Attributes of the Organizational Change and Its Influence on Attitudes Toward Organizational Change and Well-Being at Work: A Longitudinal Study

Abstract

This study investigates the role of attributes of organizational change, the attitudes toward change as an antecedent of well-being at work, and how these antecedents vary over the course of an organizational change. Organization change planning, perceived risk level, and attitudes toward organizational change are examined as antecedents. Attitudes toward change were tested as mediators in the relationship between change attributes and well-being. Hypotheses were tested in a three-wave study (N = 505, N = 390, and N = 348 respondents, respectively) of employees from a public organization in Brazil undergoing a strategic reorientation. Attitudes toward change had stable positive effects in each wave, conducted 12, 24, and 48 months after the change was initiated. This study corroborates findings that uncertainty and risk contribute to the formation of negative cognitions and feelings throughout the process of organizational change but do not necessarily result in discomfort in relation to the work. The effects of planning the change and the perceived risk level of the change were not moderated by time. This study’s results do not support the ideas of gradual shifts and discontinuous information processing in cognitive models of employees. In contrast, it is possible to conclude that perceptions are confirmed over time. Implications for managing employee reactions and well-being in different phases of change are discussed.

Keywords

attitudes longitudinal organizational change well-being

Organizational change is an agent of stress in individuals’ lives (Dahl, 2011; Kieselbach et al., 2009; Smollan, 2015). The literature points out that organizational change may be a risk factor for the health and well-being of individuals (Armenakis & Harris, 2009; Saksvik et al., 2007), because organizational changes usually involve changes in roles, overload and, therefore, shifts in the skills required to perform work and cope with the situation. Therefore, organizations are increasingly required to improve their ability to enhance employees’ support or acceptance for change initiatives (Choi, 2011).

The literature suggests that, in addition to being a stressful experience, experiencing organizational change is linked with negative reactions, such as reduced motivation and an increase in uncertainty, doubts, fear, and intention of withdrawal (Fugate, Prussia, & Kinicki, 2012; Hellgren & Sverke, 2003; Martin, Jones, & Callan, 2006). The effects of organizational change attributes that influence individuals’ acceptance of change, maintaining their health and well-being, should be understood. Ultimately, to be successful, organizational change must be accepted, supported, and implemented (Armenakis & Harris, 2009; Oreg, Vakola, & Armenakis, 2011; Vakola, 2016).

Organizational change increases several reactions (Vakola, 2016) that can be expressed in a positive or negative manner, containing cognitive, affective, and behavioral components like attitudes (Lines, 2005; Oreg et al., 2011). Evaluative models of emotions and affects (Lazarus, 1982) that presume attitudes are also associated with other types of human affects and related to the well-being of individuals in specific contexts of organizational change (Bryson, Barth, & Dale-Olsen, 2013; Dahl, 2011). Considering the influence of some attributes in the context of change, we may argue that if individuals welcome change, the consequences for their well-being and health will be less harmful. Hence, the objectives of this study are to (a) investigate the roles of the attributes of organizational change and attitudes toward change on well-being, (b) investigate the mediating roles of attitudes toward change on the correlation between the attributes of organizational change and well-being, and (c) verify the moderating effect of time on the mediation model, assessing the model’s goodness-of-fit at different moments, namely, T1, T2, and T3.

Generally, organizational studies have associated well-being with quality of life, degree of stress, satisfaction, and mental health (Van Horn, Taris, Schaufeli, & Schreurs, 2004). This term holds multidimensional features, comprising several affective and cognitive responses that emerge in the context of work, many times associated with health indicators, with multiple possible focuses (Quinlan, 2007; Van Horn et al., 2004).

The literature about organizational change points out associations between attributes of organizational change and well-being at work (Quinlan, 2007; Quinlan, Bohle, & Mayhew, 2001). According to an international review of 68 studies on the impact of downsizing and restructuring (Quinlan et al., 2001), 88% found harmful effects, such as an increased risk of unfairness at work, occupational violence, cardiovascular disease, and mental illness. Downsizing is the kind of change that affects an individual’s well-being (Quinlan et al., 2001).

Organization change requires coping and adaptation. Consistent with this view, using the stress–strain–coping paradigm, organization change causes a reduction in costs, redundancy, or delays, which induces feelings of job insecurity and increases stress. This scenario leads to a wide range of adverse health effects for those lacking the skills to effectively cope (Harenstam, Bejerot, Leijon, Scheele, & Waldenstrom, 2004; Kalimo, Taris, & Schaufeli, 2003; Lindorff, Worrall, & Cooper, 2011). These health effects are reflected in the following physical and psychological symptoms (Quinlan, 2007): turnover (Fugate et al., 2012; Rafferty & Restubog, 2010); burnout, cortisol, and increased levels of testosterone (Spreitzer & Mishra, 2000); stress and cardiovascular symptoms (Spreitzer & Mishra, 2000); and individual responses such as absenteeism (Cunningham, 2006).

