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Motor carrier strategies and performance
0191.x07/90 13.00+.00 (C 1990 Rrgamon Press pit
Tmnxppn. Res.-A. Vol. 24A. No. 3. PP. 201-210. 1990 Printed m Great BrikCt.
MOTOR CARRIER STRATEGIES AND PERFORMANCE RAYMOND D. SMITH Department of Business, Woodbury University, Burbank, CA 91510, U.S.A.
and THOMAS M. CORSI and CURTIS M. GRIMM Transportation, Business and Public Policy, College of Business and Management, University of Maryland, College Park, MD 20742, U.S.A. (Received 6 March 1989; in revised form 10 September
1989)
Abstract
-This paper examines post-deregulation strategies of less-than-truckload general freight carriers. Data on 38 strategic variables were gathered from annual reports filed with the Interstate Commerce Commission. Cluster analysis was used to categorize the one-hundred and forty firms into four strategic groups identified as follows: (1) differentiation approach coupled with a risky financial strategy; (2) a well-defined focus strategy concentrating on select types of traffic among select shipper groups; (3) a particularly sedentary strategy showing little activity across dimensions; and (4) an aggressive expansion strategy characterized by high growth rates. Major findings are: (1) a well-formulated and clearly defined strategy is an important determinant of financial performance and (2) despite the intense competition brought about by deregulation, an appropriate choice of strategy can provide financial performance comparable to “average” firms in the regulated environment.
1. INTRODUCTION
The Motor Carrier Act of 1980 (MCA) substantially reduced regulation in the U.S. motor carrier industry, providing carriers with greater freedom to enter new markets and initiate new services. The Motor Carrier Act also brought about a sharp increase in individual carrier-initiated pricing activity. Overall, the postMCA environment provides managers with much greater opportunity to design and implement specific strategies. The impact of the MCA on industry financial performance is an important issue in the debate as to the wisdom of motor carrier deregulation. The motor carrier industry had argued that deregulation would create excessive competition and reduce industry profits to insufficient levels. They predicted that ultimately the industry’s ability to offer adequate freight service would suffer as profits fell so that in the long run shippers would be disadvantaged by deregulation as well (Rakowski, 1988). Indeed, longitudinal financial analysis of the industry’s largest segment, the LTL (less-than-truckload) carriers, demonstrates profound changes since the passage of the MCA. The number of Class 1 LTL carriers (revenues above $5.78 million) and Class 2 (revenues between $5.78 and $1.16 million), fell from just under 500 in 1973 to a little less than 150 by the end of 1986 (Silberman and Hill, 1988). Furthermore, a review of the most fundamental measure of performance in the industry, operating ratio (ratio of total operating expenses to total operating revenues), denotes a definite downturn from levels of about 94 during the early 1970s to levels in the 96 to 98 range in the 1982-1987 period. The combination of these 201
factors led a pair of leading analysts to the following conclusion: “What we are really saying is there has been an enormous shakeout (among LTL carriers). And now that the shakeout has occurred for almost seven or eight years, what is left is an earnings disaster” (Silberman and Hill, 1988). The research presented here demonstrates, however, that management’s failure to adopt an appropriate strategy for the new competitive environment-not the MCA, per se-is for many firms the primary reason for adverse performance impacts in the deregulated marketplace. Many carriers formulated wellchosen competitive strategies in the new environment and have performed as well as the “average firm” did in the regulated environment. However, to a great extent, carriers failing to formulate a competitive strategy in the deregulated environment have performed poorly. In sum, the results show that the MCA is not totally to blame for the misfortunes of many carriers. Instead, the culprit may be the firms’ own failure to adapt adequately their competitive strategies in the face of the dramatic environmental change brought about by deregulation. The research also has important business policy implications in that it sheds light on the most successful strategic responses to deregulation.
II. THE IMPORTANCEOF STRATEGY According to the strategic management literature a firm’s strategy is crucial to its survival. As discussed by Grimm and Smith (1985), a firm’s strategy reflects a focus on a set of strategic variables as it attempts to reach a position consistent with its environment. A
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firm’s prices, product or service quality, and degree of innovation are examples of strategic variables. Each of these are important dimensions that a firm can use to achieve a competitive advantage. A firm’s strategy, then, can be defined as a pattern of behavior with regard to the strategic variables (Hambrick, 1983). For example, a firm may develop a pattern of having the lowest prices, the best service quality, or the most innovation. Each of these patterns of behavior would represent a distinct strategy. The strategic management literature argues that in a competitive environment a specialized strategy is required to align the organization with critical environmental resources, to capitalize on the organization’s distinct competence, and to achieve a competitive advantage (Andrews, 1972; Porter, 1985). A specialized strategy is one which emphasizes a distinct strategic dimension over all others: If a firm is to attain a competitive advantage, it must make a choice about the type of competitive advantage it seeks to attain and the scope within which it will attain it. Being all things to all people is a recipe for strategic mediocrity and below-average performance, because it often means that a firm has no competitive advantage at all. (Porter, 1985, p. 12) Researchers have also used empirical techniques to identify different strategies and examine performance differences across these strategic groups (Dess and Davis, 1984; Miles and Snow, 1978). Typically, after data are gathered on a number of strategic variables, multivariate classification methods are used to categorize firms into groups based on the strategic variables. The technique most frequently used is cluster analysis, whereby firms are grouped into distinct “clusters” with other similar firms. Strategies for each cluster are then identified based on the values of the strategic variables. This research largely confirms the theory that there are significant differences in performance across strategies and that strategic specialization tends to correlate with higher performance. An empirical study of strategies of U.S. railroads has similarly shown that strategic specialization is necessary for good performance in the aftermath of the deregulation of that industry (Smith and Grimm, 1987). The basic premise of the research then is that strategy is a critical determinant of firm performance. The overarching hypothesis is that performance will vary significantly with regard to firm strategy. We would also expect firms pursuing a wellchosen, specialized strategy will outperform firms not emphasizing a specific strategic dimension.
111.DATA ASD
METHODOLOGY
To study these relationships, data on 38 motor carrier strategic variables were gathered from data submitted by carriers in their Annual Reports to the ICC. These reports contain extensive operating and financial data on each individual company. While all carriers are required to file annual reports, the com-
puter file used may have some slight omissions due to late or incomplete filings by a small number of carriers. Nevertheless, the data base is an extensive sample of the total population. Factor analysis was used to reduce the 38 variables to 12 strategic factors.
Selection of sample Among all motor carriers, the LTL (less-thantruckload) General Freight carriers were selected for several reasons. One, the test of the linkage between strategy and performance dictates selection of a carrier subgroup facing the same uncertain environment. The LTL sector is homogeneous in its operating structure and market environment but is quite distinct from the structure of and environment faced by other industry segments. Second, the LTL segment is the largest single industry sector in terms of total revenues. Additionally, it is the segment that is generally thought to have been most adversely impacted by the regulatory changes. Thus, the LTL segment provides the most stringent test of whether or not individual firms can have significantly better performance by pursuing a well-defined strategy in the uncertain environment created by the MCA. Within the LTL segment, there were 140 carriers with complete data on all 38 analysis variables. Cluster analysis was used to categorize the 140 firms into four strategic groups. Finally, the authors investigated the relationship between strategy and performance as a test of the study’s basic research premise. The year 1984 was selected as the analysis year for several reasons. The authors believed that selection of a year prior to 1984 would have been an unfair test of the basic research premise since insufficient time had elapsed from the passage of the IMCA to allow carriers to define a strategic agenda. Additionally, the major recession of 1982 had complicated the environment and its effects would have been difficult to isolate in the analysis. The year 1984 allows enough time to have elapsed since both the MCA and the recession for carriers to have formulated a strategic response, yet is not so far removed from the date of the MCA such that other environmental factors may unduly influence the results.
Assessment of strategy There are a number of methods through which a firm’s strategy might be assessed. These methods range from self-typing, where managers of firms identify their own strategies, to more objective methods where published archival data or industry judges are used to assess strategy (Snow and Hambrick, 1980). We chose to use archival data for the study, drawn from motor carriers’ 1984 annual reports as filed with the ICC.
Strategic dimensions Data on 38 variables were drawn for each firm, providing indicators of a wide range of strategic dimensions such as cost and productivity position, traffic and customer focus, service differentiation,
Motor carrier
strategies
degree of recent expansion, and financial strategy. A complete list is provided in Appendix A. Factor analysis was used to reduce the large number of firm operating and financial variables to a more manageable set of factors. Factor analysis is a technique used to group variables which are highly correlated into factors. The principal components method of factor extraction was used and 12 significant factors emerged using the standard rule of only selecting those factors with eigenvalues greater than one. Three measures of firm performance were used: return on investment, operating ratio, and return on assets. Return on investment, defined as net income after taxes divided by transportation investment, is a widely used measure of economic performance. A firm’s operating ratio, defined as operating expenses divided by operating revenues, is the most commonly used measure of financial performance in the motor carrier industry. Return on assets is defined as net income after taxes divided by total assets and is also an important performance measure.
