The Relationship between Organizational Climates and Individual Readiness for Change within the Norwegian Police Service
Katrine Allen Egseth
Master Thesis at the Department of Psychology UNIVERSITY OF OSLO
June 2021
Ó Katrine Allen Egseth
2021
The Relationship between Organizational Climates and Individual Readiness for Change within the Norwegian Police Service
Katrine Allen Egseth
http://duo.uio.no
Acknowledgements
This study was a part of a research project between the Department of Research at the Norwegian Police University College and the Department of Psychology at the University of Oslo. I would like to thank both departments for providing the opportunity to take part in the project. Writing this thesis has been a tremendously useful learning experience for me.
Completion of this thesis would not have been possible without the support and guidance of others. There are several people I would like to thank and express my gratitude to. First of all, I would like to thank my supervisor Roald Bjørklund for his guidance on this paper, for sharing his extensive knowledge and for the interesting discussions we had. Furthermore, I would like to thank Police Inspector Trond Myklebust for his support and for being a facilitator for this research project. I would also like to thank Knut Inge Fostervold and Marius Hafstad for the very useful SEM-guidance they provided. It has been a pleasure to be a part of the team.
Additionally, I would like to thank my fellow master students, Nora Haartveit, Hanna Løkken and Martin Lunde, for the best support and cooperation I could ever have wished for.
Within the group we have been able to have useful academic discussions whilst at the same time providing encouragement for one another, not the least, periods of light relief and laughter. Lastly, I would like to thank my friends and family for their support through this process.
Abstract Author: Katrine Egseth
Title: The Relationship between Organizational Climates and Individual Readiness for Change within the Norwegian Police Service
Supervisor: Professor Roald Bjørklund, Department of Psychology, University of Oslo Individual readiness for change is considered as an important driver for organizational change success. Understanding the factors that facilitate individual readiness for change is valuable information for organizations. There is little research on how organizational climate might modulate levels of individual readiness for change. The aim of this study was therefore to explore the relationship between multiple organizational climates and individual readiness for change. More specifically, this thesis examined the direct effect from the molar climates internal process and rational goal on individual readiness for change, and the indirect effects through the facet-specific climates internal and external knowledge sharing. Internal process climate and rational goal climate were conceptualized by Quinn and Rohrbaugh’s (1983) Competing Values Framework. Internal and external knowledge sharing climates stems from Koritzinsky (2015) extension of Patterson et al’s (2005) Organizational Climate Measure (OCM). This study was a part of a research project between the Department of Research at the Norwegian Police University College and the Department of Psychology at the University of Oslo. The data material was collected through a self-report survey that was answered by employees in the Norwegian police service (n=1417). The results from this study indicated that the molar climate rational goal is important for developing individual readiness for change. A small part of the relationship between rational goal and individual readiness for change is explained by the facet-specific climate external knowledge sharing. In addition, some of the effect on individual readiness for change is explained by the molar climate internal process. This is because internal process contributes to laying the foundation for the facet-specific external knowledge sharing climate, whilst external knowledge sharing has again a small effect on individual readiness for change. This study contributes to the research field by giving a clearer and more holistic picture as to how narrow and broad climates modulate the level of individual readiness for change. This thesis also discusses the practical and theoretical implications.
Keywords: Organizational climate; Internal Process Climate; Rational Goal Climate;
Internal and External Knowledge Sharing; Individual Readiness for Change; Police Organization
Contents
Introduction ... 1
Theory ... 3
Attitudes toward organizational change ... 3
Individual readiness for change ... 6
Antecedents of individual readiness for change ... 6
Organizational context ... 7
Organizational climate ... 8
Molar climate and facet-specific climate ... 9
The Competing Values Framework ... 10
A two- molar climate focus ... 12
Internal process climate as predictor ... 12
Rational goal climate as predictor ... 14
Learning in organizations ... 15
Organizational learning ... 16
A learning organization ... 16
Knowledge sharing ... 17
Internal and external knowledge sharing as facet-specific climates ... 17
Internal and external knowledge sharing as mediators ... 18
Method ... 21
Research project ... 21
Ethical considerations ... 21
Data gathering ... 21
Sample ... 21
Measures ... 21
Individual Readiness for Change ... 22
Internal Process and Rational goal ... 22
Internal and external knowledge sharing ... 22
Analysis ... 23
Preliminary analysis ... 23
Structural Equation Modeling ... 23
Results ... 25
Measurement models- goodness of fit ... 26
Structural model- goodness of fit ... 31
Running the partially mediated model ... 33
Discussion ... 37
Theoretical Implications ... 38
Practical implications ... 40
Limitations ... 41
Further research ... 42
Conclusion ... 43
References ... 44
Appendices ... 58
Appendices A: Competing Values Framework- Internal process climate ... 58
Appendices B: Competing Values Framework- Rational goal climate ... 59
Appendices C: Internal knowledge sharing climate ... 60
Appendices D: External knowledge sharing climate ... 62
Appendices E: Individual Readiness for Change Scale ... 64
Introduction
The Covid-19 pandemic has led to rapid organizational changes (Spicer, 2020).
Colleagues previously communicated at their workplace by the side of the coffee machine.
Now they are sitting at home talking to a computer screen. This was an unplanned crisis and organizations have been forced to adapt to this rather extraordinary situation (Knowles &
Saxberg, 1988). However, organizational changes exist in many forms (Burnes, 2005). Many change initiatives are made with the intent of improving human effectiveness and the quality of service within the organization (Burnes, 2005; Hood, 1991). This with a more planned approach to organizational change. As opposed to the Covid-19 changes, the Norwegian police service is facing an extensive planned change (“Nærpolitireformen”) (Regjeringen, 2014-2015).
New technology and more globalization have given rise to rapid changes in criminality (Politiet, 2018; Yilmaz, 2013). Crime is getting more complex, digital and borderless, so the police are facing new demands and challenges (Politiet, 2018; Politiet, 2019; Yilmaz, 2013).
In addition to this, questions regarding the Norwegian police efficiency, culture and
organizational structure, prompted the introduction of the new police reform (Christensen et al., 2018; Politiet, 2018). The goal of this reform was to make the police better equipped to respond to the new demands, by improving collaboration between units and districts (Regjeringen, 2014-2015). In addition, to develop a more knowledge-based police
organization, by continuously improving their competence and performance (Christensen et al., 2018).
According to the change literature, organizational change efforts have a low success rate (Burnes & Jackson, 2011; Choi & Ruona, 2011). Multiple scholars have argued that change implementations fail because leaders underestimate, or neglect, the important role individuals play in a change process (Armenakis et al., 1993; Choi, 2011; George & Jones, 2001). Organizations that desire a successful change are wise to recognize the importance of employees’ attitudes towards change (Greenhalgh et al., 2004; Isabella, 1990; Löwstedt, 1993). Attitudes towards organizational change can be seen as the employees overall positive or negative evaluation toward the change implementation (Choi & Ruona, 2013; Lines, 2005).
This again influences the employees behavioural support for the specific change (Cunningham et al., 2002; Jones et al., 2005; Meyer et al., 2007; Weeks et al., 2004).
