Latent Variable in Business Logistics Research

8/8/2019 Latent Variable in Business Logistics Research

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JOUR NAL OF BUSINESS LOG ISTICS, Vol. 15, No. 2, 1994 145

LATENT VARIABLES IN BUSINESS LOGISTICS RESEARCH:SCALE DEVELOPMENT AND VALIDATION

bySteven C. Dunn

Idaho State UniversityRohert F. Seaker

Penn State Universityand

Matthew A. WallerWestern Michigan University

Logistics is confounded with an abundance of concepts that are not easilyoperationalized for scientific analysis . Variables such as customer service,partnerships, third parties, integration, and total quality m anagem ent are u nobservab leand termed "latent." A latent variable (a.k.a., construct) "is an unobserved entitypresumed to underlie observed variables. In science our real interest is more inthe relations am ong latent variables than it is in the relations among observed variablesbecause we seek to explain phenomena and their relations."'

Due to the difficulty of developing operational measures of latent variables,business logistics research that tests hypotheses or propositions involving suchvariables has been lacking. Conversely, research methods applied in logistics modelbuilding generally tend to be more rigorous, following well developed me thodologicalstandards common to other academic fields. The use of econometrics in transportationresearch, simulation m odeling for distribution network analysis, and analytical m odelsof inventory systems are all examples of logistics research that typically do notinvolve the measurement of latent variables.^-^''*'^'^

The majority of logistics research can be categorized into three distinct areas:(1) generalized descriptions of variables (e.g., case studies); (2) interpretation of


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JOUR NALO F BUSINESS LOG ISTICS, Vol. 15, No. 2, 1994 147

It is now appropriate to analyze the contemporary research in business logistics.Using a framework for research paradigms developed by Meredith et al.,'^ Dunnet al.'^ classified logistics research articles for the years 1986-1990. Five journalswere selected by the authors as a representative sample of logistics journals:International Journal of Physical Distribution and Logistics Management, Journalof Business Logistics, Journal of Purchasing and Materials Managem ent, Logisticsand Transportation Review, an d Transportation Journal. These journals wereincluded in Allen and Vellen ga's* ' assessment of top logistics jou rna ls. An ex planationof the Meredith et al. framework is presented in Figure 1. The Meredith modelhas two continuums that enable categorization of research based upon the underlyingtenets of its methodologythe rational/existential (R/E) and the natural/artificial(N/A).

The R/E continuum is based upon whether the research is independent of man(deductive) or relative to one's individual experiences (inductive). This continuumis more philosophical than the other. Inductive research is rooted in the researcher'spersonal knowledge and experiences of the truth, while deductive research is basedupon log ic and structure. This continuum is partitioned into four categories: axiomaticor the "theorem-proof world," the most rational; logical positivist/empiricist, inwhich "the facts are assumed to be independent of the laws and theories usedto explain them;" interpretive, a more inductive approach in which researchers studypeople rather than objects, and where facts are not considered separate from thetheory or observer; and critical theory, the most subjective approach, which transcendsthe inductive/deductive argument and attempts to generalize the phenom ena withoutscientific method.'*

The N/A continuum is concerned with the source of the infonnation utilizedin the research; in other words, its subjectivity or objectivity. This continuum ispartitioned into three categories, ranging from the empiricist extreme in whichexplanation is derived from concrete, objective data, to subjectivism, in whichexplanation is derived from interpretation and artificial reconstruction of reality.The most objective, object reality, is based on direct observation of the phenomenaby the researcher. Peoples' perception of object reality, a less direct form of


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148 DUNN, SEAKER, AND WALLER

FIGURE 1FRAMEWORK FOR RESEARCH METHODS

(MEREDITH ET AL.)*

Rational

^Existential

Axiomatic

LogicalPositivist/Empiricist

Interpretive

CdticalTheory

DirectObservation ofObject Reality

Field Studies*FieldExperiments

*ActionResearch*Case Studies

Peoples 'Perception ofObject Reality

StructuredInterviewingSurveyResearch

HistoricalAnalysisDelphiIntensiveInterviewingExpert PanelsFuturesScenarios

IntrospectiveReflection

ArtificialReconstructionof Object RealityReason/Logic/TheoremsNormativeModelingDescriptiveModelingPrototypingPhysicalModelingLaboratoryExperimentsSimulationConceptualModelingHermenuetics

J. Meredith, A. Raturi, K. Gyampah, and B. Kaplan, "AlternativeResearch Paradigms in Operations," Journal of OperationsManagement 8, no. 4 (October 1989): 297-326.


