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Brokerage and the potential for electronic marketing of produce transportation.

The last major study of exempt commodity truck brokers appeared in this Journal in 1979, Taffs "A Study of Truck Brokers of Agricultural Commodities Exempt from Economic Regulation."(1) Taft found brokers to be key factors in the produce transportation/distribution system, described several aspects of broker operations, and predicted an increased role for brokers of other commodities if interstate economic regulation of transportation was relaxed. This prediction has been realized. Between 1935 and the late 1970s there were fewer than 100 active Interstate Commerce Commission (ICC) licensed brokers, versus nearly 6,000 by 1988.(2)

It is common, though inaccurate, to refer to the past decade's interstate motor carriage regulatory reforms as "deregulation." Rather, there was a loosening (not elimination) of regulations. That step (i.e., total deregulation) remains an option for public debate. Brokers of produce trucking are the only significant example of brokering in the absence of economic regulation. Indeed, as produce trucking never has been regulated, it may be considered an example of conditions after long-run adjustments to deregulation. It seems appropriate, therefore, to reexamine produce truck brokering.

In this paper, the overall importance and specific practices of produce truck brokers will be examined and compared to earlier results by Taff and others. In addition, possibilities for alternative load arrangement methods will also be assessed. In particular, the potential for some form of electronic marketing of produce trucking services will be explored.

In the next section, the principal data sources for the study are briefly described. The discussion and analysis is divided into three sections, the first dealing with the frequency of use of brokers, the second with broker characteristics and services, and the third with prospects for electronic marketing of transport services for transport. Finally, the results are summarized and conclusions drawn.

DATA

Data for the study come from interviews with brokers and carriers. There were four distinct surveying efforts.

Broker Telephone Survey

In March 1991, telephone interviews were carried out with 139 produce truck brokers in California; the Pacific Northwest (i.e., Washington, Oregon, and Idaho); Nogales, Arizona (the principal port of entry for Mexican produce); Texas; and Florida. The 1990 edition of The Blue Book(3) lists 622 truck brokers operating in these areas. Brokers were randomly selected from this list. The distribution of completed interviews was: 38 in California, 26 in the Pacific Northwest, 6 in Nogales, 30 in Texas, and 39 in Florida. The refusal rate was fairly high, 40 percent. In the very large majority of cases, the reason given for refusing was insufficient time. This was not unexpected, given the hectic atmosphere typically found at broker offices. Examination of information on the respondent and nonrespondent establishments provided in The Blue Book revealed no apparent differences.

Broker and Shipper Interviews

At their places of business, in-depth interviews were conducted with a small sample (eight) of Florida-based truck brokers specializing in arranging transportation for produce. Telephone interviews were also conducted with a small sample (nine) of Florida produce shippers. The interviews were conducted in March 1990. The broker panel was drawn from the broker listing compiled by the National Agricultural Transportation League. The shippers were drawn from the 1989 Blue Book. Due to the small sizes of the samples and the absence of a systematic or random sampling approach, no claims regarding the representativeness of these samples can be made.

To distinguish between the two broker surveying efforts, the telephone interviews with 139 brokers will be referred to as the "broker survey," and the just described on-site interviews will be referred to as the "broker panel."

Owner-Operator Interviews

In January, March, and June of 1990, interviews were conducted with 199 owner-operators who were then hauling produce from Florida. The sites for the surveys were the Florida Agricultural Inspection Stations located on U.S. I-10, I-75, and 1-95. During the survey times, interviews were attempted with every owner-operator passing through the inspection stations. Refusal rates were low, normally under 10 percent at each site. For these reasons, the owner-operator sample is believed to be representative of all owner-operators serving the Florida produce industry.

1982-89 Produce Driver Interviews

Information for this study is also taken from interviews with drivers of vehicles hauling produce from 1982 through 1989. The sites and procedures for the interviews were identical to those described above for the owner-operator interviews. However, interviews were attempted with all drivers hauling produce, not just with owner-operators. During the seven-year survey period, 9, 194 interviews were completed.

FREQUENCY OF BROKER USE

Outbound Produce Movements

The importance of brokers appears to have grown over time. Gaibler(4) estimated that truck brokers arranged 36 percent of all over-the-road produce hauls in 1959 and 51 percent in 1974. In each year of the Florida produce hauler surveys, brokers were used for between 60 and 66 percent of the loads being hauled by the respondents at the time of the interview.

