• No results found

Beyond IT-adoption: The influence of Transformational IT Leadership and Personal Innovativeness in IT on an Individual’s IT Self-Efficacy Appraisal in the Post-Adoption Phase.

N/A
N/A
Protected

Academic year: 2021

Share "Beyond IT-adoption: The influence of Transformational IT Leadership and Personal Innovativeness in IT on an Individual’s IT Self-Efficacy Appraisal in the Post-Adoption Phase."

Copied!
44
0
0

Bezig met laden.... (Bekijk nu de volledige tekst)

Hele tekst

(1)

Beyond IT-adoption: The influence of Transformational IT Leadership and Personal

Innovativeness in IT on an Individual’s IT Self-Efficacy Appraisal in the

Post-Adoption Phase.

Master Thesis MSc Change Management University of Groningen

Faculty of Economics and Business

Joost Hulleman S2199416 Supervisor: dr. I. Maris-de Bresser Co-assessor: dr. M. L. Hage January 23, 2017 Words: 13.911

(excluding tables, references and appendices

)

Abstract

Daily activities of employees nowadays depend far more often on interacting with IT and therefore companies invest an increasing amount of their budget into IT. However, IT is often not used to its full potential by employees. IT self-efficacy plays an important role in explaining how effectively IT is utilised. Nevertheless, little

is known about the determinants of IT self-efficacy in the post-adoption phase. To address this gap, the relationship between transformational IT leadership and personal innovativeness in IT on IT self-efficacy has

been researched. Results from a sample of 266 participants provided evidence that three of the six sub-dimensions of transformational IT leadership have a significant effect on IT self-efficacy. This study also provided evidence that personal innovativeness in IT does have a significant effect on IT self-efficacy and has a

significant moderating effect on relationship between two of the sub-dimensions of transformational IT leadership and IT self-efficacy.

(2)

2

Table of Contents

1. Introduction 3 2. Literature Review 5 2.1 Self-Efficacy Theory 5 2.1.1 IT Self Efficacy 7

2.2 Transformational Leadership Theory 9

2.2.1 Transformational Leadership 9 2.2.2 Transactional Leadership 11 2.2.3 Transformational IT Leadership 11 2.3 Personal Innovativeness in IT 13 3. Methodology 15 3.1 Data Collection 15 3.2 Measurement 17 3.2.1 Measurement Instrument 17 3.3 Analysis 19 4. Results 21

4.1 Descriptive Statistics, Correlations and Reliability Analysis 21

4.2 Multiple Regression Analysis 21

5. Discussion and Conclusion 23

5.1 Discussion 23

3.1 Theoretical Implications 25

3.1 Practical Implications 26

3.1 Limitations and Future Research 26

3.1 Conclusion 28

References 29

Appendix A Questionnaire 35

Appendix B Industry Distribution 38

Appendix C Modifications Constructs 39

Appendix D Independent-Samples T-test 41

Appendix E Descriptive Statistics and Correlations 42

(3)

3

1. Introduction

Worldwide, companies are expected to invest up to $4.5 trillion in information technology (IT) in 2017 (Gartner Inc. 2013). However, in most cases organisations do not use their IT to its full potential and IT investments turn out to be unprofitable in many cases (Jasperson, Carter, & Zmud, 2005). In order to prevent IT from becoming a failure, on average 90% of the total project budget is devoted to the post-adoption phase (Erlikh, 2000). The way employees use and perceive IT is considered to be the crucial factor when evaluating the successfulness of an IT investment (Jasperson et al., 2005).

This better fit between user and IT can be created by multiple triggers, such as the ease of use or management support (e.g. Henry & Stone, 1994; Barki, Titah, & Boffo, 2007; Bala & Venkatesh, 2015). However, using IT alone is not enough to enhance task performance, hence more effective IT use is required (Burton-Jones & Grange, 2013). IT is considered successful or effective when it yields (more) benefits for the organisation and its users. The two key and most appropriate indicators of measuring these benefits are: IT usage and user satisfaction (DeLone & McLean, 2003).

Computer self-efficacy (CSE) plays an important role in explaining how effectively IT is utilised by employees (Deng, Doll, & Truong, 2004). Various studies provided evidence that CSE has a positive effect on IT usage and IT satisfaction (Henry & Stone, 1994; Compeau, Higgins, & Huff, 1999; Deng et al., 2004; Ball & Levy, 2008). The construct reflects “individual’s beliefs in their abilities to organize and execute the courses of action needed to complete specific tasks successfully in given contexts, such as in tasks involving computers” (Karsten, Mitra, & Schmidt, 2012, p. 54). CSE plays an essential role in the appraisal of IT perceptions and it is an important determinant of IT-related behaviour (Compeau & Higgins, 1995). Previous research predominantly focused on identifying the consequences and determinants of CSE in the acceptance or adoption phase of new technology (Deng et al., 2004).

Several studies found various cognitive, attitudinal, and behavioural consequences of CSE in the context of IT adoption. CSE was found to have a positive impact on the intention to use computers (Klein, 2007), computer skills (Marakas, Johnson, & Clay, 2007), attitudes towards computers (Compeau et al., 1999), actual computer use (Ball & Levy, 2008), and has a significant negative impact on computer anxiety (Thatcher, Zimmer, Gundlach, & McKnight, 2008). Research focusing on identifying determinants of CSE in the adoption phase for example, found that management support, ease of use, previous computer experience (Henry & Stone, 1994), encouragement by others, use by others, and support of the organisation (Compeau & Higgins, 1995) all have a significant effect on CSE. However, relatively little is known about the determinants of CSE in the post-adoption phase (Deng et al., 2004; Karsten et al., 2012).

(4)

4 and smartphones, which play a more important role in ongoing work activities nowadays, this research focuses on IT self-efficacy (ITSE). ITSE refers to all types of IT used at work and builds further on the literature of CSE. Hence, this research will focus on identifying important determinants of ITSE.

ITSE is considered to be a dynamic, situation-specific and individual-oriented construct (Thatcher & Perrewe, 2002). As a result, ITSE is malleable and individual responses significantly differ in specific situations. It is different from a stable trait, in the sense that self-efficacy appraisals are iterative processes in which an individual weighs and combines the contributions of personal (e.g. mood), environmental (e.g. support), and behavioural (e.g. IT use) influences (Bandura, 1977).

(5)

post-5 adoption phase and on general level, as it may provide information on whether employees who are characterised as ‘innovative with IT’, also have a higher general ITSE appraisal after IT has been adopted.

Following the research gaps identified in the paragraphs above, this research will look into a personal (PIIT) and environmental (transformational IT leadership) factors that are expected to influence the ITSE appraisal of an individual in the IT post-adoption phase. Therefore, the following research question is posed:

How do transformational IT leadership and personal innovativeness in IT influence IT self-efficacy in the post-adoption phase? And is the relationship between transformational IT leadership and IT self-efficacy

moderated by personal innovativeness in IT?

The main goal of this study is provide a new interpretation of the CSE construct by discovering determinants of the ITSE beliefs in the post-adoption phase. Overall, this study contributes to the literature streams of self-efficacy, transformational leadership, personal innovativeness and information technology. More specifically, this research contributes to the self-efficacy theory by responding to the call to identify determinants of CSE beliefs in the post-adoption phase, as most research focused on the adoption phase (Deng et al., 2004). Moreover, this study focuses on the more contemporary construct ITSE instead of CSE; this concept has never been researched as a dependent variable in the past. This study also contributes to transformational leadership theory by responding to the call to identify IT-related outcomes of transformational leadership (Bassellier et al., 2003; Cho et al., 2011). Furthermore, it contributes to the transformational leadership theory by further exploring the transformational IT leadership construct as predictor variable.

