Technology adoption within military organisations

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Technology Adoption Within Military

Organisations

Executive Programme in Management Studies – Digital Business Track Amsterdam Business School

Raoul ten Hengel 11658916

Supervisor: Dr. J.Y. (Jonne) Guyt Final version, 15 August 2018

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Statement of Originality

This document is written by student Raoul ten Hengel who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Abstract

Nowadays, information technology plays an increasingly important role in organisations. Unfortunately, the users in the organisation doe not always adopt IT. Technology adoption is poorly investigated in military organisations. To fill the research gap the following research question will be answered: To what extent do factors derived from technology acceptance models influence the intention to adopt innovative technology within military organisations?

Many models are developed to understand the drivers behind technology adoption. One of these models is the unified theory of acceptance and use of technology (UTAUT), which is elaborately tested. The main drivers of adoption are performance expectancy, effort expectancy and social influence, which are all moderated by age and gender. Social influence is also moderated by voluntariness of use. This study provides an overview of results of previous studies, after which it theorises the extent of influence of these drivers in military organisations. In order to empirically verify these predictions, a survey was sent out to employees of the Dutch armed forces.

Using a regression model, this study explains 59,7% of the variance in behavioural intention to adopt a new technology. The values for the drivers that were found did not confirm the predictions, hence it cannot be stated that military characteristics influence behavioural intention like hypothesised. One moderation was found to be significant. Only with mandatory use, social influence has an effect on behavioural intention.

The strong representation of highly specialised personnel from the air force could be the reason for not finding the predicted values. This study builds on the understanding of moderated relations within the literature, with finding a significant moderator. This study is one of the first investigating IT behavioural models within a military context, creating research space for further research. Last, this research contributed to the field of technology adoption by adding a new investigated organisation and confirming the predictive power of the UTAUT.

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List of Figures and tables

Figures

Figure 1 Basic concept underlying user acceptance models 3

Figure 2 The UTAUT-Model 5

Figure 3 Adapted UTAUT-model 13

Figure 4 Conceptual model with β estimations 16

Figure 5 The moderation of Voluntariness of Use on the relation between Social Influence and Behavioural Intention

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Figure 6 Assessment of the conceptual model 26

Tables

Table 1 Descriptive statistics of the significant relations 8

Table 2 Performance expectancy results for governmental and health related organisations

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Table 3 Effort expectancy results for governmental and health related organisations 14 Table 4 Social influence results for governmental organisations and universities/schools 15

Table 5 Measures, items and Cronbach’s α 18

Table 6 Descriptive Characteristics of the sample 20

Table 7 Factor Loadings 22

Table 8 Correlation matrix 23

Table 9 Results of regression analysis of whole model with moderating effects 24

Table 10 Comparison of β values 25

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1. Introduction

Information technology cannot be ignored nowadays. It is everywhere around, in business, social life, medical care and so on (Webster, 2014). Because of the high rate of development of information technology, it is not expected that in the future information technology will not be part of everyday life (Webster, 2014). On the contrary, it is expected that information technology will become more part of everyday life than it is already. Organisations strive to increase their sustainable competitive advantage and are convinced that information technology is going to help with that (Piccoli & Ives, 2005). Many organisations are investing in existing technologies to improve their processes. In addition, many organisations invest in the continuous development of new technology to maintain their competitive advantage. Investing alone does not imply that information technology (IT) will ensure competitive advantage or any other goal of organisations.

Unfortunately, approximately 66% of all IT implementation projects still fail (Legris, Ingham, & Collerette, 2003). Besides the fact that organisations do not want to fail IT projects, IT implementations are costly processes, which organisations cannot afford to fail. Often the reason for this failure is the unwillingness of the end users to adopt the technology. It can be found in the literature that technology adoption is an extensively researched subject. Since the uprising of IT, many researchers wrote about technology adoption and the drivers of adoption (Davis, 1989; Lee, Kozar, & Larsen, 2003; Legris et al., 2003). One of the most accepted models that studies and predicts technology adoption is the technology acceptance model (also known as TAM) (Legris et al., 2003). This model is extensively studied, adapted and elaborated throughout time (Lee et al., 2003). The original model is empirically proven to predict 40% of the variance in the adoption of technology (Legris et al., 2003).

There is also criticism towards technology acceptance models. Some of the limitations are that the research of the models is concentrated on a specific group of research subjects and that the models are not tested in many different organisations (Lee et al., 2003). One type of organisation in which the models are not empirically tested are military organisations. Only one article is found that applied an amended form of the TAM in a military organisation (Levy & Green, 2009). More research within many

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2 different organisations is needed to make technology acceptance models better (Lee et al., 2003). With this research the field will gain more knowledge about how different environments, people or any other factor influence technology adoption.

To fill the research gap, the following research question is designed: To what extent do factors

derived from technology acceptance models influence the intention to adopt innovative technology within military organisations?

With answering the research question this research contributes to further developing the knowledge about technology adoption. Specific knowledge about military organisations will be obtained. With researching a new organisation, the theory of technology acceptance models will be tested more empirically.

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2. Literature review

2.1. Technology adoption models and theories

Acceptance and use of information technology systems has been a well-researched subject for the past three decades. Several models for predicting innovations, mostly IT, are widely used and have been validated in multiple researches (Jeyaraj, Rottman, & Lacity, 2006). When looking at the literature one underlying basic concept can be recognized. This concept is presented in Figure 1.

Figure 1: Basic concept underlying user acceptance models, adapted from (Venkatesh, Morris, Davis, & Davis, 2003)

The goal of the models and theories is understanding actual usage as a dependent variable while intention to use is a predictor of behaviour. Most of the models are based on the theory of reasoned action (TRA) and the theory of planned behaviour (TPB). TRA was introduced in social psychology (Fishbein & Ajzen, 1975) and is probably the most influential theory about the prediction of human behaviour. The main determinant for human behaviour is behavioural intent (Fishbein & Ajzen, 1975), while behavioural intent is influenced by attitude towards behaviour and subjective norm (Fishbein & Ajzen, 1975). The TPB is an extension of the TRA where the TPB is expanded with an extra variable. This variable is perceived behavioural control (Ajzen, 1991). This variable was added to account for factors outside the individual’s control, which can also affect intention or behaviour.

The most researched model is probably the technology acceptance model. Many authors give an overview of the different researches and the evolution of the model (Chuttur, 2009; Kukafka, Johnson, Linfante, & Allegrante, 2003; Lee et al., 2003; Legris et al., 2003; Marangunić & Granić, 2015). The technology acceptance model was developed based on TRA and TPB. It specifically focussed on predicting behaviour within an IT context (Davis, 1989). Therefore, the variables were adjusted to this IT context. It was theorised that perceived usefulness and perceived ease of use are variables

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4 influencing intention and that perceived ease of use has influence on perceived usefulness. The definition of perceived usefulness is: “the degree to which a person believes that using a particular system would enhance his or her job performance” and the definition of perceived ease of use is: “the degree to which a person believes that using a particular system would be free of effort” (Davis, 1989).