It should be stressed that the methodologies employed by Gaibler and by the authors differ markedly and cross-study comparisons of results may not be valid. Gaibler's estimates had the advantage of being for the entire United States, whereas the estimates in this study apply only to loads originating in Florida. However, Gaibler's procedures were both complex and somewhat tenuous.(5) If Gaibler's approach in 1959 and 1974 was consistent, his estimates are indicative of long-term growth in broker importance. The Florida survey results demonstrate that brokerage is the dominant load arrangement method for produce in the nation's second most important production state.

Across the 1982/83-1988/89 study period for the Florida survey, the percent of loads arranged by brokers varied over a fairly narrow range, with no apparent trend. The results for 1988/89 are typical of the entire period. In that year, an estimated 63 percent of all produce hauled by truck from Florida was arranged through brokers. Just under a fifth of the loads were directly arranged between the carrier and either the shipper or the receiver. Eight percent of the produce hauls were private or own-account carriage. Finally, 10 percent of the loads were arranged by some other, unspecified means. For the most part, the "other" specification indicated driver ignorance of the load arrangement method, rather than a different method of arranging loads.(6)

Inbound Movements

Only about 12 percent of the carriers hauled produce both into and out of Florida. Between 75 and 80 percent of the loads carried into Florida by the outbound produce haulers are commodities subject to ICC regulation. It is not surprising, therefore, that differences exist between inbound and outbound load arrangements. Thirty-two percent of the inbound loads were arranged by non-Florida brokers. The largest single difference was with regard to the use of Florida-based brokers. Over 60 percent of the outbound loads were arranged by brokers, virtually all of which were based in Florida. In sharp contrast, only 10 percent of the inbound loads were arranged by Florida-based brokers, suggesting that produce brokers normally do not arrange return hauls. This is supported by evidence from the broker panel. The eight brokers in the panel indicated that in the past season they handled over 14,400 loads from their Florida offices. Only 17 percent of these were inbound into the state.

BROKER CHARACTERISTICS, FEES, AND SERVICES

Data used in this section come from the broker telephone survey, unless otherwise indicated.

Characteristics

Produce truck broker operations in the sample tended to be small, but very stable (see Table 1). In every region, the average number of offices in the same state was between one and two. Only 5 percent of the firms had more than two offices in the same state. The firms averaged 17 years in business, with 80 percent in business for at least 10 years. This result is consistent with a 1982 survey of 110 Florida brokers which found that 72 percent were in business for at least 10 years.(7) The average office handled 1,774 produce loads over the preceding 12 months. These loads accounted for 76 percent of all loads. Assuming gross revenues of $150 to $200 per load, the average office annually grosses between a third and half a million dollars.(8)

Broker longevity is suggestive of the importance of reputation. Shippers (carriers) rely upon brokers both to "screen" carriers (shippers) and to act as financial intermediaries. An unscrupulous or inept broker can cost its clients tens of thousands of dollars on a single transaction. It follows, then, that experienced, known brokers would have an advantage over entrants.(9) This is not meant to imply there is a low rate of entry into brokerage. Indeed, conversations with industry participants indicate considerable entry. However, the failure rate appears high among these entrants. In other words, there appears to be a stable core of brokers which handle the large majority of brokered produce loads, and a variable "fringe" group.

Produce loads accounted for an average of 76 percent of all loads handled by the brokers TABULAR DATA OMITTED in the sample. Only 23 percent of the brokers handled more nonproduce than produce loads. Seventy-one percent of the brokers had ICC broker's licenses; for this group produce freight averaged 73 percent of all hauls arranged, compared to 84 percent for those without such licenses. Having an ICC broker's license can expedite the arranging of inbound, non-produce loads for carriers.(10) Surprisingly, however, a somewhat smaller percentage of brokers with ICC broker's licenses indicated they sometimes arrange inbound loads than brokers not possessing such licenses (68 versus 76 percent, respectively). The principal difference between brokers with and those without ICC licenses is that the former are more likely to have their own trucks (37 versus 22 percent).

A third of the brokers were also carriers (i.e., they owned trucks). Taff also found that most brokers do not own trucks. He noted that brokers often felt it was difficult to have in-house trucks without there being the perception that in-house trucks were favored over those of carrier-clients with regard to securing the most profitable loads. This sentiment was echoed in conversations with the authors by both brokers and carriers.

