The complementarity of control formalization and control flexibility: The contingent effects of competitive turbulence

Abstract

We examine how the complementarity of control formalization and control flexibility influences organizational performance across contexts of varying competitive turbulence. We build contingency arguments anchored in the efficiency logic of control theory and investigate both the restrictive and facilitative views of control formalization. Our empirical evidence is based on a survey of top executives from 536 organizations across the United States, Australia, China, and Israel. We find that control formalization and control flexibility are complementary in environments of low competitive turbulence. With increasing turbulence, the complementarity diminishes and shifts toward substitutive effects.

JEL Classification: L2

Keywords

Competitive turbulence control flexibility control formalization control theory organizational control

1. Introduction

Organizational control, defined as processes that managers use to align organizational attention and efforts with organizational goals, is a central issue in the strategy and management literatures (Cardinal et al., 2017; Walter et al., 2021). In environments with high competitive turbulence, where there is an ongoing and significant level of competition-driven changes (Nadkarni et al., 2016), defining effective controls is challenging because the knowledge to specify desired behaviors and/or outcomes is frequently incomplete (Eisenhardt, 1985; Turner and Makhija, 2006). The various types of controls have been traditionally considered as independent choices in organizational control research (Choudhury and Sabherwal, 2003)—implying an “either-or” substitution logic.

In contrast, recent work following a more “holistic view of control” (Cardinal et al., 2017) has argued that the various types of controls coexist and suggested a complementary logic (Kreutzer et al., 2015, 2016; Wang et al., 2021). In this view, multiple forms of control interact to jointly influence organizational performance (Cardinal et al., 2017). These different perspectives establish a debate on whether organizational controls are substitutes or complements (Sihag and Rijsdijk, 2019). Within this debate, however, there is limited understanding on the contingencies that may shape the complement–substitute relationships. More specifically, we know little about the effects of competitive turbulence on the complementarity of controls. This is surprising given the salience of understanding organizational performance in environments with different levels of competitive turbulence (Nadkarni et al., 2016; Siggelkow and Rivkin, 2005). This lack of theoretical understanding prompts our investigation.

In environments with high competitive turbulence, numerous scholars argue that the fundamental control challenge is to effectively balance structured formalization (or the demands of efficiency) and flexibility to achieve high performance (Davis et al., 2009; Eisenhardt et al., 2010). Specifically, organizations benefit from structured formalization that guides organizational activities to be more efficient (Eisenhardt et al., 2010). At the same time, organizations benefit from flexibility to adapt to changing environments to achieve better fit (Doz and Kosonen, 2010; Nadkarni et al., 2016). We examine control formalization and control flexibility as two distinct types of organizational controls by drawing on the holistic view of controls (Cardinal et al., 2017) and the limited previous work (Naveh, 2007; Patel, 2011; Tatikonda and Rosenthal, 2000). Specifically, we examine control flexibility defined as the degree to which control processes enable the organization to easily and quickly change—including changing control processes themselves (Repenning et al., 2018; Smith and Besharov, 2019). Furthermore, we investigate control formalization defined as the degree to which rules and procedures governing behaviors are explicitly defined and monitored for compliance (Cardinal, 2001). Surprisingly, existing research is divided about whether control formalization should be viewed as restrictive (Burns and Stalker, 1961; Wally and Baum, 1994) or facilitative in times of change (Patel, 2011; Sine et al., 2006). Accordantly, we investigate the performance effects of the two views of control formalization (restrictive or facilitative) and the complementarity of control formalization with control flexibility across environments of varying competitive turbulence.

We first establish two competing hypotheses regarding the interaction of control formalization and competitive turbulence on organizational performance following the divergent views of control formalization. Then, we hypothesize a three-way interaction among control formalization, control flexibility, and competitive turbulence. Our empirical analysis is based on a survey of 536 large- and medium-sized firms across the United States, Australia, China, and Israel. To assure reliable and valid responses (Bagozzi and Phillips, 1982; Li and Zhang, 2007), our survey was administered to top executives knowledgeable on organizational operations (e.g. Chief Executive Officers (CEOs), Managing Directors, and Strategy Directors) as the key informants (Kumar et al., 1993). We find that competitive turbulence positively moderates the relationship between control formalization and organizational performance, which supports the facilitative view of control formalization. Most importantly, we find that the complementarity of control formalization and control flexibility diminishes as competitive turbulence increases.

Our work contributes to control theory research by investigating the contingent effect of competitive turbulence on the relationship between multiple controls and organizational performance. Our primary contribution is to inform the complement–substitute debate in the organizational control literature (Kreutzer et al., 2016; Sihag and Rijsdijk, 2019). We show that control formalization and control flexibility are complements in environments of low competitive turbulence. We also show that as environments increase in competitive turbulence, this complementarity diminishes and shifts toward substitutive effects. In doing so, we provide theory to explain the underpinning dynamics and present a boundary condition for the complement–substitute relationships of multiple forms of control. In accomplishing this primary contribution, we also provide insights to other discussions in the control literature. First, we explore the divergent views of control formalization as being restrictive or facilitative in changing environments (Seeck and Kantola, 2009). Our finding contrasts with the traditional restrictive view of control formalization in environments with high turbulence (Burns and Stalker, 1961; Wally and Baum, 1994) and supports the facilitative view (Patel, 2011; Sine et al., 2006). Second, our research informs the work of a small group of scholars who argue against the long-held formalization–flexibility trade-off by conceptualizing control formalization and control flexibility as two distinct control types (Naveh, 2007; Patel, 2011; Tatikonda and Rosenthal, 2000). Third, we provide novel theorizing regarding the interactions of controls in environments with varying degrees of competitive turbulence by examining the efficiency mechanisms—bureaucratic inefficiency, task uncertainty reduction efficiency, and adaptive efficiency—that are simultaneously in effect. These contributions inform both theory and practice, and open pathways for future research.

