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Determinants of shortline railroad performance.

The Staggers Act of 1980 substantially deregulated the railroad industry, providing firms with far greater opportunities and incentives to determine prices, service quality, and geographic markets. One significant trend accelerated by Staggers has been the widespread downsizing of railroads, with almost 40,000 miles abandoned since 1970.(1) However, rail service has been preserved in many cases due to the creation of more than 20,000 miles of shortline and regional railroads.(2) According to a recent Federal Railroad Administration/Interstate Commerce Commission joint study, there were 424 independent shortline and regional railroads in operation as of mid-1989, with more than half of these formed since the passage of the Staggers Act. The vast majority of these railroad are very small, operating fewer than fifty miles of line with perhaps ten to twenty employees. The heaviest concentration of these new lines is in the Northeast.(3)

Previous research on these local enterprises formed to take over lines sold by major railroads has contributed much to our understanding of this important phenomenon, particularly as to factors contributing to success. John Due has been a pioneer in this area, conducting detailed assessments of a large number of shortline railroads.(4) He has concluded that important factors influencing success include traffic volumes, distance of line, rate divisions, skill of managers, and labor costs. However, these factors have not been subjected to formal statistical testing in Due's research.

More recently, other authors have contributed to our understanding of the shortline phenomenon. Rockey and Dooley have provided rich descriptions of these railroads, including principal commodities hauled, traffic densities, number created by year, and the like.(5) Mielke has discussed in detail the typical terms of sale and other circumstances in creation of a shortline railroad.(6) Loughman has provided an overview of the shortline railroad industry in Canada.(7)

Building on this research, Wolfe has developed and tested more formal statistical models regarding shortline railroad failure. Initial exploratory work identified a number of "agents underlying failure," including limited traffic, single factor reliance, poor traffic balance, poor economic conditions, intense motor carrier competition, inexperienced management, and poor planning.(8) Following with more formal analysis, Wolfe found that failed companies had lower size, lower density, and a higher concentration of business in one commodity.(9) Wolfe has also contributed financial models which can be used to predict shortline railroad bankruptcy.(10)

While previous research has contributed greatly to our understanding of shortline railroads, statistical analysis has not been brought to bear on two major questions. First, to what extent are economic and demographic variables responsible for variations in performance among shortline railroads currently in operation? Second, to what extent can managerial actions and characteristics explain variations in shortline railroad performance?

This article contributes to the literature on shortline rail by addressing these two questions. Whereas previous research has utilized objective data on economic and demographic characteristics of shortline railroads, this research combines objective data with results from a survey of shortline railroads on management characteristics and actions. Also, the focus of this article is on determinants of performance among successful railroads, i.e., roads that are currently in operation. The empirical results indicate that both demographic and management variables are important determinants of shortline railroad performance.

The following section develops a set of hypotheses regarding economic, demographic, and managerial determinants of shortline railroad success. In addition to previous shortline railroad literature, findings from the management and entrepreneurship fields are also utilized for hypothesis development. In particular, literature relating to the determinants of success of newly formed and entrepreneurial firms is quite relevant to shortline railroads.(11) This literature has focused most heavily on the relationship between new firm performance and entrepreneur/top manager characteristics and the processes of founding, factors which have received limited attention in statistical analyses of shortline railroad performance.

DETERMINANTS OF SHORTLINE RAILROAD PERFORMANCE: HYPOTHESIS

Characteristics of a Shortline's Traffic

The traffic base that a railroad possesses has been argued as an important determinant of success. First, a railroad must have sufficient traffic to support its fixed assets and allow for adequate frequency of service. The existence of economies of density has been well documented in the railroad industry.(12) Such economies particularly apply to low density rail lines, where average costs go down as traffic density goes up. Due has underscored the importance of adequate traffic density in shortline railroads and has concluded in his case studies of shortline railroads that adequate density is important for viability. (13) Wolfe has also found that failed shortlines had significantly lower densities than successful lines.(14) Thus, it follows that:

H1: Traffic density is positively related to performance.

