"WHITE SPACE" OF LOGISTICS RESEARCH: A LOOK AT THE ROLE OF METHODS USAGE, THE

Frankel, Robert

Logisticians approach research from different perspectives and therefore utilize different research methods. Many logisticians would say that their research tends to be more positivist in nature and utilizes variations of quantitative approaches as the primary research method. Conversely, other logisticians tend to be more interpretative in nature which leads to a greater use of qualitative approaches.

The purpose of this article is not to argue for, or against, a particular research method. On the contrary, we believe, as do others (e.g., McGrath 1982) that all forms of research are needed since all research problems cannot be solved with one research approach. We also suggest that "good research is good research" regardless of method, and different types of research problems require different solutions in terms of research approach and choice of method.

There are many valid and relevant perspectives that can be adopted in researchers' search for "truth" within the logistics discipline. Indeed, as long as these are well reasoned and selfjustified - given the research design and the subsequent research methods - any of these viewpoints may be valid in their application to logistics research. An interesting question may thus be posed: How do logisticians currently view the use of research methods in the discipline? The purpose of this article is threefold:

(1) What is the current state of logistics research methods?

(2) Are there [identifiable] changes and/or trends occurring in logistics research methods?

(3) Given the answers to the first two questions, what, if any, opportunities are suggested to researchers regarding a research methods agenda in the logistics field?

We begin the paper with a discussion of general research theory and methodology, with particular attention to the distinction between paradigms and methods; what the selection of a particular method may mean for the logistics researcher; and what a general research framework encompassing multiple methods might look like. Next, we examine the past six years of publications in one of the leading academic logistics journals (Journal of Business Logistics) to investigate where the recently published logistics research "fits" into the aforementioned framework. Then we identify any relevant trends and draw insights regarding the publication picture that emerges. We conclude with suggestions for current and future logistics researchers.

RESEARCH: PARADIGMS VS. METHODS

It is important to emphasize that a research paradigm is not the same as a research method. A research paradigm consists of beliefs about knowledge, whereas research methods are specific ways of gathering data. Paradigm is our world-view, the lenses through which we view the world - our Weltanschuungen (Checkland 1993). Senge (1990) describes paradigm as our "mental model." We all view the world differently. A researcher's preferred paradigm can help determine the research methods he/she is comfortable with. Even if a researcher identifies with a particular paradigm, it does not necessarily mean that the researcher must use one particular research method. Still, the paradigm guides the researcher in choices of method, as well as ontologically and epistemologically (Guba and Lincoln 1994).

Therefore, unless we understand the existence of different world-views or paradigms, we cannot embrace different types of research methodologies. The problem is not so much whether the different paradigms, the world-views, are right or wrong. The problem is when the paradigms exist below the level of awareness. In research, this means that we need to express and explain our world-view, we need to make our paradigm clear. In order to see both the limitations and potential of different forms of research, we need to first understand the foundation (i.e., paradigm) on which one builds knowledge. Then we need to look at different methods before choosing the one(s) for best solving the problem.

The Elements of a Paradigm

A paradigm includes, at least, three elements: ontology, epistemology, and methodology (Denzin and Lincoln 1994, p.99). Ontology concerns questions about the nature of reality - whether an objective reality exists or not. Defined as the science of being (Burrell and Morgan 1985), ontology refers to the claims or assumptions that a particular approach to social enquiry makes about the nature of social reality. It includes claims about what exists, what it looks like, what units make it up and how these units interact with each other.

Epistemology deals with how we perceive the world, and the relationship between the researcher and the known. According to Burrell and Morgan (1985), epistemology deals with how one might understand the world and communicate that understanding as knowledge to others. Defined as the science of the methods of knowledge (Burrell and Morgan 1985), epistemology refers to the claims or assumptions made about the ways in which it is possible to gain knowledge of a reality, whatever it is assumed to be.

Ways of acquiring knowledge may vary with social context and with different phenomena; as a result, no single epistemology is right or wrong. For example, although social and natural sciences can be accommodated within the same ontological framework, the epistemology of the social sciences is inclined to be more complex and diverse than that of the natural sciences, reflecting the more complex phenomena associated with social science research.