2. Theoretical background

We build on prior work to investigate the moderating effect of competitive turbulence on control–performance relationships. We specifically investigate control formalization and the complementarity of control formalization and control flexibility on organizational performance contingent on competitive turbulence. In our study, complementarity means that a combination of different controls is more effective than any one individual control used on its own (Cardinal et al., 2017; Kreutzer et al., 2016). Next, we provide background on our key constructs.

2.1. Competitive turbulence

Turbulent environments are generally understood to be unpredictably changing environments (Nadkarni et al., 2016; Siggelkow and Rivkin, 2005). Turbulent environments have long received attention in the literature, and they continue to be the focus of investigations of, for example, dynamic capabilities (Teece et al., 2016) and adaptability (Doz and Kosonen, 2010). Our investigation focuses on turbulence caused by competition because it is a ubiquitous form of turbulence and thus is more generalizable. We define high competitive turbulence as an environment with an ongoing elevated level of change caused by competition (D’Aveni, 1994; Siggelkow and Rivkin, 2005); specifically, change that is radical, frequent, ongoing, and unpredictable. Across the literature, competitive turbulence has been acknowledged to have a direct, negative influence on organizational performance as well as a moderating influence on an array of behavior–performance relationships (Cheung and Jackson, 2013).

2.2. Organizational control formalization

The literature suggests that the primary reason for organizations to formalize control processes is to gain efficiency (Ahrens and Chapman, 2004; Gopal and Gosain, 2009). Formalized control processes increase the development, communication, and enforcement of rules and procedures that provide employees direction and guidance (Cardinal, 2001; Shinkle et al., 2021). Adler and Borys (1996) identified two views of formalization from the employee perspective—coercive and enabling. A coercive style of enactment follows the notion of command and control where standard procedures are designed and implemented to force compliance. In contrast, an enabling style of enactment encourages employee motivation and enables flexibility to deal more effectively with contingencies that may arise (Adler and Borys, 1996). Similarly, in changing environments, control formalization may be viewed as restrictive (Burns and Stalker, 1961; Wally and Baum, 1994) or facilitative (Patel, 2011; Sine et al., 2006) to achieving organizational performance. Overall, even though some scholars have indicated the possibility for curvilinear, diminishing returns (Huang et al., 2014), the literature generally suggests a positive linear relationship between control formalization and performance (Sihag and Rijsdijk, 2019).

2.3. Organizational control flexibility

Specific to the control area, flexibility in control has traditionally been viewed as an approach that lacks structure (Burns and Stalker, 1961; Ouchi, 1980) so that flexibility emerges through unfettering the discretion of employees and allowing improvization. However, this view of flexibility as the absence of control formalization began to change in more recent work. For example, Eisenhardt and colleagues examined the concept of simple rules—rules with little to moderate structure through which managers introduce flexibility by allowing improvization into control processes (Davis et al., 2009; Eisenhardt and Sull, 2001). This perspective of controlled flexibility is further supported by the emerging “structured flexibility view” where stable organizational features and adaptive enactment processes are indicated to interact, resulting in hybridity (Smith and Besharov, 2019). We draw on these more contemporary works to investigate control flexibility wherein flexibility is achieved through controlled processes. Prior research has largely indicated performance benefits to flexibility when environments are dynamic, uncertain, or turbulent (Davis et al., 2009; Nadkarni and Herrmann, 2010).

2.4. Control formalization and control flexibility

Scholars in the control area have taken contrasting perspectives on the relationship between control formalization and control flexibility¹ (Eisenhardt et al., 2010; Tatikonda and Rosenthal, 2000). First, traditional control theory argues that control formalization entails bureaucracy that impedes flexibility (Kurke, 1988; Wally and Baum, 1994). In this view, organizations confront a tradeoff between control formalization and flexibility (Kurke, 1988) because they are conceptualized to exist on a single-continuum, which implies that more of one thing precludes the other (Wally and Baum, 1994).

Second, scholars have indicated that control formalization and control flexibility may be viewed as distinct constructs. For example, in a study of research and development (R&D), Naveh (2007) found that formality and discretion (a form of control flexibility) are distinct constructs in R&D project execution. Each was found to positively affect R&D project performance; moreover, a positive interaction existed in their relationships with performance. Similarly, Tatikonda and Rosenthal (2000) found that project execution methods that include formality, project management autonomy, and resource flexibility are effective in isolation as well as collectively. In this study, project management autonomy and resource flexibility were viewed as providing discretion—for example, control flexibility—and the authors concluded that firms can “balance firmness and flexibility” in product development (Tatikonda and Rosenthal, 2000). Our investigation builds on this perspective and examines control formalization and control flexibility as distinct constructs.