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JOUR NALO F B USINESS LOG ISTICS. Vol.15, No. 2, 1994 149

When the two axes of the Meredith et al. matrix Jire applied, the majorityof logistics research tends to fall into three areas. The interpretive/perceptive (27%),artificial/axiomatic (24%), and logical-positivist/perceptive (23%) paradigms accountfor three-quarters of the total research articles.

Figure 2 presents the findings of Dunn et al. ' '

FIGURE 2LOGISTICS JOURNAL PARADIGMS

1986-1990

AxiomaticLog/Pos

InterpretiveCritical

Direct Perceptive Artifical

1%7%

23%27%

6%

24%3%9%

The perceptive/interpretive category (27%) consists of historical research,intensive interviewing, Delphi panels, and futures scenarios. The hallmark of thismethod is subjective interpretation by the researcher. Th e perceptive/logical positivistcategory (23%) consists of surveys and structured interviews. Th e artificial/axiom aticcategory (24%) involves rigo rous, logical analyses that can be followed and rep licatedby other researchers, such as analytical and mathematical models.

This paper focuses on the perceptive paradigm, specifically the surveys andstructured interviews of the logical positivist category. While the methods currentlyemployed in this area are appropriate for conceptualizing logistics theory, thereexists a great opportunity to apply additional methodologies that will contributeto the building and testing of the theory. Section two addresses this statement in


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150 DUNN, SEAKER. AND WALLER

THE SCIENTIFIC METHOD IN LOGISTICS RESEARCHTheory is a precursor to generalized knowledge. Sutherland succinctly describes

theory as "an ordered set of assertions about a generic behavior or structure assumedto hold throughout a significantly broad range of specific instances."^'^ Good theoryshould be the intention of logistics theorists.

Building logistics theory through research that relies on ambiguous or generaldescriptions of phenomena leaves logistics knowledge on treacherous footing.Furthermore, much of the research tends to present such phenomena in qualitatively-derived and prescriptive relationships. There needs to be more clarity in researchvariables and more rigor in the methodologies. When theory is presented in testableform and is eventually tested, knowledge becomes more objective, more dependable,and less value-laden.^'

To achieve these objectives, increased scientific scrutiny must be applied tologistics research, especially when latent variables are involved. This is importantsince research involving latent variables is appearing more frequently in the logisticsliterature. It is emphasized that this methodology is not intended to replacethe current approaches to logistics research, hut is intended to complementthem.

Some scientific research efforts, such as those in physics, are indebted to stronginference. Strong inference is achieved through a consistent application of scientificresearch methods, where they are used and taught on a systematic basis.^^ Suchmethods provide an accumulation of inductive inference and are effective inproducing significant resu lts. Logistics mu st begin to focus on a program of alternativehypotheses and disproofs by invoking a strong inference approach as part of itsmethodo logical regimen. The goal of logistics research, as with any research, shouldbe to ensure that theoretical systems and statements can be empirically tested. Anability to consistently explain and predict phenomena must exist.^^

An explanation of the scientific method may best be approached by firstexplaining the con cept of science. Science is a structured approach toward developingknowledge and requires observation, identification, description, experimental


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JOUR NAL OF BUSIN ESS LOG ISTICS, Vol. 15, No. 2, 1994 151

Within science, there is the assumption that there is a real, tangible worldwhere phenomena are independent of human interpretation or conjecture. It containsrules for analyzing natural phenomena. Science aims to observe and explain thesenatural phenomena . It provides a dependability to know ing. Science is not concernedwith ideas lying within the metaphysical or theological realm. If there is to beprogress in the construction of knowledge, it is real world phenomena that willprovide observable, measurable structure to ongoing research processes. Therefore,methodology used to test theories rooted in this objective world should also beas free of human persuasion as possible.