Broker Fees

Brokers charge a percent of the gross freight rate for their services. Normally, they receive payment for the entire transportation service directly from the receivers (or shippers), deduct their fees, and pay the carriers. Taff found that the commission rate depended, in many cases, on whether a cash advance to the carrier had been paid. This was also the case in the current study. Without a cash advance, the average broker charged a 9.4 percent commission, versus 11.1 percent when cash advances were taken. The comparable figures in Taff's study were 8 and 10 percent.

The difference in commissions between loads with and without cash advances were consistent in Taff's study and this one (about 2 percent of the freight rate). However, controlling for cash advances, the average commission rate was just over 1 percent of the freight rate higher in this study than in Taff's. The reasons for this increase in transaction costs are not clear.(11)

Cash Advances

Cash advances to cover out-of-pocket trip expenses are important to many carriers, particularly to owner-operators. Owner-operators rated cash advances as the most important service brokers perform (other than locating loads):
 Average Score
 (1=unimportant
Service Aspect 10=very important)
Cash advances 7.1
Help with freight claims 6.0
Help securing insurance 4.0
Other 1.0


The average owner-operator indicated that he or she asked for advances for 56 percent of all produce loads. This was consistent with the average response from the broker survey that 61 percent of their owner-operator clients request cash advances. In contrast, the average response by brokers to the same question regarding their fleet customers was 24 percent.

Eighty-four percent of the brokers offered cash advances. Ninety-three percent of those offering advances placed upper limits on the amounts of the advances. Most commonly, a maximum percentage was established for advances of the total payment due to the carriers (i.e., the freight rate minus the broker's commission). The average of the total carrier payment brokers were willing to advance was 42 percent. Relatively few brokers limited advances to very small percentages of the total carrier payment or allowed over half to be advanced. Three quarters of the brokers indicated limits between 40 and 50 percent of the total carrier payment, and 95 percent of the brokers would advance between 30 and 50 percent of the total carrier payment.

While cash advances are useful, if not vital, to many carriers, they are, in effect, unsecured loans at usurious interest rates. As has already been shown, broker commissions tend to be 2 percent of the freight rate higher if cash advances are taken than if there are no cash advances. Assume a broker advances 42 percent of the total carrier payment, the broker is not paid by the shipper/receiver for one month, and the broker's commission rates are 10 percent without and 12 percent with a cash advance. The effective annual (simple) interest rate charged by the broker for the cash advance is 65 percent. If payment to the broker was within 20 days, the effective annual interest rate would exceed 100 percent.

Freight Claims and Freight Rate Coverage

Unlike freight forwarders, brokers do not take direct responsibility for transport. As such, they usually do not bear responsibility for freight claims or for non-payments by receivers. However, the results from the broker panel suggest that it is common for brokers to involve themselves in these problems. One firm in the broker panel had been in business for less than one year and had never handled a movement resulting in a freight claim. All of the remaining seven brokers indicated that they take active roles in freight claim negotiations. The majority stated flatly that they always paid the shipper/receiver, regardless of their ability to collect compensation from carriers. All but one of the eight brokers stated they paid carriers even if the shipper/receiver reneged on payment.

Other Services

Half of the brokers in the broker panel indicated that they assisted carriers with securing insurance and three brokers also assisted with bookkeeping. These facets of broker services, however, appear to be of secondary importance.

POTENTIAL FOR ELECTRONIC MARKETING OF TRUCKING SERVICES FOR PRODUCE

In this section, the receptivity of produce carriers, shipper/receivers, and brokers to electronic marketing is explored. In the next subsection, electronic marketing is defined and some potential benefits are discussed. A crucial factor in the success of any innovation, such as electronic marketing, is its acceptability. In this section, this aspect is explored.

Electronic Marketing

Definition

Electronic marketing is a broad term used to describe any marketing method which uses electronic technology in some form. The differences between electronic marketing methods are in the lines of communication and electronic equipment used to process information. In the broadest sense, a simple person-to-person telephone call can be called electronic marketing. In the current study, electronic marketing refers to marketing systems that use computer technology in some fashion. This does not preclude the use of less sophisticated electronic devices, like telephones, within such a system. It only means that computers are assumed to be an integral component of the system.

The explosive growth in information technology offers a possible avenue for lowering the transactions costs faced by carriers when arranging loads. Such information systems have been developed and implemented for the buying and selling of hogs, eggs, and cotton. Two rudimentary systems currently exist for hauling general freight, but none has yet been developed for produce haulage.