This study also has practical relevance, since IT success is mainly determined by IT usage and user-satisfaction (DeLone & McLean, 2003). CSE has a positive effect on both constructs (Henry & Stone, 1994; Compeau et al., 1999; Deng et al., 2004; Ball & Levy, 2008); however, most research into ITSE focused on the adoption phase of new IT. This study provides managers practical information on how ITSE is influenced by transformational IT leadership in the post-adoption phase. This type of leadership style could easily be

incorporated by organisations. Evidence on the influence PIIT on ITSE in the adoption-phase helps organisations to better identify individuals with high or ITSE beliefs in the long run.

2. Literature Review

2.1 Self-Efficacy Theory

(6)

6 produce desired results. The belief of being capable of performing specific behaviour and obtaining desired results is essential in this process. Bandura (1977) states that two main cognitive factors affect behaviour; outcome expectations and self-efficacy. Outcome expectations influence the behaviour of an individual in the sense that a particular person is more likely to perform a behaviour when the result is expected to be positive or valued. Self-efficacy on the other hand, relates to beliefs about one’s ability to perform the specific behaviour.

Bandura (1986, p. 391) defined self-efficacy as:

People's judgments of their capabilities to organize and execute courses of action required to attain designated types of performances. It is concerned not with the skills one has but with judgments of what one can do with whatever skills one possesses. (Bandura 1986, p. 391)

Moreover, the author states that “efficacy beliefs influence how people think, feel, motivate themselves, and act” (Bandura, 1995, p. 2). The self-efficacy theory is a social-cognitive approach to behavioural causation, in which three factors (behavioural, personal and environmental) of reciprocal determinism interact with each other. Reciprocal determinism is the term introduced by Bandura (1978), which constitutes the basis of the theory of social cognition. It states that the behaviour of an individual both influences and is influenced by personal factors, such as one’s personality, morals, preferences, beliefs, demographic characteristics, and environmental factors, such as social pressures or unique situational characteristics. Therefore, “self-efficacy appraisal is an inferential process in which persons weigh and combine the contributions of personal, environmental and behavioural factors” (Pajare & Usher, 2008, p. 399).

(7)

7 At a more specific level, self-efficacy beliefs are shaped by four main sources of information (Bandura, 1995). Mastery of enactive experiences is considered to be the most important source and provides evidence whether or not the individual has been capable of performing a specific task in the past. Vicarious experience suggests that an individual perceives a certain task as manageable when he or she encounters similar people that have performed the specific task successfully. Social persuasion influences self-efficacy as a result of the encouragement or discouragement of others to successfully perform a task. The last source that influences self-efficacy is the psychological or emotional state of an individual. For example, responses to a stressful situation, such as a feeling of fear, significantly lowers one’s self-efficacy.

It is worthwhile to notice that self-efficacy conceptually differs from closely related constructs such as esteem and concept. Self-esteem mainly relates to an individual’s judgment of the worth while self-concept focuses on a composite self-image, which is based on the views and opinions of others. Self-efficacy deviates from these two concepts as it relates to judgements of personal capabilities and is domain-specific (Klassen, 2004).

2.1.1 IT Self-Efficacy

Due to increasing interest in IT and especially computer use in the late 1980’s and 1990’s, the concept of CSE was introduced by Murphy, Coover, & Owen (1989) and later further developed and redesigned by Compeau & Higgins (1995). Compeau & Higgins (1995, p. 192) defined CSE as “an individual judgment of one’s capability to use a computer”. CSE has been widely applied in research in the last two decades, but this has also led to different interpretations and measurements of the construct (Karsten et al., 2012). At the level of measurement, the concept can both be operationalised at general and specific CSE level. General CSE focuses on the overall ability to use computers and specific CSE typically concentrates on a single software package (Karsten et al., 2012). The enormous disparities between the CSE construct designs, definitions, modifications, and applications of the last decades imply a constant shift of focus (e.g. . This is not surprising as the construct covers a topic that operates in a rapidly changing environment. Consequently measurement instruments, operational definitions, and studies in general become obsolete very fast. Today, employees do not only use computers as their main IT-tool for ongoing work activities, but a much wider variety of IT (e.g. smartphone, tablet). Therefore, this study suggests researching ITSE instead of CSE. The construct ITSE has been introduced and only been used in the study of Wang, Li, & Hsieh (2013), as a moderating factor. However, since ITSE is not mentioned elsewhere in the literature and the construct is highly related to CSE, this study builds further on research that has been conducted in the area of CSE. This study is conducted on general ITSE instead of specific ITSE level due to its generalisability and as it is not interested in one specific IT-tool or application.

As mentioned earlier, self-efficacy relates to one’s belief in the ability to organise and execute courses of action. In the context of IT, this means ITSE represents one’s belief in the ability to use IT in order to accomplish a variety of work-related tasks. To clarify, in this study ITSE does not relate to the accomplishment of specific tasks (e.g. using a software package to analyse data), or the reflection of simple component skills (e.g. using a specific software feature such as bolding text in Microsoft Word) (Compeau & Higgins, 1995). ITSE has been defined in this study as: “an individual’s judgement of efficacy across multiple IT application domains”, which is a modification of the definition constructed by Marakas et al. (1998, p. 129).

(8)

8 usefulness and perceived ease of use (Wang, Xu, & Chan, 2015). Compeau & Higgins (1995) concluded that CSE has a significant impact on an individual’s feelings and behaviour. The authors stated that CSE had a significant positive impact on user enjoyment and a negative impact on computer anxiety. Venkatesh & Davis (1996) found that CSE has a positive effect on the creation of a positive perception of IT. In line with this finding, Compeau et al. (1999) concluded higher CSE beliefs lead to an increase of IT usage. Henry & Stone (1994) provided evidence that CSE has a positive impact on job satisfaction and Taylor & Todd (1995) found a positive relationship between CSE and perceived behavioural control.

CSE beliefs are considered to be dynamic, situation-specific and individual-oriented (Thatcher & Perrewe, 2002). Therefore, CSE beliefs are malleable and individual responses differ in specific situations. It is therefore different from a stable trait, in the sense that these beliefs could be influenced by a number of factors (e.g. environmental factors such as support, or personal factors such as preferences) and thereby reduce or increase their influence on behaviour over time (Thatcher & Perrewe, 2002). However, relatively little is known about the determinants of CSE, especially not in the post-adoption phase (Deng et al., 2004).

Research focusing on the adoption phase found that management support, ease of use, and previous computer experience (Henry & Stone, 1994) had a significant influence on CSE, as well as encouragement by others, use by others, and support of the organisation (Compeau & Higgins, 1995). Thatcher & Perrewe (2002) found that computer anxiety had a negative impact on CSE while PIIT had a positive impact. Deng et al. (2004) were the first and one of the few who explicitly researched CSE in the post-adoption phase; however they did so on software specific CSE level. The authors found that user autonomy, learning capabilities, and collegial support had significant impacts on CSE and called for further research on discovering determinants of CSE in the post-adoption phase.