Science can provide such tests. The scientific approach has a self-correctioncharacteristic so that scientific activities and conclusions are controlled and verifiedso that the end result is dependable knowledge.

Scientific research is composed of objective observation and systematicprocedures. The purpose of the scientific method is to test theory. However, theorycan be built or adjusted based on unexpected test results. For example, a controlvariable may unexpectedly provide a strong moderating effect to an independentvariable's explanation of the dependent v ariable. At this junc ture, the original theorymay require some additional thought.

Science attempts to provide a consistent and unbiased view of observablephenom ena and how they relate to each other. It does so while maintaining objectivityin both measurement and testing. Testing procedures are systematic and controlledin that the investigation itself provides a strong assurance that the results are accurateand consistent.

Science alone is not a panacea. There are weaknesses associated with thescientific method. Howson and Urbach^^ have exposed some of the problems ofinduction as they relate to knowledge. They probed for underlying assumptionsin scientific methodologies. For example, Hume's principle of the uniformity innature emphasizes that those ascribing to scientific research assume an a priori"truth" that the future will resemble the past. It allows the scientist to assuredlypredict future occurrences based upon current experimental results.Relat ionships between const ructs bui l t on percept ions (e .g . , cus tomer


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quantified) present an array of value-based variables in a multiple of possiblecom binations. The predictive processes are less exact and, hence, more probabilistic.As Nunnally states, "The intention of science is to try to find some underlyingorder in all the particular events that occur in nature... Many generalizations inall areas of science, and particularly in the behavioral sciences, must be statedin statistical terms, with respect to the probability of an event occurring, ratherthan specified with more exactness."^^

Although they will not be investigated at this time, other epistemologicalquestions can present challenges to the foundations of scientific research. Any andall other research paradigms are also susceptible to attacks of this nature. Thoughthe underpinnings of science contain basic assumptions, it must be understood thatthe importance of science is its ability to provide the following: (1) a consistencyin method, (2) exclusion of human values and emotions, and (3) external and internalvalidity.

External validity is a direct function of the extent to which experimental resultscan be applied to other scenarios, firms, industries, etc. Internal validity indicatesthe degree of accuracy at which the test variables and the experiment measurethe conceptualized variables and their relationship(s), respectively.

Science does not rule out the importance of qualitative methods in research.Eisenhardt^^ states that qualitative methods have the ability to indicate relationshipsthat would not otherwise be observable to the quantitative researcher. The casestudy method, for example, can provide a grasp of the interactive dynamics withina single setting. Such activities shape hypotheses and help build theory. However,it may lend itself to a constant infusion of preconceived b iases held but not consc iouslyrecognized by the researcher.

Weiss supports qualitative methods, and states that "qualitative data are superiorto quantitative data in density of infomiation...not precision and reproducibility."^Flynn et al.'^' additionally point out that "without a conscious attempt at theory-building (a focus of qualitative approaches), research can degenerate into simplydata-dredging or the ad hoc collection and analysis of data for whatever reasoncan be found."^^ Consequently, divergent, creative approaches to research, as


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JOU RNA L OF BUSIN ESS LOG ISTICS, Vol. 15, No. 2, 1994 153

Research within the field of logistics may best be approached through theapplication of multiple methods, both quantitative and qualitative. Eisenhardt,^-'Jick,'''* and Campbell and Fisk^^ among others suggest that this triangulated approachto research can create better assurances that variances are trait-related and notmethod-related.

Logis t ics knowledge should be approached wi th an array of d iversemethodological tools to ensure the coexistence of both theory-testing and theory-building. Weick^^ provides a model based on Thomgate's postulate of commensuratecomplexity illustrating the strengths and weaknesses in the various approaches toresearch in the social sciences. In this conception, the power of research outcomescan be measured by its adeptness at providing the virtues of generalizability, internalvalidity, or simplicity. There appears to be a form of mutual exclusivity regardingeach.

As any single research method attempts to satisfy any two of the three virtues,the third virtue is increasingly alienated. For example, as empirical science attemptsto provide both external and internal validity in its outcomes, simplicity is sacrificed.In contrast, as case study research attempts to capture both internal validity andsimplicity as its virtues, it loses generalizability.

