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A multidimensional examination of slack and its impact on innovation.

The concepts of organizational slack and innovation are central elements in the strategic management literature. Innovation in particular has received a great deal of attention in the recent past (Hitt et al., 1996; Hitt et al, 1997; Stuart, 2000). The focus of researchers on innovation has stemmed from its importance in creating competitive advantage through organizational adaptation and product development (Hitt et al., 1996; Sorenson, 2000). The concept of organizational slack has also recently received attention (Cheng and Kesner, 1997; Greenley and Oktemgil, 1998; Palmer and Wiseman, 1999) because of its ability to buffer firms from shortages of funds as well as its potential to foster innovation (Bourgeois, 1981; Cyert and March, 1963). It has also been argued, however, that organizational slack is wasteful, inefficient, and accumulates due to the self-serving interests of managers (Jensen and Meckling, 1976; Nohria and Gulati, 1996; Simon, 1957).

These arguments were synthesized by Nohria and Gulati (1996), who argue that the extent of slack within the firm impacts the firm's level of innovation. In their study it was hypothesized that an inverted U-shaped relationship exists between slack and innovation. In other words, they suggested that as slack increases, innovation increases as well. However, at some point excess slack will actually reduce innovation because it allows for undisciplined investment in R&D activities (Jensen, 1986, 1993). The results of Nohria and Gulati's (1996) study support this hypothesized relationship.

The purpose of this study is to extend the work of Nohria and Gulati (1996) by examining the impact of organizational slack on innovation from a multidimensional perspective. Research has found that different types of slack have different effects on firm innovation (Singh, 1986). However, Nohria and Gulati (1996) were not able to test for these differences due to the nature of the data collected. Furthermore, they call for future research to examine slack from a multidimensional perspective. Echoing these remarks, Cheng and Kesner (1997) call for future research to examine the impact of different types of slack resources on organizational outcomes.

Unfortunately, no studies to date have examined the relationships between the various slack dimensions and innovation, thus leaving us with little knowledge of how various types of slack impact innovation. It may be the case that the different types of organizational slack have different relationships with innovation (i.e., not all dimensions of slack have a curvilinear relationship with innovation). Thus, this study examines slack from three dimensions that are common in management research: available slack, recoverable slack, and potential slack (Bourgeois, 1981; Bromiley, 1991; Cheng and Kesner, 1997; Palmer and Wiseman, 1999). It is expected that the results of this study will serve to complement the findings of Nohria and Gulati (1996), while providing for a more fine-grained insight into the relationship between slack and innovation.

In the paragraphs that follow the definitions of organizational slack and innovation will first be presented. Secondly, the literature involving slack and innovation will be addressed, followed by the development of the hypotheses of interest. Next, the methodological procedures used to test the hypotheses will be described. Finally, the results of the analyses will be presented and the implications and limitations of the findings will be discussed.

Slack and Innovation

Slack has been defined in the literature in many ways. For example, Cyert and March defined slack as "the disparity between the resources available to the organization and the payments required to maintain the coalition" (1963: 36). Similarly, Dimmick and Murray defined slack as "those resources which an organization has acquired which are not committed to a necessary expenditure. In essence, these are resources which can be used in a discretionary manner" (1978: 616). Most recently, Nohna and Gulati defined slack as "the pool of resources in an organization that is in excess of the minimum necessary to produce a given level of organizational output" (1996: 1246).

For purposes of this study, slack will be defined as the resources in or available to an organization that are in excess of the minimum necessary to produce a given level of organizational output. This definition is close to that of Nohria and Gulati's (1996), with the important distinction that slack involves resources that are not only currently within the firm, but also those that are potentially available to the firm (i.e., debt), thus capturing the multidimensional aspect of organizational slack. Consistent with this definition, slack can be viewed from an internal and external perspective. Internal slack involves resources that are within the firm, either readily available or already absorbed within the organization. External slack, on the other hand, involves resources that are not currently within the firm and can best be thought of as availability of debt financing.

