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Implementing Planned Change: An Empirical Comparison of Theoretical Perspectives

Matthew W. Ford, Northern Kentucky University
Bertie M. Greer, Northern Kentucky University

Introduction

Understanding the process of planned change is imperative for managers who are charged with implementing strategic initiatives that drive the success of the organization. Planned change refers to a premeditated, agent-facilitated intervention intended to modify organizational functioning for a more favorable outcome (Lippit, Watson, and Westley 1958). This perspective largely reflects the teleological category of change theory advanced by Van de Ven and Poole (1995) in which organizational change is achieved primarily through the adaptive behavior of individuals in light of internally set goals. While other perspectives stress the role of external, Darwinian-like forces in organizational change (e.g., Alchian 1950; Hannan and Freeman 1977), a substantial volume of literature favors the teleological premise of premeditated actions to effect change (Huy 2001). Further, the notion of crafting and deploying large-scale change initiatives has been widely diffused among managers as the basis for strategic management (e.g., Andrews 1971; Thompson and Strickland 1998).

Although planned change has been viewed from a variety of conceptual perspectives (e.g., Gioia and Chittipeddi 1991; Huy 2001; Levy 1986), few models of planned change have been studied using empirical research designs. Inquiry using empirical methods could illuminate a number of issues about which we remain largely uninformed such as the relative importance of various change process factors in successful change implementation. For example, is incentive system alignment more important to change achievement than, say, skill development and delivery? Empirical studies could also assist in more accurately specifying change process models. Although many change process configurations have been proposed, few have been tested to determine how factors should be organized to best express the process of change (Pettigrew, Woodman, and Cameron 2001). Some resolution could be obtained by testing competing model configurations with empirical data, and linking the resulting measurement properties to the models appropriateness (Venkatraman 1990).

In this study, we seek to contribute to both theory and practice by investigating the process of planned change in an empirical context. First, we extract common factors from several prominent conceptualizations of planned change process. We then use these factors to configure three alternative models of change process implied by the literature. Using data gathered from over one hundred managers involved in the implementation of planned change, we employ multivariate methods such as factor analysis and structural equations modeling to evaluate and compare the models. By evaluating the construct and predictive validity of the models, we draw conclusions about the appropriateness of the three plausible configurations, and about the relative importance of various change process factors in achieving implementation success.We conclude by discussing the practical implications of the studys findings and by offering further direction for change process research.

Theoretical Background and Hypothesis Development

Change Variables

Planned change is often conceptualized as a process because of the sequence of actions or events that unfold to move the organization from one state to another (Garvin 1998). Models of change process tend to share three basic stages (Kanter, Stein, and Jick 1992). The first stage involves questioning the organizations current state and dislodging accepted patterns of behavior. The second stage is a state of flux, where new approaches are developed to replace suspended old activities. The final period consists of institutionalizing the new behaviors and attitudes. These three stages are clearly visible in many classic conceptualizations of change such as Lewins (1951) unfreezing-movement-refreezing framework. The same stages can be also be used to categorize the numerous variables proposed as contributors to the change process. For example, each of Kotters (1996) eight steps for managing change are readily categorized into the various stages.

Despite general acceptance of the process notion, there has been little agreement on the organizational factors or activities that comprise the process. Theorists have proposed a plethora of factors as contributing to the process of planned change. Tichy (1983) for example, proposed nine factors or levers that could be adjusted to facilitate organizational change; each factor required evaluation in the technical, cultural, and the political context of the organization. In most theoretical models, little guidance is offered about a change process factors relative importance to successful implementation. Some scholars weigh their change process factors equally and caution against ignoring any of them in the pursuit of successful change (e.g., Kotter 1995). However, there is reason to believe that the influence of particular change process factors on implementation success is not equally distributed, and that some factors might matter more than othersat least in particular contexts. For example, it has been argued that elaborate up-front planning may hinder change achievement, particularly when the planned change is large in scale (Mintzberg and Waters 1985).