Epistemological and ontological assumptions consequently influence methodological decisions. Basically, methodology deals with how we gain knowledge about the world. Defined as "a body of methods, procedures, concepts, and rules" (The Merriam-Webster Dictionary 2004), the research methodology is the rationale or basis for the selection of methods used to gather data, and for determining the sequence and samples of data to be collected.

Research Design, Methodology and Methods

Research design

Defined as the "overall configuration of a piece of research" (Easterby-Smith, Thorpe, and Lowe 1991, p. 21), the research design provides the opportunity for "building, revising and choreographing" (Miles and Huberman 1994, p. 16) the overall research study. In other words, a research design defines the study's purpose. Like all social science researchers, logisticians must consider what comprises a viable research design and what does not. Researchers increasingly face the practical challenges, for example, of achieving acceptable sample size and corresponding response rate. They must also determine the level of control that is plausible and optimal when utilizing individual or organizational behaviors. Research design also drives the choices of methodology and methods.

Methodology

The choice of an appropriate research methodology is influenced by several factors (Bryman 1989; Easterby-Smith, Thorpe, and Lowe 1991), some of which include: (1) the format of the research questions (i.e., "what," "how," "who," "why," etc.), each of which requires different research designs to adequately answer them (Yin 1994); (2) the nature of the phenomenon under study, i.e., contemporary or historical issues (Eisenhardt 1989); (3) the extent of control required over behavioral events in the research context (Yin 1994); and (4) the researcher's philosophical stance, i.e., his/her understanding of the nature of social reality and how knowledge of that reality can be gained (Blaikie 1993; Tsoukas 1989). In other words, the choice of research methodology must be appropriate for the research problems and objectives. Based upon these objectives, the choice of appropriate research methods is made.

Research methodologies range from the two extremes of objective, scientific (quantitative) research styles to the subjective, interpretive, more constructive (qualitative) styles. Qualitative research methodologies were developed in the social sciences to enable researchers to study social and cultural phenomena. This approach states that the world is essentially relativistic and thus one must understand it from the inside rather than the outside. It can only "be understood from the point of view of the individuals who are directly involved in the activities which are to be studied" (Denzin and Lincoln 1994). Qualitative research is criticized for being unscientific, only exploratory, or too personal and full of bias. Many (traditionalists) regard qualitative researchers as soft scientists or even journalists (Denzin and Lincoln 1994, p.4). In their view, qualitative research can be used to familiarize oneself with a setting before the serious sampling and counting begins (Silverman 1993, p. 20).

Quantitative research methodologies, on the other hand, typically incorporate statistical elements, designed to quantify the extent to which certain phenomena behave/respond to stimuli in specified ways, and the extent to which a target group is aware of, think, or believe, or behave in a certain way. Studies tend to emphasize the measurement and the analysis of causal relationships between variables. New knowledge is added to existing knowledge and false hypotheses are eliminated. Quantitative research seeks general laws as well trying to explain and predict by searching for regularities. Quantitative research methodology is criticized since information can be clouded due to the complexity of accompanying methods, the large sample sizes needed, and the difficulty in understanding and interpreting the results (Van Maanen 1982). Kaplan and Maxwell (1994) argue that the goal of understanding a phenomenon - from the point of view of the participants and its particular social and institutional context - is largely lost when textual data are quantified.

Research Methods

Research methods are the data collection techniques which refer to the specific, fact-finding procedures that yield information about the research phenomenon. The meaning of data and the measures through which data are captured are also influenced by theoretical frameworks relating in some way to the research questions. These measurement theories must not bias the test for or against one explanatory theory over its rival. In other words, researchers must be objective.

Research methods are also generally described as qualitative or quantitative, although in practice such distinctions can oftentimes be open to discussion. For example, consider a structured interview in which a respondent is asked to answer survey questions on a 1-7 scale. Is it truly qualitative research? Or consider another study in which a series of structured (or unstructured) interviews are utilized by a researcher to obtain transcriptions and then codify them (i.e., quantify the main themes from the transcriptions) for quantitative data analysis. Is the study a qualitative one?