To track the influence of organizational change, change should be characterized by specifying which attributes of the context affect individuals’ behaviors, affects, and cognitions (Kalimo et al., 2003; Maes & Van Hootegem, 2011). These attributes include past experiences with downsizing and expectations of future downsizing (Kalimo et al., 2003); the frequency of programs of change, degree of planning involved in change, and magnitude of the change (Rafferty & Griffin, 2006); the organization’s background related to change (Bordia, Restubog, Jimmieson, & Irmer, 2011; Bordia, Hobman, Jones, Gallois, & Callan, 2004); the future perspectives of the new processes of change (Cunningham, 2006; Devos, Buelens, & Bouckenooghe, 2007; Kalimo et al., 2003); and the impact of change intensity (Cunningham, 2006) on the uncertainty of individuals, listed as one of the principal consequences of change that affects well-being and performance (Bordia et al., 2004).

The association between the organization change context and well-being lies in two assumptions: (a) psychological contract and equity theory and (b) novelty and uncertainty caused by situational events. There is a psychological contract between employees and the organization for which they work. Equity theory emphasizes the importance of an equitable balance between investments and rewards for worker’s well-being. Inequity occurs if one outweighs the other. Research has shown that inequity in exchange relationships at work is associated with developing diseases (Kalimo et al., 2003). Furthermore, the situation offers a number of temporal properties that can have negative impacts, including the imminence, duration, and temporal uncertainty surrounding events and novelty of an event (Lazarus & Folkman, 1984). These properties make the situation harmful or threatening. Planning actions increase the psychological certainty and help individuals evaluate the magnitude of change (Rafferty & Griffin, 2006).

Studies have shown that negative effects on well-being resulting from organizational changes are associated with interpretations of lack of clarity and controllability (Green, 2011), workers’ overload and stress (Dahl, 2011), and increased anxiety (Bryson et al., 2013) that generates uncertainty and makes predicting the future difficult (Bryson et al., 2013).

Planned change is defined as the perception that deliberation and preparation have occurred prior to the implementation of change. When efforts are made to plan change in advance, change becomes more predictable as people are provided with information about the imminence of change and the likely duration of change. In addition, when planning occurs prior to change implementation, the novelty of a change event is likely to be reduced. (Rafferty & Griffin, 2006; p. 1155)

Planning actions involve informing the individuals about what is going to happen, preparing them to act according to the proposed goals and structure their actions in a logical manner, and in accordance with the proposed objectives (Bryson et al., 2013). Hypotheses 1 and 2 were formulated considering such influence:

Hypothesis 1: The planning of organizational changes is positively associated with well-being during the process of changes.

Hypothesis 2: The degree of risk and uncertainties of organizational changes are negatively associated with the well-being of individuals during the process of change.

Planning change and the perception of the reasons and benefits during the process are associated with positive attitudes toward organizational change (Rafferty & Restubog, 2010) and supportive behaviors (Kim, Hornung, & Rousseau, 2011). The degree of risk of change may be associated with individuals’ openness to accept the change process and to behave according to expectation during the process (Devos et al., 2007; Nery, Neiva, & Mendonça, 2016; Oreg et al., 2011). At the beginning of a process of change in an organization, the first experiences and information about change are sufficient to cause several cognitions, emotions, and feelings, such as frustration, enthusiasm, or fear (Bouckenooghe, 2010; Choi, 2011; Lines, 2005). In contrast, changes can be positively perceived when these eliminate rework and unnecessary routines as well as maximize opportunities for growth and development for workers (Kruglanski, Pierro, Higgins, & Capozza, 2007).

When the organizational change increases the workload or takes away advantages the individuals used to enjoy, the negative reactions are almost immediate (Quinlan, 2007). This is because individuals understand it as threat and typically experience suffering, anxiety, and stress (Dahl, 2011; Kiefer, 2005; Rafferty & Restubog, 2010). The organizational change tends to be positively perceived when objectives are clearly presented, individuals know what is going to happen, and interventions abolish boring routines and maximize opportunities of growth and development for individuals, bringing about excitement and motivation (Kruglanski et al., 2007). The process of “sensemaking” (Schwandt, 2005) is based on previous knowledge about assigning meaning to new information. This process is facilitated by schemes that reduce the complexity of the information received, allowing individuals to associate it with past actions and meanings (Schwandt, 2005). This facilitates understanding the process of change. For organizational changes, creating a purpose helps members to accept the need for the changes (Robinson & Griffiths, 2005). Uncertainty refers to the psychological state of doubt about what an event signifies or portends (DiFonzo & Bordia, 1998; Rafferty & Griffin, 2006). Risk level refers to losses and damage perspectives with new processes of change.

Based on the literature, the following hypotheses were created:

Hypothesis 3: Planning change positively predicts the attitude of acceptance and negatively predicts the attitudes of fear and skepticism in relation to the organizational change.

Hypothesis 4: The risk of change positively predicts the attitude of fear and skepticism and negatively predicts the attitudes of acceptance of organizational change.