The literature is also unclear on how factors reflecting the process of change are best organized (Pettigrew, Woodman, and Cameron 2001). Some scholars have suggested that the process of change is sequential to some degree, and that, when implementing change, it is more important to alter some elements of the organization before others (e.g., Hinings and Greenwood 1988). Others have noted the iterative nature of planned change and its implementation (e.g., Lindblom, 1959; Quinn 1980), which challenges the notion of planned change as orderly proceeding from one phase to the next. Moreover, contextual factors may play a role in change process sequencing. The order might depend, for instance, on whether the planned change is episodic (e.g., Romanelli and Tushman 1985) or continuous (e.g., Weick and Quinn 1999) in nature. The extent to which change process factors function in parallel or in sequence to produce successful change is a central issue in the literature that remains resolved.

Variable Selection

One way to assess the aforementioned concerns of the change process is to test competing model configurations empirically and link the resulting measurement properties to the models appropriateness (Venkatraman 1990). Since this study contributes to a relatively nascent stream of empirical change process research, we decided to limit the factors in our model to a few core building blocksfactors widely accepted as contributing to the process of planned organizational change. To obtain these factors, we studied conceptualizations of change proposed by Nadler and Tushman (1980), Tichy (1983), Burke and Litwin (1992), and Kotter (1995, 1996). These models were chosen for a few reasons. First, each of these models display some character of the teleological change theory category proposed by Van de Ven and Poole (1995). Second, these models have been widely cited in the literature; many have been featured in formal reviews of organizational change theory (e.g., Burke 1995; Werr 1995). Finally, these models represent prominent contemporary frameworks that have established a presence in the empirical world.

Our comparison found five factors common to all of these models. One factor related to activities aimed at planning or determining the organizational actions necessary to operationalize the change. A second factor reflected developing and delivering new behavior to replace old patterns of action. A third factor involved aligning incentive and reward systems to encourage behavior necessary to realize successful change. A fourth factor involved monitoring of the implementation progress and taking corrective action when necessary. Finally, there was a factor that reflected the change outcomes themselves, or the extent to which implementation was successful. The first four factors became independent process variables for our investigation while the fifth factor represented the dependent results variable. This small variable set allowed us to operationalize our research questions using a research design that was manageable in the present but scalable (i.e., open to the addition of more variables) for follow-up investigations. In the paragraphs below, we provide further evidence of the content validity of these five factors.

Variable Justification

Action planning. Scholars have historically proposed the disaggregation of high-level goals into more concrete plans of action. Barnard (1938) argued that an organizations purpose and objectives should be broken into fragments ordered in time and assignment for cooperation. Simon (1947) portrayed an organization as a hierarchy of decisions with action at lower levels. Ansoff (1965) suggested that strategic objectives were best implemented through a series of cascading goals down through the organization. Plans of specific action served as linking pins between organizational levels on the way to goal achievement (Likert 1961). Action planning processes can be highly structured, particularly in the context of planning large-scale change (e.g., Hofer and Schendel 1978; Thompson and Strickland 1998). Although many changes are incremental in their development (Quinn 1980), action planning is often viewed as an early element in temporal processes of change.

Skill development and delivery. Organizational change is realized largely through changes in individual behavior (Goodman and Dean 1982; Robertson, Roberts, and Porras 1993; Tannenbaum 1971), since the nature of individual behavior significantly influences organizational performance (Porras and Hoffer 1986). Many models of planned change emphasize the task or work related aspects of behavior change (e.g., Nadler and Tushman 1980; Weisbord 1976). Organizational change requires the development and delivery of skills in a way that will permit successful change implementation. Evidence supports the relationship between practices to acquire and develop skills and the achievement of organizational goals (e.g., Kerr and Jackofsky 1989; Terpstra and Rozell 1993). The timing of skill development and delivery must permit workers to assimilate and practice skills prior to their regular use, particularly for groups that must coordinate new skills as a work unit (Cottrill 1997). However, skills delivered too far in advance are undesirable if workers forget how to turn their knowledge into practice (Adams 1967), or if workers fail to see the connection between practicing these skills and the organizational change imperative (Baldwin and Magjuka 1997).