Many other models and theories were developed with the goal to explain the reasons for adopting new technologies. A broad spectrum of variables, moderators and different settings were identified. For example: voluntary settings and mandatory settings (Venkatesh & Davis, 2000), experienced and unexperienced users (Taylor & Todd, 1995), Image as the degree to which use of an innovation is perceived to enhance one’s image or status in one’s social system (Moore & Benbasat, 1991) and facilitating conditions as objective factors in the environment that observers agree make an act easy to accomplish (Thompson, Higgins, & Howell, 1991).

2.2. A unified theory

In 2003, an effort was made to achieve a unified view on acceptance and usage of IT, combining eight prominent theories and models. This unified view is called the unified theory of acceptance and usage of technology (UTAUT). The eight models and theories it is based on are:

- the theory of reasoned action (TRA), - the theory of planned behaviour (TPB), - the technology acceptance model (TAM), - a combination of TAM and TPB,

- the innovation diffusion theory (IDT), - the social cognitive theory (SCT), - the motivational model (MM),

- the Model of PC utilization (MPCU) (Venkatesh et al., 2003).

In the research, empirical evidence of the predictive ability of the unified theory was included. A comparison with the eight models and their predictive ability was also made (Venkatesh et al., 2003). The eight models explained between 17% and 53% of the variance in the intention to use IT (Venkatesh

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5 et al., 2003). The unified theory was tested and explained 69% of the variance, significantly outperforming the other models (Venkatesh et al., 2003). After the original research, the UTAUT has been used in many other researches, researching website usage, tax payment systems, mobile technology and many other IT applications (Williams, Rana, & Dwivedi, 2015). In many of the researches it is found to be significant that the UTAUT explains variance in intention to use and usage behaviour (Venkatesh, Thong, & Xu, 2016; Williams et al., 2015).

Four constructs were defined as drivers of intention to use and use behaviour, based on the eight models and theories. These constructs are performance expectancy, effort expectancy, social influence and facilitating conditions. It was also theorised that four moderators, namely age, gender, experience and voluntariness of use, moderate these constructs. Resulting in the conceptual model presented in figure 2.

Figure 2: The UTAUT-Model (Venkatesh et al., 2003)

Performance expectancy is defined as the degree to which an individual believes using an IT application will enhance his or her performance (Venkatesh et al., 2003). In the empirical evidence of the UTAUT and the eight models which it is based on, it was shown that the performance expectancy construct is the strongest driver of intention to use and is significant every time (Venkatesh et al., 2003). Effort expectancy is the degree of ease associated with using the application (Venkatesh et al., 2003). Social influence is defined as the degree to which an individual perceives that important others believe he or she should use the application (Venkatesh et al., 2003). It was found that social influence

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6 had a significant effect in mandatory settings, while it had no significant effect in voluntary settings (Venkatesh et al., 2003). The last determinant defined in the model is the construct facilitating conditions. This construct is defined as the degree to which an individual believes that organisational and technical infrastructure exist to support use of the system (Venkatesh et al., 2003). In the UTAUT it was theorised and proven that facilitating conditions do not have an influence on the intention to use but has a direct influence on usage behaviour (Venkatesh et al., 2003). Behavioural intention was defined as the degree to which an individual intends to use the system. Behavioural intention is a driver of actual system usage (Venkatesh et al., 2003).

As already mentioned, the initial research of the UTAUT explained 69% of the variance in intention to use. In other researches, high variances were also the result. For example, in Venkatesh, Thong, & Xu (2012) where a variance of 70% was found. Some even state that the UTAUT explains 77% of the variance in intention to use (Venkatesh et al., 2016) and are claiming that the UTAUT has reached its practical limit of explaining technology acceptance within organisations. In a literature review where 174 articles involving UTAUT were analysed it shows that every time over half of the relations examined are significant (Williams et al., 2015). The relation between intention and actual use was found to be significant in 82% of the studies, which underlines the usefulness of the UTAUT in predicting actual use (Williams et al., 2015).

Therefore, the UTAUT combined eight most accepted and used models and theories in the field to develop a new model. The model is found to be able to predict the intention of using IT and is found to be able to predict the actual usage. The UTAUT has been researched in many different settings. Researches conducted with the UTAUT have a variety of different user groups, different sectors and different technology types. Maldonado, Khan, Moon, & Rho (2011) conducted a study with a sample group of students. Zhou, Lu, & Wang (2010) used a sample group from users of a mobile service. Venkatesh, Thong, Chan, Hu, & Brown (2011) studied citizens adopting e-government services. Examples of different sectors are schools (Pynoo et al., 2011), hospitals (Chang, Hwang, Hung, & Li, 2007) and governmental organisations (Gupta, Dasgupta, & Gupta, 2008). Many different types of

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7 technologies were also studied with the UTAUT. General purpose systems like the internet (AbuShanab & Pearson, 2007) or communication systems like mobile banking (Zhou, Lu, & Wang, 2010) but also specialized business systems like electronical medical record systems (Hennington & Janz, 2007).

2.3. An analysis of the UTAUT

In this study, the extent of influence of factors from technology adoption models within military organisations is searched. As presented the UTAUT is a frequently used model, which is empirically proven to predict behavioural intention and usage. This makes the UTAUT a suitable model to search for the extent of influence within military organisations.

Certain characteristics of military organisations probably influences the UTAUT and its variables. No previous research could be found where the UTAUT is used within military organisations. Some examples of the TAM can be found, but these are scarcely represented (Levy & Green, 2009). To be able to conclude about the influence of military characteristics on the variables from the UTAUT a comprehensive overview of the research results of the UTAUT is needed. It would be interesting to look at different organisations where the UTAUT is successfully applied and search for similarities between military organisations and the studied organisations, for example governmental organisations (Gupta et al., 2008).

Recently two literature reviews on the UTAUT were published (Venkatesh et al., 2016; Williams et al., 2015). From these reviews the results of relevant articles, based on organisation and type of application, will be extracted and will be summarised in a table available in appendix A. This table will include a description of the organisation or study and the results from cross-sectional studies or first measurement results from longitudinal studies. First measurement result from longitudinal studies are chosen, because of the innovative nature of the technology used in this study. This first measurement is most similar to the situation in this research. In total 22 studies are summarized in the table and from these studies 24 results from testing the UTAUT are extracted. One meta-analysis is added to the table. In this analysis, the results of 8 studies were researched (Dwivedi, Rana, Chen, & Williams, 2011). Some descriptive statistics of the results are given in table 1.

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8 Table 1: Descriptive statistics of the significant relations.

With the overview of the results, the descriptive statistics and the characteristics of military organisations influencing the UTAUT, which will be given in the next section, an estimation to what extent the factors of the UTAUT influence the intention to adopt an innovative technology will be made.

