Research methods Summary of the book Research methods for operations management Christer Karlsson Sharmaine Linker Contents Chapter 1 Introduction of the book... 3 Chapter 2 Research in operations management... 3 2.1 Introduction... 3 2.2 Research outputs and targets... 3 2.3 Roles of the researcher... 3 2.4 The research process... 4 2.5 Research as contribution to knowledge... 4 2.6 What to research for academia and practice... 5 2.7 Research quality... 5 2.8 Assessing research quality and contribution... 6 2.9 Getting published... 6 2.10 Research ethics and ethics for researchers... 6 3. The research process... 6 3.1 Contributing to knowledge... 6 3.2 Choosing a research topic... 7 3.3 Using literature to develop the research topic... 7 3.4 Developing research question... 8 3.5 Considerations in choosing a research approach... 8 Chapter 4 Surveys... 9 4.1 Introduction... 9 4.2 The survey research process... 9 4.3 What is needed prior to survey research design?... 10 4.4 How a survey should be designed... 10 1
4.5 Pilot testing the questionnaire... 11 4.6 Advancements in theory formalization and survey design... 12 4.7 Survey execution... 12 4.8 Data analysis and interpretation of results... 13 4.9 Information that should be included in articles... 13 4.10 Ethical issues in survey research... 13 Chapter 5 Case research... 13 5.1 Introduction... 13 5.2 When to use case research... 13 5.3 The research framework, constructs and questions... 14 5.4 Choosing cases... 14 5.5 Developing research instruments and protocols... 15 5.6 Conducting the field research... 15 5.7 Reliability and validity in case research... 15 5.8 Data documentation and coding... 15 5.9 Analysis... 16 Chapter 9 Modelling and simulation... 16 9.1 Introduction... 16 9.2 Origins and development of model-based research in operations management... 16 9.3 Methodologies in quantitative modelling... 16 9.4 How to conduct quantitative research in operations management... 17 2
Chapter 1 Introduction of the book Nothing special Chapter 2 Research in operations management 2.1 Introduction Research aims at the creation and development of knowledge and research has to be of a high quality in terms of validity and reliability. Key components of research are new knowledge and/or new applications. Mode 1 research is traditional pure knowledge creation and development. Mode 2 research includes the purpose of implementation or using knowledge to develop applications. Operations are a transformation activity: transforming resources (machines and people) into outputs of goods and services. The scope of OM is very wide and includes for example the following strategic perspective: focus on the role of and the objectives for the function of operations. Operations play an important role in business model innovation and development. 2.2 Research outputs and targets A fundamental characteristic of good research is that is significant contribution to knowledge. Typically it consists of additions to pre-existing knowledge. It also might be significant in terms of the contribution to theory or practice. Another important factor is the potential value that can be created when applying the research results. Research is expected to provide trustworthy knowledge. The aim of research is the creation and development of knowledge and the output is contribution to knowledge. Research may have different purposes: confirmation, falsification and exploration. Confirmation is often done by confirming previous findings in other settings. Falsification needs to identify the rejection of an earlier hypothesis or theory. Exploration offers endless opportunities since there are always issues and problems from empirical situations that have not been studied before. 2.3 Roles of the researcher Since there are many different types of research, research approaches, methodologies, used methods, intended products, contributions and target group(s), there are also many different types of researchers. - Observer - Surveyor ((opzichter) being able to grasp large amounts of data and review it systematically) - Analyst (having the ability to break down data and classify into categories) - Communicator - Sounding board (calls for an ability to listen, analyse, and then relate to models and / theories that inform the practitioner) - Actor and other partner roles - Consultant (ability to give advice based on the identified problems and developed knowledge) - Clinician (curing the research object while at the same time use it as a database) 3
2.4 The research process 1. Identification of a problem or issue to research 2. Literature review 3. Specifying the aim, objective, or purpose of the intended research 4. Determine specific research questions 5. Choice of research approach and methods 6. Development of a conceptual framework 7. Data collection 8. Analysing and interpreting the data 9. Synthesizing and concluding 10. Evaluating the research and suggesting further research 11. Reporting and communicating the research findings 2.5 Research as contribution to knowledge Explicit knowledge is knowledge that can be captured, documented, transferred, shared and communicated easily. Tacit knowledge cannot be traced in documents and publications, it is personal, context-specific and therefore difficult to articulate; it resides only with the individual. Another distinction is the one between knowledge and skills/craft. Knowledge is built on a rational formula, while skills can be called know-how. Also a distinction can be made between skill/craft, virtue and attitude. Virtue are the attributes, attitudes