The organizational change processes trigger reactions of an affective, cognitive, and behavioral nature (Lines, 2005) that may be understood by the evaluative theories that approach the relationship among cognition–emotion–coping (Fugate, Harrison, & Kinicki, 2011). The theory of evaluation of emotion, for example, is based on the proposition that individuals evaluate their environments, and these evaluations cause emotions (Lazarus & Folkman, 1984) that many times are associated with longer-lasting affective states (Lazarus, 1982). Yet the relationship between emotion, affect, and cognition is always dynamic. These factors have synchronic mutual relationship that provides an integrative perspective in which emotion and cognition take place together, emphasizing the potential of the cognitive and emotional information to work as a Gestalt harmonic (Fugate et al., 2011).

Evaluation, emotion, and coping are three core constructs of the theory of evaluation. Cognitive evaluation considers what is at stake for an individual in a specific situation (Lazarus & Folkman, 1984). The employees’ evaluations about the organizational change are particularly crucial because they show how employees respond to changes. Surveys showed that individuals typically evaluate organizational changes in a negative way, perceiving the changes as damage or threats (Fugate, Kinicki, & Prussia, 2008). Emotions emanate as “short-term responses about something” (Seo, Barrett, & Bartunek, 2004, p. 423) and usually provide crucial and prompt information that may indicate the relative certainty or difficulty of a situation (Seo et al., 2004). Negative emotions are usually associated with organizational change (Kiefer, 2005) and become “instructions for action” (George & Jones, 2001, p. 427). These actions usually take the form of coping (Fugate et al., 2011). Coping, in turn, comprises behaviors and cognitions that try to restrict negative evaluations and emotions and potentially allow individuals to re-read change as positive and beneficial (Robinson & Griffiths, 2005). Coping helps employees to feel capable of handling the stressors and challenges associated with change. If coping fails and employees engage in intentions of giving up, they may feel incompatible with the organization and develop negative feelings in relation to the work and organization (Fugate et al., 2011).

With change, employees should learn to open new paths and build new strategies to achieve the redefined objectives. Employees develop resilience to overcome the setbacks that are unavoidable during the process of change. Moreover, in a successful organizational change, the employees undergoing these changes must be motivated and have alternative paths defined (i.e., hope) for them when obstacles are encountered and make optimistic attributions when things go wrong, so they have a positive perspective of the future. These joint affects consist of an individual’s well-being at work and in organizations (Avey, Wernsing, & Luthans, 2008).

In summary, the main arguments of this study are based on the idea that perceived risk and planning are contextual features that foster the employees’ positive and negative evaluations of change. The perceived risk is related to the possibility of losses, an increased workload, role ambiguity, and so on. This context, when evaluated negatively, generates anxiety and negative emotions that contribute to a lower level of well-being at work. Planning, in turn, is characterized by the provision of information and rational logic of actions, so employees know what to expect. Positive attitudes are generated from positive evaluations brought about by the presence of these contextual features. Positive evaluations generate positive effects associated with work and, therefore, well-being. Planning is also characterized by employee preparedness, which generates readiness for change and, consequently, well-being. Therefore, the following hypotheses have been developed:

Hypothesis 5: Well-being is positively predicted by the attitude of acceptance and negatively predicted by the attitudes of skepticism and fear to organizational change.

Hypothesis 6: Attitudes toward organizational change mediate the relation between attributes of change (planning and risk) and well-being.

Some authors caution that time is often underestimated understanding organizational change (George & Jones, 2001; Pettigrew, Woodman, & Cameron, 2001; Sonnentag, 2012), but few empirical surveys have examined how the response by those affected by change may vary over time (Kim et al., 2011). In this sense, this study proposes that these mutual relationships between affect and cognition change over time. The core argument is that attitudes toward organizational change, the perception of the attributes of the context, and the employee’s well-being, in addition to the relationship among them, change over time (Sonnentag, 2012). Gradual changes in the employees’ cognitive models of their employment and organizational relationships have been proposed as the psychological mechanisms underlying this moderating role of time (Kim et al., 2011).

The organization change process is a disruptive event that leads employees to ask and interpret clues about the gains and losses involved for them. Uncertainty regarding the consequences of an organizational transition and the ambiguity about the role of employees are typically high in the earlier phases of change. The longer a change program is in place, the more integrated into the organizational structures and processes it should become (Kim et al., 2011). In addition, additional information and personal experiences about change are available to employees. As the novelty and the uncertainty of the situation subsides, the employee responses should become increasingly habitual and oriented by automatic routines or mental and behavioral schemes (Louis & Sutton, 1991).

Hypothesis 7: Time moderates the proposed model in such a way that T1, T2, and T3 will present a significant difference.

Figure 1 presents the model to be tested in the present study.

Figure 1.

Investigation model of the study.

