Measuring Complexity: A Confirmatory Factor Analysis Approach

Abstract

This study examines complexity as a measure of support for organizational redirection. This study considers whether 16 items (culture, mission, values, decentralization, policies and procedures, administrative reporting practices, weapons, contract, pay, benefits, patrol boundaries, equalization of workload, size of boundaries, communications, 10-codes, and car numbers) appropriately characterized a suppressed measure of complexity related to complex organizational change, a police department merger. The current study utilizes data collected from 390 sworn officers from two merged law enforcement agencies in Kentucky. The results of the structural equation model analysis supported the view that four factors (mission, logistics, benefits, and policy) fashion an underlying construct for measuring complexity related to organizational change/redirection. The implications of these findings are also considered.

Keywords

complexity factor analysis organizational change organizational redirection police consolidation merger

Introduction

Change and the characteristic complexity that accompanies much of it exists in all organizations, especially during large-scale events such as a government merger. The research on organizational change is plentiful, generating a mass of insights. However, to date, the research on complexity related to organizational change, and support for the redirection, is less abundant. Academics and practitioners are increasingly seeing complexity theories as a way of understanding organizations and promoting change (Black, 2000; Macbeth, 2002). However, the complexity paradigm uses a method of universal inquiry to build vague and ambiguous representations of reality configured at different levels and structured from many disciplines (Dooley, 1997).

One underlying theoretical stance is that an individual’s perception of the complexity of merging certain organizational change components (OCCs) might affect attitudes of support for the consolidation. Complexity could be important, as successful organizational redirection is reliant upon generating officer support and enthusiasm for the changes (Piderit, 2000). While the existing literature examines the complex behavior of individuals, systems, and subsystems of organizations, complexity to redirect different organizational components of change is not so clear and warrants concurrence in definition. Lack of a definitive measure of complexity may inhibit literature consistency and a congruous conceptualization of complexity.

Complexity theories have transcended from the natural sciences to the field of organizational science to argue chaos is a necessary condition for the growth of dynamic systems, but that these systems are prevented from destruction by the presence of “simple order-generating” rules (Gell-Mann, 1994; Gould, 1989). Many different interpretations of complexity exist among researchers. Three key theories are most cited: chaos, dissipative structures, and complex adaptive systems (Stacey, Griffin, & Shaw, 2002). The chaos and dissipative structures theories focus on entire sets and populations. In contrast, the complex adaptive systems approach attempts to make meaning of the behavior of the individual elements of systems and populations (Stacey et al., 2002). However, there has been no examination of complexity variables and/or factors relating to merging OCCs, nor has a complexity scale been proposed. In addressing policy and operational concerns, this may prove precarious or misrepresentative of the exact issue at hand.

This research probes methodologies for conceptualizing and operationalizing the model of complexity related to organizational redirection. We examine 16 OCCs utilized in research at a large metropolitan police agency in the United States during a government consolidation (Reed, 2013) to identify the practicality of this measure for assessing complexity related to merging components of change. By utilizing structural equation modeling (SEM), and in particular confirmatory factor analysis (CFA), we seek to contribute to a more thorough understanding of the validity of this measure and how it relates to the perception of complexity in merging OCCs.

The study begins by reviewing the literature on complexity related to organizations and an examination of the implications of complexity on organizational redirection and on merging OCCs. Following this examination, we describe the OCCs utilized for merging a police agency. Thereafter, we provide a case calling for a valid and uniform measure of complexity. Subsequently, we report the methods and results, and conclude with a discussion of the findings.

Literature Review

Complexity

Complexity is a state or quality of being complicated or intricate. Organizations are complex systems made up of people and groups, sometimes forming coalitions, each of which having their own areas of interest, values, beliefs, preferences, and perspectives (Shafritz, Ott, & Jang, 2005). Complexity theories, initially utilized in the natural sciences, have found application as a way of understanding organizations and promoting organizational change (Black, 2000; Macbeth, 2002). Researchers in the natural sciences posit chaos is a requisite condition for the growth of dynamic systems, but that these systems are averted from destruction by rules that produce stability (Gell-Mann, 1994; Gould, 1989).

