A summary and analysis of multi-item scales used in logistics research
Publication: Journal of Business Logistics
Date: Tuesday, January 1 2002
Early in the development of modem logistics thought, researchers and practitioners applied econometrics, simulation modeling, and management science analytical techniques to evaluate cost tradeoffs between manufacturing, storing, and transporting raw materials, component parts, and finished goods
Research focusing on attitudinal and behavioral concepts differs notably from traditional approaches applied to studying inventory levels, facility locations, and business logistics network designs in that they are not directly measurable. As a result, researchers increasingly have been forced to examine these "latent" concepts using tools and techniques developed in the social sciences. Such measures require rigorous development and testing to ensure that they accurately and completely capture the meaning of the concepts that lead to sound, generalizable implications (Mentzer and Flint 1997; Mentzer and Kahn 1995).
The evolution of more established business fields suggests that logistics researchers should continuously work to develop, test, and strengthen a complete set of measures for latent logistics concepts. Researchers in more mature business disciplines including marketing and management have established and documented a set of field-tested, and statistically robust measures, to support conceptual principles and to guide future research (e.g., Bruner, James, and Hensel 2000). Significantly, no consolidated documentation of reliable and valid measures of latent concepts in logistics exists. Such documentation will help to ensure the continuance of a strong theoretical foundation and the advancement of logistics as a discipline.
Richard Powers, in his keynote address to the Council of Logistics Management 2000 Transportation and Logistics Educators Conference in New Orleans, stated that academic research should aim to separate truth from hype (Powers). Measurement precision is required to pursue this ambitious goal. The primary objectives of this research, therefore, are as follows:
1. To provide a directory that offers broad categorization of all multi-item scales published in leading logistics journals.
2. To document the scale-development techniques used to assess the reliability and validity of the measures.
3. To identify logistics concepts in need of further scale development and testing.
4. To offer preliminary measurement property comparisons between leading journals in logistics and marketing disciplines.
Prior to presenting the measurement scales, the search procedures used to identify the relevant articles, the criteria used for categorizing scales, and the format and presentation of the scales are presented. Next, general research findings review scale characteristics, trends, and methodologies used to establish sound psychometric properties. Implications are drawn from the analyses to identify areas of significant development and to provide researchers and practitioners with a useable guide from which to access more meaningful measures for advancing logistics concepts, theory, and practice.
SEARCH PROCEDURES AND SELECTION CRITERIA
The research focuses on survey research employing multi-item measures published in the International Journal of Logistics Management (IJLM), International Journal of Physical Distribution and Logistics Management (IJPDLM), Journal of Business Logistics (JBL), and Transportation Journal (TJ). These journals have been consistently recognized as leading logistics journals with respect to logistics theory development, influence on the discipline, breadth of articles, and pragmatic orientation (Emmelhainz and Stock 1989; Fawcett, Vellenga, and Truitt 1995).
Multi-item scales are especially useful for researching latent concepts in logistics and must withstand rigorous statistical evaluation to assure meaningful measurement characteristics. Churchill (1979, p. 66) explains the deficiencies of utilizing single-item measures. He and other researchers advocate the application of summated items to achieve more valid and reliable responses that better represent the entire domain of a latent construct (Churchill 1979, pp. 66, 67; Dunn, Seaker, and Waller 1994, p. 157; Mentzer and Flint 1997, p. 208). It is not enough, however, simply to create several items to measure a concept. Researchers must employ the appropriate process of scale development and validation to ensure the highest quality measures lead to the most reliable conclusions (Dunn, Seaker, and Waller 1994).
The intent of this paper is to include all scales that have been subjected to at least minimal development procedures to assess the reliability and validity of the measures as part of the research process. It is important to note that the authors did their best to carefully review the journals to methodically identify and include all articles employing the selection criteria. While a first and second review of each volume was undertaken, instances may exist where articles or scales that meet the inclusion criteria have been overlooked. Moreover, every effort was taken to identify relevant scale-development procedures employed in each article. The complexity involved with such an undertaking imposes the possibility of error. If such an error exists it is not intentional, and the authors apologize for any occurrence. Specific criteria for inclusion are as follows:
1. The scale was developed or used in research published in IJLM, IJPDLM, JBL, or TJ.
2. The scale is comprised of two or more items.
3. Researchers reported at least some minimal procedures for assessing the usefulness of the scale (e.g., reliability and/or validity steps were taken and/or identified).