Scholars have shown that individual readiness for change is especially critical for organizational change success (Choi & Ruona, 2011; Goksoy, 2012; Haffar et al., 2014; Holt
et al., 2010; Olafsen et al., 2020). Understanding the antecedents of individual readiness for change is valuable information for organizations that are planning for or facing change (Choi
& Ruona, 2011). The organizational context is seen as an important contributor to the specific change attitude (Eby et al., 2000; Holt, Armenakis, Feild, et al., 2007). There is, on the other hand, little research on organizational climate as an antecedent (Kirrane et al., 2017; Oreg et al., 2011). The aim of this study is therefore to add to this research by studying the
relationship between organizational climate and individual readiness for change.
The relationship between climate factors and individual readiness for change is thought to be complex (Kirrane et al., 2017; Oreg et al., 2011). It has been suggested that research should include mediating variables in the relationship between climate variables and individual readiness for change. There is also a need for research that examines multiple climate types simultaneously, both broader and more general climates, named molar climates, and more specific and strategic climates, named facet-specific climates (Kuenzi, 2008). Taken together, this thesis therefore examines the direct effect from the molar climates internal process and rational goal on individual readiness for change, and the indirect effects through the facet-specific climates internal and external knowledge sharing. This thesis is based on bandwidth-fidelity theory, that implies that a general molar climate has an effect on a specific outcome (individual readiness for change in this case) when mediated for a facet-specific climate (Kuenzi, 2008). This study adds to the research by establishing a more holistic understanding of the relationship between organizational climate and individual readiness for change. This is achieved by including mediator variables and multiple climates (both narrow and broad) in the structural model.
Internal process (IP) climate and rational goal (RG) climate were defined by the Competing Values framework (Quinn & Rohrbaugh, 1983). The framework consists of four different molar organizational climates (Human relations, Open systems, Internal process and Rational Goal) (Kuenzi, 2008; Quinn & Rohrbaugh, 1983). There appears to be a rather insufficient focus on the rigid climates, internal process and rational goal, in the research field. This thesis therefore contributes to fill this research gap. Furthermore, it is thought that the rigid structure of these two climate types can be fruitful in the forefront and during an organizational change. This is because organizational changes can be related to uncertainty and chaos (Abrahamson, 2000; Bordia et al., 2004). Therefore, employees could need some elements of predictability, stability and order (characteristics of IP and RG), to get some overall control over the situation (Burnes, 2005; Caldwell, 2013; Cameron & Quinn, 2011;
Lewis, 1994; MacIntosh & MacLean, 2001; Vakola, 2014). Having some organizational
structure can make employees more ready for change. Furthermore, internal and external knowledge sharing climates have been proposed as potential antecedents for individual readiness for change (Armenakis et al., 1993; Choi & Ruona, 2011; Vakola, 2014; Watkins &
Marsick, 1993). This because a knowledge sharing climate is closely related to a context of learning, information and communication (Jackson et al., 2006; Spinello, 2000; Van Den Hooff & De Ridder, 2004; Yang, 2007). These are again elements that are important in making employees more ready for change (Armenakis et al., 1993; Choi & Ruona, 2011;
Vakola, 2014; Watkins & Marsick, 1993). It is also believed that internal process climate and rational goal climate can help to lay the foundation for internal and external knowledge sharing climates (Kuenzi, 2008; Kuenzi & Schminke, 2009; Schneider et al., 2013). This is because preliminary evidence suggests that molar climates can work as a basis for facet- specific climates (Kuenzi, 2008; Kuenzi & Schminke, 2009; Schneider et al., 2013).
In the following part of this thesis, there will be presented a theoretical foundation for each of the constructs, individual readiness for change, internal process, rational goal and internal and external knowledge sharing. Based on empirical climate research, a structural model, comprising twelve hypotheses is presented and tested. To examine these hypotheses structural equation modelling was applied. This on a Norwegian police sample (n=1417) acquired from a cross-sectional survey.
Theory Attitudes toward organizational change
The study of organizational change is a popular topic in the literature (Bouckenooghe, 2010). Kurt Lewin’s three-step model (unfreeze-move-refreeze) is regarded by many scholars as the fundamental model for managing planned change (Cummings et al., 2016; Kaminski, 2011). On the other hand, his model has been criticised for being overly simplistic. Despite this, his theory is regarded as important and change models are frequently based on his work (Cummings et al., 2016; Kaminski, 2011).
According to Lewin’s theory there is one group pushing for change whilst another group is striving for the maintenance of the current state (status quo) (Choi & Ruona, 2013).
When there is an equal number of people pushing for change and people striving to maintain the status quo, there is a stable state called “quasi-stationary equilibrium”. In this state behaviour stays the same. The first step to change this behaviour, is to unfreeze (destabilize) the equilibrium, by creating an increase in the number of people pushing for change and a
reduction in the people wanting to maintain the status quo. The second step (moving-phase) encompasses the process of change. This involves changing the current state to the more desired and improved state (Hussain et al., 2018; Kaminski, 2011). New structures and processes are implemented (Kaminski, 2011). The third and final step (refreezing) is to make the new structure a permanent and accepted part of the organization. Without this last step, it is easy to regress back into the old behaviour.
The failure of change projects is often related to an insufficient unfreezing process before implementing the change (Choi & Ruona, 2011; Kotter, 1996; Lewin, 1997; Schein, 1987; Schein, 1999). The unfreezing-step involves affecting employee’s beliefs and attitudes towards the organizational change and making them recognize the change as necessary (Choi
& Ruona, 2011). Making employees recognize the need for the change can increase the number of employees pushing for change and reduce the number of employees wishing to maintain the status quo (Choi & Ruona, 2013; Choi & Ruona, 2011).
Unsuccessful change implementations are often related to employees’ attitudes toward change (Jones et al., 2005). Attitudes towards organizational change can be defined as the employees overall positive or negative evaluation of the change implementation in the organization (Choi & Ruona, 2013; Lines, 2005). There are multiple constructs in the
literature representing attitudes towards change (Bouckenooghe, 2010; Hafstad, 2020). They are mainly divided in favourable and unfavourable attitudes. In the unfreezing step, it is recognized that there can be unfavourable attitudes, like cynicism about organizational change and resistance to change, and favourable attitudes like commitment to change, openness to change and readiness for change (Bouckenooghe, 2010; Choi, 2011; Choi & Ruona, 2013).
Before the 1990’s research on attitudes towards change had a negative-orientated mindset (Bouckenooghe, 2010). Traditionally, the focus of attitudes towards change has been on resistance towards change (Choi & Ruona, 2011). There are multiple definitions regarding the construct in the research field, most authors providing their own definition in their articles (Bouckenooghe, 2010). Research has frequently considered resistance to change as
employees’ belief that the change is unnecessary, that they have negative emotions regarding change and that their behaviour hinders successful change (Bouckenooghe, 2010; Oreg, 2006;
Peng et al., 2020; Piderit, 2000). Another unfavourable attitude is cynicism about organizational change, which is a factor that can contribute to resistance for change
(Bouckenooghe, 2010; Stanley et al., 2005). There are some differences in the definitions of cynicism. In the definitions there is a general agreement that cynicism involves employees having a pessimistic or negative view regarding the potential success of change
(Bouckenooghe, 2010). Research focuses on the two unfavourable attitudes as an obstacle to organizational change and how critical it is to overcome the problem of resistance and cynicism (Bouckenooghe, 2010; Choi & Ruona, 2013).