Like complex systems in nature, an organizational change such as a consolidation of police agencies is dynamic and characterized by continuous change, activity, or progress. Because of the nonlinear nature of the system, rules are devised to provide a focus, restraint, and/or order. The key to survival is for all organizations to develop a set of rules, which keep the organization operating “on the edge of chaos” (Stacey et al., 2002). Too much stability results in absence of change, while too much chaos creates an overwhelming atmosphere where change cannot occur. Although several interpretations regarding complexity exist, the most prominent are chaos, dissipative structures, and complex adaptive systems (Stacey et al., 2002).

Chaos theory is resultant from research conducted on weather systems by Lorenz (1993) and is defined as processes that appear to advance according to chance, even though their behavior is in fact determined by precise laws. Chaos theory involves dynamic systems that are continually changing themselves in an irreversible and thus evolutionary manner (Bechtold, 1997; Haigh, 2002). According to the theory, small changes in the environment can be augmented by chaos, which causes instability. This instability is integral to the converting of an existing pattern of behavior into a new, more suitable one. In a police department involved in consolidating OCCs, the changes causing instability can occur internally or externally and can be planned, spontaneous, or anywhere in between (Reed & Higgins, in press). In fact, managing stability may be as important as managing the organizational change (Jacobs, Christe-Zeyse, Keegan, & Polos, 2008).

The dissipative structures theory recognizes that unless energy is fed in from the outside, structures will “dissipate.” Dissipative structures are similar to chaotic systems. A dissipative structure is a somewhat constant configuration that operates in harmony with nonlinear logic (Prigogine, 1997). In certain positions, the structure can attract considerable external pressure, while in others it can be completely changed by even the smallest disturbances (Styhre, 2002). These structures can also experience periods of instability and at certain points reorganize to form a structure or behavior that cannot be foretold from knowledge of the prior condition, but rather from an internal dynamic (Stacey, 2003).

While chaos and dissipative structures theories concentrate on entire sets and populations, the complex adaptive systems take an individualistic approach to organizations attempting to make meaning of the behavior of the individual elements of systems and populations (Stacey et al., 2002). It comprises many different components or “agents” working together with one another under a set of rules so as to improve their behavior and the behavior of the group which they comprise. These systems require each agent to modify its behavior to that of other agents (Stacey, 2003) as behavior is not influenced by a single entity but rather simultaneous and corresponding actions of agents within the system itself. Learning takes place during this interaction. In this system, all of the complex adaptive systems form a larger system, which “learns its way into the future” (Stacey, 1996, p. 183), and evolve.

We maintain police agencies involved in organizational redirection and, in particular consolidation initiatives, have elements in common with each of these theories. We further theorize police organizations are dynamic systems fluid during and after the consolidation phase. As new initiatives are implemented, the organization is constantly changing and evolving. Some of the changes involve complex initiatives such as consolidating or redefining cultures, policies and procedures, communications, collective bargaining contracts, and patrol boundaries. During these changes, the department(s) display complex patterns of behavior. This occurs not only at the organizational and group level but also at the individual level. While these behaviors are chaotic, restraining rules that govern behavior are established that allow for innovation, but discourage excessive behaviors.

Organizations need to change to cope with instability in their external environments (Gioia, Schultz, & Corley, 2000) such as with dissipative structures. The external environment includes customers, markets, and associations that influence the responses of the organization. During a consolidation of police agencies, external influences adding to complexity can include political considerations, labor unions, community members, businesses, and special interest groups.

There are also components of complex adaptive systems encountered during a police consolidation. Individuals work and learn from other members of the organizational system, especially in specialty units such as narcotics, communications, investigations, and so on. These systems work with other systems as part of the entire larger conglomerate.

Much of the complex organizational change during a consolidation process can be generally categorized as cultural or structural. Organizational change is often related to the violation of an organization’s core cultural values (Hannan, Pólos, & Carroll, 2007). We theorize that even if many organizational changes are structural, conceptually the change influences procedures, which induces habit-forming behavior. At this level, behavior becomes a characteristic of the organization’s culture.

