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Chandler, Scott, Stodder, and Tworoger                                                                                                                       Advances in Business Research 

2011, Vol. 2, No. 1, 1-15 

Innovation and Recoverable Slack Interaction: How Does It Affect Firm Performance? 
 

Lisa Lucarelli Chandler, Quinnipiac University 

Elizabeth Scott, Eastern Connecticut State University 

James Stodder, Rensselaer Polytechnic Institute 

Thomas Tworoger, Nova Southeastern University 
 

 

This paper investigates the impact of the interaction effect between a slack variable and   innovation on 
financial performance. Specifically, the interaction effect between recoverable slack resources and an 

innovation input on financial performance were analyzed using financial data of U.S firms in aerospace 
and computer science industries. An extensive review of literature on slack, innovation, and 
environmental shock develops the conceptual model. This study examines the relationship as 

representative of a specific bundle of resources governed by the strategic direction of management. The 
results suggest evidence of a positive and significant interaction effect under certain conditions.    
 

 

Innovation has been accepted as a necessary and important resource for firm sustainability and growth 

(Bourgois, 1981; Nohria and Gulati, 1996; Zahra and Covin, 1993) and has been an important topic of 

interest in organizational theory. Slack is another construct that has garnered the attention of 

organizational theorists (Bourgeois, 1981; Cyert and March, 1963; Tan and Peng, 2003; Voss et al., 

2008). The relationship of excess resources above that necessary to produce a product or service has been 

argued to both contribute and hinder innovation in firms. It has been theorized that above optimal levels 

of slack are counterproductive and wasteful while below optimal levels of slack inhibit innovation 

(Geiger and Cashen, 2002; Nohria and Gulati, 1996). Empirical research has clarified the relationship to 

be curvilinear. There is also established literature that discusses the relationship of firm-specific factors 

which would include the slack-innovation relationship to the sustained generation of above normal levels 

of return or rents (Barney, 1991; Wernerfelt, 1984). As firms increasingly face greater pressures to be 

both more innovative and to manage resources most efficiently, a possible dilemma arises. If firms 

maintain lean levels of slack resources, innovation may be inhibited. Decisions for resource allocation are 

made at a specific point in time, but the environment is not static. Is there justification then for keeping 

excess above minimum levels of slack resources and if so, what type of slack resources? This presses 

the question. What happens to this relationship when an event takes place that is outside of the industry 

norms, such as during an environmental shock?  

 This study suggests that the slack-innovation relationship represents a specific bundle of resources 

governed by the strategic direction of management, and as such, testing of this relationship can give 

further insight into slack innovation allocation strategies (Sirmon et al., 2007). Another purpose of this 

research was to provide greater insight into what Sirmon et al., (2007) described as the “black-box”.   

The results of this research provide a model for practitioners making strategic decisions grounded in 

the resource-based view of the firm. The resource-based view of the firm holds that heterogeneous firm-

specific bundles of resources and capabilities provide sources for sustained competitive advantage 

(Barney, 2001, 1991; Conner, 1991; Dierickx et al., 1989; Sirmon et al., 2007). Resources and capabilities 

within the firm can be categorized into tangible assets, such as physical and financial capital resources 

and intangible assets, such as human and organizational capital resources (Barney, 1991). Generally, 

resources are considered available assets that can be traded in the market place; alternatively capabilities 

are considered firm-specific attributes. Amit and Schoemaker state: “capabilities are based on developing, 

carrying and exchanging information through the firm’s human capital” (1993: 36). As noted in the 

literature, it takes more than just the presence of resources to provide a firm with a distinct competitive 

advantage (Prahalad and Hamel, 1990; Priem and Butler, 2001; Sirmon et al., 2007; Van de Ven, 1986). It 

is the identification and bundling of specific resources in combination with the effective and efficient use 

of capabilities that can ultimately create a sustainable competitive advantage (Amit and Schoemaker, 

1 

 



Chandler, Scott, Stodder, and Tworoger                                                                                                                       Advances in Business Research 

2011, Vol. 2, No. 1, 1-15 

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1993; Pralahad and Hamel, 1990).  Sirmon et al., state, “Bundling refers to the processes (i.e., stabilizing, 

enriching, and pioneering) used to integrate resources to form capabilities” (2007: 273). 

Competitive firms design organizational systems to operate under industry specific environmental 

pressures (Anand and Ward, 2004; Sharfman, 1985). Optimal slack resources would then be expected to 

be maintained to create environments that support and protect innovation during environmental 

turbulence. Ruiz-Moreno et al., state, “The key issue is not whether slack is good or bad for innovation, 

but that the managers, depending whether or not they have slack, organized and managed differently the 

resources that the organization can bundle and apply to the maintenance and development of competitive 

advantage” (2006: 520). Following the resource-based tradition, the allocation of slack resources designed 

to create an optimal amount of innovation meet the empirical construct of a valuable resource (Amit and 

Schoemaker, 1993; Barney, 1991). There has been recent interest in the dynamic capability framework as 

a way to explain why some firms are better able than others at bundling resources and capabilities to 

address rapidly changing environments. (Danneels, 2008; Teece et al., 1997). Teece et al., (1997) argue 

competitive advantage can be attained through the exploitation of existing internal and external firm-

specific capabilities and developing additional ones (Prahalad and Hamel, 1990). Danneel classified 

“competence to build new competencies” as the first form of dynamic capabilities. The study also 

identified specific organizational antecedents that had an effect on firm’s dynamic capabilities including 

organizational slack. The findings suggested surplus resources be set aside to “foster second-order 

competences, which will allow the firm to engage in exploration to avert the threat of environmental 

shifts or to take advantage of new opportunities (2008, p.536). Therefore, this study contributes to the 

existing research in the following ways: Firstly, it offers a model to test the recoverable slack-innovation 

interaction effect on financial performance. Secondly, two industries with different investment horizons 

will be examined for industry effects on the recoverable slack-innovation relationship’s effect on financial 

performance. 