Successful organizational changes are not just dependent on overcoming resistance or cynicism but also in facilitating employee’s enthusiasm and support for the change
(Bouckenooghe, 2010; Cameron et al., 2003; Piderit, 2000). It is important to identify elements that facilitate, enable and motivate employee’s openness and readiness for change (Bouckenooghe, 2010). There are different favourable attitudes in the research field, including openness to change, commitment to change and readiness for change. Openness to change has been described as the willingness to support change initiatives and having a belief in a
positive outcome of the change (Bouckenooghe, 2010; Wanberg & Banas, 2000).
Commitment to change has been described as employees having a mindset that binds the individual to actions that are crucial for a change to be successful (Hafstad, 2020; Herscovitch
& Meyer, 2002).
Of the three favourable attitudes, readiness for change is the most comprehensive construct, with a strong basis in theory, offering a framework and has a strong consensus regarding its content (Bouckenooghe, 2010; Choi & Ruona, 2011). The construct readiness for change has been defined as “…the cognitive precursor to the behaviours of either resistance to, or support for, a change effort” (Armenakis et al., 1993, p.681). Readiness for change is a comprehensive concept that captures employee’s thoughts about the change- specific efficacy, appropriateness of the change, management support regarding the change and personal benefit from the change implementation (Armenakis et al., 1993; Choi & Ruona, 2011; Eby et al., 2000; Holt, Armenakis, Harris, et al., 2007). Readiness for change can be examined at an individual, group and organizational level, and the different analyse levels have different antecedents and consequences (Rafferty et al., 2013). A limitation in the research field is a lack of differentiation between for example individual and organizational readiness for change (Vakola, 2014).
Individual readiness for change is seen as critical for change implementations to end in success (Choi & Ruona, 2011; Goksoy, 2012; Haffar et al., 2014; Holt et al., 2010; Olafsen et al., 2020). Therefore, this study is interested in studying the concept individual readiness for change.
Individual readiness for change
Individual readiness for change has been defined in the literature with marginal differences (Rafferty et al., 2013). Armenakis et al. (1993) has defined the construct as
“organizational members beliefs, attitudes, and intentions regarding the extent to which changes are needed and the organization`s capacity to successfully make those changes”
(1993, p.68). Jansen (2000) and Eby et al. (2000) also defined individual readiness for change as the necessity for the change and the organizational capacity for implementing the change successfully. Hence, Jones et al. (2005) defined the concept as employees acknowledging the need for the specific change but also included if the change would have a positive impact on themselves. Despite the small differences in the definitions, authors agree that individual readiness for change contains employees thoughts about the need for the change, their individual and organizations capacity for the change and the benefits resulted by the change both for the employee and the organization (Holt, Armenakis, Harris, et al., 2007).
Because of the importance of individual readiness for change in a change implementation it is valuable information to understand factors that facilitate individual readiness for change (Choi & Ruona, 2011; Goksoy, 2012; Haffar et al., 2014; Holt et al., 2010). Understanding the antecedents can guide leaders to create the right change strategies (Goksoy, 2012).
Antecedents of individual readiness for change
The antecedents of individual readiness for change can be classified into three major categories, content, process and context (Bouckenooghe, 2010). The first category that can affect individual readiness for change, is the content of the change, which is related to employee’s perception of what has been changed in their workplace (Bouckenooghe, 2010;
Rafferty et al., 2013). The content of the change is often related to administrative, procedural, technological or structural elements of the organization (Holt, Armenakis, Feild, et al., 2007).
The second category (process) is how the change process is handled (Bouckenooghe, 2010).
Elements in the change process are quality of change management, employee participation and effective communication which are elements that affects readiness for change (Holt, Armenakis, Feild, et al., 2007; Reichers et al., 1997).
In a change process the change message and communication is seen as especially important (Armenakis et al., 1993; Vakola, 2014). Employees have to be fully informed about the background for the change, the progress and eventual problems that can arise (Reichers et al., 1997). To communicate the need for the specific change can make employees more ready for the change (Armenakis et al., 1993). This can be done by communicating the discrepancy
between the current and the desired end-state (Katz & Kahn, 1978). One can illustrate results in the internal context (e.g. unacceptable product quality) and the changes in the external context (social, economic, political and competitive environments) (Armenakis & Harris, 2002; Pettigrew, 1987). To make employees ready for change one must not only communicate the discrepancy, but also that employees are capable of handling the change (Armenakis et al., 1993). Employees will only be motivated to change if they have the confidence that they can succeed (Armenakis & Harris, 2002). It can also be advantageous to communicate the positive outcome the change will have on the individual. Individuals that recognise the change will have a positive outcome for themselves are more likely to support the change (Oreg et al., 2011).
The last antecedent category is the context, which is related to what type of work environment the change occurs in (Bouckenooghe, 2010). The organizational context is seen as an important contributor for individual readiness for change (Eby et al., 2000; Holt, Armenakis, Feild, et al., 2007). However, there is little research on how organizational climate might affect levels of readiness for change (Kirrane et al., 2017; Oreg et al., 2011).
Therefore, this thesis is going to assess organizational climates effect on individual readiness for change.
Organizational context
Organizational climate and organizational culture are the most common descriptors for work environment (Sleutel, 2000). The two constructs have overlapping meaning, both
constructs rest upon employees shared understanding of certain aspects of the organizational context (Ostroff et al., 2012). Their understanding of the organizational context is formed through interaction with other employees (Kuenzi & Schminke, 2009). Thus, organizational culture and climate have been challenging to define, conceptualize and measure (Kuenzi, 2008; Kuenzi & Schminke, 2009; Schneider et al., 2013). There is no agreement as to what culture is (Schneider et al., 2013). In addition, defining organizational climate is not an easy task (Kuenzi & Schminke, 2009). Schneider (1990) described it as trying to “nail Jell-O to the wall” (p. 1).
Kuenzi (2008) has stated that climate and culture are two distinctive constructs.
It is possible to separate the constructs from each other, as organizational culture can be defined as the shared meanings of beliefs, assumptions, core values, symbols and underlying ideologies (Denison, 1996; Schein, 2010). Organizational culture can be conceptualized as the deep structures of an organization, which often are unconscious, taken for granted and hidden from the employees (Denison, 1996; Kuenzi & Schminke, 2009; Schein, 2010). The construct
has strong roots in history and is difficult to manipulate (Denison, 1996). Organizational climate has most commonly been defined as the employees shared perception of the policies, practices, and procedures and behaviours that are rewarded, supported and expected (Kuenzi, 2008; Schneider et al., 1996; Schneider et al., 2013; Schneider & Reichers, 1983). Climate exists on the surface level of an organization and is more salient and visible for individuals as compared to culture which is deeper and less visible (Denison, 1996; Kuenzi, 2008; Ostroff &
Schulte, 2014). Climate is an expression of the underlying values of the culture (Beus et al., 2020). Although the two constructs share similar features, climate can be described as the perception of what happens and culture explains why it happens (Beus et al., 2020; Ostroff et al., 2012).