3. Hypotheses development

3.1. Control formalization and competitive turbulence

Building on the anticipated positive performance effect of control formalization (Sihag and Rijsdijk, 2019), we examine how the relationship between control formalization and organizational performance varies with the level of competitive turbulence in the environment. Surprisingly, existing research is divided about whether the benefits of control formalization are strengthened or weakened in environments with high level of competitive turbulence. Mapping to these divergent views, we develop two competing hypotheses.

The restrictive view (Burns and Stalker, 1961; Wally and Baum, 1994) argues that competitive turbulence weakens the positive relationship between control formalization and organizational performance. Control formalization is widely viewed as a “mechanistic’’ approach that is efficient in compelling organizational action under normal (e.g. relatively stable) environmental conditions (Galbraith, 1973, 1977). Following the traditional work, more mechanistic control processes, characterized by formalized procedures, tend to constrain performance as competitive turbulence increases (Burns and Stalker, 1961). This is because formalized control processes are inefficient in adapting, and therefore lose fit with the changing environment due to bureaucratic inefficiency. Much of the existing literature indicates that a high level of control formalization tends to constrain performance in environments with high level of competitive turbulence (Aiken et al., 1980; Glisson and Martin, 1980; Wally and Baum, 1994). Hence, we propose:

Hypothesis 1a: Competitive turbulence moderates the relationship between control formalization and organizational performance. Specifically, the positive relationship between control formalization and organizational performance is weakened as the level of competitive turbulence increases.

The facilitative view argues that, as the level of competitive turbulence increases, performance benefits of control formalization can also accrue (Patel, 2011; Sine et al., 2006). The reasoning underpinning this argument is that formalized processes increase organizational efficiency by reducing uncertainty regarding the task to be undertaken by organization members. That is, there is efficiency in reducing task uncertainty (Rustagi et al., 2008). More formalized control processes facilitate a higher level of clarity on the action to be implemented and channels (e.g. narrows) organizational focus. This is critical for firms in environments with high competitive turbulence where it could be difficult to identify the precise area deserving of attention (Ocasio, 1997). Such a reduction in uncertainty regarding the task and attention focus facilitates more consistent and action-oriented behaviors of organizational members, which thereby assists organizations in developing largely effective and more timely responses (Adler and Borys, 1996; Patel, 2011). The task certainty of formalized processes is especially relevant in comparison to competitors that have limited formalized guidance and thus rely mostly on individual employee search, which is often divergent and requires effort and time to realign at the organizational level (cf. Siggelkow and Rivkin, 2005). Empirical support for the performance benefit of more control formalization as competitive turbulence increases is also evident in a large-scale survey conducted by the US Census Bureau of over 30,000 manufacturing plants across more than 10,000 companies (Bloom et al., 2017). This study indicated that, in environments with “tougher competition,” organizations with more structured (e.g. formalized) management practices reported higher levels of productivity, profitability, innovation, and growth. Hence, we propose:

Hypothesis 1b: Competitive turbulence moderates the relationship between control formalization and organizational performance. Specifically, the positive relationship between control formalization and organizational performance is strengthened as the level of competitive turbulence increases.

3.2. Control formalization, control flexibility, and competitive turbulence

Next, we examine the complementarity of control formalization and control flexibility contingent on competitive turbulence. This joint-effect examination resonates with work in the holistic approach where the simultaneous use of various types of controls are argued to offset the weaknesses of any one type of control (Kreutzer et al., 2015, 2016). This emerging work generally finds that controls are complementary (see the meta-analysis by Sihag and Rijsdijk, 2019) but has limitedly examined contextual contingencies. In correspondence with the competing logic in Hypotheses 1a and 1b and the anticipated complementarity, we develop two competing hypotheses for the three-way interaction among control formalization, control flexibility, and competitive turbulence on organizational performance.

To undergird these competing hypotheses, we begin by examining an environment of low competitive turbulence. When competitive turbulence is low, we argue that control formalization and control flexibility will exhibit complementarity in their influence on performance. With a low level of competitive turbulence, uncertainty and unpredictability are low, which allows higher formalization to provide explicit organizational direction and guidance that enhances efficiency of organizational activity toward organizational goals (Ahrens and Chapman, 2004; Turner and Makhija, 2006). In such environments, we contend that this efficiency benefits accrues regardless of whether control formalization is viewed as restrictive or facilitative following the general performance expectation of the literature (Sihag and Rijsdijk, 2019). Simultaneously, higher control flexibility, via its adaptive efficiency mechanism, enables the incremental, infrequent, and predictable control adaptations of the low turbulence environment to be easily integrated with the formalized control processes through the bureaucratic system (Patel, 2011). In this situation, we contend that the speed of adaptation has limited performance effect due to the slow pace of change in this environment. Thus, we argue that control formalization and control flexibility are complementary when competitive turbulence is low.