 In the sections that follow, a literature review summarizing the relationship between the multiple 

dimensions of slack and innovation are discussed, followed by associated hypotheses. It considers various 

aspects contributing to the measures used in our research for slack resources, innovation, and 

environmental shock. The methodology, analysis, and implications of the study follow. In the final 

section, a discussion of the findings and the potential for future research is offered. 
 

LITERATURE REVIEW 

 

Environmental Shock 

 

This study proposes that subtle yet important differences exist between the concept of environmental 

shock and the notion of environmental pressure (Anand and Ward, 2004; Dess and Beard, 1984; Meyer, 

1982; Porter, 1980).  Figure I provides a graphical comparison between the two concepts.   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



Chandler, Scott, Stodder, and Tworoger                                                                                                                       Advances in Business Research 

2011, Vol. 2, No. 1, 1-15 

3 

 

Environmental pressure has been discussed in the literature from both economic and organizational 

theory perspectives (Dess and Beard, 1984). There is agreement in the literature on the constructs of the 

environment reflected by Sharfman’s definition of environmental pressure, “Environmental pressure is 

defined as the product of the dynamism in the environment and the firm’s resource relationships and 

dependencies” (1988: 33). 

Research indicates firms are less able to cope with unpredictable demands and rapid changes in 

technologies than with predictable normal business pressures (Anderson and Tushman, 2001). Firms in 

dynamic industry environments can create coping strategies with the allocation of resources allowing for 

greater flexibility as a response to discontinuous change. One cause for a lack of capability to respond has 

been related to inertia resulting in an inability to adapt. Cheng and Kesner (1997) argued that firms will 

target slack resources as a response to environmental pressure depending on the firm’s strategic focus. 

The response will either be one of applying greater available slack as an external response or one of 

applying adjustments to recoverable slack to gain greater internal efficiencies. Anand and Ward (2004) 

argued firm structure should be designed to fit the dynamics within an industry. A review of the literature 

has shown a need to more clearly define the terms “environmental pressure” and “environmental shock” 

to gain greater understanding of the effect of organizational strategies, structures, or outcomes (Dess and 

Rasheed, 1991).  

Environmental pressure stems from changes to market factors resulting in a change to economic 

equilibria. An example has been noted in the technological change that took place in the photographic 

manufacturing industry with the introduction of consumer digital imaging systems (Christensen, 2002). 

Although the probability for the introduction of a new competitive technology was known by the firms, 

they did not react with successful business strategies in the short-term in part because of inertia or internal 

political concerns.  

As stated previously, the nuances between environmental pressure and shock are subtle, yet important 

for clarity in environmental research findings. Sirmon et al., refer to the occurrence of an environmental 

shock as, “the introduction of discontinuous innovation or a major political catastrophe as in the event of 

9/11” (2007: 287). However, there is no commonly referred to definition for environmental shock. It has 

been argued in the literature that organizations can allocate resources and design organizational forms to 

be responsive to dynamic environments (Anand and Ward, 2004; Barney, 1991). Thus, the introduction of 

discontinuous (radical) innovation may cause an increase of dynamism or reduce munificence, while not 

necessarily cause an environmental shock (Sirmon et al., 2007). In Meyer’s (1982) study of hospital 

administrative reactions to policy changes, he distinguished normal business surprises from 

environmental shocks as: “environmental events that were transient perturbations whose occurrences are 

difficult to foresee and whose impacts on organizations are disruptive and potentially inimical” (1982: 

515). 

Criteria for an environmental shock would comprise events that are difficult or impossible to foresee 

within the timeframe of a business quarter and out of the firm’s internal control. For example, it could 

be reasonably argued that deregulation or privatization are political processes that  give some advance 

notification of potential change, and therefore provide some measure of strategic decision making. This 

paper suggests the basis for environmental shock is one where there is minimal warning for an event 

that brings about a radical and disruptive change. 

This study presents a definition that builds upon the work of Meyer (1982) and Sirmon et al., (2007) 

using the events of 9/11 as a reference point for analysis. Environmental shock is defined as an external 

event that is large, infrequent, and structural, whose occurrence is difficult or impossible to foresee within 

the timeframe of a business quarter, out of the firms internal control, and whose impact on organizations 

is disruptive and potentially inimical. 
 