It can be concluded that prior research primarily focused on identifying the consequences and determinants of CSE in the context of IT adaption and IT acceptance. However, it is extremely relevant to research the determinants of ITSE in the post-adoption phase as goals, time horizon, practice environment, user behaviour, and the nature of support significantly differ when compared to the adoption-phase (Deng et al., 2004; de Guinea & Webster, 2013). For example, during the adoption phase goals are often short-term oriented and more intensive support is offered by managers. Hence, constructs that have been identified as antecedents of CSE in the past, such as management support or other’s use are not representative for actual behaviour because they are often manipulated in the adoption phase (Deng et al., 2004). Therefore, findings of adoption-oriented studies can not just simply be generalised into a adoption context. Moreover, identifying determinants of ITSE in the post-adoption phase explains more about how IT is actually used in the organisation, instead of only adopted. This is extremely relevant since the two main appropriate measures of IT success are IT usage and user satisfaction (DeLone & McLean, 2003). Since higher CSE beliefs increase IT usage and IT satisfaction (e.g. Henry & Stone, 1994; Ball & Levy, 2008), it is relevant to identify determinants that influence ITSE in the post-adoption phase so companies can anticipate on these results in the long run and increase the success of their IT.

(9)

9 The decision has been made to not include a behavioural factor since personal behaviours are in general more reliably measured with observations instead of self-reporting measurement scales. However, observations are beyond the scope of this research. PIIT and transformational IT leadership have been chosen since previous research concluded that both constructs positively influence CSE, however in a different context (Thatcher & Perrewe, 2002; Li & Hsieh, 2007; Cho et al., 2011). These researches were conducted in the acceptance or adoption phase of a new technology, primarily on software specific CSE level and the constructs have never been tested in a single model. Evidence showed that determinants of IT-related outcomes, which have been identified in an adoption-context, can not simply be generalised into the post-adoption phase since they often significantly differ (Deng et al., 2004; de Guinea & Webster, 2013). Furthermore, evidence is provided that determinants of specific CSE beliefs significantly differ from general CSE beliefs (Marakas et al., 1998).

2.2 Transformational Leadership Theory

Burns (1978) was the first who introduced the concepts of transformational and transactional leadership in literature. Transformational leadership was defined by Burns (1978, p. 20) as a process where “leaders and followers raise one another to higher levels of morality and motivation”. The author argues that transformational leaders create significant changes in the lives of people and organisations, by redesigning perceptions and values and by stimulating employees to change their expectations and aspirations. The main difference between transformational and transactional leadership according to Burns, relates to what a leader and his or her followers have to offer one another. Transactional leaders predominantly focus on the exchange of resources, which mainly comprises of contingent reward and management-by-expectation (Rafferty & Griffin, 2004). On the other hand, transformational leaders determine short-term goals, focus on higher order intrinsic needs, and make subordinates aware of the importance and value of the task’s outcomes (Podsakoff, MacKenzie, Moorman, & Fetter, 1990; Conger & Kanungo, 1998). Burns (1978) claimed that transformational leaders are perceived as role models by their followers, since they function as moral exemplars and work towards the benefit of the team, organisation, and community.

Bass (1985) built further on the two conceptualizations of Burns (1978) and introduced the transformational leadership theory. The author modified and extended the concepts of Burns, since the author disagreed with several of his statements and ideas. For example, Bass disagreed with the fact that Burns positioned the two leadership styles on both ends of a single continuum. Bass (1985) perceived the concepts as independent and even claimed that the best leaders posses both transformational and transactional leadership styles. This conclusion is based on the augmentation effect, which states that “transformational leadership styles build on the transactional base in contributing to the extra effort and performance of followers” (Bass, 1998, p. 5). Therefore, transformational leadership is considered to be ineffective in total absence of a transactional relationship (Bass, Avolio, & Goodheim, 1987).

2.2.1 Transformational Leadership

(10)

10 been described as an important component of transformational leadership by all studies that have been taken in consideration (e.g. Bass, 1985; Bennis & Nanus, 1985; Conger & Kanungo, 1987). However, fostering acceptance at group goals has been described by various studies but has not been covered by the sub-dimension of transformational leadership designed by Bass (1985) (Podsakoff et al., 1990). As a result, Podsakoff et al., (1990) contributed to the transformational leadership theory by expanding Bass’ (1985) four dimensions to six. In this research the concept of transformational leadership of Podsakoff et al. (1990) has been applied to the IT context, which is discussed in section 2.2.3. The definitions of the sub-dimensions of transformational leadership and transformational IT leadership are provided in Table1.

Hence, transformational leadership can be described as a leadership style which involves influencing followers to act beyond immediate self-interest (Bass, 1985). Followers who experience trust, admiration, loyalty, and respect towards their leader could be motivated to move beyond their own self-interests, prior set tasks, and work for the aims of the team (Andressen et al., 2012). In order to move followers beyond their self-interest and act to the benefit of the organisation, it is important that leaders motivate followers by increasing awareness of the importance of the task outcomes (Yukl, 1999). In addition, transformational leaders seek for new opportunities and novel ways of working. Therefore, transformational leaders are less likely to support the status quo. Avolio & Bass (1988) argue that transformational leaders do not simply react to environmental and situational conditions but instead try to shape and create them.

Table 1. Sub-dimensions Transformational Leadership

Sub-dimension Transformational Leadership Transformational IT Leadership

Articulating a

vision Behaviour on the part of the leader aimed at identifying new opportunities for his or her unit/division/company, and developing, articulating, and inspiring others with his or her vision of the future

Behaviour on the part of the leader aimed at identifying new opportunities in IT for his or her unit/division/company, and developing,

articulating, and inspiring others with his or her IT vision of the future.

Providing an appropriate

model

Behaviour on the part of the leader sets an example for employees to follow and is consistent with the values the leader espouses

Behaviour, in the context of IT, on the part of the leader that sets an example for employees to follow and is consistent with the IT-values the leader espouses.

Fostering acceptance at

group goals

Behaviour on the part of the leader aimed at promoting cooperation among employees and getting them to work together toward a common goal.

Behaviour, in the context of IT, on the part of the leader aimed at promoting cooperation among employees with IT, and getting them to work together toward a common goal with the use of IT.

High performance expectations

The leader’s expectations for excellence, quality, and/or high performance on the part of followers.

Behaviour that demonstrates the leader’s expectations for excellence, quality, and/ or high performance in the use of IT on the part of followers.

Providing individualised

support

Behaviour on the part of the leader that shows that he/she respects followers and is concerned about their personal feelings and needs.

Behaviour on the part of the leader that indicates that he/she respects followers and is concerned about their personal feelings and needs within the field of IT.

Intellectual

stimulation Behaviour on the part of the leader that challenges followers to re-examine some of their assumptions about their work and rethink how it can be performed

Behaviour, in the context of IT, on the part of the leader that challenges followers to re-examine some of their assumptions about their use of IT and rethink how it can be performed.

(11)

11 Research from the past few decades has predominantly focused on identifying transformational behaviours and development of the transformational leadership theory, including determinants of the construct and consequences. Focusing on consequences, Bass (1985) concluded that transformational leadership has a positive impact on employees’ satisfaction, self-reported effort, and job performance. Furthermore, transformational leadership has a significant affect on: organisational commitment (Bycio, Hackett, & Allen, 1995), satisfaction with supervision (Podsakoff et al., 1990), extra effort (Seltzer & Bass, 1990), job satisfaction and team performance (Braun, Peus, Weisweiler, & Frey, 2013), turnover intention (Bycio et al., 1995), organisational citizenship (Podsakoff, MacKenzie, Paine, & Bachrach, 2000), overall employee performance (Yammarino, Spangler, & Bass, 1993), and negatively affects an employee’s resistance intentions to change (Oreg & Berson, 2011).