It is also desirable for a railroad not to be too dependent upon an individual shipper or commodity. As discussed by Porter, an important environmental force is the bargaining strength and leverage of customers; customer leverage typically increases when sales are concentrated in a relatively small number of customers.(15) Moreover, concentration in a commodity or customer leaves the railroad very vulnerable to declines of that customer's business or declines in demand overall for a particular commodity. Due provides an example of a shortline overly dependent on one major shipper; the shortline failed because this shipper suspended operations.(16) Wolfe has noted that failed shortlines had a higher percentage of their traffic in one commodity than did successful carriers.(17) The second hypothesis, then, is:

H2: Degree to which traffic is concentrated in one commodity is negatively related to performance.

Given that traffic of shortline railroads generally is concentrated in a relatively small number of commodities, key characteristics of these principle commodities can very much dictate railroad performance. Since transportation is a derived demand, strength of demand for the principle commodities carried will directly influence the demand for the carrier's service. Also, the strength of demand for the rail service will be a function of the degree of intermodal competition, particularly trucking. Wolfe has pointed out the importance of the intensity of motor carrier competition, which varies with commodities hauled, as a determinant of shortline railroad success, while Due has argued that the ability of a shortline to win traffic from trucks is critical.(18) In addition, macroeconomic conditions are important for railroad performance, which generally suffers in times of economic downturn. The strength of the economy, particularly the local economy in which the carrier provides service, has also been noted as important for shortline railroads.(19) Three related hypotheses follow from the above discussion:

H3: Total sales of major commodity is positively related to performance.

H4: The strength of intermodal competition is negatively related to performance.

H5: The strength of the economy in the area served by the railroad is positively related to performance.

Shortline railroads can often find a successful niche after abandonment to the extent that their small size, local presence, and entrepreneurial orientation facilitate superior customer service. Due has documented the importance of good customer service in his studies of shortline railroads.(20) However, if a railroad is largely terminating traffic, its ability to generate additional traffic by direct customer contact may well be hampered. A company can be in a better position if it originates a high percentage of its traffic. This can provide greater leverage in dealing with connecting railroads, and can also afford the railroad greater opportunity to gain traffic through customer service excellence. Our sixth hypothesis, then, is:

H6: Percentage of traffic originated is positively related to performance.

Demographic and Financial Characteristics

It is not uncommon for new businesses in any industry to experience start-up problems, and shortline railroads may be particularly vulnerable in this regard. The situation inherited by many shortlines is less than ideal, in that relationships with shippers often deteriorate as the Class 1 railroad pursues abandonment. Also, deferred maintenance by the predecessor carrier can be a problem for many new shortlines. Moreover, managers may need a period of time to effectively carry out their duties. Accordingly, it follows that:

H7: The number of years that the railroad has been in existence is positively related to performance.

Related to a firm having adequate traffic densities, it has also been argued that a shortline benefits from greater overall size. For example, Wolfe has provided data revealing that successful shortlines were bigger than their failed counterparts.(21) A larger carrier can have greater leverage in dealing with customers and suppliers, and can spread administrative expenses over greater output. Hypothesis eight follows, then:

H8: Size is positively related to performance.

Another shortline characteristic discussed in previous research is a carrier's financial position. In particular, the degree to which a shortline is financially leveraged is critical, in that a highly leveraged position results in financial inflexibility. Wolfe has provided evidence that failed railroads tended to be more highly leveraged than successful carriers.(22) It follows that:

H9: Degree of leverage is negatively related to performance.

Managerial Characteristics and Actions

Management and entrepreneurship literature have argued that previous experience and training of top executives are key determinants of their firms' success.(23) Not surprisingly, most theorists have focused on the value of industry-specific experience as being the most critical dimension of experience for top managers. Familiarity with the industry is expected to enhance the probability that management can draw on knowledge of product and market preferences and government regulations to make effective decisions and that it can utilize previous formal and informal contacts and relations in the industry and with the industry's customers to implement plans in a timely fashion. If a local railroad's CEO has had little industry experience prior to assuming leadership of the firm, information gathering, analysis, and interpretation are more onerous tasks. Less than optimal decisions and significant time delays may result. Due has echoed this point with regard to shortline railroads, arguing on the basis of his case studies that shortlines least likely to be successful are those headed by persons without railroad management experience.(24)

H10: Previous railroad industry experience is positively related to performance.