Electronic marketing systems could take one of two forms. First, a system could be developed that reduced the need for intermediaries in produce transportation. Similar to electronic banking, which reduces the need for tellers, such a system could allow shipper/receivers and carriers to identify opportunities for matching carriers and shipper/receivers and, possibly, to negotiate freight rates via computers. One of the most serious problems that would have to be addressed with such a system is asymmetric and/or insufficient information. The negotiators would likely have little or no previous experience with one another. Depending on the type of system, there may be few opportunities for the parties to interact either visually or verbally. Each party, therefore, would have little indication of the reliability and qualifications of the other party.

In part due to the just described problem, the development of electronic information systems may actually enhance the role of brokers and other intermediaries. Intermediaries could also benefit from the development and implementation of a system similar to computerized airline reservation systems. Intermediaries might play the same role for shipper/receivers and motor carriers as travel agents do for travelers and airlines.

Potential Benefits

While there have been no applications of electronic marketing for produce transportation, there exist two systems for arranging transport of (primarily) regulated dry freight: DAT and Comdata Transportation Services. Conversations with officers in these firms suggest their services cost 1 percent or less of the freight rate. In 1990, approximately 25 million tons of produce was transported interstate in the United States.(12) This is the equivalent of 1.14 million truckloads, assuming 22 tons per truckload. If 60 percent of this traffic is brokered at 10 percent commissions and the average freight rate is $2,000, the annual costs of truck brokering of produce in the U.S. is $137 million. If an electronic marketing system could operate for the equivalent of 1 or 2 percent of the freight rate, the potential exists for lowering transactions costs by over $100 million per year.

Reduced transactions costs should also result in reduced empty mileage. For example, it is estimated that a produce load is unsuccessfully sought for a third of all the refrigerated vehicles which exit Florida without a load |during the October-June produce season~. With reduced transactions or search costs, loads might be located for many of these vehicles. Moreover, produce loads would be sought by a portion of the empty vehicles not currently seeking such loads.

An electronic marketing system also might facilitate the availability of short-term credit to carriers on more favorable terms than brokers typically grant with cash advances. This might be accomplished by the electronic marketing system providing direct access to alternative, third-party lenders or by providing greater opportunities for carriers to express their credit needs to shipper/receivers.(13)

Satisfaction with Brokers and Overall Receptivity to Electronic Marketing

Nearly 70 percent of owner-operators felt brokers earned too much money, given the services they provided. When asked what percentage of brokers they felt lied about their actual freight rates on hauls, the response averaged 73 percent.(14) When asked what percentage of brokers failed to pay an agreed-upon rate, the average response was 40 percent.

The average shipper uses about 21 brokers on a regular basis, and enjoys good business relationships with them. This can be inferred from the shippers' feelings about dishonesty among brokers. On average, shippers felt that only 13 percent of produce brokers lied to carriers about what actual transport charges for a haul would be.

Those in the broker panel, on average, perceive that only 15.3 percent of their peers are dishonest. Of course, one would not expect brokers to blithely announce that their profession is riddled with dishonesty. When asked whether brokers have an image problem (rightly or wrongly), however, half the brokers responded affirmatively. While brokers do not see themselves as dishonest, they acknowledge that many do not share their assessment.

Owner-operators, shippers, and the broker panel were questioned regarding their desire for electronic marketing of produce transport services. Owner-operators responded favorably to the idea of electronic marketing by a margin of three to one. Brokers were evenly split in their interest in electronic marketing. Three-quarters of the brokers surveyed, however, did feel that electronic marketing systems would become more important in their industry in the future, even if the respondent's firm had no interest. Only 22 percent of the shippers contacted currently were interested in electronic marketing. Even so, two-thirds felt that electronic marketing of transport services would be an important part of their industry in the future.

As previously noted, on average, produce shippers felt only 13 percent of brokers are dishonest, almost identical to the average broker response to the same question of 15 percent. This should explain the wide disparity between shippers, brokers, and owner-operators regarding their interest in electronic marketing systems. Owner-operators see electronic marketing as a possible way to avoid brokers, whom they perceive as often overpaid and less than honest. Brokers see electronic marketing as a way to possibly get more business from truckers because of greater access to load information. The low interest of shippers seems to indicate indifference rather than hostility. The large majority of them subscribe to the premise that this technology will become increasingly important.