Method

Participants

The sample of research organization was defined by a stratified random sample based on 11 states in Brazil that housed the organization offices. An electronic raffle was used in combination with the online software available at www.random.org. This procedure allowed the random selection of the research participants. The main objective of selecting the participants in this manner was to obtain proper samples of each population layer (Rea & Parker, 1997). During the first data collection phase, N = 505 questionnaires were selected. The questionnaires provided demographic information and the responses to the scales. In the second and third phases, attrition phenomenon has led to a cumulative reduction in the initial sample size over time. In the second phase, 390 participants were kept in the survey. In the last stage, the study involved only 348 respondents. In all the three phases, the sample was mainly composed of male respondents (77%, 72.6%, and 71%, respectively). In each phase, the sample was composed of professionals who had completed higher education and graduation courses (like specializations and MBAs). All the participants voluntarily answered the questionnaire, and the sample comprised all hierarchical levels and functions in the organization. All the participants were directly affected by the organizational change programs.

Data Collection Procedure

To collect the data, the organization provided a contact list with the names and email addresses of the participants. After each participant was selected, their data were recorded with the online software Lime Survey, which enables the collection of structure data through scales. All the instruments were presented in a random order, and the respondents received the link to access the questionnaire by email. The data were collected in three stages. The time span between the first and the second collection was approximately 1 year. Between the second and the third collection, the time span was about 2 years. Figure 2 represents the data collection timeframe.

Figure 2.

Timeframe of data collection.

The data collection periods were defined by considering a statement by Pettigrew et al. (2001) that highlights the importance for a researcher to have an expedient plan, that is, flexibility to apply the survey. The data collection took place three times: from May to June 2011, September to October 2012, and October and November 2014. For each data collection (T1, T2, and T3), the instruments were emailed to the same participants with an explanatory text, a link to the research, and the password to access the questionnaire. In the last week of collections, a reminder was emailed to those who had not completed the research. Before accessing the instruments, the respondents accessed the Term of Free and Informed Consent, which contained a brief text explaining the objective of the research and requested the participant’s consent. The instructions for completion was a part of the questionnaires.

Instruments

This research employed three instruments to assess the attributes of the organizational change context, attitudes toward organizational changes, and well-being at work. The instruments were subjected to confirmatory factorial analyses with samples other than the research sample, involving five other organizations in Brazil.

Organizational Change Attributes: Planning and Risk Level

Planning and degree of risk (Franco, Neiva, Nery, & Demo, 2016) were evaluated by 21 items with factorial loads above 0.45 and Cronbach’s alphas higher than .74. All the items were evaluated according to the scale, ranging from 1 (fully disagree) to 10 (fully agree). The items were translated and adapted from previous works (Kalimo et al., 2003; Rafferty & Griffin, 2006), for example, “There was planning prior to the process of changes,” “Organizational changes trigger losses for employees,” “The employees were prepared to act according to proposed change goals,” “The organization underwent lots of changes in the last few years.” For the structural equations-based confirmatory factorial analysis, a second sample of 475 respondents from five Brazilian organizations was used. The test of goodness-of-fit of the proposed models considered the following indexes: the ratio between chi-square (χ²) and degrees of liberty (dl), normed fit index (NFI), Tucker–Lewis index (TLI), comparative fit index (CFI), goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), and root mean square error of approximation (RMSEA) (Hox, 2010). For this sample, structural equation modeling was also performed (Hair et al., 2009) and the results supported the previously tested structure of the instrument. In this study, the goodness-of-fit indexes found in the confirmatory factorial analysis for the attributes of organizational change were acceptable: χ²(31, N = 475) = 113.24, p < .005; χ²/dl = 3.53; NFI = 0.95; TLI = 0.97; CFI = 0.97; GFI = 0.95; AGFI = 0.92; RMSEA (confidence interval, CI) = 0.07 (0.06-0.08).

Attitudes Toward Organizational Change

The attitudes toward organizational change were measured by the Scale of Attitude Toward Organizational Change (ATOC), with three factors (acceptance, fear, and skepticism) that consisted of 46 items with factorial loads above 0.50 and a Cronbach’s alpha higher than .85 (Neiva, Garcia, & Paz, 2005). Examples of items are “Changes oxygenate the organization” and “Changes raise risks to the organization.” The confirmatory factorial analysis of the scale of attitudes toward organizational change was carried out with 419 participants from five organizations in Brazil. Only the items with a factorial load above 0.50 were selected. The results showed goodness-of-fit for the three factors: χ²(38, N = 419) = 108.24, p < .005; χ²/dl = 2.84; TLI = 0.96, CFI = 0.96, GFI = 0.95; NFI = 0.95, RMSEA (CI) = 0.06 (0.04-0.08). For this sample, the measure model test using structural equation modeling was also performed (Hair et al., 2009). The results supported the previously tested structure of the instrument.

