Relationships of work improvement program experience and logistics quality management factors.

The importance of quality or continuous improvement programs in logistics has been asserted by a number of scholars.(1) Furthermore, Total Quality Management (TQM), process reengineering, and other topics related to formalized continuous improvement programs have been prevalent in the logistics

A recent article reported that existing empirical studies have not shown that TQM firms consistently outperform non-TQM firms, though they conclude that TQM does produce value. The author further noted that most of these studies were conducted by individuals or organizations with a vested interest in their outcomes, and most did not meet generally accepted standards of methodological rigor.(2) The author developed a method to compare the performance of TQM and non-TQM firms using years of experience as the basis for distinguishing these two groups. He found no significant difference in the average total firm performance (a scale comprising several performance factors) of the two groups.(3)

A 1991 study of logistics quality programs explored a number of issues including the reasons for the quality program initiative, how logistics fit with the firm's overall quality program, the organization structure to manage the quality improvement process, what aspects of logistics performance are measured and how, barriers to successful quality programs, and general satisfaction with the programs. The results indicated that there was not a high level of satisfaction with the programs' results at that time. The authors attributed this largely to the "immaturity" or newness of the logistics quality programs, noting that the quality process is an extended one.(4)

The primary purpose of this article is to investigate the effect of logistics quality programs on logistics performance and on the logistics workplace. Because the results of quality programs are not realized immediately, the sample firms are categorized into three groups based on experience with a formal logistics quality program: firms without such a program, firms with short-term experience, and firms with long-term experience. The three groups are compared on a number of key logistics performance, work environment, and quality management factors.

The article is organized in the following manner: First, a brief background of continuous improvement programs is provided to identify key quality management concepts and factors. Second, research propositions and the research design are delineated. Third, the results are presented. Lastly, conclusions and implications are discussed.

A BRIEF BACKGROUND OF CONTINUOUS IMPROVEMENT

Work improvement efforts reach back to Taylor's view of scientific management. However, recent emphasis has been on process improvement, rather than functional effectiveness, through total quality management (TQM) and re-engineering programs. TQM's origins can be traced to a committee of the Union of Japanese Scientists and Engineers, formed in 1949, to improve productivity and enhance the postwar quality of life in Japan.(5) The committee developed a statistical quality control course, and worked to disseminate the evolving Deming philosophy among Japanese manufacturers.(6) Deming broadened and amplified his early emphasis on statistical methods, to fourteen points necessary to achieve superior quality management. The Deming framework focused on organizational processes rather than individuals or functions, and stressed the importance of leadership, teamwork, and the need to reduce process variation.(7)

Alternatives to the Deming framework have been specified by Juran, Crosby, and the Baldrige Award criteria.(8) Juran presented a trilogy of activities - quality planning, control, and improvement - that concentrate on a customer focus orientation, performance evaluation, and coordinated skilled efforts. Crosby's fourteen quality steps focused on zero-defects through management commitment, teamwork, training, and adequate measurement. The Malcolm Baldrige Quality Award, instituted in 1987 by the U.S. Department of Commerce, places greatest importance on customer focus, operational results, employee involvement, training, and fulfillment, and process management.

In 1990, Hammer articulated six principles of re-engineering which emphasized business processes, information technology, and employee empowerment.(9) The stated objectives of re-engineering are to achieve improvements in critical measures of performance, such as cost, quality, service, and speed by fundamental rethinking and radical redesign of business processes.(10) The fundamental rethinking part of re-engineering has contributed to the extensive downsizing and outsourcing activities of the 1990s. Although re-engineering was originally positioned as relatively unique and separate from TQM, it may be more appropriately considered a continuation of the TQM efforts. Thus, TQM and re-engineering are viewed in this article as the two major programs that underlie continuous work improvement.

The above review of the TQM and re-engineering movements suggests that organizational work should be defined and supported by senior leadership and accomplished from cooperative processes to yield desirable employee and customer outcomes. Although Deming, Juran, Crosby, Baldrige, and Hammer differ in semantics and somewhat on the relative importance of quality elements, there is a great deal of invariance in the prescriptions. Each work improvement advocate, either explicitly or implicitly, is concerned with the quality management concepts of Leadership, Cooperation, Learning, Process Management, Employee Outcomes, and Organizational Performances. Each concept may be defined by primary contributing factors, and each factor may be characterized by specific indicators that are measurable activities or results.