2.2.2 Transactional Leadership

Bass (1985) considered transactional leaders as individuals who are risk-avoidant, work within the existing and predetermined system or culture, have a focus on time constraints and efficiency, and favour process over substance in the case of maintaining control. Bass (1985) also identified four subcomponents of transactional leadership. These four main characteristics of transactional leadership are; contingent reward, management by expectation (active), management by expectation (passive), and laissez-faire. Contingent reward significantly differs from transformational leadership, since the main ideas of transactional leadership are based on a ‘contract of exchange’, which involves leader support in exchange for resources. Leaders recognise accomplishments of employees and clearly communicate rewards in case of good efforts and performance (Bass, 1990). Management by expectation (active) entails active monitoring and searching for deviations from standards and rules. In case leaders encounter performances of individuals that deviate from what is expected from them, the leader takes corrective actions (Bass, 1990). Management by expectation (passive) entails more passive monitoring, the leader only takes corrective actions in case standards are not met and problems become serious (Bass, 1990). Laissez-faire, also often described as non-leadership, is the avoidance or absence of leadership. It entails a type of leadership, where leaders offer very little guidance, avoid decision-making and hesitate to take action (Bass, 1990). However, Laissez-faire is considered as a poor, ineffective leadership style and is highly dissatisfying for followers (Judge & Piccolo, 2004).

Hence, transactional leadership becomes increasingly ineffective as a leader’s involvement declines. Therefore, contingent reward is perceived as the most effective component of transactional leadership and laissez-faire as the least effective. However, various researchers argue that laissez-laissez-faire is not part of transactional leadership (Judge & Piccolo, 2004). A well-performing transactional leader is mainly effective in relatively stable and predictable environments. The best operating strategy for this type of leader in these environments is comparing activities against prior performance (Bass 1985; Lowe, Kroeck, & Sivasubramaniam, 1996).

A clear example that illustrates the differences between transformational and transactional leadership is that transactional leaders elucidate the task structure and provide information on the ‘right way’ to execute tasks, in a way that maintains dependence on the leader for preferred problem solutions. Conversely, transformational leaders, provide a new strategy or vision which helps subordinates to solve the problem, but sovereignty is endowed in decision-making and problem solving (Lowe et al., 1996).

2.2.3 Transformational IT Leadership

(12)

12 motivate employees in comparison to other leadership styles (Howell & Avolio, 1993). However, as Bassellier et al. (2003) and Cho et al. (2011) claim, relatively little is known about transformational leadership in relation to IT-related outcomes. This is remarkable, since daily activities depend far more often on interacting with IT, an increasing amount of the company budget is invested in IT development (Li & Hsieh, 2007), and IT is often not used to its full potential (Jasperson et al., 2005; Cho et al., 2011).

As mentioned earlier, IT is considered successful or effective when it yields (more) benefits for the organisation and individuals (DeLone & McLean, 2003). The authors argue that the two main appropriate measures of IT success are: IT usage and user-satisfaction. Rockart, Earl, & Ross (1996, p. 53), address the important influential role managers have, with regard to IT usage of employees:

The success or failure of an organisation’s use of IT, however, is only partially dependent on the effectiveness of the IT organisation. It is even more dependent on the capability of line managers at all levels to understand the capabilities of the IT resource and to use it effectively. (Rockart et al., 1996, p. 53)

Bassellier et al. (2003) concluded that a manager’s level of IT knowledge and his or her IT experience has a significant influence on their intention to champion IT use. The authors (p. 322) argue it should be further explored how a manager’s “vision to transform the organisation with IT” and the “personal use of computers” influence the IT use of employees. These components suggest that the sub-dimensions of transformational leadership such as ‘providing an appropriate IT-role model’ and ‘articulating an IT-vision’ could have a potential effect on IT use.

In addition, Cho et al. (2011) and Ekiko (2014) state that transformational leaders can enhance IT success. Cho et al. (2011) argue that by ‘articulating an vision’, the leader could improve the user’s confidence in using the IT and articulating a high level of expectation and optimism about the user’s ability to use the IT. Moreover, by ‘intellectual simulation’, transformational leaders can challenge employees to be more creative in problem-solving with IT, stimulate them to take risks, and to look at problems from different angles. Afshari et al. (2009) concluded that transformational leaders positively stimulate employees’ IT use. However, the specific sub-dimensions of transformational leadership have only been applied to the IT context by a few theses (e.g. Biernath, 2014; Sietsma, 2014; Hendriksen, 2015). Transformational IT leadership is defined in this study as the process of inspiring subordinates to share and pursue the leader’s vision concerning IT and motivating other to move beyond their own self-interests in IT and work for the aims of the team by the use of IT (Sietsema, 2014). An overview of the modified definitions of the six sub-dimensions of transformational leaders of Podsakoff et al. (1990) are provided in Table 1.

Li & Hsieh (2007), provided evidence that transformational leadership positively affects software specific CSE in the adoption phase, as well as intrinsic motivation to use IT and intention to explore. Cho et al. (2011), also found that transformational leadership has a significant positive affect perceived organisational support and software specific CSE in the adoption phase. However, no research has been conducted on general ITSE level, during the post-adoption phase and with a specific transformational IT leadership construct.

Deng et al. (2004) argue that:

(13)

13 capabilities may encourage one to set more challenging goals or be more committed to the goals one sets, and the achievement of challenging goals that one is committed to is intrinsically satisfying. (Deng et al., 2004, p. 409)

Thus, intrinsic motivation is a direct result of achieving these goals, which subsequently leads to an increase of engagement in IT-related work and more perseverance (Deng et al., 2004). The belief in one’s capabilities, pursuing more challenging goals, and support to achieve IT-related goals all correspond with key elements in transformational leadership such as ‘high performance expectations with IT’, ‘providing individualised support’, ‘intellectual stimulation with IT’ and ‘fostering the acceptance of group goals through IT’. Li & Hsieh (2007) also indicate that transformational leaders with high expectations and focus on the development of competencies and independence could intrinsically motivate employees to perform beyond expectations and thereby increase self-confidence. Based on these findings and as transformational IT leadership specifically concentrates on applying the key elements into an IT context, it is expected that transformational IT leadership has positive impact on ITSE beliefs in the post-adoption phase. This results in the following hypothesis:

H1: Transformational IT leadership has a positive impact on ITSE. 2.3 Personal Innovativeness in IT

The concept of PIIT has been introduced by Agarwal & Prasad (1998) in the domain of IT. Prior to the introduction of PITT, personal innovativeness was predominantly studied in a wider context of technology (Agarwal & Prasad, 1998). Research into personal innovativeness especially focused on the diffusion of innovations theory (Rogers, 1983), specifically in the domain of marketing (e.g., Midgley & Dowling, 1978, Flynn & Goldsmith, 1993). The global concept of personal innovativeness involves the willingness to change and one’s tolerance of risk (Hurt, Joseph, & Cook, 1977; Bommer & Jalajas, 1999). Hence, an employee who is willing to take more risks, is more likely to perform innovative behaviour (Agarwal & Prasa, 1998). However, domain-specific innovativeness has greater influence on behaviour than global innovativeness (Flynn & Goldsmith, 1993). Therefore, Agarwal & Prasad (1998, p. 206) developed the PIIT construct and defined it as; ‘the willingness of an individual to try out any new information technology”. The concept is perceived as an individual’s trait (Webster & Martocchio, 1992). Hence, PIIT is considered to be a relatively stable descriptor of individuals in situations that involve IT (Agarwal & Prasad, 1998). Furthermore, in general traits are not influenced by personal factors, such as beliefs, or environmental factors, like management support (Webster & Martocchio, 1992).

