THE INFLUENCE OF
AGE, TENURE, AND THE HISTORY OF CHANGE
ON READINESS FOR CHANGE
Master Thesis, MscBA, specialization Change Management University of Groningen, Faculty of Management and Organization
March 5, 2010 J.J.A. (ARJAN) WESTRIK
Studentnumber: 1839934 Floresstraat 1i 8022 AD Zwolle tel.: +31 (0)6-14329266 e-mail: j.j.a.westrik@student.rug.nl Supervisor/ university D. Schaap Supervisor/ field of study
I. Tacoma
ABSTRACT
TABLE OF CONTENTS
ABSTRACT ... 2
1. INTRODUCTION ... 6
1.1. Problem Definition and Objectives ... 7
2. THEORETICAL BACKGROUND ... 7
2.1. Readiness for Change ... 7
Appropriateness. ... 9 Management support ... 9 Self-efficacy. ... 9 Personal valence. ... 10 2.2. Age ... 10 2.3. Tenure ... 11 2.4. History of Change ... 12 2.5. Conceptual Model ... 14 3. METHODOLOGY ... 15 3.1. Research Design ... 15
3.2. Data Collection Methods ... 15
3.3. Data Source ... 18 3.4. Measurements ... 19 Dependent variables. ... 19 Independent variables. ... 19 3.5. Data Analysis ... 19 Factor analysis. ... 19 Correlation analysis. ... 19 Regression analysis. ... 20 4. RESULTS ... 21
4.2. Regression Analysis Results ... 23
Appropriateness. ... 23
Management support. ... 24
Self-efficacy. ... 24
Personal valence. ... 25
5. DISCUSSIONS AND IMPLICATIONS ... 25
5.1. Implications... 28
5.2. Limitations and Further Research ... 29
6. REFERENCES ... 30
7. APPENDICES ... 35
Appendix A – Results of Factor Analysis ... 35
Appendix B – Descriptive Statistics per Function ... 36
INDEX OF FIGURES AND TABLES
Figure 1 – Conceptual Model of Correlations ... 14
Figure 2 – Revised Model of Correlations ... 23
Table 1 – Descriptive Statistics for Demographic Variables ... 17
Table 2 – Group Statistics and Equality of Means ... 20
Table 3 – Correlations and Descriptive Statistics ... 22
Table 4 – Two-Stage Multiple Regression Analysis... 24
Table 5 – Three-Stage Mulitple Regression Analysis... 25
Table 6 – Results of Factor Analysis (appendicix A) ... 35
“There is nothing more difficult to take in hand, more perilous to conduct, or more uncertain in its success than to take the lead in the introduction of a new order of things”
– Niccolò Machiavelli
1. INTRODUCTION
Changing demographic patterns and cultural influences has led to an increasing older population and therefore an increasing older workforce (Morris & Venkatesh, 2000; Brigman & Cherry, 2002). According to the U.S. Census Bureau the number of people aged 55 and older is expected to increase between 2004 and 2014 by 49%, while this will only be by 2.9% for the younger workers. This means that by 2014 this older population will represent 21% of the American civilian labor force, up from 15% in 2004 (Brown, 2007). This graying of the workforce is not only present in the U.S., it is a worldwide phenomenon; an aging population and lower fertility rates in Europe, Japan, and other places around the globe promise a similar demographic shift in their workforces (Rappaport et al., 2003). This shift towards older workforces causes organizations to face a number of challenges which they do not yet know how to deal with (Capowski, 1994).
In addition, organizations are facing today an environment where organizational change is becoming more and more important. Although change is from all times, it is argued that the frequency and magnitude of change is nowadays greater than ever before. In order to survive in the increasing turbulent environment organizations need to change continuously. But, a well-known and concerning key number within the area of organizational change is that approximately 70 percent of all change programs fail (Balogun & Hailey, 2004; Van Es, 2009). Although this number varies widely, depending on the interpretation of „failure‟ and „success‟, academics and practitioners are clearly concerned that many still get change wrong (By, 2007).
psychologically ready (Jones et al., 2005). It is stated that if employees are not motivated or ready for change, the organizational change initiative is simply doomed to fail (Bouckenooghe & Devos, 2007).
Taken these facts together an image emerges that it becomes essential for researchers and practitioners in the field of organizational change to understand the influence of (an older) age on readiness for change. Nonetheless, very little research is done is this area (e.g. Hanpachern et al., 1998; Hargreaves, 2005; Madsen et al., 2005). Furthermore, the results of these studies are not conclusive. Therefore this study will research to what extent age has an influence on the individual level of change readiness. Because people are aging in a certain context two other time related factors, tenure and history of change, will be examined on this relationship as well.
1.1. Problem Definition and Objectives
This study will research to what extent age, tenure, and history of change have influence on the individual dimensions of the construct of readiness for change. In the first place it will try to find correlations between age, tenure, and history of change and the dimensions of readiness for change, and in the second place it will try to determine the impact of these correlations on readiness for change. It will research this for the individual level of readiness rather than the organizational level of readiness because, as Schneider et al. (1996, p.7) ones stated, “…if people do not change, there is no organizational change”.
2. THEORETICAL BACKGROUND
2.1. Readiness for Change
problem: it might be true that people resist loss of pay, loss of status, or loss of comfort, but these are not the same as resisting change (Dent & Goldberg, 1999). In other words, people do not resist change per se, but rather they resist because they belief that the change has negative consequences for them or the organization as a whole (Piderit, 2000). It is therefore the beliefs of people about the change that provide the foundation for resistance or adoptive behaviors (Holt et al., 2007). In order to implement a change successfully it is thus essential to align the beliefs and cognitions of the organizational members‟ with those of the change leaders: in essence, a state of readiness must be created (Holt et al.,2007). By acknowledging this, the focus of researchers and practitioners should change from „how can we overcome resistance‟ to „how can we create readiness‟ .