Van de Ven defines innovation as a new idea, which may be a recombination of old ideas, a scheme that challenges the present order, a formula, or a unique approach which is perceived as new by the individuals involved" (1986: 591). His definition is drawn from the work of Zaltman et al. (1973) and Rogers (1982). However, in this study innovation will be defined using Dougherty and Hardy's definition, which describes product innovation as "the generation of multiple new products, as strategically necessary over time, with a reasonable rate of commercial success" (1996:1121). Thus, innovation in this study will refer to the creation of new products within the firm.

The Inverted U-shaped Argument for Slack and Innovation

The existence of an inverted U-shaped relationship between organizational slack and firm innovation was first presented by Nohria and Gulati (1996). This notion rests on the argument that slack can be both good and bad for innovation. In an effort to reconcile existing arguments concerning the relationship between slack and innovation, Nohria and Gulati (1996) sought to synthesize the literature. The basis for whether slack has positive or negative impacts on innovation was argued to be a function of how much slack existed within the organization. Moderate levels of slack were hypothesized to positively impact firm innovation, but at some point too much slack was argued to negatively impact innovation within the firm.

Slack has been argued to have positive impacts on firm innovation for several reasons. First, slack leads to the easing of managerial controls (Cyert and March, 1963; Nohria and Gulati, 1996). This allows managers more discretion as to whether the firm should pursue new projects. Secondly, slack involves resources that can be used during times of distress. As organizations become protected from the uncertainty of experimental projects (because of the presence of slack resources), innovative cultures are likely to develop (Bourgeois, 1981; Nohria and Gulati, 1996). Members of the firm are less likely to worry about the risk of failure because the firm has extra resources to buffer the losses from such failures. This allows for the pursuit of innovative projects because slack protects organizations from the uncertainty and risk associated with experimentation (Bourgeois, 1981). Empirical studies have shown this relationship to exist (Damanpour, 1987; Nohria and Gulati, 1996; Singh, 1986; Zajac et al., 1991).

Slack has also been argued to have negative impacts on firm innovation (Child, 1972; Leibenstein, 1969; Nohria and Gulati, 1996; Palmer and Wiseman, 1999; Simon, 1957; Williamson, 1964). Following this line of thinking, slack is argued to be wasteful, and a result of self-serving managers, or a result of managers "satisficing" (Simon, 1957). From an agency theory perspective (Jensen and Meckling, 1976), it is argued that slack results from the conflict of the principal-agent relationship (Nohria and Gulati, 1996). Thus, the negative impact of slack on innovation occurs because agents do not always have the incentive to behave in the best interest of the firm. Moreover, principals do not always have perfect information to monitor agents. As such, agents can use the information asymmetries to their advantage (Williamson, 1964).

In this scenario, managers are likely to use slack resources to maximize their own personal wealth or pursue their own personal interests (Jensen, 1986). Thus, managers may use slack resources in ways that decrease innovation and experimentation. For example, decision-making groups may pursue unrelated diversification (Denis et al., 1999; Jensen, 1986), or adopt organizational structures that more closely follow their personal preferences (Amihud and Lev, 1999; Bourgeois, 1981; Child, 1972). Moreover, in the presence of excess resources, options viewed as unacceptable in the absence of slack may be viewed as satisfactory (Cheng and Kesner, 1997). Empirical support has also been found for the negative relationship between excess slack and firm innovation (Nohria and Gulati, 1996).

Given these two opposing influences of slack on firm innovation, Nohria and Gulati (1996) argue that firms with zero slack are forced to focus on short-term performance and thus little innovation is fostered. As firms accumulate slack, an increase in experimentation and new projects occurs. Thus, as slack begins to increase, innovation also increases. However, it is expected that possibilities for innovation will diminish as slack continues to increase (Nohria and Gulati, 1996). Moreover, it can be expected that decision making involving new and existing projects will become more relaxed as slack increases (Jensen, 1993; Simon, 1957). As excess resources accumulate beyond a certain point, poor resource decisions may occur. Therefore, it can be argued that slack beyond a certain level may reduce firm innovation.