Slack Resources and Innovation 

 

The notion of slack resources as having a relationship to innovation has been widely discussed (see 

Bourgeois, 1981, for a review). Nohria and Gulati offer a definition that presents the common notion of 



Chandler, Scott, Stodder, and Tworoger                                                                                                                       Advances in Business Research 

2011, Vol. 2, No. 1, 1-15 

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slack as excess resources: “the pool of resources in an organization that is in excess above the minimum 

necessary to produce a given level of output.” (1996: 32). It is from this definition that perceptions of the 

term, “excess above the minimum”, vary. Proponents of slack resources argue that slack is a positive and 

a significant determinant of performance outcomes as slack provides a cushion that protects the firm from 

environmental shocks (Bourgeois, 1981) as well as acts as a facilitator of creative behavior (Bourgeois, 

1981; Cyert and March, 1963). Alternatively, this reserve or cushion has been argued to reflect 

inefficiencies and waste (Nohria and Gulati, 1996; Sharfman, 1985) as well as poor internal control 

practices (Jensen, 1993).  

Most recently, research indicates the relationship between organizational slack and innovation to be 

curvilinear. In essence, limiting slack resources may negatively impact firm capabilities to innovate, while 

an excess over a certain amount may result in inefficiencies (Bourgeois, 1981; Bourgeois and Singh, 

1983; Cheng and Kesner, 1997; Geiger and Cashen, 2002; Nohria and Gulati, 1996). Most common 

measures of slack resources are framed as available, potential, and recoverable.  

Available Slack. The term “available slack” reflects the readily available (unabsorbed) nature of the 

resource for use by management. A common example is cash or credit lines (Voss et al., 2008). This 

category of resource holds the greatest level of discretionary use and thus is considered highly accessible 

in a short time frame. Ease of accessibility may act as a buffering mechanism against environmental 

variability (workflow variability) and function as a catalyst for innovation and strategy formulation 

(Bourgeois, 1981). Alternatively, as optimum levels are exceeded, control mechanisms may become lax 

resulting in inefficiency and waste (Jensen, 1993).  

Potential Slack. The literature presents an alternative dimension of slack, “potential slack”, which 

measured the amount of debt available to the firm. The findings are range from a positive linear 

relationship between potential slack and innovation (Geiger and Cashen, 2002) to a relationship that does 

not appear to be direct nor predictive (Herold et al., 2006). Findings indicate further research is needed to 

investigate the impact of the different debt structures on R&D intensity as modeled in this study (David et 

al., 2008).  

Recoverable Slack. Firms also hold recoverable (absorbed) resources or excess overhead that has been 

absorbed into the system with the potential to be recovered or redistributed. Studies that have examined 

operational slack have indicated recoverable slack acts very similarly to available slack in relationship to 

innovation as both a buffering mechanism and catalyst for innovation, and alternatively as a potential for 

inefficiencies. At optimal levels, it fulfills the purpose of slack which is to absorb irregularities and 

shocks in the environment (e.g. Bourgeois, 1981; Cyert and March, 1963; Thompson, 1967). Proponents 

of slack argue for high levels of uncommitted resources while also stating the need for finding the balance 

between buffering levels that would protect the core with associated costs. As such, prior research 

suggests recoverable slack and innovation will have a curvilinear relationship (Geiger and Cashen, 2002; 

Nohria and Gulati, 1996; Singh, 1986). Examples range from additional employees kept on the payroll 

during slower economic times to additional distribution channels that serve as buffers in dynamic 

environments.  

One example of recoverable slack is redundancy. The importance of redundant and diverse systems 

was evident after the 9/11. Organizations that had a physical presence at the World Trade center on 

September 11, 2001 suffered tremendous, immediate and incalculable loss. Managers reported after 

addressing critical concerns for employee their attention needed to focus on restoring capabilities within 

their departments. Those companies maintaining backup systems and shared services with offsite 

departments were able to respond in innovative ways to meet the needs of the organization more quickly 

(Gallagher, 2001; Yossi, 2001).  

Innovation. Interestingly, there has not been equal development or agreement for multiple measures of 

innovation based on specific determinants. The main criterion that differentiates innovation from mere 

change is the introduction of something new, something rare and unique (Gopalakrishnan and 

Damanpour, 1997; Nohria and Gulati, 1995; Schumpeter, 1939). The various dimensions of innovation 

that contribute to outcomes of the innovation process have been an important focus of the literature on 

innovation since Schumpeter (1939). This research has evolved into a rich area of interdisciplinary study 



Chandler, Scott, Stodder, and Tworoger                                                                                                                       Advances in Business Research 

2011, Vol. 2, No. 1, 1-15 

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resulting in heterogeneity of concepts, yet with no consistent model linking various disciplines. 

Contributing researchers from various disciplines and even within the same discipline conceptualize 

innovation from different dimensions. To this point, an integrated concept has not been introduced 

(Castellacci et al., 2005; Gopalakrishnan and, Damanpour, 1997; Johannessen et al., 2001). Therefore, 

there is a need to discuss or identify which dimension of innovation is being addressed and at what level 

of analysis. For example, Herold et al., (2006) argued for the use of patent-based statistics as a proxy for 

innovation. Although patent-based activity can be considered an important determinant of innovation, it 

specifically looks at innovation output. Helfat states, “This focus on patents, however, obscures the fact 

that in many industries, an important part of developmental research in particular entails alterations and 

enhancements to existing firm assets, production processes, and products.” (1994: 176). Thus it is 

important to address which factor is being examined within the innovation process. Alternatively, a 

variable commonly used to measure innovation, R&D intensity, is a well-respected measure of an 

innovation input.   