The concept of readiness itself is certainly not new in the field of organizational change. The term
readiness was first mentioned by Jacobson (1957), but the concept is even older. Bernerth (2004) explains
that creating readiness and Lewin‟s concept of unfreezing (1951) describe a similar organizational phenomenon; the process of “altering cognitions of employees in an effort to facilitate organizational
change” (p.39). Successful alteration will therefore result in employees who are change ready.
Armenakis et al. (1993) define this change readiness as the cognitive state that occurs when organizational members have positive attitudes, beliefs, and intentions toward the change. What this cognitive state entails is nicely described by Bernerth (2004): “readiness is more than understanding the
change, readiness is more than believing in the change, readiness is a collection of thoughts and intentions toward the specific change effort” (p.40).
It is important to note that change readiness is not a one-dimensional construct, but rather a multidimensional construct with complex relationship to behavior, cognition, and environmental context (Carey et al., 1999). Also Holt et al. (2007) acknowledge this multidimensional character, by stating that readiness for change is a comprehensive attitude influenced simultaneously by the content, the process, the context, and the individuals involved. In their study about an instrument for gauging readiness for organizational change Holt et al. (2007) found in the context of these dimensions four relevant readiness
factors; appropriateness (content, context), management support (process), self-efficacy, and personal
beneficial (individual attributes).
Appropriateness. An important readiness factor is appropriateness. Bernerth (2004) even states that
perceptions of necessity and appropriateness are at the very heart of employee readiness. A proposed change is perceived as appropriate when employees belief it will bridge the gap between the current state and the desired state (Walker et al., 2007) or belief it is the correct reaction to the current state of affairs (Armenakis & Harris, 2009; Bernerth, 2004). If employees see the proposed change as the wrong answer to the problems, they will not be willing to make the change work. Therefore change agents must convince organizational members that the proposed change is the appropriate one (Bernerth, 2004). A great deal of what change theorists call creating a vision (e.g. Kotter, 1995) is in fact just communicating the appropriateness of the proposed change (Bernerth, 2004).
Management support. To ensure change readiness it is also important to have key organizational leaders
supporting the particular change (Armenakis et al., 1993). This because individuals who are functioning within the context of an organization do not stand alone and so they will look to their coworkers and organizational leadership to see if support for the change exists (Bernerth, 2004). Especially if key organizational leaders do not clearly demonstrate their support, employees may not be willing to change (Neves, 2009). Also Kotter (1995) acknowledges this by bombarding the head of the organization as one of the key persons during major changes. Although Armenakis and Harris (2009) also acknowledge the importance of the formal leader(s), they also point out to the informal leaders in their description of management support:
“Principal support is the belief that the formal leaders (vertical change agents) in an organization are committed to the success of a change and that it is not going to be another passing fad or program of the month. Furthermore, we include as principals the opinion leaders who can serve as horizontal change agents” (p.129).
Self-efficacy. The third readiness factor is self-efficacy and this construct has received increasing
attention in the organizational behavior literature (Gist & Mitchell, 1992). Self-efficacy is formally defined as the “beliefs about the ability to coordinate skills and abilities to attain desired goals in
particular domains and circumstances” (Snyder & Lopez, 2002, p.278). It was found that perceived
self-efficacy affects the amount of effort and persistence employees are willing to put forth to reach particular outcomes (Bandura & Adams, 1977). Consequently it is reasonably relevant in organizational changes. Self-efficacy is often even defined in terms of change, e.g. “efficacy refers to the belief that the change
recipient and the organization can successfully implement a change” (Armenakis & Harris, 2009, p.129)
p.377). As can be derived from these definitions, self-efficacy is necessary since it builds confidence in the individual and organization‟s ability to successfully implement the changes (Walker et al., 2007); it creates the belief that „we can do this‟ (Bernerth, 2004). In contrast, employees who perceive the proposed change as exceeding their coping capabilities will believe that they can‟t do the change and may therefore resist the change (Cunnigham et al., 2002). In this light it is important to note that self-efficacy is a dynamic construct that changes over time as new information and experience are acquired (Gist & Mitchell, 1992). Managers and change agents should therefore find ways to assure employees that they have the ability to succeed in the change process (Bernerth, 2004).
Personal valence. The fourth and final relevant readiness factor, as defined by Holt et al. (2007), is
personal valence. Personal valence refers to the extent to which employees believe that he or she will or will not benefit from the implementation of the proposed change (Armenakis & Harris, 2009; Holt et al., 2007). It is also described as an appraisal process in which employees evaluate the intrinsic and extrinsic benefits of the proposed change (Bernerth, 2004). Prochaska et al. (1994) found that there two major categories on which individuals base their decision for making behavior changes, being pros and cons. Consequently, if the cons outweigh the pros and the change is seen as potentially harmful, employees are likely to be non-receptive to the change effort. However, if the pros outweigh the cons and the change is viewed as an opportunity, employees will be more receptive and willing to embrace the proposed change (Bernerth, 2004).
2.2. Age
One of the most important demographic variables in scientific and organizational research is age. This because chronological age is one of the few human experiences that is universal and it provides a basic structural link between individuals and social systems (Lawrence, 1988). Groups of individuals of similar age tend to have common non-work-related experiences, regardless of their status, expertise, or tenure in an organization (Zenger & Lawrence, 1989). It is suggested that a particular age group takes on “a
distinctive composition and character reflecting the circumstances of its unique origin and history”
of similar age (Rhodes, 1983), and therefore it seems that age plays an important role in a wide range of employee behaviors (Lawrence, 1988).