Slack as a Multidimensional Concept

Slack has been proposed as having three components based on financially derived data (Bourgeois, 1981; Bourgeois and Singh, 1983). These components consist of available, recoverable, and potential slack. Similarly, Singh (1986) defined slack based on two components--absorbed and unabsorbed. The essence of both of these methods used to operationalize slack involves differentiating between slack that is available and slack which is already being used. Thus far in the literature, slack has been treated uniformly from a theoretical standpoint (i.e., Nohria and Gulati), while mostly being operationalized as a multi-component concept (i.e., Bourgeois, 1981; Bromiley, 1991). It is likely that the relationship between slack and innovation may differ between the different components of slack. Bourgeois and Singh (1983) found that the separate dimensions of slack had differing relationships with political behavior. In addition, Singh (1986) found that the different components of slack had different relationships with o rganizational risk-taking. In the following paragraphs, arguments will be presented that highlight the expected differences in the relationship between the varying components of slack and firm innovation.

Available Slack and Innovation. Available slack has been measured in previous studies using the current ratio (current assets/current liabilities) of the firm (Bourgeois and Singh, 1983; Bromiley, 1991; Cheng and Kesner, 1997). This component of slack serves to capture the extent to which firms have resources that are untapped, but readily available. It has been argued in the literature that slack provides a pool of resources that eases the ups and downs in the flow of innovation (Bourgeois 1981; Nohria and Gulati, 1996). Because slack exists, managers are more likely to pursue projects with promising outcomes. It seems likely that slack that exists within the firm and is readily available should impact decision makers such that managers will pursue promising projects. Thus, it is expected that increases in available slack lead to increases in firm innovation.

However, Nohria and Gulati (1996) argue that as slack increases the possibilities for innovation diminish. Therefore, at a certain level of available slack, innovation should peak. Furthermore, as slack increases, controls that are used in selecting, supporting and terminating projects may become relaxed (Jensen, 1993; Leibenstein, 1969). Given this, slack may dull a firm's response to environmental shifts (Cheng and Kesner, 1997). Thus, suboptimal levels of innovation may become acceptable due to the presence of excess resources within the firm.

It seems logical that slack, which is readily available, could lead managers to become more relaxed in their decision making. As such, available slack should impact managerial decision malting such that moderate increases in available slack increase innovation, but beyond a certain point, innovation will decrease. Thus, it is expected that the relationship between available slack and innovation will follow an inverted U-shaped pattern. This leads to the first hypothesis.

H1: An inverted U-shaped relationship exists between available slack and innovation such that as available slack increases beyond an intermediate optimal level, innovation decreases.

Recoverable Slack and Innovation. Recoverable slack has been operationalized in previous studies using selling and general administrative expenses divided by sales (SG&A/sales) of the firm (Bourgeois and Singh, 1983; Bromiley, 1991). This component of slack serves to capture the extent to which resources are embedded in the firm as excess costs, but could be recovered when firms experience financial difficulty (Bourgeois and Singh, 1983). This type of slack has also been referred to as absorbed slack (Singh, 1986). Recoverable, or absorbed slack, can best be thought of as resources that are absorbed into the firm in the form of expenses which are greater than those needed by the firm. For example, firms may employ more individuals than necessary to operate effectively year round. While this may increase expenses and reduce efficiency, it does, however, provide a cushion or buffer from disruptions in output (Cyert and March, 1963). Moreover, this type of slack may also aid in allowing firms to be more innovat ive (Nohria and Gulati, 1996). For example, Ben & Jerry's once retained factory workers and allowed them to work in the ice cream plant and within the community for over three months when a production line was closed (Laabs, 1992). This may have not only allowed for innovative projects by employees while the plant was closed, but also allowed for minimal downtime once operations were continued.