Well-Being in Organizations

To measure well-being in organizations, a one-factor instrument that comprised 14 items with factorial loads above 0.45 and a Cronbach’s alpha of .91 were used. The instrument was designed according to information from the work by Van Horn et al. (2004), which proposed a structure of well-being at work based on the tradition of psychological well-being and used Warr’s (1987, 1994) model. The authors assumed that well-being at work consists of the positive evaluation of several work characteristics and includes affective, motivational, behavioral, cognitive, and psychosomatic aspects. This instrument had evidence of validity in a previous study (Dessen & Paz, 2010). Here is an example of item: “I feel good working here.” The confirmatory factorial analysis comprised a sample of 367 cases from five Brazilian organizations. The results of the one-factor structure of the 14 items showed proper goodness-of-fit: χ²(34, N = 367) = 129.49, p < .005; χ²/dl = 3.81; TLI = 0.95, CFI = 0.96, GFI = 0.93; NFI = 0.95, RMSEA (CI) = 0.09 (0.74-0.11). For this sample, the measure model test using structural equation modeling was also performed (Hair et al., 2009). The results supported the previously tested structure of the instrument.

Data Analysis Procedure

The analysis was conducted in stages. In the first stage, after verifying the statistical assumptions (Hair et al., 2010; Tabachnick & Fidell, 2013), the descriptive and correlational statistical analyses were performed. The second stage consisted of the verification of the goodness-of-fit of the investigation model and was performed on each of the three databases after data cleaning. Next, there were two stages to evaluate the model and test the hypotheses: the measurement model and structural model (Hair et al., 2010). The models were estimated with the AMOS 20 software through maximum likelihood, to verify the goodness-of-fit of the models based on the following indicators: GFI, CFI, and RMSEA (Byrne, 2010; Kline, 2010). The proposed model was compared with alternative models because this practice is a more rigorous trial than the isolate analysis of goodness-of-fit (Hair et al., 2010). The third stage consisted of the verification of the hypotheses tests. The mediation model in this study is considered complex because it has more than one mediating variable (Preacher, Zyphur, & Zhang, 2010). Therefore, mediation was verified using structural equations, observing the magnitude and significance of indirect effects through bootstrapping. The indirect effects were based on a population estimated sample and the results from the estimates of the regression coefficients. Significant indirect effects are a measure analogous to the use of other methods that suggest the significance of the regression interaction effects (Preacher & Selig, 2012). Hence, several analyses were performed with submodels to check the sign, magnitude, and significance of the coefficients.

The fourth stage checked the moderating effect of time. The goodness-of-fit of the investigation model was compared in T1, T2, and T3 using the chi-square statistics. The nonsignificant change in the chi-square, or χ²(dl), shows that the model does not vary over time, that is, there is not a moderation effect. If any significant changes are found, then there is evidence that time is a moderating effect, and the effect size for the moderation extension should be calculated through t tests (Cohen’s d).

Results

Correlations between variables are supported by many hypothesized in this research. The descriptive statistics and correlations of factors are presented in Table 1. The goodness-of-fit of the model was verified separately for the three groups (T1, T2, and T3). The goodness-of-fit indexes for the structural model proposed in T1, T2, and T3 are presented in Table 2.

Table 1.

Means, Standard Deviations, and Correlations of the Research Variables.

	M	SD	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17
1. Planning1	5.95	1.66
2. Planning2	5.71	1.65	.139**
3. Planning3	5.53	1.46	0.07	−0.01
4. Risk1	6.53	1.68	−0.05	0.03	0.09
5. Risk2	6.45	1.69	.103*	−0.05	0.06	0.05
6. Risk3	6.57	1.75	.138**	0.05	.140**	−0.05	0.00
7. Acceptance1	5.65	1.24	.721**	0.02	.119*	−0.02	0.06	0.03
8. Acceptance2	5.50	1.62	.115*	.755**	0.02	0.07	−.135**	0.03	0.02
9. Acceptance3	5.48	1.69	0.09	−0.04	.691**	0.01	0.01	−.110*	.140**	−0.01
10. Fear1	6.14	1.37	−.142**	0.02	0.01	.495**	−0.07	0.01	0.00	−0.02	−0.03
11. Fear2	6.12	1.22	.164**	−.171**	0.06	0.04	.468**	.160**	.154**	−.116*	−0.03	0.01
12. Fear3	6.20	1.35	.215**	0.07	0.01	−0.01	0.02	.512**	.162**	.126*	−.156**	0.01	.165**
13. Skepticism1	4.34	1.64	−.386**	−0.08	−0.04	.348**	−0.08	−0.03	−.235**	−0.09	−0.09	.638**	−0.02	−0.07
14. Skepticism2	4.52	1.51	−0.09	−.321**	0.01	−0.08	.356**	0.05	−0.08	−.318**	−0.04	−0.03	.584**	−0.01	0.08
15. Skepticism3	4.67	1.64	−0.08	−0.01	−.182**	−0.04	0.05	.385**	−0.05	−0.01	−.302**	0.04	.111*	.647**	0.10	0.08
16. Wellbeing1	7.78	1.39	.625**	.223**	.192**	0.05	.129*	.171**	.511**	.214**	.153**	−0.05	.255**	.274**	−.346**	−0.10	−0.09
17. Wellbeing2	7.64	1.39	.351**	.636**	.111*	0.05	0.02	.106*	.233**	.549**	0.09	−0.04	−0.06	.169**	−.237**	−.288**	−0.02	.599**
18. Wellbeing3	7.54	1.41	.318**	.112*	.566**	0.00	0.08	0.06	.274**	.115*	.534**	−0.04	0.07	0.00	−.195**	−0.03	−.284**	.611**	.410**

p = .05. **p = .01.