Education is among the most widely studied variables in the entrepreneurship literature. Interestingly, empirical results on the relationship between education and business performance have been mixed, whether the measure has been in number of courses completed, the level of the highest degree completed, or the type of degree achieved. Researchers have generally argued that more education should lead to better performance, but empirical results have not always supported this view. As Cooper and Gascon state:

...it is not clear whether the things which are learned in school are enough to achieve success. Commitment and determination, obsession with opportunities, and tolerance for ambiguity may be critical for success with some ventures, yet these may not be the product of formal education.(25)

Thus, it is less than clear what impact greater and greater education might have on factors believed to be critical to crafting and executing strategies for success in creating and building a new business. Nonetheless, the existence of such institutions as Small Business Development Centers and Entrepreneurship Education Centers nested in the business departments of universities suggest that practitioners and educators believe that founders of new businesses can benefit from exposure to formal business courses. Still, some have argued that too much formal training through business schools results in indecisiveness, unrealistic assumptions about competition and labor, and an inclination to overemphasize data collection and analysis. Peters and Waterman labeled this phenomenon the "paralysis of analysis."(26) In summary, few have argued that no formal business education is superior to some, but many support the idea that beyond a certain point further education does more harm than good. Accordingly, we offer the following hypothesis:

H11: The number of business courses taken by a CEO is positively related to performance up to a point beyond which the number of business courses taken is negatively related to performance.

The relationship between ownership and firm performance has been of central interest to theorists since the emergence of the corporation as a dominant business form in the U.S. A key implication of the emergence of this form of business has been that the separation of ownership and control results in a divergence of goals and incentives for owners and managers acting as owners' agents.(27) As managers' stake in the enterprise diminishes, their incentive to consume resources of the firm increases because they bear only a fraction of the cost of this consumption; as a result, owners' incentive to create elaborate contractual and monitoring devices to prevent opportunistic behavior is increased.(28) Thus, the value of the business is reduced by the cost of insuring against such behavior (agency loss); the value is less than it would be if the business were entirely owner-managed. Walsh and Seward argued that the central governance role of outside ownership in publicly owned firms is to ensure that management has the requisite skill and exerts the requisite effort in achieving superior performance; they point out that equity incentive plans have often been used to insure and reward effort and to bind good managers to the firm.(29) In the context of the local railroad this logic implies that those CEOs who have a greater personal stake in the railroad in the form of equity will expend greater effort to see that their firm will succeed; this greater effort should translate into better firm performance.

H12: The greater the ownership level of the CEO, the higher will be performance.

To a certain extent, the experience, education, and ownership stake of the CEO can be seen as firm-specific contextual variables which influence the skill and effort directed toward achieving strategic superiority. Some theorists have speculated that certain strategic approaches or philosophies can be potent competitive weapons if they properly match the larger competitive environment. Covin and Slevin examined the performance of small manufacturing firms in hostile and benign environments and found no direct relationship to performance; however, they found that in hostile environments performance was positively related to an entrepreneurial strategic posture, while in benign environments performance was positively related to a conservative strategic posture; they defined strategic posture as the firm's overall strategic orientation. Because they were studying firms in a variety of industries for which direct comparison of objective data was difficult, Covin and Slevin used survey data to assess the level of hostility on a continuum from a hostile environment (one with severe threats to survival, dearth of investment opportunities, and strong competitive and political forces dominant) to a benign one (which was relatively threat-free, rich in investment and marketing opportunities, and easily manipulable and competition-free). In essence, they argued that survival and success could not ordinarily be achieved in hostile environments without a proactive, innovative, opportunity-seeking orientation.(30) The current study is a single-industry one in which comparison of hostility of this environment to all other environments is not feasible. However, the rate of railroad "deaths," the limited ability of railroads to create and invest in "new products" or marketing opportunities, and the often intense competition from alternative forms of transportation suggest that the environment may indeed be "hostile." Accordingly, we expect that a more entrepreneurial profile or philosophy will be associated with better performance.

H13: An entrepreneurial, innovative strategic philosophy will be positively related to better performance.

METHOD

Two sources were used for the data: a data base compiled by the Association of American Railroads (AAR) and a survey of shortline managers. More specifically, the data base was used to draw the traffic and railroad measures, while the survey was used to develop measures for the managerial and performance measures.