Previous studies have identified R&D intensity as an input to innovation within the innovation process 

at the firm level (Bourgeois and Singh, 1983; Geiger and Cashen, 2002; Hitt et al., 1996; Hambrick and 

Macmillan, 1985). This paper reflects the spirit of Schumpeter’s definition of innovation to include 

positive outcomes with respect to customers, stakeholders, and the organization with the creation of new 

products within the firm (Geiger and Cashen, 2002; Rainey, 2005; Schumpeter, 1939). As such, 

innovation was conceptualized in this study as R&D intensity and operationalized as (R&D/sales). This is 

reflective of the investment the firm makes in research and development as a percent of sales (R&D/sales) 

(Baysinger and Hoskisson, 1989; Geiger and Cashen, 2002; Hitt et al., 1996; Hambrick and Macmillan, 

1985).   

Immediately following an environmental shock, firms will seek to deploy both available and 

recoverable slack resources. Under some circumstances, firms with additional cash resources may be able 

to respond more vigorously than those who have not maintained such available slack resources. At other 

times, the resources needed will not be available at any price. Firms with recoverable slack resources may 

have the competitive advantage. Research analyzing multiple dimensions of slack resources using 

regression analysis has shown recoverable slack to have the most significant relationship to innovation 

(Geiger and Cashen, 2002). Therefore, based on the research findings discussed regarding the resource-

based view of the firm, the relationship between recoverable slack and innovation was chosen as the most 

salient measure to examine the slack-innovation relationship as it relates to firm financial performance. 
 

Relationship between Recoverable Slack - Innovation and Financial Performance 
 

No previously published research studies have examined the recoverable slack-innovation relationship 

as it relates to firm financial performance. The recoverable slack-innovation relationship is representative 

of a specific bundle of resources governed by the strategic direction of management (Barney, 1991; 

Sirmon et al., 2007). Recoverable slack includes excess overhead that has been absorbed, but has the 

potential to be recovered or redistributed. Testing of this relationship can give further insight into what 

has been termed the “black box” of the resource-based view of the firm (Sirmon et al., 2007). It is 

expected the interaction effect between recoverable slack-and innovation will have a positive influence on 

financial performance Recoverable slack has features that can be categorized as ease of implementation 

and the time it takes to implement (Sharfman et al., 1988; Singh, 1986). The identification and bundling 

of recoverable resources in combination with the effective and efficient use of innovation can ultimately 

create a sustainable competitive advantage (Amit and Schoemaker, 1993; Pralahad and Hamel, 1990). 

Based on research findings discussed regarding the resource-based view of the firm, slack and innovation, 

the following hypothesis is offered: 
 

Hypothesis 1: The interaction effect between recoverable slack and innovation will be positively 

related to firm performance.  
 



Chandler, Scott, Stodder, and Tworoger                                                                                                                       Advances in Business Research 

2011, Vol. 2, No. 1, 1-15 

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There is an emerging literature addressing the organizational effects from the tragic events of 

September 11, 2001. Findings specifically looking at the Aerospace Product and Parts Manufacturing 

industry showed the industry suffered a significant demand shock (and Lee, 2004). Gittell et al., (2006) 

presented a model that clarified the role between relational reserves (people) and financial reserves during 

a crisis. The study analyzed the effects to organizational resilience in airline industry post 9/11. The 

authors concluded: “…financial reserves coupled with a strong commitment to employees are pivotal to 

an organization's ability to cope with environmental jolts” (2006, p.325).  
 

Hypothesis 2: The interaction effect between recoverable slack and innovation will be positively 

related to firm performance in the aeronautics industry, but diminish after an environmental shock. 
  

 According to Standard and Poor’s industry reports, every product segment in the computer science 

industry was negatively affected by the events on 9/11 in both business and consumer sectors. Although 

business conditions caused a decline in demand earlier in the year following September 11, 2001, the 

global economic recession negatively affected world-wide computer sales (Bouwman et al, 2003; 

Graham-Hackett, 2002; Rudy, 2002) 
 

Hypothesis 3: The interaction effect between recoverable slack and innovation will be positively 

related to firm performance in the computer science industry, but diminish after an environmental 

shock. 
 

Methods 
 

The sample was drawn from secondary data from Standard and Poor’s Research Insight (Compustat® 

North America) database (Davis and Duhaime, 1992; Venkatraman and Ramanujam, 1986). This sample 

was drawn from the following industry sectors: (33641) Aerospace Product and Parts Manufacturing, 

(334) Computer and Electronic Product Manufacturing sectors and (511210) Software Publishers. The 

Aerospace Product and Parts Manufacturing sector was reported to be directly affected by environmental 

shock on September 11, 2001 (Ito and Lee, 2005). In addition, the Computer and Electronic Product 

Manufacturing, and Software Publishers industry are representative of innovation. Firms represent both 

short and long cycle industries with differing R&D investment horizons. It is also likely these firms 

would have reported research and development expenditures in the 1999 - 2005 period. Two additional 

conditions for selection were: the firms providing data must be listed as a United States Fortune 1000 

company from the years 1999 through 2005, and the firms must report R&D expenditures for all years 

included in the study. The following assumptions were made to determine appropriate sample size for 

statistical analysis. An alpha level of .05, a power level of .80, and a medium effect size of .05 was used 

and predictor variables were set at 15 as a conservative sample size (Cohen, 1992; Green, 1992; Geiger 

and Cashen, 2002). It was determined in that study that a sample size of at least 138 firms would be 

necessary.  In order to capture the effects of environmental shock, the years 1999 through and inclusive of 

2005 were captured.  
 