In the organizational literature one can find several things about the relationship between age and (readiness for) change. So is it by Wiersema and Bantel (1992) suggested that flexibility decreases and rigidity and resistance to change increase as people age. In line with this, Rosen and Jerdee (1977) found that older employees are perceived by their managers to be relatively inflexible and resistant to change. Morris and Venkatesh (2000) suggested that older workers find it more difficult to adapt to changes in the work environment and are likely to refuge in methods that are familiar to them. They add to this that habits become stronger as one grows older because it becomes more difficult to change routines. It is further suggested by them that self-efficacy and cognitive skills decreases as people age.
In his qualitative research about responses toward educational changes at different ages among 50 Canadian school teachers, Hargreaves (2005) concluded that most teachers become resistant and resilient toward change towards the end of their careers. This for two reasons. First, most teachers suffer from the so-called repetitive-change syndrome, referring to organizations that encounter an overload of change initiatives (Abrahamson, 2004); second, their impending retirement weakens the grip that others have over them and consequently most teachers do what they find important themselves.
In contrast to all these suggestions in the literature and the study of Hargreaves (2005), two empirical studies about readiness for change and age didn‟t find any significant relationships (Hanpachern et al., 1998; Madsen et al., 2005). A possible explanation for this discrepancy is that both studies measured readiness for change as a one-dimensional construct. As explained by Piderit (2000), a one-dimensional view of readiness is not sufficient to capture the full complexity of readiness for change and therefore a multidimensional view of readiness will provide a better understanding of the relationships between readiness for change and its antecedents. This study will therefore measure readiness for change along its multiple dimensions.
2.3. Tenure
Organizational tenure is another demographic characteristic that can be linked to an individual‟s receptivity to change according to the organizational literature (e.g. Wiersema & Bantel, 1992). Tenure refers to the length of employment and Hanpachern et al. (1998) discovered that it was significantly related to readiness for change. They showed that employees who were relatively new to the organization had higher levels of change readiness than those employees who had worked for years at the same organization.In the literature one can find several explanations for this phenomenon.
have participated in this 'social construction of reality' for the longest time are most convinced of its correctness, particularly as they are successful and move up the hierarchy in the organization. These employees have invested a great deal psychologically and tangibly in the status quo and have often far more to lose than to gain from organizational change (Hambrick et al., 1993). Consequently these employees have become committed to the status quo and without a high degree of discomfort with the status quo there is no emotional or rational case for change (Kaufman et al., 2003). Besides that, Hambrick et al. (1993) argued that since long-tenured employees are so convinced of the correctness of the status quo they may have difficulty in envisioning anything but the status quo.
Katz (1982) states that as employees work together for a long time, it is likely that they will reinforce their common views, commitments, and solutions strategies. These shared perceptions act as powerful constraints on the attitudes and behaviors of the individual employee, since they provide a great deal of assurance to the group members. Given the certainty these homogeneous tendencies facilitate, it is understandable that a group of employees with shared systems of meaning and beliefs develop great stability, become committed to established policies and practices, and consequently, become resistant to change (Katz, 1982).
In sum one can state that the literature gives the impression that organizational tenure has negative correlation with readiness for change because tenure has negative correlations with at least three of the four readiness for change factors; negative correlation with appropriateness because long-tenured employees have become committed to the status quo; negative correlation with self-efficacy because long-tenured employees may have difficulty conceiving alternative logics else than the status quo; and negative correlation with personal valence because long-tenured employees have often far more lose than to gain from alteration of the status quo due to their large investment in it.
2.4. History of Change
determinant of an organization‟s future success or failure at implementing change initiatives (Russel & Russel, 2006).
According to the literature one can explain organizational change history as an enabler of readiness for change by joining the expectancy theory (Vroom, 1964) and the schema theory (Lau & Woodman, 1995). The expectancy theory assumes that beliefs or expectancies about the outcome influence the type of behaviors an individual chooses to execute, while the schema theory states that people use schemata to understand and make sense of external events. A schema can be seen as a “cognitive structure that
represents organized knowledge about a given concept or type of stimulus" (Fiske & Taylor, 1984,
p.140), and it helps people to simplify, effectively manage, and make sense of information about events or objects (Lau & Woodman, 1995). A schema about an event or object is developed based on an individual‟s past experiences and believes (Lau & Woodman, 1995) and is accompanied by sentiments that create positive or negative expectancies (Vroom, 1964). Thus, when an organizational change initiative is announced, employees will trigger a schema of previous change experiences with the accompanying sentiment (i.e. positive or negative) and based on this schema they will determine the effort they want to put into the change initiative. Contextually, bad experiences with organizational change initiatives results in (and is represented in) a schema of poor organizational change history and consequently in lower effort putted into new change initiatives (Bordia et al., 2007).
In line with this, Reichers et al. (1997) and Wanous et al. (2000) found that negative experiences with organizational change causes people to develop cynicism about new organizational change. Individuals who are cynical about organizational change have lost faith in the organization‟s ability to manage change (Bordia et al., 1997) and are less likely to be willing to engage in future change efforts (Reichers et al., 1997). This relationship suggest that cynicism may be a self-fulfilling prophecy to some extent, since the lack of support of cynics may cause new change initiative to fail or to have very limited success. Subsequently this failure will reinforces cynical beliefs, which will further reduce the willingness of this group to try again (Reichers et al., 1997). Because cynicism creates lack of openness for change efforts (Bordia et al., 2007) it can be viewed as an important barrier to change (Reichers et al., 1997).
FIGURE 1
Conceptual Model of Correlations
to merge, acquire, reengineer, advance technologically, and change other fundamental operational processes (Bernerth, 2004).