The selection and data collection process involved a complete review of the articles in every issue of ULM, IJPDLM, JBL, and TJ to identify those meeting the inclusion criteria. IJLM began in 1990, IJPDLM (previously, International Journal of Physical Distribution and Materials Management-IJPDMM, and International Journal of Physical Distribution-UPD) in 1970, JBL in 1979, and TJ in 1961. At least two authors independently reviewed each issue to identify the articles. The authors compared their selections to confirm that all articles were correctly identified. Multi-item scales and development/validation procedures reported were extracted from the articles identified. While each article did not contain the same data regarding measurement evaluation, the available key information was obtained and documented. The intent was to offer the most complete, yet concise, information pertaining to each scale and the minimal criteria set forth.
Multi-item scales were categorized according to major logistics conceptual dimensions. Categories were created based upon the results of a Delphi process conducted among the authors. The purpose of the Delphi technique is to elicit rational judgments from participants to facilitate consensus decision-making. The process is designed to take advantage of participants' creativity and facilitate collaborative group output. Individually, the authors provided input regarding how she/he would organize the multi-item scales into primary logistics categories according to research topic. Six iterative rounds of systematic modifications were necessary to achieve an aggregated group response for all categories. The final list represents a consensus regarding the predominant topical groupings. Categorizations are based on broad interpretive assignment. Intentions, therefore, are not to offer a strict categorical interpretation of each article, concept, scale, or item. Categorizations simplify the presentation and reference of the material and offer broad directives for use and future scale development.
RESULTS AND DISCUSSION
From 1961 to 2000, IJLM, IJPDLM, JBL, and TJ collectively published 476 journal issues. The issues contained approximately 2,063 articles. Of these articles, 5.6%Jo or 116 were identified as having reported the usage of 690 different multi-item scales. Consequently, the results of this research are based on a total sample of 690 multi-item scales reported in the articles.
Scale Categorization
The Appendix presents all multi-item scales meeting the inclusion criteria along with key information pertaining to the authors, source, concept, sample description and size, primary validity and reliability checks, scale anchors, and item wording. The listing is organized alphabetically by category and construct title. While in some cases the reported scales may have been adapted from work appearing in other publications, it is the purpose of this research to reference the authors who have utilized the scales for research appearing in logistics journals. On occasion, an article provided explicit reference to previous research from which a scale was derived. In such cases, the citation for the previous article also appears in the Appendix.
Scale categories identified by the authors are listed in Table 1. Seventeen broad categories were identified. The top four categories based on the number of scales per category and representing 389 scales or 56.4% of the total number of scales are Channel Relationships, Performance Evaluation, Internal Relationships/Personnel, and Information Sharing Processes and Technology.
The most highly represented category, Channel Relationships, contains 120 scales and represents 17.4% of the total logistics scales. Channel Relationships categorizes measures as the interactions between suppliers and customers at various supply chain levels. One example is, "My firm has supply chain agreements with suppliers and customers that operate under principles of shared rewards and risks" (Stank, Keller, and Daugherty 2001). Performance Evaluation ranked second with 117 scales and pertains to the output of logistical processes. An example item reads, "Indicate your best supplier's performance on each of the following measures of customer service:..." (Emerson and Grimm 1996). Seventy-seven scales make up the category titled Internal Relationships/Personnel. This group represents attitudes and behaviors within and across functional areas and between employee levels. Under this heading is the item, "Management makes an effort to talk with you about your career aspirations" (McElroy et al. 1995). Lastly, Information Sharing Processes and Technology comprises 75 scales and represents topics related to the use of information systems and technology to provide visibility to information across logistics entities. "We use software compatible with our customers' systems," is an example (Gardner, Cooper, and Noordewier 1994).
IMAGE TABLE 14TABLE 1
It is clear that the majority of the scales developed for use in logistics research pertain to the interactions and exchanges between people and the performance outcome of these interactions. Such research focuses on supply chain relationships used by logistics managers to break down internal functional silos and strengthen exchanges with customers and suppliers.
Each of the remaining categories represents less than 10% of the total scales used in logistics research. This by no means indicates a lack of importance of the variables or under-developed theory. It simply means that multi-item scales for these categories are fewer compared to the top four categories. The final 13 categories also can be grouped into "themes." Logistics and Transportation Operations, Service Processes, Strategy and Planning, and Flexibility, Responsiveness, and JustIn-Time seem to pertain to the competitive-advantage focus of a firm. That is, the variables are required for achieving better positioning with respect to competition. For example, an item placed under the Logistics and Transportation Operations category reads, "Loss and damage in transit is an important factor in determining my company's choice of mode" (McGinnis 1979). This category represents 64 multi-item scales. Also, the Service Processes category represents assessments of service elements used as input to help a focal firm create or change service position. An example item is, "My firm has a commitment to the organization-wide concept of customer service" (Foster, Strasser, and Thompson 1992). One example pertains to the "Flexibility" group. The item reads, "In my company/division logistics system responsiveness to changing needs is more important than cost efficiency per se" (McGinnis and Kohn 1990).