Dependent Variable 
  

Performance. Return on Assets (ROA) was used as an indicator of firm performance. ROA is defined 

as net income plus interest expense divided by sum of total assets. 
 

Independent Variables  
 

Innovation. The input to innovation (IN) is conceptualized as R&D intensity of the firm and was 

operationalized using the ratio R&D/sales (Zachariadis, 2003; Geiger and Cashen, 2002; Hitt et al., 1991). 

Recoverable Slack. It is common in slack-innovation relationship research to use financially derived 

data to conceptualize slack (Herold et al., 2006; Geiger and Cashen, 2002; Bourgeois and Singh, 1983; 

Bourgeois, 1981). Recoverable (absorbed) slack (RS) captures resources within the firm that can be 



Chandler, Scott, Stodder, and Tworoger                                                                                                                       Advances in Business Research 

2011, Vol. 2, No. 1, 1-15 

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identified as excess costs, but can be utilized to respond in times of financial difficulty or to take 

advantage of new opportunities (Singh, 1986; Bourgeois and Singh, 1983). Recoverable slack has been 

conceptualized using selling and general administrative expenses divided by sales (SG&A/sales) of the 

firm (Geiger and Cashen, 2002; Bourgeois and Singh, 1983; Bromiley, 1991).  

Interaction Effect. Empirical studies regarding the slack innovation relationship relied upon 

accounting performance measures to operationalize recoverable slack and innovation (Geiger and Cashen, 

2002; Cheng and Kesner, 1997; Baysinger and Hoskisson, 1989; Hitt et al., 1996; Hambrick and 

Macmillan, 1985). This study used the interaction effect between recoverable slack and innovation as an 

indicator of a specific bundle of resources governed by the strategic direction of management (Sirmon et 

al., 2007; Barney, 1991). Also, Lin et al., 2006 used an interaction effect to model the relationship 

between commercial orientation, R&D intensity, and firm performance. The interaction in this paper was 

calculated as the ratio of SGA (selling, general and administrative) to total sales multiplied by the ratio of 

R&D expense divided by total sales (Agresti and Finlay, 1986). 
 

Control Variables 
 

Relying on methods from prior studies, the variables of firm size, risk, product diversification, 

industry characteristics, and time were included in this study. Research has shown a positive relationship 

between size and R&D spending (Baysinger and Hoskisson, 1989). As such, firm size would be directly 

related to innovation. Sorenson (2000) suggested that as large firms have the advantage of economies of 

scale and size, these would act as buffers from short term shocks. One could reasonably argue firm size 

may have an impact on the slack-innovation relationship. This study measured firm size by using the log 

of total sales. Systematic risk is a common measure to assess firm risk (Geiger and Cashen, 2002; 

Chatterjee and Lubatkin, 1988). Information on firm beta was retrieved from the Compustat® database. 

The level of unrelated product diversification has been shown to have a significant and negative 

relationship to innovation (Hoskinsson and Hitt, 1988; Hitt et al., 1996). Product diversification was 

calculated using an entropy measure that takes into account the number of segments in which a firm 

operates weighted by sales in each segment (Palepu, 1985; Hitt et al., 1996; Geiger and Cashen, 2002). 

 !"#$%&'()*#+#,#-j x In (1/P), where Pj represents the percentage of firm sales in segment j and ln (1/p) is 

the weight for each segment (Hitt et al., 1996).  

Firms were divided into Computer Science and Aerospace industries to control for industry 

characteristics (Damanpour and Gopalakrishnan, 1998; Hitt and Ireland, 1985). A Time trend variable 

was included to test for long and short term structural sifts.   
 

Data and Analysis 
 

An initial panel data least squares series was conducted to test the significance of the control variables 

as they related to the innovation recoverable slack relationship. The nature of the data - a cross section of 

firms, each with a few periods worth of observations (years 1999 - 2005) makes panel estimation the 

natural technique for estimates (Wooldridge, 2002). To capture fixed individual differences for each firm, 

a fixed-effects or ‘dummy variable’ formalization was used, where the intercept is different for every firm 

and does not change over time. 

In addition to the panel data formation, strong evidence of serial correlation and unit roots for many of 

the variables made it desirable to pursue an approach that takes account of cointegrated series. A Vector 

Error Correction model (VEC) was able to handle most of the panel data. The longer-term trends are 

accounted for in the error-correction (EC) portion of the equation, showing stable relationships between 

key variables. In earlier specifications of the VEC model shown in Table 1, it was noted that constant and 

residual terms were highly significant in the V portion of the VEC, but most other coefficients were not. 