Qualitative methods primarily create meanings and explanations to research phenomena. Data collection methods typically associated with (but not limited to) qualitative methodologies include observation and participant observation (fieldwork), interviews and questionnaires, diary methods, documents and texts, case studies, and the researcher's impressions, and reactions to observed phenomena. Qualitative studies typically involve words, and as such often feel "untidy" because it is harder to control their pace, progress, and end-points. Qualitative studies are also typically given low credibility based on perceived problems with validity.

Quantitative research methods can provide wide coverage of a range of situations, are fast, and can be economical, particularly when statistics are aggregated from large samples. Questionnaires and survey methods are easily used in a quantitative way and as such typically provide the framework around which quantitative methods evolve. Other commonly used quantitative research methods, and examples of those now well accepted in the social sciences include laboratory experiments, formal methods (e.g., econometrics), and numerical methods and techniques such as mathematical modeling.

Many of these "scientific" methods have been applied to logistics research, typically with the objective of testing theoretical systems and statements (Seaker, Waller, and Dunn 1993). As the field of logistics develops, however, so do its research requirements and opportunities. As such many of the single-dimensional frequency counting based research methodologies may be considered limited due to their lack of vigor in terms of the resultant testing of the relationships existing between variables and the ability of researchers to substantiate their findings to the research community. As a result, there is an ever increasing need for logistics research to employ rigorous methods and techniques that can provide in-depth understanding of theory, the variables and the relationship(s) that exist between them while still maintaining relevance to practitioners (Garver and Mentzer 1999). Increasingly, researchers argue that one should attempt to mix methods to some extent, because it provides more perspectives on the phenomena being investigated. Several researchers advocate the use of both qualitative and quantitative methods and provide examples of how they have been able to combine these different forms of data to good effect in social science research (examples: Fielding and Fielding 1986; Fielding and Schreier 2001; Thomas 2003).

THE CURRENT STATE OF LOGISTICS RESEARCH METHODS

What is the state of logistics research methods today? To answer this question, we examine the last six years of articles published in the Journal of Business Logistics (JBL). We use this timeframe because the most recent comprehensive analysis of JBL publications occurred in 1999 (see Miyazaki, Phillips, and Phillips 1999), although the analysis focused on the scope of author contributions rather than research methods. JBL has been selected as a proxy for the state of logistics research given its reputation (see Gibson, Hanna, and Menachof 2001) to logistics researchers, whether they are academicians or industry professionals, or a combination of the two. Table 1 summarizes the 108 articles published in JBL, listed by year, by author(s), and by research method. It should be noted that several brief (i.e., less than 1-2 page) "commentaries" published in JBL have been excluded from the analysis, given that they do not meet the admittedly subjective guidelines of what we would term a "journal article."

The authors utilized the following procedure for the rating categorization process. Each author provided definitions for the research methods from sources they were most familiar with, and discussion ensued until agreement was reached on each method's definition. Individual articles were selected as representative samples for each method to assist in clarifying the categorization process. Classification of the article research method(s) was independently (i.e., separately) determined by the authors. Intercoder reliability was then calculated. Intercoder reliability, defined as the number of agreements divided by the number of combined agreements and disagreements, was 0.96, which is greater than the recommended 0.90 minimum (Miles and Huberman 1994). Any disagreements with regard to coding were examined and settled by consensus as well as referral back to the articles (Eisenhardt 1989; Miles and Huberman 1994; Perreault and Leigh 1989).

The research methods represented in the 108 articles are as follows: (1) surveys (or questionnaires); (2) interviews; (3) observation; (4) focus groups; (5) case studies; (6) experiments; (7) literature reviews; and (8) content analysis. Each method is briefly defined next.