Building from Hypothesis 1a, which follows the restrictive view that formalized control processes are inefficient in changing and, therefore, constrain adaptation (Burns and Stalker, 1961; Wally and Baum, 1994), we argue that the complementarity of control formalization and control flexibility is strengthened as competitive turbulence increases. A higher level of competitive turbulence generally increases the benefit of adaptive efficiency (Davis et al., 2009; Nadkarni and Herrmann, 2010) that arises with control flexibility. Combining control flexibility with control formalization will relax the anticipated constraints of bureaucratic inefficiency from formalized controls to more effectively cope with competitive turbulence (Naveh, 2007; Patel, 2011). As a result, in combination, high formalization and high flexibility are mutually supportive and performance increases as competitive turbulence increases. Following the restrictive view, we argue that this is due to the combined efficiency effects wherein the adaptative efficiency of control flexibility offsets the bureaucratic inefficiency of control formalization. Hence, we propose:

Hypothesis 2a: There is three-way interaction among control formalization, control flexibility, and competitive turbulence for organizational performance. Specifically, the complementarity of control formalization and control flexibility is strengthened as competitive turbulence increases.

Building from Hypothesis 1b, which follows the facilitative view that formalized control processes increase organizational efficiency by reducing task uncertainty (Patel, 2011; Sine et al., 2006), we argue that the complementarity of control formalization and control flexibility is weakened, shifting toward being substitutive as competitive turbulence increases. When competitive turbulence is high, frequent and significant departures from standard practices as well as changes to control processes, which result from high control flexibility, degrade the efficiency of reducing task uncertainty from high control formalization. This is because the adaptive efficiency of increasing flexibility imparts greater on-going changes to both the existing practices and frequently to the formalized procedures themselves. As a result, organization members are left with less clarity because there is difficulty in unlearning, relearning, and re-accumulating knowledge regarding the new practices and procedures (Fiol and O’Connor, 2017). Consequently, the practices to use and the procedures to follow are less certain to organizational members; such continuous disturbances frequently cause change frustration and organizational confusion (Kovoor-Misra and Smith, 2008). As a result, the performance benefit of the combination of high formalization and high flexibility decreases as competitive turbulence increases. Following the facilitative view, we argue that this benefit decrease is due to the combined efficiency effects wherein the adaptative efficiency of control flexibility conflicts with the efficiency of reducing task uncertainty of control formalization. Hence, we propose:

Hypothesis 2b: There is three-way interaction among control formalization, control flexibility, and competitive turbulence for organizational performance. Specifically, the complementarity of control formalization and control flexibility is weakened as competitive turbulence increases.

4. Data and methods

4.1. Sample and data collection

Our empirical analysis is based on data collected through a survey undertaken in 2015, of large- and medium-sized firms throughout the United States, Australia, China, and Israel. A multi-country sample has distinct advantages in increasing generalizability (Harrigan, 1983). These four countries are representative in the global market in exhibiting diverse levels of economic development (China vs the United States) and socio-political dynamism (Israel vs Australia). Our survey was administered to top executives knowledgeable about organizational operations—that is, key informants such as CEOs, Managing Directors, and Strategy Directors. The underlying logic of the key informant approach is that individuals, by virtue of their position in the organization’s hierarchy, are able to provide perspectives that reflect those of other key decision-makers and the firm in general (Kumar et al., 1993).

Our sample in Australia and in the United States was collected with the collaboration of a third-party online survey administration company following the approach in the study by Long et al. (2011). First, the third-party firm screened the informants to only include top managers (based on job positions). In addition, the expertise of the respondents was verified by asking their knowledge regarding setting strategic goals, determining strategy, and strategy implementation processes on a 7-point Likert-type scale (cf. Atuahene-Gima and Li, 2002). Respondents scoring under 4 were filtered out, leaving the final means of the level of knowledge in these three areas above 5.5. Our sample was supplemented in Australia with responses from top executives in the university alumni network (36 firms provide full data).

We collected responses in Israel in collaboration with a bilateral international Chamber of Commerce, while responses in China were collected with the support of local government agents. To increase the reliability of the information collected in China, we primarily adopted a face-to-face interview approach (80%), supplemented by an online survey approach. The survey was administered personally by one member of our research team. This approach has been endorsed as a way of obtaining reliable and valid data in emerging economies (Hoskisson et al., 2000). The translation and back translation procedure was implemented (Brislin, 1970). That is, an English-language version of the survey was developed, before being translated into Chinese, and then translated back into English for comparison. No inconsistencies were observed—indicating equivalence.

We tested the differences in sampling approaches with dummy variables, and no statistical differences were indicated (e.g. including dummy variables did not influence findings, and the dummy variables were not significant at p < 0.05). We checked non-response bias by comparing the demographics of earlier responses and later responses (first- vs fourth-quartile responses) on 363 respondents receiving the survey at the same time (Armstrong and Overton, 1977). The results of the t-tests suggested there was no significant difference, indicating that non-response is of limited concern in this research. In total, we gained insights from 555 firms (202 in Australia, 203 in the United States, 101 in China, and 49 in Israel) across a broad array of industries (see Appendix A of the Supplemental Material for sample characteristics).