Organizational climate has been more widely studied than organizational culture (Beus et al., 2020; Hafstad, 2020). The reason being that climate is more visible in an organization.
This makes the construct easier to measure and manipulate in the unfreezing stage.
Organizational climate
Organizational climate research has a long history, and it stems from Lewinian psychology (Kuenzi & Schminke, 2009; Schneider, 1990). There is an inconsistent use of organizational climate in the literature, which has led to confusion for scholars regarding the content of the construct (Kuenzi & Schminke, 2009).
The definition of organizational climate used in this thesis (specified in previous section), identifies the construct as a perceptual phenomenon rather than an objective characteristic (Kuenzi & Schminke, 2009; Schneider & Reichers, 1983). This means the organizational climate is not actual characteristics of an organization, but the employee’s interpretation of their work environment (Carr et al., 2003; Kuenzi, 2008). Furthermore, organizational climate is seen as a collective phenomenon, meaning that climate is the employees shared perception of their work context (Kuenzi & Schminke, 2009; Schneider et al., 2013). The individual employees’ perceptions of their work environment reflect the psychological climate (James et al., 2008; Kuenzi, 2008; Schneider et al., 2013). When these individual perceptions are aggregated to an appropriate level within the organization it
represents organizational climate (James et al., 2008; Kuenzi, 2008; Schneider et al., 2013). A meaningful measure of organizational climate is when employees evaluate their environment with some degree of agreement (Kuenzi, 2008).
Molar climate and facet-specific climate
Climate research has typically either focused on molar climates or facet-specific climates (Kuenzi, 2008). Molar-climate can be seen as the general work environment. Kuenzi (2008) defined molar climate as the shared perception of the priorities in the broad
environment. This relates to the general focus of the social system. Facet-specific climate differs from molar-climate, by being related to a specific and narrow part of an organizational context. Some examples of facet-specific climates are safety, justice and service climate.
Research was initially dominated by the molar climate perspective. Researchers were ambitious, by trying to understand the entire organizational context (Kuenzi, 2008; Kuenzi &
Schminke, 2009). This was somewhat challenging. This initial research also had difficulties with definition, theory and method. To try to deal with these difficulties, climate researchers switched their focus to facet-specific climates (Kuenzi, 2008). Previous challenges from molar climate research was the lack of caution when matching predictor with outcome (Schneider et al., 2013). Schneider (1975) was the first to recognize the concern about the bandwidth-fidelity issue. Bandwidth has been explained as the amount of information or complexity in a construct, this ranging from narrow to broad (Cronbach & Gleser, 1957). The theory indicates that that prediction is enhanced, when the bandwidth of the predictor is matched with the bandwidth of the outcome (Kuenzi, 2008; Schneider, 1975). The criterion variable should guide the choice of predictor variable. Bandwidth-fidelity theory suggests that if researchers are interested in predicting a specific outcome, for example safe behaviour, then it is wisest to focus on a predictor of a specific climate, for example climate for safety (Carr et al., 2003; Kuenzi, 2008).
Both molar and facet-specific climate research have given a better understanding regarding organizational climate and how organizations function (Kuenzi & Schminke, 2009).
However, the two research fields exist distinct from each other. Organizations are made up of several climate types, with several molar and facet-specific climates existing at the same time (Kuenzi, 2008; Kuenzi & Schminke, 2009). Kuenzi (2008) has suggested that future research should focus on studying multiple climates at the same time and examine the relationship between different molar climate types and facet-specific climates. It has been stated that molar climates can work as an antecedent for a facet specific climate. An integration of both molar and facet-specific climate research may give a more informative picture of the
organizational context (Kuenzi & Schminke, 2009).
To explore the relationship between molar climates and facet-specific climates, researchers has used bandwidth-fidelity theory as a framework (Carr et al., 2003; Kuenzi,
2008). Bandwidth-fidelity theory suggests that if researchers are interested in predicting a broader outcome, for example job performance, then a molar climate should be used as a predictor (Carr et al., 2003; Kuenzi, 2008). Nevertheless, the theory implies that molar climates can be indirectly related to specific outcomes (Kuenzi, 2008). This is achieved by facet-specific climates mediating the relationship between molar climates and specific outcomes. Kuenzi (2008) has shown this relationship in her doctoral dissertation.
This master thesis is contributing to the literature by studying multiple climate types. It studies both molar climates and facet-specific climates simultaneously, which are matched based on bandwidth-fidelity theory. To measure molar climates one can use the Competing Values Framework (CVF) (Kuenzi, 2008; Quinn & Rohrbaugh, 1983). To measure the molar climates, internal process and rational goal, the framework was used.
The Competing Values Framework
The competing values framework was originally developed as a framework for
organizational effectiveness (Cameron, 2009; Cameron & Quinn, 2011; Quinn & Rohrbaugh, 1983). The framework has later been used in a variety of areas and has, for example, been used as a tool for defining and categorizing organizational context. CVF has frequently been applied in culture research (Chatman & O’Reilly, 2016; Ostroff et al., 2012). The framework consists of underlying strategically organizational values (Beus et al., 2020; Quinn &
Rohrbaugh, 1983; Schneider et al., 2011). When defining the culture in an organization, employees directly evaluate the underlying organizational values from the framework.
Since climate is an expression of underlying organizational values, this framework can also be applied in measuring organizational climate (Beus et al., 2020; Kuenzi, 2008; Patterson et al., 2005). Climate studies tend to assess employees’ perceptions of the practical and visible expression of the underlying organizational values (Beus et al., 2020; Schneider et al., 2011).
It is therefore credible to use this framework for this study, when measuring the two molar climates, internal process and rational goal (Beus et al., 2020; Kuenzi, 2008). The framework is also well accepted and has been found to have face and empirical validity (Cameron &
Quinn, 2011).
The competing values framework consists of two major dimensions, structure
(flexibility versus rigid) and focus (internal versus external) (Cameron & Quinn, 2011; Quinn
& Rohrbaugh, 1983). The crossing of these two dimensions, makes up four possible molar climates, human relations (HR), open systems (OS), internal process (IP) and rational goal (RG) (this shown in Figure 1.)(Beus et al., 2020; Cameron & Quinn, 2011). Each quadrant has its distinct values, beliefs, means (different processes) and ends (final outcomes) which
makes up four unique climates (Cameron & Quinn, 2011; Hartnell et al., 2011; Quinn &
Rohrbaugh, 1983; Schneider et al., 2011). The first dimension is called structure (Cameron &
Quinn, 2011). On the flexibility side of the structure dimension fall HR and OS which are adaptable and dynamic climates. On the rigid side of the dimension falls IP and RG which are climates that value stability, order and predictability. The second dimension is called focus.
On the internal side of the dimension fall HR and IP which value integration and unity. On the external side of the dimension falls OS and RG which focus on differentiation, competition and the external changing environment (Cameron & Quinn, 2011; Quinn & Rohrbaugh, 1983).