2.5. Conceptual Model
Based on the literature review a conceptual model was made (figure 1). In this model a positive correlation is suggested between history of change and all the four readiness for change factors as well as between age and tenure. A negative correlation is suggested between tenure and appropriateness, self- efficacy, and personal valence; and between age and self-efficacy.
Tenure
Readiness for Change
3. METHODOLOGY
3.1. Research Design
From the literature review it became clear that age, tenure, and the history of change initiatives are likely to have a relationship with (some of) the dimensions of the individual level of readiness for change. Based on what was stated in the literature a conceptual model was produced in which the dependent variables (the dimensions of readiness for change) and the independent variables (age, tenure, and history of change initiatives) were incorporated, as well as their expected correlations.
This study has used a quantitative research approach to test if there are indeed statistically significant correlation coefficients between and among the independent and dependent variables as suggested in the conceptual model. Because it statistically tests whether certain relationships or correlations between phenomena exist this study can be defined as empirical testing research (Van der Velde et al., 2004).
3.2. Data Collection Methods
A self-administered questionnaire was used for data collection. As stated by Verschuren and Doorewaard (1999), the collection of quantitative data by the use of a survey is the ideal method to test suggested correlations or relationships on a relatively large sample in a time and cost efficient way. It was chosen to make this survey available online as well as a on paper. Online surveys have the advantage of being very effective at a relative low cost. In addition, the data of the survey is organized automatically, which will save the researcher a lot of time when entering the data for its analyses. Despite these advantages it was chosen to complement this online survey with a paper one. This because a large part (more than 50%) of the employees in the department under investigation (in the function of mechanic) are working most of their time „out in the field‟ and do not use computers on a daily basis. More response was to be expected from them using a paper survey.
The survey itself consisted of items with statements regarding the different variables. Respondents were asked to give a reaction on a Likert-scale with a five-point response format (1 = strongly disagree, 3 = neutral, 5 = strongly agree). A Likert-type scale was used because (1) they have already proven their reliability, validity and practical relevance; (2) they are relative easy to construct, administer and score; and (3) the employees in the department under investigation were accustomed to use these scales. Furthermore, the structure of closed questions using a Likert-scale makes it possible to statistically measure individual‟s attitudes and opinions about the different variables1.
After the survey was made available online an invitation was sent to the email addresses of 491 employees of the target population. These email addresses were retrieved from the company‟s human resource database. The invitation consisted of a short explanation of the study with a link to the online survey. A total of 41 invitation reported an „Undelivered Mail Returned to Sender‟ and 34 attempts resulted in the return of an „Out of Office‟ message. Eventually, 416 invitations were successfully received by the target population. In total 181 individuals started the online survey, which resulted in a response rate of 43.5%. However, only 141 completed surveys were deemed suitable for data analysis, because the other respondents didn‟t complete the survey. Therefore, the final effective response rate for the online survey was 33.9%.
In addition 590 letters, with a paper version of the survey, were send to the home addresses of the mechanics. These letters included, as the email, a short explanation and a paper version of the survey. An retour envelope was included to improve the response rate. The home addresses of the mechanics were also retrieved from the company‟s human resource database. A total of 148 paper surveys were sent back, resulting in a response rate of 25.1%. A number of them (14) were incomplete and therefore the effective response rate was 22.7%.
Overall a total of 1006 invitations were send to the target population which resulted in 329 responses (32.7%) of which 275 were deemed suitable for data analysis. This implies an overall effective response rate of 27.3%. This might seem a bit low, but in this kind of surveys it is actually relatively high. Besides that, Keeter et al. (2006) have found that that lower response rates do not necessarily have an impact on the quality of survey estimates.
The effective response rate of the paper version was evidently lower than the effective response rate of the online version (22.7% vs. 33.9%). This can easily be explained by the factors time and effort. The paper version required more effort, since respondents had to bring the survey to a mailbox. Additionally it was also likely to require more spare time, since respondents received the questionnaire at their home
1 In the academic world there is a debate about whether Likert scale data can or cannot be analyzed parametrically.
TABLE 1
Descriptive Statistics for Demographic Variables
Variables FTE % N = 275 % N = 207 % Age > 25 88 8,5% 10 3,6% 8 3,9% 25 - 35 126 12,2% 23 8,4% 17 8,2% 35 - 45 233 22,7% 60 21,8% 42 20,3% 45 - 55 362 35,2% 115 41,8% 94 45,4% 55 < 220 21,3% 67 24,4% 46 22,2% Total 1028 100,0% 275 100,0% 207 100,0% Tenure 0 - 2 197 19,2% 22 8,0% 12 5,8% 2 - 5 84 8,2% 25 9,1% 15 7,2% 5 - 10 45 4,3% 9 3,3% 8 3,9% 10 - 20 146 14,2% 28 10,2% 22 10,6% 20 - 30 350 34,0% 114 41,5% 94 45,4% 30 < 207 20,1% 66 24,0% 48 23,2% Missing 0 0,0% 11 4,0% 8 3,9% Total 1028 100,0% 275 100,0% 207 100,0% Gender Male 963 93,7% 251 91,3% 204 98,6% Female 65 6,3% 24 8,7% 3 1,4% Total 1028 100,0% 275 100,0% 207 100,0% Function Management 15 1,5% 6 2,2% 6 2,9% Team Leader 52 5,1% 26 9,5% 26 12,6% Section Engineer 103 10,0% 28 10,2% 28 13,5% Planner 19 1,8% 5 1,8% 5 2,4% Mechanic 573 55,7% 142 51,6% 142 68,6% Administration 49 4,8% 20 7,3% 0 0,0% Data Management 124 12,1% 23 8,4% 0 0,0% Other 93 9,0% 25 9,1% 0 0,0% Total 1028 100,0% 275 100,0% 207 100,0%
addresses. This implies that they were only able to read it after work time. Contrary, respondents of the online version received the questionnaire at their work email during office time and were therefore more likely to fill it in during office hours.