4.2. Measures

4.2.1. Organizational performance

We measured organizational performance with three survey items drawn from the study by DeSarbo et al. (2005) (See Appendix B in Supplemental Material for survey items). Respondents were asked to compare their performance relative to their competitors in terms of return on capital, relative market share, and sales growth over the past year on a 7-point Likert-type scale (1 = far below average; 4 = average; 7 = far above average). The three items loaded on one factor with a Cronbach’s alpha of 0.87, above the accepted threshold of 0.7 (Nunnally, 1978). We used the mean score on the three items to measure organizational performance.

4.2.2. Control formalization

The measure of control formalization, consisting of six items, is adapted from the study by Cardinal (2001). While Cardinal’s (2001) formalization measure highlights the documentation and formulation of rules and procedures, we added extra items regarding monitoring and enforcements to provide a more comprehensive measure of the control formalization construct (Chen et al., 2009; Turner and Makhija, 2006). The Cronbach’s alpha for the six items was 0.87. The values of composite reliability (CR) and average variance extraction (AVE) of the measures were 0.9 and 0.6, both of which were above the respective thresholds of 0.7 and 0.5 (Fornell and Larcker, 1981). These evaluations indicated a high level of reliability.

4.2.3. Control flexibility

We measured control flexibility using the prominent aspects mentioned in the organizational flexibility literature (Nadkarni and Herrmann, 2010; Nadkarni and Narayanan, 2007)—ease and speed of adaptation of management processes. From a control perspective, these aspects include both changing activity within the boundaries of control processes (Brown and Eisenhardt, 1997; Eisenhardt et al., 2010) and changing control processes themselves (Adler et al., 1999). We captured these aspects with two items regarding how the organization was managed on a daily operational basis. Using a 7-point Likert-type scale, the items are (1) our management processes enable us to adapt quickly to meet changing needs and (2) our management processes are easily changed in response to new conditions.

As the scale measuring the degree of control flexibility was new, we conducted a series of reliability and dimensionality assessments. Initially, we calculated Cronbach’s alpha, CR, and AVE to evaluate the reliability of the measures. The Cronbach’s alpha for the two items was 0.82. The value of CR was 0.92 and the value of AVE was 0.85. These evaluations indicated a high level of measurement reliability. To test unidimensionality of the measures, we conducted factor analysis on the two items measuring control flexibility. The items loaded on one latent factor, and the factor loadings for all items were above 0.75 (Steenkamp and Van Trijp, 1991). We also evaluated and confirmed the face validity of the construct based on our interviews of managers and our pilot survey.

Multiple approaches were taken to evaluate the discriminant validity of our measures on control formalization and control flexibility. First, following the guidance of Anderson and Gerbing (1988), we constrained the estimated correlation parameter between control formalization and control flexibility to be 1 and then performed a Chi-square difference test on the values obtained from the constrained and unconstrained models. We found a significant difference in Chi-square (△χ² = 13.7; △df = 1; p < 0.001) indicating that control formalization and control flexibility are not perfectly correlated, and that discriminant validity was achieved. Also, following the procedure that Fornell and Larcker (1981) proposed, we found that the square root of the average variance extracted by control formalization (0.77) and control flexibility (0.92) exceeded their correlation (0.34), indicating robust discriminant validity of the two constructs. Finally, following prior work (Poppo and Zenger, 2002), complementarity between the two control types is identified via a significant, positive interaction term while a negative interaction term indicates substitutive effects.

4.2.4. Competitive turbulence

We adopted five items to measure competitive turbulence following the work of D’Aveni (1994). These items were selected to capture the role of competition causing turbulence (e.g. unpredictability of changes). The items measure how firms (managers) perceive changes in their competitive environment. This measurement scale matched our theorizing, and respondents were asked to evaluate these items in the previous three years, providing temporal sequencing with our dependent variable. This measure had a Cronbach’s alpha of 0.84, a CR value of 0.86, and an AVE value of 0.57.

4.2.5. Control variables

We also controlled for alternative explanations of organizational performance. Demographic variables included firm size, firm age, firm ownership, listed company (a dummy variable), firm types, and industry. We measured firm size as the number of employees (in eight categories), firm age as the number of years the firms had been established (in nine categories), and firm ownership in terms of the percentage that was government-owned and foreign-owned, while using a dummy variable to control for the status of formerly state-owned firm. Organization type was measured by categories including multi-business organization, business unit of multi-business organization, and single business, with these categories coded as dummy variables. Our model also included industry- and country-fixed effects.

4.3. Data analysis

The descriptive statistics and Pearson correlations are shown in Table 1. To support our theorizing of control formalization and control flexibility as distinct constructs, we provide a scatterplot (see Figure 1). While the firms in our data tend toward more formalization than flexibility (there is a significant difference between means), the dispersion in the scatterplot sustains the distinction of these variables while the positive correlation (0.34) is consistent with prior research on organizational controls (Sihag and Rijsdijk, 2019).

Figure 1.

Scatterplot between control formalization and control flexibility.

Table 1.

Descriptive statistics and correlations.

