The effect of compensation program and structure on sbu competitive strategy: A study of...

Strategic Management Journal, Vol. 12, 353-370 (I 991)

THE EFFECT OF COMPENSATION PROGRAM AND STRUCTURE ON SBU COMPETITIVE STRATEGY: A STUDY OF TECHNOLOGY-INTENSIVE FIRMS CRAIG S. GALBRAITH Krannert Graduate School o f Management, Purdue University, West Lafayette, Indiana, U.S.A.

GREGORY 6. MERRILL School o f Business and Public Administration, California State University, San Bernardino, California, U.S.A.

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In this paper we examine the impact various compensation programs have upon business- level strategy for technology-intensive firms. Similarly, we examine the effect of centralization of R&D and non-R&D decision-making, formality of procedures, and SBU size on competitive strategy. Analysis of data from 79 SBUs suggest that there is a resource trade- off between marketing-oriented strategies and R& D-oriented strategies, and that managers who operate under certain types of compensation programs will tend to favor R& Dlinnovation strategies and capital investment over other alternatives. Structure and competitive position also appear to play a significant role in determining technology and investment strategy.

Executive compensation has long been recognized as an important mediating mechanism between the wishes of a firm’s owners and the strategic behavior of that firm’s management. Most theoretical development and empirical analysis to date, however, has primarily focused on the effect various compensation programs have upon ‘corporate-level’ or diversification strategy. In addition, most of this work is based upon economic and finance theory with little regard for the nuances of strategic measurement, although recently some significant efforts have been made to place the issue of executive compensation within the context of strategic management theory (Gomez-Mejia, Tosi and Hinkin, 1987; Kerr, 1985; Kerr and Bettis, 1987). In spite of this, surprisingly little work has been accomplished with respect to the effect executive compensation has upon ‘business-level’ or competitive strategy.

Key words: SBU strategy, compensation, R&D, structure,

0143-2095J911060353-18$09.00 0 1991 by John Wiley & Sons, Ltd.

Just as the form of executive compensation affects diversification strategy, so should the form of executive compensation affect issues of marketing, research and development, innovation, capital investment, employee training, and product quality. The purpose of this study is to examine the extent to which compensation programs for SBU managers reflect variations in competitive strategy within the context of technology-intensive firms; in addition, structural issues such as size, formality, and centralization are also examined in this study.

LITERATURE REVIEW

Incentive programs and competitive strategy

It is well understood that the basic, underlying objective of an incentive program is to directly influence the actions and behavior of those managers covered under that program. When the

Received August 9 1989 Revised January 30 1991

354 C. S . Galbraith and G . B. Merrill

affected manager is in a position of strategic responsibility, a properly designed program must correspond, in terms of motivating relevant decisions, to the desired strategic outcome. Thus, if a firm wants to pursue a particular strategy, such as a strategy of increased investment in R&D and technology leadership, the compensation program needs to include those elements that motivate that particular investment behavior. Since, by definition, compensation schemes create economic wealth for individuals, an examination of such programs, and their effect on strategic behavior, must be placed in the context of the total individual wealth that a manager enjoys.

The total wealth of a manager, W,, may be classified under three broad categories or elements: the total amount of equity held, or potentially owned through options, etc., in the firm that employs the manager, W,; his or her human capital, which is equivalent to the present value of the manager’s stream of future earnings, Wh; and all other personal assets held by the manager that are unrelated to the manager’s firm, W, . W , is valued by the market, and is influenced by changes in the firm’s present and future cash flows. Likewise, the manager’s human capital, W,, is also influenced, albeit differently, by changes in the firm’s cash flows since cash flow may influence both base salary and non- equity bonuses. Thus, the classic agency problem arises (Jensen and Meckling, 1976). A manager who attempts to maximize his or her total wealth, W,, may, in fact, make decisions that are dramatically different from those the owners might have otherwise have preferred. Two major behavioral elements are related to the compensation issue: risk behavior and decision horizon.

Compensation and risk behavior

It has been theoretically established that managers who are overinvested in human capital, Wh. and who have not sufficiently diversified their unrelated personal wealth, W,,, are encouraged to make decisions that reduce the risk to their own personal income stream; that is, these managers choose investments that reduce the variability of returns on the firm’s assets. In contrast, actual or potential ownership of equity provides the opposite incentive. Based upon various option-pricing models we know that

under certain conditions (such as the existence of risky assets) the market value of equity will increase as income stream variability increases (Black and Scholes, 1973). Thus, ceferus paribus, for managers who are equity-rich, or covered by compensation programs tied to equity value, there are strong incentives to follow strategies, that perhaps increase the variance of corporate cash flows, whereas compensation systems primarily based on salary encourage risk-hedging strategies.

There is, in fact, increasing empirical evidence that equity-compensated managers are indeed more likely to make corporate-level strategic decisions that are in the shareholder’s best interest (as defined by the capital market) than those managers who do not have a stake in equity. Several studies (Agrawal and Mandelker, 1987; Amihud and Lev, 1981; Bentson, 1985), for example, found that executives’ security holdings induced them to make non-conglomerate diversification decisions, while executives overinvested in human capital were more likely to pursue variance-reducing strategies such as conglomerate diversification even though conglomerate diversifiers are typically poor per- formers when compared to other diversification alternatives (i.e. Porter, 1987; Rumelt, 1982). This is a particularly important issue in technology-intensive industries since there already is a strong underlying incentive to diversify as a hedge against the inherent technological uncertainty of these industries (Galbraith and Kay, 1986; Galbraith, Samuelson, Stiles and Merrill, 1986).

Compensation and decision horizon

While the salary/equity relationship addresses the risk-aversion issue, another, albeit related, issue is that of decision horizon. The interests of owners and managers can also diverge if the manager focuses only on short-term profitability when making decisions. Bonus plans that emphasize short-term evaluation can lead to ‘quick profit’ strategies; strategies that may, for example, result in decisions to divest valuable assets that are not currently generating profits regardless of those assets’ potential for long-term profits, or to investment in quick payback but lower present value projects (Narayanan, 1985). In response, many firms have developed ‘value-based’ compen-

SBU Compensation and Competitive Strategy 355

sation programs oriented toward long-term performance to lengthen the manager’s decision- making horizon and overcome short-term bias (Rappaport, 1983,1986). Long-term performance plans typically have three features: (1) incentives are linked to future objectives versus past performance, (2) evaluation of performance takes place over several years versus 1 year, and (3) the collection of any incentives is usually deferred (Tehranian, Travlos and Waegelein, 1987).