Table 2.

Model Fit Indexes.

Model Fit
	χ″	dl	χ″ γ.λ.	NFI	TLI	CFI	GFI	AGFI	RMSEA (95% CI)	Δχ² *	Δdl
Initial model T1	211.12	4	52.78	0.82	0.32	0.82	0.89	0.44	0.32 (0.27-0.32)
Respecified model T1	13.22	3	4.40	0.98	0.95	0.99	0.99	0.94	0.08 (0.07-0.09)	197.9	1
Initial model T2	117.33	4	29.33	0.86	0.81	0.86	0.92	0.59	0.27 (0.24-0.29)
Respecified model T2	13.22	3	4.40	0.98	0.94	0.98	0.98	0.92	0.09 (0.08-0.10)	104.11	1
Initial model T3	151.02	4	37.75	0.80	0.77	0.80	0.88	0.41	0.32 (0.29-0.35)
Respecified model T3	14.43	3	4.81	0.98	0.92	0.98	0.98	0.90	0.10 (0.09-0.11)	174.86	1

Note. dl = degree of liberty; CI = confidence interval; ∆χ² = chi-square difference; Δdl = difference of degrees of liberty.

p < .001.

In principle, the model was tested according to Figure 1. However, the goodness-of-fit was not considered satisfactory. Based on suggestions from the AMOS software, the relations of correlation between the factor components of the scales were included, generating respecified models. Based on data presented in Table 2, we found that the respecified model has a proper goodness-of-fit in T1—χ²(3) = 13.22, ns; χ²/dl = 4.40; NFI = 0.98; TLI = 0.95; CFI = 0.99; GFI = 0.99; AGFI = 0.94; RMSEA (CI) = 0.08 (0.07-0.09), ns; T2—χ²(3) = 13.22, ns; χ²/dl = 4.40; NFI = 0.98; TLI = 0.94; CFI = 0.98; GFI = 0.98; AGFI = 0.92; RMSEA (CI) = 0.09 (0.07-0.09), ns; and T3—χ²(3) =14.43, ns; χ²/dl = 4.82; NFI = 0.98; TLI = 0.92; CFI = 0.98; GFI = 0.98; AGFI = 0.90; RMSEA (CI) = 0.10 (0.09-0.10), p < .05. The first hypothesis was supported by indicators showing that planning of organizational changes is positively associated with well-being in T1 (β = 0.62, p < .001), T2 (β = 0.63, p < .001), and T3 (β = 0.56, p < .001). This result means that planning the organizational change affects the employee’s well-being throughout the implementation process.

The second hypothesis was rejected. The degree of risk and uncertainty of organizational changes was not negatively associated with the well-being of individuals. The relation had a weak and nonsignificant effect in T1 (β = 0.005, ns), T2 (β = 0.02, ns), and T3 (β = 0.06, ns). According to these results, the degree of risk and uncertainty entailed by organizational change will not necessarily affect the employee’s well-being throughout the process being investigated. The third hypothesis was partially supported. Well-being was positively associated with the attitude of acceptance in T1 (β = 0.42, p < .001), T2 (β = 0.50, p < .001), and T3 (β = 0.46, p < .001). Well-being was negatively associated with attitude of skepticism in T1 (β = −0.36, p < .001), T2 (β = −0.19, p < .05), and T3 (β = −0.31, p < .001). However, well-being was not negatively associated with attitude of fear in T1 (β = 0.18, p < .001), T2 (β = 0.11, p < .05), and T3 (β = 0.27, p < .001). These results suggest that employees’ well-being is affected by the attitudes of acceptance and skepticism throughout the process, and the attitudes of acceptance present an effect of greater magnitude than the attitudes of skepticism. However, individuals who feared the change process did not experience a negative effect on their well-being at work: this was a weak positive relation.

The fourth hypothesis was partially supported. Change planning positively predicted the attitude of acceptance with a high and significant effect in T1 (β = −0.72, p < .001), T2 (β = 0.75, p < .05), and T3 (β = 0.69, p < .001). Attitudes of skepticism are negatively predicted by change planning in T1 (β = −0.38, p < .001), T2 (β = −0.32, p < .001), and T3 (β = −0.18, p < .001). The relation between the variables fear and planning has weak and significant effect only in T1 (β = −0.14, p = .001). These data demonstrate that attitudes of fear are only affected by the lack of planning perceived early in the process of change, while the attitudes of skepticism and acceptance are affected by planning in different moments during the research.