Surveys are structured questionnaires given to respondents and designed to elicit specific information; they may be administered by telephone, person, mail, or electronically (Malhotra 2004). Interviews cover a wide variety of formats, but generally are designed as personal meetings between an interviewer and respondent(s). Often criticized for its high propensity to encourage interviewer and respondent bias, interviews represent a targeted method of collecting data and are often insightful - providing perceived causal inferences. The type of interviews range from unstructured, semi-structured, to completely structured in format. The completely structured interview is basically a verbal survey with fixed response options. On the other end of the scale, the "depth" interview is an unstructured and personal interview in which a single respondent is probed by a highly skilled interviewer to uncover underlying motivations, beliefs, attitudes, and feelings on a topic (Breech 2002). In general, the looser the interviews, the less comparable data become (Miles and Huberman 1994, p. 89).

Observation involves recording the behavioral patterns of people, objects, and events in a systematic manner to obtain information about the phenomenon of interest (Malhotra 2004). The range of observation methods includes personal, mechanical, audit, content analysis, and trace analysis. The role of the observer can range from complete observer to complete participant (Silverman 1993). The complete observer does not question or communicate with the subject(s) being observed; the complete participant, on the other hand, performs the research covertly as an organizational member and the scientific intentions are hidden from the organizational members (Malhotra 2004). It is important to note here that although content analysis is defined as a form of observation, its popular application as a stand-alone data collection method justifies its inclusion as a separate category as we have done in Table 1. The content analysis of documents, websites, archival records, etc. provide a somewhat stable and repeated review process which is often unobtrusive, and can provide a broad coverage of data over an extended time span (e.g., Stock 2001). Data sources can include published and unpublished documents; company reports; memos; letters; reports; email messages; faxes; newspaper articles; web-pages, etc. Typical problems associated with this form of data collection method include difficulties involved in retrieving data, and inherent researcher bias in source selection and reporting. Focus groups are conducted by a trained moderator among a small group of respondents in an unstructured and natural manner; their main purpose is to gain insights by listening to a group of people from an appropriate target market/ group talk about issues of interest to the researcher (Malhotra 2004). For the purpose of this article, we define Delphi panels as a form of iterative focus group, aimed at achieving consensus among panel members.

A case study is "an empirical inquiry that investigates a contemporary phenomenon within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident" (Yin 1994, p.13). The case study is an ideal method when a holistic, in-depth investigation is needed (Feagin, Orum, and Sjoberg 1991). By allowing the use of multiple sources of data, case studies are also designed to bring out the details of the research from the perspective of the participants. This advantage ensures that all the relevant information is available to the researcher. Similarly, the case study method allows "multi-perspectival" analysis i.e., it allows researchers to consider not just the perspective of the actors within the study but also of the interaction between them (Tellis 1997). There are different forms of, and interpretations of, case studies. Yin (1994) identified three specific types of case studies: exploratory, explanatory, and descriptive. Exploratory case studies are sometimes considered as a prelude to theory testing research; they are used to explore issues within an area whereby there is limited availability of empirical data and/or a theoretical framework. In a more "positivist" approach, interviews with a number of respondents would be a case study. Others would argue that case studies require much more in-depth knowledge of the research subject, perhaps including observation over long periods of time. This is one contributing factor to the discussion whether case studies require one or many subjects to study. This depth also allows for the contexts of people's beliefs and values to be explored. Yin (1994) also presents at least four applications of the case study method: (1) to explore situations in which the intervention being evaluated has no clear set of outcomes; (2) to explain complex causal links in real-life phenomena; (3) to describe real-life context within which the phenomenon exists; and (4) to describe the phenomenon itself. case studies often include comparisons of many cases, yet Stake (1994) argues that a case study is mainly about what can be learned from the in-depth investigation of a single case. The strength of the case study approach lies in its ability to uncover subtle distinctions and provide a richness of understanding and multiple perspectives that experienced researchers are able to obtain.

Experiments are formed when a researcher manipulates one or more independent variables and measures their effect on one or more dependent variables, while controlling for the effects of extraneous variables (Brown and Melamed 1990). This may include "pure" experiments, descriptive experiments, simulation, and modeling methods. Literature reviews involve an in-depth analysis and critical summary of previously collected data (e.g., secondary data) for the purpose of identifying a research "gap" that needs to be addressed through future studies. Often used in exploratory and conceptual studies, a review of relevant literature assists researchers with producing a meaningful "map" depicting the existing connections between the different areas of literature and the research gaps identified relating to this research.