(14)

14 PIIT encompasses risk-taking behaviour, as innovations are related to greater risk. Agarwal & Prasad (1998) claim that individuals that are characterised as innovators or early adopters, are willing to take greater risks, are able to cope with a higher amount of uncertainty, and are more venturesome and more prone to try out new IT. Likewise, early adopters are perceived as less fatalistic, where fatalism relates to the extent that an employee feels a lack of ability to control the future (Yi et al., 2006). Individuals that are perceived as innovative overall are more self-confident in the context of performing new tasks or in a new situation (Thatcher & Perrewe, 2002). Wang, Oh, Courtright, & Colbert (2011) claim that employees with a high level of PIIT are more eager and willing to experiment with IT, and are more focused on finding novel ways for using IT. These characteristics of PIIT are highly related to key components of high ITSE beliefs.

Agarwal et al. (2000) found that PIIT has a significant influence on CSE in a training context. Similarly, Thatcher & Perrewe (2002) provided evidence that PIIT has a positive effect on software specific CSE in the adoption phase. No research has been conducted on whether PIIT has a significant affect on general ITSE in the post-adoption phase. It is relevant to research this relationship in the post-adoption phase and on a general level, as it provides evidence whether employees, who are characterised as ‘innovative with IT’, also have a higher ITSE appraisal after IT has been adopted and for IT in general instead of merely for one software package. Therefore the following hypothesis has been proposed:

H2: Personal innovativeness in IT has a positive impact on IT self-efficacy.

(15)

15 H3: Personal innovativeness in IT moderates the relationship between transformational IT leadership and IT self-efficacy such that the relationship is less strong for users with high PIIT than for users with a low PIIT.

These hypotheses result in the following conceptual model:

3. Methodology

3.1 Data Collection

In order to analyse and test the hypotheses of this study primary data has been gathered (Ghauri, Grønhaug, & Kristianslund, 1995). The primary data of this study has been collected via an online questionnaire, which was hosted via ‘www.qualtrics.com’. An online questionnaire is considered to be the best tool for data collection of this study as the research is conducted on individual level and a relatively high amount of respondents is required to test the three hypotheses in a short timeframe. Therefore, the online questionnaire has been favoured over a paper questionnaire mainly for flexibility, speed and efficiency reasons (Lumsden & Morgan, 2005). Participants of the study were contacted more effectively via the Internet, and data could directly be exported to Excel and SPSS, which lowers the chance of human errors. Qualtrics has been chosen as platform to host the online questionnaire primarily due to reliability reasons.

The requirements for an employee to participate in the survey were: 1) the respondent should interact with IT on daily basis; 2) the respondent is headed by a manager or leader; 3) the respondents are Dutch; and 4) the participant is not working at a start-up. Since previous research provided evidence that CSE appraisals differ across cultures and nationalities, the sample only consisted of Dutch participant (Durndell, Haag, & Laithwaite, 2000). The English and the translated Dutch questionnaires have been reviewed by native Dutch and English MSc BA students, the thesis supervisor, and a professor of the University of Groningen. The decision to exclude employees who work at a start-up was taken because start-ups usually have not reached the post-adoption phase for their IT. To ensure participants meet the specific criteria, the restrictions were communicated twice to every participant through an instruction e-mail, and is stated on the instruction page of the survey. The survey is provided in Appendix A.

(16)

16 This study uses nonprobability sampling due to the arbitrary and subjective sampling selection procedure (Blumberg, Cooper, & Schindler, 2011). The primary data has been gathered in two ways: collectively via nine Dutch companies and individually via family and close non-family. Firstly, these companies have mainly been selected due to strong personal relations with, in most cases a manager of this company. The manager of the specific company has received a comprehensive information document with a clear description of the aim of the study, the participation procedure, participation restrictions and the added value for the company to participate in the study. In order to convince the companies to participate, an information document with the results and practical implications of the study will be sent to the nine companies after this study has been completed. Additionally, in most cases a telephone call has been made in order to verify whether the data collection procedure and restrictions were clear and to answer any questions. An email-template has been sent to the contact, which the particular person subsequently distributed among colleagues that met the criteria of the study. This email-template again included clear information about the study, the participation restrictions and an anonymous survey-link. Secondly, respondents have been reached through snowball sampling (Blumberg et al., 2011). Family and close non-family have been informed with the same information as the managers (aim of the study, the participation procedure and participation restrictions) and subsequently distributed the survey and instructions among colleagues and acquaintances that met the study-criteria. In order to decrease the non-response bias, follow-ups or reminders were sent to participants. However, survey distribution via mail has been criticized for non-response bias (Armstrong & Overton, 1977). In case responses of non-respondents significantly differ from responses of survey participants, findings can not be generalised to the population. Therefore, Armstrong & Overton (1977) stress to test whether the ITSE means of both the first and second half of the respondents significantly differ. The second half of a homogenous population filled out the survey in a later moment in time and therefore have more in common with non-respondents since they often needed an additional stimulus (e.g. reminder) to fill out the survey (Armstrong & Overton (1977). Since, one (healthcare) organisation distributed the survey at a later moment in time and represents a large proportion of the total sample (n=115), this group has been perceived as separate homogenous group. Two independent sample T-tests were run to test the differences between the means of the subgroups. As shown in Appendix D, the first and second half of the participants, both of the healthcare organisation and ‘other participants’ group, do not significantly differ. Therefore, it can be concluded that the non-response bias has been kept to a minimum.

In general, no restrictions were imposed on the type of IT since the study focuses on general ITSE instead of software specific ITSE (Marakas et al., 1998). Moreover, there were no restrictions regarding the type of industry or the size of company that respondents were working in, as the research is conducted on individual level, thus company differences are not part of this study. In order to analyse the data accurately a minimum number of respondents was required. For every question of the study, a minimum of five respondents is required, which in this study should amount to minimum 145 respondents (Hair, Black, Babin, & Anderson, 2010). After deleting participants with incomplete surveys, 266 employees participated in the survey and delivered valid data (for a compressive explanation of the deleted participants, see section 3.3).

(17)

17 university of applied science (HBO) or at the university. Most respondents are employed in the healthcare industry (56%) followed by the B2B services industry (15,8%) and the B2C services industry (9,8%). A comprehensive overview of the industry distribution can be viewed in Appendix B.

3.2 Measurement

The measurement scales have primarily been selected on the basis of the measurement scales that have been used in similar studies, recognition of the journal in which these instruments have been used or introduced, the frequency these measurement instruments have been used, the suitability of instruments for the context of this study (e.g. is the measurement scale applicable for the post-adoption phase), and the length of the scale.

3.2.1 Measurement Instruments

In order to test the dependent variable and the independent variables of the conceptual model, validated constructs of previous researches will be used and slightly modified if needed.