Quality Management Concepts and Factors

The defining factors of the quality management concepts are summarized in Table 1, and discussed in this section. The Leadership concept is viewed as being defined by two quality management factors, vision and commitment. The central role of senior management in defining, communicating, and motivating quality management efforts has been widely recognized. It is the responsibility of senior management to articulate a realistic, credible, attractive future, currently labeled as a vision. The influence of an organizational vision on logistics processes and outcomes is indicated, in part, by the extent to which logistics is viewed as a key capability and as contributor to growth and returns expectations. The commitment of senior management to the philosophy of work improvement is viewed as necessary to achieve progress in cost and delivery performances.(11) This commitment is a form of transformational leadership, requiring communication and reinforcement efforts to implement the work improvement philosophy.

Cooperation, the degree of internal and external collaboration, is a dominant concept in the quality literature. Internal cooperation is defined in terms of two related factors, teamwork and employee involvement. The teamwork factor measures the usage of work teams empowered to improve work performances. Indicators of the employee involvement factor include the degree of participation in planning and decision making. Supplier management is the factor used to define external collaboration. The contributing role of the supplier in quality management initiatives has also been widely recognized in the purchasing literature.(12) Juran has recommended that long-term partnerships with suppliers be established.(13)

Organizational Learning is most directly [TABULAR DATA FOR TABLE 1 OMITTED] reflected by the training efforts of the firm. Employee training in work improvement concepts and tools is necessary for understanding of quality-related issues and for stimulating a greater level of participation.(14) Efforts to improve specific work-skills, management commitment, and resource availability are indicators of the Training factor. The set of practices that combine methodological approaches with human resource management has been labeled the Process Management concept.(15) The key defining factors that provide identification, information, and analysis of processes are benchmarking, data availability, and statistics usage. Benchmarking involves identifying best practices and making comparisons with key competitors.(16) Adequate, accurate, and timely information on warehousing, inventory, and order processing are taken as indicators of the data availability factor. The usage of quantitative and graphical methods are the indicators of the statistics usage factor.

The Employee Outcomes concept is defined by three factors that measure the results of the work effort to those employed by the firm: fulfillment, stress, and economics. Fulfillment is indicated by the degree of morale and company loyalty on the part of managers and non-supervisory employees. Stress is indicated by the degree of work-induced strain, and measures of monetary rewards reflect the economics factor. In general, the three outcomes are taken as measurements of the degree to which an organization satisfies employee needs.

The factors of the Organizational Performances concept measure the end value of work improvement efforts. Such efforts should produce improvements in the key logistics performance factors of cost, speed, dependability, and customer needs. Ordering, inventory, and transit performances are indicators of the cost, speed, and dependability factors. Abilities to handle unique customer requirements and to expedite orders are indicators of meeting customer needs. Performances in relation to key competitors should also be affected by work improvement efforts. Indicators of competitive performance include cost, transaction processes, order cycle time, delivery, availability, flexibility, and assessment ability.

The degree to which the quality management factors vary over organizations should be related to the improvement program experience of a firm. Organizations require time to adapt, assimilate, and stabilize under a work improvement philosophy. Consistent performance advantages are expected to require three or more years of implementation experience.(17)

RESEARCH PROPOSITIONS

As noted earlier, the purpose of this article is to investigate differences in logistics quality management factors in relation to the level of continuous improvement program experience. Continuous improvement program experience is defined by the maximum length of time of a formal TQM program or a formal re-engineering program. The levels of program experience are none or 0 years, short-term equal to 1-4 years, and long-term equal to 5 or more years.

The following propositions are to be evaluated:

Proposition 1: Differences in the quality management factors, shown in Table 1, between firms with short-term continuous improvement program experience and firms without continuous improvement program experience will not be significant.

Proposition 2: Firms with long-term continuous improvement program experience will have higher scores on the quality management factors than firms without continuous improvement program experience.

RESEARCH DESIGN

The Sample

The directory of the American Society of Transportation and Logistics was used to generate the sample for this study. As the focus of the research is on changes in logistics outcomes performance and the logistics work environment, only members from shipper firms were selected (i.e., carrier, consultant, and educator members were not included). The logistics personnel selected for the sample had job titles reflecting middle and senior management level responsibilities. All potential respondents were employees in separate firms. The questionnaire was a mailed computer disk, which provided computer-assisted interviewing, and eliminated potential questionnaire to data coding errors.