3.3. Data Source
The sample used for the analysis consisted of individual employees within a department of a large Dutch organization operating in the energy sector. With over 5,000 employees and 2.7 million customers, this organizations is the legal administrator of the gas and electrical power network in a large part of the Netherlands. Their activities include the construction and the maintenance of the gas and electrical power network in their (legal) area. The department under investigation was responsible for fixing gas and electrical failures and for the maintenance of the network. This department houses a total of 1040 employees who were all approached, either by email or mail, to participate in a self-administered questionnaire.
The department under investigation was at the time of the study in a large scale change effort concerning the way operational processes were managed. Heart of the change effort was the development and implementation of Operational Management; managing processes by steering on operational performance. Central parts were (re)describing the processes in detail, attaching the matching roles and responsibilities to the processes, developing and implementing a new structure of key performance indicators (with the accompanying reports), and implementing a new machinery of internal consultation.
Respondents completed the questionnaire voluntarily and were promised that their responses were completely confidential. A comparison of the sample and the whole population (the department under investigation) on their most apparent characteristics – age, tenure, gender, and function – is displayed in table 1.
The average age and tenure of the respondents is respectively 47.28 and 21.64 (n = 275) and 47.21 and 22.45 (n = 207). This is slightly older and longer then the department‟s average. This can easily be explained by the nature of this research; older people (with longer tenure) felt more necessity to participate in this research since it tries to find out to what extent (an older) age and (a longer) tenure influence the individual level of change readiness. The relative high averages of age and tenure of the whole department can partly be explained by the fact that this company was formerly government owned; it is proven that managers of government-owned (public) firms have longer tenure in office than managers of privately owned firms (DeAlessi, 1974; Mixon Jr. & McKenzie, 1999).
These two are the functions where most of the women in this department are occupied. In the sample n = 275 relatively much respondents came from these function, while in the sample n = 207 these functions were excluded (see 3.5. Data Analysis).
3.4. Measurements
Dependent variables. In this study there are four dependent variables who are believed to be dependent
on how the independent variables are manipulated or varied. They were all measured by scales developed by Holt et al. (2007). The reliability (α) for these scale were respectively 0.91 for appropriateness (e.g. „There are legitimate reasons for us to make this change‟), 0.81 for management support (e.g. „Every senior manager has stressed the importance of this change‟), 0.85 for self-efficacy (e.g. „When we implement this change, I feel I can handle it with ease‟), and 0.85 for personal valence (e.g. „This change will disrupt many of the personal relationships I have developed). All these Cronbach Alpha‟s can be considered as good, since they are all higher than 0.8 (Van de Velde et al., 2004).
Independent variables. The scale to measure the history of change was adapted from Metselaar (1997).
The Cronbach Alpha for this scale was 0.79, only slightly lower than the 0.8 that is considered as good. An example of an item in this scale was „Past changes were generally successful‟. The demographic variables age and tenure were simply asked in the questionnaire.
3.5. Data Analysis
Factor analysis. A principal component analysis with an orthogonal rotation (varimax) was conducted on
the 29 items of the survey (see table 6 in the appendices). The size of the used sample (n = 275) does fit the required sample size for such an analysis (Tabachnick & Fidell, 2001). During the factor analysis a total of 8 items were eliminated because their convergent or discriminant validity was considered too low. One item (HistCh2) that exceeded one of the discriminant validity limitations, a factor loading of more than 0.3 on another component, was not eliminated because it only exceeded it slightly (0.016) and the difference between the highest loading and the second highest loading was more than 0.4 (0.457). Furthermore, eliminating this item would reduce the reliability of the variable history of change from 0.79 to 0.74.
Correlation analysis. A Pearson correlation analysis was performed in order to test whether the
TABLE 2
Group Statistics and Equality of Means
Variables Group N Mean S.D. t Sig. (2-tailed) Appropriateness 1 207 3,173 0,719 -0,765 0,445 2 68 3,248 0,656 Management Support 1 204 3,417 0,683 0,341 0,733 2 68 3,382 0,845 Self-Efficacy 1 204 3,626 0,716 -2,064 0,040* 2 68 3,838 0,790 Personal Valence 1 202 3,500 0,950 -2,605 0,010** 2 68 3,838 0,851
NOTE: N = 275. T-test with equal variance assumed. 1 = Employees in functions severely affected by change; 2 = Employees in functions hardly affected by change.
** p < 0.01; * p < 0.05
either a positive or a negative relation between the different variables, a one-tailed significance test was used. It was chosen to exclude, based on their function, 68 respondents from this analysis. This because it turned out that employees in the functions of administration, data management, and „other‟ (see table 1) were hardly affected by the change under investigation, which resulted in significantly higher scores on two of the four dependent variables; „Self-Efficacy‟ and „Personal Valence‟ (see table 2). The remaining sample (n = 207) is large enough for correlation analysis, since it is considerably larger than the 30 subjects required for establishing relationships (Hill, 1998).
Regression analysis. In order to determine to what extent age, tenure, and history of change account for
the variance in the dimensions of readiness for change a multiple regression analysis was carried out. The same sample as with the correlation analysis (n = 207) was used. The size of this sample does fit the requirements of 10 to 15 observations per independent variable (Hair et al., 1998).
coefficients. In order to prevent incorrect contribution values of the individual predictors because of this, it was chosen to add age and tenure at the same time into the model.
The resulting two-stage multiple regression analysis was conducted for each dependent variable. In the first stage only history of change was regressed on the four dimensions of readiness for change. In the second stage tenure and age were added. This order of adding the history of change before tenure and age into the model was chosen based on the strength of their correlations with the dimensions of readiness for change.