As with available slack, however, it is expected that innovation will fail to increase with recoverable slack beyond a certain level. It seems likely that only a certain amount of innovation could result from having excess resources absorbed within the organization. It could also be expected that as recoverable slack increases, control over innovative projects will suffer (Jensen, 1993) and undisciplined resource allocation will occur (Nohria and Gulati, 1996). Thus, excess recoverable slack may decrease innovation within the firm. For example, Ben & Jerry's not only retained employees while a plant was shut down, they also elected to pay above-market prices for one of their major supplies, milk. While it is not the point of this article to argue this as a poor business decision, it is useful to note this as an example of managers using organizational slack to pursue their personal interests. In this case, they made sure that their suppliers (Vermont farmers) survived a reduction in federal subsidiaries. Whi le certainly a noble business decision, it is hard to argue that the resources used to pay excess milk prices served to increase innovation within the firm. Thus, the next hypothesis is offered.

H2: An inverted U-shaped relationship exists between recoverable slack and innovation such that as recoverable slack increases beyond an intermediate optimal level, innovation decreases.

Potential Slack and Innovation.

The last component, potential slack, has been operationalized using a firm's debt to equity ratio (Bourgeois and Singh, 1983; Bromiley, 1991; Palmer and Wiseman, 1999). This measure represents the ability of a firm to secure resources with the use of debt financing. It could be expected that as potential slack increases, experimentation and product innovation is encouraged. This is attributable to the resources potentially available which allow for less anxiety and concern about the risks of research and development and short-term performance issues. This relationship coincides with the other two dimensions of slack. However, it seems unlikely that increased levels of potential slack will decrease innovation within a firm. First, firms can only achieve a maximum level of potential slack (0% debt). Moreover, as potential slack increases, it is difficult to imagine that undisciplined experimentation will occur because potential slack is not a current resource within the firm. The use of potential slack involves the firm incurring future expenses (in the form of interest expenses). The use of potential slack (debt) may also involve changes in analyst opinions (i.e., bond ratings), which in turn impacts the future cost of debt and the value of the firm's stock. Because of these factors, the use of potential slack will likely involve greater scrutiny by managers, and thus it does not seem likely that managers will become more relaxed in their decision making because of the existence of potential slack. For example, Bourgeois and Singh (1983) found that potential slack increases political behavior within the firm. Thus, managers may expend greater time and energy on decisions involving potential slack. While the relationship between slack (available and recoverable) and innovation should follow an inverted U-shaped pattern, it is expected that the relationship between potential slack and innovation is positive and linear (i.e., a negative relationship between debt/equity and innovation) This leads to the last hypothesi s.

H3: A positive relationship exists between potential slack and innovation.

METHODS

Sample

To determine the necessary sample size for statistical analyses, a desired level of power, alpha, and effect size were assumed. Given a power level of .80, alpha level of .05, a medium effect size, and 15 predictor variables, it was determined that a sample size of at least 138 firms was necessary (Cohen, 1992; Green, 1992). This sample size was considered conservative because it was expected that less than 15 predictor variables would be included in the analyses. Given the N needed to achieve the desired statistical power in the analyses, 250 Fortune 500 firms were randomly selected to insure that the desired power was obtained. A ten-year sample time frame from 1982-1992 was selected for data collection. The beginning, midpoint, and ending years (1982, 1987, and 1992) of the sampling time frame were used for data collection. Using a ten-year time frame allowed for the capturing of changes in firm characteristics over time. Moreover, the use of these years allowed for the comparison of this study to previous research. Lastly, the use of these years provided for a broad range of economic conditions. This decreased the likelihood that findings from this study would result from internal or external conditions in any single year. Annual reports and the Compustat database were utilized to collect data on the variables of interest. Data were not available for 22 of the firms in the initial sample and thus the final sample included 228 firms.

Dependent Variable

Innovation was operationalized as the R&D intensity of the firm. R&D intensity was calculated as R&D expense/sales. This method measures inputs into the innovation process and thus captures the extent of innovation opportunities within firms. In addition, this method has been common in the management literature (Hansen and Hill, 1991; Hitt et al., 1996; Hitt et al., 1997). It can be argued that current levels of slack will impact not only current levels of innovation, but also future levels of innovation. Thus, for purposes of analyses, R&D intensity was calculated as the mean of R&D intensity in year 0 and year 1 ((R&D intensity 1982 + R&D intensity 1983)/2).