Clustering technique The K-means clustering algorithm of numerical classification was used with squared Euclidian distance measures to classify the motor carriers into strategic groups. This technique is one of the most popular with social scientists and is appropriate for our purposes (McKelvey, 1982). The use of factor scores as inputs into cluster analysis has been used in numerous empirical studies of strategy (Dess and Davis, 1984; Hambrick, 1984).
Iv.
RESULTS
Factor analysis The factors which were derived and their loadings by variable are presented in Appendix B. Table 1 presents a summary of the 12 factors sorted into five strategic dimensions: low cost, focus, differentiation, expansion and financial strategy. The factors were sorted into the dimension which their pattern of variable loadings seemed to most suggest. For example, if the variables loading most heavily on a factor were associated with the cost dimension, it was placed into Table 1. Factors grouped by dimension Factors
Dimension Low cost
F, F6
Focus
Differentiation
Expansion Financial
Lease/Buy Vehicle Efficiency Linehaul Cost LTL Concentration High Value LTL Shipper Concentration Common Carriage Reliance
F II El Fz E, El, FZ F7 ElfI
Loss and Damage Single Line Service
F¶
Asset Growth
F3
Debt-Equity Reliance
hlaintenance-
Advertising
and performance
203
the cost dimension and used as a measure of activity along that dimension. The five dimensions and associated factors are described in more detail below: Cost dimension factors. The cost dimension is represented in the analysis by the following factors: lease-buy preference, which measures a carrier’s preference for leasing or buying as a means of providing fleet capacity; vehicle efficiency, relating to average load and length of haul indicating the intensity of vehicle use; and linehaul cost, which measures linehaul expenses per mile as an indicator of unit cost. Focus dimension factors. The focus dimension is represented by four factors. The first relates to a primary focus on LTL traffic, while the second concerns a focus on a particular type of LTL traffic-that is, that portion generating a high revenue per ton-mile for the carrier. The third factor from this dimension measures the degree to which carriers are focusing on particular shipper groups. This type of focus approach is measured indirectly through pickup and billing-collecting economies resulting from a narrowing of customer base. Conversations with shippers have also indicated a preference for consolidating their freight with fewer carriers and so, this effort is being supported from both sides (Alex Brown & Sons, December 1987). The fourth factor measures activity along the focus dimension in terms of decisions to emphasize common carriage to the exclusion of other services such as contract carriage. This represents an element of the dimension related to service offerings. Differentiation dimension factors. The first factor measures two aspects of the differentiation dimension: service reliability through its domination by maintenance and insurance/safety expense variables and image uniqueness through the advertising variable. The next factor concerns rates of loss and damage and so captures the service quality aspect of the dimension. The third factor measures the dimension through its orientation with single line service. This is a desired service offering for most shippers given its directness and simplicity. Therefore, a firm’s ability to offer single line service constitutes an important service offering that would differentiate the firm from competitors unable to provide such a service. Expansion dimension factor. The expansion dimension is captured by one factor relating to asset growth. Specifically, it measures the extent to which firms grow during a year in terms of revenue equipment as well as total investment. Financial strategy dimension factor. The degree to which a firm relies on debt versus equity financing is the main component of the financial strategy factor. Financial theory indicates that firms relying more heavily on debt than on equity in periods of environmental uncertainty are riskier than are those who rely on equity, since debt must be paid back in fixed installments whether the firms are doing well or poorly. In contrast, equity payments in the form of stockholder dividends can be postponed in times of most severe downturn. Corsi and Scheraga (1989) docu-
20-l
R. D.
ment that this behavior carrier industry.
has occurred
SMITH,
CORSI. and C. Xl. GRIMM
T. M.
variables which load positively on the factor and/or highly positive on those variables loading highly negative. Firms with this score pattern would have an inverse association with the factor. Factor scores close to zero would indicate insignificant activity along the part of the strategic dimension suggested by the factor. By analyzing the factor scores of a firm by dimension, one is then able to ascertain the degree of association between the firm and each strategy dimension. Overall strategy of the firm is identified through an evaluation of the specific dimensions with which the firm is most associated. To facilitate interpretation, the clusters were ranked by the mean scores of their member firms on each of the factors from each dimension. Next, these rankings were summed for all factors within the dimension to give the composite rank of each cluster on the factors in that dimension. Since the factors each represent a particular dimension, this rank was used to determine the extent to which a cluster’s strategy embodies each dimension. For example, if a cluster ranked first in terms of its scores on the factors in the low cost dimension, then that cluster would contain firms with a strategy centered around the low cost dimension. If, however, the cluster did not rank highly on any dimension its strategy would be more ambiguous, falling into the stuck-in-the-middle category. Finally, it should be noted that factors seven, eight and twelve are reverse indicators of a dimension, so
in the motor
Cluster analysis: Overview of results A crucial decision in using the K-means algorithm is the choice of “k”, or the number of clusters to use. As explained by Hartigan (1975), this decision is left up to the researcher and does not result from the algorithm itself. In fact the typical approach is to run the algorithm with several different values of k and apply judgement as to which provides the best solution (Hambrick, 1984; Harrigan, 1985). In this instance, as the number of possible clusters increased up to four, different patterns of firm behavior emerged. However, when examining cluster solutions beyond four, the existing clusters from the four cluster solution were merely fragmented. Thus, the four cluster solution appeared to provide the best overall solution. Table 2 provides the factor scores for each of the four clusters. Factor scores are a measure of a firm’s association with the variables loading most strongly on a particular factor. A high positive factor score, for example, indicates that the firm scored highly positive the
factor
on those
variables
and/or
highly
which negative
load
positively
on those
on
variables
loading highly negative and would be representative of firms practicing the strategic activity suggested by the factor. A high negative factor score would indicate that the firm scored highly negative on those
Table 2. Factor score centroids
I
Cluster: Strategy: Dimension
by dimension
2
Differ.
No. Firms:
Low cost 4 Lease/Buy* 6 Veh. Effic.* I1 Linehaul Cost
grouped
14
.239(3)
2.630(l) - .079(2, 6
3
Focus
3
Inactive
Expan.
50
45
31
.237(2) - .298(3) - .046(1)
-.365(l) .174(2) -.239(j)
.406(4) - .388(4) - .244(;1)
6
6
12
Focus
1 LTL Reliance* 2 8 12
High Rated LTL* Shipper Concentration Common Carrier Reliance*
.196(2) - .229(3) .292(4) .259(4) 13
Differentiation 5 Maint/Advt.* 7 Loss & Damage 10 Single Line Service*
- .212(2) -. 194(2) .239(1)
Financial Strategy 3 Debt/Equity Expansion 9 Asset Growth*
-
.101(3) .072(l) -.097(l) .103(3) 8 .975( 1) -.058(3) .071(2,
.317(l) - .336(4) - .057(2) - .264(2) 9 - .293(3) .135(4) - .289(4) -
-. -
152(4) .068(2) .053(3) .278(1) 10
-.483(j) -.320(l) .015(3)
I1
8
-.117@
-.142(j)
5
6
.392(1)
.031(2)
.osscz,
- .072(3)
- .447@
97.0 5.1 9.6 2
99.6 -.8 0.5 4
1.15 (1,
Performance Results Operating Ratio (Sig. at .05) Return on Assets (Sig. at .05) Return on Investments (Sig. at Rank *,Mean factor score significantly
.lO) different
98.7 .7 6.0 3 across groups
at .05 level
94.3 12.2 13.0 1
205
Motor carrier strategies and performance that it is negative scores rather than positive ones which imply that the cluster is active along the dimension. In this case, a large negative factor score would be ranked higher than a large positive score.
Cluster analysis: The four strategies Cluster I: Differentiation with risky financial strategy. This cluster consists of 14 firms and ranks highest on the differentiation dimension, with a higher degree of single line service than the other three groups. This cluster also ranked highest with respect to the degree of risk in their financial strategy, as they exhibited a strong reliance on debt rather than equity financing. In addition, this cluster had moderate rankings on the low-cost and expansion dimensions, while ranking last on the focus dimension. Cluster 2: Focus. Cluster two consists of 50 firms; the dimension which figures most prominently in these firms’ strategy is focus, highlighted by a specialization in a limited cross-section of traffic tendered by a concentrated group of shippers. This focused approach is supported by the presence of differentiation in the strategy approach of these firms as illustrated by their second place rank overall on the factors of that dimension. Cluster 3: Stuck-in-the-middle. Cluster three consists of 45 firms. The cluster’s most salient feature is its lack of strength in any of the five dimensions. Any strong performance on one factor of a particular dimension is offset by very weak performance on another factor of that same dimension. Cluster 4: Expansion. Cluster 4 contains 3 1 firms. To assess this cluster’s strategy, we note the following attributes: (1) a markedly high rate of growth, given their first place rank on the asset expansion factor; (2) a lack of emphasis on LTL traffic in light of their last place rank on the LTL reliance factor; (3) a high degree of reliance on common carriage given their first place score on the common carriage reliance factor; and (4) a superior loss and damage record given their first place score on that factor. Together, these strategy characteristics suggest firms that are not focusing exclusively on a particular dimension but which rate highly on selected key measures from each dimension.