The above arguments can be considered true only in the broad sense. By incorporating additional issues such as asymmetric information where the manager knows more about income from projects than the owners, the existence of pecuniary ‘perks’ such as corporate jets and free trips to exotic seminars (the value of which is unknown to shareholders), convertible financing instruments, and labor market inefficiencies, the exact theoretical effect of long-term performance plans remains unascertained. For example, a manager who is operating under a long-term performance plan may still elect a quick-profit investment in order to ‘trick’ the executive labor market regarding his or her worth, then make a job move prior to the realization of any negative long-term effects of the earlier investment decision (see Darrough, 1987; Narayanan, 1987). Regardless of the potential for such game-playing, however, long-term performance plans do appear to have a significant effect on corporate investment decisions (Tehranian et a l . , 1987), and even the announcement of such plans in the press often results in positive market returns (Brickley, Bhagat and Lease, 1985).

Delegation, formality, and competitive strategy

It has long been argued that structural elements, such as delegation and formality, are important mediating mechanisms between environmental conditions and management behavior. Only recently, however, have empirical studies actively sought to link structural elements to issues of competitive strategy and strategy making (Fredrickson, 1986). For example, as Miller (1987) succinctly points out,

There may well be intrinsic association between strategy making and structure. The structure of an organization importantly influences the flow of information and the context and nature of human interactions. It channels collaboration,

specifies modes of coordination, allocates power and responsibility, and prescribes levels of formality and complexity. How then could it fail to influence the making of strategy? (p. 7)

The underlying argument that relates structural conditions to the strategic problem is the way an organization perceives and processes information, particularly strategic stimuli. March and Simon (1958), for example, get to the crux of this problem by noting that organization structure imposes certain boundaries of rationality, but is necessary due to the individual’s limited cognitive capabilities. Indeed empirical evidence is begin- ning to mount that suggests that delegation (when the right to make decisions and evaluate activities is not concentrated (Fredrickson, 1986; 285)) and formality (the presence of rules and procedures that influence decision-making behavior (Fredrickson, 1986: 286)), are related to the strategy-making process and the manner in which a firm deals with strategic information (i.e. Miller and Toulouse, 1986; Romanelli and Tushman, 1986). Miller (1987), for example, found that formality was related to rationality and interaction in strategy-making; but decentralization was less related to strategy-making. Interestingly, Miller (1987) also found that risk-taking behavior was associated with formality and delegation; the argument being that ‘CEOs who are risk averse with a high need for achievement favor formalized, integrated, centralized structures’ (Miller and Droge, 1986: 547). Continuing in a later paper using the same data, Miller, Droge, and Toulouse (1988) expanded and empirically examined the hypothesized link between environment, structure, and strategy to suggest that environmental uncertainty leads to innovation, and that innovation was directly related to formalization and integration, and inversely to centralization.

Based upon the previous discussion there appears to be a strong theoretical argument that it is the existence of an overall gestalt, or organizational configuration that includes compensation issues, such as reliance on salary and length of decision horizon, and organizational structure issues, such as formality and delegation, play important roles in determining strategic behavior. In this study we examine both compensation and structural issues simultaneously and, in contrast to previous studies, focus on the effect

356 C. S. Galbraith and G . B. Merrill

of these factors on SBU competitive strategy, rather than corporate-level diversification strategy. In addition, since we are particularly interested in the trade-offs between R&D and marketing-oriented strategies, we limit this study to firms in technology-intensive industries.

Compensation and SBU strategic position

As Rappaport (1986) notes, linking divisional or SBU strategic behavior to compensation also requires taking into account the strategic position of the individual SBU, and how it fits within the overall corporate strategy. Unlike corporate strategic decisions, which tend to be portfolio- oriented (the collection of various SBUs), SBU decisions are more productharket-driven. Thus market structure and competitive position must also be considered when examining SBU activity (Porter, 1980). The genesis model of SBU strategy was developed by Bruce Henderson of the Boston Consulting Group, and now represents the well-known ‘BCG matrix’ (BCG, 1968). Here market structure is defined by market growth, and competitive position is defined by the SBU’s market share. The cells of the resulting matrix, high share/low growth (cash cow), high sharelhigh growth (star), low sharelhigh growth (question mark), and low share/low growth (dog) suggest appropriate SBU general strategies: extract profit for a high sharellow growth SBU, maintain market position for a high shareihigh growth SBU, increase share for a low sharelhigh growth SBU, and divest a low share/low growth SBU. While a number of alternative approaches have been offered over the past two decades (e.g., General Electric’s ‘business screen’, the Bain Company’s ‘bubble’, Hofer and Schendel’s (1978) ‘productimarket evolution matrix’, and Porter’s (1985) ‘generic strategies’), the BCG matrix, and its various offspring, remain some of the most widely used models in business practice. Its appropriateness, however, is contingent upon several important assumptions being met; if the assumptions are met then the BCG matrix becomes a very powerful tool; if not, then other models are more appropriate-unfortunately, the underlying assumptions of the BCG approach are poorly understood by many. The assumptions underlying the market share dimensions are twofold: (1) the existence of industry-wide experience

curve effects (which in turn involves three conditions: a relatively high labor input, mass production type operations, and long production runs of similar design technology); and (2) relatively low product differentiation (all competitors are generally forced to similarly price their products). The assumption behind the market growth dimension is that industry sales approxi- mately follow an ‘s-shaped’ Gompertz or logistic growth model, where early rapid growth declines over time as substitute technologies are introduced. In general, it should be noted that these assumptions are well met by many technology- intensive environments. Not surprisingly, many high-technology firms tend to follow the BCG approach of trying to achieve market dominance early in the life cycle, and with each subsequent new design technology.

This raises two important empirical questions. First, to what extent does an SBU’s strategic position relate to the design of its compensation systems? For example, while compensation programs are designed to motivate certain strategic behaviors, the desire to pursue a particular strategy often stems from a firm’s competitive position. Second, to what extent does this strategic position represent an intervening factor when examining the relationship between compensation and SBU strategy? Both of these issues will be investigated in the course of this study.