To enhance the results in Table 1, Figure 1 illustrates the research methods within a broad philosophical framework. We define the descriptors on the "y" axis of the framework as an "involved researcher" or a "detached researcher," in order to display the extent of researcher involvement in the data collection method, and to reflect the extent of researchers' closeness to the phenomenon at hand. We define the descriptors on the "x" axis as an "objective, external social reality" or a "subjective, cognitive social reality, to reflect the extent of researcher bias possibility in each method. There are, of course, limitations that should be recognized. As one would expect, creating a summary map of methods is not an easy task. The complexities encountered in projecting a one-dimensional perspective (for example, objective vs. subjective) severely complicate the use of secondary (or more) dimensions to the classification (for example, involved vs. detached researcher). However, the benefit that such a map provides us is twofold. First, it gives us the ability to better visualize the range of [potential] logistics research methods. Second, when applied to the actual use(s) of current research methods in published articles, it provides a "snapshot" of the current state of the discipline. To further emphasize the trends in method usage, we trace the development of methods used in logistics research in each year over the last six years (1999-2004) in Table 2.

Given the difficulty of selecting a single position in Figure 1, our approach is to place the methods as vectors. This emphasizes the potential variety in both interpretation and execution of the different methods. One could easily argue that for many methods vectors are needed in both dimensions. For case studies, for example, the level of involvement of the researcher is obviously very important, yet the fundamental difference in approach is more significant across the objective-subjective dimension. Similarly, the subjective-objective dimension is very important for classifying different forms of observations, yet the level of involvement is, in our opinion, even more important.

The "White Space" of Logistics Research

As Table 1 illustrates, the majority of logistics research as published in JBL is based on methods within the detached, objective, external perspective (i.e., experiments, surveys, literature/document studies) with surveys as the primary research method - 51% (55 articles) utilized surveys as the primary research method. Conversely, very few research methods take the form of an involved, subjective, cognitive perspective (i.e., case studies, observations). If one were to place the number of such articles on Figure 1 (in the manner of a marketing "perceptual map"), that area could almost be described as a "white space" (e.g., blank). To be more numerically specific, of the 108 articles published in JBL during the relevant timeframe, only seven articles (six case, one observation) utilized case methods and observation as the primary research methods, while six articles (three case, three observation) used these as the secondary research methods.

Trends in Logistics Methods

An analysis of Table 1 identifies a number of interesting and potentially important trends in logistics research. One relates to the use of survey methods in logistics. In the past few years, a considerable proportion of the survey-based method of data collection has been analyzed with structural equation modeling (SEM). There were eight articles published in 2004 that utilized SEM, which is double the number of such articles in 2003. Prior years exhibited smaller numbers of such articles; there were two in 1999 and only one each in 2000, 2001, and 2002.

We also see a trend toward greater application of case methods. While only seven articles used case studies as the primary research method, three of these were published during 2004. In addition to the increase in the use of case studies, we observed two related aspects: more thorough descriptions of the logic behind using a case study and more thorough descriptions of the actual case(s) used. Examples of this approach to case studies in logistics are provided by Flint and Mentzer (2000); Garver and Mentzer (2000); Lambert, Knemeyer, and Gardner (2004); and Manrodt and Vitasek (2004). In the past, the term "case" research in logistics was a phrase oftentimes used out of its proper methods context. In actuality, most logistics researchers who stated that they were utilizing "case" research methods were neither describing the rationale for using a case study nor how the case study was conducted.

Another trend is the increased application of a multi-method (i.e., triangulation) approach in logistics research. Many of the articles utilize at least two methods. Of the 108 articles analyzed, 37 articles (35%) utilized two or more methods. This trend is on the rise as evidenced in the year 2004, when 10 of the 19 articles (53%) utilized two or more methods. For example, articles with surveys as the primary method have increasingly used secondary support methods such as interviews. A somewhat related observation concerns the use of a research method (i.e., case study) for an objective not typically associated with it (i.e., causality). For example, Lambert, Emmelhainz, and Gardner (1999) investigated partnering through a case study approach. The purpose of the article, however, is not only to explore and understand but to explain and predict the partnering phenomenon. The aforementioned articles by Flint and Mentzer (2000) and Garver and Mentzer (2000), as well as Zacharia and Mentzer (2004), utilized a similar approach.