4. RESULTS
4.1. Correlation Analysis Results
Table 3 presents an overview of the overall means, the standard deviations and the correlations coefficients between age, tenure, history of change and the readiness for change factors. Two other variables that were also included in the correlation analysis, the number of functions and changes, are displayed in this table as well.
All the correlations that were expected, as displayed in the conceptual model, were confirmed. So does the history of change initiatives positively correlate with all the four dimensions of readiness for change (all p < 0.001); does tenure negatively correlate with appropriateness (p < 0.01), self-efficacy (p < 0.01), and personal valence (p < 0.05); and does age negatively correlate with self-efficacy (p < 0.01) and positively with tenure (p < 0.001).
Contrary to the expectations it was also found that tenure was negatively correlated with management support (p < 0.01) and history of change (p < 0.10); that age was negatively correlated with appropriateness (p < 0.05), management support (p < 0.05), personal valence (p < 0.05) and history of change (p < 0.05). Based on these findings a revised version of the conceptual model was made, see figure 2.
FIGURE 2
Revised Model of Correlations
4.2. Regression Analysis Results
An overview of the results of a two-stage multiple regression analysis with the four readiness for change dimensions as dependent variables is presented in table 4. The first model tested consisted of the predictor history of change (model 1), whereas the second model tested consisted of the predictors history of change, tenure and age (model 2).
Appropriateness. History of change accounts for a large amount of the variance in appropriateness
(20.0%, p < 0.001), whereas tenure and age add together an additional 3.5% (p < 0.05) of explained variance. In total 23.4% of the variance in appropriateness can be explained by this model. The adjusted
Readiness for Change
Personal Valence Tenure Age Self-Efficacy Appropriateness Management Support History of Change (+) (-) (+) (-) (-) (-) p < 0.001 p < 0.01 p < 0.05 p < 0.10
TABLE 4
Two-Stage Multiple Regression Analysis: History of Change, Tenure and Age
Dependent variables Model R R² Adjusted R² R² Change F Change Sig. F Change Appropriateness 1 0,447 0,200 0,195 0,200 47,153 0,001*** 2 0,484 0,234 0,222 0,035 4,235 0,016* Management Support 1 0,321 0,103 0,098 0,103 21,701 0,001*** 2 0,375 0,141 0,127 0,038 4,132 0,018* Self-Efficacy 1 0,417 0,173 0,169 0,173 39,673 0,001*** 2 0,433 0,187 0,174 0,014 1,571 0,211 Personal Valence 1 0,207 0,043 0,038 0,043 8,488 0,004** 2 0,235 0,055 0,040 0,012 1,208 0,301
NOTE: N = 207. Multiple linear regression enter method. 1 = Predictors: History of Change; 2 = Predictors: History of Change, Tenure and Age.
*** p < 0.001; ** p < 0.01; * p < 0.05
R², indicating how much variance in the outcome is accounted for if the model had been derived from the whole population from which the sample was taken, is just slightly lower (22.2%).
Management support. The variance in management support can be explained for 10.3% by the history of
change (p < 0.001). If tenure and age are added into the model the amount of explained variance increases by 3.8% (p < 0.01) to a total of 14.1%. Because the correlation analysis showed also a correlation between management support and the number of functions, a third model was tested in which the number of functions was added (see table 5). In this new regression analysis history of change, tenure and age accounted together for 13.3% in the variance of management support. The number of functions adds an additional 3.3% (p < 0.10) of explained variance, resulting in a total of 16.6% (adjusted R² = 14.6%) of explained variance.
Self-efficacy. As with appropriateness and management support, history of change accounts for the
TABLE 5
Three-Stage Multiple Regression Analysis:
History of Change, Tenure and Age, and the Number of Functions/Changes
Dependent variables Model R R² Adjusted R² Change R² Change F Change Sig. F
Management Support 1 0,296 0,088 0,082 0,088 16,403 0,001*** 2 0,365 0,133 0,118 0,045 4,431 0,013* 3 0,408 0,166 0,146 0,033 6,698 0,010* Self-Efficacy 1 0,374 0,140 0,127 0,140 10,417 0,002** 2 0,434 0,188 0,149 0,048 1,852 0,165 4 0,513 0,263 0,215 0,075 6,190 0,016*
NOTE: N = 207. Multiple linear regression enter method. 1 = Predictors: History of Change; 2 = Predictors: History of Change, Tenure and Age; 3 = Predictors: History of Change, Tenure and Age, and the Number of Functions; 4 = Predictors: History of Change, Tenure and Age, and the Number of Changes;
*** p < 0.001; ** p < 0.01; * p < 0.05
efficacy (see table 5). In this three-stage regression analysis the predictors history of change, tenure and age accounted for 18.8% in the variance of management support. The number of changes adds an additional 7.5% to this to a total of 26.3% (adjusted R² = 21.5%) of explained variance (p < 0.05).
Personal valence. As can be seen in table 4, the total amount of variance in personal valence that can be
explained by the predictors is the lowest of all four dimensions of readiness for change. History of change accounts for 4.3% (p < 0.01) of this variance, whereas tenure and age do not significantly explain more (1.2%). The total explained variance in personal variance by the three predictors is 5.5% (adjusted R² = 4.0%).
5. DISCUSSIONS AND IMPLICATIONS
Age. Although the literature suggested that age and readiness for change were negatively correlated, two
empirical studies about readiness for change and age didn‟t found any significant relationships (Hanpachern et al., 1998; Madsen et al., 2005). Contrary to these two studies, this study did find significant negative relationships between age and all of the four readiness for change factors. As was already suggested in the theory section this could be explained by the fact that both Hanpachern et al. (1998) and Madsen et al. (2005) measured readiness for change as a one-dimensional construct, while this study measured readiness along its four dimensions.