HYPOTHESES R&D and marketing decisions

Generally accepted accounting principles (GAAP) require marketing and R&D expenditures (expenditures designed to discover new knowledge, or new uses of current knowledge, not directly related to a firm’s existing, market- ready product or design) to be expensed in the current accounting period, and are excluded from a firm’s asset base, primarily because of the claimed difficulty of determining the long-term benefits of these efforts. To examine this issue, a number of studies have attempted to determine the longevity of marketing and R&D expenditures. The evidence of various time-series studies suggests that the benefits of R&D expenditures indeed have a much longer time horizon than marketing expenditures. Ravenscraft and Scherer

SB U Compensation and Competitive Strategy 357

(1982), for example, found a mean lag of 3-6 years between R&D expenditure and its derived benefit in terms of patents and profitability while, in contrast, similar studies examining various marketing expenditures suggest a much shorter- lived benefit (see Norris, 1984). Additional support of this argument is provided by recent studies which employ various market-return methodologies to examine the longevity of R&D and marketing expenditures (Ettredge and Bublitz, 1988; Hirschey and Weygandt, 1985). Bublitz and Ettredge (1989), for example, found a long-term association between unexpected changes in R&D expenditures and post-period cumulative abnormal security returns (CAR), while no such association was noted for marketing expenditures.

It can also be argued that R&D expenditures are associated with a larger cash flow variance than marketing expenditures. The uncertainty, and thus risk, associated with R&D expenditures are well known. Depending on the degree of innovative skill, competitive developments, and scientific ’luck’, a dollar spent on R&D may or may not result in profitable new products or patents. Although some marketing investments, such as initiating new channels, are certainly risky, in general the returns from marketing expenditures will typically have a smaller variance-marketing dollars spent will likely provide more consistent and certain returns over time. For the reasons provided in the previous section, a risk-averse manager with limited resources will be more likely to follow variance- reducing strategies, such as marketing (versus R&D), even though the present value of the future income streams may actually be smaller than those associated with other, more innovative, strategies.

We therefore hypothesize that compensation programs with larger equity and long-term performance elements will be positively associated with R&D expenditures, and its likely derivatives, new product introduction, technological innovations, and technology quality, and negatively associated with marketing expenditures. In addition, we expect that compensation programs linking rewards to short-term objectives and growth targets will be positively related to marketing expenditures since the direct objective of most marketing expenditures is to increase current period sales. Formally then:

H I : There will be a positive relationship between marketing-oriented expenditures and compensation programs emphasizing salary, bonus tied to exceeding prior performance and bonus tied to growth measures, and a negative relationship between marketing-oriented expenditures and compensation programs emphasizing long-term performance. For R& Dlinnova- tion activity the opposite relationship will hold.

In discussing the structural elements of delegation and formality we partially accept the arguments presented by Miller, Droge and Toulouse (1988). With respect to centralized, non-delegated decision-making, we hypothesize (as Miller et al., 1988) that centralization will inhibit risk-taking strategies and thus will be positively related to marketing expenditures and negatively related to R&Dlinnovation strategies. With respect to formalization it is difficult to support Miller’s argument that R&D strategies require formality of structure (1988: S O ) , particularly in rapidly changing technology-intensive environments. As Kay (1979, 1984) points out, successful R&D strategies typically require scientists and engineers to leverage technology and design know-how synergistically across depart- mental boundaries through ever-changing infor- mal organizational networks, often circumventing the formal decision process. This is well supported by studies of corporate culture (e.g. Deal and Kennedy, 1982; Leonard-Barton and Deschamps, 1988). In fact, one of the specific objectives of formalized reporting and allocation systems is to control such behavior. Specifically then:

H2: There will be a positive relationship between marketing-oriented expenditures and the centralization of management decisions, and a negative relationship between centralization of R& D decisions and R& Dhnovat ion activity. There will be a negative relationship between the structural element of formality and both marketing and R& Dlinnovation activity.

Product-line diversification

As previously discussed, the objective of product diversification (or any type of diversification) is typically risk reduction. Unless there are incentives to do otherwise, an SBU manager will attempt to maximize his or her human capital,

358 C. S. Galbraith and G. B. Merrill

W,, by actively pursuing risk-reduction strategies such as product diversification. By diversifying a division’s product line the manager can not only hedge against divisional ‘bankruptcy,’ thus protecting his or her job and human capital in the firm, but also smooth out divisional revenues and profits, thereby establishing an equivalency of yearly profit contribution to the firm- an important consideration in many corporate environments. While SBU-level product diversification will typically take greater advantage of potential scope economies and synergies than corporate level diversification, we hypothesize that, ceterus paribus:

H3: There will be a positive relationship between product-line diversification and compensation programs emphasizing salary and bonus tied to growth measures, and a negative relationship between compensation programs emphasizing long-term performance.

We also expect that the decentralization and informality of R&D decision-making will lead to the proliferation of products and technologies. In the continuing effort for technological advance- ment, high-technology firms are usually simultaneously involved in a large number of research projects, most of which have only limited potential for commercial success. In spite of this, each of these projects will have at least one ardent champion, usually the scientist andlor engineer working on their ‘pet’ projects, who will actively lobby for any form of commercial introduction. Centralization of R&D project decisions forces an organization to prioritize the commercial viability of technology alternatives, and limit technology efforts according to available resources. Formalization of processes typically supports this effort by requiring the various arms of the organization to justify various projects in terms of expected paybacks and cash flows.

The centralization of non-R&D decision-making should have the opposite effect, however. Technology intensity creates a general risk to all firms within that industry. The technological base of any SBU’s products, along with its production and marketing capacity, can be rendered obsolete by a competitor’s discoveries-a form of technological ‘mugging’ (Galbraith and Kay, 1986). Organizations pursuing a focused, or highly synergistic strategy, will create common points

of vulnerability. Thus, there is an inherent underlying incentive for high-technology firms to pursue hedging strategies, such as a diversified product-line, that provide a degree of security against such environmental uncertainty. In an organization with a centralized structure, the cognitive limits and desires of those few who dominate will determine the strategic outcome. In essence, in the case of technology-intensive organizations, centralized management decision- making places the diversification decision in the very hands of those senior executives who have the most to lose in the event of a technological downturn. A number of studies, for example, have found significant relationships between the degree of unrelated diversification and the technological intensity of industries (Galbraith et al., 1986; Kay, 1984; Wolf, 1977). Taking these arguments together, we expect somewhat opposite results relating to the centralization of R&D and non-R&D decisions. Centralized control of R&D-related decision constrains and directs the enthusiasm of those scientists and engineers supporting their individual projects, while centralized management activity provides greater opportunity for following hedging strategies. In essence, the motivations between scientist and managers are probably quite different. Scientists will likely pursue a wide variety of projects because of the perceived technical opportunities, whereas managers are more likely to pursue diversification as a hedge against the inherent uncertainty of high-technology environments.