Performance effects of each strategy Table 2 also provides performance data for each of the four clusters. The top performing clusters for 1984 were clusters two and four, which exhibited a focus strategy and leadership-expansion strategy, respectively. The average ROA of 12.2% for cluster four is significantly different from the others at the .OS level, as is its operating ratio of 94.3. The average ROI for cluster four is 13%, significantly different from the others at the .10 level. During the regulated environment of 1977, LTL (Class 1 and 2 carriers) had an average operating ratio of 94, an average return on assets of 12%, and an average return on investment of 13%-comparable to the 1984 performance of the firms in cluster four. Cluster two lit(A)24:3-o
averages a 5.1% ROA, a 9.6% ROI, and a 97.0 operating ratio (Table 2). The poorest performing group of firms was cluster 3, which had a stuck-in-the-middle strategy. Thus, well-formulated strategies appear to bring the most rewards in a competitive market.
V.
DISCUSSION
In overall order of performance, the strategies for 1984 are as follows: (1) aggressive expansion strategy characterized by high growth rates (Cluster four); (2) a well-defined focus strategy concentrating on select types of traffic among select shipper groups and judicious use of advertising to target these groups (Cluster two); (3) a differentiation approach coupled with a risky financial strategy (Cluster one); and (4) a particularly sedentary strategy showing little activity across dimensions, almost a “non’‘-strategy (Cluster three). The strategy-performance linkage demonstrates a primary relationship between the degree of strategic focus and financial success as evidenced by the performance of clusters two and four versus that of clusters one and three, and a second level relationship between the degree of strategic activity and firm performance as evidenced by the relative performance of clusters one and three. Both results concur with existing strategy research findings calling for greater emphasis by firms on strategy development as the operating environment becomes more competitive and volatile (Mahon and Murray, 1981). We turn now to a more detailed analysis of the individual strategies. To supplement the cluster analysis results, we also draw on other data across the four groups (Table 3).
Cluster I: Differentiation financial strategy
with risky
The 14 firms in cluster one make it the smallest in terms of membership and the largest in average firm size. Firms in cluster one average $263.5 million in total assets, eight times larger than cluster three and over ten times that of firms in clusters two and four (see Table 3). This cluster of firms was notable for its emphasis on differentiation, ranking first overall and on the single-line service factor. This result indicates a high proportion of moves by carriers in this cluster are single line in nature and is consistent with the previously discussed profile of firms in the cluster: large firms able to offer through service as a result of their extensive operating networks. This result is borne out further by the high number of terminals per state for firms in this cluster (5.4 compared to 4.4 average; see Table 3), indicating that these firms are able to provide single-line service given their extensive terminal network. Other salient characteristics of this cluster, which include long average length of haul, high vehicle use, and an emphasis on single line service are typical of large, diversified trucking firms.
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CON, and C. M.
GRLMM
Table 3. Cluster centroids for supplementary variables
Variable Linehau! cost/mile Linehau! cost/ton-mile Gen. & admin. expenses as % of total Ave. load (in tons) Ave. haul (in miles) % owned vehicle miles Tons per terminal (000) % LTL shipments 70 LTL revenue Total assets (in millions) 70of tariffs published independently c/oexpenses on advertising % revenue equip. growth
This cluster combines its differentiation strategy with a risky financial approach as demonstrated by its highly positive score on the debt/equity factor indicating a strong reliance on debt rather than on equity financing. Such a reliance is illustrative of a more risky financial strategy than one based upon equity financing.
Cluster 2: Focus The firms in cluster 2 are, on average, the smallest in the study year, averaging 15 million dollars in total assets, versus an average of 46 million overall (Table 3). Concerning the focus dimension, the cluster’s first place rank on the shipper concentration factor and the high-rated LTL factor is noteworthy. The cluster’s joint association with these two factors suggests an interesting profile of firms specializing in a limited cross-section of traffic tendered by a specific group of shippers. The benefits of such an approach are greater efficiencies in pick-up and delivery and billing expenses as well as a resulting increased security in their market niche. Porter (1980) cited high market share with its accompanying competitive advantage as one of the principal benefits of the focus strategy. Also, in support of this approach, Woo and Cooper (1981) suggested a similar strategy in their study of successful small businesses. Regarding the remaining dimensions, differentiation is also present in these firms’ strategies as evidenced by the cluster’s second place rank overall on the factors of that dimension. The primary cause of this is the cluster’s high score on the maintenanceadvertising factor: .975. Given the variables loading on this factor, this score indicates that these firms are using advertising to separate themselves from their competitors and are also emphasizing maintenance to preserve service quality. Their expenditures for advertising equalled 2.1% of total operating expenses compared to an industry average of 1.5% (see Table 3). Also, their use of advertising supports their previously identified shipper/commodity focus by allowing the carriers to target particular groups of shippers through advertising. In conclusion, the overall strategy suggested by
I
2
3
4
1.29 .18 4.82 13.92 1024 77.10 1249 93.62 72.80 263 42 1.5 18.7
1.12 .50 6.33 9.98 237 82.36 213 94.85 79.41 15 46 2.1 15.9
1.04 .32 5.32 10.31 385 57.13 426 95.95 74.53 32 46 1.1 6.7
1.03 .36 5.55 8.35 226 78.94 421 83.13 60.65 18 53 1.2 36.6
Mean
I .08 .38 5.68 10.12 360 72.97 452 92.50 73.03 46 47 1.5 17.8
the foregoing is one of firms focusing on a select group of shippers who seem to ship similar commodities and supporting this approach through advertising and service quality. This is supported by the cluster’s first place scores on the shipper concentration factor and high-rated LTL concentration relating to the focus dimension and the maintenance/advertising factor from the differentiation dimension. As mentioned previously, this focus/differentiation approach has been associated in a previous study with successful small businesses.
Cluster 3: Stuck-in-the-middle The firms of cluster three are of approximately average size; 32 million total assets versus 34 million overall (Table 3). Since the cluster does not, as mentioned, exemplify strongly any one dimension, its low performance is in accordance with strategy theory. Cluster three does rank highly on certain factors from within the focus and low-cost dimensions. Within the focus dimensions, the cluster exhibits a strong preference toward mainline LTL freight, given its .3 17 score on that factor. This result contrasts with cluster two, which focused on higher-valued LTL freight. Further, the positive score (.3 17) on factor 1, taken together with its highly negative score (- .336) on factor 2 (high-valued LTL), indicates firms which are concentrating on ordinary LTL traffic to the exclusion of other types of freight, even other types of LTL traffic. A further indication of this cluster’s reliance on LTL traffic is provided by their high percentage of such shipments 96%, versus 91% for other firms as indicated in Table 3. The cluster also ranks first on the lease-buy factor from the cost dimension indicating a reliance on leased vehicles. Overall, however, the cluster does not stand out on this dimension. Its average linehaul costs per ton-mile of $.32 versus $.38 average support this finding (see Table 3). Also of interest are the cluster’s distinctly unfavorable scores on two important service factors. The cluster’s unfavorable scores on the single line service and loss and damage factors (-.289 and .135, respectively) signifies firms engaging heavily in con-
Motor carrier strategies and performance netting service and incurring high rates of loss and damage. Generally, single line service is preferred by shippers, and loss and damage is particularly troublesome; these attributes would not appear to bode well for their service quality or their overall profitability. Their negative score on the maintenance/advertising factor, (- .293), when considered in light of the above factor scores, indicates further that these firms are clearly not incorporating the differentiation dimension into their overall strategy. In summary, this overall strategic position appears to be rather dismal. The clearest signals of strategic emphasis the cluster displays is its dedication toward traditional LTL freight along with a reasonably strong reliance on common carrier, another traditional source of motor carrier revenue. The results point to a malaise regarding their strategic responses to the challenges of deregulation. They seem to be clinging to traditional service offerings and not attempting to distinguish themselves from their competitors by developing new strategies. Such characteristics suggest an inert or stuck-in-the-middle strategy. Cluster 4: Expansion The firms of cluster four have an average size of 18.2 million total assets, substantially below the average of 46.1 million for all firms. Also, this cluster contains significant variance in firm size, with the largest firm being twice the industry average and the smallest possessing assets of only 480 thousand. The most striking feature of cluster four is its high score on the asset expansion factor. Its score of 1.15 is significantly different from the other clusters at the .05 level and is precipitated by an average growth rate of 36% (see Table 3). The next highest rate of expansion (16%) is exhibited by the firms of cluster two. A secondary characteristic of these firms is their low score on the vehicle use factor. This is reasonable given their below average figures for load and haul indicated in Table 3: 8.3 tons (10.1 average) and 226 miles (361 average). It should not be inferred, however, that such results indicate inefficiency, as examination of the number of states served reveals that these firms serve a smaller geographic area than average: 9.9 states versus the 13.8 average. Lower than average loads can occur because these firms are less able to rationalize their traffic given their smaller base of operation or because they are choosing to emphasize fast service over high loads and thus not consolidating to as high a degree as other firms. The cluster does not stand out on the differentiation dimension, although it does possess a superior loss and damage record as indicated by its favorable score (- .320) on that factor. This positive indication of a differentiated strategy is offset by their large negative score (- .453) on the advertising/maintenance factor, indicating a lack of attention to advertising (a key component of a differentiated strategy), and their small score of .015 on the single line service factor demonstrating that these firms do not vigorously pursue these other aspects of the differentia-
207
tion dimension. Since the cluster ranks third overall on this dimension, it is apparent that differentiation is not a major part of their overall strategy. The cluster’s scores for the focus dimension factors reveal that this dimension is also not prominent in their strategy, although the firms are committed to common carriage, outscoring all clusters on this one factor (.278). Also noteworthy is their lack of reliance on LTL freight, particularly standard LTL freight as evidenced by their last place rank on the LTL reliance factor. This result highlights firms which are branching out from the traditional LTL freight market to pursue new revenue opportunities such as long-term contract agreements with shippers and TL backhauls to complement unbalanced LTL traffic. In conclusion, the overall strategy suggested by the above evidence is the expansion strategy. This is clearly supported by the firms’ high rate of growth and their pursuit of other than LTL sources of revenue. The firms are contributing to the successful strategy position through superior loss and damage control, an aspect of service that has not diminished in importance with passage of the MCA. The expansion strategy is similar to the explosion strategy of Wissema, Van Der Pol, and Messer (1981) and the aggressive initiator strategy of Hawes and Crittenden (1984).