or basic values of an individual. At the base of research philosophy is a view of reality that reflects the researcher s understanding of what exists and what hence can be said to be, this is called ontology (called the science of being). Just as there can be different realities, there can be different truths. The study of reality ontology and the study of knowledge is called epistemology. There must be good (justified) reasons for believing that something can be verified as absolutely true regardless of the context, before it can be considered as knowledge. An aspect of importance is how arguments are build. An argument is said to consist of premises, inference and conclusions. A premise of an argument is something that is put forward as a truth, but which is not proven. The conclusion/claim is the statement of the result of the analysis. The inference is the reasoning process between the premises and conclusions. There are different types of reasoning: Logic of argument Components Rules Observations Results Argumentations Deduction Induction Abduction Rule Observation Result Observation Result Rule Result Rule Observation 4
Rule: concerns how the world is structured and functions Observation: the database or the research material There are different levels of knowledge. The first level is the data, this are those facts that can be gathered and for which there are measures. When data are put into a system, we form information. The information can be put in a context and used, and, as a result, add experience. This enables an interpretation of the information, its applicability, effects and other strengths and weaknesses. Knowledge is developed. 2.6 What to research for academia and practice In making a contribution to knowledge, the first step is to think about the intended contribution. The exact contribution is depended on the outcomes of the research, but an aim could and should be made. The starting point may also be a research project, a project possibility, an available case or other empirical material on which to do research. From the intended contribution it is practical to follow a form of reverse/backward logic, considering what has to be done to reach the intended contribution. After deciding on the intended contribution, a more thorough literature review can be performed. Then the researcher can start to find a way to achieve knowledge development and plan a study, creating a methodology, analysing the methodology and creating an empirical/theoretical foundation. The researcher can also plan how to set the contribution in the research field context. A researcher should forward a chain of evidence. A report is expected to start with a problem discussion based on the relevant problem from practice and the theoretical formulation. A problem definition should be developed. Next a thorough literature review should be given to develop a picture of the relevant knowledge and gaps in the knowledge. Then the research questions should be formulated (explorative, hypothesis generating/testing, or whatever). A conceptual framework of what will be studied should be presented. The next section should be about the methodology and thereafter the results can be presented. The last section should contain a conclusion. 2.7 Research quality To gain research quality, the first question asked should be whether there is a need for the product and whether it will be demanded. Other question are whether the research was performed in a reliable way and if the research can repeated by a reader. Furthermore, the reader should be able to follow the text, understand the chain of logic, and come to the same conclusions. There are four requirements that are relevant to OM research: - Construct validity the operational measures used to measure the construct should actually measure the concepts that are intended to measure - Internal validity that the study actually measures what it is meant to measure and that demonstrated relationships are explained by the factors described and not by other factors - External validity that the results are valid in similar settings outside the studied 5
objects - Reliability that the study is objective in the sense that other researchers should reach the same conclusion in the same setting 2.8 Assessing research quality and contribution A well-done manuscript review should contain general aspects and careful examination. The general overview should bring up what has been discussed in quality of research and especially analyse the chain of evidence. In the book (p. 32-34) is an extensive explanation on how to perform a good review. 2.9 Getting published Nothing relevant for the exam 2.10 Research ethics and ethics for researchers In this section the rules and principles about ethics are explained. 3. The research process 3.1 Contributing to knowledge A theory can be defined as a set of interrelated constructs, definitions and propositions that present a systematic view of phenomena by specifying relationships among variables, with the purpose of explaining and predicting the phenomena. Theories explain facts and provide stories as to why phenomena work as they do. Three elements in a theory: - Conceptual definitions of the theory s construct - Defined relationships between constructs - A limitation of the theory s domain; the boundary conditions within which the proposed relationships hold Theories are not built, they are invented, they cannot be systematically constructed or deduced from facts, but require inspiration and creativity. They cannot be proved, but only disproved by findings that run counter to their predictions/explanations. - References are not theory - Data are not theory (data is what was observed, theory why it is observed) - Lists of variables or constructs are not theory - Diagrams are not theory - Hypotheses are not theory Other research products that could constitute valid contribution, but that should not be confused with theory - Proposition there is a relationship between propositions and hypotheses, hypotheses require measures whereas propositions involve concepts. - Model is a way to operationalize theory for empirical testing - Conceptual model sets out in broad terms the definition of the particular problem to 6