Independent Variables

Following the methods of previous research, three measures were gathered for organizational slack (Bourgeois and Singh, 1983; Cheng and Kesner, 1997; Palmer and Wiseman, 1999). Available slack was measured using a firm's quick ratio. Recoverable slack was measured as selling, general, and administrative expenses divided by sales. Potential slack was measured using the debt to equity ratio of the firm.

Control Variables

Risk. The variable systematic risk was measured using the market model defined as follows:

Rit = ai + [beta]iRmt + eit,

where Rit is the individual firm's return to common stockholders in period t, Rmt is a proxy for the return of all risky assets in period t, and [beta]i is the slope for the relationship between the firm's return and the market return. This slope ([beta]i) is known as beta or systematic risk and measures the risk of the firm relative to market. The market return is estimated using a value-weighted portfolio of firms. This method is common for determining systematic risk and has been used in previous studies (Chatterjee and Lubatkin, 1990).

Size. Firm size has been shown to impact R&D expenditures (Baysinger and Hoskisson, 1989) as well as a firm's ability to withstand short-term shocks (Sorenson, 2000). Thus, it was necessary to control for size as well. Size was measured using the log of total sales.

Product Diversification. Diversification has been shown to have a significant and negative impact on R&D activity and thus it was necessary to control for its impact in the analyses (Hitt et al., 1996). Product diversification was measured using a weighted product count measure. Specifically, the entropy measure was calculated to determine the corporate diversification of firms. Using the Compustat business segment database, this method relies on the 2 and 4 digit SIC codes for each business to determine the relationship among businesses. Businesses are deemed related if the 4 digit SIC codes for a set of businesses are within the same 2 digit SIC code. Alternatively, businesses are considered unrelated if the 4 digit SIC codes for a group of businesses are not within the same 2 digit SIC code. Furthermore, the degree of related diversity within a group of businesses with the same 2 digit SIC code is determined by the different 4 digit SIC codes given to each business. Another characteristic of the entropy m easure is that the computation involves sales within each SIC code. Thus, the relative importance and relatedness of each business segment is included in the entropy measure. The entropy measure of diversification is defined as:

DT = [[sigma].sub.i] [P.sub.i] In (I/[P.sub.i]),

where DT equals total diversification and [P.sub.i] equals the percent of total corporate revenues generated in industry i.

Time. Because data were collected for three different years it was also necessary to control for the effect of time. A dummy variable was created to determine the impacts of the three different years on R&D intensity.

Administrative Structure. Structure may also impact the R&D expenditures of a firm. It has been argued that greater diversification levels lead to the reliance of firms on M-form competitive structures (Chandler, 1962, Rumelt, 1974). However, it has also been argued in the literature that the use of M-form structures results in lower levels of innovation (Hoskisson and Hitt, 1994). Thus, it is important to control for this firm characteristic in the analyses. Administrative structure was measured as either functional or divisional based on product or geography. Functional organizations can be considered those in which the major subunits are defined according to business functions such as marketing or manufacturing. Divisional firms have a central office, and a group of operating divisions that each have the responsibility and resources necessary to engineer, produce and market a product, group of products, or manage a geographic region. For purposes of analyses, functional firms were assigned a value of 0 an d divisional firms received a value of 1. Annual reports were utilized to gather these data.

Data Analysis

Data from the 228 companies were analyzed using multiple linear regression. This method is appropriate because of the expected relationship of the dependent variable with the multiple independent variables (Cohen and Cohen, 1983). To determine the effects of organizational slack on innovation, the variables available slack, recoverable slack, and potential slack were regressed on R&D intensity. The variables risk, size, diversification, time, and structure were also included as control variables. The relationship between a firm's R&D intensity and the independent variables was modeled as follows:

Yi = B0+ B1X1 + B2X2 + B3X3 + [B4X1.sup.2] + [B5X2.sup.2] + control variables + [epsilon],

where Yi is the risk for firm i, X1 represents available slack, X2 represents recoverable slack, X3 represents potential slack, and [X1.sup.2] and [X2.sup.2] represent the quadratic variables for the two slack components predicted to have a curvilinear relationship with R&D intensity. In the regression model, the control variables were entered in the first stage, available slack, recoverable slack, and potential slack were entered in the second stage, and the quadratic variables were entered in the third stage.