Figure 1
The Competing Values Framework
Note. The figure portrays the four molar climates in the competing values framework. From “The competing values framework: Understanding the impact of organizational culture on the quality of work life”, by E.A.
Goodman and B.D. Gifford, 2001, Organization Development Journal, 19(3), p.58. Copyright 2021 by Organization Development Journal.
Original research on CVF used an ipsative scale, because it was believed that the four climate types were competing (competing values), and excluding each other out (Cameron &
Quinn, 2011; Quinn & Rohrbaugh, 1983). Later it was pointed out that all four climate types could exist simultaneously in an organization, but with different strengths (Cameron & Quinn, 2011; Koritzinsky, 2015; Kuenzi, 2008; Patterson et al., 2005). Recent research has therefore used a normative scale where respondents are allowed to freely evaluate and rate the different climate types (Cameron & Quinn, 2011; Koritzinsky, 2015; Kuenzi, 2008; Patterson et al., 2005).
The belief that the climate types can coexist and the observed high correlation between the four climates, have made scholars investigate if the four climates could represent a
second-order factor (Cameron & Quinn, 2011; Kuenzi, 2008; Patterson et al., 2005). This means that the four climates represent one general factor. Kuenzi (2008) stated that there is no support for the climates representing a second-order factor. The two climates, internal process and rational goal, have also been acknowledged as especially similar conceptually (Kuenzi, 2008). Hartnell et al. (2019) investigated if internal process and rational goal shared a mechanistic structure. There was no support for this hypothesis. Taken together, there is strong support for the four climate types being distinct from each other (Kuenzi, 2008).
A two- molar climate focus
Based on the bandwidth-fidelity theory and the strong support for the four climate types being distinct from each other (Kuenzi, 2008), this thesis is including internal process and rational goal as predictors. The rationale behind choosing these two climate types, is because of an insufficient focus in the research field on the rigid climates (in the CVF).
Internal process climate has typically been included in studies when authors examine the whole framework (CVF), with a rather small focus on internal process climate (Kværne, 2018). To the authorsknowledge internal process and rational goal have not been seen studied together (as predictors), without including the whole framework, and with individual
readiness for change as criterion variable. Both climates have a rigid structure, although they are different in their organizational focus, internal process having an internal focus and rational goal having an external focus (Cameron & Quinn, 2011).
Internal process climate as predictor
Internal process falls within the dimension directions of rigid structure and internal focus (Quinn & Rohrbaugh, 1983). Workplaces having an internal process climate are governed by procedures, rules, routines, formalization and structure (Cameron & Quinn,
2011; Hartnell et al., 2019). Information management and communication are central aspects of this climate (Quinn & Rohrbaugh, 1983). Work situations with distributed and coordinated information will get supported, which again will provide employees with a sense of security and continuity (Quinn & Rohrbaugh, 1983). There is also a belief that employees meet
expectations when they have clear roles (Hartnell et al., 2011). Internal process has an internal focus, with an inward and micro focus on the employees, rather than external conditions of the environment (Cameron & Quinn, 2011; Quinn & Rohrbaugh, 1983; Yu & Wu, 2009). The long-term effects from internal process climate is stability, predictability, control and
efficiency (Cameron & Quinn, 2011; Quinn & Rohrbaugh, 1983).
In an unfreezing stage, it is believed that an internal process climate could increase levels of individual readiness for change. This is because organizational changes can be related to chaos, uncertainty and employees feeling a lack of control (Abrahamson, 2000;
Bordia et al., 2004). This leads to psychological strain and stress for the employees (Bordia et al., 2004). Therefore, employees will appreciate some stability, rules, procedures and
formality in the chaotic situation to feel some control in the process (Burnes, 2005; Lewis, 1994; MacIntosh & MacLean, 2001). This control can make employees more ready for change.
The logic of appreciating some stability in a change implementation is grounded in the complexity theory (which is an umbrella label for a number of theories) (Burnes, 2005;
Lewis, 1994). Organizations have three different zones, the stable, the complex and the chaotic. In the stable zone, no learning occurs because rules and procedures are followed, and no changes are occurring. In the chaos zone, also no learning occurs because there is a large amount of flexibility and to many changes are occurring. If too many changes are occurring and there is excessive flexibility the system tips into chaos. In the complex zone, which is the preferred zone, there is maximum potential for growth and learning. This is because there is an optimal amount off rules, procedures and changes. Since the Norwegian police are going through an extensive change, it could be rational for the employees to desire some procedures and rules rather than flexibility. This will give some control and prevent the change from tipping the system into chaos.
As previously mentioned, in the section regarding the antecedents of individual readiness for change, communication and information regarding the change are important contributors for making employees more ready for change (Armenakis et al., 1993; Vakola, 2014). Organizations with internal process climates are more likely to communicate and give employees adequate information in the unfreezing and change process, because
communication and information lays in the values of the climate (Quinn & Rohrbaugh, 1983).
This again will make employees more ready for change (Armenakis et al., 1993; Vakola, 2014).
Kværne (2018) found that internal process had a direct effect on individual readiness for change. It has been argued that internal process has an effect on change readiness in certain organizational contexts, contexts resembling police settings (Burnes, 2009).
Having a climate which includes elements like procedures, structure, importance of communication and information can give the basis for employees being more ready for change (Lewis, 1994; Vakola, 2014). The following hypothesis is therefore made:
H1a: Internal process climate has a direct positive effect on individual readiness for change
Rational goal climate as predictor
Rational goal falls within the dimension directions of rigid structure and external focus (Quinn & Rohrbaugh, 1983). The climate has an external focus, by following the changing trend in the external environment (Cameron & Quinn, 2011; Cameron et al., 2006; Kuenzi, 2008). Organizations with this climate type focus on external demands and produce outputs valued by environmental sectors. Rational goal climate focuses on competition, competence and improvement in quantity and quality of services (Beus et al., 2020; Cameron & Quinn, 2011; Hartnell et al., 2011). There is also a strategically future focus, with attention to planning, clear goals and emphasis on achievement (Hartnell et al., 2011; Quinn &
Rohrbaugh, 1983). Communication is also a central aspect of the climate (Hartnell et al., 2011). Rational goal climate leads to productivity, efficiency, results and product quantity and quality (Cameron & Quinn, 2011; Hartnell et al., 2011; Quinn & Rohrbaugh, 1983).
In an unfreezing stage, it is believed that a rational goal climate could increase levels of individual readiness for change. This will be elaborated in more detail.
As previously mentioned, communication and information regarding the change are important contributors for making employees more ready for change (Armenakis et al., 1993;
Vakola, 2014). Organizations with rational goal climates are more likely to communicate and give employees adequate information in the unfreezing and change process, because
communication lies in the values of the climate (Hartnell et al., 2011). This again will make employees more ready for change (Armenakis et al., 1993; Vakola, 2014).