Based on the literature review it was further expected that age had the highest impact on the dimension of self-efficacy, since it was found that age reduces cognitive skills and self-efficacy (Brigman & Cherry, 2002; Morris & Venkatesh, 2000). Although the correlation with self-efficacy was indeed the strongest of all of the four correlations, the regression analysis showed no significant more explanation of the variance in self-efficacy when age and tenure were added into the model. In fact the results indicated that age and tenure together only significantly explained more of the variance in appropriateness (R² change = 3.5%, p < 0.05) and management support (R² change = 3.8%, p < 0.05).
The low influence of age on self-efficacy could be explained by the nature of the change under investigation. Heart of this change effort was the development and implementation of Operational Management, with as central parts the (re)description of the processes in detail, the attachment of the matching roles and responsibilities to the processes, the development and implementation of a new structure of key performance indicators (with the accompanying reports), and the implementation of a new machinery of internal consultation. These are all changes that do not require employees to learn a wide range of new skills or knowledge, such as working with a new technology does. The relative high mean (x = 3.68) on the independent variable self-efficacy does confirm this. The studies in which it was found that age does reduce cognitive skills and self-efficacy employees did have to learn new skills (Brigman & Cherry, 2002) or to work with new technologies (Morris & Venkatesh, 2000).
Tenure. This study confirms the in the literature suggested negative correlations of tenure with
appropriateness, due to commitment to the status quo; self-efficacy, due to difficulty in conceiving alternative logics else than the status quo; and personal valence, due to large investment in the status quo (Hambrick et al., 1993). Although this study confirms these negative correlation, it has also showed that the impact of these correlations are low to non-significant: adding tenure and age into the model resulted only in significant more explanation of the variance in appropriateness (3,5%, p < 0.05).
model was 3,8% (p < 0.05). The negative correlation between tenure and management support could be explained by the relative low score on history of change (x = 3.08), which is even lower if management is excluded (see table 7 in the appendices). A combination of a long tenure with a (long) bad history of change could causes cynicism by employees about the support of the management (Reichers et al., 1997; Wanous et al., 2000). Also Devos et al. (2007) suggested this by stating that the failure of management in dealing with change in the past decreases employees‟ trust in the management. It is therefore possible that in organizations with a more positive history of change a positive correlation between tenure and management will be found.
History of change. The results of this study support that the history of change is a major factor
influencing readiness for change. This study has found that history of change is significant positively related with all the four readiness for change factors (all with p < 0.001) and furthermore significantly explains a certain part of the variance of the readiness for change factors: appropriateness (R² = 20.0%), self-efficacy (R² = 17.3%), management support (R² = 9.8%), and personal valence (R² = 3.8%).
The theoretical foundation for these findings can be found in the expectancy theory (Vroom, 1964) and the schema theory (Lau & Woodman, 1995). When an organizational change initiative is announced, employees will trigger a schema of previous change experiences with the accompanying sentiment (i.e. positive or negative) and based on this schema they will especially determine if the change is appropriate and if they have the ability to successfully accomplish the change. To a lesser extent they will also, based on this schema , determine if the change will be supported by the management and if the change will have positive consequences for them.
Other findings. As displayed in table 7 (in the appendices), it was found that the management scored
Walston & Chadwick (2003) further showed that even if change initiatives improved performance, knowledge of these improvements do not seem to disseminate across organizational levels.
This study further found that the number of functions an employee had occupied in the organization had a positive correlation with the perceived management support for this particular change initiative. Although no explanation for this finding was found in the literature, one could argue that the more functions an employee has occupied the more he feels respected, appreciated and trusted by its management. This because an new function often involves a promotion or at least its shows the confidence of the organization in the abilities of the employee. As a form of social exchange (Blau, 1964), this could result in more trust in the management in general, and therefore in an higher perceived management support for an announced change initiative.
In addition it was found that the number of changes an employee had experienced was positively correlated with self-efficacy. This is in line with the literature, since self-efficacy, the confidence in individual and group‟s ability to make the change succeed (Bernerth, 2004), is thought to raise from experiences (Knowles & McLean, 1992). A number of studies (e.g. Morgan & Cleave-Hogg, 2002) has showed that confidence grows with experience, since experiences causes someone to know what he can reasonably expect.
5.1. Implications
Based on theoretical arguments and the empirical findings presented in this research, several important implications for theory, research and practice can be identified. In the first place this research contributes to the theory by identifying the level of influence that age, tenure, and the history of change have on the individual dimensions of readiness for change. Although most of the correlations were already suggested in the literature, this study was able to confirm them empirically where other empirical studies (Hanpachern et al., 1998; Madsen et al., 2005) had failed. The reason why this study did succeeded lies in the method used to measure readiness for change; this study has measured readiness for change along its multiple dimensions, where the other studies measured it as a one-dimensional construct. As explained by Piderit (2000), a multidimensional view of readiness will provide a better understanding of the relationships between readiness for change and its antecedents. The results of this study therefore suggests that further research about readiness for change should use the multidimensional approach.
an employee‟s perception of the change history of the organization, since the influence of the perceived history of change on the readiness for change dimensions is far bigger than age and tenure together. This implies that organizations and change agents should actively manage the perceived history of change of employees in order to improve their readiness for change. Hereby one can think about interventions such as celebrating successes and ceremonial ending of change stages during a change effort, evaluations with all participants after a change effort, and (if necessary) discussing the (bad) history of change with all participant before a new change effort. All these suggested interventions are of even more importance lower down the hierarchy, since Walston & Chadwick (2003) showed that even if change initiatives improved performance (are successful), knowledge of these improvements do not seem to disseminate across organizational levels.