Figure 3 incorporates Weick's model into an encompassing research framework.By combining the large array of multiple logistics theories and multiple methodsinto a collective research effort, a more powerful means of extracting new, meaningful,and dependable knowledge is made available.Research in business logistics tends to span most of the approaches in Figure3. Although there is much survey research on business logistics topics, it frequentlylacks scientific rigor. This is especially true in terms of construct measurement.The next section of this paper provides a framework for construct measurementthat should facilitate scientific survey research in business logistics.

MEASURING LATENT VARIABLES


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FIGURE 3

METHODOLOGICAL TOOLS FORLOGISTICS RESEARCH

SNOWUDCS

Lab AnalyticModels

Accuracy

CaseMultipleCase Studies

' ScientificSurveyReseaichy


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JOUR NALO F BUSIN ESS LOG ISTICS, Vol. 15, No. 2, 1994 155

Items or questions can then be listed that would measure, indirectly, theconstructs. For example, to measure decentralization several questions might beasked. The following are examples of possible survey questions:

1. Routine logistics decisions are not formalized. (Circle one.)A. Strongly AgreeB . AgreeC. NeutralD. DisagreeE. Strongly Disagree

2. Logistics problems are resolved immediately without committees ormemoranda.A. Strongly AgreeB . AgreeC. NeutralD. DisagreeE. Strongly Disagree

These are only two of the possible items that might reflect a decentralizedlogistics function. As mentioned above, suppose the researcher developed sevensuch questions to measure, indirectly, the extent of decentralization in a logisticsfunction. Should all of these questions be included in the measurement ofdecentralization? What statistical techniques are available to decide which questionsto use in measuring this construct? Do these seven questions adequately coverthe scope of what is meant by decentralization? Do these seven questions measuredecentralization per se or do they also measure another construct which is includedin the theory?

The balance of this section is aimed at answering these and other questionsrelated to latent variable measurement. Figure 4 is a flow chart of the process


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FIGURE 4THE PROCESS OF SCALE DEVELOPMENT AND VALIDATION

nono

nono

1n n

nono

no 111

Define Constructs\ yes

Develop Potential Items^ yes

Check Content Validity^ yes

Confirm Substantive Validity\ yes

Pilot Survey/Surveyyes

Item PurificationExploratory Factor Analysis

Item to Total Correlationi yes 1

Confirmatory Factor Analysis1 no

Dim ensionality Purification |\ yes

yesReliability

\ yes1 Con vergent Validity

\ yesDiscriminant Validity

yesCriterion Related Validity

Predictiv e Validity |


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JOUR NAL OF BUSIN ESS LOG ISTICS, Vol. 15, No. 2, 1994 157

Content ValidityIn order to measure latent variables, construc ts should be carefully defined

from the literature and the autho r's und erstanding of the constructs. A set of tentativeitems can then be produced to measure each construct. This approach helps ensurecontent validity:

One can imagine a domain of meaning that a particular construct is intendedto measure . Content val id i ty refers to the degree that one hasrepresentatively sampled from that domain of meaning. ...[T]he researchershould search the literature carefully to determine how various authorshave used the concept which is to be measured. ...[A lso], researchers shouldrely on their own observations and insights and ask whether they yieldadditional facets to the construct under consideration.

Content validity exists when the scope of the construct is adequately reflected bythe items as a group. Unfortunately, there is no rigorous way to assess contentvalidity.^^ Multiple items are typically used to measure constructs so that constructmeasurement will be thorough.^' Another term for measure is scale, that is, acollection of items mapped into one variable. If content validity does not exist,then there is no reason to proceed with the analysis because the desired constructis not being properly represented by the group of items. This means that theresearchers will not be able to use the scale to test the hypothesis.

A logistics researcher may wish to measure customer service. If the researcheruses only number of stockouts as a measure, the scale may lack content validitybecause it does not sufficiently span the scope of the meaning of custom er serv ice.The researcher may wish to change the name of the construct to "number ofstockouts." However, if the researcher is really interested in measuring customerservice, then other items should be included in the scale.Substantive Validity

It is not possible for a scale to have content validity without having substantivevalidity.'*^ If a measure has substantive validity, then its items are conceptually


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purifying the set of items, using a test for substantive validity."^^ Item purificationinvolves eliminating those items that do not agree with the set of items.