2.4. Military organisations characteristics

Military organisations are a specific kind of organisation and maybe one of the oldest organisation there is. The specific traits come from the goals, tasks, means and personnel who need to fulfil the tasks and goals. In this section, specific characteristics from military organisations that influence the factors from the UTAUT and the way how they influence it will be presented.

Descriptive Statistics

DV: Intention Count Mean

R2 20 0.504 Adjusted R2 4 0.438 IV Count all relations Mean β all relations Min β all relations Max β all relations Count significant relations Mean β Significant relations Min β significant relations Max β significant relations PE 30 0.317 -0.120 0.686 26 0.365 0.120 0.686 SI 30 0.248 0.030 0.660 24 0.285 0.100 0.660 EE 29 0.181 -0.040 0.487 24 0.210 -0.040 0.487 PE (GDR, AGE) 4 0.439 0.260 0.687 3 0.357 0.260 0.520 FC 6 0.169 0.030 0.513 3 0.278 0.160 0.513 EXP 4 0.310 0.030 0.673 2 0.570 0.467 0.673 SI (GDR, INC, EXP, VOL) 2 0.508 0.250 0.766 2 0.508 0.250 0.766 PE (GDR, INC) 2 0.315 0.270 0.359 2 0.315 0.270 0.359 EE (GDR, AGE, EXP) 3 0.398 0.300 0.583 2 0.305 0.300 0.310 SI (GDR, AGE, EXP) 2 0.280 0.270 0.290 2 0.280 0.270 0.290 EE (GDR, AGE) 3 0.336 0.210 0.579 2 0.215 0.210 0.220 SI (GDR, AGE, VOL) 3 0.242 -0.250 0.707 2 0.010 -0.250 0.270

AGE, VOL, EXP 1 0.696 0.696 0.696 1 0.696 0.696 0.696

VOL, EXP 1 0.682 0.682 0.682 1 0.682 0.682 0.682 EE(EXP) 3 0.205 0.020 0.566 1 0.566 0.566 0.566 EE (GDR, INC, EXP) 2 0.339 0.250 0.427 1 0.427 0.427 0.427 EE(GDR) 7 0.132 -0.06 0.468 1 0.170 0.170 0.170 VOL 4 0.201 0 0.662 1 0.100 0.100 0.100 SI (AGE, VOL) 3 0.194 -0.17 0.691 1 -0.170 -0.170 -0.170

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9 The military organisation is a well-known form of a bureaucracy like many authors defined. Some say it is possibly the prime example of a bureaucracy (Lang, 1965). One of the first authors who wrote about the bureaucracy in a theoretical way was Max Weber. He defined an ideal list of characteristics that an organisation needs to have to become a good bureaucracy (Swedberg & Agevall, 2016). Other authors acknowledged these characteristics and some other characteristics were added (Hall, 1963). The characteristics that most authors agree upon are:

- hierarchy of authority, - division of labour, - standardised procedures, - rule-based organisation,

- disciplined working environment, - formalised structures

- technical competent employees (Hall, 1963; Olsen, 2006; Swedberg & Agevall, 2016). All these characteristics can be observed when looking at military organisations. These characteristics are in other researches redefined as the degree of specialisation, standardisation, centralisation and formalisation (Child, 1972).

Weber theorised that the ideal bureaucracy will have a high degree in these dimensions. Because this research is investigating the intention of adoption of the individual, it is necessary to look for the perceived degree of the bureaucracy dimensions. Before theorising the extent of influence of the bureaucracy dimensions, it is needed to clearly explicate the exact definitions of the four dimensions. Adapted from other research the following definitions will be used. Specialisation: the number of different specialisations in labour present in the company and the degree of different technological knowledge needed to sustain the specialisations (Child, 1972; Damanpour, 1991). Standardisation: the extent of coverage and application of standard procedures, rules and regulations within the organisation (Child, 1972; Walton, 2005). Centralisation: the extent to which authority to make decisions affecting the organisation is assigned to the higher levels of hierarchy (Child, 1972).

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10 Formalisation: the extent to which rules, procedures and instructions are written down and filed (Child, 1972; Walton, 2005).

Next to the specialisation dimension from the bureaucracy characteristics, military organisations are known for their technological driven mind-sets. History points out that many inventions, which are used nowadays, can be derived from inventions driven by military organisations. Think about the jet engine (Constant, 1980), radar systems (Rosen, 1988) and even the invention of the internet (Leiner et al., 2009). Military organisations are still technology driven. Every military organisation needs high-end technological weapon systems to be able to join and win in combat. The need for military organisations to communicate with all assets on a battlefield pushes them to use state of the art technologies. Nowadays new tasks for military organisations arises. The tasks of defending against cyber warfare or even be active in offensive cyber warfare.

Specialised personnel in tasks and technological knowledge and the perceived degree of the specialisation is expected to influence performance expectancy. With a higher perceived degree of specialisation, individuals are likely to identify the possible advantages of new IT applications. With as result that they believe that a new application will improve their performance. This expectation is also stated in research on drivers of adopting innovations. In several studies, it was hypothesised and proven that a high degree of specialisation has a positive effect on adopting innovations (Damanpour, 1991; Kimberly & Evanisko, 1981). It is expected that this works the same for adopting innovative technologies and that it has effect within the construct performance expectancy. This implicates that it will influence the effect of performance expectancy on behavioural intention positively, resulting in a higher β value than the average β value.

For standardisation and formalisation, it was found that a high degree of standardisation and formalisation has a negative effect on adopting innovations (Damanpour, 1991; Kimberly & Evanisko, 1981; Zmud, 1982). Individuals who work according to standard procedures are less likely to easily adopt a new application, which forces them to change their standard procedures, thus having an effect within effort expectancy. The same as high degree of standardisation has an effect on effort

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11 expectancy; the perceived degree of formalisation has an effect on effort expectancy. When procedures, rules and regulations are all written down and filed, individuals will believe it is not easy to adopt a new application due to the changes needed in the formalised documents. Both negative effects coming from standardisation and formalisation result in a decreasing β value compared to the average β value of effort expectancy on behavioural intention.