A total of 340 were mailed, 99 were returned, and 87 were usable for a 26 percent effective response rate.(18) The most frequent indicated job titles were Traffic Managers (29 percent), Director of Transportation (13 percent), and Vice-President (12 percent). In terms of continuous improvement programs, 50 firms had a TQM program and 39 firms had a re-engineering program. Twenty-seven firms had neither a TQM nor a re-engineering program, and 29 firms had both a TQM program and a re-engineering program.

The organizational environment of the sample is described, in part, by experience with formal work improvement programs, work improvement practices, the amount of out-sourcing, and the degree of downsizing. Thirty-eight firms had short-term experience of 1 to 4 years with a TQM program, a re-engineering program, or both. Twenty-two firms had long-term experience of 5 or more years with a TQM program, a re-engineering program, or both. Of the firms with short-term program experience, 14 had only a TQM program, 10 had only a re-engineering program, and 14 had both TQM and re-engineering programs. In the long-term program experience category, 7 firms had only a TQM program and 15 firms had both TQM and re-engineering programs. Of course, by definition, no formal TQM or re-engineering programs exist for the 27 firms with 0 years of experience. Figure 1 shows the adoption percentages of selected practices that may be associated with work improvement. The firms with long-term continuous improvement program experience had the largest adoption percentage for each practice. The relative changes in outsourcing of activities are measured by index numbers, with 100 equal to no change in the past five years, and displayed in Figure 2. Very little change was reported for the outsourcing of order processing and inventory management in any of the program experience groups. Outsourcing of warehousing and transportation increased from 5 to 15 percent across the groups. The downsizing indices, displayed in Figure 3, show a distinct trend of the number of non-supervisory and managers decreasing with increasing continuous improvement program experience.

Measures and Analysis

Multiple indicators provided measurements for each of the quality management factors shown in Table 1. Each indicator was measured on a 5-point scale. Indicators for the factors of Leadership, Cooperation, Learning, and Process Management had a response set of 1=very low, 2=low, 3=medium, 4=high, 5=very high.(19) The competitive performance factor (in Organizational Performances concept) also had the very low to very high response set. The indicators for the factors of Employee Outcomes and Organizational Performances had the response set of 1=greatly decreased, 2=decreased, 3=no change, 4=increased, 5 =greatly increased. Composite variables were used to provide measurements on the quality management factors by summing the appropriate indicators. All scores on indicator and composite variables were scaled to index numbers so that 100 represents a medium response or a no change response. For example, a composite variable score of 3.3 would have an index number of 110 (i.e., 3.3/3 x 100). The use of index numbers allows improved graphical presentations, and roughly allows a percent change interpretation when dealing with averages (e.g., the performance factor cost in the no experience group has an index number of 111, or an 11 percent perceived improvement in the past 5 years).

The propositions were evaluated by testing the significance of average score differences on the quality management factors using multivariate and univariate analysis of variance, with the appropriate pairwise contrasts. Tests of differences were also conducted for each of the indicators of the quality management factors.

RESULTS

Research Propositions

Summaries of the statistical tests of mean differences for the quality management factors over the experience groups are given in Table 2. [TABULAR DATA FOR TABLE 2 OMITTED] The columns of Table 2 are organized to present pairwise comparisons of the program experience groups. Each pairwise comparison provides a p-value, the univariate probability that an observed mean difference in a quality management factor between specified experience groups is due to random variation. The strength of the relationship between a quality management factor score and experience group membership is measured by the polyserial correlation coefficient, labeled "corr." The multivariate p-values for the overall test of mean differences are presented at the bottom of Table 2. Also, the percent of the firms that can be correctly classified as to experience group membership by a discriminant analysis of the quality management factors is shown for each pairwise comparison at the bottom of Table 2.

The first research proposition stated that differences in the quality management factors between firms with short-term continuous improvement program experience and firms without continuous improvement program experience would not be significant. The multivariate test of no differences in the 17 composite variables representing the quality management factors yielded a p-value of .081. Thus, the hypothesis of no mean differences would be retained at the traditional .05 level of significance. However, classification of firms based on factor scores is 80 percent accurate. The univariate p-values and correlations, summarized in Table 2, provide evidence that the [TABULAR DATA FOR TABLE 3 OMITTED] benchmarking (p=.017), teamwork (p=.024), and dependability (p=.055) factors have average scores that are greater in the short-term experience group.