Incentives. Incentives induce action and motivate effort (Cummings and Schwab 1973). In addition, incentive and reward systems constitute a primary governance mechanism for the organization (Jensen and Meckling 1976). The primary controlling feature of incentive systems is the inducement for practicing behavior consistent with performance objectives (Kerr 1988). Some work has found that reward system design and usage helps explain inter-organizational differences in successful change implementation (e.g., Agarwal and Singh 1998). Accountability is a critical element of incentive and reward systems (Bourdon 1982). Individuals are said to be accountable when their performance is monitored and when there are consequences (tangible or intangible) associated with the evaluation (Siegel-Jacobs and Yates 1996). Degree of accountability appears to affect decision-making and judgment. In particular, high levels of accountability appear to encourage more information gathering and examination and to lessen the possibilities of opportunistic behavior (Fandt and Ferris 1990; Hattrup and Ford 1995), and may be particularly important in motivating performance in situations of high interdependent behavior (Fandt 1991).

Monitoring and control. Monitoring has long been considered a core activity of managers (e.g., Newman 1940). Managers commonly employ diagnostic control systems (Anthony 1965) when monitoring planned change. In diagnostic control systems, managers gather information about the initiative of interest, assess the current state of performance against goals or objectives, and act on significant differences between actual and desired performance (i.e., the performance gap) to achieve better results. As such, diagnostic controls help managers keep things on track (Merchant 1985, 1). The effectiveness of diagnostic control systems is reduced when comparative performance standards are imprecise or do not exist, or when output or behavior cannot be accurately measured (Lawler and Rhode 1976; Otley and Berry 1980). Despite its limitations, diagnostic control is thought to be central to the implementation of intended change, particularly those large in scale (Simons 1995).

Implementation success. Outcomes or results of a change initiative are frequently treated as a multidimensional variable. To assess the effectiveness of implementation, Tushman and OReilly (1997) suggested evaluating the extent to which the organization actually reached the intended future state, how well the organization functioned in its new state, and the cost of change to both organization and individual. Nadler and Tushmans (1980) congruence model, Tichys (1983) TPC framework, and the Burke-Litwin (1992) model all connect implementation success to both organizational performance and the effect or influence on the individual. Miller (1997) proposed three dimensions that captured the degree of implementation success associated with a planned change: completion, achievement, and acceptability. Completion was the degree to which intended actions were implemented as planned. Achievement was the degree to which implemented actions were performed as intended. Acceptability was the degree to which the method of implementation and outcomes were satisfactory to those involved in, or affected by, the implementation. A well-rounded measure of implementation success, then, should assess change achievement at both the organizational and individual levels as well as dimensions that capture the notions of completion, achievement, and acceptability.

Configuration Alternatives

Using these factors as building blocks, we proceed to configure three alternative representations of the planned change process that are plausible expressions of existing theory. These configurations provide working models that can be subjected to empirical assessment.Diagrams of the three configurations appear in Figure 1.

Direct effects model (M1). The most straightforward configuration of our five building block factors involves simply linking each of the four variables of change process to the implementation success variable (Figure 1a). This configuration resembles a multiple regression model in which several independent variables are hypothesized to have a direct relationship on a single dependent variable. Several studies have employed this approach to examine the relationship between single change process variables and performance. Perhaps no change process variable has been studied in this fashion more so than planning, particularly in the context of its relationship to large-scale change achievement (e.g., Pearce, Robbins, and Robinson 1987). Metastudies of the confusing, often contradictory results flowing from the stream of planning-performance studies have suggested that the models used to test hypothetical relationships require more accurate specification (e.g., Miller and Cardinal 1994). The M1 model specified here reflects the potential effects of several change process factors on implementation outcomes. M1 also expresses a non-sequential arrangement of the process variables. This specification supports the incremental, non-linear perspective of change process proposed by some theorists (e.g., Lindblom 1959; Quinn 1980). Configured in this fashion, change process factors such as action planning and skill development and delivery proceed mostly in parallel rather than in sequence to influence implementation success. This model reflects the following hypothesis:

H1: Change process factors (action planning, skill
development and delivery, incentives and monitoring and control) are positively related to implementation success.