be solved and it specifies the variables that will be used to define the nature of the problem. - Taxonomy used to classify things according to their similarities - Framework explain either graphically, or in narrative form, the main things to be studied Ways of making theoretical contribution - Adding or subtracting factors from an existing theory - Find alternative explanations for the occurrence of a phenomenon - Address the domain limitations of the theory Other ways of making theoretical contribution are to contribute to existing knowledge, or to use existing theory to explain phenomena. Another way is to examine and test relationships between variables. Maturity of existing knowledge Types of studies Types of contributions Nascent theory Exploratory, theory building Identifying new phenomena and suggesting relationships among them Intermediate theory Mature theory Confirmatory, theory testing Testing, confirming, extending or refining existing theory The contribution on knowledge is also depending an individual s regards to ontology & epistemology. 3.2 Choosing a research topic Practical problems are not research problems. Practical problems are solved by action, by doing something that eliminates the cause of the problem. Research problems are defined by what you do not know or understand about something. Research problems can arrive from, but not exclusively: - Industry problems - Industry trends - Literature studies - Previous research - Personal experience - Networking - Funding bodies 3.3 Using literature to develop the research topic 8 pages of how to use literature and how to find it. Use of existing literature - To motivate and position the research - Create a framework for the research - Support more hands-on action in the research 7
- Interpreting and explaining research findings - Discuss the research findings in relation to existing literature Common misuse of literature - Only describing the literature and not analyse it - Review literature without using it - Employ too many (perhaps even incompatible) grand theories - Usage of high-level theories is taken as a guide for contributing to theory 3.4 Developing research question Research questions help you narrow your search to the data that you need to answer the question. Research question are fundamental in bringing focus to research. Other mechanisms that help to achieve this focus are the purpose, aim, propositions and hypotheses. The purposes comprise a statement of what the research is trying to achieve and how this end is to be achieved. An aim is the purpose of doing something: what somebody is trying to achieve. The aim and purpose are often used interchangeable. Types of research questions - Exploratory (who, what, where and when) - Descriptive (who, what, where and when) - Explanatory (how and why) - Prescriptive Nature of research question Types of research questions Nascent theory Intermediate theory Mature theory Open-ended inquiry Proposed relationships about a phenomenon between new and of interest established constructs existing constructs Exploratory Descriptive Focused questions and/or hypotheses relating Explanatory Prescriptive Basic gap spotting modes Specific versions of basic gap spotting modes Neglect spotting Looking for areas in existing literature that are overlooked or under-researched or that lack empirical support Confusion spotting Looking for competing explanations in existing literature Application spotting Extending and complementing existing literature 3.5 Considerations in choosing a research approach Overall considerations in choosing an approach: methodological fit, philosophical position and practical considerations. The core notion of methodological fit is that there should be internal consistency among all the main elements of a research project (research question, maturity of existing knowledge, research approach and contribution). The philosophical position refers to the reflection of the researcher s position with regard to fundamental issues of ontology and epistemology. Practical factors that have to considered are for example the access, the institutional factors and skills & interests. 8
