197 2.0738 .0030 .1568 .0680 Yes .0176 .1450 .0680 Yes 1
M=ATT PSOD on
ATT
.0693 .2136 1.8348 .0001 .2965 .1214 No .0202 .2731 .1214 Yes 2
X=Remaining
variables
ATT on
BI
.0001 .5683 4.0424 - - - - - - - 2
Y=BI
Processmodel4
.4776
.0000
16.0931
PSOD on
PFC
.0041 .4024 2.9324 .0019 .2812 .0951 Yes .0131 .2451 .0951 Yes 3
M=PFC PEOU on
PFC
.0002 .3236 3.8728 .0031 .2018 .0764 Yes .0147 .1812 .0764 Yes 4
X=Remaining
variables
PFC on
BI
.0452 .2362 2.0269 - - - - - - - 4
Y=ATT
Processmodel4
.7904
.0000
11.3777
VOL on
SI
.0000 -.5083 -5.8337 -.1412 -.0028 -.0608 Yes -.1359 -.0137 -.0608 Yes 5
M=SI PSOD on
SI
.0429 -.3903 -2.0493 -.1693 -.0033 -.0467 Yes -.1294 -.0050 -.0467 Yes 6
X=Remaining
variables
PU on SI .0013 .2740 3.3001 .0017 .0911 .0328 Yes .0063 .0822 .0328 Yes 7
SI on
ATT
.0405 .1197 2.0738 - - - - - - - 7
Y=BI
Linear
Regression
.4780
.0000
9.6900
PE on BI .0060 .4590 2.7800 - - - - - - - 8
X= Remaining
variables
POS on
BI
.0280 .0890 2.2270 - - - - - - - 8
Table7–Resultsofadditionaltestingforrelationshipsbetweenconstructs
Seeappendix12forSPSSOutput
39. 38
Table 7 provides the results that show relationships among constructs which were not
theorized in the preliminary conceptual model. Besides testing all variables directly on BI,
and testing PFC and ATT as mediators between BI and the remaining variables, SI was tested
as a mediator on outcome variable ATT with all other variables included as an X to see what
drives SI. In figure 3 below all relationships are presented within the empirical model of this
research. Only the unstandardized beta coefficients are mentioned due to the fact that this is
the only coefficient output that is generated by PROCESS. For variables that achieve an effect
through a mediator on Y, the indirect effect coefficients are reported without parentheses. The
direct effect of a variable X on the mediator it achieves its indirect effect through, is presented
in parentheses. So for example PEOU practices a direct effect on ATT with b= .2034, and
achieves an indirect effect on BI through ATT of b= .1156.
§4.6 Other Findings
To conclude on this chapter, a number of techniques were used to compare for significant
differences between groups. Regression analysis with a selection variable (e.g. gender) was
not chosen due to a lack of cases. Therefore, independent samples T-test and ANOVA were
chosen to compare for differences regarding all the variables that turned out significant in the
empirical model in figure 3. At first the assumptions will be discussed after which the tests
will be performed.
Figure 3: Empirical model of factors influencing BI of IT self-service
Significance Levels
* = P < .05
** = P < .01
.0951** (.4024)
.0764** (.3236 on PFC)
.1156** (.2034 on ATT)
.0680* (.1197 on ATT)
.0890*
-.0467* (-.3903 on SI)
.0328** (.2740 on SI) -.0608** (-.5083 on SI)
.4153** (.7308 on ATT)
.4590**
.5683**
.2360*
POS
PSOD
SI
ATT
PE
PFC
BI
VOL
PEOU
PU
40. 39
§4.6.1 Assumptions
Assumptions to perform both a T-test and an ANOVA which already were checked when
performing the regression analysis are random sampling, independence of observations and a
normal distribution of the variables. The homogeneity of variance will be checked when
performing the tests.
§4.6.2 Independent Samples T-Test
Independent samples T-tests were conducted for gender and management versus non-
management on all variables displayed in the empirical model in figure 3. None of the
variance in any of the variables was explained by difference in gender. However, when
comparing management versus non-management employees, three variables showed
significant differences between management and non-management which are presented in
table 8 below.
According to the guidelines of Cohen (1988), the effect of having a management position or
not explains a moderate amount of the variance of PU, ATT and BI. Management employees
perceive the usefulness higher, have a higher ATT and have a higher BI compared to non-
management. For output of the independent samples T-tests of gender and management
versus non-management, see appendix 13.
§4.6.3 ANOVA
At first an ANOVA was performed on all continuous variables by to see for differences
regarding the different generations. There were no statistical differences mentioned by SPSS
on any of the variables. After that, a second ANOVA was performed to check for differences
based on the HNW user group. Five variables showed significant differences which will be
discussed below. The test of homogeneity of variance showed non-significant results for all
the variables. For SPSS output on the ANOVA, see appendix 14.
Regarding PE, there was a statistical significance at the p<.05 level scores for the four
HNW user groups [F(3, 113.)=2.967, p=.035]. The actual difference in mean scores was quite
Construct Mean
management
Mean non-
management
SD.
management
SD. non-
management
T-
Value
Sig. 2
tailed
Eta
Squared
Variance
explained
PU 3.85 3.10 1.22 1.24 2.101 .038 0.0369 3.7%
ATT 4.22 3.31 1.11 1.37 2.369 .019 0.0465 4.7%
BI 5.07 4.05 1.57 1.65 2.187 .031 0.0390 3.9%
Table 8: Independent samples T-test for differences between Management and Non-Management