A final trend relates to the increasing use of the Internet for data collection. Several articles (e.g., Ellinger et al. 2003) utilize the Internet to perform content analysis data collection. Griffis, Goldsby, and Cooper (2003) specifically address the issues involved in web-based research methods.

IMPLICATIONS AND FUTURE RESEARCH OPPORTUNITIES

The trends identified in Table 1 may be interpreted in different ways. We offer our interpretation of those trends in the balance of this paragraph. As noted previously, the evolution of the logistics discipline has generated a call for logistics research to become more rigorous with respect to theory development and practitioner application. We see several different responses to this challenge. First, the increased use of SEM as well as simulation would seem to reflect a research purpose driven to discover and explain causality. In the specific instance of SEM, its increased usage may also be evidence of researchers who have already looked at an issue with "proven" techniques but now become aware of a new technique which may provide them with additional insight into a situation - and thus they are likely to try and utilize that technique. In other words, if all (or most of the new analytical techniques) are causal, whether they are quantitative or qualitative in nature, researchers will tend to use those techniques to explore research hypotheses. second, the use of more stringent case methods reflects a research purpose driven to discover greater depth/richness in the understanding of problems as well as accompanying potential solutions (Naslund 2002; Yin 1994). The case studies published recently in JBL indicate that case based research can be as useful and as rigorous as other research methods. Third, the use of triangulation suggests that logistics researchers see value in combining methods to increase theory development and improve data collection. Finally, the somewhat eclectic approach of using a research method for a purpose not typically associated with it, for example, using a case study to explore causality, suggests that a small number of researchers have broadened the more traditional world-view of research. The logistics discipline oftentimes appears to view the above as being mutually exclusive. This perspective, we believe, is an unfortunate and unnecessarily restrictive one.

With regard to future research opportunities in the "white space" one might consider that there may be, relatively speaking, a lack of understanding of what is required to effectively conduct good research using the methods in the "white space." For example, many researchers believe that good research always requires a large number of respondents/subjects. The basis for, and an understanding of, well-grounded case research methods would suggest that this is not true. Or, perhaps there is an inherent perception that research utilizing methods in the "white space" is not as rigorous as survey or experiment methods. Again, an understanding of properly conducted case study research or observation should dispel that notion.

Logistics problems are often ill-structured, even messy, real-world problems. Logistics uses multi-disciplinary and cross-functional approaches. In order to provide value to industry, education in the classroom, the discipline as a whole, logistics researchers must gain "extreme relevance" (Bowersox 1996) by understanding what is going on within and between organizations. Therefore logistics research will benefit by the researcher spending time in organizations and observing and/or communicating with professionals performing logistics in action. By being in the "real world" researchers gather first-hand information to develop knowledge and gain extreme relevance. It can be argued that to gain relevance, for logistics researchers, "a one paradigm, one approach" perspective should not automatically be the obvious choice. In fact, as Burrell and Morgan write (1985, p. 2): "the possible range of choice is indeed so large that what is regarded as science by the traditional 'natural scientist' covers but a small range of options."

What benefits are available from a perspective that offers a wider range of options, for example, in the "white space?" There would seem to be a number of potential opportunities provided by the use of multiple methods of data collection in the logistics discipline. One, as this paper indicates, a significant use of research methods in the 'white space" is not evident. Becoming adept with the skill to use multiple methods might be advantageous on an individual basis (i.e., diverse skills versus specialization) with respect to one's career. Second, case and observation research methods appear to be very useful and appropriate for much of the relationship-based (individual to individual and/or organization to organization) research that is becoming increasingly important and popular in the logistics discipline. Third, as the scope of logistics research expands on a global scope, a familiarity and skill with "white space" methods is a "plus" because a considerable portion of global research is particularly well-suited for exploring the "how" and "why" of many cross-cultural issues. Fourth, while an overall balance might someday occur in the future, that balance would likely be created by relatively equal numbers of academics being focused at each end of the spectrum(6). However, the skill to utilize both types of methods might be advantageous on an individual basis (i.e., diverse skills versus specialization). Finally, of course, such methods remain invaluable for multi-stage research designs by providing a foundation for survey development.