The second proposition, that firms with long-term continuous improvement program experience will have higher scores on the quality management factors than firms without continuous improvement program experience, is supported by the rejection of the hypothesis of no overall mean differences, based on a p-value of .018. Approximately 86 percent of the firms can be correctly classified into their experience group category based on the factor scores. However, the univariate tests show vision, data availability, each of the Employee Outcomes factors, and each of the factors defining Organizational Performances to be non-significant between the long-term and no experience groups.

Comparisons of the long-term experience firms with the short-term firms are also provided in Table 2. Significant differences between the two groups should exist based on research propositions. The overall multivariate test indicated significant differences exist between the two experience groups, and approximately 87 percent of the firms can be classified correctly from the factor scores. But, the univariate tests show only supplier cooperation and cost performance to be significantly greater in the long-term experience group.

Only partial support was found for the research propositions based on the statistical analysis. Descriptive analyses of composite factor scores and indicator measurements for [TABULAR DATA FOR TABLE 4 OMITTED] each quality management concept are summarized below.

Leadership Factors

Figure 4 displays the average indices for the vision and commitment factors by experience group. The magnitude of the vision factor is in the moderate range across the experience groups, with no significant differences. The average commitment factor score did increase with experience. Each of the commitment indicators, senior management commitment, department head acceptance, and goal specificity, exhibited an increasing trend with program experience, as summarized in Table 3.

Cooperation Factors

The scores on the involvement, teamwork, and supplier factors each increased with continuous improvement program experience, as shown by Figure 5. The involvement factor indicators averages and experience group comparisons are displayed in Table 4.

The performance feedback, involvement in planning, and decision-making autonomy involvement indicators showed monotonic increases with program experience. The no [TABULAR DATA FOR TABLE 5 OMITTED] experience and short-term experience groups can be characterized as having moderate employee involvement in planning, decision-making participation, and decision-making autonomy, as reflected by the indices ranging from 90 to 107. The long-term experience group yielded indices of 100, 115, and 112 on the planning, participation, and autonomy indicators. However, only the performance feedback index for the long-term experience group, 124, reflected a high level of employee involvement.

The only statistically significant differences between the none and the long-term experience groups were for the employee performance feedback and decision-making autonomy indicators. No significant differences were found between the none and the short-term experience groups. The teamwork factor strongly differentiated the experience groups. The composite indices were 76, 98, and 114, across the [TABULAR DATA FOR TABLE 6 OMITTED] groups, and the indicators of Table 4 showed similar trends. The no experience group was weak in team building, and the short-term experience group indicated only moderate usage. Even the long-term experience group showed only somewhat higher than moderate usage.

Supplier management provided strong separation of the long-term experience group from the other groups. The supplier factor score index and the indicator indices were high ratings, ranging between 133 and 135. Other experience groups would be classified as having moderate ratings, ranging from 99 to 117.

Learning

The average training factor scores, shown in Figure 6, increased with program experience, as did each of the averages for the four indicators of training support, given in Table 5. Also shown in Table 5 are the usage percentages of executive education programs, computer training, customer relations training, time management training, and personal health management training. Each usage percentage also increased as program experience increased, though the differences between the no experience and long-term experience groups were not statistically significant for executive education and computer training.

Process Management

The average scores on the benchmarking and statistics usage factors increased with program experience, as displayed by Figure 6. Table 6 shows that the average scores of the indicators [TABULAR DATA FOR TABLE 7 OMITTED] of benchmarking and statistics usage also increased with program experience. The highest average score for the data availability factor was in the short-term experience group. The no experience and long-term experience groups had similar index averages, ranging from 85 to 99.

Employee Outcomes

Figure 7 shows the average scores on the employee fulfillment factor decreased with program experience. Table 7 provides the indices for the morale and company loyalty indicators of the employee fulfillment factor. Averages decreased for both managers and non-supervisory employees on the morale and loyalty indicators as experience increased, [TABULAR DATA FOR TABLE 8 OMITTED] though only the decrease for non-supervisory employees was statistically significant.

The stress factor averages increased slightly as experience increased. The high values of the stress factor indices in each experience group, 134, 136, and 140, reflect the large increase in perceived strain over the past five years. The average indices were somewhat lower for non-supervisory logistics employees than for logistics managers, as summarized in Table 7.