IT self-efficacy – This construct was measured by a modification of the CSE measure of Deng et al. (2004). CSE has been measured with multiple instruments in the past (e.g. Murphy et al., 1989; Compeau & Higgins, 1995; Barbeite & Weiss, 2004). Most studies used the measurement scale that has been introduced by Compeau & Higgins (1995). However, for this research it is important that the measurement scale is appropriate for measuring general ITSE instead of software specific ITSE and is in addition suitable for the post-adoption phase (Marakas et al., 1998). Most of the CSE instruments specifically focus on the adoption phase, such as the measurement scale of Compeau & Higgins (1995), and are therefore not appropriate for this study. If slightly modified, the ‘generic self-efficacy’ measurement scale of Chen et al. (2001) and the ‘computer self-efficacy’ instrument of Deng et al. (2004) have been found appropriate for measuring the ITSE construct of this study. All in all, the instrument of Deng et al. (2004) has been chosen due to the fact that the construct of Chen et al. (2001) is a generic organisational construct which has not been adopted or specifically been used in the IT context in the past. Modifying this construct to the IT context has a higher potential of changing the meaning of the construct, compared to the minor modifications that are required to make the measurement scale of Deng et al. (2004) suitable for this study. Moreover, the measurement scale of Deng et al. (2004) is short, has adequate reliability, and is easily worded, which makes it easier to adapt it to the purpose of this study (Spreitzer, 1995; Deng et al., 2004). Hence, by using a modification of the instrument of Deng et al. (2004) the risk of jeopardising the construct validity has been minimised.

(18)

18 into transformational leadership most often (77%) used the ‘Multifactor Leadership Questionnaire’ (MLQ) developed by Bass & Avolio (1995), followed (18%) by the measurement scale that was constructed by Podsakoff et al. (1990) (Wang et al., 2011). However, the measurement scale of Podsakoff et al. (1990) has been chosen as measurement instrument for this research. The main reason for this decision is that the instrument of Podsakoff et al. (1990) is more comprehensive compared to the instrument of Bass & Avolio (1995). As explained in section 2.2.1., the measurement instrument of Podsakoff et al. (1990) incorporates all sub-dimensions of Bass & Avolio, but also two additional sub-dimensions that are considered to be part of transformational leadership in the literature. Furthermore, the instrument of Podsakoff et al. (1990) is better suited to be adjusted to the IT context (e.g. items of the MLQ;‘is absent when needed’ or ‘instills pride in me for me for being associated with him/her’ are difficult to modify to the IT context). Hence, no formal measurement instrument of transformational IT leadership exists. However, Podsakoff et al. (1990) instrument has been used and adapted in previous theses in the same way without any difficulties or reliability issues (e.g. Hendriksen, 2015; Biernath, 2014). The measurement scale has been transformed to the IT context and individuals will therefore answer questions that focus on how they perceive their manager in regard to the stimulation of IT usage. For example, the item ‘fosters collaboration among team groups’ (Podsakoff et al., 1990) has been adapted to ‘fosters collaboration between teams by using IT tools’. All adjustments and items are provided in Appendix C.

The construct transformational IT leadership consists of six subthemes and a 7-point Likert scale has been used, where 1 indicates strongly disagree, 4 indicates neutral, and 7 indicates strongly agree: (1) articulating an IT-vision (five items, e.g. ‘The leader has provided me with new ways of looking at IT, something that used to be a puzzle for me’), (2) providing an appropriate IT-role model (three items, e.g. ‘The leader leads by being an exemplary IT user himself/ herself’), (3) fostering the acceptance of group goals through IT (four items, e.g. ‘The leader develops a positive team attitude towards IT tools’), (4) high performance expectations with IT (three items, e.g. ‘The leader insists on using IT tools to ensure best performance’), (5) individualised support (four items, e.g. ‘The leader behaves in a manner that is thoughtful of my personal needs’), (6) intellectual stimulation with IT (four items, e.g. ‘The leader has stimulated me to think about old IT problems in new ways’). The items of the sub-dimensions have been asked in random sequence, to ensure the participant considers each question separately and does not base his or her answer on the previous question (Blumberg et al., 2011).

Personal innovativeness in IT – The construct PIIT was measured by the measurement scale developed by Agarwal & Prasad (1998). This instrument has been chosen as most appropriate measurement scales, since most instruments of personal innovativeness are generic instead of domain-specific (e.g. Rogers, 1995). Additionally, most domain-specific measurement scales focus on marketing (e.g. Flynn & Goldsmith, 1993). Agarwal & Prasad (1998) have constructed the first and only (reliable) instrument of personal innovativeness in the IT context and has been used by most studies in this domain (e.g. Lu, Yao, & Yu, 2005; Sun, 2012). The construct focuses on the likelihood that an individual chooses to interact with any IT or not. It consists of four items and is scored on a 1-7 Likert-scale; where 1 indicates strongly disagree, 4 indicates neutral, and 7 indicates strongly agree: e.g. ‘If I heard abut a new IT, I would look for ways to experiment with’. An overview of the items of the construct can be viewed in Appendix C.

(19)

19 level of education) and are therefore included as a control variable in this study. Comparable studies also incorporated these three control variables, but several studies also stress the need to incorporate tenure as a control variable (Agarwal & Prasa, 1998; Venkatesh et al., 2003; Wang et al., 2013). Hence, in order to rule out alternative explanations for the dependant variable the variables ITSE, age, gender, level of education and tenure have been included as control variables in the analysis.

3.3 Analysis

The output of the surveys has been checked for missing data and incomplete surveys have been deleted from the data set in preparation for the analysis. At the start, the data set consisted of 373 ‘participants’, however 103 incomplete forms have been identified and deleted. It is hard to draw conclusions on this relatively high number, since Qualtrics identifies a ‘session’ as a unique survey/participant. Hence, an individual who reads the instruction page, continues to the second page, closes the webpage and fills out the survey at a later point in time, generates two unique surveys/participant ID’s. Similarly, four participants have been deleted since they had less than one month experience at their current company. The answers of these individuals were not considered valid since these participants are unable to answer the questions about the transformational IT leadership capabilities of their manager appropriately because the questions relate to specific situations, which have most likely not been encountered yet. Likewise, these employees are probably in their training period, and therefore behaviour, beliefs, and support of the manager are not representative for the post-adoption phase. Ultimately, this resulted in a sample size of n=266, which has been used for further analysis.

(20)

20 All analyses have been carried out with IBM SPSS Statistics 23. In the next section descriptive statistics are provided in order to provide insights into the mean and standard deviations of the variables. A Pearson product-moment correlation analysis has been carried out in order to determine the linear associations between the individual variables (Blumberg et al., 2011). In addition, a reliability analysis has been run in order to test for the internal consistency of the items as a group. Lastly, a multiple regression analysis has been performed, in order to estimate the relationships between the constructs of the conceptual model (Hair et al., 2010). The multiple regression analysis for example provides insight into how the typical value of ITSE changes when either PIIT or one of the sub-dimensions of transformational IT leadership is varied. The multiple regression analysis includes three models in order to test the affects of the control variables, the main affects, and moderating affects. As shown in Table 4, and more extensively in Appendix E, the first model only consists of the four control variables and ITSE as dependent variable. The second model consists of the elements of the previous model and in addition, the six subthemes of transformational IT leadership and PIIT as independent variable. The third model consists of the elements of the previous model and six newly created variables that represent the moderating effects of PIIT. For the moderation analysis, mean centered versions of the variables concerned have been used according to the recommendations of Aiken & West (1991) and Kenny & Judd (1984). As shown in Table 4, the adjusted R2, increases from model 1 to 2 with a delta R2 of .366 and from model 2 to model 3 with a delta R2 of .04. Model 3 is considered to be the best model to interpret the results because compared to model 2, model 3 has a significant increase of the R2. This means the proportion of the variance in the dependent variable of model 3 that can be explained by the independent, moderating and control variables is significantly higher than in model 2 (p < .01, R2 = .04). Therefore, the results and discussion section are based on the results of model 3 of the multiple regression analysis. As shown in Table 4 and Appendix E, overall the independent and control variables significantly predict ITSE F(17,248) = 14.299, p < .0005, R2 = .460.