The organizational structure and the organizational culture provide the answer to the basic question of who we are as an organization, our purpose, our mission, and so on (Albert & Whetten, 1985). This process of recognition takes place internally and is complex. These fundamental, long-term, and exclusive to the organization elements institutionalize the requirements for a collective belief structure (Jacobs et al., 2008; Jacobs, van Witteloostuijn, & Christe-Zeyse, 2013; Van Rekom & Whetten, 2007). These elements guide behavior and make uniform and stable action possible. Mutual beliefs can successfully influence organizational action due to inchoate policies, rules, and procedures, and the inability to project every contingency that may occur. Thus, certain actions are often derived from the common understanding of who we are as an organization (organizational identity). Adding to the complexity is that some organizational changes or issues are not supported or affiliated with the organizational identity. This may result from a practice that has yet to be formalized or one that presents fundamental challenges to valued organizational identities (Gioia et al., 2000; Rousseau, 1998; Van Knippenberg, Van Knippenberg, Monden, & de Lima, 2002) which adds to further complexity regarding the mechanics of organizational change.

The complex nature of organizational redirection is also evidenced by multiple audiences, both internal (i.e., different divisions, departments, and/or units) and external (i.e., the public, businesses, government offices) to the organization. With a police agency merger, individuals may have different cultural interpretations of expectations or relationships adding to the unification’s complexity. Ambiguous procedures and regulations are interpreted in numerous ways, and different parties develop varied responses in relation to procedural ambiguities (Magala, 2009; Weick, 1995) which also added to the complexity of change.

OCCs

Culture is the most dominant dynamic in an organization and refers to the deep structure of organizations, which is grounded in the values, beliefs, and assumptions held by organizational members (Denison, 1996). It is thought to influence individuals’ attitudes concerning outcomes, such as support, motivation, morale, and satisfaction. These fundamental, long-term, and exclusive to the organization elements institutionalize the requirements for a collective belief structure (Jacobs et al., 2008; Jacobs et al., 2013). A mission statement is a stainable statement of purpose that characterizes an organization from other like organizations (David, 1989). It exemplifies the culture, principles, and beliefs of the organization. Values are the norms and basic truths employees feel are important to their organization (Schein, 1985). They are integral to the agency’s operation as they guide the behavior of personnel. Decentralization is a complex organizational change related to the structure and ultimately the culture of the agency.

For an organization to be viable, its policies and practices must be adaptable to its current environment (Khandwalla & Mehta, 2004). Besides policies, all administrative reporting practices needed to be revised during the consolidation to conform to the values and mission. In addition, to ensure uniformity, a weapon choice was made as each agency carried a different weapon system.

At the time of the consolidation of the two organizations, 11 contracts existed with different pay rates, benefit packages, and miscellaneous contractual provisions. Many of these contracts were merged, redefined, or renegotiated to reconcile parity in pay and benefits and minimize the number of contracts that existed. Restructuring of patrol divisions was essential for optimal organizational effectiveness and efficiency as each agency, prior to merger, had a disparate number of patrol divisions. When redefining the patrol boundaries, equalization of workload and geographical size were important factors for consideration. Calls for service, response times, population density, distance considerations, and officer safety were evaluated for each division and patrol areas within each of the divisions.

Communications at the time of organizational change were handled by two different radio systems with dissimilar wavelength ranges, both an ultra high frequency (UHF) system and a very high frequency (VHF) system. These systems did not allow for effective radio communications, as they provided no interoperability unless a “link” was manually selected. At the time of the consolidation, one agency used approximately 100 10-codes while the other used ten to twelve 10-codes. In addition, the car numbers used and assigned by each agency were entirely different. One agency assigned car numbers individually while the other agency assigned them to areas/units.

The Need for a Standardized Measure of Complexity

Due to the frequency of organizational change and the interests of scholars, an array of research has been conducted. However, the complex nature of the organizational change(s) has been far less studied. This is especially true when measuring the complexity of change as it applies to different types of organizations, especially police agencies. It is vital to have a standardized and validated method of measuring complexity related to organizational change.

There is very little consensus on how to evaluate complex organizational change processes (Oreg, Vakola, & Armenakis, 2011). However, there is a prevalent practice in the organizational change research to ignore the dominant influence of cultural and institutional differences of diverse organizations (Sorge, 2005). While this position has merit, it is also apparent that there are many commonalities among types of organizations that can be standardized. The analysis of organizational change needs an approach that can account for typologies of organizations. While there may be exclusive differences in organizations such as culture, mission, values, and so on, these organizational attributes are generally found in all organizations and changing them could be more consistently measured by a standardized method. We have formulated a list of 16 conjectural components transformed to four factors that provide examples of core insights we believe to be interesting to explore in future work measuring organizational change complexity.