Traffic and Railroad Measures

The AAR maintains a publicly available data base for railroads, entitled Profiles of U.S. Railroads. For the purposes of this article, the term "shortline railroad" includes two types of line haul railroads in the AAR data base: local (less than 350 miles of road and annual revenues less than $40 million) and regional (non-Class 1 with more than 350 miles of road and/or annual revenues more than $40 million). Switching and terminal railroads were not included in the analysis. The 1990 version of this data base was used, which contained information on 293 railroads.

This data base was used to calculate measures for the first eight hypotheses. Traffic density (DENSITY) was calculated by dividing carloads by miles of road. The percent of carloads for the top three commodities was provided on the data base. From this, the concentration of traffic in the leading commodity (COMMOD1PCT) was measured by the percentage of carloads in that commodity. To assess overall demand for the leading commodities, data were derived from Forbes on annual sales for the product itself (PRODSALES).(31) The strength of truck competition (TRUCKCOMP) was derived from commodity-specific data on the comparative costs of rail and truck in cents per mile, drawn from Keeler.(32) The strength of the economy in the area served by the railroad was measured by drawing on Profiles data on states served by each railroad in conjunction with unemployment rates by state from the Department of Labor (STATEUNEMP).(33) Percentage of traffic originated (ORIGPCT) was drawn from the data base, which also contained the year each railroad was created (RRAGE). Number of employees, also drawn from the data base, was used as a measure of size (SIZE). Finally, the degree to which the shortline was financially leveraged (LEVERAGE) was drawn from the survey, which will be discussed in more detail below.

Performance and Managerial Measures

Surveys were sent to 285 shortline railroads. In nine cases envelopes were returned to sender by the post office, indicating that the railroad no longer existed. One hundred forty-nine responses were received from the remaining 276, a response rate of 54 percent. For this study, 142 responses contained information on the performance measure and therefore were usable. The survey was used primarily to gather managerial and performance measures, details of which follow.

Performance. Because it is such a key variable in our study, explanation of our choice and construction of railroad performance is warranted. A subjective measure of performance was chosen in part because of the lack of publicly available objective measures of financial performance such as profitability. Further, objective measures often fail to capture the goals of the firms,(34) ignore important stakeholders other than shareholders,(35) and are subject to the inconsistencies in accounting methods and manipulation by management.(36)

Multi-dimensional subjective measures of firm performance which ask top managers to compare their own performance with that of similar firms have been used in a variety of contexts in which objective measures have been unavailable(37) and have been shown to be reliable substitutes for objective counterparts in those contexts.(38) The central advantage of such measures is that they simultaneously capture several dimensions of performance not reflected in financial indicators, allow comparison of performance with the relevant set of competitors, are not subject to the timing of accounting cycles or choice of methods, and reflect the goals of management.(39)

In line with the suggestions of Chakravarthy,(40) our measure is designed to capture the goals and performance of a shortline railroad vis-a-vis its three major stakeholders: its owners, its customers, and its employees. A weighted multi-dimensional measure of performance was created by asking CEOs to assess the relative importance of achieving growth in sales, short-term profits, customer satisfaction, and employee satisfaction by distributing 100 points across these four categories; the first two items help capture tradeoffs which might be made between building share and reaping short-term returns; such tradeoffs should reflect the goals of shareholders. The other two items clearly reflect the attention paid to satisfying the other two major stakeholder groups: customers and employees. The CEOs were then asked to assess (on a one-to-five scale in which 1=inferior, 2=below par, 3=average, 4=good, 5=superior) how well the railroad had done over the last one-year period relative to similar firms. These assessments were weighted by the importance rating. Thus, if a firm believes that a particular performance measure (such as profitability) is much more important than the others, the firm can weight this measure heavily. The performance value for this firm would then be primarily determined by the assessment of profitability on the one-to-five scale.