As part of the multiple regression analysis, the conceptual model has been tested for multicollinearity. This test has provided evidence whether two or more predictor variables of ITSE are highly correlated and

Table 2. Factor Loadings from Exploratory Factor Analysis (using Oblimin rotation)

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8

TFITL_PAM2 ,809 ,008 -,015 ,122 -,120 -,045 ,032 -,080 TFITL_PAM1 ,798 ,022 ,068 ,045 ,021 ,081 ,054 -,032 TFITL_PAM3 ,662 ,075 ,002 ,246 -,095 -,039 -,047 -,124 ITSE_3 -,037 ,953 -,001 -,051 ,116 ,025 -,098 -,125 ITSE_1 -,005 ,899 ,014 -,001 -,001 ,028 ,008 ,063 ITSE_2 ,159 ,669 ,009 -,059 -,084 ,022 ,159 ,109 TFITL_ISU1 ,028 ,002 ,945 ,013 ,042 -,040 ,017 ,083 TFITL_ISU4 -,033 -,017 ,897 -,033 -,046 ,038 -,071 ,070 TFITL_ISU3 ,036 ,044 ,633 ,030 ,016 -,029 ,078 -,338 TFITL_IST1 ,101 -,049 -,002 ,842 ,005 ,055 -,126 -,021 TFITL_IST2 ,155 -,113 ,003 ,813 ,077 ,125 ,038 -,008 TFITL_AV1 ,233 -,112 ,031 -,021 -,718 ,190 -,038 -,109 TFITL_AV2 ,216 -,015 ,046 -,068 -,466 ,335 -,072 -,224 TFITL_HPE1 -,024 ,027 -,040 ,009 ,023 ,879 ,090 -,049 TFITL_HPE2 -,021 ,065 ,031 ,147 -,118 ,819 -,051 -,011 PIIT_2 ,136 -,017 -,031 -,141 ,153 ,088 ,975 ,014 PIIT_4 -,163 ,251 ,056 ,062 -,285 ,007 ,618 ,078 PIIT_1 -,240 ,182 -,014 ,255 -,312 -,168 ,591 -,121 TFITL_FAG1 ,122 -,009 -,018 ,003 -,099 ,021 -,056 -,833 TFITL_FAG2 ,076 ,002 ,056 ,120 ,025 ,258 ,073 -,646

(21)

21 therefore jeopardize the estimate power of the individual predictors (Hair et al., 2010). A cut-off point of a score of 10 has been applied (Hair et al., 2010). As shown in Table 3 of appendix E, all variables have an VIF-score between 1.198 and 3.089, Therefore, it can be concluded that two or more explanatory variables of the multiple regression model are not highly linearly related.

4. Results

4.1 Descriptive Statistics, Correlations and Reliability Analysis

As stated in Table 3, the mean of the variables provide evidence that ‘individualised support’ is most frequently used and ‘intellectual stimulation with IT’ is least used by transformational IT leaders. Moreover, a Pearson product-moment correlation was run in order to measure the strength and direction of the associations between the different variables of the study. As shown in Table 3, the sub-dimensions ‘individualised support’ and ‘intellectual stimulation with IT’, and the constructs PIIT, have a strong statistically significant correlation with ITSE (n = 266, p = .01). Moreover, the sub-dimension ‘high performance expectations with IT’ also has a statistically significant correlation with ITSE (n = 266, p = .05).

A reliability analysis has been carried out in order test the internal consistency of the newly created variables, comprising the selected items of the EFA. The Cronbach’s alpha, which provides an overall reliability coefficient for the variables has been used in order to determine the internal consistency. A Cronbach’s alpha cut-off point of 0.7 has been maintained (Blumberg et al., 2011). As shown in Table 3, all variables are considered reliable based on the cut off point of the Cronbach’s alpha. An extensive overview of the correlations and the descriptive statistics is provided in Appendix E.

4.2 Multiple Regression Analysis

Hypothesis one, which describes the positive relationship between the six sub-dimensions of transformational leadership and ITSE has been partially confirmed. Based on the multiple regression analysis and as shown in Table 4, it can be concluded that the sub-dimension ‘fostering the acceptance of group goals through IT’ has a significant impact on ITSE (B = 0.108, p < .1). Similarly, the sub-dimensions ‘high performance expectations with IT’ and ‘individualised support’ have a significant impact on ITSE (B = 0.11, B = 0.116, p < .05). However, the

sub-Table 3. Results Descriptives, Pearson Correlation & Reliability Analyses

Mean St. Dv. TFITL_ AV TFITL_ PAM TFITL_ FAG TFITL_ HPE TFITL_ ISU TFITL_ IST PIIT ITSE TFITL_AV 4,80 1,33 0,831 TFITL_PAM 4,43 1,31 0,650** 0,829 TFITL_FAG 4,67 1,40 0,714** 0,698** 0,831 TFITL_HPE 4,78 1,28 0,630** 0,536** 0,607** 0,806 TFITL_ISU 5,10 1,15 0,219** 0,239** 0,276** 0,088 0,809 TFITL_IST 3,52 1,41 0,486** 0,649** 0,540** 0,413** 0,093 0,864 PIIT 4,18 1,36 0,107 -0,021 0,034 0,095 0,044 -0,092 0,826 ITSE 5,14 1,04 0,042 -0,033 0,037 0,126* 0,164** -0,584** 0,584** 0,835

The meaning of each question in the first column is shown in Appendix C. Descriptive statistics are based on the average scores of the composite scales. * Significant at p < 0.05 (2-tailed)

(22)

22 dimension ‘intellectual stimulation with IT’ has a negative significant affect on ITSE (B = -0.240, p < .001). Finally, ‘articulating an IT-vision’ has an insignificant negative affect on ITSE while ‘providing an appropriate IT-model’ has an insignificant affect on ITSE (B = -0.080, B = 0.026). As only three of the sub-dimensions had a significant positive affect on ITSE and the analysis provided evidence that one sub-dimension even has a negative significant affect on ITSE, hypothesis one is partially confirmed.

The second hypothesis states that PIIT has a positive effect on ITSE. As shown in Table 4 and Appendix E, PIIT has a significant positive affect on ITSE (B = 0.409, p < .001). Therefore, hypothesis two is accepted. The third hypothesis, the moderating effect of PIIT on the relationship of transformational IT leadership on ITSE can only be partially confirmed as shown in Table 4 and Appendix E. PIIT has a significant affect on the positive relationship between the sub-dimensions ‘providing an appropriate IT-model’ and ITSE and a significant affect on the negative relationship between ‘intellectual stimulation with IT’ and ITSE (B = 0.081, p < .1; B = 0.084, p < .05). PIIT has no significant affect on the relationship between the other four sub-dimensions of transformational IT leadership and ITSE. Therefore, the hypothesis on the moderating effect of PIIT has been partially confirmed.