Nonetheless, the R-squared was fairly high, suggesting significant multi-collinearity between these 

nominally ‘independent’ right-hand-side variables. By eliminating most of the control variables, this 

problem was mitigated in the specification shown in Table 1. That is, of the three control variables, RS 

was the only one reporting consistently significant coefficients in the V portion of the equation when, as 



Chandler, Scott, Stodder, and Tworoger                                                                                                                       Advances in Business Research 

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in Table 1, both aeronautics and computer science industries were included in the dataset. Prior research 

also confirms a lack of demonstrated significance of additional control variables (Latham and Braun, 

2009).  
 

Table 1 

Dependent Variable 
 

Change in Innovation 

D(IN) 

(A) 

1999-2005 

Inc. Per. = 7 
C.S. = 214 

Obs. = 812 

(B) 

1999-2005 

Inc. Per. = 7 
C.S. = 214 

Obs. = 812 

(C) 

1999-2005 

Inc. Per. = 7 
C.S. = 214 

Obs. = 812 

Cointegrating 
Equation: 

   

IN(-1) 1,000 1,000 1,000 

Constant 
-0. 2063 

[-3.427]*** 
0.0359 
[0.689] 

-0.1492 
[-2.220]** 

RISK(-1) 
-0.1164 

[-3.305]*** 

-0.0723 

[-1.749]* 

-0.1440 

[-3.276]*** 

SIZE(-1) 
0.0769 

[4.824]*** 
0.0145 
[1.549] 

0.0643 
[3.859]*** 

RS(-1)  
-0.3521 

[-4.021]*** 
 

POST01(-1) 
-0.0149 

[-5.162]*** 
  

R-squared 

Adjusted R-squared 

0.8256 

0.7767 

0.8537 

0.8127 

0.8196 

0.7693 

Independent Variables:    

Cointegrating Equation 
-0.9167 

[-6.371]*** 

-0.9708 

[-6.392]*** 

-1.0316 

[-7.732]*** 

Constant 
0.0036 

[3.170]*** 

-0.0012 

[-1.414] 

0.0034 

[3.988]*** 

D(IN(-1)) 
0.1818 

[2.308]** 
0.0535 
[0.657] 

0.1865 
[2.587]*** 

D(RISK(-1)) 
-0.0677 

[-1.301] 

-0.0820 

[-1.456] 

-0.0472 

[-0.948] 

D(SIZE(-1)) 
0.0351 

[2.168]** 
0.0861 

[4.388]*** 
0.0258 
[1.759] 

D(RS(-1))  
0.0383 

[0.776] 
 

D(POST01(-1)) 
-0.0097 

[-4.306]*** 
  

R-squared 

Adjusted R-squared 

0.5279 

0.3543 

0.4851 

0.2958 

0.6007 

0.4548 

Log likelihood 

F-statistic 

1849.723 

3.0413 
  

Akaike AIC 
Schwarz SC 

-4.0166 
-2.7491 

-3.9298 
-2.6624 

-4.1866 
-2.9249 

t-statistics in [ ]; *** -- p-val < 0.01, **-- p-val < 0.05, * -- p-val <0.10. 

Inc. Per – Included Periods, C.S – Cross Sections, Obs. - Observations 

 



Chandler, Scott, Stodder, and Tworoger                                                                                                                       Advances in Business Research 

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The Wooldridge-Wald test on the regressions in Table 1 rejects the null of no serial correlation at the 5 

percent level. Thus serial correlation is likely to be a serious problem. To mitigate this problem, however, 

in Table 1 the authors are using White (1980) period estimators, which Arellano (1987) shows to be 

robust to within-cross-section serial correlation.   

The analysis will now turn to the “interaction effect” regressions. A single-equation method was used 

to test the hypotheses. A model of the interaction of Residual Slack (RS) and Innovation (IN), and their 

effect upon Return on Assets (ROA) in the final period was based on the average levels of RS and IN 

over the entire period - 1 to 7 years.  Thus, the authors relied on a simple cross-sectional data set, as this 

test did not require panel methods. Ordinary Least Squares was sufficient. As the R-squared was little 

changed by removing the control variables, this invites the option of removing all the control variables 

other than RS, as was done for each of the regressions below (Latham and Braun, 2009; Geiger and 

Cashen, 2007).   

Hypothesis 1 proposes that the interaction effect between recoverable slack and innovation will be 

positively related to firm performance. It can be seen that the interaction effect between innovation and 

recoverable slack has an overall positive effect on financial performance. In a larger sense the interaction 

is positive, that the effect of the individual terms is positive, and there is just a small corrective for the 

joint effect (see Table 2). By adding the interaction term, the authors transform the combined influence of 

IN and RS from a linear to a non-linear effect - which the R-squared statistics in column A of Table 2 

show to be much more accurate than the other specifications.   
 