Chapter 4 Surveys 4.1 Introduction Exploratory survey research takes place during the early stages of research on a phenomenon when the objective is to gain preliminary insight into a topic, and provides the basis for more in-depth survey research. Confirmatory (or theory testing or explanatory) survey research takes place when knowledge of a phenomenon has been articulated in a theoretical form using well-defined concepts, models and propositions. Data collection is carried out with the specific aim of testing the concepts developed in relation to the phenomenon, the linkages hypothesized among the concepts and the validity boundaries of the model. Holistic construal: this approach is neither rigidly deductive nor purely exploratory. Rather it subsumes a process by which theories and hypotheses are tentatively formulated deductively, tested on data and, later, reformulated and retested until a meaningful outcome emerges. Descriptive survey research is aimed at understanding the relevance of a phenomenon and describing the incidence or distribution of the phenomenon in a population. A survey is a suitable method when knowledge of the phenomenon under investigation is not too underdeveloped, when generalization is an important intended contribution, when the variables and the context can be detailed and when the empirical evidence sought concerns how variables are related, where the relations hold and to what extent a given relation is present. 4.2 The survey research process Link to theoretical level Design Pilot test Collect data Analyse data Generate report 9
4.3 What is needed prior to survey research design? 1. Researcher should have a clear idea of what the problem is about and should discuss it in depth 2. Researcher should map the problem in the existing literature in order to be cognizant of what is already know and what is still unknown 3. Researcher should clarify the intended contribution of the research. Exploratory survey research may pose several problems with regard to framing the research, as little support is provided by the available literature. In contrast, theory-testing is based on more established knowledge and, therefore, the focus of the researcher is easier to communicate clearly. Establish a conceptual model 1. Construct names and nominal definitions 2. Propositions; presentation and discussion of the role of the constructs, the important linkages between them and an indication of the nature and direction of the relationships 3. An explanation 4. Boundary conditions; definition of the conditions under which the researcher might expect these relationships to hold Before a researcher can talk about how to collect data, it is necessary to - Define the unit of analysis corresponding to the level of reference of the theory - Provide and test the operational definitions for the various constructs - Translate the propositions into hypotheses The unit of analysis refers to the level of data aggregation during the subsequent analysis. Defining the unit of analysis at a theoretical and empirical level is important to avoid the cross-level inference problem and even the possibility that a reader might fail to understand the level to which the research refers. In developing operational definitions, the first problem is to translate the theoretical concepts into observable and measurable elements. The list of observable elements in each construct should be developed before writing the items/questions that constitute the measure. When the operational definition has been developed, the researcher should test it for content validity. The content validity of a construct measure can be defined as the degree to which a measure s items represent a proper sample of the theoretical context domain of a construct. Evaluating the face validity of a measure can indirectly assess its content validity. A hypothesis is a logically conjectured relationship between two or more variables (measures), expressed in the form of testable statements. A hypothesis can also test whether there are differences between two groups with respect to any variable(s). 4.4 How a survey should be designed Before embarking on a survey, the research should consider the suitability of the survey method and the overall feasibility of the research project. In survey research there is a trade- 10
off between time and cost constraint, on the one hand, and minimization of four types of errors, on the other hand: - Sampling error - Measurement error - Statistical conclusion error - Internal validity error The sample - Population refers to the entire group of people, firms, plants or things that the researcher wishes to investigate - An element is a single member of the population - The population frame is a list of all the elements in the population from which the sample is to be drawn - A sample is a subset of the population it comprises some members selected from the population - A subject is a single member from the sample - Sampling is the process of selecting a sufficient number of elements from the population so that by studying the sample, and understanding the properties or the characteristics of the sample subjects, the research will be able to generalize the properties or characteristics to the population elements. Sampling overcomes the difficulties of collecting data from the entire population, which can be impossible or prohibitive in terms of time, costs and other human resources. Data collection methods - Postal survey - Face-to-face - Telephone - Email/online And a lot of other very specific information on how to design a survey 4.5 Pilot testing the questionnaire After designing the survey, the research has to test it. The pre-test should be done by submitting the questionnaire to colleagues, industry experts and target respondents. The role of colleagues is to test whether the questionnaire accomplishes the study objective. The role of industry experts is to prevent the inclusion of some obvious questions that might reveal the investigator s ignorance in some specific areas. The role of the target respondents is to provide feedback on everything that could affect whether and how the targeted respondents answer the questions. Non-respondent can limit the generalizability of the results, in the pilot-testing the researcher should identify a way to address this problem. Reliability indicated dependability, stability, predictability, consistency and accuracy and refers to the extent to which a measuring procedure yields the same results in repeated trials. - Test-retest method - Alternative form method 11