A high degree of centralisation means that authority to make decisions is assigned to the higher levels in hierarchy. For the members of centralised organisations this means that they are obliged to accept the orders given by the persons acknowledged to be one’s superiors by their position (Merton & Merton, 1968; Peabody, 1962). The relation that arises means that the people in the organisation experience pressure to oblige to the authority. Especially in military organisations, which are known for the strong hierarchy and central authority (Lang, 1965), it can be expected that this pressure is felt by the organisation’s members. Therefore, it is expected that a high degree of centralisation and authority have a positive effect on the behavioural intention on adopting an innovative technology through the construct social influence. In other research, it was found that centralisation has a positive effect on adopting innovations (Zmud, 1982). In the original research of the UTAUT, it was also found that in mandatory settings the social influence construct has an effect and is more often found to be significant (Venkatesh et al., 2003). In organisations with a high degree of authority, it is possible that individuals feel they are obliged to use certain innovations even if they are not obliged. Next to a high degree of authority, military organisations are also known for the strong social values present amongst personnel. Ethics, righteousness and integrity are important traits within military organisations. Group cohesion is also a very important factor within military organisations (Oliver, Harman, Hoover, Hayes, & Pandhi, 1999; Siebold, 2007), because of the tasks and the mentally challenging training military personnel need to follow with each other and the communal life which is present in military organisations (Lang, 1965). These social factors working within military groups implicate that members of military organisations are expected to be influenced by what the members of their group think and believe. These two factors work within the social influence construct and contribute to a higher β value

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12 compared to the average β value on behavioural intention. It is expected that compared to the effects of the UTAUT in other organisations social influence will have proportionately the highest increase of the β value.

So, based on the factors in military organisations it is expected that there will be a proportionally higher β value for performance expectancy and social influence and proportionally a lower β value for effort expectancy. Where social influence will increase the most. In the next section, the conceptual model will be presented and estimations of the β values will be given.

2.5. Conceptual model

To determine to what extent the factors from the UTAUT influence behavioural intention the analysis of the UTAUT and the influence of military characteristics need to be combined. Because of the limited time frame of this thesis it will not be possible to study all the relations in the UTAUT. Furthermore, this research question is searching for the extent of influence on the adoption of innovative technology within military organisations. When looking in the dictionary for innovative and technology the following definitions are given. Innovative: ‘featuring new methods; advanced and original.’ (Oxford English Dictionary, 2018) and technology: ‘The application of scientific knowledge for practical purposes, especially in industry.’ (Oxford English Dictionary, 2018). So, based on these definitions an innovative technology can be described as an application of scientific knowledge for practical purposes, which is advanced and original. In scientific literature the definition of innovation given by the organisation for economic co-operation and development is often used which states: “An innovation is the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organisational method in business practices, workplace organisation or external relations.” (Mortensen & Bloch, 2005). This means that it is difficult for any user to have experience in an innovative technology. If a user would have experience then the technology would not be innovative. Therefore, the moderator experience is left out of the model. Another implication of an innovative technology is that it is possibly not ready to use yet. Because this is the case in this thesis, actual use cannot be measured and therefore behavioural intention will

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13 become the dependent variable in the conceptual model. In the UTAUT, it was defined that facilitating conditions has significant effect on usage. With excluding usage out the model of this thesis, facilitating conditions are not relevant to measure. These implications give the model as presented in figure 3. Figure 3: Adapted UTAUT-model, adapted from (Venkatesh et al., 2003)

From the original research of the UTAUT the hypotheses for the model in figure 3 were developed. These hypotheses will be used in this research but an estimation of the value of the β coefficient will be added to the hypotheses.

As theorised above the β value of performance expectancy will be higher due to the characteristics of military organisations. When looking at organisations with similar characteristics as military organisations table 2 can be found. Organisations with similar characteristics in this case are governmental organisations and health related organisations. Governmental organisations are chosen because military organisations are also governmental organisations. Health related organisations are chosen because they also have a high degree of specialisation to achieve their goals (Axelsson & Axelsson, 2006; Kimberly & Evanisko, 1981).

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14 Table 2: Performance expectancy results for governmental and health related organisations.

Based on these numbers and the total overview, the estimation will be made that the performance expectancy β value will not be higher than the max β value from the similar organisations, so not above 0.484. It is also estimated that due to the military characteristics the β value will be at least a half standard deviation higher, so higher than 0.435. This estimation combined with the original hypothesis from Venkatesh et al. (2003) gives the following hypothesis.

Hypothesis 1: The β value of performance expectancy (PE) on behavioural intention will be between 0.435 and 0.484 and will be moderated by age and gender, such that it will be stronger for men and particularly younger men (Venkatesh et al., 2003).

The same as for performance expectancy a comparison will be made with the results from governmental and health related organisations due to their similarities. These organisations have similar characteristics for effort expectancy as military organisations. Within health organisations, a high degree of formalisation and standardisation can also be found (Greenhalgh, Robert, Macfarlane, Bate, & Kyriakidou, 2004). Results of effort expectancy from studies from these organisations can be found in table 3. It is expected that the β value of effort expectancy will be lower than the mean, but not lower than the minimum value, which is 0.165. A decrease from at least half of the standard deviation is estimated, so the β value of effort expectancy will be lower than 0.199. These numbers combined with the original hypothesis give hypothesis 2.

Hypothesis 2: The β value of effort expectancy (EE) on behavioural intention will be between 0.165 and 0.199 and will be moderated by age and gender, such that it will be stronger for women and particularly younger women (Venkatesh et al., 2003).

Count all relations Mean all relations STDEV all relations Min all relations Max all relations Count significant relations Mean Significant relations STDEV Sig relations Min significant relations Max significant relations PE 6 0.306 0.167 0.080 0.484 5 0.351 0.167 0.120 0.484 PE (AGE) PE (GDR) 1 0.192 0.192 0.192 PE (GDR, AGE)

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15 Table 3: Effort expectancy results for governmental and health related organisations.

It is theorised that social influence will be the factor with the highest increase in the β value due to the specific characteristics of military organisations. When analysing the available results from similar organisations table 4 can be presented. Similar organisations in the case of social influence are governmental organisations, universities and schools. Governmental organisations are again included because a military organisation is a governmental organisation. Findings from universities and schools are included because of the fact that students are more influenced by their social environment than other groups (Park & Lessig, 1977). This can be seen in the results from the similar organisations. The mean β value of social influence of these organisations is 0.099 higher than the mean β value of all results (0.384 β - 0.285 β).

Table 4: Social influence results for governmental organisations and universities/schools.

An increase of at least a standard deviation is expected which means a social influence β value higher than 0.482. The maximum β value of the significant relations is 0.486. This would mean that the expected β value should be between 0.482 and 0.486. The difference is too small, so the maximum of the significant relations cannot be used the highest significant value of all the studies is 0.660, so 0.660 will be used as the upper limit for the social influence β value. Combining this with the original hypothesis gives hypothesis 3.

Count all relations Mean all relations STDEV all relations Min all relations Max all relations Count significant relations Mean Significant relations STDEV Sig relations Min significant relations Max significant relations EE 5 0.263 0.128 0.165 0.487 5 0.263 0.128 0.165 0.487 EE (AGE) EE (GDR) 1 0.124 0.124 0.124 EE (GDR,AGE) Count all relations Mean all relations STDEV all relations Min all relations Max all relations Count significant relations Mean Significant relations STDEV Sig relations Min significant relations Max significant relations SI 7 0.384 0.098 0.200 0.486 7 0.384 0.098 0.200 0.486 SI(AGE) SI(GDR) 2 0.262 0.262 0.262 SI(VOL) SI(AGE,VOL) SI(GDR,AGE) SI(GDR,VOL) SI(GDR,AGE,VOL)

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16 Hypothesis 3: The β value of social influence (SI) on behavioural intention (BI) will be between 0.482 and 0.660 and will be moderated by age and gender, such that it will be stronger for women, particularly older women and particularly in mandatory settings (Venkatesh et al., 2003).