The fifth hypothesis of the study was partially supported. Risk of change positively predicted the attitudes of fear in T1 (β = 0.49, p < .001), T2 (β = 0.46, p < .05), and T3 (β = 0.51, p < .001). Risk of change positively predicts the attitudes of skepticism in T1 (β = 0.34, p < .001), T2 (β = 0.35, p < .05), and T3 (β = 0.38, p < .001); however, the risk of change presents a weak and negative effect, significantly predicting the attitude of acceptance of organizational change only in T2 (β = −0.13, p < 0.05) and T3 (β = −0.11, p < .05).

The sixth hypothesis predicts that attitudes toward organizational change mediate the relation between attributes of change (planning and risk) and well-being. The literature affirms that including the mediating variable in the regression equation reduces or neutralizes the impact of the independent variable on the dependent variable (Tabachnick & Fidell, 2013). Table 3 presents the results of the mediation analyses.

Table 3.

Result of the Mediation Test of the Criterion Variable.

Variables	Well-Being
	T1		T2		T3
	β	Indirect Effect	β	Indirect Effect	β	Indirect Effect
Planning	0.62*		0.63*		0.54*
Risk	0.05 ns		0.02 ns		0.06 ns
Planning → Acceptance	0.48*	0.08 ns	0.50*	0.11**	0.33*	0.18**
Planning → Fear	0.50*	0.17**	0.53*	0.002 ns	0.35*	0.03**
Planning → Skepticism	0.48**	0.65**	0.52*	0.03**	0.35*	0.05**
Risk → Acceptance	0.11**	−0.003 ns	0.09**	−0.02**	0.06 ns	−0.03**
Risk → Fear	0.11**	0.04**	0.09**	0.03**	0.05 ns	0.06**
Risk → Skepticism	0.11**	−0.01 ns	0.09**	−0.02 **	0.60 ns	−0.02 ns

p ≤ .001. **p ≤ .05. ns = nonsignificant.

The results of the test of the mediation of attitudes toward organizational change regarding the variable of well-being weakly support the hypothesis that the attitude of skepticism mediates the relation between planning and well-being in the three data collections (i.e., T1, T2, and T3). The seventh hypothesis predicts that time moderates the relations; as such, planning and risk have a decreasing influence on well-being over time. To compare the model in the three groups (T1, T2, and T3), the index of change of the chi-square or χ²(dl) was used. The alteration value was not significant, indicating no moderating effect of time.

Discussion and Conclusions

The findings of this study indicate that the investigation model had good fit indexes for T1, T2, and T3. However, the hypotheses were partially supported because the attribute of risk of change did not predict a negative effect on well-being. This finding is striking and does not support most research in the field, which affirms that risk of change influences rates of disease, absenteeism, and well-being (Devos et al., 2007; Rafferty & Griffin, 2006; Rafferty & Restubog, 2010; Robbins, Ford, & Tetrick, 2012; Spreitzer & Mishra, 2000). Surveys showed organizational changes are typically evaluated in a negative way: as damaging or as a threat (Fugate et al., 2008). In this study, the relative certainty or difficulty of the situation became “instructions for action” (George & Jones, 2001, p. 427). However, these actions usually took the form of coping (Fugate et al., 2011), which helped the employees feel capable of coping with the stressors and challenges associated with the change. Moreover, individuals must define alternative paths (i.e., hope, resilience) when obstacles are encountered and make optimistic attributions when situations go awry, projecting a positive perspective of the future. These joint effects consist of an individual’s well-being at work (Avey et al., 2008).

This result may be a bias of this sample because the research was conducted in a public corporation where employees enjoy an apparent stability and, thus, do not assimilate the threats of changes implemented in the organization. A comparative study on the perception of changes among managers in the United Kingdom and Australia identified that organizational change was less effective in the public sector than in the private sector (Lindorff et al., 2011). In the light of these findings, we suggest investigating the relation proposed in this article in private organizations to verify the generalization of our results. Notably, the results herein indicated a relation between the prediction of attribute of risk of change with attitudes toward changes. This relation corroborates the findings that support that uncertainty and risk contribute to the formation of negative cognitions and feelings throughout the process of organizational change (Fugate et al., 2008; Fugate et al., 2012; Kiefer, 2005), but do not necessarily result in discomfort, in relation to the work and the organization. These data could indicate that during the coping process, individuals resort to internal resources to maintain their well-being, despite risk, as suggested by Avey et al. (2008).

The attribute of planning organizational change had a strong relation with the attitude of acceptance. This finding supports other studies (Bouckenooghe, 2010; Devos, et al., 2007; Kalimo et al., 2003; Rafferty & Griffin, 2006) and is relevant because an individuals’ perception about planning and preparation of organizational changes may interfere with an individuals’ positive evaluation of the interventions and proposals presented by the organization. Planning contributes to acceptance, insofar as it presents a path and the logic of an action. Acceptance can foster other feelings (i.e., hope, resilience) that make optimistic attributions when things go wrong, generating a positive perspective about the future (Avey et al., 2008).