5.2. Limitations and Further Research
Despite the large sample used in this research, the generalizability of the findings is limited by the fact that research was done in one (department of an) organization. In addition, due to the nature of the work most of the respondents in this department were male. Additional research is therefore necessary to see if the same results can be found in other organizations and in samples with more females.
Another limitation of this research stems from the high correlation between age and tenure in the sample used. Due to this high correlation this study was not able to retrieve the contribution values of the predictors age and tenure individually. Although the combining contribution values does give us insights, individual contribution values would be better. Additional research is therefore required.
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7. APPENDICES
Appendix A – Results of Factor Analysis
TABLE 6
Results of Factor Analysis
Questionnaire Item (a) I II III IV V
Appr4 0,825 0,137 0,146 0,023 0,201 Appr5 0,792 0,134 0,177 0,129 0,128 Appr1 0,787 0,115 0,184 0,169 0,169 Appr6 0,778 0,267 0,076 0,126 0,091 Appr3 0,764 0,102 0,209 0,053 0,136 Appr7 0,710 0,073 -0,038 0,155 -0,006 Appr10 0,634 0,162 0,174 0,108 0,174 SelfEf4 0,184 0,826 0,173 0,071 -0,010 SelfEf6 0,252 0,784 0,119 0,134 0,149 SelfEf5 0,104 0,776 0,158 0,185 0,096 SelfEf3 0,196 0,691 0,053 0,234 0,229 ManSup2 0,071 0,171 0,779 0,172 0,092 ManSup4 0,133 0,294 0,763 0,055 0,005 ManSup3 0,230 0,007 0,721 0,080 0,221 ManSup1 0,186 0,053 0,720 0,151 0,107 PersVal2 0,179 0,172 0,150 0,823 0,104 PersVal1 0,129 0,271 0,124 0,813 -0,073 PersVal3 0,213 0,114 0,181 0,812 0,185 HistCh1 0,091 0,170 0,142 0,048 0,811 HistCh4 0,166 0,140 0,168 0,139 0,779 HistCh2 0,316 0,040 0,060 0,007 0,773
NOTE: N = 275. Extraction Method: Principal Component Analysis, Rotation Method: Varimax with Kaiser Normalization. All loadings above 0,3 are displayed in italic.
Appendix B – Descriptive Statistics per Function
TABLE 7
Descriptive Statistics per Function
Function N Appr. Man. Sup. Self Ef. Pers. Val. Hist. Ch. Mean S.D. Mean S.D. Mean S.D. Mean S.D. Mean S.D.
Management 6 4,452 0,293 4,445 0,586 4,667 0,438 4,890 0,170 3,498 1,048
Team Leader 26 3,300 0,915 3,641 0,783 3,664 0,652 3,898 1,002 2,975 0,885
Planner 5 3,572 1,004 3,268 0,797 3,700 0,737 3,543 1,610 2,800 0,652
Section Engineer 28 2,847 0,748 3,481 0,797 3,648 0,809 3,431 0,905 2,653 0,782
Mechanic 142 3,146 0,608 3,319 0,601 3,586 0,687 3,378 0,889 3,177 0,768
Appendix C – Questionnaire
The paper version of the questionnaire is displayed, although the online version consisted of the exact same text and questions. Both questionnaires were preceded by a short introduction and explanation of the study itself.
--- Questionnaire
The completed forms will be processed anonymously. Your details are only asked in case I need to contact you for further information or for clarification of your answers.
Background information
Name
District
Age year
First year of employment in this organization
Temporary function
Number of functions within this organization
Appropriateness
"this change" = working according the new
way operational processes are managed disagree Totally Disagree Neutral Agree Totally agree
1. I think that the organization will benefit from
this change.
O
O
O
O
O
2. It doesn‟t make much sense for us to initiate
this change.
O
O
O
O
O
3. There are legitimate reasons for us to make
this change.
O
O
O
O
O
4. This change will improve our organization‟s
overall efficiency.
O
O
O
O
O
5. There are a number of rational reasons for
this change to be made.
O
O
O
O
O
6. In the long run, I feel it will be worthwhile
for me if the organization adopts this change.
O
O
O
O
O
7. This change makes my job easier.
O
O
O
O
O
8. When this change is implemented, I don‟t
9. The time we are spending on this change
should be spent on something else.
O
O
O
O
O
10. This change matches the priorities of our
organization.
O
O
O
O
O
Management Support
"this change" = working according the new way operational processes are managed
Totally
disagree Disagree Neutral Agree
Totally agree 1. Our senior leaders have encouraged all of us
to embrace this change.
O
O
O
O
O
2. Our organization‟s top decision makers have
put all their support behind this change effort.
O
O
O
O
O
3. Every senior manager has stressed the
importance of this change.
O
O
O
O
O
4. This organization‟s most senior leader is
committed to this change.
O
O
O
O
O
5. I think we are spending a lot of time on this change when the senior managers don‟t even
want it implemented.
O
O
O
O
O
6. Management has sent a clear signal this
organization is going to change.
O
O
O
O
O
Self-Efficacy
"this change" = working according the new
way operational processes are managed disagree Totally Disagree Neutral Agree Totally agree
1. I do not anticipate any problems adjusting to the work I will have when this change is
adopted.
O
O
O
O
O
2. There are some tasks that will be required
when we change that I don‟t think I can do well.
O
O
O
O
O
3. When we implement this change, I feel I can
handle it with ease.
O
O
O
O
O
4. I have the skills that are needed to make this
change work.
O
O
O
O
O
5. When I set my mind to it, I can learn everything that will be required when this
change is adopted
O
O
O
O
O
6. My past experiences make me confident that I will be able to perform successfully after this