Managerial Variables. The amount of railroad experience possessed by the CEOs prior to involvement with the current local railroad (PRERREXP) was measured as the number of years the CEO had worked in the railroad industry prior to working for the current local railroad. The extent of formal business education training received by the CEO was measured by the sum of the number of business courses taken by the CEO at either the undergraduate or graduate college levels. It should be noted that about half the CEOs in our sample held either an undergraduate or a graduate degree in business. To test the hypothesized relationship that the number of business courses would be positively related to performance up to a certain point and negatively related beyond this point, the number of business courses was standardized and then squared. The variable as so constructed (BUSINESS) would take a large value if for extreme numbers of business courses, either very many or very few, and take a low value for a mean number of courses. A negative coefficient for this variable would then indicate support for the hypothesis. In order to assess the personal financial stake of the CEOs in the local railroad (OWNERSHIP), CEOs were asked to indicate the percent of the railroad owned by the CEO, his or her spouse, or family members. A five-point scale was used (1=0-19 percent; 2=20-39 percent; 3=40-59 percent; 4=60-79 percent; and 5=80-100 percent) in order to reduce possible reluctance to reveal specific financial information. The mean score on this measure was 2.16, with responses in each of the five ranges.

Finally, we sought to create a scale to capture the extent to which the management philosophy of each firm reflected an entrepreneurial, proactive posture versus an administrative, conservative one (PHILOSOPHY). The scale for the management philosophy of the railroad was adapted from Covin and Slevin.(41) Three of the five items used to create our scale were taken from items they used to assess the "strategic posture" of firms in hostile and benign environments. We selected one each of the items they used to assess a firm's innovativeness, proactiveness, and level of risk-taking (other items they used were not included here because they appeared inappropriate in this environment). Following Miller,(42) they argued that these three components comprised a basic, undimensional strategic orientation; i.e., firms which tended to favor innovation, proactiveness, and risk-taking could be characterized as having an "entrepreneurial" strategic posture whereas those that emphasized product improvement over new product introduction, market responsiveness over proactiveness, and a cautious, wait-and-see attitude over opportunity creation or pursuit were at the "conservative" end of the strategic posture scale. Accordingly, we asked CEOs to indicate on a seven-point scale the extent to which their firm favored incremental, minor changes in services (at the "1" end of the scale) versus dramatic, major changes (at the "7" end of the scale); responding to competitor actions (1 to 7) versus initiating actions to which competitors respond; and taking a cautious posture to avoid costly mistakes (1 to 7) versus adopting a bold posture to maximize the probability of exploiting opportunities.
Table 1. Summary Statistics for Study Variables
Variable Mean Std Dev Minimum Maximum
DENSITY 253.84 486.50 1.00 3387.67
COMMOD1PCT .62 .25 .16 1.00
PRODSALES 3106.94 6574.69 567 34832
TRUCKCOMP 1.05 .45 .50 2.95
STATEUNEMP 5.50 .88 3.7 8.3
ORIGPCT .44 .34 .00 1.00
RRAGE 31.02 35.73 1.00 108.00
SIZE 21.31 31.34 0 210
LEVERAGE 3.13 1.93 1 7
PERFORMANCE 3.79 .69 1.25 5.00
PRERREXP 9.73 10.85 0 42
BUSINESS(1) 9.68 10.86 0 50
OWNERSHIP 2.16 1.60 1 5
PHILOSOPHY 21.82 4.36 11.00 33.00
1 The values for this variable indicate the raw number of
business courses taken, not the final variable used in the
regression.


Because we felt they were consistent with the administrative-entrepreneurial continuum we are trying to capture, we also adopted for our scale two of the items which Covin and Slevin used to assess management philosophy: the extent to which the firm emphasized sticking to policies regardless of business conditions (1 to 7) versus changing practices as the situation demanded; and having personnel always follow formal procedures (1 to 7) versus getting things done even if it means ignoring formal procedures. Thus, the scale for administrative versus entrepreneurial management philosophy was created by adding the scores on all five items, with lower scores indicating a more administrative bent and higher scores a more entrepreneurial one.(43)

RESULTS

Summary statistics for all the variables are shown in Table 1. An ordinary least squares TABULAR DATA OMITTED regression was used to test the hypotheses, with performance as the dependent variable and the thirteen other measures described above as independent variables. The regression results, as reported in Table 2, reveal strong support for the model. A large number of the variables are significantly related to shortline railroad performance.

More specifically, regarding traffic determinants, traffic density was positively related to performance, as expected, with marginal statistical significance (.06). The coefficient on commodity concentration was negative and statistically significant, indicating that performance declines as a railroad is more dependent on a particular shipper/commodity. The percentage of traffic originated was also positive and significant, indicating that there are benefits to originating as opposed to terminating traffic. The coefficient for the overall sales of the primary products carried by the railroad had a positive sign, as expected, with marginal significance (.09). Other factors relating to demand for rail travel, the strength of truck competition, and the strength of the local economy, were not significant determinants of performance.