VI. CONCLUSlON
The results clearly show that financial success is possible in the LTL general commodity trucking industry in the era of deregulation. The key to success is the formulation of a well-developed, specialized strategy as opposed to a nebulous undefined one. The results affirm existing theoretical and empirical findings regarding the importance of strategy formulation to performance and the necessity of strategic adaptation to changes in the firm’s competitive environment. They establish that firms with the most unified, comprehensive strategy (Cluster 4) in a deregulated environment can equal the performance of “average firms” in the regulated environment. Contrary to the views of certain industry representatives, the advent of deregulation has not been uniformly detrimental to carriers. The key to survival in the new competitive environment appears to be a coherent, well thought out competitive strategy.
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Porter M. (1980) Competifive Sfrutegy. Free Press, New York. Porter M. (1985) Competifive Advanfage. Free Press, New York. Rakowski J. P. (1988) Marketing economies and the results of trucking deregulation in the less-than-truckload sector. Trans. Journal, 27, I I-22. Silberman I. and Hill H. (1988) State of the LTL trucking industry. Transportation Executive Update. 2. 6-13. Smith K. G. and Grimm C. M. (1987) Environmental variation, strategic change and firm performance: A study of railroad deregulation. Strut. Manug. J., 8, 363316. Snow C. C. and Hambrick D. C. (1980) Measuring organizational strategy: some theoretical and methodological problems. Acad. of Manag. R., 5, 527-538. Wissema J. G., Van Der Pol H. W. and &lesser H. M. (1980) Strategic management archetypes. Strur. Manag. ‘J., 1; 37-47. Woo C. Y. and Cooper A. C. (1981) Strategies of effective low-share businesses. Strat. Manug. 1, 2. 301-308.
APPENDIX.4
Variables constructed from carriers’ annual reports as: Indicators of the cost dimension
2. Pickup and delivery expense per ton 3. Billing and collecting expense per shipment
Unit Cost
Indicators of the differentiation dimension 1. 2. 3. 4.
Linehaul expenses per ton-mile Linehaul expenses per mile Total operating expenses per ton-mile Platform and terminal expenses per shipment
Productivity 1. Average length of haul 2. Average load (in tons) 3. Average annual miles per power unit 4:Paid time-off expenses as a percentage expenses
of total operating
Overhead
Leasing
I. Percentage owned vehicle miles 2. Vehicle rents without drivers as a percentage of total operating expenses 3. Revenue equipment rents and purchased transportation as a percentage of total operating expenses Indicators of the focus dimension Traffic Focus LTL revenue as a percentage of total operating revenue LTL tons as a percentage of total tons Freight revenue per ton-mile Common revenue as a percentage of total operating revenue
Customer
1. Percentage of tonnage originated and terminated 2. Loss and damage claims as a percentage of total operating revenue 3. Maintenance expense per vehicle mile 4. Insurance expenses as a percentage of total operating expenses 5. Insurance and safety expense per vehicle mile 6. Average salary and wages per employee 7. Officers and supervisors salary as a percentage of total operating expenses
Additional indicators of differentiation dimension
1. General and administrative expenses as a percentage of total operating expenses 2. Operating supplies and expenses as a percentage of total operating expenses
1. 2. 3. 4.
Service Differentiation
Focus
1. Pickup and delivery expense per shipment
Uniqueness 1. Advertising expense as a percentage of total operating expense 2. Traffic and sales expense per shipment 3. Tariff and schedule expenses as a percentage of total operating expenses 4. Total assets
Indicators of the expansion dimensions Expansion 1. Revenue equipment additions during year as a percentage of beginning revenue equipment 2. Total investment during year as a percentage of total assets 3. Accounts receivables as a percentage of sales
Indicators of the financial strategy dimension Debt Reliance 1. 2. 3. 4.
Debt-equity ratio Current ratio (current assets/current liabilities) Total liabilities as a percentage of total assets Total equity as a percentage of total liabilities and equity
Motor carrier
strategies
Appendix
209
and performance
B. Factor
Loadings Factors
Variables 1) % owned veh. mi. 2) op. supp. as a 70 total op. exp. 3) rev. equip. rents and purchased transp. as a % tot. op. expenses 4) veh. rents w/o drivers as a % tot. op. expenses 5) avg. length of haul 6) tot. assets 7) pltf. and terminal expenses per shipment 8) avg. load 9) gen. & adminis. expense as a % tot. op. exp. 10) linehaul exp. per mile 11) LTL rev. as a % tot. rev. 12) LTL tons as a 70 tot. tons 13) paid time off as a % tot. op. expenses 14) pickup & delivery expense per ton 15) ave. salary & wages per employee 16) traffic & sales expense per shipment 17) ave. annual mi. per unit 18) tot. oper. expense per tonmile 19) freight revenue per ton-mile 20) linehaul expense per tonmile 21) pickup & delivery expense per shipment 22) billing & collecting exp. per shipment
(4) Lease Buy Factor
(11) Linehaul cost
(1) LTL Concent.
(2) High Value LTL Concent.
(8) Shipper Concentration
,826 ,698
- .690 -.650 .792 .781 .611 .468 ,610 -.522 .733 .680 ,627 ,600 ,584 -.66-I - .596 .922 .904 ,864 ,850 ,777 (12) Common Carrier
23) common revenue as a % total revenue 24) ave. load 25) ins. & safety exp per veh. mile 26) salaries officers Br sprvsr. as a % operating exp. 27) maintenance expense per veh. mile 28) advertising as a % of tot. oper. expense 29) loss & damage claims 30) tot. ins. expenses as a % tot. oper. expenses 31) accnts. rec. as a % of total revenue 32) % tonnage originated & terminated 33) tariff & sched. exp. as a % tot. oper. expense 34) tot. liab. as a % of tot. assets 35) debt equity ratio 36) equity as a 70 of tot. liab. & equity
(6) Vehicle Efficiency
(5) Maintenance Advertising
(7) Loss & Damage
(10) Single Line
(3) Debt vs. Equity
- .696 .537 .636 .622 .598 .576 .739 .725 .625 .747 - .795 .656 .575 -.926
(9) Expansion
210
R. D. SMITH, T. M. CORSI.and C. .&I.GRIMM (12) Common Carrier
(5) Maintenance Advertising
(7) Loss & Damage
37) current ratio 38) rev. equip. add’ns as a % of beg. revenue equip. 39) tot. invest during yr. as a % total assets Nofe. Only factor loadings with an absolute value in excess of ,450 are shown.
(10) Single Line
(3) Debt vs. Equity
(9) Expansion
-.721 .885 .862
Tmnxppn. Res.-A. Vol. 24A. No. 3. PP. 201-210. 1990 Printed m Great BrikCt.
MOTOR CARRIER STRATEGIES AND PERFORMANCE RAYMOND D. SMITH Department of Business, Woodbury University, Burbank, CA 91510, U.S.A.
and THOMAS M. CORSI and CURTIS M. GRIMM Transportation, Business and Public Policy, College of Business and Management, University of Maryland, College Park, MD 20742, U.S.A. (Received 6 March 1989; in revised form 10 September
1989)
Abstract
-This paper examines post-deregulation strategies of less-than-truckload general freight carriers. Data on 38 strategic variables were gathered from annual reports filed with the Interstate Commerce Commission. Cluster analysis was used to categorize the one-hundred and forty firms into four strategic groups identified as follows: (1) differentiation approach coupled with a risky financial strategy; (2) a well-defined focus strategy concentrating on select types of traffic among select shipper groups; (3) a particularly sedentary strategy showing little activity across dimensions; and (4) an aggressive expansion strategy characterized by high growth rates. Major findings are: (1) a well-formulated and clearly defined strategy is an important determinant of financial performance and (2) despite the intense competition brought about by deregulation, an appropriate choice of strategy can provide financial performance comparable to “average” firms in the regulated environment.