To make employees more ready for change as also previously mentioned is to
communicate the need for the specific change (Armenakis & Harris, 2002; Armenakis et al.,
1993). To show the need for change, on can communicate the current and the desired end- state, this can be illustrated by communicating results in the internal context (e.g.,
unacceptable product quality) and the changes in the external context (social, economic, political and competitive environments) (Armenakis & Harris, 2002; Katz & Kahn, 1978;
Pettigrew, 1987). Rational goal climate has an external focus (Quinn & Rohrbaugh, 1983).
This makes it more likely for employees working in an organization with rational goal climate to know the current external status (Kuenzi, 2008; Paulsen, 2019), which again can make employees more ready for change, because they are fully aware of the need for the change (Armenakis & Harris, 2002; Armenakis et al., 1993).
Rafferty et al. (2013) has argued that a strong future focus and strategic and structural characteristics are antecedents for readiness for change. When a change is planned, the novelty and uncertainty of the change is reduced (Rafferty & Griffin, 2006). It can also be assumed that goal setting may reduce uncertainty related to organizational change (Nilsen, 2018). Making implementations more predictable is stated to affect employee’s readiness for change (Caldwell, 2013). Important elements in rational goal climate are planning and goal setting (Drolsum, 2019; Nilsen, 2018; Quinn & Rohrbaugh, 1983; Rafferty & Griffin, 2006).
It is therefore expected that having an RG climate will reduce employee’s uncertainty which again will affect the level of individual readiness for change.
Nilsen (2018) and Fosse (2019) found that rational goal had a direct effect on individual readiness for change. Taken together, it is believed that a climate that places
emphasis on communication, the external environment, planning and goal setting can increase levels of individual readiness for change (Armenakis & Fredenberger, 1997; Armenakis et al., 1993; Beus et al., 2020; Kuenzi, 2008; Paulsen, 2019; Rafferty et al., 2013; Vakola, 2014).
The following hypothesis is therefore made:
H1b: Rational goal climate has a direct positive effect on individual readiness for change
Learning in organizations
Researchers have been very interested in how organizations learn and how to increase learning amongst employees, which has led to an extensive amount of literature on the topic (Argyris & Schön, 1978; Easterby-Smith & Lyles, 2011; Hafstad, 2020; Kirkpatrick, 1967).
The literature regarding learning in organizations has mainly been divided into four main approaches; organizational learning, the learning organization, organizational knowledge and knowledge management (Easterby-Smith & Lyles, 2011). This thesis is going to focus on
organizational learning and the learning organization, because knowledge sharing (which is a part of this thesis) is closely related to the two approaches (Levinthal & March, 1993;
Schneider, 2014). The two approaches will further be elaborated.
Organizational learning
Organizational learning has been defined by different researchers with different perspectives (Schneider, 2014). Argyris and Schön (1978) postulated that organizational learning is either single- or double-loop learning (Walston, 2017). The organization learns when employees are acting as agents, detecting and correcting errors in the organization (Argyris, 1982, 1995). Single-loop learning is when errors are detected, and adjustments are made to the existing procedure, goals, rules and values. Double-loop learning occurs when modifications are made to the underlying norms, policies and objectives, which is a more radical change of the organization (Kaufman & Kaufman, 2015; Walston, 2017).
Furthermore, Kirkpatrick’s (1967) model is also related to organizational learning
(Augustsson et al., 2013; Klein, 2009). His model evaluated training on four different levels (Cheng & Hampson, 2008). The first level (reaction) is the individual reaction after receiving training; the second level (learning) is the cognitive increase of knowledge; the third level (behaviour) is related to if employees apply and transfers the knowledge and skills to the working context; and the last level (results) is the effect the training has had on the
organization (Augustsson et al., 2013; Baldwin & Ford, 1988; Kirkpatrick, 1998; Schneider, 2014). Level three (behaviour) is connected to if the individual learning gets transferred to the structure of the working context (Augustsson et al., 2013; Klein, 2009). This transfer of knowledge is the core of organizational learning (Klein, 2009).
Most of the literature uses individual learning theory as a basis for understanding organizational learning (Easterby-Smith & Lyles, 2011). Individual learning does not necessarily guarantee organizational learning (Klein, 2009). For organizational learning to take place it is important for the individual to share and transfer their knowledge to other employees (Gherardi et al., 1998). It is a process of employee interaction and participation (Easterby-Smith & Lyles, 2011). Taken together, organizational learning can be seen as a process of developing and sharing new knowledge (Levinthal & March, 1993).
A learning organization
A learning organization is an organization that is constantly trying to improve itself (Schneider, 2014). This by having a culture that values organizational learning, where
employees identify and correct errors. In addition, a focus of improving employees’ skills and
behaviours and developing and sharing knowledge. A learning culture inspires employees to be engaged in learning and builds up organizational capacity to make successful
organizational change (Choi & Ruona, 2011). A central part of a learning organization is knowledge sharing (Levinthal & March, 1993; Schneider, 2014). The construct knowledge sharing will be elaborated in the next section.
Knowledge sharing
Knowledge has been recognized as the most important resource for organizations and is critical for success (Ipe, 2003; Nahapiet & Ghoshal, 1998; Nonaka & Takeuchi, 1995;
Spender & Grant, 1996). Knowledge has been defined as information processed by the individual (Alavi & Leidner, 2001; Bartol & Srivastava, 2002; Wang et al., 2014). This includes facts, ideas, expertise and judgements which is relevant for organizational
performance. To get value out of the individual knowledge, the knowledge must be shared throughout the organization (Chen & Huang, 2007; Cho et al., 2007; Grant, 1996; Rusly et al., 2014; Teece, 2000). Knowledge sharing is closely related to learning, and enables ongoing learning through the organization (Spinello, 2000; Yang, 2007).
Knowledge sharing is a form of communication (Van Den Hooff & De Ridder, 2004).
The concept can be understood as a process of delivering task information and “know-how”
to help other colleagues (Cummings, 2004; Jackson et al., 2003; Wang & Noe, 2010). It also includes collaborating with colleagues to develop new ideas, solve problems and in the implementation of new procedures or policies “Know-how” can be described as practical skills and expertise, that enables the individual to do a task more efficiently and smoothly (Hippel, 1988; Kogut & Zander, 1992). Knowledge sharing arises when employees are willing to share and learn from others (De Vries et al., 2006; Yang, 2007). It is a process of both bringing and getting knowledge.
Internal and external knowledge sharing as facet-specific climates
Using the recommendations from Koritzinsky (2015) this thesis has measured knowledge sharing on two different levels, both internal and external. Internal knowledge sharing is the communication across groups in the work unit and external knowledge sharing is the communication across work units in the district (Fosse, 2019). This thesis is measuring internal and external knowledge sharing as two climate-constructs. Both internal and external knowledge sharing can be regarded as climates (Gupta, 2008). This because the constructs are tactical elements of an organization, which is a part of the employee’s workplace. Both internal and external knowledge sharing climates can be considered as facet specific climates,
because they can be seen as a specific aspect/part of the broader organizational context (Kuenzi, 2008).
Internal and external knowledge sharing as mediators
Organizational change always involves learning (Choi & Ruona, 2011; Meyer, 1982).