	Variable	1	2	3	4	5	6	7	8	9	10	11	12
1	Organizational performance
2	Control flexibility	0.31
3	Control formalization	0.24	0.34
4	Competitive turbulence	−0.02	0.06	0.11
5	Organizational size (category)	−0.01	−0.14	0.14	−0.06
6	Organizational age (category)	0.01	−0.20	0.06	−0.09	0.43
7	State ownership	−0.03	−0.11	−0.02	−0.20	0.14	0.07
8	Foreign ownership	−0.02	0.00	0.02	0.10	0.16	0.04	−0.19
9	Single business	0.04	0.05	−0.09	−0.07	−0.26	−0.13	−0.06	−0.20
10	Business unit	−0.09	−0.13	0.01	−0.01	0.12	0.12	−0.01	0.21	−0.61
11	Listed company	0.02	−0.03	0.16	0.15	0.34	0.20	−0.15	0.25	−0.31	0.26
12	Formerly state owned	−0.03	−0.09	0.02	−0.03	0.05	−0.08	0.34	−0.07	−0.05	−0.03	−0.01
	Max	7	7	7	7	8	9	7	7	1	1	1	1
	Min	1	1	1	1	1	1	1	1	0	0	0	0
	Mean	4.80	4.66	5.23	4.47	5.55	5.09	2.24	2.11	0.45	0.31	0.34	0.13
	SD	1.10	1.34	0.99	0.98	1.93	2.36	2.29	2.14	0.50	0.46	0.47	0.34

SD: standard deviation.

Values in bold indicate a significant correlation at ⩾ 95% level.

Our primary analysis uses ordinary least squares (OLS) regression with country and industry fixed-effects and we use two-stage least squares (2SLS) regression in sensitivity tests. We mean-centered independent variables to reduce multicollinearity concerns and to aid interpretation. Variance inflation factors in all models are less than 5, suggesting limited multicollinearity (Aiken et al., 1991). Table 2 shows our results with a stepwise model build-up to test hypotheses. Via country fixed-effects, we test for country differences and find no statistical difference (e.g. the dummy variables are not significant at p < 0.05).

Table 2.

Regression results on organizational performance.

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Intercept	4.923 (< 0.0001)	4.856 (< 0.0001)	4.668 (< 0.0001)	5.087 (< 0.0001)	4.809 (< 0.0001)	4.810 (< 0.0001)
Industry fixed-effects	Yes	Yes	Yes	Yes	Yes	Yes
Country fixed-effects	Yes	Yes	Yes	Yes	Yes	Yes
Control variables
Organizational size	−0.006 (0.838)	−0.010 (0.748)	0.002 (0.934)	−0.024 (0.430)	−0.007 (0.803)	−0.003 (0.930)
Organizational age	0.015 (0.543)	0.042 (0.082)	0.049 (0.042)	0.020 (0.409)	0.044 (0.068)	0.041 (0.085)
State ownership	0.000 (0.986)	0.003 (0.901)	0.002 (0.927)	0.000 (0.998)	0.005 (0.826)	0.003 (0.909)
Foreign ownership	0.001 (0.970)	−0.003 (0.897)	−0.006 (0.810)	0.006 (0.808)	−0.002 (0.934)	−0.000 (0.989)
Single business	−0.023 (0.865)	0.039 (0.753)	0.032 (0.801)	0.023 (0.856)	0.060 (0.634)	0.049 (0.697)
Business unit	−0.229 (0.102)	−0.118 (0.373)	−0.104 (0.439)	−0.187 (0.167)	−0.107 (0.424)	−0.106 (0.424)
Listed company	0.086 (0.476)	0.045 (0.695)	0.083 (0.476)	0.006 (0.961)	0.026 (0.824)	0.020 (0.864)
Formerly state owned	−0.081 (0.626)	−0.014 (0.928)	0.001 (0.997)	−0.068 (0.672)	−0.010 (0.948)	−0.039 (0.803)
Independent variables
Control flexibility (CFL)		0.226 (< 0.0001)	0.276 (< 0.0001)		0.214 (< 0.0001)	0.223 (< 0.0001)
Control formalization (CFM)		0.180 (0.001)		0.324 (< 0.0001)	0.218 (< 0.0001)	0.203 (0.000)
Competitive turbulence (CT)		−0.076 (0.134)	−0.060 (0.249)	−0.078 (0.137)	−0.079 (0.130)	−0.048 (0.362)
CFL*CT			0.022 (0.521)		−0.010 (0.770)	−0.007 (0.844)
H1: CFM*CT				0.130 (0.004)	0.104 (0.024)	0.066 (0.174)
CFL*CFM					0.012 (0.707)	0.005 (0.888)
H2: CFLCFMCT						−0.079 (0.016)
F value	0.62 (0.899)	3.79 (< 0.0001)	3.2 (< 0.0001)	2.54 (< 0.0001)	3.57 (< 0.0001)	3.69 (< 0.0001)
Adjusted R²	0.000^a	0.107	0.086	0.062	0.111	0.119

N = 536, two-tailed tests, reported unstandardized coefficients with p-values in parentheses.

The negative adjusted R² value was rounded to zero.

Before examining our hypothesized relationships, we consider the underpinning relationships. Our results in Table 2 indicate that control flexibility (b = 0.226; p < 0.0001) and control formalization (b = 0.180; p = 0.001) are positive and significant in their direct effects on organizational performance as generally expected (Sihag and Rijsdijk, 2019). We observe that competitive turbulence (b = −0.076; p = 0.134) is negative but not significant. An additional interesting observation is that the interaction of control flexibility and competitive turbulence (b = 0.022; p = 0.521) is not significant.