The Present Study

The purpose of the present study is to examine the structure of the complexity of OCCs as a measure during organizational redirection. We examine 16 components of change (OCCs) to determine whether they culminate in a measure of complexity for organizational redirection in police agencies. To accomplish this, we use CFA by means of SEM. This research seeks to enrich the understanding of complexity and organizational change in police agencies and possibly in other organizations with like components of change.

Method

Procedures and Sample

The data for this study came from a population of 669 sworn police officers who were working for a large metropolitan police agency in Kentucky, but had previously worked for two police agencies, which were consolidated. Of the entire population, 435 or 65% were employed with one of the former agencies and 234 or 34.9% were formally employed by the other. The officers included in the study held the rank(s) of Officer through Lieutenant Colonel. Patrol officers accounted for the greater part (67%) of the population, followed by Sergeants and then members of middle management. The majority of the population was male (85.7%) and Caucasian (86%) followed in frequency by Black/African Americans, Hispanics or Latinos, and Asians.

Procedures

A survey instrument from which the data were drawn was divided into five sections: (a) support (prior support and current support), (b) merger experience and participation, (c) satisfaction, (d) perceived complexity, and (e) demographic information/officer characteristics. The instrument consisted of 32 questions and was administered to personnel in August 2012 via departmental email utilizing SurveyMonkey®.

The 16 OCCs used to test complexity were scored on a 7-point scale ranging from 1 (very easy) to 6 (very difficult). Seven was used for neutral responses (“no opinion” responses were excluded from the data set prior to final analysis). The specific question for the complexity items was as follows: “In your opinion, how difficult was it to merge the following OCCs of the two agencies?” Exploratory factor analysis revealed that the 16 items coalesced into four factors that were used as subscales, and the subscales accounted for 75.97% of the variation. The four subscales were as follows: Mission (culture, mission statement, decentralization of personnel from specialty units, and value statement; Cronbach’s α = .74), Logistics (patrol division boundaries, equalization of workload in merged patrol divisions, geographical size of the patrol divisions, and communication sections; Cronbach’s α = .88), Benefits (type of weapon patrol officers carry, contracts, reconcile pay scales, and reconcile employee benefits, as defined by contract; Cronbach’s α = .88), and Policy (10-codes, car numbers, policy and procedure manuals, and administrative reporting practices; Cronbach’s α = .71).

Statistical Analysis

The focus of the study was to provide information about the measurement of complexity. Specifically, three steps produce the results. The first step was the development of the descriptive statistics (means and standard deviations). The second step was the bivariate analysis via correlations. Correlations determine the amount of shared variation among the subscales. The third step was the CFA via Mplus Version 6.12. CFA allowed for the examination of the measurement properties. Specifically, the CFA provided information about the size of the factor loadings. Factor loadings above 0.50 are considered strong factor loadings (Kline, 2010). Furthermore, four indexes were used, in the CFA, to assess model fit. The first index was the chi-square statistic. The chi-square statistic should be nonsignificant. The likelihood of this occurring was small because the amount of data was large. This indicated consulting other indexes. Other indexes consisted of the root mean square error of approximation (RMSEA < 0.08), comparative fit index (CFI > 0.95), and standardized root mean square residual (SRMR < 0.05) (Hu & Bentler, 1999).

Results

Step 1

Table 1 presents the first step and is the report of the descriptive statistics. Primarily, the means for each of the subscales are as follows: mission (M = 9.99), logistics (M = 15.55), benefits (M=13.22), and policy (M = 12.65).

Table 1.

Descriptive Statistics.

Measure	M	SD	Internal consistency
Mission	9.99	3.03	0.74
Logistics	15.55	4.65	0.88
Benefits	13.22	4.83	0.88
Policy	12.65	3.94	0.71

Step 2

Table 2 presents the bivariate correlations. The bivariate correlations show whether the measures share appropriate amounts of variation to proceed to the CFA. The correlations are significant and range from .21 to .31. This suggests that proper amounts of shared variation exist among the measures.

Table 2.

Bivariate Correlations.