H4: There will be a negative relationship between product-line divers$cation and the centralization and formality of R&D decisions; there will be positive relationship for the centralization of non-R&D decisions.

Investment in capital equipment and employee training

Investment in capital equipment is typically considered commitment to a project exceeding the current time period, and is accounted for under GAAP as a fixed cost to be expensed over several periods. We therefore expect that managers operating under long-term performance plans will be more likely to invest in capital equipment than managers oriented toward the short-term. We also believe that a risk-averse


manager (with high salary involvement) will avoid long-term fixed costs in favor of short-term costs because it is easier to control and smooth an SBU’s cash flow by adjusting variable cost factors, such as labor and materials, and ‘discretionary costs,’ such as advertising. This argument assumes that for pre-owned capital equipment, markets are relatively inefficient, or have large transaction costs involved; in other words, there are substan- tial barriers to quickly buying, selling, and transporting plant and capital equipment. Thus:

H5: There will be a negative relationship between investment in capital equipment and compensation programs emphasizing salary and bonus based upon exceeding prior performance, and a positive relationship for compensation programs emphasizing long-term performance.

High levels of investment in capital equipment also require an organization to severely ration its limited resources, thus forcing an executive team to prioritize and focus its efforts on a more global or strategic perspective. Forcing non-R&D decision-making to lower levels will often create a bias against long-term capital investment for two reasons: (a) lower-level managers will favor short-term investments that allow them to achieve short-run, operational objectives, such as weekly sales quotas or production schedules; and (b) given limited resources, a major capital investment would require a consensus for some to sacrifice resources in favor of other projects. Successful capital rationing therefore requires a more centralized managerial process. Similarly, formality of decision processes also supports capital rationing, since under these conditions capital requests must be better justified in terms of financial returns. However, the opposite argument can be made for decentralization of R&D decisions. With unmatched passion, research scientists and design engineers are continuously arguing for the acquisition of expensive, state-of-the-art laboratory equipment, testing devices, and computer hardware. The decentralization of decision-making for R&D should, therefore, encourage investment-albeit not necessarily wise investment-in R&D-related capital equipment. In juxtaposition, centralization of R&D decisions at the top will likely have a negative impact on the amount of R&D-related capital expenditures.

H6: There will be a positive relationship between investment in capital equipment and the centralization and formalization of non- R&D decisions; there will be a negative relationship for centralization of R&D decisions.

A parallel argument can be made for employee training expenditures. A manager with a long- term perspective will typically invest in employee training since it is perceived that the present value of the future cash flows associated with a well-trained employee base will likely exceed the current training cost, whereas a short-term orientation will typically minimize training costs since the short-term cost of training often exceeds the short-term benefits. In addition, a short- term-oriented manager will always be cognizant of the inherent risk of employee training investments-eer since the abolition of employee ownership in most industries (professional sports being the most notable exception) it is essentially impossible for a firm to recover a significant amount of an employee’s training cost should that employee choose to leave the organization. Therefore,

H7: There will be a negative relationship between employee training expenditures and compensation programs emphasizing salary, and a positive relationship between employee training expenditures and compensation programs emphasizing long-term performance.

With respect to formality and centralization a ‘strategic perspective’ argument similar to that provided for capital expenditures can also be made-lower-level managers are primarily interested in ‘getting the product out the door,’ not sitting through management training sessions- in effect, these managers tend to be ‘local’ in their orientation. Thus, top management often takes the lead in establishing and requiring attendance at various training programs, such as management education seminars. However, following the same argument as above, we also believe that the decentralization of some R&D- related decisions will, in fact, lead to increased training expenditures. Since research scientists and design engineers tend to be more ‘cosmopoli- tan’ in their orientation, and are keenly aware of the need to constantly update technical know-


how (or ‘retrain’ newly acquired personnel), they often take the first initiatives in establishing various training programs, such as scientific conferences and technical retraining seminars. In effect, scientists and design engineers often appear to place a much higher priority on these types of expenditures than other employees and management.

H8: There will be a positive relationship between employee truining expenditures and the centralization and formalization of non- R b D decisions; and a negative relationship for the centralization of R& D decisions.

Organization size

An additional structural characteristic, SBU size (SIZE), is introduced as an explanatory variable. While size of firm has been positively linked to marketing expenditures and product-line diversification in a number of industrial organization studies (see Spence, 1980), the theoretical relationship between firm size and innovative activity remains unknown. Schumpeterian-based arguments posit that large firms are more proportionally innovative than smaller firms-an argument strongly endorsed by John Kenneth Galbraith. The argument is twofold; first that large firms are likely to tacitly agree to limit price competition, thus replacing price competition with innovative activity, and second, it is a more successful strategy to gain advantage over smaller competitors who have fewer resources to finance R&D. A counter-argument is that smaller firms are less bureaucratic and closer to market signalling and evolving technology. Most, although certainly not all, empirical studies in the past appear to support the smaller firm hypothesis (see Kamien and Schwartz, 1982. for a complete discussion).

SAMPLE AND VARIABLES

The sample consists of responses from 79 high- technology firms located in the western United States. To obtain this sample, SBU managers from 100 high-technology firms were contacted by phone and agreed to respond to a questionnaire regarding their SBU’s strategy, organizational structure, competitive environment, and their

individual compensation programs. Each SBU surveyed had discretion over at least R&D, marketing, product introductions, and capital investment (given budgetary constraints); and was physically located in a separate facility from corporate headquarters. In addition, no two SBUs were from the same corporate entity. Also, so as not to confuse equity ‘ownership’ with ‘compensation tied to equity value,’ all respondents were considered non-owners under accounting rules. A total of 86 managers subsequently returned the questionnaire; however, seven responses were considered unusable, for various reasons. High technology for this study was defined by industry sectors: computers, SIC (357); communication equipment, SIC (366); electronic components, SIC (367); instruments, SIC (38), aerospace, SIC (372), research labs SIC (737), and space systems, SIC (376).