Relationships Between Acceptability of Electronic Marketing and Owner-Operator Characteristics

In addition to determining the overall level of support among owner-operators for electronic marketing, it was of interest to explore if certain subgroups of owner-operators were more or less favorably disposed toward electronic marketing. This is important both to gauge future levels of support for electronic marketing and to provide information regarding target groups for those marketing such services.

Theoretical Development

The implementation of any technology can be greeted with enthusiasm, hostility, or indifference. The degree of success in adopting a new technology depends upon the degree of enthusiasm or acceptance. Because the cost of searching will vary from owner-operator to owner-operator, it is hypothesized that some owner-operators are more likely to look favorably on the utilization of new types of information technology than others. It is also hypothesized that the differences in the way individual owner-operators react to the idea of electronic marketing are associated with their characteristics, such as age, education, satisfaction with current load arranging options, and prior experience with computer technology. These characteristics function as proxies or indicators of the differences among owner-operators in the costs that would be incurred with the adoption of electronic marketing technology. Henri Theil's(15) theory of rational random behavior provides the theoretical framework for this approach.

Perfect information (like perfect competition) is a reality that exists only in the pages of an economics text. In the real world, a decision maker rarely, if ever, possesses full information about all the variables that would affect his decision. Thus, in evaluating a decision under uncertainty, the decision maker often formulates an initial assessment of the probability that the uncontrollable variables (states of nature) affecting the decision will take certain values (prior probabilities). At this point, the decision maker has the option of trying to obtain additional information that can help him make a better decision. The additional information will allow the decision maker to revise the initial (prior) assessment of probabilities to a new set of revised (posterior) probabilities.(16)

Theil has taken these basic information and decision making concepts and formulated a theory of, rational random behavior. Let x=|x1, x2, ... xk~' be defined as a vector of variables controlled by the decision maker. Let x vary over some region F, which is the feasible region for the vector. The set F contains all possible alternatives for x, even those for which the decision maker has no knowledge. Now, let X (where X is an element of F) be defined as the optimal value of the vector x, and 1(x,X) be the loss function which describes the decision maker's loss when x is chosen instead of X.

The optimal choice of X depends on many factors. The decision maker does not possess knowledge of all these factors, and must operate with imperfect information.

The decision maker can improve the chances of getting closer to X by acquiring additional information. If information were free, the decision maker would acquire all that was available until the optimal X were reached. Free information, like perfect information, is an unrealistic assumption, however. Therefore, the decision maker has two options in the real world: acquire no information or acquire some finite amount of information.

If no additional information is acquired, the decision maker makes a random decision with the prior density function of p0(x). It is reasonable to assume randomness since the decision maker does not know the values of the factors determining the optimal X. As information is acquired, the factors that determine X transform the prior density function p0(x) into the posterior density function p(x). Thus, information can be defined in terms of changes in the probabilities assigned to possible choices of x. If p(x) is equal to p0(x) for every x in F, then the decision maker has obtained no additional information since perfect information was already possessed. In other words, if there is no change in behavior, then it is evident that no relevant (i.e., choice-altering) information was obtained. If p(x) is not equal to p0(x), then a positive amount of information was acquired.

Information has a cost, as do all inputs. The cost of information is defined as c=c(I), where I is defined as the amount of information gathered. I depends on p(x), so c(I) will depend on the choice of p(x). Another important determinant is the expected loss L from choosing x instead of X. The loss function can be expressed as 1(x,X). L is the expected value of the loss given x and X. If x=X, then L will, of course, be 0. Likewise, L will always be greater than 0 when x is not equal to X.

The cost of information is assumed to be differentiable with a positive first derivative (dc/dI |is greater than~ 0) and a non-negative second derivative (|d.sup.2~c/|dI.sup.2~ |is greater than or equal to~ 0). In other words, the maginal cost of information (dc/dI) is therefore positive and non-decreasing. The decision maker's objective is to minimize the value of c(I)+L. Theil calls behavior displayed under these conditions rational random behavior. To recap, the decision distribution of rational random behavior involves three determinants: a prior density function, the loss function, and the marginal cost of information.