Many times researchers attempt to purify a set of items in a pretest by usingitem-to-total correlations or contribution to Cronbach's coefficient alpha. The problemwith this approach is that the correlations are typically not statistically significantbecause the sample size is often small in a pilot survey. As a result, the researchermay be eliminating items that should not be eliminated or may be retaining itemsthat will weaken construct validity.

On e solution would be to increase the sample size of the pretest. If the researcheris to use exploratory factor analysis, the sample size must be fairly large. Exploratoryfactor analysis is a statistical technique used to find which variables or items areagreeing with one another. Those that do not agree with a given scale can beeliminated as long as content validity is not jeopardized . This raises another problem ,namely, cost. To use exploratory factor analysis there should be at least tenobservations for each item'*-' and at least 200 observations'*'* even if there are fewerthan twenty items on the questionnaire. Therefore, use of exploratory factor analysisin a pilot study is potentially very costly.

One solution to this dilemma is to assess substantive validity in a pretest settingusing the approach of Anderson and Gerbing.^^ They performed an empirical studywhere their substantive validity assessment technique was able to predict whichitems should have been retained and which should have been deleted. The resultsof the substantive validity assessment were stable across multiple pretest samples.The methodology for evaluating substantive validity is an item-sort task.'*^Experts or a sample of the population to be surveyed are given a sheet of paperthat defines each of the constructs and another sheet of paper that contains allof the items in random order. The participants are asked to match the items tothe constructs that they best represent. When this method is used as the pretest,then the pretest population should be representative of the study population."*^

Anderson and Gerbing'** have developed two indices for evaluating thesubstantive validity. One index is the proportion of substantive agreement (PSA),


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JOUR NAL OF BUSIN ESS LOG ISTICS. Vol. 15, No. 2, 1994 159

that a given item is assigned to. For example, if an item has been assigned toa given construct by eight of the ten participants, the PSA value is 0.80.

The other index is the substantive validity coefficient (CSV). CSV is thedifference between the number of participants assigning an item to its hypothesizedconstruct and the highest number of assignments of an item to any other construct;that quantity is then divided by the number of observations. For example, supposethat six of ten participants assigned an item to the hypothesized construct, threeassigned it to another construct, and one assigned it to another construct. In thiscase CSV = (6-3)/10 or 0.30.

The CSV index is a member of the interval from -1.0 to +1.0. Some itemsare often eliminated in this stage in the research methodology. A critical valueof 0.5 was used to determine which items should be eliminated in the Gerbingand Anderson study."*^ This stage helps assure substantive validity.

The substantive validity test together with the pilot study is intended to improvethe efficiency of the questionnaire as much as possible before data are collected.This helps ensure high quality research. A rigorous statistical analysis of bogusdata is of little value. If substantive validity does not exist for an item and theitem is still included in the scale, then that item will probably have a low correlationwith the other items in the scale. Therefore, it will probably be eliminated duringthe ensuing scale refinement stage of the analysis.

The research of Hunt, Sparkman, and Wilcox^ shows that the most commontype of error detected in a pretest is missing alternative errors, i.e., questionnaireerrors that result from not including the correct alternative in the set of alternatives.The use of inappropriate vocabulary was the second most frequently detected error.Telephone interviews were found superior to personal interviews for detecting errors.

After the pretest has been accomplished, the survey can be sent out. Severalbooks and articles exist on survey research^^ and related topics.^Scale Refinement

Scale reliability, hereafter referred to as reliability, refers to the internal


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of the scale.^-' Reliability is most commonly estimated using Cronbach's coefficientalpha.^"^ Cronbach's coefficient alpha is given by the following expression:

a = Np/[1 + p(N -l)] (1)In equation (1) N is the number of items and p is the mean inter-item correlation.Cronbach's coefficient alpha is an element of the closed unit interval (i.e., 0

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Latent Variable in Business Logistics Research