Competitive strategy

Following the form established by PIMS, and other studies examining competitive strategy (see Galbraith and Schendel, 1983 and Miller. 1987, for detailed discussions regarding measuring competitive strategy), managers were asked to evaluate components of their SBU’s overall strategy, relative to competitors and industry average. Strategy variables included relative price,’ overall marketing expenditures, advertising expenditures, expenditures on sales personnel, product R&D expenditures, process R&D expenditures, capital equipment expenditures, assessment of the product’s or technology’s overall quality, and the degree of product line diversification. Strategy questions were rated on a seven-point scale, with the mid-point representing the industry average. Two other technology-related variables were created: relative long-term technological advances and relative long-term new product introduction. The relative long-term new product introduction variable was created by the ratio of the responses on two questions: the number of products introduced by

I Relative price was collected as a strategy variable. However, since pricing is most strongly influenced by industry structure, and since no strong theoretical arguments could he rnadc why compensation program or structural characteristics alone would impact pricing. it was dropped from the analyses.


the firm over the past three years, and how many products were introduced by the industry over the past three years. Similarly, the relative long- term technological advances variable was created by the ratio of the responses on two questions: the respondents’ evaluation on the frequency in the previous three years of ‘major’ technological advances for the firm and the industry, respectively.

Company and industry demographics

Data was collected regarding primary industry growth and profitability, and SBU market share and profitability. SBU size, in terms of employees, was also obtained. The median size of the SBU in our sample was 675 employees, ranging from a low of 125 to a high of 7000.

Compensation

Respondents (the SBU managers) were asked to provide information regarding their individual compensation program; most of the information provided was not available from public sources, although when possible 10-K reports were checked to verify accuracy of information. Com- pensation data collected were: (1 ) percentage of total compensation that comes from the various elements of salary, bonus, and equity-based incentives; ( 2 ) for bonus and equity-based incentives respectively, the weight attached to short- term (1 year or less) versus long-term objectives; (3) for bonus and equity-based incentives respectively, the weight attached to: (a) revenue growth, (b) profitability, and (c) non-quantifiable elements such as leadership; and (4) for bonus and equity-based incentives respectively, the weight attached to exceeding prior-period performance versus established future objectives.

On the average, in our sample, some 77 percent for the SBU manager’s total pay came from fixed salary, while the remaining 23 percent was derived from various incentive plans. For our sample, about 91 percent of the SBU managers received some type of incentive pay and 27 percent operated under some type of long-term performance plan. This compares with a recent survey by Tehranian et al. (1987) of 146 NYSE/ASE firms who found that 100 percent of their sample had incentive plans, and 45 percent

had long-term performance plans. Considering our sample contained many smaller firms it appeared fairly representative of the population.

Centralization and formality

Centralization was measured with a nine-item scale. Respondents were asked to evaluate the degree that various decisions were delegated to lower levels in the organization. The decisions evaluated were: large-scale capital investment, budgeting decisions, R&D investment, direction of R&D effort, management personnel decisions, R&D personnel decisions, pricing, and marketing decisions. The dimensions of centralization were drawn from the Miller stream of research (e.g. Miller et al., 1988), supplemented by dimensions or terminology more appropriate for high-technology operations. The centralization variables were then factor-analyzed utilizing SPSS principal-components, with varimax rotation. Two factors with eigenvalues > 1.0 resulted. Based upon the associated factor loadings, factor 1, which combined the centralization of capital investment, budgeting, management personnel, and pricing/marketing decisions, was labeled ‘MGMT CENT.’ Factor 2, which combined the centralization of R&D-related decisions, was labeled ‘R&D CENT.’

Likewise, formality was measured with a 10- item scale. Respondents were asked to evaluate how formalized certain descriptions and procedures were in the organization. These included: formalization of job descriptions, operating descriptions, performance evaluation procedures, strategic planning procedures, and budgeting procedures. These five items were evaluated for (a) top management activities and (b) lower management activities. The formality dimensions built upon previous studies by Miller, and issues specifically discussed by Galbraith and Nathanson (1978), take into account the argument by Fredrickson (1986), that organizational formality needs to be examined relevant to the level in the hierarchy. Factor analysis with SPSS, utilizing varimax rotation, again resulted in a two-factor solution with eigenvalues > 1.0. Based upon the associated factor loadings, factor 1 (labeled ‘PROC FORM’) combined elements of pro- cedural formality, while factor 2 (labeled ‘DESC FORM’) combined variables measuring document description formality. All centralization and


formality questions were measured with a seven- point Likert scale.

For this analysis, centralization was measured by the two factors, R&D CENT and MGT CENT, and formality was measured by the single factor PROC FORM, since formality of document descriptions did not appear relevant to this study. Four specific compensation variables were constructed: SALARY = percentage of total pay from salary; PAST = percentage of incentive pay (non-salary) based upon standards relative to prior-year performance (versus future performance); GROWTH = percentage of incentive pay based upon growth (versus profitability) standards; and LONG = percentage of incentive pay based upon long-term performance (versus short-term performance). Based upon the discussion above, Table 1 presents the hypothesized relationships between the various dependent and explanatory variables.

RESULTS

The data were analyzed in two phases. First, the relationship between strategic position and compensation program was investigated using a matrix approach to define strategic position. Second, to determine the relationship of compensation and structure with SBU competitive strategy a multivariate regression analysis was performed with data pooled across industry sectors.

The relationship between compensation program and strategic position

The sample was divided into four sub-groups of strategic position based upon market share and industry growth. The cutpoint for market share was 20 percent, with those SBUs reporting less than a 20 percent share of the ‘served market’ classified as ‘low share’ and those greater than 20 percent as ‘high share.’ Similarly, the cutpoint for industry growth was 8 percent annual growth, less than this classified as ‘low growth,’ greater than 8 percent as ‘high growth.’ Table 2 displays the characteristics of the compensation programs for SBUs in each of the four sub-groups.

It appears that there are significant differences in compensation programs depending on strategic position (oneway ANOVA, a < 0.05). For example, only 64.7 percent of the low share/low growth SBUs have incentive programs; far less than the other categories. In addition, as expected under the BCG model, SBUs in high growth industries were more likely to have incentive programs linked to long-term performance versus those in lower growth markets.

Examining the various elements of the compensation programs also reveals significant differences. In particular, the managers of high share/low growth SBUs have a much higher weighting placed on profit achievements, and less on growth-oriented elements, when computing incentive pay. This is again consistent with the ‘cash cow’ strategy of extracting profits from the organization versus the growth-oriented strategies

Table 1. formality

Hypothesized relationships between SBU strategy, executive compensation program, delegation, and

~ ____ ~ ~ ~~

Independent Dcpendcnt variablcs (strategy) variable

Market Advert Sdlcs Product Proccss Ncw Techno- Techno- Product Capital Employee expend cxpcnd expend R&D R&D products logicdl logicdl brcadth investment training

ddVdnCCS qUdlltY

Salary + Past + Growth + Long - Size + R&D Cent ? Mgmt Cent + Proc Form -

~

+ + + -

+ ’? + -

+ = Hypothesized positive relationship, - = hypothesized negative relationship, +I- = theoretical arguments exist for either direction, ? = no strong theoretical argument can be made for relationship.