1. INTRODUCTION
The Motor Carrier Act of 1980 (MCA) substantially reduced regulation in the U.S. motor carrier industry, providing carriers with greater freedom to enter new markets and initiate new services. The Motor Carrier Act also brought about a sharp increase in individual carrier-initiated pricing activity. Overall, the postMCA environment provides managers with much greater opportunity to design and implement specific strategies. The impact of the MCA on industry financial performance is an important issue in the debate as to the wisdom of motor carrier deregulation. The motor carrier industry had argued that deregulation would create excessive competition and reduce industry profits to insufficient levels. They predicted that ultimately the industry’s ability to offer adequate freight service would suffer as profits fell so that in the long run shippers would be disadvantaged by deregulation as well (Rakowski, 1988). Indeed, longitudinal financial analysis of the industry’s largest segment, the LTL (less-than-truckload) carriers, demonstrates profound changes since the passage of the MCA. The number of Class 1 LTL carriers (revenues above $5.78 million) and Class 2 (revenues between $5.78 and $1.16 million), fell from just under 500 in 1973 to a little less than 150 by the end of 1986 (Silberman and Hill, 1988). Furthermore, a review of the most fundamental measure of performance in the industry, operating ratio (ratio of total operating expenses to total operating revenues), denotes a definite downturn from levels of about 94 during the early 1970s to levels in the 96 to 98 range in the 1982-1987 period. The combination of these 201
factors led a pair of leading analysts to the following conclusion: “What we are really saying is there has been an enormous shakeout (among LTL carriers). And now that the shakeout has occurred for almost seven or eight years, what is left is an earnings disaster” (Silberman and Hill, 1988). The research presented here demonstrates, however, that management’s failure to adopt an appropriate strategy for the new competitive environment-not the MCA, per se-is for many firms the primary reason for adverse performance impacts in the deregulated marketplace. Many carriers formulated wellchosen competitive strategies in the new environment and have performed as well as the “average firm” did in the regulated environment. However, to a great extent, carriers failing to formulate a competitive strategy in the deregulated environment have performed poorly. In sum, the results show that the MCA is not totally to blame for the misfortunes of many carriers. Instead, the culprit may be the firms’ own failure to adapt adequately their competitive strategies in the face of the dramatic environmental change brought about by deregulation. The research also has important business policy implications in that it sheds light on the most successful strategic responses to deregulation.
II. THE IMPORTANCEOF STRATEGY According to the strategic management literature a firm’s strategy is crucial to its survival. As discussed by Grimm and Smith (1985), a firm’s strategy reflects a focus on a set of strategic variables as it attempts to reach a position consistent with its environment. A
202
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M. CORSI,and C. M. GRIMM
firm’s prices, product or service quality, and degree of innovation are examples of strategic variables. Each of these are important dimensions that a firm can use to achieve a competitive advantage. A firm’s strategy, then, can be defined as a pattern of behavior with regard to the strategic variables (Hambrick, 1983). For example, a firm may develop a pattern of having the lowest prices, the best service quality, or the most innovation. Each of these patterns of behavior would represent a distinct strategy. The strategic management literature argues that in a competitive environment a specialized strategy is required to align the organization with critical environmental resources, to capitalize on the organization’s distinct competence, and to achieve a competitive advantage (Andrews, 1972; Porter, 1985). A specialized strategy is one which emphasizes a distinct strategic dimension over all others: If a firm is to attain a competitive advantage, it must make a choice about the type of competitive advantage it seeks to attain and the scope within which it will attain it. Being all things to all people is a recipe for strategic mediocrity and below-average performance, because it often means that a firm has no competitive advantage at all. (Porter, 1985, p. 12) Researchers have also used empirical techniques to identify different strategies and examine performance differences across these strategic groups (Dess and Davis, 1984; Miles and Snow, 1978). Typically, after data are gathered on a number of strategic variables, multivariate classification methods are used to categorize firms into groups based on the strategic variables. The technique most frequently used is cluster analysis, whereby firms are grouped into distinct “clusters” with other similar firms. Strategies for each cluster are then identified based on the values of the strategic variables. This research largely confirms the theory that there are significant differences in performance across strategies and that strategic specialization tends to correlate with higher performance. An empirical study of strategies of U.S. railroads has similarly shown that strategic specialization is necessary for good performance in the aftermath of the deregulation of that industry (Smith and Grimm, 1987). The basic premise of the research then is that strategy is a critical determinant of firm performance. The overarching hypothesis is that performance will vary significantly with regard to firm strategy. We would also expect firms pursuing a wellchosen, specialized strategy will outperform firms not emphasizing a specific strategic dimension.
111.DATA ASD
METHODOLOGY
To study these relationships, data on 38 motor carrier strategic variables were gathered from data submitted by carriers in their Annual Reports to the ICC. These reports contain extensive operating and financial data on each individual company. While all carriers are required to file annual reports, the com-
puter file used may have some slight omissions due to late or incomplete filings by a small number of carriers. Nevertheless, the data base is an extensive sample of the total population. Factor analysis was used to reduce the 38 variables to 12 strategic factors.
Selection of sample Among all motor carriers, the LTL (less-thantruckload) General Freight carriers were selected for several reasons. One, the test of the linkage between strategy and performance dictates selection of a carrier subgroup facing the same uncertain environment. The LTL sector is homogeneous in its operating structure and market environment but is quite distinct from the structure of and environment faced by other industry segments. Second, the LTL segment is the largest single industry sector in terms of total revenues. Additionally, it is the segment that is generally thought to have been most adversely impacted by the regulatory changes. Thus, the LTL segment provides the most stringent test of whether or not individual firms can have significantly better performance by pursuing a well-defined strategy in the uncertain environment created by the MCA. Within the LTL segment, there were 140 carriers with complete data on all 38 analysis variables. Cluster analysis was used to categorize the 140 firms into four strategic groups. Finally, the authors investigated the relationship between strategy and performance as a test of the study’s basic research premise. The year 1984 was selected as the analysis year for several reasons. The authors believed that selection of a year prior to 1984 would have been an unfair test of the basic research premise since insufficient time had elapsed from the passage of the IMCA to allow carriers to define a strategic agenda. Additionally, the major recession of 1982 had complicated the environment and its effects would have been difficult to isolate in the analysis. The year 1984 allows enough time to have elapsed since both the MCA and the recession for carriers to have formulated a strategic response, yet is not so far removed from the date of the MCA such that other environmental factors may unduly influence the results.
Assessment of strategy There are a number of methods through which a firm’s strategy might be assessed. These methods range from self-typing, where managers of firms identify their own strategies, to more objective methods where published archival data or industry judges are used to assess strategy (Snow and Hambrick, 1980). We chose to use archival data for the study, drawn from motor carriers’ 1984 annual reports as filed with the ICC.
Strategic dimensions Data on 38 variables were drawn for each firm, providing indicators of a wide range of strategic dimensions such as cost and productivity position, traffic and customer focus, service differentiation,
Motor carrier
strategies
degree of recent expansion, and financial strategy. A complete list is provided in Appendix A. Factor analysis was used to reduce the large number of firm operating and financial variables to a more manageable set of factors. Factor analysis is a technique used to group variables which are highly correlated into factors. The principal components method of factor extraction was used and 12 significant factors emerged using the standard rule of only selecting those factors with eigenvalues greater than one. Three measures of firm performance were used: return on investment, operating ratio, and return on assets. Return on investment, defined as net income after taxes divided by transportation investment, is a widely used measure of economic performance. A firm’s operating ratio, defined as operating expenses divided by operating revenues, is the most commonly used measure of financial performance in the motor carrier industry. Return on assets is defined as net income after taxes divided by total assets and is also an important performance measure.
Clustering technique The K-means clustering algorithm of numerical classification was used with squared Euclidian distance measures to classify the motor carriers into strategic groups. This technique is one of the most popular with social scientists and is appropriate for our purposes (McKelvey, 1982). The use of factor scores as inputs into cluster analysis has been used in numerous empirical studies of strategy (Dess and Davis, 1984; Hambrick, 1984).
Iv.