- Split halves method - Internal consistency method While content validity refers to the degree to which the meaning of a set of items represents the domain of the concept under investigation, construct validity refers to the degree to which scores obtained from using a set of items behave as expected. You can only have construct validity if there is content validity. When checking construct validity, first the construct unidimensionality has to be assessed. A measure must satisfy two conditions in order to be unidimensional. First, an empirical indicator must be significantly associated with an underlying latent variable. Second, it can be associated with one and only the latent variable. The second step in checking construct validity is assessing the convergent and discriminant validity. Convergent validity refers to the degree to which multiple attempts to measure the same concept are in agreement. Discriminant validity refers to the degree to which measures of different concepts are distinct. Criterion-related validity is when an instrument is intended to perform a prediction function, validity depends entirely on how well the instrument correlates with what it is intended to predict. Criterion-related validity is established when the measure differentiates subjects on a criterion it is expected to predict. Establishing concurrent validity or predictive validity can do this. Concurrent validity is established when the scale discriminates subjects that are known to be different. Predictive validity is the ability of the measure to differentiate among subjects with respect to a future criterion. 4.6 Advancements in theory formalization and survey design If a construct is conceptualized as a reflective construct, then the latent variable determines the construct indicators. If the construct is conceptualized as a formative construct, then the indicators determine the latent variable. Researchers agree that the criteria used to evaluate reflective constructs, should not apply to formative constructs. Control variables are extraneous, not directly connected with the main theory and hypothesis, but presumably able to cofound with them. The idea behind control variables is that they essentially remove the variation explained by a predictor variable that has some readily identifiable alternative explanation. 4.7 Survey execution Fundamentally, in this phase the researcher has to repeat the pilot-testing activities with a large sample: - Approaching companies/respondents and collecting data - Controlling and reducing the problems caused by non-respondents - Performing data input and cleaning - Treating missing data - Assessing measurement quality - Providing feedback to respondents 12
When data is missing three strategies can be used - Deletion - Replacement based on estimation - Mean based - Regression based - Model-based - Hot-deck imputation - Model-based strategy 4.8 Data analysis and interpretation of results In order to acquire knowledge about the characteristics and properties of the collected data, some preliminary data analyses are conducted, usually before performing measurement quality assessments or testing the hypotheses. Preliminary data analysis is performed by checking central tendencies, dispersions, frequency distributions and correlations. Significance tests can be grouped into two classes: parametric and non-parametric. Parametric tests are considered more powerful because their data are typically derived from interval and ratio measurements whose likelihood model is known, except from some parameters. Nonparametric tests are used with nominal and ordinal data. 4.9 Information that should be included in articles Readers should be able to: - Understand what has been done - Critically evaluate what the work has achieved - Replicate the work or compare the results with similar studies 4.10 Ethical issues in survey research Read chapter 2 Chapter 5 Case research 5.1 Introduction Pure case research is based on analysis of a limited number of cases to which, at best, only limited statistical analysis can be applied. There are several challenges in case research: it is time consuming, it needs skilled interviewers and care is needed in drawing generalizable conclusions. It can lead to new and creative insights and development of new theory, and have high validity with practitioners. 5.2 When to use case research Strengths of case study - The phenomenon can be studies in its natural setting and meaningful, relevant theory generated from the understanding gained through observing actual practice - The case method allows the questions of why, what and how to be answered with a relatively full understanding of the nature and complexity of the complete phenomenon 13