Second order change process model (M2). An alternative perspective views each change process variable as reflecting a common, higher order change process construct (Figure 1b). Garvin (1998) viewed change processes as sequences of behaviors or events that altered the scale, character, or identity of the organization. From this perspective, a change process is more than just a collection of independent variables. Rather, the variables covary in a systematic way to reflect the higher order construct. In this configuration, the gestalt effect of the variables is proposed as a more powerful way of predicting implementation success. This configuration emphasizes the overall strength of the organizations change process. Inside this process, the variables interact dynamically. M2s configuration de-emphasizes individual variables and stresses the organizations overall change process. This model reflects the plausible notion that the process for achieving change may differ between organizations. Some organizations, for example, may realize successful change largely through exceptional planning while others rely heavily on effective reward systems. Organizations may differ in their profile of enacted change process variables while the relative strength of their overall change processes may be similar. These observations reflect the following hypothesis:

H2: Each change process factor (action planning, skill development and delivery, incentives and monitoring and control) reflects a higher order change process construct that is positively related to implementation success.

Sequential Model (M3). Implied in many models of planned change is a sequential progression that begins with planning activities and moves through variables that facilitate the execution of plans in order to realize effective change (e.g., Andrews 1971; Lewin 1951; Kotter 1996; Tichy 1983). While intuitively appealing, the notion that some actions must be done before others when implementing change has received surprisingly little research attention (Pettigrew, Woodman, and Cameron 2001). This investigations four change process factors can be categorized into the three general stages of planned change process (Kanter, Stein, and Jick 1992). Action planning is a stage one activity that helps dislodge the organization from old patterns of behavior. Skill development is a stage two activity which serves to move the organization to new patterns of action. Incentives, and monitoring are stage three activities that govern behavior and help the organization institutionalize new patterns of action.

Skill development, incentives, and monitoring can also be viewed as execution variables. These factors should be directly linked to implementation success since they make change happen by altering behavioral patterns in the organization. Levels of these factors should be related to action planning, since the plans provide the objectives and marching orders that must be operationalized. Moreover, since monitoring and incentives are mechanisms for governing behavior (Eisenhardt 1989; Fama1980), these two factors should also influence skill development and delivery due to their institutionalizing character (Figure 1C).

An interesting feature of this model is the mediating effect of three execution variables between action planning and implementation success. This planning execution outcomes sequence reflects a common conceptualization of how intended organizational change occurs (e.g., Andrews 1971; Tichy 1983; Van de Ven and Poole 1995; Thompson and Strickland 1998) that is worthy of empirical testing in lieu of the rival view that such sequential order rarely occurs or is ill-advised (e.g., Mintzberg and Waters 1985). We should also note that the inclusion of execution variables in M3 highlights the role of implementation as a bridge between planning and performancea role thought by some to have been largely unaccounted for in the planning-performance studies (e.g., Smith and Kofron 1996). These observations reflect the following hypotheses:

H3a: Action planning is positively related to change process factors (skill development, incentives, monitoring).

H3b: Change process factors (skill development, incentives, monitoring) are positively related to implementation success.

H3c: Change process factors (incentives and monitoring) are positively related to skill development.

Method

Sample data for this study were obtained from participants in change management seminars sponsored by the Center for Quality of Management. The Center for Quality of Management is an international consortium of over one hundred organizations focused on improving performance through the development and application of structured managerial processes. During the seminar, participants completed a questionnaire to assess the extent to which their organizations employed various activities during the implementation of a particular change in which they were involved. A complete description of the assessment process and the full questionnaire can be found in Center for Quality of Management (2001).

We secured 107 useable questionnaires from individuals representing forty-three organizations. The primary unit of analysis in this study was an individuals assessment of the organizations change management processes in light of a specific planned change (individuals were asked to record this reference change in the questionnaire). During the data collection, individuals from the same parent organization often identified different initiatives to serve as their reference change, hence multiple respondent issues were not deemed an overly significant concern. Indeed, the standard deviation between respondents in the full sample was found equal to or slightly higher than standard deviations between respondents in assorted sub-samples where respondent were restricted to one per organization. Sixty-four percent of the respondents worked for service organizations and fifty-six percent worked for manufacturing organizations. About two thirds of the respondents were from private, for-profit enterprises; others were about equally split between public, for-profit and public sector/government agencies. Ninety-two percent of the respondents were from organizations of more than 100 employees; 25 percent of respondents were from organizations of greater than 1000 employees. Over 90 percent of respondents were at least middle-level managers; more than half were upper-level managers.

Questions designed to reveal stage and impact of planned change indicated that about 45 percent of the changes were estimated to be at least 50 percent completed at the time of the evaluation. Once implemented, over half of the planned changes were forecast to impact at least 40 percent of the organizations employees, suggesting that the majority of changes evaluated in this study were strategic, rather than incremental, in nature (see Nadler and Tushman 1989).