RESULTS
Factor analysis The factors which were derived and their loadings by variable are presented in Appendix B. Table 1 presents a summary of the 12 factors sorted into five strategic dimensions: low cost, focus, differentiation, expansion and financial strategy. The factors were sorted into the dimension which their pattern of variable loadings seemed to most suggest. For example, if the variables loading most heavily on a factor were associated with the cost dimension, it was placed into Table 1. Factors grouped by dimension Factors
Dimension Low cost
F, F6
Focus
Differentiation
Expansion Financial
Lease/Buy Vehicle Efficiency Linehaul Cost LTL Concentration High Value LTL Shipper Concentration Common Carriage Reliance
F II El Fz E, El, FZ F7 ElfI
Loss and Damage Single Line Service
F¶
Asset Growth
F3
Debt-Equity Reliance
hlaintenance-
Advertising
and performance
203
the cost dimension and used as a measure of activity along that dimension. The five dimensions and associated factors are described in more detail below: Cost dimension factors. The cost dimension is represented in the analysis by the following factors: lease-buy preference, which measures a carrier’s preference for leasing or buying as a means of providing fleet capacity; vehicle efficiency, relating to average load and length of haul indicating the intensity of vehicle use; and linehaul cost, which measures linehaul expenses per mile as an indicator of unit cost. Focus dimension factors. The focus dimension is represented by four factors. The first relates to a primary focus on LTL traffic, while the second concerns a focus on a particular type of LTL traffic-that is, that portion generating a high revenue per ton-mile for the carrier. The third factor from this dimension measures the degree to which carriers are focusing on particular shipper groups. This type of focus approach is measured indirectly through pickup and billing-collecting economies resulting from a narrowing of customer base. Conversations with shippers have also indicated a preference for consolidating their freight with fewer carriers and so, this effort is being supported from both sides (Alex Brown & Sons, December 1987). The fourth factor measures activity along the focus dimension in terms of decisions to emphasize common carriage to the exclusion of other services such as contract carriage. This represents an element of the dimension related to service offerings. Differentiation dimension factors. The first factor measures two aspects of the differentiation dimension: service reliability through its domination by maintenance and insurance/safety expense variables and image uniqueness through the advertising variable. The next factor concerns rates of loss and damage and so captures the service quality aspect of the dimension. The third factor measures the dimension through its orientation with single line service. This is a desired service offering for most shippers given its directness and simplicity. Therefore, a firm’s ability to offer single line service constitutes an important service offering that would differentiate the firm from competitors unable to provide such a service. Expansion dimension factor. The expansion dimension is captured by one factor relating to asset growth. Specifically, it measures the extent to which firms grow during a year in terms of revenue equipment as well as total investment. Financial strategy dimension factor. The degree to which a firm relies on debt versus equity financing is the main component of the financial strategy factor. Financial theory indicates that firms relying more heavily on debt than on equity in periods of environmental uncertainty are riskier than are those who rely on equity, since debt must be paid back in fixed installments whether the firms are doing well or poorly. In contrast, equity payments in the form of stockholder dividends can be postponed in times of most severe downturn. Corsi and Scheraga (1989) docu-
20-l
R. D.
ment that this behavior carrier industry.
has occurred
SMITH,
CORSI. and C. Xl. GRIMM
T. M.
variables which load positively on the factor and/or highly positive on those variables loading highly negative. Firms with this score pattern would have an inverse association with the factor. Factor scores close to zero would indicate insignificant activity along the part of the strategic dimension suggested by the factor. By analyzing the factor scores of a firm by dimension, one is then able to ascertain the degree of association between the firm and each strategy dimension. Overall strategy of the firm is identified through an evaluation of the specific dimensions with which the firm is most associated. To facilitate interpretation, the clusters were ranked by the mean scores of their member firms on each of the factors from each dimension. Next, these rankings were summed for all factors within the dimension to give the composite rank of each cluster on the factors in that dimension. Since the factors each represent a particular dimension, this rank was used to determine the extent to which a cluster’s strategy embodies each dimension. For example, if a cluster ranked first in terms of its scores on the factors in the low cost dimension, then that cluster would contain firms with a strategy centered around the low cost dimension. If, however, the cluster did not rank highly on any dimension its strategy would be more ambiguous, falling into the stuck-in-the-middle category. Finally, it should be noted that factors seven, eight and twelve are reverse indicators of a dimension, so
in the motor
Cluster analysis: Overview of results A crucial decision in using the K-means algorithm is the choice of “k”, or the number of clusters to use. As explained by Hartigan (1975), this decision is left up to the researcher and does not result from the algorithm itself. In fact the typical approach is to run the algorithm with several different values of k and apply judgement as to which provides the best solution (Hambrick, 1984; Harrigan, 1985). In this instance, as the number of possible clusters increased up to four, different patterns of firm behavior emerged. However, when examining cluster solutions beyond four, the existing clusters from the four cluster solution were merely fragmented. Thus, the four cluster solution appeared to provide the best overall solution. Table 2 provides the factor scores for each of the four clusters. Factor scores are a measure of a firm’s association with the variables loading most strongly on a particular factor. A high positive factor score, for example, indicates that the firm scored highly positive the
factor
on those
variables
and/or
highly
which negative
load
positively
on those
on
variables
loading highly negative and would be representative of firms practicing the strategic activity suggested by the factor. A high negative factor score would indicate that the firm scored highly negative on those
Table 2. Factor score centroids
I
Cluster: Strategy: Dimension
by dimension
2
Differ.
No. Firms:
Low cost 4 Lease/Buy* 6 Veh. Effic.* I1 Linehaul Cost
grouped
14
.239(3)
2.630(l) - .079(2, 6
3
Focus
3
Inactive
Expan.
50
45
31
.237(2) - .298(3) - .046(1)
-.365(l) .174(2) -.239(j)
.406(4) - .388(4) - .244(;1)
6
6
12
Focus
1 LTL Reliance* 2 8 12
High Rated LTL* Shipper Concentration Common Carrier Reliance*
.196(2) - .229(3) .292(4) .259(4) 13
Differentiation 5 Maint/Advt.* 7 Loss & Damage 10 Single Line Service*
- .212(2) -. 194(2) .239(1)
Financial Strategy 3 Debt/Equity Expansion 9 Asset Growth*
-
.101(3) .072(l) -.097(l) .103(3) 8 .975( 1) -.058(3) .071(2,
.317(l) - .336(4) - .057(2) - .264(2) 9 - .293(3) .135(4) - .289(4) -
-. -
152(4) .068(2) .053(3) .278(1) 10
-.483(j) -.320(l) .015(3)
I1
8
-.117@
-.142(j)
5
6
.392(1)
.031(2)
.osscz,
- .072(3)
- .447@
97.0 5.1 9.6 2
99.6 -.8 0.5 4
1.15 (1,
Performance Results Operating Ratio (Sig. at .05) Return on Assets (Sig. at .05) Return on Investments (Sig. at Rank *,Mean factor score significantly
.lO) different
98.7 .7 6.0 3 across groups
at .05 level
94.3 12.2 13.0 1
205
Motor carrier strategies and performance that it is negative scores rather than positive ones which imply that the cluster is active along the dimension. In this case, a large negative factor score would be ranked higher than a large positive score.
Cluster analysis: The four strategies Cluster I: Differentiation with risky financial strategy. This cluster consists of 14 firms and ranks highest on the differentiation dimension, with a higher degree of single line service than the other three groups. This cluster also ranked highest with respect to the degree of risk in their financial strategy, as they exhibited a strong reliance on debt rather than equity financing. In addition, this cluster had moderate rankings on the low-cost and expansion dimensions, while ranking last on the focus dimension. Cluster 2: Focus. Cluster two consists of 50 firms; the dimension which figures most prominently in these firms’ strategy is focus, highlighted by a specialization in a limited cross-section of traffic tendered by a concentrated group of shippers. This focused approach is supported by the presence of differentiation in the strategy approach of these firms as illustrated by their second place rank overall on the factors of that dimension. Cluster 3: Stuck-in-the-middle. Cluster three consists of 45 firms. The cluster’s most salient feature is its lack of strength in any of the five dimensions. Any strong performance on one factor of a particular dimension is offset by very weak performance on another factor of that same dimension. Cluster 4: Expansion. Cluster 4 contains 3 1 firms. To assess this cluster’s strategy, we note the following attributes: (1) a markedly high rate of growth, given their first place rank on the asset expansion factor; (2) a lack of emphasis on LTL traffic in light of their last place rank on the LTL reliance factor; (3) a high degree of reliance on common carriage given their first place score on the common carriage reliance factor; and (4) a superior loss and damage record given their first place score on that factor. Together, these strategy characteristics suggest firms that are not focusing exclusively on a particular dimension but which rate highly on selected key measures from each dimension.
Performance effects of each strategy Table 2 also provides performance data for each of the four clusters. The top performing clusters for 1984 were clusters two and four, which exhibited a focus strategy and leadership-expansion strategy, respectively. The average ROA of 12.2% for cluster four is significantly different from the others at the .OS level, as is its operating ratio of 94.3. The average ROI for cluster four is 13%, significantly different from the others at the .10 level. During the regulated environment of 1977, LTL (Class 1 and 2 carriers) had an average operating ratio of 94, an average return on assets of 12%, and an average return on investment of 13%-comparable to the 1984 performance of the firms in cluster four. Cluster two lit(A)24:3-o
averages a 5.1% ROA, a 9.6% ROI, and a 97.0 operating ratio (Table 2). The poorest performing group of firms was cluster 3, which had a stuck-in-the-middle strategy. Thus, well-formulated strategies appear to bring the most rewards in a competitive market.