4.4. Findings

Hypothesis 1 includes two competing hypotheses. The positive, significant coefficient (b = 0.13; p = 0.004) of the interaction term between control formalization and competitive turbulence for organizational performance supports Hypothesis 1b and the idea that the benefits of formalization are strengthened as the level of competitive turbulence increase. This result supports the facilitative view of control formalization. To assist in the interpretation of the moderation effect, we present the interaction graph in Figure 2.

Figure 2.

Two-way interaction of control formalization and competitive turbulence on organizational performance (plotted at one and two standard deviations above and below the mean of the data).

Hypothesis 2 includes two competing hypotheses regarding a three-way interaction among control flexibility, control formalization, and competitive turbulence. The negative and significant coefficient of the three-way interaction term (b = −0.08; p = 0.016) supports Hypothesis 2b, and the general idea that the complementarity of control formalization and control flexibility is weakened as the level of competitive turbulence increases. We also confirm this result in a simple-slope analysis (Dawson and Richter, 2006) (see Appendix C of the Supplemental Material) and visualize the empirical relationships in Figures 3 and 4. Figure 3 illustrates that, in the condition of low competitive turbulence, increasing control formalization results in higher performance when control flexibility is high. However, in the condition of high competitive turbulence, increasing control formalization results in higher performance when control flexibility is low. These results suggest that control formalization and control flexibility are complements when competitive turbulence is low, and this complementarity diminishes—tending toward substitutive effects—as competitive turbulence increases; supporting Hypothesis 2b. Finally, Figure 4 depicts the control–performance relationships across various levels of competitive turbulence. The four graphics demonstrate the substantial effect of competitive turbulence and how it influences the relationships. In particular, substantial performance differences exist with very high levels (approaching +2SD) of control formalization and control flexibility in comparison to moderate levels.

Figure 3.

Three-way interaction of control formalization, control flexibility, and competitive turbulence on organizational performance (plotted at one and two standard deviation(s) above and below the mean of the data).

Figure 4.

Control–performance relationships across various levels of competitive turbulence (plotted at one, two, and three standard deviation(s) above and below the mean of competitive turbulence).

4.5. Robustness tests

To evaluate the robustness of the analysis, we conducted numerous sensitivity checks (please see Appendix D of the Supplemental Material for additional details and tests). First, endogeneity may be a concern in our model. Instrumental variable regression (2SLS) is an approach to alleviate endogeneity concerns (Bascle, 2008; Certo et al., 2016). Our 2SLS regression results were consistent with those reported using OLS, providing confidence in our findings.

Second, to alleviate the concern of common method variance, we adopted several recommended approaches (Jordan and Troth, 2020; Podsakoff et al., 2003). As one example, we obtained revenue data on 61 of our examined firms (10 in Israel, 25 in Australia, and 26 in China) from secondary sources. We found revenue change between the end of 2013 and the end of 2014 has a strong correlation with our dependent variable (r = 0.32, p = 0.013). Overall, these evaluations indicate that our findings are unlikely to be attributed to common-method bias (Podsakoff et al., 2003).

Third, we performed a correlation analysis to evaluate the efficiency effect of control formalization and control flexibility. Measuring organizational efficiency with a proxy of goal achievement on business process improvement, both control formalization (b = 0.95, p < 0.0001) and control flexibility (b = 0.57, p < 0.0001) are positively and significantly associated with this proxy for efficiency. This finding supports our general efficiency arguments, while at the same time pointing to the potential benefits of future research on our theorized mechanisms.

Finally, to account for individual decision makers’ differences, we also controlled for respondent demographics such as work experience, education level, knowledge in strategic management, and organizational position. The results are consistent.

5. Discussion

Our research informs the complement–substitute debate in the organizational control literature by examining the contingent influence of competitive turbulence (Kreutzer et al., 2015; Kreutzer et al., 2016; Sihag and Rijsdijk, 2019). This literature has only started to understand when controls are substitutes and when they are complements. Our theorizing, following the holistic view of control, joins a small group of scholars who conceptualize control formalization and control flexibility as two distinct control types (Naveh, 2007; Patel, 2011; Tatikonda and Rosenthal, 2000). In contrast to the generally accepted notion, we show that control formalization and control flexibility are complements in environments of low competitive turbulence—and that this complementarity diminishes, shifting toward substitutive effects, as environments increase in competitive turbulence.

Our theorizing in Hypothesis 1 included competing hypotheses, given the divergent views of formalized control processes. Our finding challenges the long-standing restrictive view that highly formalized control processes constraint performance in environments with high competitive turbulence due to bureaucratic inefficiency (Burns and Stalker, 1961). However, our finding supports the facilitative view that formalized control processes have features that are beneficial—such as efficiency derived from reducing task uncertainty in environments with higher competitive turbulence (Rustagi et al., 2008; Sine et al., 2006). While our data support the facilitative view of formalized control processes, there is reason to further investigate systematic contingencies that may shape these divergent views. Our study examined a wide array of industries in four countries with different levels of competitive turbulence. However, investigating additional contexts and examining industry differences in more depth may provide novel insights for theory. For example, it may be fruitful to study different kinds of turbulence such as a global financial crisis, war, or pandemic. Furthermore, our findings support the facilitative view and, therefore, the prevalence of an enabling style of control formalization. The literature in information systems has identified that different styles of control enactments (styles of implementation) influence effectiveness (Shinkle et al., 2021; Wiener et al., 2016). We, therefore, encourage research on different styles of control enactments in general management contexts.