Measurement

Eleven items, those meant to reflect the five latent variables of our change model, were utilized from the questionnaire (Table 1). With 107 samples and eleven indicators, our ratio of samples to indicators was nearly 10:1, comfortably above the five-to-one level often specified in multivariate studies (Hair, Anderson, Tatham, and Black 1998). The four independent latent variables of change process, action planning, skill development and delivery, incentives, and monitoring were each reflected by two items (Table 1). As indicated by the associated alphas, each scale exhibited acceptable reliability. Response to each item consisted of five choices organized on a Likert scale meant to reflect the extent to which a formal system existed and was effectively implemented. A 1 represented little or no formal system in place with few results; a 5 represented a formal, effective system. Each response choice was behaviorally anchored to reduce the response scale drift that can confuse the detection of actual behavior changes when using questionnaires to measure change (Lindell and Drexle 1979).

Implementation success was treated as a single dependent latent variable represented by three self-rated measures intended to reflect the completion, achievement, and acceptability dimensions proposed by Miller (1997) (see Table 1). Responses consisted of five behaviorally anchored choices meant to reflect the effectiveness of results achieved. A 1 represented little or no results to speak of; a 5 represented highly effective results. Descriptive statistics and correlations for the eleven indicators used in this study appear in Table 2.

Self-reported measures of performance are commonly noted as concerns due to the potential for common methods variance. However, self-reported measures have been broadly employed in empirical studies of organizations (Nahm et al 2004; Ward and Duray 2000; King and Tao 2000). One method for evaluating whether response bias impairs the unidimensionality of measured variables is confirmatory factor analysis (Gerbing and Anderson 1988). A confirmatory factor analysis of the five latent variable, 11-indicator measurement model representing the four independent change process variables of action planning, skill development and delivery, incentives, and monitoring and control, and the single dependent implementation success variable was conducted using LISREL 8 (Joreskog and Sorbom 2001). Significant path coefficients (t values of 5.9 or higher) between each of the five latent factors and their corresponding items resulted. Goodness of fit statistics suggested acceptable model fit1 (x2 = 42.19; df = 34; p = .158; RMSEA = .048; GFI = .93; AGFI = .87; NNFI = .96). Results from the confirmatory factor analysis suggested a measurement model with acceptable convergent and discriminant validity.

Results

The three hypothetical change process model configurations were evaluated using the structural equation modeling methods of LISREL 8 (Joreskog and Sorbom 2001). Figure 2 includes the coefficients obtained from analysis of the direct effects model (M1). Only one of the path coefficients, the relationship between monitoring and control and implementation success, was found highly significant (p .001). The path coefficients between skill development and delivery and implementation success, and between incentives and implementation success, were found marginally significant (p .10). The path between action planning and implementation success was not significant (t = 1.41). The squared multiple correlation for the implementation success latent variable was .70. Goodness of fit statistics implied that the model fit the data well. The chi-square was not significant (x2 = 42.2; df = 34; p = .158). Additional indicators (RMSEA = .048; GFI = .93; AGFI = .87; NNFI = .96) met or exceeded benchmarks indicative of reasonable fit. These findings suggest that Hypothesis 1 as stated should be rejected, since three of the four levers were found to have marginal or insignificant relationships to implementation success.

Figure 3 includes the coefficients from analysis of the second order change process model (M2). All path coefficients were found highly significant (p .001). The strong path coefficient between the second order change process construct and implementation success supports the proposed relationship between this higher order change process variable and change achievement. The squared multiple correlation for the implementation success latent variable was .86. The fit of this model was incrementally better than the fit of M1. The chi-square statistic remained non-significant (x2 = 46.2; df = 39; p = .201), and other indicators approached or exceeded benchmarks of reasonable fit (RMSEA = .042; GFI = .93; AGFI = .88; NNFI = .97). These findings suggest that Hypothesis 2 should not be rejected.