The economics factor, indicated by salary levels for non-supervisory employees and managers, showed no significant differences across the experience groups. The magnitude of the indices showed the change in salary level tended to match the stress level increases for non-supervisory employees in the no experience and short-term experience groups. However, for the managers, the stress increases have out-distanced [TABULAR DATA FOR TABLE 9 OMITTED] the salary level increases in all three groups.

Performance Improvement Factors

Figure 8 shows performance increases for each of the factors over the past five years. The magnitude of the improvement in the customer needs indicators, 28 to 35 percent, across all groups over the past five years is impressive. [TABULAR DATA FOR TABLE 10 OMITTED] However, very few performance improvement advantages are associated with continuous improvement program experience. The long-term experience firms showed significant cost improvements in inventory and storage and handling over the short-term program firms, and in inventory cost over the no program firms. Differences in the averages for indicators of the speed, dependability, and customer needs factors were non-significant, as summarized in Table 8.

Competitive Performance

The average indices for the competitive performance factor, given in Figure 8, show non-significant differences across the experience groups. Each of the seven indicators of competitive performance also had non-significant differences across the groups, as displayed in Table 9.

Overall Evaluations

Figure 9 and Table 10 provide summaries of global performance measures on logistics quality, customer satisfaction, and logistics contribution to financial performance. The differences across program experience groups are non-significant. However, the perceived increases in logistics quality, 31 to 35 percent, and financial contribution, 37 to 42 percent, over the experience groups in the past five years are very large improvements.

CONCLUSIONS AND IMPLICATIONS

The results of this study indicate that the no experience and long-term experience groups are significantly different from one another. In particular, firms with five or more years' experience scored much higher on the Cooperation and Learning concepts. These firms utilize cross-functional teams and empowered work teams to a much greater extent. They also provide greater specification clarity to their suppliers, conduct more thorough analyses of their suppliers, and seek longer-term relationships with them. Commitment to employee training is also greater in the long-term experience group, especially in the areas of customer relations, time management, and personal health management. Finally, the long-term group reported greater use of continuous improvement tools such as benchmarking and use of statistical methods.

Given the forementioned differences, it is surprising that the long-term experience group exhibited no significant relative gains with respect to organizational logistics performance. One explanation might be that the areas of no revealed differences have more effect on performance. Most notably, the long-term quality firms fare worse on most of the data availability factors. This is very surprising since formal quality programs are generally perceived to be more measurement-oriented. It also seems inconsistent with the greater use of statistical methods reported by the long-term group. Interestingly, the 1991 study on logistics quality programs also cited lack of information systems to support performance measurement as a key stumbling block to quality efforts.(20) These results suggest that firms need to improve their logistics information and data systems.

Quality initiatives with employees (e.g., empowerment, involvement) are generally expected to result in more satisfied workers. The results show, however, that employee fulfillment and stress have generally worsened over the last five years for the long-term experience firms while the no experience group reported slight improvement in fulfillment and less increase in stress. In particular, the company loyalty of non-supervisory logistics employees is significantly different between the two groups, having declined considerably for the long-term group.

A possible explanation for the deterioration of employee morale and increased stress at the long-term quality firms is the extensive down-sizing they have experienced. Perceptions of lessened job security and greater work loads may have more than offset the positive aspects of employee empowerment and involvement. The attitudes of managers and non-supervisory employees may be further exacerbated if they perceive that the improvement in performance has not been great (relative to the negative effects of downsizing). Regardless of the cause, these firms need to address the apparent dissatisfaction of their employees as employee "buy in" to the quality program is important to its success.

Finally, the results on training programs merit comment. Though the long-term quality firms provide more training than the no experience firms, their ratings on four of the five areas of education and training are well below the midpoint of the scale (i.e., lower than a medium rating). Computer training has the highest rating and it is at the midpoint. Given quality programs' emphasis on the customer and given the extensive downsizing that has occurred, it is surprising that the long-term experience group has such low scores on customer relations training and time management training. This appears to be an area of opportunity for the quality firms.

It is important to note that each of the three comparison groups has made substantial improvements in logistics performance over the last five years, both in an absolute sense and relative to competitors. Though the firms that have formal quality programs have not made greater improvements than those that do not, they have certain factors in place (i.e., employee involvement, teamwork, supplier management, benchmarking, and use of statistics) that may allow them to achieve differentially greater performance improvements in the future. To capitalize on these strengths, the results of this study suggest that key areas needing improvement are information and data availability, employee outcomes, and employee training.