V.
DISCUSSION
In overall order of performance, the strategies for 1984 are as follows: (1) aggressive expansion strategy characterized by high growth rates (Cluster four); (2) a well-defined focus strategy concentrating on select types of traffic among select shipper groups and judicious use of advertising to target these groups (Cluster two); (3) a differentiation approach coupled with a risky financial strategy (Cluster one); and (4) a particularly sedentary strategy showing little activity across dimensions, almost a “non’‘-strategy (Cluster three). The strategy-performance linkage demonstrates a primary relationship between the degree of strategic focus and financial success as evidenced by the performance of clusters two and four versus that of clusters one and three, and a second level relationship between the degree of strategic activity and firm performance as evidenced by the relative performance of clusters one and three. Both results concur with existing strategy research findings calling for greater emphasis by firms on strategy development as the operating environment becomes more competitive and volatile (Mahon and Murray, 1981). We turn now to a more detailed analysis of the individual strategies. To supplement the cluster analysis results, we also draw on other data across the four groups (Table 3).
Cluster I: Differentiation financial strategy
with risky
The 14 firms in cluster one make it the smallest in terms of membership and the largest in average firm size. Firms in cluster one average $263.5 million in total assets, eight times larger than cluster three and over ten times that of firms in clusters two and four (see Table 3). This cluster of firms was notable for its emphasis on differentiation, ranking first overall and on the single-line service factor. This result indicates a high proportion of moves by carriers in this cluster are single line in nature and is consistent with the previously discussed profile of firms in the cluster: large firms able to offer through service as a result of their extensive operating networks. This result is borne out further by the high number of terminals per state for firms in this cluster (5.4 compared to 4.4 average; see Table 3), indicating that these firms are able to provide single-line service given their extensive terminal network. Other salient characteristics of this cluster, which include long average length of haul, high vehicle use, and an emphasis on single line service are typical of large, diversified trucking firms.
206
R. D.
SMITH, T. M.
CON, and C. M.
GRLMM
Table 3. Cluster centroids for supplementary variables
Variable Linehau! cost/mile Linehau! cost/ton-mile Gen. & admin. expenses as % of total Ave. load (in tons) Ave. haul (in miles) % owned vehicle miles Tons per terminal (000) % LTL shipments 70 LTL revenue Total assets (in millions) 70of tariffs published independently c/oexpenses on advertising % revenue equip. growth
This cluster combines its differentiation strategy with a risky financial approach as demonstrated by its highly positive score on the debt/equity factor indicating a strong reliance on debt rather than on equity financing. Such a reliance is illustrative of a more risky financial strategy than one based upon equity financing.
Cluster 2: Focus The firms in cluster 2 are, on average, the smallest in the study year, averaging 15 million dollars in total assets, versus an average of 46 million overall (Table 3). Concerning the focus dimension, the cluster’s first place rank on the shipper concentration factor and the high-rated LTL factor is noteworthy. The cluster’s joint association with these two factors suggests an interesting profile of firms specializing in a limited cross-section of traffic tendered by a specific group of shippers. The benefits of such an approach are greater efficiencies in pick-up and delivery and billing expenses as well as a resulting increased security in their market niche. Porter (1980) cited high market share with its accompanying competitive advantage as one of the principal benefits of the focus strategy. Also, in support of this approach, Woo and Cooper (1981) suggested a similar strategy in their study of successful small businesses. Regarding the remaining dimensions, differentiation is also present in these firms’ strategies as evidenced by the cluster’s second place rank overall on the factors of that dimension. The primary cause of this is the cluster’s high score on the maintenanceadvertising factor: .975. Given the variables loading on this factor, this score indicates that these firms are using advertising to separate themselves from their competitors and are also emphasizing maintenance to preserve service quality. Their expenditures for advertising equalled 2.1% of total operating expenses compared to an industry average of 1.5% (see Table 3). Also, their use of advertising supports their previously identified shipper/commodity focus by allowing the carriers to target particular groups of shippers through advertising. In conclusion, the overall strategy suggested by
I
2
3
4
1.29 .18 4.82 13.92 1024 77.10 1249 93.62 72.80 263 42 1.5 18.7
1.12 .50 6.33 9.98 237 82.36 213 94.85 79.41 15 46 2.1 15.9
1.04 .32 5.32 10.31 385 57.13 426 95.95 74.53 32 46 1.1 6.7
1.03 .36 5.55 8.35 226 78.94 421 83.13 60.65 18 53 1.2 36.6
Mean
I .08 .38 5.68 10.12 360 72.97 452 92.50 73.03 46 47 1.5 17.8
the foregoing is one of firms focusing on a select group of shippers who seem to ship similar commodities and supporting this approach through advertising and service quality. This is supported by the cluster’s first place scores on the shipper concentration factor and high-rated LTL concentration relating to the focus dimension and the maintenance/advertising factor from the differentiation dimension. As mentioned previously, this focus/differentiation approach has been associated in a previous study with successful small businesses.
Cluster 3: Stuck-in-the-middle The firms of cluster three are of approximately average size; 32 million total assets versus 34 million overall (Table 3). Since the cluster does not, as mentioned, exemplify strongly any one dimension, its low performance is in accordance with strategy theory. Cluster three does rank highly on certain factors from within the focus and low-cost dimensions. Within the focus dimensions, the cluster exhibits a strong preference toward mainline LTL freight, given its .3 17 score on that factor. This result contrasts with cluster two, which focused on higher-valued LTL freight. Further, the positive score (.3 17) on factor 1, taken together with its highly negative score (- .336) on factor 2 (high-valued LTL), indicates firms which are concentrating on ordinary LTL traffic to the exclusion of other types of freight, even other types of LTL traffic. A further indication of this cluster’s reliance on LTL traffic is provided by their high percentage of such shipments 96%, versus 91% for other firms as indicated in Table 3. The cluster also ranks first on the lease-buy factor from the cost dimension indicating a reliance on leased vehicles. Overall, however, the cluster does not stand out on this dimension. Its average linehaul costs per ton-mile of $.32 versus $.38 average support this finding (see Table 3). Also of interest are the cluster’s distinctly unfavorable scores on two important service factors. The cluster’s unfavorable scores on the single line service and loss and damage factors (-.289 and .135, respectively) signifies firms engaging heavily in con-
Motor carrier strategies and performance netting service and incurring high rates of loss and damage. Generally, single line service is preferred by shippers, and loss and damage is particularly troublesome; these attributes would not appear to bode well for their service quality or their overall profitability. Their negative score on the maintenance/advertising factor, (- .293), when considered in light of the above factor scores, indicates further that these firms are clearly not incorporating the differentiation dimension into their overall strategy. In summary, this overall strategic position appears to be rather dismal. The clearest signals of strategic emphasis the cluster displays is its dedication toward traditional LTL freight along with a reasonably strong reliance on common carrier, another traditional source of motor carrier revenue. The results point to a malaise regarding their strategic responses to the challenges of deregulation. They seem to be clinging to traditional service offerings and not attempting to distinguish themselves from their competitors by developing new strategies. Such characteristics suggest an inert or stuck-in-the-middle strategy. Cluster 4: Expansion The firms of cluster four have an average size of 18.2 million total assets, substantially below the average of 46.1 million for all firms. Also, this cluster contains significant variance in firm size, with the largest firm being twice the industry average and the smallest possessing assets of only 480 thousand. The most striking feature of cluster four is its high score on the asset expansion factor. Its score of 1.15 is significantly different from the other clusters at the .05 level and is precipitated by an average growth rate of 36% (see Table 3). The next highest rate of expansion (16%) is exhibited by the firms of cluster two. A secondary characteristic of these firms is their low score on the vehicle use factor. This is reasonable given their below average figures for load and haul indicated in Table 3: 8.3 tons (10.1 average) and 226 miles (361 average). It should not be inferred, however, that such results indicate inefficiency, as examination of the number of states served reveals that these firms serve a smaller geographic area than average: 9.9 states versus the 13.8 average. Lower than average loads can occur because these firms are less able to rationalize their traffic given their smaller base of operation or because they are choosing to emphasize fast service over high loads and thus not consolidating to as high a degree as other firms. The cluster does not stand out on the differentiation dimension, although it does possess a superior loss and damage record as indicated by its favorable score (- .320) on that factor. This positive indication of a differentiated strategy is offset by their large negative score (- .453) on the advertising/maintenance factor, indicating a lack of attention to advertising (a key component of a differentiated strategy), and their small score of .015 on the single line service factor demonstrating that these firms do not vigorously pursue these other aspects of the differentia-
207
tion dimension. Since the cluster ranks third overall on this dimension, it is apparent that differentiation is not a major part of their overall strategy. The cluster’s scores for the focus dimension factors reveal that this dimension is also not prominent in their strategy, although the firms are committed to common carriage, outscoring all clusters on this one factor (.278). Also noteworthy is their lack of reliance on LTL freight, particularly standard LTL freight as evidenced by their last place rank on the LTL reliance factor. This result highlights firms which are branching out from the traditional LTL freight market to pursue new revenue opportunities such as long-term contract agreements with shippers and TL backhauls to complement unbalanced LTL traffic. In conclusion, the overall strategy suggested by the above evidence is the expansion strategy. This is clearly supported by the firms’ high rate of growth and their pursuit of other than LTL sources of revenue. The firms are contributing to the successful strategy position through superior loss and damage control, an aspect of service that has not diminished in importance with passage of the MCA. The expansion strategy is similar to the explosion strategy of Wissema, Van Der Pol, and Messer (1981) and the aggressive initiator strategy of Hawes and Crittenden (1984).