Five important concepts can be extracted from Theil's rational random behavior theory: (1) information is transformed into decisions in uncertain situations affected by random events which make a priori ascertainment of the best option impossible; (2) a decision made with more information is not automatically better than a decision made with less information; (3) information has a positive and non-decreasing cost; (4) these costs must be viewed from the individual decision maker's perspective; and (5) the rational decision maker will garner additional information only as long as the marginal costs of that information are exceeded by the perceived marginal decline in losses.(17)

Individual firms within the produce/ornamentals transportation sector have different loss and cost functions. There is no single function for either loss or cost that can be applied to all market participants. Given that this is true, it follows that minimizing the combined loss and cost functions will result in differing amounts of information and differing costs of information among firms in the industry. For example, giving the same amount of identical information to two owner-operators may result in two very different decisions. This is because the ability to process information and make decisions will be different from decision maker to decision maker.

Theil's theory has been used previously to investigate the potential for electronic marketing of several agricultural products. Tilley and Dickey(18) conducted a study to find if there were certain characteristics among Midwest grain producers that made them amenable to the adoption of electronic marketing for grain. Turner, Epperson, and Fletcher(19) conducted a similar study for agricultural producers in southwest Georgia. Oklahoma State University(20) has conducted five surveys of producer attitudes toward electronic marketing of corn, feedgrains, grain, soybeans, and wheat. There have been no studies to date, however, for the potential acceptance of electronic marketing of produce/ornamentals transportation services.

Empirical Model

A logit model was developed to test the hypothesis that certain types of owner-operators would be more amenable to electronic marketing than others. The model is as follows:

L = |beta~0 - |beta~1(YEARS) + |beta~2(FUTURE) + |beta~3(EDUC) - |beta~4(ICC) + |beta~5(EMPOUT) + |beta~6(EMPIN) + |beta~7(INTEARN) + |beta~8(INTLIE) + |beta~9(INTFAIL) + |beta~10(COMPUSE) + |mu~

L is the log of the odds ratio |1n (Pi/(1-Pi))~, which represents the odds in favor of the response being 1 (the respondent is interested in electronic load posting and arranging). |beta~0 is an intercept term, |beta~1, through |beta~10 are unknown coefficients, and |mu~ is an independently distributed error term. The independent variables are defined in Table 2 and the rationales for their inclusion are discussed below:

YEARS. The longer the respondent has been in trucking, the more likely it is that he or she has become accustomed to a certain range of load arrangement options. If this supposition is true, those who have been in trucking for many years should be more resistant to technological change than those who have just entered the industry.

FUTURE. Learning to use the new technology that would be available is bound to have some fixed costs for the owner-operator (in time, if nothing else). Remaining in the trucking industry for a longer period would tend to spread those fixed costs out over time.

EDUC. Education was expected to influence owner-operator attitudes toward the use of computer technology in a positive manner. Higher levels of education were expected to lower the costs of adopting electronic marketing (less time spent on learning the system).

ICC. ICC authority was expected to have a negative impact on attitudes. Owner-operators with authorities have many more types of loads directly available to them. Therefore, a load posting system for produce would likely be of less value.

EMPOUT. It was hypothesized that those with empty movements would be more apt to look favorably on electronic marketing as a possible means of lowering the empty miles out of Florida.

EMPIN. The rationale for the hypothesized positive impact of this variable is the same as for EMPOUT.

INTEARN. If the owner-operator felt that the intermediary was making an unfair return, he or she would be more likely to look favorably on alternative methods of load arranging, such as electronic marketing.

INTLIE. The higher the percentage of intermediaries that owner-operators felt were lying to them, the more apt owner-operators were to look favorably on electronic marketing.

INTFAIL. The higher the percentage of intermediaries that owner-operators felt failed to pay them agreed upon rates, the more likely owner-operators were to have a more positive attitude toward electronic marketing.

COMPUSE. The costs of learning to use information technology would tend to be less for those who have had previous experience with computers than for those who have had no such experience.

Results--Differences in Receptivity to Electronic Marketing Among Owner-Operators

The chi-square values for the individual parameter estimates show that none of the tested variables were statistically significant at conventional levels except ICC, significant at the .02 level of probability, and INTFAIL, significant at the .01 level of probability (Table 3). The negative sign of ICC is not surprising, but the negative sign of INTFAIL is most peculiar. It implies that the higher the percentage of intermediaries the owner-operator distrusts, the less likely he or she is to favor electronic marketing.