The group of categories including Organizational Characteristics, Global/International, and Environmental Characteristics seem to be change-oriented concepts relating to environments both internal and external the firm. Together they comprise 71 scales equaling 10.3% of the total measures reported. For example, "The company's decision making is highly centralized at top management levels," is an item depicting organizational characteristics (Williams 1994).
The least represented categories include 46 scales or 6.7% of the total scales published in logistics journals. They include a Miscellaneous category (12 scales that are included to offer a complete listing of the multi-item scales reported in logistics research), Customer Satisfaction and Loyalty (11 scales), Measurement (10 scales), Inventory (7 scales), Benchmarking (3 scales), and Green Logistics (3 scales). Measurement, Inventory, and Benchmarking issues are least associated with latent constructs such as attitudes and behaviors, and therefore are more commonly measured using objective assessments and ratio variables rather than psychometric, multi-item scale methodologies. Green Logistics is a relatively new area of heightened interest. In recent years, firms have more seriously considered the business implications associated with being more environmentally conscious. It is surprising that a minimal number of multi-item measures have been utilized to research Customer Satisfaction and Loyalty. Perhaps one explanation may be that traditional measures were "hard numbers." Therefore, greater levels of customer purchases may have been interpreted as indication of a satisfied customer. With the advent of relationship management concepts, today's managers must reach beyond order frequency and volume and address qualitative issues related to retaining the most valuable customers. While the number of scales representing these categories is minimal, they represent key logistics concepts that require further development and testing.
Researchers that use the appendix are encouraged to closely review and refine the scales representing each concept to enhance their usefulness to explore important logistics theories. The multiple scales used to represent similar constructs may provide an indication of the multidimensionality of a concept. In such circumstances, researchers should seek to adapt, develop, and test the scales to specifically represent the ideas they are trying to capture.
Historical Trends
The bar chart in Figure 1 illustrates the total number of articles that have employed multi-item scales in leading logistics journals. Logistics research utilizing scale development procedures first appeared in 1973 in IJPDLM (at the time called IJPDMM). However, the use of multi-item scales was marginal until the early 1990s and substantially increased throughout that decade. For example, twenty articles appeared from 1981-1990 and 89 articles appeared from 1991-2000, a difference of 69 articles. This illustrates a 345% increase in scale usage. Among the many possible variables that could have influenced this sharp increase, include:
1. Increased competition required firms to seek greater cost minimization and improved service output. Firms focused on evaluating and implementing process reengineering and fostering long-term business relationships.
2. Modern logistics thought achieved greater attention in the marketplace and demand for logistics education (hence, an increased demand for logistics faculty) and knowledge escalated.
3. Researchers' motivations to more rigorously develop and test logistics theory. A primary research objective is to offer results generalized to a target population rather than to a specific individual or company. Survey research is a traditional analytical tool for better achieving such a goal and is well accepted by peer disciplines. Researchers of logistics concepts continued to adopt the analytical techniques of their peers.
4. Improvements and availability of personal computers with the processing power to more easily conduct higher-level statistical analyses.
5. Development and affordability of statistical software for theory testing utilizing latent variables. Availability also influenced the education of researchers with respect to more rigorous methods.
The data indicate research reporting scales were non-existent in JBL for its first eight years in press. However, JBL experienced a sharp increase to represent 42% (37 articles) of the articles reporting multi-item scales during the decade of the 1990s. IJPDLM followed closely and included 27 articles or 30% of the articles reporting multi-item scales for the same period. This indicates that more research based on latent constructs or directly unobservable measures was published in these two journals.
IMAGE CHART 23FIGURE 1
The use of multi-item scales has steadily increased in research published in most of the leading logistics journals. This is an indication that the use of latent constructs for exploring new dimensions of logistics theory is gaining acceptance. Historically, transportation and logistics variables consisted of more objective economic-type measures, and operational theorists often employed simulation modeling. While these methodologies continue to be highly useful and relevant today, researchers and managers also highly value the latent conceptual aspects of logistics management. Consequently, appropriate measures are being developed for exploring the nature and impact of such business dimensions. The measures must be conceptually relevant to researchers and managers in the field and must pass well accepted rigorous statistical testing.