Table 2 

Dependent Variable: 

 

Return on Assets (ROA) 

(A) 

2005 

 

Inc. Per. = 1 

C>S> = 136 

Obs. = 136 

(B) 

2005 

 

Inc. Per. = 1 

C>S> = 136 

Obs. = 136 

© 

2005 

 

Inc. Per. = 1 

C>S> = 136 

Obs. = 136 

(D) 

2005 

 

Inc. Per. = 1 

C>S> = 136 

Obs. = 136 

(E) 

2005 

 

Inc. Per. = 1 

C>S> = 136 

Obs. = 136 

Independent Variables: 

Constant 

3.5908 

[3.054]*** 

11.5206 

[9.1694]*** 

10.2144 

[8.8068]*** 

6.9575 

[8.5618]*** 

9.2884 

[13.8292]*** 

IN 34.2077 -0.6630 -29.0976   

RS 11.5743 

[2.3889]*** 

14.1850 

-[-2.45888]*** 

 -02313 

[-1.5773] 

 

IN*RS -76.3984 

[-10.8063]*** 

   -40.9424 

[-9.9041]*** 

R-SQUARED 

Adjusted R-squared 

0.546586 

0.536281 

0.145466 

0.132616 

0.6622 

0.099955 

0.017836 

0.010667 

0.422640 

0.418331 

Log likelihood 

F-statistic 

-444.8075 

53.04154 

-487.9025 

11.32021 

-490.9253 

15.99251 

-508.8770 

2.487968 

-461.2404 

98.09081 

Akaike AIC 

Schwarz SC 

6.60011 

6.68578 

7.219154 

7.2834404 

7.248902 

7.291735 

7.350748 

7.392971 

6.812359 

6.855192 
 

Hypothesis 2 proposes that the interaction effect between recoverable slack and innovation will be 

positively related to firm performance in the aeronautics industry, but diminish after an environmental 

shock. The results as shown in Table 3 support a positive and significant relationship between the 

recoverable slack and innovation interaction effect and financial performance. This result is significant 

even after accounting for the combined effect. The significance of these results is impressive, and the 

Wooldridge-Wald test can only reject the null of no serial correlation for the regression in column A at 

the 15% level. Thus, serial correlation is not likely to be a serious problem.  

Hypothesis 3 suggests the interaction effect between recoverable slack and innovation will be 

positively related to firm performance in the computer science industry, but diminish after an 

environmental shock. The results as shown in Table 3, show that over the long term (i.e., in the EC 

equation) the interaction effect is positive and significant in aerospace (Column A), but not in computer 

science (Column B).   
 
 



Chandler, Scott, Stodder, and Tworoger                                                                                                                       Advances in Business Research 

2011, Vol. 2, No. 1, 1-15 

10 

 

Table 3 

Dependent 

Variable: 

Change in Return on Assets (ROA) 

 

Industry 

(A) 

1999-2005 

 !"#$%&'#$($)$$$*+,+$-$./ 

Obs. = 163 

0 

(B) 

1999-2005 

 !"#$%&'#$($)$$$*+,+$-$01 

Obs. = 423 

1 

Cointegrating Equation:   

ROA(1) 1.00000 1.00000 

Constant -12.28649 

[-7.2106]*** 

-34.2151 

[-3.8280]*** 

IN(-1) 92.9082 

[4.0073]** 

-26.8449 

[-0.4122] 

RS(-1) 40.3497 

[2.5297]*** 

92.1327 

[-4.0930]*** 

IN(-1)*RS(-1) *-449.9306 

[-2.7194]** 

-24.9587 

[-0.7868] 

@TREND 0.3125 

[1.8869 

0.5878 

[2.0920]** 

R-squared 

Adjusted R-squared 

0.574777 

0.448971 

0.4138 

0.2758 

Independent Variables   

Cointegrating Equation   

Constant -0.32006 

[-6.70922]*** 

-1.0505 

[-3.1852]*** 

RES_ROA(-1) -1.19794 

[-13.0600]*** 

-1.3256 

[-42.8084]*** 

D(ROA(-1) 0.18376 

[1.7922]* 

0.5764 

[14.0125]*** 

D(IN(-1) 35.54318 

[2.51888]*** 

100.2749 

[0.7509] 

D(RS(-1)) 21.63838 

[2.3769]** 

4.9062 

[0.0689] 

D(IN(-1)*RS(-1)) -213.33300 

[-3.7337]*** 

-25.5237 

[-1.8486]* 

R-squared 

Adjusted R-squared 

0.57799 

0.41064 

0.624537 

0.507933 

Log likelihood  

F-statistic 

-384.4050 

3.45382 

-1842.699 

5.356074 

Akaike AIC 5.29331 9.190066 

Schwarz SC 6.18538 10.15646 
 

Please note that coefficient signs are reversed in the form of the EC equation. Thus the original 

equation behind the EC term shows a coefficient on the interaction term in column A that is positive, 

significant, and very large. The interaction coefficient is negative in the V part of the regressions, 

however, and is again quite large and significant in the case of column A. The post 2001 shock effect 

shows a significantly negative trend in both columns. 
 

DISCUSSION 
 

The implications of this study provide greater insight into conditions under which recoverable slack 

resources have on organizational functioning (Cheng and Kesner, 1997). This research provides empirical 

evidence that recoverable slack and innovation allocation decisions can affect financial performance. 

These findings indicate - specifically in the aeronautics industry - investments in this particular bundle of 

resources can provide a competitive advantage after an environmental shock. The authors presented a 

model with three hypotheses and conducted an empirical study of U.S. aerospace and computer firms. 