Key to our contribution is Hypothesis 2. Our finding that control formalization and flexibility demonstrate a complementary performance effect when the level of competitive turbulence is low indicates that higher performance in more stable environments is achieved with control processes that are both formalized and flexible. Most interestingly, we found that this complementarity diminishes in environments with higher competitive turbulence; specifically, organizations that utilize control processes that are both highly formalized and highly flexible are likely to underperform. Furthermore, our findings add a new perspective to, and extend the discussion of, the holistic approach to organizational controls, which considers the simultaneous use of multiple control types (Cardinal et al., 2017; Sitkin et al., 2010). Our findings also shed light on the formalization–flexibility literature (Eisenhardt et al., 2010) by adding competitive turbulence as a critical boundary condition. Our research resonates with the work of Kreutzer et al. (2016) who observed the complementarity of formal controls and informal control diminishes as market exploration increases.

5.1. Managerial implications

Our investigation provides important managerial guidance on the design of organizational control by highlighting the pertinent contingent influence of the external environment, specifically competitive turbulence. Practically speaking, many organizations find their competitive environments increasingly dynamic and unpredictable (Damanpour, 2010; RuedaManzanares et al., 2008). As such, organizations may benefit from reconsidering their levels of control formalization and control flexibility while striving for competitiveness. Our research suggests formalization and flexibility should be viewed as two prominent and distinct attributes of organizational control processes. In environments with a high level of competitive turbulence, our results indicate highly formalized control processes can be beneficial. Most importantly, organizations that employ a highly formalized and highly flexible control process, simultaneously, will have higher performance, but only in environments with low or moderate levels of competitive turbulence. In environments with a high level of competitive turbulence, our results indicate decreased complementarity. Therefore, with high level of competitive turbulence, managers should carefully select the levels of control formalization and control flexibility—emphasizing one or the other but not both at very high levels.

5.2. Limitations and future research

Several limitations of our investigation suggest avenues for future research. First, our investigation focused on control flexibility designed into the control system as opposed to flexibility based on the discretion of individual actors within boundaries. We encourage future scholars to disentangle these two types of control flexibility since they could affect performance differently with increasing competitive turbulence (see sensitivity test in Supplemental material on other control types). This work could distinguish the benefits of organizational members each undertaking individual searches and actions rather than having the design of systems be the agent of change. Second, our data do not allow us to directly empirically test the efficiency mechanisms we theorized. Future research could more thoroughly investigate the efficiency effects of controls. Third, our measurement of control flexibility is based on two survey items. While this measurement is generally aligned with the construct conceptualization, future research is encouraged to further improve the reliability and validity in the measurement of controlled forms of flexibility. Fourth, while our measurement of competitive turbulence assesses firms’ perceptions following the literature (Huber et al., 1975), and is in line with our theorizing, it is constrained by the recall bias of the respondents. Future research is encouraged to use alternative measurements of competitive turbulence, such as secondary measurements. Fifth, the literature has indicated that various types of control may be inter-related; for example, formalization may enable flexibility or vice versa (Cardinal et al., 2017). More specifically, the potential for control co-evolution deserves future research and may benefit by considering duality or hybridity perspectives (Farjoun, 2010; Smith and Besharov, 2019). Finally, our examination is based on multi-country and multi-industry data. While we control for country and industry fixed-effects, we encourage future research to explore these contextual factors in depth.

6. Conclusion

In sum, our work injects new insight into the substitute–complement debate in control theory and extends work on controlled flexibility. Our investigation of competitive turbulence as a contingency on the complementarity of organizational controls opens a pathway for the study of a broader set of contingencies. In doing so, our arguments and results offer important implications to the research and practice of organizational control.

Supplemental Material

sj-docx-1-aum-10.1177_03128962211067648 – Supplemental material for The complementarity of control formalization and control flexibility: The contingent effects of competitive turbulence

Supplemental material, sj-docx-1-aum-10.1177_03128962211067648 for The complementarity of control formalization and control flexibility: The contingent effects of competitive turbulence by Feifei Yang, George A Shinkle and Mirjam Goudsmit in Australian Journal of Management

Footnotes

Acknowledgements

We are grateful for the insightful comments and guidance from the editor, Andrew Jackson, as well as two anonymous reviewers.

Final transcript accepted 1 December 2021 by Andrew Jackson (Editor-in-Chief ).

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We gratefully acknowledge the support of the University of New South Wales (Sydney, Australia). The second-named author’s contribution to this work and the data collection effort were funded through the support of the Australian Research Council (Award DE130100840). This work was supported by Shanghai Planning Office of Philosophy and Social Science (2019EGL009).

ORCID iD

Feifei Yang

Supplemental material

Supplemental material for this article is available online.

Notes

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