- The case method lends itself to early, exploratory investigations where the variables are still unknown and the phenomenon not at all understood Case studies can be used for different types of research - Exploration: in the early stages of many research programmes exploration is needed to develop research ideas and questions - Theory building: theory can be considered as being made up of four components: definitions of terms/variables, a domain, a set of relationships, and specific predictions. - Theory testing: often in conjunction with survey-based research in order to achieve triangulation - Theory elaboration/refinement: In inductive research, propositions or frameworks are the outcome of the analysis. Deductive research starts with hypotheses that are then empirically tested. In abductive research, an extant theory is identified and explored through an iterative process eventually leading to propositions or frameworks and extension to existing theory. 5.3 The research framework, constructs and questions Case research is especially good in answering how and why questions. Such question can lead to both theory testing and theory development. First a conceptual framework needs to be created. The next step is to design the initial research question. Underlying the research questions, it is likely to be one or more constructs; a priori specification of constructs is valuable since: it permits researchers to measure constructs more accurately. If these constructs prove important, then researchers have a firmer empirical grounding for the emergent theory. 5.4 Choosing cases For a given set of available resources, the fewer the case studies, the greater the opportunity for depth of observation. Single in-depth case studies are often used in longitudinal research. In embedded cases a single firm is studied, but this firm is involved in a number of different cases or unit of analysis. But single cases have some limitations: first of all the generalizability, the misjudging of a single event, and of exaggerating easily available data. Multiple cases may reduce the depth of study when resource is constrained, but can both augment external validity and help guard against observer bias. Sampling cases involves two actions: - Setting boundaries that define what you can study and connect directly to the research question. - Creating a sample frame to help uncover, confirm, or qualify the basic processes or constructs that underpin the study Each case should be selected so that it either predicts similar results (a literal replication) or that it produces contrary results, but not for predictable reasons (theoretical replication). When selecting cases it is also important to consider what are the parameters or factors that define the population and are to be held constant across the sample. Controls rely on the 14
selection of the phenomena during the study s experimental stage for their control. This allows particular factors to be, in essence, held constant while other are left free to vary as they would naturally. 5.5 Developing research instruments and protocols The primary source of data in case research is structured interviews, often backed up by unstructured interviews and interactions. Other sources of data can include personal observation, informal conversations, attendance at meetings and events, surveys administered within the organization, collection of objective data and review of archival sources. Reliability is the degree to which a measure is free from random error components. Validity is the extent to which a measure only reflects the desired construct without contamination from other systematically varying constructs. A common format for the protocol is the funnel model. This start with broad and open-ended questions first, and as the interview progresses, the questions become more specific and the detailed questions come last. Saturation occurs when the addition of respondent does not add any different insights or theoretical advancement. 5.6 Conducting the field research An underlying principle of case research in collection of data is that of triangulation, the use and combination of different methods to study the same phenomenon. The chapter contains a very specific explanation of how to conduct field research, steps are the same as in the assignment and in the slides. 5.7 Reliability and validity in case research Construct validity is the extent to which we establish correct operational measures for the concepts being studied. If the construct as measured can be differentiated from other constructs, it also possesses discriminant validity. Internal validity is the extent to which we can establish a causal relationship whereby certain conditions are shown to lead to other conditions, as distinguished from spurious relationships. External validity is knowing whether a study s finding can be generalized beyond the immediate case study. Reliability is the extent to which a study s operations can be repeated, with the same results. 5.8 Data documentation and coding The first step in documentation is a detailed write up of each site following the research protocol structure. Another step is to the coding of the observations and data collected in the field. It is important to try to reduce data into categories. In coding, the first step is open coding - data are fragmented or taken apart. The next step is axial coding putting together the data in new ways. The final step is selective coding selecting a core category and relating it to other categories. 15