Figure 4 includes the coefficients obtained from analysis of the sequential change process model (M3). While many of the path coefficients were found significant, note that the strongest relationships surrounded the monitoring and control variable. The models sole insignificant path was between incentives and skill development, suggesting that incentives had little direct influence on skill development and delivery, which provided only partial support for Hypothesis 3c. The squared multiple correlation for the implementation success latent variable was .68. While the fit of this model was weaker than the fit of the previous two models, the fit statistics remained at acceptable levels (x2 = 55.5; df = 36; p = .02; RMSEA = .071; GFI = .91; AGFI = .84; NNFI = .92).1 These findings suggest that Hypotheses 3a and 3b should not be rejected.

To summarize, analysis of M1s configuration found monitoring and control as the most significant change process lever linked to implementation success. Hypothesis 1 should be rejected, since action planning, skill development and delivery, and incentives were also proposed as directly related to change achievement. Analysis of M2s configuration found highly significant paths (p .001) emanating from the second order change process construct to the other model variables, including implementation success. The strong path coefficients and measured fit support Hypothesis 2s notion the gestalt effects of individual levers as part of a higher level change process construct. The strength of the path coefficients and measures of overall fit also provided reasonable support for the sequential ordering of change process variables (M3) as proposed by Hypotheses 3a, 3b, and 3c. Of particular note was the strong relationship between the monitoring and control factor and other variables of the model, which suggests the importance of this variable on the achievement of planned change.

Discussion

Viewing the change process using a perspective similar to the direct effects M1 configuration in Figure 1 appears nave. Strength of the results from the analysis of M2 and M3 suggests a more dynamic perspective of the planned change process. One such perspective is that of a high-level change process construct which captures patterns of covariation among the individual change process variables. It is consistent with the non-linear path through which many changes are realized (e.g., Lindblom 1959; Quinn 1980). Emphasizing the higher order change process construct rather than the individual levers supports a view that different organizations might emphasize different levers at their disposal for the implementation of change. Such a perspective is intuitively appealing since it emphasizes the uniqueness by which each organization might approach the implementation problem.

Our findings also suggest the plausibility of modeling some sequential organization among change process variables. Many researchers have suggested that the process of change is sequential to some degree, and that, when implementing change, it is more important to alter some elements of the organization before others (e.g., Hinings and Greenwood 1988; Gersick 1994). Our findings suggest that this is a reasonable view from a measurement perspective, which should motivate further inquiry into causal order among change process variables.

Of the four independent change process variables considered in this investigation, monitoring and control appeared to have the strongest effect on implementation success. The path coefficients associated with the monitoring and control variable were relatively strong in each of the three models examined (see Figures 1-3). Monitorings salience to change achievement may relate to the dynamic, revisionist nature of planned change. Most planned changes, particular those large in scale, require midstream corrections to the initial course of action (Mintzberg and Waters 1985), which may necessitate formal monitoring of implementation progress.

This study has some limitations. Our sample was confined to respondents from organizations that were members of the same industrial organization. Moreover, the sample size was relatively small in comparison to other multivariate studies, and included respondents from the same organization. While the resulting demographics of the sample appeared reasonable and multiple respondent influences were deemed minimal, a larger, broader sample would be desirable in future studies. By design, the change variables selected for this study were limited to a few widely accepted factors in order to explore some fundamental empirical questions. Of course, other factors have been proposed to impact the process of change, such as climate and culture (Burke and Litwin 1992), previous decision history (Nadler and Tushman 1980), politics (Tichy 1983), and communication (Kotter 1995). Entering additional factors of change process would make for a more comprehensive analysis. In addition, the two- and three-item measurement scales were smaller than those often employed in structural equation modeling studies. Future research could explore larger measurement scales to round out the content validity of the model. Finally, researchers have noted concerns with self-rated measures of change, based largely on the argument that a raters basis for comparison shifts as the organization itself changes (e.g., Zmud and Armenakis 1978). While objective measures of organizational change are certainly desirable, finding them has been problematic for both researchers (Cameron 1980; Lewin and Minton 1986) and practitioners (Troy 1994). We should note that despite such concerns, self-rated measures have been effectively employed in a number of insightful implementation studies (e.g., Nutt 1986; Miller 1997; Nahm,Vonderembse and Koufteros 2004).