Table 2. Profile of SBU manager compensation

Strategic Position

Market share: Low High Low High Market growth: Low Low High High BCG name: ‘Dog’ ‘Cow’ ‘?’ ‘Star’

(n=17) (n=lO) (n=24) (n=28)

Percentage from different sources: Fixed salary* Bonus (short-term plan)* Bonus (long-term plan) Equity (short-term plan) Equity (long-term plan)

For incentive pay, weight on: Profitability* Revenue growth* Non-quantifiable (leadership)*

Exceeding prior 1-year performance Exceeding prior multiyear performance Future standards, 1-year* Future standards, multiyear*

For incentive pay, weight on:

Percentage of sample with incentive plans (bonus andlor equity)* Percentage of sample with long-term performance plans*

0.86 0.12 0.01 0.00 0.01

0.44 0.19 0.37

0.13 0.07 0.65 0.11

64.7%

11.8%

0.75 0.20 0.02 0.03 0.00

0.76 0.09 0.15

0.19 0.02 0.78 0.02

100.0%

10.0%

0.75 0.17 0.01 0.01 0.05

0.53 0.17 0.30

0.18 0.01 0.71 0.10

100.0%

37.5%

0.74 0.17 0.03 0.01 0.05

0.67 0.15 0.18

0.15 0.00 0.77 0.08

96.4%

29.6%

* Significant at a < 0.05, one-way ANOVA.

of the ‘question mark’ and ‘star.’ Another interesting finding is the significantly greater emphasis placed upon ‘non-quantifiable’ elements for the low share/low growth category. For many of these SBUs the senior staff is explicitly charged with implementing liquidation, turnaround, or some other draconian strategy. Clearly, in these situations, behavioral skills such as leadership are critical for success. From our sample it appears that, in fact, the incentive plans developed for weaker positioned SBUs do consider these non- quantifiable skills to a greater degree.

Regression analysis

For the regression analysis the dependent variables were those variables of competitive strategy. Independent variables included the four elements of the SBU compensation program and the four structural variables of formality, R&D centrality, management centrality, and size. When a theoreti-

cal relationship could not be well supported for a particular independent variable it was excluded from that particular model (see Table 2). In addition, strategic position (as defined by the four categories described above) was included in the regression as dummy control variable. Both linear and log-linear relationships were specified and estimated; however, the log-linear models appeared to fit the data best and are reported here. A possible explanation for this is because an SBU manager’s funding discretion is essentially asymptotic, that is, limited to corporate budgetary constraints on the high end of the funding spectrum (one cannot support an activity beyond budgetary constraints), and the practical reality to at least partially support a wide range of functional activities at the other end (some marketing, some R&D, some capital equipment purchases, etc.), a log-linear model may better capture this behavior. Because multicollinearity did not appear as a severe problem (as indicated

364 C. S . Galbraith and G. B. Merrill

by the estimated tolerances for each of the independent variables and Pearson correlations (see Table 3)) estimation was by ordinary least- squares. Table 4 presents the results of the regression analysis.

The effect of compensation programs

Marketing and R& Diinnovation

The results presented in Table 4 appear to support Hypothesis 1, which posits the relationship between the various elements of compensation programs and both marketing- and R&D/innovation-related strategies. With respect to the three marketing equations only three of the estimated twelve coefficients are in directions opposite from the hypothesized relationship, and all the statistically significant coefficients are in the expected direction. Of the four compensation variables, SALARY and LONG appear to be most significant, although incentive pay linked

to exceeding past performance is also significant in two of the models. This suggests that compensation programs that (a) overemphasize a manager's human capital, W,, via base salary, and (b) provide incentive pay linked to both short- term objectives (versus long-term) and bettering past results, will result in greater marketing efforts. In all, the explanatory power for the marketing equations is relatively good, with R2 values ranging between 0.21 and 0.29.

The five R&D/innovation equations provide somewhat mixed results. Three of the five R&D/innovation equations have R2 values less than 0.15, and two of these (process R&D and technology advances) are essentially statistically insignificant models relative to the compensation variables. However, almost all of the sixteen compensation coefficients, and all but one of the seven statistically significant coefficients, are in the hypothesized direction. Again, SALARY and LONG seem to have the greatest explanatory power, and in every case show the anticipated

Table 3. Descriptive statistics and Pearson correlation coefficients

Variables Dcpcndcnt variahlci ($tratcgy)

Mcdn S D Id Za ta 4a 5a 6a 76 8a 9a 1Oa

la Market expenditure 2a Advert expenditure 3a Sales expenditure 4a Product R&D 5a Process R&D 6a New products 7a Technological advances 8a Technological quality Ya Product breadth

10a Capital investment l l a Employee training

3.90 1.30 3.48 1.44 4.10 1.06 4.03 1.65 3.78 1.56 0.96 0.36 1.04 0.34 5.31 1.20 4.86 1.43 4.44 1.22 3.92 1.13

o.hx*** 0.48"'

-0.22* -0.40*** - -0.13

0.10 - 0.29** 0.27" 0.23" 0.38'. *

0.40'" 0.16

4.31 * * 0. I3

-0.03 0.11 0.W 0.23" 0.40"'

0.13 0.27" 0.85"'

0.01 0.14 0.20* 0.28;' 0.25** 0.37*** 0.39*** 0.23** 0.30"' 0.18 0.26" 0.34*** 0.35*** 0.20* 0.40*** 0.25" 0.44"' 0.57*** 0.32*** 0.18 0.23" 0.37*** 0.42'"' 0.49"' 0.59"' 0.35*** 0.26** 0.33"' 0.44*** 0.60**'

-0.02 0.29" 0.26"

Variables Independent variables (compensation and structure)

Mean S.D I h 2b 3h 4h 5h 6b 7b

Ib Salary 2h Past 3h Growth 4h Long 5b Size 6b R&D Cent 7h Mgmt Cent 8h Proc Form

0.77 0.15 0.02 0.04 -0.20 0.08 0.08 -0.66** * 0.03 0.06 -0.47"' 1563 1WY -0.36"' - 3.94 2.02 0.09 2.81 1.53 -0.07 4.33 I .3Y 0.14 -

0.23* 0.19'

-0.07 0.22" 0.13 0.16 -0.08 -0.04 -0.w 0.04 -0.08 0.03 -0.00 0.57."