Figure 2 reports the number of multi-item scales developed and/or utilized for logistics research. As expected, after seeing the positive trend of articles reporting multi-item scales, the actual number of scales employed has steadily risen. The bar chart indicates that for each five-year period the growth rate in number of scales reported has reached at least 14% (for example, the increase between 35 scales in years 1976-80 and 40 scales in years 1981-85). Most periods exceeded 72% growth. In fact, the number of scales in logistics research over the last decade (1991-2000) compared with all prior periods combined has grown 266%. A closer look at the line graphs representing the number of scales reported in each journal reveals that JBL and IJPDLM are the greatest contributors to this growth. The growth indicates a substantial improvement in the level of knowledge researchers have achieved with respect to developing measures that represent latent variables in logistics.
The use of multi-item scales provides more confidence that researchers are capturing the full conceptual domain of interest. The bar chart in Figure 3 illustrates a fairly consistent range of the number of items per scale. However, the line charts indicate the average number of items per scale has declined and leveled-off to between four and five measures. While during the time period 1991 to 1995, TJ published manuscripts with a sharply higher average number of items per scale (7.46), trends between the various journals have been rather similar. Again, the "leveling-off" seems to indicate that researchers have worked to create efficient item sets that capture particular traits of well-defined concepts.
Researchers have steadily employed more rigorous statistical standards for evaluating and developing useful measures. Consequently, more items may be eliminated during the stringent scale purification process. This may also be an indication that researchers are attempting to more accurately define the domain of logistical concepts, resulting in the use of a narrower array of measures to capture particular conceptual details.
IMAGE CHART 28FIGURE 2
IMAGE CHART 32FIGURE 3
Figure 4 reveals an overall increase over time in the reported average coefficient alpha, a commonly used statistic to indicate scale reliability. During the most recent 15 years (1986-2000), the average coefficient alpha reported has steadily increased, indicating an improvement in the reliability and internal consistency of the scales. The most important finding is that for all five-year periods the average has exceeded the acceptable coefficient alpha advocated in the literature for exploratory research and, in all but one case, for confirmatory research. However, the bar chart in Figure 4 illustrates that throughout the years, the range of reported coefficients has widened. Specifically, the minimum published average coefficient alpha was .64 in the late 1970s and steadily declined to .52 and .53 by the decade of the 1990s. This is indicative of the increased number of published multi-item scales and reflects the more complex logistics relationships and issues examined in the published research. It also suggests the need to further develop particular multi-item measures to achieve robust psychometric properties for more accurate theory testing.
Figure 4 also offers a comparison of the reported average coefficient alpha appearing in individual logistics journals. The data indicate that overall reliability levels have steadily increased in all journals since their inception. The average statistic reported in each journal consistently has exceeded the baseline reliability of .60.
IMAGE CHART 37FIGURE 4
Table 2 indicates the percentage of articles containing the results of analyses commonly reported to evaluate the validity and reliability of multi-item measures. These include item pre-testing, performing factor and principal components analyses, assessing internal consistency and reliability of the scale, reporting item-to-total correlations analyses, conducting confirmatory factor analyses, and comparing average variance extracted to shared variance of construct pairs. The data reveal that 47% of the logistics articles reported that a formal pre-test was conducted, 72% reported the results from factor and principal components analyses, and 63% reported reliability statistics. Less often reported were the more rigorous tests for assessing dimensionality and trait validity. Confirmatory factor analysis was reported in 13% of the articles while only 3% of the articles reported statistical difference tests between average variance extracted and shared variance for construct pairs.
With respect to comparisons between logistics journals, the majority of publications have relied upon exploratory factor/principal components analysis and coefficient alpha to assess reliability and validity. JBL stands out as having published a greater percentage of research articles employing more rigorous psychometric property analyses. However, only 23% of the articles reported results from confirmatory factor analyses and 8% indicated rigorous tests for discriminant validity such as comparisons of variance extracted and shared variance. IJLM and IJPDLM published the next highest percentages (14% and 9%, respectively) of confirmatory factor analyses.
IMAGE TABLE 42TABLE 2
While high-level statistical analyses have been demonstrated in logistics publications, the case is made for researchers to more often subject their multi-item measures to these rigorous tests for demonstrating important psychometric properties. The next section offers preliminary comparisons of descriptive statistics between logistics and marketing publications.
Comparisons with Peer Discipline
Marketing is generally considered to be a discipline that is closely related to logistics. Therefore, it is interesting to explore comparisons between the areas with respect to multi-item scale utilization and validation during the last five years. It is not within the scope of this research to offer a complete review of the top marketing journals, however a comparison of two leading journals representing the disciplines offers some insight into the level of advancement achieved and improvement needed with respect to logistics survey research. Previous results indicate JBL as the leading logistics journal in publishing articles employing multi-item scales and in reporting the most complete validity and reliability statistics. Therefore, Table 3 contains data representing JBL and a leading marketing journal, the Journal of Marketing (JM).