Measure	1	2	3	4
1. Mission	1.00
2. Logistics	.21*	1.00
3. Benefits	.29*	.28*	1.00
4. Policy	.28*	.28*	.31*	1.00

p < .05.

Step 3

Table 3 presents the CFA. The CFA determines the strength and fit of the data to the model. When the factor loadings are strong—above 0.50 (Kline, 2010)—the model fits the data and adequate measurement properties exist. Table 3 shows that half of the factor loadings meet Kline’s threshold for large factor loadings. Two of the factor loadings do not meet this threshold, but they do not indicate severe problems. We believe the factor loadings are substantial. Furthermore, the factor loadings show the subscales should be retained. In addition, the model fits the data (χ² = 0.72, p = .70; RMSEA = 0.00, CFI = 1.00, and SRMR = 0.01).

Table 3.

Confirmatory Factor Analysis.

Measure	Factor loading
1. Mission	0.49
2. Logistics	0.48
3. Benefits	0.58
4. Policy	0.56

Discussion

The present study tested the factor structure of 16 complexity OCCs from a large police department consolidation. We assert these 16 items form a four-factor model: mission, logistics, benefits, and policy. This model demonstrated desirable fit and strong fit implications. The fit of the model in relation to the data indicates that the model has discriminant validity, and the factor loadings indicate that convergent validity has been identified in the data. In addressing sworn police agency employees related to complexities of organizational change, it appears that this scale is appropriate.

Even though past research related to organizational change could be considered sufficient, complexity related to organizational redirection is not. Organizations, especially police organizations, are constantly changing and redirecting their resources. Until this study, measuring the complexity of components of change by discipline typology was virtually nonexistent. This study may lead to a more sound development of policy to dispel prechange misconceptions, ease the chaotic nature of the redirection, and help to mold positive perceptions and ultimately lead to support of the change (Reed & Higgins, in press).

The CFA measure comprises items and ultimately factors that net several different aspects of complexity. These items/factors are lacking in the literature. Specifically, the measure contains items relevant to mission, logistics, benefits, and policy. Although succinct, the items allow for a discussion and exchange that the measure is exploiting several parts of the complexity concept. Also significant is that the measure affords sufficient data to understand complexity among organizational change in police agencies.

It should be noted that the validity of the scale should be kept within the confines of the study’s limits. While the population is sizable and adequate, the population comes from one police agency involved in merging two law enforcement agencies. Despite the limits of this study, it augments the literature by examining the structure of complexity related to organizational change in a police agency. The results show that the factors of mission, logistics, benefits, and policy constitute an unrealized measure of complexity of organizational change in police agencies. Further study is warranted that investigates a more theoretically focused approach. However, for judicious inquiry related to complexity and organizational change in police agencies, the measure implies validity to understand the complexity of redirection in police organizations.

Conclusion

When organizational redirection occurs, a multitude of complex changes can take place. Understanding complexity during organizational redirection or change is important due to the effects it could have on employees. Complexity can be a very important obstacle to adoption or support for innovations such as organizational redirection/change (Rogers, 2003). This study was undertaken to determine a standardized measure of complexity. The findings provide a more comprehensive understanding of the role of complexity in relation to organizational change initiatives in police agencies and ensure validity of information interpretation in future research. This will help to gain support and to dispel negative perceptions of complexity, such as believing changes are more complex than they really are or avoiding changes that are believed to be too difficult.

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.

Author Biographies

John C. Reed Jr., PhD, is an associate director of the Southern Police Institute and an assistant professor in the Department of Criminal Justice at the University of Louisville. He holds a BS in police administration, an MS in justice administration, and a PhD in urban and public affairs from the University of Louisville. He served in the law enforcement profession for approximately 35 years, 28 of which with the Louisville Metro Police Department and seven as chief of police with the Henderson, Kentucky Police Department. He has been a consultant to law enforcement agencies as well as to the national police forces of Hungary and of Slovakia. His research interests include organizational behavior, organizational change, and policing issues.

George E. Higgins is a full professor in the Department of Criminal Justice at the University of Louisville. He received his PhD in criminology from Indiana University of Pennsylvania in 2001. He is the current editor of the Journal of Criminal Justice Education. His most recent publications appear or are forthcoming in Journal of Criminal Justice, Criminal Justice and Behavior, Justice Quarterly, Deviant Behavior, and Youth & Society.

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