- 0 . 1 1 -0.M -0.22** -0.23** -0.14 -0.30***

* * * - - p < 0.01, * * = p < 0.05, * = p < 0.10.

Tabl

e 4.

R

esul

ts o

f lo

g-lin

ear

regr

essio

n of

SB

U s

trat

egy

with

exe

cutiv

e co

mpe

nsat

ion

prog

ram

, del

egat

ion,

form

ality

, and

siz

e

Dep

ende

nt v

aria

bles

(st

rate

gy)

~~

~ ~~

~ ~~

Inde

pend

ent

Mar

ket

Adv

ert

Sale

s Pr

oduc

t Pr

oces

s N

ew

Tech

nolo

gica

l Te

chno

logi

cal

Prod

uct

Cap

ital

Empl

oyee

va

riab

le

expe

nditu

re

expe

nditu

re

expe

nditu

re

R&

D

R&

D

prod

ucts

ad

vanc

es

qual

ity

brea

dth

inve

stm

ent

trai

ning

Con

stan

t 1.

01

0.40

1.

12

1.64

0.

86

0.13

0.

36

2.04

1.

57

1.16

1.

55

Sala

ry

0.22

*"

0.69

**

0.04

-0

.21'

-0

.15

-0.0

6 -0

.12

-0.3

3**

0.19

* -0

.10'

-0

.03

Past

0.10

**

-0.0

2 0.

06"

-0.0

3 0.

00

-0.1

2***

0.

02

0.08

' -

0.01

-

-

-

-0.0

3 -0

.02

-

0.03

G

row

th

-0.0

1 0.

03

-0.0

4 -

Long

-0

.06'

-0

.07"

-0

.06:

0.

07**

0.

00

0.06

* 0.

03

0.00

-0

.06"

0.

05'

OM

* Si

ze

0.06

**

0.09

**

0.05

" -0

.04

0.02

-0

.05

-0.0

5 O

M*

0.

00

0.01

0.

00

R&

D C

ent

-

Mgm

t C

ent

-0.0

2 0.

04

0.09

' -

-

-

-

0.22

"' 0.

18*

0.04

0.

09'

Proc

For

m

-0.0

9 0.

08

-0.1

2*

0.04

0.

19'

-0.0

9 -0

.02

-0.2

0"

-0.1

8'

0.06

0.

24**

'D

og'

-0.2

8'"

-0.0

9 -0

.10

-0.1

6*

-0.1

5'

-0.0

7 0.

00

-0.0

2 -0

.05

-0.1

1'

0.00

'C

ow'

0.05

0

.W'

-0.0

5 -0

.34"

-0

.38*

* -0

.09

0.03

0.

11

-0.2

1**

-0.0

1 -0

.09

'?'

0.05

0.

03

0.05

-0

.24.

-0

.28*

* 0.

00

0.00

-0

.07

-0.1

4'

0.04

-0

.06

R2

0.29

0.

21

0.25

0.

11

0.13

0.

15

0.05

0.

24

0.23

0.

16

0.28

-

-

-

-0.0

7 -0

.10'

-0

.09'

-0

.06

-0.0

6 -0

.1 1

**

-0.0

9'

-0.2

1***

p <

0.0

1,

** =

p <

0.0

5,

* =

p <

0.1

0,

***

=

Q 3 h a 3

Q.

366 C. S. Galbraith and G . B. Merrill

trade-off effect between R&D/innovation-oriented strategies and marketing expenditures. In sum, compensation programs that emphasize equity elements (which promotes risk-taking behavior) and incentive pay based upon long- term results (which extends a manager’s decision horizon) appear to significantly affect the resource trade-offs between marketing and R&D/innovation activities in favor of the latter.


Hypothesis 3 is essentially supported. Of the three compensation elements, two are statistically significant at the 0.10 level, and are in the hypothesized direction. The estimated coefficients of the SALARY and LONG compensation elements support the theoretical arguments presented earlier: managers who are tied to salary and short-term performance incentive plans will prefer diversification strategies as a hedge against the risk of losing their personal income stream.

lnvestment in capital equipment and employee training

Hypotheses 5 and 7, which deal with the relationship between compensation program and investment in capital equipment and employee training, respectively, are also supported-albeit weakly in statistical significance for the elements of compensation. For capital investment, SAL- ARY and LONG are statistically significant at the 0.10 level, and are in the hypothesized direction; for the employee training model, LONG has the only significant coefficient. As with the R&D/innovation equations, managers operating under long-term performance plans, and thus longer decision horizons, appear to make heavier investments in capital equipment and employee training than those managers with more short-term incentives.

The effect of Centralization, formality, and size

Marketing and R&D/innovation

Hypothesis 2 posits a negative relationship between formality and both marketing and R&D/innovation activities, a negative relationship between centralization and R&D/innovation

activities, and a positive relationship between centralization and marketing activities. In addition, we expected a positive relationship between size and marketing while both positions could be argued for the relationship between innovative activity and size. For the marketing equations the hypothesized positive relationship for size is supported. This finding parallels most of the studies in industrial organization economics which have noted that large firms typically account for proportionally larger marketing expenditures than smaller firms. However, for the other two structural variables (MGMT CENT and PROC FORM) only the sales force expenditures equation provides significant results in the hypothesized direction.

For the R&D/innovation equations all the estimated coefficients for R&D CENT are in the hypothesized direction, with two being statistically significant whereas mixed results are noted for the PROC FORM variable. Interestingly, the technology quality equation shows a positive and highly significant coefficient on the centralization of management decision-making. Taken together, these findings suggest that while the generation of technology options and new ideas is stimulated by decentralized research decisions (R&D CENT), the successful assurance of ‘high-quality’ technology-based products, particularly those in commercial production, may require centralized management processes (MGMT CENT), such as quality assurance programs, company project review sessions, and other project management techniques.


In Hypothesis 4 we suggest that product-line diversification should be positively related to the informality and delegation of R&D decisions (all research scientist and design engineers want to see their efforts brought to fruition), and positively related to the centralization of management decisions (managers in technology-intensive industries have a natural incentive to diversify as a hedge against technological ‘mugging’). The regression results support these arguments for the three structural variables of R&D CENT, MGMT CENT, and PROC FORM. No significant relationship is noted for SIZE.