RESULTS

Collinearity Diagnostics

Examination for multicollinearity among the independent variables was necessary to perform the analyses. The presence of multicollinearity can produce large variances for parameter estimates and high standard errors. If extreme correlation occurs among independent variables, parameter estimates become unreliable (Lewis-Beck, 1980). In addition, if severe multicollinearity exists, regression coefficients obtained in one sample are unreliable if applied to the overall population. Thus, when multicollinearity is present, the confounded effects of the predictors make the importance of predictors more difficult to determine (Stevens, 1992).

A visual inspection of the correlation matrix suggested that multicollinearity may exist. To test for the existence of multicollinearity, procedures recommended by Belsley et al. (1980) were used. Using SAS, condition indexes were developed to determine if a high condition index contributed greatly to the variance of two or more variables. The collinearity diagnostics performed revealed that no component associated with a high condition index contributed substantially to the variance of more than one variable. Thus, the collinearity diagnostics performed revealed that no multicollinearity problems existed.

Autocorrelation

Tests of hypotheses using cross-sectional time series data are vulnerable to the existence of autocorrelation and thus it was also necessary to determine if autocorrelation existed. The existence of autocorrelation means that the error variances among variables are correlated. If this condition exists, parameter estimates become unreliable and alternative methods must be used to correct for the autocorrelation problem. To test for autocorrelation, a Durbin-Watson statistic was generated, which if significant indicates that an autocorrelation problem exists. The Durbin-Watson statistic was not significant, indicating that no autocorrelation problem existed. Thus, the data were tested using ordinary least squares procedures.

Correlation and Regression Results

Summary statistics for all variables are provided in Table 1 and the presentation of the model results are reported in Table 2. The correlation matrix suggests that risk had a significantly positive correlation with R&D intensity. Also, consistent with prior research (Hitt et al., 1996), the correlation between a firm's level of diversification and R&D intensity was negative. The structure of the firm had a significantly positive correlation with R&D, indicating that firms using divisional structures had greater R&D intensity.

The research hypotheses suggest that a significant relationship exists between organizational slack and innovation. Specifically, Hypothesis 1 suggests that an inverted U-shaped relationship exists between available slack and innovation. In the regression analysis, the main effect of available slack for the dependent variable R&D intensity was positive and significant (p<.0l) and the quadratic variable was negative and significant (p<.05), providing support for Hypotheses 1. Thus, in the current study, the amount of available slack within the firm played a significant role in the firm's level of innovation. At lower levels of available slack innovation suffers. However, as available slack increases innovation increases as well, but beyond moderate levels of available slack innovation appears to suffer.

Hypothesis 2 suggests that an inverted U-shaped relationship exists between recoverable slack and innovation. As hypothesized, in the regression analysis the main effect of recoverable slack for the dependent variable was also positive and significant (p<.0l), and the quadratic variable was negative and significant (p<.05). These results support Hypothesis 2. Thus, recoverable slack and available slack have a similar relationship with innovation in this study.

The third hypothesis suggests that the relationship between potential slack and innovation is positive and linear. The main effect of debt/equity in the regression analysis was negative and significant (p<.01), indicating a positive relationship between potential slack and innovation. Thus, Hypothesis 3 was also supported. However, given that the other two types of slack were found to have a curvilinear relationship with innovation, it seemed necessary to determine if potential slack also followed such a pattern. Therefore, a quadratic term for potential slack was added to the previously specified regression model. As expected, this quadratic term was not significant (p=.93). Thus, the results presented represent the analyses of the theoretically specified model (presented in the methods section) that does not include the quadratic term for potential slack.