In general, the three dimensions of attitudes toward change seem to mediate (although partially) the relation between planning and well-being. These results are in line with the literature and reinforce the findings of studies conducted by Nery et al. (2016), suggesting that the perception of planning as an attribute of organizational change in the formation of individuals’ attitudes toward change is crucial, which further suggests that the organization’s preparedness denotes to its employees that it has capacity to implement the initiatives of change (Robbins et al., 2012).

To confirm the mediation model found by the modeling of structural equations, the indirect effects were verified as instructed by Preacher and Selig (2012). Amazingly, some relations confirmed the mediation criteria, but were rebutted by the adoption of indirect effects and their degree of significance. This significance could have been the result of the bootstrapping that uses sample, pointing to bias in the present sample.

Although Kim et al. (2011) found evidence of the moderating effect of time, the results achieved with this sample did not. The effects of planning of the change and the perceived risk level were not moderated by time. The results of this study do not support the idea of gradual shifts and discontinuous information processing in employee’s cognitive models. In contrast, it is possible to conclude that such perceptions have been confirmed over time.

This finding could be because of the time span of the data collection, which may not have been adequate to identify the effect, or from the type of organization researched—public enterprise. In Brazil, public organizations typically do not undergo a lot of changes, partially because of the high level of institutionalization of public policies in the country. Thus, new studies could investigate the effect of time on private organizations or other productive sectors.

Surprisingly, we found very low correlations between the same measures at T1, T2, and T3. The set of findings could be an outlier, but we should consider that the respondents in all the three samples were not the same. We had attrition during the data collection, which could have reduced the correlations between the same measures. Furthermore, time could change these measures considering the organization had undergone many interventions. To evaluate the validity in longitudinal studies, the procedures indicated by Taris (2007) were performed.

This study contributes to the literature, first, by investigating the role of time in the gradual shifts of employee’s cognitive models of their individual–organization relationships, which has been progressively approached in the literature (George & Jones, 2001; Lee & Liebenau, 1999; Sonnentag, 2012). Second, this study contributes to organization change theory by investigating attributes of change that, albeit frequently reported as relevant, remain neglected by many studies.

Regarding methodological improvements, this study conducted three data collections with the same subjects in a context of organizational changes. Despite the setbacks during the research (the impossibility of collecting data with the same time interval), the research objectives have been achieved and some surprising results can foster other studies. Moreover, as our findings suggest, we expect these individual–work–organization interactions to change with time and that fear will not influence well-being during all organization change processes; although, this influence may occur in some circumstances. This major finding will have a valuable influence on the direction of future research. Alternative explanations, particularly regarding to the nonobserved moderating effects of time, must be considered. First, to what degree are the previously mentioned constructs affected by time sensitivity? Second, how do interventions occur over time?

The mapping of context interventions is crucial to understanding how and why these indices did not vary significantly over time. Regarding the perception of the attributes, the changes in the results are directly influenced by the intervention’s initial moments and by actions performed in an organizational environment. In this case, a monthly (or daily) monitoring of this context would be required to understand the relations between the variables. Unfortunately, this study did not meet this demand.

Changes in well-being at work are also very susceptible to the effect of time. Attitudes toward change have been shown to be very constant over time because they depend on the previous repertoire of organizational members (Bordia et al., 2011). Considering these aspects, the stability of the attitudes was probably important to the nondifference of the results over time.

In addition, low correlations among the T1, T2, and T3 measurements could indicate that time effects the intensity of the variables; however, the structure of the model did not change (the fit differences were not significant). If the coefficients of relation between the variables are considered, they are relatively different in the three moments. In other words, the intensity of relations may vary, but not enough to significantly affect the entire structure of relationships.

The large-scale organizational changes can only succeed based on the change of thoughts, feelings, and behaviors of the organization’s members (Smollan, 2015). Therefore, understanding how attitudes influence well-being at work is a differential of success. As a suggestion, organizations can promote internal events to disseminate the stages of the process of change and improve communication and transparency of the processes of change, which fosters positive attitudes and promotes engagement with the proposed process of change. The negative attitudes could be minimized with programs about quality of life and well-being during the implementation of the changes, which would reduce the negative impacts on the employees’ well-being. The negative aspects of organizational change assessment can be minimized if employees are informed and coping strategies are triggered. Thus, well-being will not suffer deleterious effects. According to the literature and our results, when resistance is well-managed, it tends to facilitate the process of change; when resistance is not well-managed, it could lead to unbearable anguish and discomfort (Bordia et al., 2011).

Limitations

The response rate to the questionnaires was better than in other studies (Laurie, 2008), but our results may be affected by nonresponse bias. Although we tested for nonresponse bias and found no evidence, the possibility cannot be completely ruled out. We speculate that if nonresponse bias did exist and if we were able to collect data from nonrespondents, our results would be stronger or more pronounced. Another limitation is regarding common method variance. To test the influence of the variance of common method, a model of one factor that did not present an acceptable fit was used (NFI = 0.46, CFI = 0.39, and NNFI = 0.49). That single-factor model was used to assess if there was an effect from the common method variance (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). We concluded that the variance of the common method does not explain the results found when there is no adjustment in this model.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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