Investment in capital equipment and employee training

Based upon a ‘strategic perspective’ argument, Hypotheses 6 and 8 suggest that centralization of management decision-making and formality of procedures will lead to increased investment in capital equipment and employee training. We also posit that the decentralization of R&D decision-making will lead to increased capital investment and training, since research scientists and design engineers in high-technology firms clearly value both state-of-art laboratory equipment and currency of technological know-how. Hypotheses 6 2nd 8 are supported by the analysis. For the employee training equations all the estimated coefficients are statistically significant and in the expected direction; likewise for the capital equipment investment equation all coefficients are as hypothesized, but only the R&D CENT variable is statistically significant. Again, SIZE did not appear important.

The effect of strategic position

As discussed above, the strategic position variables are coded as dummy variables. For example, the ‘dog’ variable was coded 1 if the SBU was low share/low growth, 0 otherwise. Because the strategic position variables would be linearly dependent if all four variables were entered into the regression at the same time (perfect multicollinearity), only the ‘dog,’ ‘cow,’ and ‘?’ variables were entered. Thus, the base model (when all three dummy variables are set to 0) represents the ‘star’ category. Under these conditions, then, the coefficients estimated for the dummy variables should be interpreted as the position of that strategic position relative to the ‘star’ position.

From Table 3 we find that low share/low growth (dog) SBUs have significantly less marketing expenditures than SBUs in the other strategic positions. In contrast, ‘cash cows’ show the highest marketing’ expenditures, particularly in the area of advertising. This, however, needs to be compared with the results on the R&D equations. Here, ‘cash cows’ show the lowest amount of product R&D and process R&D expenditures, whereas SBUs enjoying a high

sharelhigh growth (star) position appear to show the greatest R&D efforts. In addition, ‘stars’ diversify their product-lines more than others. Taken together several conclusions are suggested. Assuming BCG arguments, the theoretical strategy of ‘cash cows’ should be to extract profit, with a minimum of long-term risky expenditures. In fact, the regression results suggest that ‘cash cows’ are indeed hesitant about investing in R&D, but willing to expend major marketing efforts. Two possible explanations can be offered. First, for these firms marketing, compared to other investment opportunities, may represent the least expensive method of keeping a presence in the marketplace-at least in the short run. Second, organizational ‘inertia’ may be at work. An often-observed phenomenon of dominant organizations is the constant aversion to developing new, innovative products and technologies; hence possibly explaining the relatively low R&D investments for these SBUs. In contrast, ‘stars’ appear willing to make major commitments to R&D, and subsequently market a number of products. ‘Dogs,’ as expected, display significantly lower expenditures of all types.

SUMMARY AND CONCLUSIONS

This study examined the impact of two important organizational factors on SBU competitive strategy: the nature and type of the executive compensation package for the senior manager, and the structural characteristics of centralization, formality, and size. In contrast to most previous empirical efforts, which have studied the effect of executive compensation programs and equity ownership on corporate-level, or diversification strategy, we focused on competitive strategy at the SBU level. In addition, we concentrated on firms in technology-intensive industries where the resource trade-offs between relatively long-term investments, such as R&D and capital equipment, and relatively short-term investments such as marketing expenditures, are particularly critical to the success or failure of a firm.

Based upon a human capital argument, we suggested that managers who are over-reliant on salary, and less reliant on incentive pay, will

368 C. S . Galbraith and G. B. Merrill

make less-risky competitive decisions and thus favor product-line diversification and increased expenditures for marketing efforts as a hedge against the inherent uncertainty in technology- intensive industries. In contrast, we hypothesized that less reliance on salary would result in the more-risky investments of R&D and innovation- oriented activities. In addition, we argued that compensation programs containing long-term performance elements would encourage a long- term perspective, and thus greater investment in R&D, capital equipment, and employee training.

Using data from 79 high-technology SBUs, the results essentially support our argument. Managers who had a larger salary component in their total compensation did, in fact, favor marketing expenditures over R&D expenditures; they also showed an inclination to diversify their SBU product-line, and did not favor investment in capital equipment. Managers with a higher percentage of incentive pay were associated with the opposite strategies. Similarly, the analysis supported the contention that long-term performance plans would lead to strategic behavior oriented toward the long term. Long-term performance plans were positively associated with R&D/innovation activity, and investment in both capital equipment and employee training. In contrast, managers operating under compensation programs which provided greater rewards to short-term objectives were associated with higher marketing expenditures and greater product-line diversification.

We also examined the impact of structural characteristics, such as decision centralization, formality of procedures, and organization size, upon competitive strategy. The results suggest that, for at least technology-intensive operations, delegation can work in two directions. The delegation of R&D-related decisions to lower levels acts to encourage investment in R&D activities, capital equipment, and employee training while the opposite behavior is noted for the delegation of non-R&D-related decisions. In addition, the results also strongly suggest that the centralization of non-R&D decisions has a significantly positive effect in controlling the relative level of technology/product quality. Although formality of procedures was positively related to both level of quality and employee training, in general it did not provide much explanatory power within our model; similarly

organizational size was primarily related only to marketing expenditures.

Finally, we investigated the impact of competitive position on both the design of compensation programs and the overall strategy of the SBU. We found that an SBU’s competitive position does, indeed, influence how the compensation package of the senior SBU manager is put together. Competitive position also assists in explaining the SBU’s strategy when included as an intervening variable in the analysis.

Clearly there are a number of additional factors beyond what we have examined that can influence the ultimate design of a compensation package; these range from issues of corporate culture to simply matching a successful competitor’s incentive plan. The present study, however, adds to the expanding body of literature which examines executive compensation issues within the context of strategic management theory. By providing a theoretical basis for risk behavior and decision horizon it was possible to examine the impact which different compensation program elements and structural characteristics have upon business-level, or competitive strategy. The results of this study, however, may not generalize to non-technology-intensive populations because of the unique underlying behavioral incentives created by the technological uncertainty within these industries. Much more work also needs to be accomplished in order to understand the complete interaction of compensation, structure, and size. Recent research (e.g. Conlon and McLean, 1988; Eisenhardt, 1985; Gomez-Mejia et a l . , 1987), for example, suggests that the design of an organization’s compensation system is based not only upon agency concerns, but also upon the institutional factors of tradition, history, and structure.

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