At last, the control variables age, gender, tenure and education were expected to significantly influence ITSE. However, as shown in Table 4, all control variables have an insignificant affect on ITSE.

Table 4. Unstandardized Results from Multiple Regression Analyses

Model 1 Coefficient Estimate (Standard Error) Model 2 Coefficient Estimate (Standard Error) Model 3 Coefficient Estimate (Standard Error) Age -0,015* -0,007 -0,005 Gender -0,057 0,141 0,141 Tenure -0,007 -0,005 -0,007 Education -0,241 -0,063 -0,048 Articulating an IT-vision -0,073 -0,080 Individualised support 0,095* 0,116*

Providing an appropriate IT-role model 0,059 0,026

High performance expectations with IT 0,129* 0,110*

Fostering the acceptance of group goals trough IT 0,062 0,108*

Intellectual stimulation with IT -0,231** -0,240**

Personal innovativeness in IT 0,404** 0,409**

Personal innovativeness in IT x Articulating an IT-vision 0,004

Personal innovativeness in IT x Individualised support -0,022

Personal innovativeness in IT x Providing an appropriate IT-role model 0,081†

Personal innovativeness in IT x High performance expectations with IT -0,006

Personal innovativeness in IT x Fostering the acceptance of group goals -0,054

Personal innovativeness in IT x Intellectual stimulation with IT 0,084*

F value 6,376 19,269 14,299

R2 0,089 0,455 0,495

Adjusted R2 0,075 0,431 0,46

Δ R2 0,089 0,366 0,04

(23)

23

5. Discussion and Conclusion

5.1 Discussion

Previous research concluded that CSE has an important role in explaining how successful and effectively IT is utilised by employees (Deng et al., 2004). CSE has a significant positive affect on two of the main determinants of IT success; IT usage and job satisfaction (e.g. Henry & Stone, 1994; Ball & Levy, 2008). However, research regarding CSE predominantly focused on the identifying determinants of the construct in an adoption context. This does not provide enough information on how the IT is effectively utilised in the long run. Moreover, the results of these studies can not simply be interpreted in the post-adoption phase, as for example the evironment and behaviour of managers signifcantly differ (Deng et al., 2004; de Guinea & Webster, 2013). This research provided evidence that this is indeed the case. Contrary to the literature, this study provided evidence that transformational IT leadership only has a partially significant affect on ITSE in the post-adoption phase. Consistent with literature focusing on the adoption phase, this research concluded that PIIT also has a significant impact on ITSE beliefs in the post-adoption phase (Thatcher & Perrewe, 2002; Agarwal et al., 2000). Contrary to the literature, PIIT only partially moderated the relationship between transformational IT leadership and ITSE. The results of this study indicate that three of the six sub-dimensions of transformational IT leadership: ‘fostering acceptance at group goals through IT’; ‘high performance expectations with IT’; and ‘providing individualised support’ have a significant positive affect on ITSE. The social cognitive theory states that self-efficacy beliefs are influenced by observing other people’s behaviour and the consequences of this behaviour (Bandura, 1977). However, behaviour is not simply mimicked, but instead interpreted and meaning has been given to this observation, for example by an increase of ITSE beliefs (Bandura, 1977; Bandura, 2001). With the exception of the sub-dimension ‘intellectual stimulation with IT’, the participants of this study have observed and described that all other sub-dimensions of transformational IT leadership are performed at a relative equivalent level, as shown in Table 3 (means vary from 4,43 to 5,1). Hence, individuals observe these different types of behaviours at a relative equal level, but give a different meaning to each of the sub-dimensions, as three of the sub-dimensions result in an significant increase of ITSE and the other two have no affect on ITSE beliefs.

(24)

24 Hence, this lack of impact consequently leads to insignificant affects of these sub-dimensions on ITSE beliefs. Cho et al. (2011) & Li & Hsieh (2007) both concluded that transformational leadership has a significant influence on CSE in the adoption phase. A direct comparison between the sub-dimensions of both studies (Cho et al., 2011; Li & Hsieh, 2007) is not possible, as the authors chose to combine the sub-dimensions of transformational leadership into a single indicator. However, the complexity of the IT environment and the post-adoption context clarify the discrepancy between the findings of Cho et al. (2011) and Li & Hsieh (2007) and this study. Contrary to this study, both studies have been conducted on software specific CSE level, in a relatively complex IT environment and in the adoption phase of IT. The self-efficacy theory states that ‘mastery of enactive experiences’ is the most important source of information that influences self-efficacy beliefs (Bandura, 1995). Hence, evidence that an individual has been capable of performing a specific task in the past leads to higher ITSE beliefs. In a complex IT environments and during the IT adoption phase, individuals increasingly encounter challenging IT-situations and are frequently not able to rely on similar situations or behaviours. The self-efficacy theory states that ‘vicarious experience’ and ‘social persuasion’ therefore play an important role in order to increase ITSE beliefs (Bandura, 1995). Hence, as most participants of this study do not operate in complex IT environments, are in the post-adoption phase and do have high ITSE beliefs (mean = 5,14), a managers articulation of an IT-vision and the ability to be an IT-role model has less impact, since these individuals can mainly rely on previous experiences. Consequently, employees respond less to their leader and therefore do not give meaning to the IT-vision and IT-role model function of the manager.

It was expected that transformational IT leaders could increase ITSE beliefs through challenging employees by creative problem solving, stimulating to take risks, and convincing them to look at problems from different angles (Cho et al., 2011). Contrary to the literature, this study provided evidence that ‘intellectual stimulation with IT’ has a negative significant affect on ITSE. Compared to the other sub-dimension of transformational leadership, as shown in Table 3, ‘intellectual stimulation with IT’ has been demonstrated significantly less often by managers (mean = 3,52). Following the social cognitive theory, individuals observe that managers do not often challenge employees with IT and do not stimulate to take risks. In case managers increasingly do so, individuals give negative meaning to this observation that decreases ITSE beliefs. The social cognitive theory states that the ‘psychological or emotional state’ of an individual affects ITSE appraisals (Bandura, 1995). Hence, individuals might perceive this type of behaviour for example as threatening, as they are not used to be challenged and to take risk with regard to IT. Evidence of this study showed that individuals have a relatively high ITSE belief and most often do not operate in a complex or challenging IT environment. Hence, in case managers are challenging and stimulating to take risk this could evoke for example uncertainty, which could lower ITSE beliefs .

Referenties

GERELATEERDE DOCUMENTEN

The participants were asked to fill in the survey, which with the help of survey instruments was directed towards personal innovativeness in IT, age,

Altogether, this causes enough reason to believe that transformational IT leadership acts as a moderator of the relationship between each of the abovementioned triggers

” In this example, the tediousness of the request procedures that have to be followed resulted in an enhanced IT self-leadership, but it also occurs that these type

H4: The expected mediating relationship of work engagement on the relation between transformational IT leadership and innovative behavior with IT is moderated by a

Ultimately, this paper has shown that IT self-leadership as a whole has a positive relationship with team innovativeness while the two different levels of IT

The climate for innovation moderates the relationship between IT self-leadership and innovative behaviour with IT such that the effect of this leadership on

P1: The idea exploration and generation process of innovation is positively influenced IT constructive thought pattern strategies through communication, networking

Overall, this research will shed light on the concepts of transformational leadership and self-leadership in the IT- context and investigates whether leaders can