Although the model correctly categorized two thirds of the sample, the model was fairly weak. The model chi-square of 17.22 had an attained significance level of only .0696. The failure of the logit model to identify many characteristics associated with predispositions towards electronic marketing does not mean that no valuable information was obtained. As mentioned previously, nearly 75 percent of the interviewed owner-operators expressed interest in electronic marketing systems. The weakness of the logit model indicates that the support for electronic marketing systems is spread fairly evenly across all owner-operators. These results suggest that developers of electronic load arranging systems will have an easier time marketing those systems than if they had to target sub-groups among owner-operators.

TABULAR DATA OMITTED

SUMMARY AND CONCLUSIONS

In the mid-1970s, Taff and Gaibler described the pivotal role of brokers in arranging transport for produce. The current study reveals that that role has been maintained, if not enhanced. The transport is brokered for over 60 percent of the produce shipped by truck from the nation's number two produce state, Florida. It is estimated that annually in the United States, $137 million is spent for truck brokering of produce. The importance of brokering in this never-regulated sector bodes well for the future of brokering in transportation markets in which regulations have been relaxed or eliminated.
Table 3. Owner-Operator Acceptance Model Results
Variable Beta Standard Error Chi-Square P
INTERCEPT 3.06186 1.8387 2.77 0.0959
YEARS -0.01694 0.0209 0.65 0.4186
FUTURE 0.34346 0.4476 0.59 0.4429
ICC -1.22936 0.5239 5.51 0.0190
EDUC -0.04054 0.1375 0.09 0.7682
EMPOUT -0.00217 0.0101 0.05 0.8309
EMPIN 0.00729 0.0137 0.28 0.5962
INTEARN -0.40079 0.5445 0.54 0.4617
INTLIE 0.00481 0.0077 0.39 0.5341
INTFAIL -0.01751 0.0071 6.11 0.0134
COMPUSE 0.63999 0.5861 1.19 0.2749
Model chi-square: 17.22 with 10 D.F., P = 0.0696
Percent of sample correctly predicted by model: 68 percent


Some of the study's findings were disturbing. First, commission rates for brokering rose during the 1980s by approximately 1 percent of the freight rate. Considering the advances over this period in information processing and communications technologies, the rise in brokerage fees as a percent of total transport costs is surprising. Second, brokers commonly give carriers cash advances in return for higher commission rates. While this short-term loan service is vital to many carriers, the effective interest rates are exorbitant. Finally, there is widespread dissatisfaction and distrust among owner-operators with brokers. Both the absolute size of the national brokerage bill and the aforementioned disturbing factors suggest the potential for alternative load arrangement methods, such as electronic marketing.

There are high levels of receptivity to the idea of electronic marketing of transportation for produce. Interest is strongest among owner-operators and weakest among shippers. Multivariate analysis failed to detect strong relationships between owner-operator characteristics and interest in electronic marketing. This suggests that electronic marketing is attractive to a wide range of owner-operators, rather than to specific subsegments.

Electronic marketing of transportation services for produce is technically feasible and, as is suggested by this study, would be welcomed by many in the industry. Indeed, the sentiment expressed by the large majority of brokers and shippers is that electronic marketing is inevitable. This technology has the potential for greatly lowering transactions costs, thereby reducing the overall costs of marketing produce and, in addition, improving the efficiency of the transportation industry. It also has the potential of altering the structure of the produce transportation sector. The electronic marketing system(s) which emerges may promote direct negotiations between shipper/receivers and carriers or, alternatively, may enhance the importance of those able to specialize in information management, i.e., brokers and larger carriers and shipper/receivers.

ENDNOTES

1 C. Taff, "A Study of Truck Brokers of Agricultural Commodities Exempt from Economic Regulation," Transportation Journal, 19, 3 (1979): 4-15.

2 T. Brown, The Role of Intermediaries in Unregulated Markets: Transportation Brokers, report prepared for the Small Business Administration, 1989.

3 United Fresh Fruit and Vegetable Association, The Blue Book, 1990, United Fresh Fruit and Vegetable Association, 1990.

4 F. Gaibler, Truck Brokers: An Integral Part of Exempt Agricultural Commodity Movements, U.S. Department of Agriculture, Economic Research Service, NEAD Working Paper, 1976.