Firm-specific variables demonstrated the following relationships: The degree of financial leverage was negatively and significantly related to performance, as expected. The size of the railroad was positively and significantly related to performance, Finally, the number of years the railroad had been in existence was not significantly related to performance.

Among the managerial determinants, the following can be observed: Degree of CEO ownership and previous experience in the railroad industry were not significantly related to performance. It may well be that in the deregulated railroad environment, where pricing, customer service, cost control, and other fundamental business elements are critical, business experience gained in other industries is just as valuable as prior railroad experience. However, a proactive, aggressive management philosophy was positively related to performance, significant at the .01 level. The number of business courses was related to performance as hypothesized, with the square of the number inversely related to performance. This indicates that performance increases with initial increases in the number of business courses, but declines with additional courses past a certain point (the mean number of business courses for CEOs in our sample was 10).

CONCLUSION

The growth of shortline railroads has been a very important development in the post-Staggers environment. This study has utilized survey data as well as objective information on 142 carriers and found that there are important managerial as well as environmental predictors of shortline railroad performance. The study has direct implications for shortline managers striving for better performance and for shippers attempting to assess carrier performance. The study also is relevant to public policy makers, who have a strong interest in the determinants of performance for shortline carriers. Public policy makers have been greatly involved with nurturing shortline railroads, providing communication and intermediary service between Class 1 carriers and prospective shortline companies, and providing expertise on dealing with the legalities of abandonment and assistance with acquisition negotiations. These policies are pursued because of the role shortline railroads play in business location and economic development. A better understanding of shortline railroad performance will assist policy makers in their investment of time and public resources.

ENDNOTES

1 F. Dooley, "The Evolution of the Shortline Railroad Industry," Journal of the Transportation Research Forum Vol. XXXI, No. 1, 1990, pp. 180-194.

2 Dooley, p. 182.

3 U.S. Department of Transportation/Interstate Commerce Commission, "A Survey of Shipper Satisfaction with Service and Rates of Shortline and Regional Railroads," Joint Staff Study, August 1989.

4 J. Due, "The Experiences of Local Enterprises Formed to Take Over Railway Lines Abandoned by Major Companies, Logistics and Transportation Review June 1987.

5 C. Rockey, "The Formation of Regional Railroads in the United States," Transportation Journal, 1987, Winter, pp. 5-13; and Dooley, 1990.

6 J. Mielke, "Short Line Railroad Creations: Terms of Sale, Impacts of Viability, and Public Policy Implications," Journal of the Transportation Research Forum Vol. XXIX, No. 1, pp. 138-148.

7 M. Loughman, "Shortline Railroad Industry in Canada," Journal of the Transportation Research Forum Vol. XXXI, No. 2, pp. 378-391.

8 K. E. Wolfe, "The Downside Risk: An Analysis of Local and Regional Railroad Service Failures," Journal of the Transportation Research Forum Vol. XXIX, No. 1, 1988, pp. 124-137.

9 K. E. Wolfe, "Long-Run Financial and Demographic Differences Between Failed and Successful Local and Regional Railroads," Transportation Journal, Vol. 28, No. 3, Spring 1989, pp. 13-23.

10 K. E. Wolfe, "Financial and Demographic Conditions Associated with Local and Regional Railroad Service Failures," Transportation Quarterly Vol. 43, No. 1, January 1989, pp. 3-28.

11 A review of this literature can be found in: A. Cooper and F. Gascon, "Entrepreneurs, Processes of Founding, and New-Firm Performance," in The State of the Art of Entrepreneurship, D. L. Sexton and J. D. Kasarde (eds.), (Boston, MA: PWS-Kent, 1992).

12 For a recent study documentating economies of density, see T. Barbera, C. Grimm, K. Phillips and L. Selzer, "Railroad Cost Structure--Revisited," Journal of the Transportation Research Forum Vol. 28, No. 1, 1987.