This study suggests the value of survey-based empirical research for studying organizational change. Pettigrew, Woodman, and Cameron (2001) identified issues related to temporality, sequencing, and linkage to organizational outcomes among the challenges facing researchers of organizational change. Although researchers often suggest only qualitative or case based methods for gathering change process knowledge, survey-based empirical research can help researchers pursue such issues. For example, periodically gathering questionnaire-based data over the life of an implemented change could provide insight into when organizations employ particular change process factors, the degree to which such factors were employed, and how outcomes responded to the various process adjustments.

As noted previously, the validity of M2s configuration raises the possibility that organizations possess unique change process profiles for implementing planned change. The profile, reflective of the degree to which various process variables are enacted during implementation, might relate to the organizations particular set of skills or competences (e.g., Barney 1991). Organizations that possess strong communication skills, for instance, might emphasize change process factors with high communication content to a greater extent than less fluent organizations. Empirical designs could investigate the existence of such change process profiles and the extent to which they may be linked to an organizations underlying resources.

Our study has practical implications for managers accountable for successfully implementing planned change. Findings from our evaluation of M2 suggest the possibility of developing a unique change process for each organization. Instead of subscribing to one particular set of change process factors, it appears plausible that an organization might be able to develop their own change style or combination of process factorsperhaps based on particular organizational skills or strengths. For example, an organization with poor planning skills might still realize implementation success if it can compensate with effective skill development and delivery during the change process. In addition, our evaluation of M3 suggests some sequential character to the change process, which supports the notion that timing or pace may be an important consideration when implementing change (Gersick 1994).

Do some change process factors matter more than others? In our study, monitoring and control were consistently found to be related to implementation success. Since modifications to an initial course of action are highly probable (Mintzberg and Waters 1985), diagnostic control systems may be essential for managers to detect performance gaps that impair implementation success and require corrective action. Effective monitoring and control requires organizational skills in objective setting, in information retrieval and analysis, and in selecting the appropriate corrective action if a significant deviation from plan is detected (Simons 1995).

Many of these skills are similar to factors thought to embody learning organizations (Nevis, DiBella, and Gould 1995). In other words, an organizations effectiveness in diagnostic monitoring and control may reflect general capacity for organizational learning and change management (Kloot 1997). Managers who are accountable for change outcomes might benefit from establishing monitoring and control systems that permit tracking of implementation progress and effective intervention when necessary.

Finally, we should note that, while a number of researchers have also observed the empirical importance of monitoring and control in achieving change (e.g., Charan and Colvin 1999; Kotter and Schleisinger 1979), many elements of modern organization design may not be conducive to monitoring. Managers have been busy shedding bureaucracy, decentralizing decision-making, and establishing more workplace autonomy to help the organization move faster and become more innovative (Burns and Stalker 1961; Ouchi 1980). Although such practices might help get change going, lack of formal control structure might impair effective execution of the plan. Such a premise is consistent with the Were great starters, but terrible finishers assessment we often hear from managers characterizing the change processes in their organizations. Many organizations may be reaching or exceeding advisable limits for decentralized control (Bungay and Goold 1991). Further inquiry into the role of monitoring in the process of change is prudent.

Conclusions

Given the dynamic work environments that exist in most organizations, understanding planned change and its components for success is a necessary skill for managers involved in implementing short and long-term strategic objectives. Indeed, the growing use of the term execution in the lexicon of management (e.g., Bossidy 2003) suggests that the value placed on knowing how to manage change is increasing. Knowledge about change models and the factors that compose them can only benefit managers who must pull the proper levers that lead to successful implementation.

The findings from this study support configurations that reflected dynamic change process conceptualizations. The dynamic change processes were found to possess favorable measurement properties when compared to a direct effects model. Of the change process variables considered in this investigation, monitoring and control demonstrated the strongest relationship to implementation success. These findings support a dynamic, perhaps sequential perspective of change and its implementationa perspective that should benefit from further empirical investigation.

Notes

  1. We utilize several commonly reported goodness of fit indicators and the thresholds suggested by Hair et al. (1998) as desirable. x2 is the chi-square statistic (a non significant p-value of at least p .01 is desirable). RMSEA is root mean square error of approximation ( .08). GFI is goodness of fit index (no consensus threshold but .90 often viewed as minimum acceptable value). AGFI is adjusted goodness of fit index ( .85). NNFI is non-normed fit index, also known as the Tucker-Lewis index ( .90).

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