VI. CONCLUSlON
The results clearly show that financial success is possible in the LTL general commodity trucking industry in the era of deregulation. The key to success is the formulation of a well-developed, specialized strategy as opposed to a nebulous undefined one. The results affirm existing theoretical and empirical findings regarding the importance of strategy formulation to performance and the necessity of strategic adaptation to changes in the firm’s competitive environment. They establish that firms with the most unified, comprehensive strategy (Cluster 4) in a deregulated environment can equal the performance of “average firms” in the regulated environment. Contrary to the views of certain industry representatives, the advent of deregulation has not been uniformly detrimental to carriers. The key to survival in the new competitive environment appears to be a coherent, well thought out competitive strategy.
REFERENCES
Andrews K. (1972) The Concept of Corporate Strategy. Irwin, Homewood, IL. Dess G. G. and Davis P. S. (1984) Porter’s (1980) generic strategies as determinants of strategic group membership and organizational performance. Acad. Manag. J.,
27.467-488. Corsi, T. M. and Scheraga, C. A. (1989) Pre- and postderegulation financial strategies and linkages to performance in the motor carrier industry: an application of
canonical scores. Tiansp. Res., 23A, 161-171. Grimm C. M. and Smith K. G. (1985) Impact of deregula-
208
R. D. SMITH,T. M. Cc IRSI, and C. M. Gat.v~
tion on railroad strategies and performance. Trans. Res. Forum Proceedings, 540-544. Hambrick D. C. (1983) High profit strategies in mature capital goods industries: a contingency approach. Acad.
Manag. J., 26,687-707. Hambrick D. C. (1984) Taxonomic approaches to studying strategy: some conceptual and methodological issues. J. of Manag., 10, 27-42. Harrigan K. R. (1985) An application of clustering for strategic group analysis. Strat. Manag. J, 6, 55-74. Hartigan J. A. (1975) Clustering Algorifhms. John Wiley & Sons, New York. Hawes J. M. and Crittendon W. F. (1984) A taxonomy of competitive retailing strategies. Strut. Munug. J., 5, 275287. Xlahon J. F. and Murray E. A. (1981) Strategic planning for regulated companies. Straf. Munag. J., April-June, 251-262. hlcKelvey B. (1982j Organizational Systetnatics. University of California Press,Berkeley, CA.. .Miles R. E. and Snow C. C. (1978) OrganizationalStrategy, Structure and Process. McGraw-Hill. New York.
Porter M. (1980) Competifive Sfrutegy. Free Press, New York. Porter M. (1985) Competifive Advanfage. Free Press, New York. Rakowski J. P. (1988) Marketing economies and the results of trucking deregulation in the less-than-truckload sector. Trans. Journal, 27, I I-22. Silberman I. and Hill H. (1988) State of the LTL trucking industry. Transportation Executive Update. 2. 6-13. Smith K. G. and Grimm C. M. (1987) Environmental variation, strategic change and firm performance: A study of railroad deregulation. Strut. Manug. J., 8, 363316. Snow C. C. and Hambrick D. C. (1980) Measuring organizational strategy: some theoretical and methodological problems. Acad. of Manag. R., 5, 527-538. Wissema J. G., Van Der Pol H. W. and &lesser H. M. (1980) Strategic management archetypes. Strur. Manag. ‘J., 1; 37-47. Woo C. Y. and Cooper A. C. (1981) Strategies of effective low-share businesses. Strat. Manug. 1, 2. 301-308.
APPENDIX.4
Variables constructed from carriers’ annual reports as: Indicators of the cost dimension
2. Pickup and delivery expense per ton 3. Billing and collecting expense per shipment
Unit Cost
Indicators of the differentiation dimension 1. 2. 3. 4.
Linehaul expenses per ton-mile Linehaul expenses per mile Total operating expenses per ton-mile Platform and terminal expenses per shipment
Productivity 1. Average length of haul 2. Average load (in tons) 3. Average annual miles per power unit 4:Paid time-off expenses as a percentage expenses
of total operating
Overhead
Leasing
I. Percentage owned vehicle miles 2. Vehicle rents without drivers as a percentage of total operating expenses 3. Revenue equipment rents and purchased transportation as a percentage of total operating expenses Indicators of the focus dimension Traffic Focus LTL revenue as a percentage of total operating revenue LTL tons as a percentage of total tons Freight revenue per ton-mile Common revenue as a percentage of total operating revenue
Customer
1. Percentage of tonnage originated and terminated 2. Loss and damage claims as a percentage of total operating revenue 3. Maintenance expense per vehicle mile 4. Insurance expenses as a percentage of total operating expenses 5. Insurance and safety expense per vehicle mile 6. Average salary and wages per employee 7. Officers and supervisors salary as a percentage of total operating expenses
Additional indicators of differentiation dimension
1. General and administrative expenses as a percentage of total operating expenses 2. Operating supplies and expenses as a percentage of total operating expenses
1. 2. 3. 4.
Service Differentiation
Focus
1. Pickup and delivery expense per shipment
Uniqueness 1. Advertising expense as a percentage of total operating expense 2. Traffic and sales expense per shipment 3. Tariff and schedule expenses as a percentage of total operating expenses 4. Total assets
Indicators of the expansion dimensions Expansion 1. Revenue equipment additions during year as a percentage of beginning revenue equipment 2. Total investment during year as a percentage of total assets 3. Accounts receivables as a percentage of sales
Indicators of the financial strategy dimension Debt Reliance 1. 2. 3. 4.
Debt-equity ratio Current ratio (current assets/current liabilities) Total liabilities as a percentage of total assets Total equity as a percentage of total liabilities and equity
Motor carrier
strategies
Appendix
209
and performance
B. Factor
Loadings Factors
Variables 1) % owned veh. mi. 2) op. supp. as a 70 total op. exp. 3) rev. equip. rents and purchased transp. as a % tot. op. expenses 4) veh. rents w/o drivers as a % tot. op. expenses 5) avg. length of haul 6) tot. assets 7) pltf. and terminal expenses per shipment 8) avg. load 9) gen. & adminis. expense as a % tot. op. exp. 10) linehaul exp. per mile 11) LTL rev. as a % tot. rev. 12) LTL tons as a 70 tot. tons 13) paid time off as a % tot. op. expenses 14) pickup & delivery expense per ton 15) ave. salary & wages per employee 16) traffic & sales expense per shipment 17) ave. annual mi. per unit 18) tot. oper. expense per tonmile 19) freight revenue per ton-mile 20) linehaul expense per tonmile 21) pickup & delivery expense per shipment 22) billing & collecting exp. per shipment
(4) Lease Buy Factor
(11) Linehaul cost
(1) LTL Concent.
(2) High Value LTL Concent.
(8) Shipper Concentration
,826 ,698
- .690 -.650 .792 .781 .611 .468 ,610 -.522 .733 .680 ,627 ,600 ,584 -.66-I - .596 .922 .904 ,864 ,850 ,777 (12) Common Carrier
23) common revenue as a % total revenue 24) ave. load 25) ins. & safety exp per veh. mile 26) salaries officers Br sprvsr. as a % operating exp. 27) maintenance expense per veh. mile 28) advertising as a % of tot. oper. expense 29) loss & damage claims 30) tot. ins. expenses as a % tot. oper. expenses 31) accnts. rec. as a % of total revenue 32) % tonnage originated & terminated 33) tariff & sched. exp. as a % tot. oper. expense 34) tot. liab. as a % of tot. assets 35) debt equity ratio 36) equity as a 70 of tot. liab. & equity
(6) Vehicle Efficiency
(5) Maintenance Advertising
(7) Loss & Damage
(10) Single Line
(3) Debt vs. Equity
- .696 .537 .636 .622 .598 .576 .739 .725 .625 .747 - .795 .656 .575 -.926
(9) Expansion
210
R. D. SMITH, T. M. CORSI.and C. .&I.GRIMM (12) Common Carrier
(5) Maintenance Advertising
(7) Loss & Damage
37) current ratio 38) rev. equip. add’ns as a % of beg. revenue equip. 39) tot. invest during yr. as a % total assets Nofe. Only factor loadings with an absolute value in excess of ,450 are shown.
(10) Single Line
(3) Debt vs. Equity
(9) Expansion
-.721 .885 .862