ENDNOTES

1 For example, see: C. John Langley, Jr. and Mary C. Holcomb, "Creating Logistics Customer Value," Journal of Business Logistics, Volume 13, Number 2, 1992, pp. 1-24; John T. Mentzer, "Managing Channel Relations in the 21st Century," Journal of Business Logistics, Vol. 14, No. 1, 1993, pp. 27-41; Mary C. Holcomb, "Customer Service Measurement: A Methodology for Increasing Customer Value Through Utilization of the Taguchi Strategy," Journal of Business Logistics, Volume 15, Number 1, 1994, pp. 29-52.

2 Thomas C. Powell, "Total Quality Management as Competitive Advantage: A Review and Empirical Study," Strategic Management Journal, Volume 16, 1995, pp. 16-20.

3 Ibid., pp. 26-27.

4 William F. Read and Mark S. Miller, "The State of Quality in Logistics," International Journal of Physical Distribution and Logistics Management, Vol. 21, No. 6, 1991, pp. 32-47.

5 Powell, pp. 15-37.

6 M. Walton, The Deming Management Method (New York: Pedigree, 1986).

7 W. E. Deming, Quality, Productivity, and Competitive Position. (Cambridge: MIT, Center for Advanced Engineering Study, 1982); W. E. Deming, Out of the Crisis. (Cambridge: MIT, Center for Advanced Engineering Study, 1986).

8 J. M. Juran, Juran on Quality by Design. (New York: The Free Press, 1992); P. B. Crosby, Quality is Free. (New York: Mentor Publishing, 1979); S. George, The Baldrige Quality System (New York: Wiley, 1992).

9 M. Hammer, "Re-engineering Work-Don't Automate Obliterate," Harvard Business Review, July-August, 1990.

10 M. Hammer and J. Champy, Re-engineering the Corporation - A Manifesto for Business Revolution (New York: Harper Business, 1993).

11 D.A. Garvin, "Japanese Quality Management," Columbia Journal of Worm Business, Vol. 19, Number 3, 1984, pp. 3-12.

12 R.G. Newman, "Insuring Quality: Purchasing Role," Journal of Purchasing and Materials Management, Vol. 24, Number 3, 1988, pp. 14-21; L.C. Giunipero and D.J. Brewer, "Performance Based Evaluation Systems under Total Quality Management," International Journal of Purchasing and Materials Management, Vol. 29, Number 1, 1993, pp. 35-41.

13 J.M. Juran, "Product Quality: A Prescription for the West (Part I)," Management Review, Vol. 70, Number 6, 1981, pp. 8-14; J.M. Juran, "Product Quality: A Prescription for the West (Part II)," Management Review, Vol. 70, Number 7, 1981, pp. 57-61.

14 P.A. Galagan, "How to get your TQM Training on Track," Nation's Business, 1992, pp. 24-28; D.A. Garvin, "Building a Learning Organization," Harvard Business Review, 1993, pp. 78-91.

15 J.C. Anderson, M. Rungtusanatham, and R.G. Schroeder, "A Theory of Quality Management Underlying the Deming Management Method," Academy of Management Review, Vol. 19, No. 3, 1994, pp. 472-509.

16 R.C. Kamp, Benchmarking (Milwaukee, WI: ASQC Quality Press, 1989).

17 W. Schmidt and J. Finnigan, The Race without a Finish Line: America's Quest for Total Quality (San Francisco: Jossey-Bass, 1992).

18 Twelve diskettes were returned with no data. The program does not retain answers if the respondent quits the program before completing the questionnaire. Thus, the 87 usable responses have no missing data.

19 J.V. Saraph, P.G. Benson, and R. G. Schroeder, "An Instrument for Measuring the Critical Factors of Quality Management," Decision Sciences, Vol. 20, 1989, pp. 810-829.

20 Read and Miller, p. 36.

21 Ibid., p. 46.

Mr. Anderson is American United Life professor of business administration, Indiana University, Indianapolis, Indiana 46202-5151; Mr. Crum, EM-AST&L, is associate professor of transportation and logistics, Iowa State University, Ames, Iowa 50011-2063; and Mr. Jerman, EM-AST&L, is professor of business administration, Indiana University, Indianapolis, Indiana.