The results show that fewer articles employing multi-item scales for theory development and extension have been published in JBL over the last five years compared to JM. JM has published 63 articles while JBL has published 27, a 133% difference. Moreover, JM has published research that has developed and utilized 202% more multi-item scales than JBL.
IMAGE TABLE 48TABLE 3
This discrepancy may indicate JM's focus on publishing theoretical research that often requires the measurement and testing of latent variables (e.g., attitudinal and behavioral variables relating to consumer behavior, marketing strategy, etc.). As previously discussed, logistics research is relatively a newcomer to the study of latent concepts in the field. With this in mind, it is expected that logistics research will continue its positive trend to publish research employing multi-item scale methodologies.
The results in Table 3 reveal that, on average, logistics is similar to marketing in meeting the acceptable benchmark for the average number of items per scale and for achieving statistical reliability demonstrated through the average coefficient alpha statistic. For the five-year period (1996-2000) JBL reported an average of 4.92 items per scale and JM reported an average of 4.48 items per scale. Most revealing is that the average coefficient alpha for assessing scale reliability in JBL and JM was equivalent to .81 for both journals.
Table 3 also reports the results of validity and reliability analyses reported in the journals for the years 1996 to 2000. For the most part, the articles published in JBL and JM were similar in reporting pretesting, reliability analyses, and item-to-total correlations. The data indicate differences with respect to reporting factor/principal components analyses and confirmatory factor analyses. Research articles published in JM that utilized multi-item scales most often (65% of the time) reported rigorous testing including confirmatory factor analysis. On the other hand, research published in JBL has most often (81% of the time) relied on the results of common factor or principal components analyses, and only 26% of the time indicated more stringent testing.
Lastly, tests for assessing discriminant validity by comparing average variance extracted to the shared variance between constructs has been reported in 32% of the articles employing scales in JM. Logistics researchers reported the analyses in 11% of the articles appearing in JBL. While other methods are available for analyzing discriminant validity, this method is by far the most often reported in the literature (for a thorough review of the process of scale development and validation see, for example, Dunn, Seaker, and Waller 1994; Garver and Mentzer 1999; Mentzer and Flint 1997; Mentzer and Kahn 1995). In summary, Table 3 illustrates that leading logistics research using multi-item scales has achieved a higher level of measurement strength and in many aspects is rendering results very comparable to that reported in JM.
CONCLUSION
Several conclusions may be drawn from the previous analysis and discussion. First, it is clear that researchers are increasingly relying upon survey research for exploring business logistics concepts that are not directly observable. Consequently, they are faced with developing and validating multi-item scales. Toward this end, the advancement and availability of computer technology and statistical software have provided the tools necessary for conducting high-level and more efficient research analyses.
Secondly, concepts such as logistics channel relationships, performance evaluation, internal relationships, and information sharing processes have received considerable research attention in the last ten years. On the other hand, less attention has been given to developing and utilizing summated scales for evaluating customer satisfaction and loyalty, measurement, inventory, benchmarking, and green logistics issues. Researchers are encouraged to utilize the existing scales, especially those well represented in the literature, and when necessary combine and refine the measures in an effort to achieve more accurate and valuable research conclusions. Categories with few multi-item scales offer opportunities for further research and development.
Thirdly, logistics researchers have demonstrated the skills for constructing sound variables for meaningful theory development and testing. Results for logistics research appearing in JBL are very comparable to that in JM. In an effort to continue the advancement of logistics as a peer research discipline, researchers, editors, and reviewers are encouraged to follow the example set forth by JM and consistently employ more rigorous techniques with the objective to achieve measurement strength.
Scale development and validation help to build the foundation upon which logistics theory will continue to advance. In light of the increasing importance of attitudinal and behavioral issues in logistics today, researchers and practitioners also are encouraged to embrace diverse methodologies to aid in the quest for understanding business environments and the people hired to manage complex organizations. Survey research is only as good as the measures employed. Therefore, it is imperative that such measures be theoretically and pragmatically conceptualized in an effort to produce the most useable research for academics and practitioners alike. Without fully considering both parties, researchers fail to utilize the complete potential of the methodology. More importantly, without careful conceptualization multi-item scales may actually produce faulty results and managers may act on erroneously derived implications. A first step in developing useable multi-item scales is to insure that the measures have strong face validity. Without this level of detail, statistical procedures will not "produce" better measures, but they may support poorly conceived ones. Procedures such as in-depth interviews with managers, employees, customers, and suppliers, and field observations help ensure that ideas are most useful and accurately conceptualized.
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