DISCUSSION

Research involving the impact of slack on the strategic behavior of firms has been sparse in the strategic management literature. Thus, studies involving organizational slack have been limited with the exception of topics such as slack's impact on risk-taking, political behavior, and innovation. In these studies slack has been defined and measured in numerous ways. However, to date no known research has examined the relationship between organizational slack and innovation from a multidimensional perspective. In response to this gap in the literature, the current study examined the relationship between the various components of organizational slack and firm innovation. It was theorized that internal slack (available and recoverable) has an inverted U-shaped relationship with innovation, while external slack (potential) has a positive linear relationship with innovation.

Findings indicate that both types of internal slack have an inverted U-shaped relationship with innovation. This suggests that available and recoverable resources may in fact buffer firms from the ebb and flow of innovative projects. However, available and recoverable resources may also create relaxed environments conducive to managers neglecting the best interest of the firm. Thus, if decreases in innovation are detrimental to the future of the firm, as much of the literature suggests, then solutions to such agency problems should be sought. This may require more careful consideration of organizational budgets and the discretion allowed to managers to access funds within such budgets.

Findings also indicate a linear relationship between external slack and innovation. Specifically, the greater the level of potential slack (less debt/ equity), the greater the innovation within the firm. Thus, firms with lower levels of debt tend to display greater innovation. This finding is consistent with previous studies that investigated the relationship between diversification level and innovation. Higher levels of diversification are likely to be significantly associated with greater levels of debt (Hitt et al., 1996). Williamson (1988) argued that higher returns are required by investors when projects are undertaken that have assets that are not redeployable. For example, debt used to purchase land should have a lower cost than debt used to develop technology because land is an asset that can be subsequently sold, while the resources used for developing technology may result in unusable assets or in the worst case nothing at all. Thus, managers are more likely to use debt for purposes of acquiring bu sinesses than for pursuing R&D activities (Hitt et al., 1996).

The results of this study suggest that we must not question what amount of slack is optimal, but what is the optimal amount of each type of slack? It appears from the findings of this study that greater levels of potential slack only stand to benefit the firm from an innovation perspective. However, as Nohria and Gulati (1996) suggest, a number of factors may impact the optimal level of slack for each firm. For example, internal controls may play a large part in determining the optimal slack level. It was argued in this study that the use of external slack is subject to greater scrutiny than internal slack. Thus, it may be important to determine if differences in managerial control methods impact the relationship between slack and innovation. Firms that have much tighter control methods over spending may have higher optimal levels of slack, while firms with more relaxed controls may have much lower levels of optimal slack. Future research should consider this possibility.

The results of this study also have methodological implications. First, researchers must be careful to differentiate between the various components of slack. As shown in this study, and previous studies (Bourgeois and Singh, 1983), the various components of slack have different impacts on organizational outcomes. Specifically, in this study available and recoverable slack were found to differ from potential slack with regards to their relationship with innovation. Researchers should be careful to correctly model the various components of slack with particular variables of interest.

This study also suggests important managerial implications. Managers should be aware of the various levels of available and recoverable slack within the organization and attempt to manage this slack effectively. The findings from this study, along with previous research, suggest that some slack is good for the organization. Thus, it may be important for managers to secure moderate levels of internal slack to alleviate risk- adverse behavior. However, it also seems important that managers stay alert as to how much internal slack exists within the firm. Too much slack appears to lead to the inefficient use of resources. As such, managers should be careful to not accumulate so much slack that resources are no longer valued by employees. It may be the case that firms with too much internal slack may be better off returning greater amounts of the firm's earnings to investors in the form of dividends.

Like most research efforts the current study does have some limitations associated with it that provide opportunity for future research efforts. For example, while the use of financial data provides objective measures of slack and is consistent with prior research, it fails to capture managerial perceptions of slack within an organization. Thus, future research may benefit from the use of managerial perceptions in measuring organizational slack. Moreover, alternative measures of innovation may also be useful in future studies. The use of manager's perceptions of innovation could provide more insight into the slack and innovation relationship.