Organizations are constantly changing and therefore need to be capable of learning (Armenakis et al., 1993; Hübner, 2002). Watkins and Marsick (1993) proposed that
organizations need a “culture that is learning oriented, with beliefs, values, and policies that support learning”. Having a learning culture can make employees more positive and ready when change occur in the organization (Choi & Ruona, 2011). This because learning has become an element that they are used to and is embedded in their daily working life. Based on the same rationale, it is believed that having internal and external knowledge sharing climates could also increase levels of individual readiness for change. This because a climate that supports knowledge sharing, is also a climate closely related to learning (Spinello, 2000;
Yang, 2007).
As previously mentioned (in the section regarding internal process and rational goal), communication and information regarding the change, are important contributors to make employees more ready for change (Armenakis et al., 1993; Vakola, 2014). Organizations with internal and external knowledge sharing climates are thought to communicate and give
employees adequate information in the unfreezing and change process (Jackson et al., 2006;
Van Den Hooff & De Ridder, 2004). This is because communication and sharing of information lay in the values of the facet-specific climates. It has also been stated that the information regarding the change should preferably come from multiple sources, which internal and external knowledge sharing climates enables (Armenakis & Harris, 2002;
Armenakis et al., 1993). This again can make employees more ready for change (Armenakis et al., 1993; Vakola, 2014).
Taken together, having an organization with internal and external knowledge sharing climates, which is climates closely related to learning, communication and information, is thought to affect levels of individual readiness for change (Armenakis et al., 1993; Choi &
Ruona, 2011; Spinello, 2000; Vakola, 2014; Van Den Hooff & De Ridder, 2004; Yang, 2007). These following two hypotheses are therefore made:
H2a: Internal knowledge sharing climate has a direct positive effect on individual readiness for change
H2b: External knowledge sharing climate has a direct positive effect on individual readiness for change
One can assume that both internal process and rational goal climates are built up on several facet-specific climates (Beus et al., 2020; Kuenzi, 2008; Kuenzi & Schminke, 2009;
Schneider et al., 2013). It can be though that the molar climates internal process and rational goal are build up amongst other facet-specific climates by external knowledge sharing and internal knowledge sharing. This is because internal process climate focus on information and communication and rational goal emphasis communication and competence, and this is again natural elements of a knowledge sharing climate (Hartnell et al., 2011; Jackson et al., 2006;
Quinn & Rohrbaugh, 1983; Van Den Hooff & De Ridder, 2004; Yang, 2007). Based on this rationale, the following four hypotheses are made:
H3a: Internal process climate has a direct positive effect on an internal knowledge sharing climate
H3b: Internal process climate has a direct positive effect on an external knowledge sharing climate
H3c: Rational goal climate has a direct positive effect on an internal knowledge sharing climate
H3d: Rational goal climate has a direct positive effect on an external knowledge sharing climate
Bandwidth-fidelity theory indicates that a facet-specific climate can mediate the relationship between a molar climate and a specific outcome (Kuenzi, 2008). Based on this theory, it is expected that the molar climates internal process and rational goal will have an effect on individual readiness for change (specific outcome), through the facet-specific climates internal knowledge sharing and external knowledge sharing. This creates the following four hypotheses:
H4a: Internal process climate will have an indirect effect through an internal knowledge sharing climate on individual readiness for change
H4b: Internal process climate will have an indirect effect through an external knowledge sharing climate on individual readiness for change
H4c: Rational goal climate will have an indirect effect through an internal knowledge sharing climate on individual readiness for change
H4d: Rational goal climate will have an indirect effect through an external knowledge sharing climate on individual readiness for change
Figure 2
The structural model for this thesis
Method Research project
This study was a part of a research project between the Department of Research at the Norwegian Police University College and the Department of Psychology at the University of Oslo. The Norwegian police are going through extensive changes, which is still ongoing. The aim of this project is to examine different organizational climate factors in relation to
individual readiness for change.
Ethical considerations
This master thesis follows the Norwegian Centre for Research Data (NSD) procedures on national ethical standard for research on human beings. The data has been registered in TSD 2.0, which is a security system. The system is for registering, storing and analysing data.
The participation was voluntary, and the participants could withdrawal from the study at any time. The participants got information about how the data was stored and handled, and that the individual answers would not be revealed.
Data gathering
The data was gathered in cooperation between Norwegian Police University College and the Department of Psychology at the University of Oslo. The data was collected from four different police districts. The survey was distributed by an e-mail invitation, containing nine measures, with 146 items. District one received the survey in 2016, district two received the survey in 2018, district three in 2019 and district four in 2020.
Sample
A total of 1562 employees answered the survey. 145 of the participants did not complete the full survey and these respondents were deleted from the data. 1417 employees completed the entire survey, which is considered an acceptable sample size in structural equation modelling (Kline, 2016).
There were 45.6% females and 54.4% male respondents in the final sample. There were twelve age categories, that ranged from <23 years to > 64 years. The largest majority of the respondents were between 24-27 years (13.6%), followed by 48-51 years (13.3%).
Measures
When answering the questions, the respondents were asked to use their own
experiences as basis for their answers. The items were based on a 5-point Likert scale, ranging from “definitely false” (1), a middle value of “neither true or false” (3) and the highest score of “definitely true” (5). The items used in the study can be found in Appendices A.
Individual Readiness for Change
This study measured individual readiness for change using a scale developed by Vakola (2014). Her scale was based on work carried out by Holt, Armenakis, Feild, et al.
(2007). The measurement scale consists of six items. The items have been translated into Norwegian by Koritzinsky (2015). The scale contains items like “When changes occur in my work unit, I believe that I am ready to cope with them” and “I don’t worry about changes in my work unit because I believe that there is always a way to cope with them”.
Internal Process and Rational goal
The internal process climate and rational goal climate scales are based on the
competing values framework and Kuenzi (2008) examination of the scales. The measurement scales have been adapted for measuring the police climate and were also translated to
Norwegian by Koritzinsky (2015). Both scales contain seven items.
The internal process scale contains items like “Rules and policies are clearly
communicated to us in our work unit”, “In our work unit, established procedures and policies generally govern what we do in our jobs” and “In our work unit, we make sure that work activities are organized and predictable”. Samples of items in the rational goal scale are “We have an emphasis on setting goals for the work unit”, “It is important that we, in our work unit, plan for the future” and “In our work unit, we are always planning to make
improvements”.
Internal and external knowledge sharing
The scale used for measuring internal and external knowledge sharing climates is based on the integration scale from the Organizational Climate Measure (OCM) (Patterson et al., 2005). Koritzinsky (2015) made some adjustments to the original scale of Patterson et al.
(2005). The first alteration was to the content of the concept integration. This to include items concerning knowledge sharing in addition to existing ones regarding trust and cooperation.
The second adjustment was to separate internal and external knowledge sharing from one scale to two separate scales.
Both scales for internal and external knowledge sharing contain 12 items. The items in the two different scales are almost identical in wording and meaning, although there are structural differences between the two scales. The difference is that the internal knowledge sharing items are using words to get measures across groups in the work unit, whilst external knowledge sharing items are using words to get measures across work units in the district. An example item from the internal knowledge sharing scale is “People are prepared to share
information across the groups in our work unit” and from external knowledge sharing “People are prepared to share information across the work units in our district”.