As can be seen there is not enough research on the moderated effects on behavioural intention, so no β value estimations can be made of these effects. For the moderators the original hypotheses will be tested and the presence of a significant relation will be investigated. Combining the estimations with the conceptual model gives the model below and this model will be tested in this research. How it is going to be tested will be explained in the next chapter.

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3. Research Design

3.1. Research strategy

To answer the research question, the three hypotheses with their estimated values need to be tested. When reading the research question, it can be determined that the nature of this research is explanatory. The extent of influence is searched to be answered. As can be found in earlier literature and researches, the survey is an adequate research strategy for looking into the relations of the UTAUT. 155 studies of the 174 studies using the UTAUT examined by Williams et al. (2015) used a survey. Next to the theoretical basis for the survey, the research question also fits the strategy of a survey. To determine the extent of influence of the factors of the UTAUT in a military organisation context, statistical analysis is needed. The best way to achieve enough data to be able to do the analysis is the survey method. The study will be a cross-sectional study, because of the limitation in time.

3.2. Research object

To analyse the hypotheses derived from the conceptual model we need users within a military organisation. An information technology, which can be used as research object, is also needed. The researcher has access to the armed forces of the Netherlands, which makes this military organisation a feasible option to use as research subject. The Dutch armed forces are responsible for ensuring national security, combating international unrest, providing disaster relief globally and above all protecting the territory of the Kingdom of the Netherlands and her allies. (Ministerie van Defensie, 2018). For these purposes, the armed forces comprise of 7 organisational elements, highly-qualified personnel and many different weapons systems and supporting equipment (Ministerie van Defensie, 2018).

Currently the armed forces of the Netherlands are investigating options of using applications based on blockchain technology. These applications are not operational yet, but tests are being prepared to do within the armed forces. Because of the innovative nature of the technology needed for this research, a blockchain application is a good option to serve as research object. The blockchain application used in this research is an application that is also being used outside the armed forces. The blockchain application ensures supply chain transparency without discouraging trust between

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18 organisations (IBM, 2016). The blockchain application also replaces paper trails with digitized trails secured in the distributed shared ledger, without losing security and making fraud possible (IBM, 2018). The application makes the supply chain more efficient by building smart contracts into the blockchain. These smart contracts can release shipments after all requirements are met, like customs papers (IBM, 2018). All this can be achieved while all transactions are visible for all participants in the supply chain (IBM, 2017). This application is also very useful within military organisations because of their long supply chains, many partners to work with and often high-quality standards that need to be met.

3.3. Measurements

The conceptual model is adapted from the original UTAUT. To test the extent of the relation between the independent and the dependent variables in the model the measures from the original research are adapted to this research context. This implies changing the subject in the questions from system to blockchain application. For example: ‘I would find the system useful in my job’ becomes ‘I would find the blockchain application useful in my job’. All the items were also translated into Dutch because most employees within the Netherlands armed forces are Dutch native speakers. The measures and the number of items and the average Cronbach’s α over six times of measurement of the original research will be given in the table below.

Table 5: Measures, items and Cronbach’s α, adapted from (Venkatesh et al., 2003).

All of the items were measured with a 7-point Likert scale ranging from 1 strongly agree to 7 strongly disagree. To determine if voluntariness of use has a moderating effect on the relation between SI and BI two separate surveys were handed out to two different groups. One group is told that use of the new application is mandatory while the other group is told nothing. The goal of the survey and an

Measure Definition Items Cronbach’s α

PE The degree to which an individual believes using an IT application will enhance his or her performance.

4 0.91

EE The degree of ease associated with use of the application. 4 0.92 SI The degree to which an individual perceives that important

others believe he or she should use the application.

4 0.92

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19 explanation of the blockchain application was given as introduction. After creating the survey, the survey was administered to six persons within the armed forces, varying in rank, education, age and gender as pilot survey. Based on the feedback from the pilot survey some small adjustments to the introduction were made, the rest of the survey was clear and understandable. The survey, in Dutch, can be found in appendix B.

3.4. Data collection

As already stated the survey was conducted within the armed forces of the Netherlands. The blockchain application used in the research is usable in a supply chain. Therefore, the employees of the Dutch armed forces that are part of a supply chain are potential respondents. Within the armed forces this are people responsible for transport and logistics, support and facility but also maintenance. This group of employees within the armed forces belong to the material logistics domain. To gather enough respondents, commanders of several departments were requested to spread the survey among their personnel. An equal separation between the mandatory and voluntary setting was made. After selecting the commanders, the number of employees in their departments were summed up resulting in 4240 possible respondents in a mandatory setting and 4196 possible respondents in a voluntary setting. Not all commanders were able or willing to spread the survey among their personnel. This resulted in possible respondents of 4240 in a mandatory setting and 1848 in a voluntary setting. The survey was available for answering for three weeks. In these three weeks, 168 respondents started the survey and 145 completed the survey. When the surveys were not completely filled in and not all the items of the variables were answered these cases were deleted. This resulted in 122 usable cases, which is a response rate of 2%. Descriptive characteristics of the sample are given in the table 6.

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20 Table 6: Descriptive Characteristics of the sample.

As can be seen there are not many missing items. Therefore, pairwise deletion of cases will be used throughout the analysis. This study focuses on the material logistics domain, so all respondents from other disciplines were filtered out in further analysis. The people from the disciplines logistics and transport, maintenance and facility and support were used, resulting in 115 cases.

Descriptive Characteristics

Measure N = 122 Item Frequency Percent

Gender Male 102 83.6% Female 19 15.6% Missing 1 0.8% Age 20-25 11 9,0% 26-30 20 16.4% 31-35 18 14.8% 36-40 13 10.7% 41-45 16 13.1% 46-50 14 11.5% 51-55 17 13.9% 56-62 11 9,0% Missing 2 1.6%

Voluntariness of use Mandatory 58 47.5%

Voluntary 64 52.5% Education VMBO 3 2.5% HAVO 4 3.3% MBO 37 30.3% HBO 41 33.6% WO Bachelor 19 15.6% WO Master 17 13.9% Missing 1 0.8% Civilian/Military Civilian 20 16.4% Military 101 82.8% Missing 1 0.8%

Organisational element Central Staff 1 0.8% Royal Netherlands Navy 10 8.2% Royal Netherlands Army 17 13.9% Royal Netherlands Air Force 78 63.9% Joint Support Command 10 8.2% Defence Materiel Organisation 5 4.1%

Missing 1 0.8%

Discipline Logistics and Transport 68 55.7%

Maintenance 43 35.2%

Communications and IT 3 2.5% Facility and Support 4 3.3% Care. Health and Medicine 1 0.8%

Different 2 1.6%

Missing 1 0.8%

Rank or equated Scale Enlisted 6 4.9% Non-commisioned officers 39 32,0% Commisioned officers 75 61.5%

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21 To determine a representative sample size rules of thumb were developed. Green (1991) developed two rules of thumb, one to test a whole model and one to test the individual relations in a model. The rule of thumb for the sample size for the whole model is 50+8m, where m is the number of predictors or independent variables (Green, 1991). The rule of thumb for the individual relations is 104 + m (Green, 1991). The research model in this research consist of three independent variables, when applying the rules of thumb this gives a sample of 74 and 107. To test everything the largest sample needs to be chosen (VanVoorhis & Morgan, 2007). So, for this research a sample of 107 employees is needed to be representative. This demand was met because the sample consisted of 115 cases.