5 Gaibler's estimation procedure had two principle weaknesses: First, the assumption was made that the average volume of all brokers was equivalent to that for a sampling of brokers (246 and 63 brokers, respectively, in 1959 and 1974). It seems likely that the brokers successfully located tended to be the larger ones, with established offices. The second and more serious potential flaw relates to the estimate of the total number of brokers. This number "was identified from several trade organization lists" (Gaibler, p. 3). There is no way of ascertaining what proportion of brokers belong to any such organization. The difficulty of determining the total numbers of brokers was graphically described by Taff: "One state, for example, that maintains a list of licensed brokers domiciled in its state lists 55 brokers. Yet, 40 were counted by the author in one 25-mile area and another 25 in two truck stops..." (Taff, p. 6).

6 This assertion is based primarily on conversations with respondents. Further evidence of this are the disproportionate numbers of drivers for for-hire fleets and private carriers who so answered compared to owner-operators (15, 11, and 8 percent, respectively). It seems reasonable that company rivers would be less likely to know how loads are arranged than would owner-operators. At least a portion of the owner-operator "other" arrangement situations are leasing.

7 R. Beilock, F. Stegelin, and J. Freeman, "The Agricultural Truck Brokers' Perspective of Florida's Motor Carrier Deregulation," Journal of Food Distribution Research, 13,2 (1982): 10-14.

8 The gross revenue estimates are probably somewhat conservative. With a 10 percent commission (about the average received), an average freight rate of about $2,000 per load would be required to realize $500,000. The average freight rates for produce for each of the growing regions examined in this study have, in recent years, averaged at or above $2,000.

9 Brokerage seems to be a good example of what Satterthwaite calls a "reputation good" (Satterthwaite, M. A. "Consumer Information, Equilibrium Industry Price, and the Number of Sellers," Bell Journal of Economics, 10 (1979):483-502). These are goods or services for which consumers must rely primarily upon recommendations of others when searching for new vendors. Satterthwaite demonstrates that the search process becomes less efficient as the number of sellers increases and, as a result, the equilibrium market price can actually rise. This suggests an interesting area for future research.

10 The results of the Produce Truck Survey indicate that 79 percent of the loads which |outbound~ produce haulers bring to Florida are regulated commodities. Without an ICC brokers license, a broker would be legally barred from arranging carriage for these commodities. The mix of regulated and exempt goods moving inbound to the other growing regions is not known. However, it seems likely that, in all cases, the preponderance are regulated.

11 The authors have additional, albeit anecdotal, evidence of that commission rates did rise. Throughout the 1980s, the authors had numerous contacts with brokers in Florida. In the early 1980s, most brokers indicated that their commissions were 8 percent without an advance and 10 percent with an advance. In the middle of the 1980s, most of these brokers began charging 10 percent without an advance and 12 percent with an advance.

12 U.S. Department of Agriculture, Fresh Fruit and Vegetable Shipments by Commodities, States, and Months: 1990, USDA, Agricultural Marketing Services, 1991.

13 It appears that most shipper/receivers are not cognizant of the high effective interest rates carriers pay for cash advances. As it seems likely that they (the shipper/receivers) ultimately bear all or part of those charges, they have an interest in ameliorating the situation.

14 Brokers normally act as the financial middlemen between shipper/receivers and carriers. There is the potential for brokers to claim to carriers that lower freight rates were paid than in actuality. For example, a shipper/receiver might pay $1,800 for a shipment, but the broker tells the carrier that the freight rate was only $1,500. The broker would then keep the $300 difference ($1,800-$1,500) as well as the commission on the $1,500. This practice is known as "skimming."

15 H. Theil, The System-Wide Approach to Microeconomics, University of Chicago Press, 1980.

16 E. Turban and J. Meredith, Fundamentals of Management Science, Plano, TX: Business Publications, 1985.

17 S. Turner, J. Epperson, and S. Fletcher, The Potential Acceptance of Electronic Marketing by Agricultural Producers in Southwest Georgia, University of Georgia Agricultural Experiment Station, Research Bulletin 316, September 1984.

18 D. Tilley and M. Dickey, "Factors Influencing the Adoption of Electronic Marketing for Grain Producers," North Central Journal of Agricultural Economics, 9, 1 (1987):29-36.

19 Same reference as note 17.

20 D. Tilley, M. Dickey, and J. Russell, Corn Producers' and Elevator Managers' Attitudes Toward a Grain Electronic Market (GEM), Oklahoma State University, Agricultural Experiment Station, Research Bulletin 316, September 1984.
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Author:Beilock, Richard; Shell, Timothy
Publication:Transportation Journal
Date:Jun 22, 1992
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