Analysis
Preliminary analysis
IBM SPSS version 27 was used for the preliminary analysis. The analysis evaluated missing data, outliers and normality and this is reported in the results section. Mean, standard deviations, Cronbach alpha and bivariate correlations is reported in table 1.
Structural Equation Modeling
Structural equation modeling (SEM) was used to test the hypothesis of this study. IBM AMOS version 27 was used. There was carried out 10 000 bootstrapping of the estimates to obtain the 95% confidence intervals of the effects. SEM is not a single statistical technique, since it can perform multiple statistical procedures (Kline, 2016). SEM has been described as a combination of confirmatory factor analysis and multiple regression (Schreiber et al., 2006).
SEM can assess the quality of the measurement scales and at the same time examine the relationships between the latent variables (Kelloway, 2014). An advantage with this technique is that researchers can estimate the relationships between “pure” latent variables, which are not polluted by measurement errors. Another advantage with SEM is that it allows researchers to answer complex questions about their data, for example the use of mediation relationships.
To evaluate model fit of measurement models and the structural model, there are multiple indicators in the SEM-literature that can be used (Kline, 2016). SEM relies greatly on the researcher’s judgement (Kline, 2016). This is because fit statistics do not provide a simple yes or no answer. The model can be accepted, adjusted or rejected based on evaluation of these indicators. Based on Kline’s (2016) recommendation this thesis has used the Chi- square test (X2), Root Mean Square Error of Approximation (RMSEA), Comparative Fit index (CFI) and Standardized Root Mean Square Residual (SRMR). This empirical study used Hu and Bentler (1999) threshold recommendations for RMSEA, CFI and SRMR. As recommended by Kline (2016) Bayesian Information Criterion (BIC) was used to compare models.
Bayesian Information Criterion (BIC). BIC is a predictive fit index, where sample size is taken into consideration (Kline, 2016). The model with lowest BIC-value implies a better fit (Raftery, 1995). When the difference between models is between 6-10 BIC-values,
there is a robust indication of a difference between the models. When the value is over 10 there is a very strong indication of difference.
The Chi-square test (X2). The Chi-square test is a badness-of-fit index, that assesses if the model is significantly different from the population (Hair et al., 2014; Kline, 2016).
For other techniques, the goal is to find small p-values (<.05). In this case, the (X2 test) goal is to find non-significant results. This indicates that there is no significant difference between the model and the population. This again will indicate a good fit (Hoe, 2008). The chi-square test is extremely sensitive to sample size (Lei & Wu, 2007). The model may fit the data, but the chi-square test may reject the model due to a large sample size. Therefore, it is rational to use other fit indexes in addition to the Chi-square test.
Root Mean Square Error of Approximation (RMSEA). RMSEA is a badness-of-fit index, where values closer to 0 give the best fit (Kline, 2016; Xia & Yang, 2019). Hu and Bentler (1999) recommended a value below .06. RMSEA examine how far the hypothesized model is from a perfect model (Xia & Yang, 2019). This index is sensitive to sample size and degrees of freedom, and gives a better fit with a higher degree of freedom and larger sample size (Kline, 2016).
Comparative Fit index (CFI). CFI is a goodness-of-fit statistic (Kline, 2016). The values range from 0-1, where 1 is the best value. Hu and Bentler (1999) recommended a CFI- value greater than .95 (Hu & Bentler, 1999). CFI compares the researcher’s model against the null model (model fitting perfectly) (Kline, 2016). Because of its insensitivity to model complexity the index is widely used (Hair et al., 2014).
Standardized Root Mean Square Residual (SRMR). SRMR is a badness-of-fit statistic (Kline, 2016). Hu and Bentler (1999) recommended a SRMR-value lower
than .08 as an indication of a good fit. SRMR is a measure of the absolute correlation residual, which is the difference between observed and predicted correlations (Kline, 2016).
Results
Firstly, deletions were carried out for the cases that did not answer the full survey.
Before the SEM-analysis, the variables were examined for outliers and normality. By inspection of boxplot, there were two extreme outliers. By inspection of the data, these two cases did not portray incorrect entered answers. Based on this, the outliers where retained.
Furthermore, skewness and kurtosis were evaluated, which assess normality (Field, 2013).
The skewness varied from -.60 to -.03, and kurtosis varied from -.003 to .83. All the variables were within the recommended limit of skewness <.3.0 and kurtosis <.10.0 (Kline, 2016).
Internal process, external knowledge sharing and individual readiness for change were leptokurtic, which is a distribution with a positive kurtosis. Rational goal and internal knowledge sharing were platykurtic, which is a distribution with a negative kurtosis.
Furthermore, inspection of histograms indicated a reasonably normal distribution for all the variables. On the other hand, the Shapiro-wilk test was significant for the variables (p<.05).
This indicates a violation of the normality assumption (Field, 2013). However, this test often portrays a significant result when the sample size is large. Since skewness and kurtosis are within the recommended limits and the histograms portray normality, the analysis could be continued.
Table 1 represents descriptive statistics, Cronbach’s alpha and bivariate correlations between the constructs before the confirmatory factor analysis. The strongest correlation was observed between internal process and rational goal. This was expected due to the two
climates having similar traits. A Cronbach’s value off .7 or higher is acceptable (Field, 2013).
The Cronbach’s alpha for all the constructs was therefore acceptable.
Table 1
Descriptive statistics, reliability and bivariate correlation between indicators
M SD α 1. 2. 3. 4.
1. Internal Process 3.80 .61 .85 1
2. Rational Goal 3.55 .67 .85 .71** 1 3. Internal
knowledge sharing
3.82 .66 .85 .56** .56** 1
4. External
knowledge sharing
3.37 .63 .90 .52** .54** .55** 1
5. Individual
readiness for change
3.78 .55 .76 .23** .31** .21** .26** 1
Note. Mean (M), Standard Deviation (SD), Cronbach’s alpha (α), and zero-order correlations for all constructs.
N= 1417. **Correlation is significant at .01 level (2-tailed).
Measurement models- goodness of fit
There is some confusion in the research field regarding the meaning of the term’s measurement model and structural model. In this thesis a measurement model is the relationship between a latent variable and its indicators/items (Williams et al., 2009).
The structural model is the overall model, with relationships between the different latent variables.
It is important to get satisfactory fit statistics for the measurement models before making the structural model (Williams et al., 2009). This is because an unsatisfactory fit of the measurement models can significantly influence the fit of the structural model. Firstly therefore, there was conducted a confirmatory factor analysis separately for all the five measurement models. Actions were carried out to improve the fit statistics for each
measurement model. The next step was to make the structural model. The structural model was made with the modifications from the measurement models.
The fit of the construct’s individual readiness for change, internal process, rational goal, internal knowledge sharing, and external knowledge sharing was first evaluated
separately (Table 2). None of the constructs met the criteria for a good model fit. To improve the fit statistics parceling of items and deletion of items were completed. Parceling of items was preferred over deletion of items, for the improvement of model fit. This to retain as much meaning as possible behind the different constructs. The modifications to the models were