CONCLUSION

Research methods are really only tools used to solve a problem. To really be able to use the complete set of tools in the research portfolio, we need to understand which tool(s) are best utilized to solve a particular type of research problem. Similar to Dunn et al. (1993, p. 3), we are not advocating that each researcher take on all methods in their "logistics research toolbox." If we truly want to develop logistics, to make it broad and rich, to develop and test new theories, then we need to question our paradigms, methodologies, and choice of methods. We need to increase our knowledge about these issues, and then we need to continually debate them with open minds.

In this paper, we have identified eight primary methods of data collection used in logistics research over the past six years: surveys, interviews, observations, focus groups, case studies, experiments, literature review, and content analysis (of documents, web-sites, archival records, etc.). No single source has a complete advantage over the others and not all of these are essential when addressing every research question, although the importance of multiple sources of data to the reliability of the study is well established. Rather, these sources could well be complementary to each other and as such should be used in tandem. In spite of the strides made in research such as this study, there are still potential limitations with respect to data collection methods. Consequently this limits the validity of research findings and as such must be acknowledged. In the first instance, pre-conceived researcher biases are the most common limitation of any research method. Also, lack of adequate training in and knowledge of the methods involved, as well as the lack of explicit statements that connect research strategy with selection of appropriate data collection methods, may lead to researchers asking the wrong questions or lacking the ability to link the research data with its original conceptual propositions. This risk is inherent in any type of research method for which the researcher lacks adequate training and skills. A broadened approach to this paradigmatic positioning of research strategy would address these issues. As also noted in the introduction to this paper, "good research is good research" wherever its methods fall on the framework in Figure 1. It is important that in an evolving and applied field such as logistics that we utilize multiple kinds of good research. In other words, researchers must provide good examples of the application of different paradigms and methods. The key to making that condition a reality is to understand, appreciate, and encourage a diversity of perspectives and methods. Thus, we encourage logisticians to consider the "white space" of logistics research.

NOTES

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Robert Frankel

University of North Florida

Dag Naslund

University of North Florida

and

Yemisi Bolumole

University of North Florida

ABOUT THE AUTHORS

Robert Frankel is a Kip Associate Professor of Marketing and Logistics at the University of North Florida. A Fulbright scholar, he received his Ph.D. in Marketing and Logistics from Michigan State University. His research interests include supply chain management, international marketing, and pedagogy. His research has been published in Journal of Business Logistics, International Journal of Physical Distribution and Logistics Management, International Journal of Logistics Management, Journal of Business-to-Business Marketing, and Marketing Education Review, among others.

Dag Naslund is an Assistant Professor of Logistics at the University of North Florida. He earned an MS from the Lund Institute of Technology in Civil Engineering, an MBA from the University of California at Irvine, and a Ph.D. from the Lund Institute of Technology in Process & Supply Chain Management. His research focuses on Process Management, with examinations of Enterprise Resource Planning (ERP), Performance Measures, and Supply Chain Management. He was awarded a research grant from SAP University Alliance for his work on ERP. He currently serves as Captain in the Royal Swedish Air Force Reserves.

Yemisi Bolumole is an Assistant Professor of Logistics at the University of North Florida. She received her Ph.D. in Logistics & Supply Chain Management from Cranfield University in the UK where her thesis, titled "Logistics Outsourcing in the UK Forecourt Convenience Retail Sector: The Supply Chain Role of Third Party Service Providers," was awarded the best Ph.D. prize for 2001. Dr. Bolumole has extensive research and professional experience in the third-party service, retail and logistics sectors and her areas of research interest include logistics outsourcing, the supply chain implications of third party logistics, partnering for optimizing supply chain performance, and the strategic implications of supply chain management.

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