It may also be beneficial for future researchers to examine some issues not investigated in the current study. Of particular interest may be the relationship between corporate refocusing and slack Refocusing has become a common strategy among diversified firms and thus its impact on organizational slack may be important. Research suggests that innovation increases following corporate refocusing (Hitt et. al, 1996). Is this increase in innovation partially a result of a change in slack? Answers to this question may prove important to our continuing understanding of corporate refocusing as well as organizational slack. Moreover, what is the relationship between organizational growth and slack? Do firms experiencing greater growth benefit from the existence of organizational slack? Again, future research is needed to examine this question.

Overall, it is hoped that this study will provide a more fine-grained understanding of organizational slack and its impact on innovation. Previous studies have examined slack from numerous perspectives, and it is important that researchers seek for consistency among findings. This study provides a useful extension of Nohria and Gulati's (1996) work by examining slack from a multidimensional perspective. In addition, this study also examines the relationship between slack and innovation using the organization as the unit of analysis, which also serves to extend Nohria and Gulati's (1996) efforts.
Table 1

Descriptive Statistics for Dependent and Independent Variables

Variable Mean S.D. Correlations
 1 2

 1. Innovation .035 .034
 2. Risk .891 .335 .21 (**)
 3. Size 7.650 1.426 .05 .17 (**)
 4. Diversification .764 .525 -.27 (**) .02
 5. Year 1982 .317 .466 -.05 -.02
 6. Year 1987 .350 .478 -.01 .01
 7. Structure .913 .273 .12 (*) .08 (+)
 8. Recoverable Slack .228 .129 .55 (**) .14 (**)
 9. Potential Slack 22.160 12.140 -.30 (**) .03
10. Available Slack 1.043 .458 .32 (**) .04

Variable Correlations
 3 4 5 6

 1. Innovation
 2. Risk
 3. Size
 4. Diversification .13 (**)
 5. Year 1982 -.21 (**) .11 (*)
 6. Year 1987 .05 -.01 -.50 (**)
 7. Structure .10 (*) .22 (**) -.01 -.04
 8. Recoverable Slack -.20 (**) -.24 (**) -.05 .01
 9. Potential Slack -.01 .19 (**) -.13 (**) .04
10. Available Slack -.35 (**) -.24 (**) .09 (*) .04

Variable Correlations
 7 8 9

 1. Innovation
 2. Risk
 3. Size
 4. Diversification
 5. Year 1982
 6. Year 1987
 7. Structure
 8. Recoverable Slack .04
 9. Potential Slack .08 (+) -.17 (**)
10. Available Slack -.14 (**) .36 (**) -.31 (**)

N = 334

(+)p<.10

(*)p<.05

(**)p<.01
Table 2

Results of Regressing R&D Intensity on Organizational Slack

Three State Entry

Independent Variables Effect

Stage 1
Risk .192 (**)
Size .036
Diversification -.308 (**)
Year 1982 -.014 [R.sup.2] = .143
Year 1987 -.020
Structure .165 (**)

Stage 2
Risk .104 (+)
Size .173 (**)
Diversification -.l42 (**)
Year 1982 -.022
Year l987 -.033 [R.sup.2] = .428
Structure .138 (**)
Recoverable Slack .448 (**)
Potential Slack -.160 (**)
Available Slack .155 (**)

Stage 3
Risk .093 (*)
Size .196 (**)
Diversification -.157 (**) [R.sup.2] = .439
Year 1982 -.023 [R.sup.2] change = .011
Year l987 -.030 Adj [R.sup.2] = .419
Structure .134 (**) Sig of [DELTA][R.sup.2]
Recoverable Slack .754 (**) = .044
Potential Slack -.163 (**)
Available Slack .437 (**)
Recoverable Slack Squared -.313 (*)
Available Slack Squared -.292 (*)

Beta Coefficients are standardized.

N = 334

(+)p<.10

(*)p<.05

(**)p<.01


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