The testing of the relationship between the recoverable slack, innovation and financial performance 

presented in this research has given further insight into what has been termed the “black box” of the 

resource-based view of the firm (Sirmon et al., 2007). This model has implications for future research in 



Chandler, Scott, Stodder, and Tworoger                                                                                                                       Advances in Business Research 

2011, Vol. 2, No. 1, 1-15 

11 

 

the area of the resource-based view by providing an additional measure that can be used to predict the 

relationship of firm-specific resources to financial performance. The results support a positive and 

significant relationship between the recoverable slack and innovation interaction effect and financial 

performance in the long term for aeronautics industry. Investments in innovation and recoverable slack in 

this industry can provide a competitive advantage after an environmental shock. Consequently, this 

analysis will assist  managers to make more informed decisions with regard to investments in buffering 

mechanisms against environmental variability (workflow variability), and the return on investments in 

innovation.  

In addition, industries included in this study were both long and short cycle. For example, aerospace 

firms can have R&D cycles of twenty years while software companies can have short-term R&D 

requirements of nine months. The recoverable slack-innovation relationship presented in this research 

model is representative of a specific bundle of resources governed by the strategic direction of 

management. Implicit in the innovation variable used in this study is R&D investment. A consequence of 

this investment is a contribution to the knowledge-base within the firm that ultimately translates to 

goodwill (David et al., 2008; Helfat, 1994). Helfat (1994) suggests R&D’s value is in large part linked to 

the nature of the cumulative learning that takes place both corporately and individually. Management may 

find decisions to reduce funding in R&D will inadvertently affect the routines that take place in the 

organization. Learning that is disrupted may not be preserved (Dosi, 1988; Dougherty, 1992; Helfat, 

1994). As managers in this industry must have long term investment horizons these results have practical 

implications. The results suggest the aeronautics industry did see a change in the relationship between the 

interaction effect and financial performance over time. Although the industry was severely affected by the 

events of September 11th
, the interaction effect played a positive role in financial performance outcomes 

in the long term. However, the results are quite different for the computer science industry. The 

interaction effect between recoverable slack and innovation did not have a significant relationship to 

financial performance in either the short or long term. The results could be interpreted as a tendency 

toward greater short-term instability post 2001. There may have been other factors. The industry was 

feeling the effects of poor business conditions. Even before the events of September 11th
, the computer 

industry was expecting dramatically slowing growth (Graham-Hackett, 2002; Rudy, 2002). These 

findings suggest industry characteristics are an important factor in this stream of research.  

Further, this paper offers insight into the differences between environmental pressure and 

environmental shock. It has been suggested within this paper that the basis for environmental shock is one 

where there is minimal warning for an event that brings about a radical and disruptive change. Having the 

benefit of more precise definitions of environmental events will assist researchers in developing and 

testing models using explicit criteria (see Figure I). 

It has been acknowledged in the literature that additional measures are needed to predict the 

relationship between the use of firm resources and competitive advantage (Sirmon et al., 2007). This 

paper presented an important contribution in this area of research. However, future research can continue 

to add value by assembling additional data to test this interactivity.  
 

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Chandler, Scott, Stodder, and Tworoger                                                                                                                       Advances in Business Research 

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Chandler, Scott, Stodder, and Tworoger                                                                                                                       Advances in Business Research 

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Chandler, Scott, Stodder, and Tworoger                                                                                                                       Advances in Business Research 

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Chandler, Scott, Stodder, and Tworoger                                                                                                                       Advances in Business Research 

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Lisa Lucarelli Chandler is a visiting assistant professor at Quinnipiac University. She received her 

D.B.A from Nova Southeastern University. Her current research includes developing business models on 

innovation, leadership, and gender perceptions. She worked in the telecommunications industry where she 

designed, and directed merger and acquisition integration programs for both Fortune 500 and start-up 

companies. She continues to advise managers and specializes in the effects of disruptive innovation.  
 

Elizabeth Scott is a professor at Eastern Connecticut State University. She received her Ph.D. in 

management from University of Pennsylvania. Her research interests include honesty, prejudice, moral 

values, and employee-organization fit. She has published in Business & Society, Business Ethics 

Quarterly, Teaching Business Ethics, and Journal of Personality and Social Psychology.   
 

James Stodder is a clinical associate professor at Rensselaer Polytechnic Institute. He received his Ph.D. 

in economics from Yale. He is a prolific author and researcher. He has had a long term interest in the 

evolution of exchange systems seen in "primitive" economies through anthropology, in economic history, 

and in current technological developments. His latest publication, “Complementary Credit Networks and 

Macro-Economic Stability: Switzerland’s Wirtschaftsring,” was published in Journal of Economic 

Behavior and Organization.  
 

Thomas Tworoger is an associate professor at Nova Southeastern University and the chair of the 

entrepreneurship department which includes a concentration in the MBA program as well as an 

undergraduate minor in entrepreneurship. He received his DBA at Nova Southeastern University. His 

current research interests include entrepreneurship, microfinance, and leadership. He has published in 

Journal of Leadership & Organizational Studies, Journal of Business and Leadership, Academy of 

Information Management Sciences Journal, and Academy of Information and Sciences Journal.  