3.5. Analysis

In order to test the hypotheses and to answer the research question, regression analysis was conducted. To make sure that the results of the analysis are useable, a reliability and validity analysis is needed. Although the survey was adapted from a reliable and validated survey (Venkatesh et al., 2003), it is necessary to make sure that results are derived from a reliable and validated dataset. The reliability will be tested with a Cronbach’s Alpha test. A scale is considered reliable when the Cronbach’s Alpha is above 0.7 (DeVellis, 2016). All Cronbach’s Alphas are above 0.7, but the reliability of PE is 0.717, which is quite low compared to the others. One item of this scale has a corrected item-total correlation of 0.088, which is extremely low. This indicates that this item is measuring something different from the other items of the scale. Removing this item will increase the reliability from 0.717 to 0.9. Factor analysis confirms the issue with this item. A principal axis factor analysis was conducted on the scales. Both the KMO and the Bartlett’s test were passed which means the results of the factor analysis can be used, KMO = 0.870 and Bartlett’s χ² (105) = 1296.449, p < 0.001. The four independent variables were used as factors and these four factors were rotated with a Varimax with Kaiser normalisation rotation. Table 7 shows the factor loadings. The same item caused difficulties in the factor analysis as it did in the reliability analysis. This strongly suggest that this item needs to be removed from the scale. This can be explained due to the content of the item. This item is about a

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22 paygrade raise after using the blockchain application. Within the armed forces, a paygrade raise is connected to the rank or scale. The respondents do not believe that using the application is connected to a promotion in rank or scale. It was decided that item 4 of the PE construct needed to be removed from further analysis, improving the reliability of the PE scale with 0.183.

Table 7: Factor Loadings

The Cronbach’s Alphas are reported in the correlation matrix, table 8, on the diagonal. Scale means were computed and standardised to perform the regression analysis. With the standardised variables, the interaction variables of the independent variables and the moderators were computed. With the reliability and the validity of the scales checked and the standardised variables and the interaction variables computed the regression analysis was conducted.

Factor Loadings

Item PE EE SI BI

Ik verwacht dat de blockchain applicatie nuttig is voor mijn werk. 0.717 0.233 0.212 0.379

Ik verwacht dat gebruik van de blockchain applicatie mij in staat stelt mijn

taken sneller te volbrengen. 0.799 0.24 0.16 0.299 Ik verwacht dat gebruik van de blockchain applicatie mijn productiviteit

verhoogt. 0.745 0.235 0.169 0.215

Ik verwacht wanneer ik de blockchain applicatie gebruik dat de kansen op

een salaris verhoging groter worden. 0.12 -0.201 0.207 0.049 Ik verwacht dat de interactie met de blockchain applicatie voor mij duidelijk

en begrijpbaar is. 0.282 0.717 0.2 0.206

Ik verwacht dat het makkelijk is voor mij om bekwaam te worden in het

gebruik van de blockchain applicatie. 0.244 0.811 0.174 0.346 Ik verwacht dat de blockchain applicatie makkelijk te gebruiken is. 0.271 0.814 0.267 0.175 Ik verwacht dat leren werken met de blockchain applicatie makkelijk is voor

mij. 0.194 0.801 0.138 0.274

Ik verwacht dat personen die mijn gedrag beïnvloeden vinden dat ik de

blockchain applicatie moet gebruiken. 0.029 0.076 0.726 0.215 Ik verwacht dat personen die belangrijk voor mij zijn vinden dat ik de

blockchain applicatie moet gebruiken. 0.084 0.111 0.685 0.263 Ik verwacht dat de hogere leiding binnen mijn organisatieonderdeel

behulpzaam is bij het gebruik van de blockchain applicatie. 0.233 0.282 0.717 0.03 In het algemeen verwacht ik dat mijn organisatieonderdeel ondersteunend is

bij het gebruik van de blockchain applicatie. 0.289 0.367 0.587 0.089 Ik ben voornemens om de blockchain applicatie te gaan gebruiken. 0.326 0.282 0.275 0.767 Ik voorspel dat ik de blockchain applicatie ga gebruiken. 0.261 0.219 0.318 0.594 Ik ben van plan om de blockchain applicatie te gaan gebruiken. 0.358 0.286 0.141 0.814

Note: Factor loadings over 0.4 appear in bold. Extraction Method: Principal Axis Factoring. Rotation Method: Varimax with Kaiser Normalisation.

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23

4. Results

4.1. Correlation analysis

An overview of the descriptive characteristics, correlations and scale reliabilities is given in table 8. As can be seen all independent variables have a significant relation with the dependent variable. So, regression analysis can be used. All independent variables also have a significant relation with each other. The independent variables are not highly correlated, with r = 0.581, p < 0.01 as the highest value between PE and EE. As can be seen in the factor analysis the items of these constructs do not have high cross loadings, so no multicollinearity occurs. No multicollinearity is needed to be able to conduct regression analysis. A remarkable observation is the significant correlation between age and BI. It was hypothesised that age is a moderator for the relations between all the independent variables and dependent variables, but it appears that age on its own has an effect on BI. The relation being positive implies that with an increase of age, BI also increases, or with an increase of BI, age also increases. An increase in BI cannot have an effect on age, so in this research it can be stated that how older people get the more intention they have to use the blockchain application. Which is a result contrary to what was expected.

Table 8: Correlation matrix

4.2. Regression analysis

In order to conduct regression analysis some assumptions need to be met. These assumptions are related to sample size, multicollinearity and singularity, outliers, normality and linearity (Pallant, 2016). The assumption of sample size was already met. The other assumptions were checked with the regression analysis. No multicollinearity occurs, as already seen in the correlation matrix there are no

Means, Standard Deviations, Correlations, Reliabilities

Number

of items M SD 1. 2. 3. 4. 5. 6. 7.