13 Due, 1987, p. 127.

14 Wolfe, 1988.

15 M. Porter, Competitive Strategy: Techniques for Analyzing Industries and Competitors (New York: Free Press, 1980).

16 Due, 1987, p. 117.

17 Wolfe, 1988. The percentages were 77 (failed) versus 68 (successful).

18 Wolfe, 1988; Due, 1987.

19 Wolfe, 1988.

20 Due, 1987, p. 124.

21 Wolfe, 1988.

22 Wolfe, Transportation Journal, 1989.

23 See, for example, Cooper and Gascon, 1992.

24 Due, 1987, p. 129.

25 Cooper and Gascon, 1992, p. 306.

26 T. J. Peters and R. H. Waterman, Jr., In Search of Excellence: Lessons from America's Best-Run Companies (New York: Warner, 1982).

27 A. A. Berle Jr. and G. C. Means. The Modern Corporation and Private Property (New York: MacMillan, 1982).

28 M. H. Jensen and W. C. Meckling, "Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure," Journal of Financial Economics, 1976, p. 305-360.

29 J. P. Walsh and J. K. Seward, "On the Efficiency of Internal and External Corporate Control Mechanisms," Academy of Management Review, 1990, 15, pp. 421-458.

30 J. G. Covin and D. P. Slevin, "Strategic Management of Small Firms in Hostile and Benign Environments," Strategic Management Journal, 1989, 10, pp. 75-87.

31 Forbes, January 7, 1991, pp. 232-233.

32 T. Keeler, Railroads, Freight and Public Policy, Brookings, 1983, p. 76.

33 United States Department of Labor, Bureau of Labor Statistics, "State and Regional Unemployment in 1990," Washington, D.C.

34 R. Quinn and J. Rohrbaugh, "A Spatial Model of Effectiveness Criteria, Management Science, 1984, 29, pp. 363-372; H. J. Sapienza, K. G. Smith and M. J. Gannon, "Using Subjective Evaluations of Organizational Performance in Small Business Research," American Journal of Small Business, 1988, 12, pp. 45-53; and N. Venkatraman and V. Ramanujam, "Measurement of Business Performance in Strategy Research: A Comparison of Approaches," Academy of Management Review, 1986, 11, pp. 801-814.

35 B. S. Chakravarthy, "Measuring Strategic Performance," Strategic Management Journal, 1986, 7, pp. 437-458.

36 R. S. Kaplan, Advanced Managerial Accounting (Englewood Cliffs: Prentice-Hall, 1982); and R. S. Kaplan, "Yesterday's Accounting Undermines Production," Harvard Business Review, 1984, 62 (4), pp. 95-101.

37 See, for example, Covin and Slevin, 1989; and A. K. Gupta and V. Govindarajan, "Business Unit Strategy, Managerial Characteristics, and Business Unit Effectiveness at Strategy Implementation," Academy of Management Journal, 1984, 27, pp. 25-41.

38 C. G. Brush and P. A. Vanderwerf, "A Comparison of Methods and Sources for Obtaining Estimates of New Venture Performance," Journal of Business Venturing, 1992, 7, pp. 157-172; G. Dess and R. B. Robinson, Jr., "Measuring Organizational Performance in the Absence of Objective Measures," Strategic Management Journal, 1984, 5, pp. 265-273.

39 H. J. Sapienza, K. G. Smith, and M. J. Gannon, "Using Subjective Evaluations of Organizational Performance in Small Business Research," American Journal of Small Business, 1988, 12, pp. 45-53.

40 Chakravarthy, 1986.

41 Covin and Slevin, 1989.

42 D. Miller, "The Correlates of Entrepreneurship in Three Types of Firms," Management Science, 1983, 29, pp. 770-791.

43 The mean for our scale was 21.82 with a standard deviation of 4.36; the minimum was 11 and the maximum 33. A check of scale reliability was examined by calculating a coefficient alpha. The alpha for our managerial philosophy scale is .57; there is no universally accepted cutoff point, but some have argued for anywhere between .45 and .60 as minimum acceptance levels for organization level constructs.

Mr. Grimm, EM-AST&L, is associate professor of transportation, business and public policy, University of Maryland, College Park, Maryland 20742; Mr. Sapienza is assistant professor of management, University of South Carolina, Columbia, South Carolina 29208.

The support of the University of Maryland's Dingman Center for Entrepreneurship is gratefully acknowledged, as is the outstanding research assistance of Dana Eitner. Carol Emerson and April Hardison also provided assistance and helpful comments.
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Date:Mar 22, 1993
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