1. Gender (0=Male, 1=Female) 1 0.17 0.37

2. Age 1 39.72 10.8 -0.250**

3. Voluntariness of use (0=Mandatory, 1=Voluntary) 1 0.54 0.5 0.036 -0.027

4. Performance Expectancy 3 2.79 1.19 -0.100 0.239* 0.099 (0.900) 5. Effort Expectancy 4 2.59 1.03 -0.112 0.277** 0.023 0.581** (0.932) 6. Social Influence 4 3.41 1.16 -0.072 0.115 0.027 0.453** 0.502** (0.824) 7. Behavioural Intention 3 2.92 1.22 -0.078 0.308** 0.122 0.657** 0.611** 0.512** (0.901) Note: N = 115. Reliabilities are reported along the diagonal.

* Correlation is significant at the .05 level (two-tailed). ** Correlation is significant at the .01 level (two-tailed).

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24 strong relationships among independent variables. This is supported by the tolerance and VIF values of the collinearity statistics. The lowest tolerance value is 0.349, which is not near the threshold of below 0.1 (Pallant, 2016). The highest VIF value is 2.862, which is also not near the threshold of above 10 (Pallant, 2016). Normality and linearity are checked by inspecting the normal probability plot of the regression standardised residual and the scatterplot of the standardised residuals (Pallant, 2016). After inspection of the plots normality and linearity for this dataset is ascertained. Several checks to determine possible outliers were done. No outliers were found, so no cases were excluded from the analysis. Further elucidation of these checks can be found in appendix C. After confirming the assumptions for regression analysis, the analysis was conducted. The results of the regression analysis are given in table 9.

Table 9: Results of regression analysis of whole model with moderating effects.

The model is statistically significant, F (13, 100) = 11.39; p < 0.001, and explains 59.7% of the variance in behavioural intention. All the predictors are significant with PE recording the highest β value (β = 0.564, p < 0,001), then SI (β = 0.365, p < 0.01) and the lowest β value for EE (β = 0.283, p < 0.05). In other words, if someone’s PE goes up with one, their BI will go up with 0.564. If someone’s SI

Behavioural Intention B SE β R R² Performance Expectancy 0.564*** 0.109 0.461 Effort Expectancy 0.283* 0.126 0.232 Social Influence 0.365** 0.124 0.298 Gender 0.155 0.235 0.047 Age 0.199* 0.087 0.163 Voluntariness of Use 0.110 0.163 0.045 PExGender -0.504 0.328 -0.120 PExAge -0.017 0.116 -0.013 EExGender 0.080 0.331 0.021 EExAge -0.036 0.143 -0.027 SIxGender 0.243 0.281 0.069 SIxAge 0.115 0.098 0.089 SIxVOL -0.421* 0.164 -0.229 Total 0.773 0.597*** *p < 0.05 **p < 0.01 ***p < 0.001

Results of Performance Expectancy. Effort expectancy and Social Influence as predictors for Behavioural Intention moderated by Gender, Age and Voluntariness of Use.

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25 goes up with one, their BI will go up with 0.365 and if someone’s EE goes up with one their BI goes up with 0.283. The β value of PE is not between 0.435 and 0.484, so hypothesis 1 can be rejected. The β value of EE is not between 0.165 and 0.199, so hypothesis 2 can also be rejected. The β value of SI is not between 0.482 and 0.660, so hypothesis 3 can also be rejected. All the predictors do have a positive effect on BI but the β values were not as high or as low as was expected. It can be concluded that military organisations characteristics do not influence the predictors of BI in the way that was suggested. The results of the analysis compared to the predicted values are presented in table 10. Table 10: Comparison of β values.

Age is found to have a significant positive effect on BI with β = 0.199, p < 0.05. In other words, if someone’s age goes up with one their BI will increase with 0.199. This is contrary to what was expected and what is theorised in the literature (Venkatesh et al., 2003). Only one of the expected moderating effects occurred. This is the effect of voluntariness of use on the relation between SI and BI with β = -0.421, p < 0.05. To interpret this relationship, it was probed and plotted in a graph, see figure 5. The result of probing the interaction is that the moderation is only significant in the mandatory setting (β = 0.365, p < 0.01). Meaning that in mandatory settings people perceive an effect of SI on BI.

Behavioural intention Hypothesised β value Found β value Difference Performance expectancy 0.435 < β > 0.484 0.564 +0.08 Effort expectancy 0.165 < β > 0.199 0.283 +0.084 Social influence 0.482 < β > 0.66 0.365 -0.117

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26 Figure 5: The moderation of Voluntariness of Use on the relation between Social Influence and

Behavioural Intention.

Looking at the hypotheses and the results of the analysis it can be stated that none of the hypotheses can be accepted, but a significant moderating effect of voluntariness of use on SI and BI was found. An assessment of the conceptual model is given in figure 6, where the insignificant relations are transparent.

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5. Discussion

5.1. Accomplishment of the research objective

The goal of this study was finding out to what extent factors derived from technology acceptance models influence the intention to adopt innovative technology within military organisations. To reach this goal the UTAUT was used. The UTAUT is an extensively researched model, which focusses on the acceptance and use of technology, but the UTAUT was never before used within a military organisation. This research contributes to the literature of technology adoption by studying a new organisation. In this way, it broadens and deepens the literature on technology adoption. This study hypothesised that certain military organisations characteristics influence the effect of the drivers of the UTAUT on behavioural intention to use a new technology. In the original development of the UTAUT, several moderators moderate the relations . In studies after the original these relations are rarely found to be significant. This research also investigated the moderation and the significance of these relations. In this way contributing to the understanding of these moderations. The research was conducted within the armed forces of the Netherlands.

First, the direct effects of performance expectancy, effort expectancy and social influence on behavioural intention were investigated. It was found that all relations were significant but did not meet the hypothesised β values. The relations were positive the same as in prior studies. From these direct effects, performance expectancy had the highest β value. According to the hypotheses, this is an unexpected result. According to previous research, this is an expected result. In prior studies, performance expectancy is often the factor with the strongest β value and is most often significant. Therefore, this research confirms the influential power of performance expectancy on behavioural intention. The reason why performance expectancy was higher than originally hypothesised could come from the fact that mostly air force personnel filled in the survey (63.9%). Air force personnel is highly specialised and thus more willing in adopting innovations (Damanpour, 1991; Kimberly & Evanisko, 1981), resulting in stronger performance expectancy than other factors.

Just the same as prior research, effort expectancy has the weakest β value. It was also found that effort expectancy was the most correlated with another variable which was performance expectancy,

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28 r = 0.581 p < 0.01. In the original technology acceptance model (Davis, 1989) it was stated that effort expectancy has a mediating effect through performance expectancy on behavioural intention, as well as a direct effect on behavioural intention. The weak effect from this study could come from the fact that effort expectancy still has a mediating effect through performance expectancy on behavioural intention. A reason why effort expectancy was not as low as was hypothesised could also be the highly specialised personnel of the air force. People who tend to be more specialised believe that is easier for them to work with a new application or system. In this way, the effect of standardisation and formalisation could be weakened. There is probably still an effect of the standardisation and formalisation because effort expectancy is the lowest scoring β value, but this is weakened by the highly specialised personnel of the air force.

One moderated relation is found to be significant in this research confirming the existence of this moderation. This is the effect of voluntariness of use on the relation between social influence and behavioural intention. Compared to previous studies this was an unexpected outcome. In previous researches, it is frequently found that moderation is often not significant and the specific moderation of voluntariness of use is only significant in four cases out of 12. The reason why this relation is found to be significant in a military organisation setting is probably the strong hierarchal structure. In a strong hierarchal structure, the opinion of higher management is seen as very important and often seen as leading. So, in a mandatory setting where use is obliged by management, people perceive the social influence as effect on their intention. The fact that in voluntary settings the relation is not significant points out that the comradery does not have the proposed effect within the social influence construct. Although often pointed out that the comradery within military organisations is a distinctive factor of this organisation, it is found that the effect is not present as was suggested. It could also mean that the comradery as a whole is not present. Overall, it can be said that the UTUATs predictive power is confirmed in a new organisation type with explaining 59.7% of the variance in behavioural intention.

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5.2. Theoretical and practical implications

This study contributes to further developing the knowledge about technology adoption. Specific knowledge about military organisations is obtained. With the adding of a new environment to the literature of technology acceptances, the theories are better empirically proven. Next to that, this study was one of the first studies investigating IT behavioural models from a business context within a military context, which could invite to do many more of this kind of researches.

With proving the significance of the model within military organisations, this model becomes usable in practice. Military organisations are government organisations and government organisations are known for their failure in implementing IT. With the UTAUT, military organisations can investigate before implementation what the intention on usage is. Although in this research the predictive power of intention on actual usage is not proven this relation is proven in many other studies (Davis, 1989; Venkatesh et al., 2003). Meaning that with knowing the intention, military organisations have an accurate insight in the possible failure or success of new IT implementation. The way the drivers work can be used to convince future users to actually wanting to use the new system. For example, with the highly specialised air force employees the organisation needs to make clear what the performance enhancements will be. The users will conclude for themselves if they are going to use the new system or application.

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6. Conclusion

Many companies rely on technological innovations and systems to achieve their goals. The implementation of new systems and innovations can only be successful when the users accept and adopt them. Often this is not the case and new technological systems do not become as successful as hoped and expected. To anticipate this problem many studies have been executed in order to find the reasons influencing the adoption of technological innovations. Within the field, several models are developed where most of them are based on the same drivers that influence technology adoption. These models and drivers are used in many different settings with many different technologies and users. Nevertheless, within a military organisation these models are scarcely studied. In this study, drivers influencing technology adoption are investigated in a military organisation context. The extent of influence of these drivers is searched. To do so the unified theory of acceptance and use of technology (UTAUT) and its drivers is used. A conceptual model based on the UTAUT is developed and based on military organisations characteristics effects of the drivers on behavioural intention are hypothesised.

The conceptual model and the hypotheses were tested through regression analysis, after gathering data through a survey within a military organisation. This approach was chosen because of many other researches who followed the same approach and because of the explanatory, statistical nature of the research question and hypotheses. To evaluate the research question, it can be stated that drivers from the UTAUT do influence the intention to adopt an innovative technology within military organisations, but they do not influence to the extent that was suggested in this study as can be seen in table 11.

Table 11: Comparison of β values.

Behavioural intention Hypothesised β value Found β value Difference Performance expectancy 0.435 < β > 0.484 0.564 +0.08 Effort expectancy 0.165 < β > 0.199 0.283 +0.084 Social influence 0.482 < β > 0.66 0.365 -0.117

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31 The model was proven to be significant and is able to explain 59.7% of the variance in behavioural intention. Making this model an applicable model for prediction of behavioural intention of adopting IT applications or systems within military organisations. Most moderated relation were not significant, as was found in previous research. Only voluntariness of use had a significant effect on the relation between social influence and behavioural intention, in such a way that in a mandatory setting the effect of social influence is present.

Although the predicted values were not found, the model and the constructs appear to be influenced by military organisations characteristics. In practice, the model is usable for predicting the intention to adopt a new innovative technology. This study contributes to the technology acceptance literature by adding a new organisational context in which technology acceptance is tested. Many limitations need to be considered in this research so no definite conclusions can be made, but the first step of investigating technology acceptance in a new organisation is made.

6.1. Limitations

There are limitations to this research on several different aspects. A longitudinal study would have been a better research method. The several factors would have been measured over time making sure the actual opinion about the drivers was measured. It would have been better if besides the behavioural intention to use, the actual use was also measured. Because of the chosen technology and the limited time frame this was not possible. The chosen application, a blockchain application, could also be a limitation. As said above actual usage was not possible to test because of the choice of application. Blockchain technology is perceived as highly technical, which means people believe that it is difficult to use or even understand. Blockchain technology is also strongly connected with cryptocurrency. People could have certain thoughts and feelings about cryptocurrency and these could influence the actual results of this study. Another limitation is the number of studies on which the predicted β values are based. A more comprehensive overview of results from other studies would have been a better base for predicting the β values. Not much literature on military organisations characteristics is available. Especially if these characteristics are linked to IT applications or

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32 innovations. In this study, the direction of military organisation as a bureaucracy is chosen to search for defining characteristics. The limitation is not that this was the wrong choice, but it is hard to define the amount of influence of these certain characteristics on the technology acceptance model. One could question if a military organisation is still a bureaucracy, or should it be seen as another organisational form.

6.2. Further research

To resolve issues encountered in this research, further research is needed. First, this study should be replicated with another innovation to exclude the possibility that results from the survey are biased because of the chosen technology. These results need to be compared to the results of this study to determine the actual influence of the hypothesised characteristics of military organisations. Next to another innovation, the way in which the β values are predicted needs to be investigated and it needs to be determined what the best way is to predict these values. This probably needs extensive research and is not easy to achieve. Another interesting finding, which needs further research, is the way effort expectancy works in the model. In some models, effort expectancy has a mediating effect on performance expectancy and behavioural intention, but not in the UTAUT. The correlation found between performance expectancy and effort expectancy and the lower β value implies that the mediating effect is present in the current model. If this appears to be true, the model and the way people need to interpreted the model changes. The construct of social influence within military organisation is an interesting topic for future studies. It was hypothesised that the comradery within the organisation would have some kind of effect on the social influence construct. From this research, it appears that this effect is not present. This could mean that the comradery is not present in military organisations, although this is a specific trait of military organisations. To truly understand comradery within military organisations more research is needed. It would be particularly interesting to investigate the difference between peacetime and wartime situations. Maybe a difference can be observed or maybe there is also less comradery in wartime situations. How this could affect technology adoption, if it does, could be an interesting angle of approach.

Technology adoption within military organisations