5.9 Analysis There are two steps in analysis: analysis of within-case data, and searching for cross-case patterns. To start the analysis of within-case data, it is useful to create an array or display with all the data. The overall idea is to become intimately familiar with each case as a stand-alone entity, and to allow the unique patterns of each case to emerge before you seek to generalize across cases. Now, the researcher can start looking for explanation and causality. Two options are suggested: using a case dynamics matrix or making predictions and then use the case to test them. A third method is using the causal network. When searching for cross-case patterns, also a display should be created first. An effective analytical approach is then to pick a group or category and to search within groups for similarities or differences. Chapter 9 Modelling and simulation 9.1 Introduction Model-based quantitative research is research where models of causal relationships between control variables and performance variables are developed, analysed or tested. Quantitative models are based on a set of variables that vary over a specific domain, while quantitative and causal relationships have been defined between these variables. 9.2 Origins and development of model-based research in operations management This section explains how research in operations management has developed over the years. 9.3 Methodologies in quantitative modelling Quantitative model-based research can be classified as a rational knowledge generation approach. It is based on the assumption that we can build objective models that explain (part of) the behaviour of real-life operational processes or that can capture (part of) the decision making problems that are face by managers in real-life operational processes. It is important to stress that the relationships between the variables are describes as causal, meaning that it is explicitly recognized that a change of value α in one variable will lead to a change of f(α) in another variable. The first class of research, axiomatic research, is primarily driven by the model itself. The primary concern of the researcher is to obtain solutions within the defined model and make sure that these solutions provide insights into the structure of the problem as defined within the model. Axiomatic research produces knowledge about the behaviour of certain variables in the model, based on assumptions about the behaviour of other variables in the model. Axiomatic research is most of the times prescriptive; prescriptive research is primarily interested in developing policies, strategies and actions to improve over the results available 16
in the existing literature, to find an optimal solution for a newly defined problem or to compare various strategies for addressing a specific problem. The second class of research, empirical research, is primarily driven by empirical findings and measurements. The primary concern of the researcher is to ensure that there is a model fit between observations and actions in reality and the model made of that reality. This type of research can be both prescriptive and descriptive. Descriptive empirical research is interested in creating a model that adequately describes the causal relationships that may exist in reality, which leads to understanding of the processes going on. The methodology for both types is very roughly - Conceptualization - Modelling - Model solving - Implementation 9.4 How to conduct quantitative research in operations management Axiomatic descriptive research aims at creating managerial insights into the behaviour of operational processes and their control. Axiomatic prescriptive research aims at developing rules and tools for managerial decision making. Axiomatic descriptive research start with a condensed description of the characteristics of the operational process or the operational decision problem that is going to be studied. The conceptual model description should use as much as possible concepts and terms that are accepted as standards published in scientific literature. The second phase in the research is the specification of the scientific model of the process or problem. The scientific model must be presented in formal, mathematical term such that a mathematical/numerical/simulation analysis or study can be performed. Analytical research aimed at managerial insights does not try to provide the manager with a direct answer to his question; instead, it construct an idealized model of the problem, ensuring that an answer for the idealized problem can be found with the analytical methods and tools available. At this point validity gets important, three ways to claim validity: - Researcher may refer to scientifically accepted axiomatic descriptions of the system studied that contain evident of the occurrence of the characteristics in real life - Researcher may refer to published empirical research that shows the existence in real life of the characteristics captured in the model - Researcher may refer to earlier published empirical research that uses the same modelling assumptions An extensive example can be found in this section Decision rule oriented research is more ambitious than managerial insight orientated research. Its goals is to provide the manager with decision rules that, when applied, achieve optimal or near optimal performance with respect to some criterion function. Results in this field are more difficult to obtain and modelling assumptions are often less strict. In particular, the 17
results must be obtained for realistic large-scale models, or must be easily scalable to a realistic size. A Slightly different approach is taken when the result is obtain with computer simulation instead of mathematical analysis. This model is used when the model/problem is too complex for formal mathematical analysis. This type of research generally leads to lower scientific results than research using mathematical analysis, but the scientific relevance of the process/problem studies can be much higher. The first step in simulation study is to build a (very rich) conceptual model. Secondly, a justification of the research method has to be given since the contribution to the scientific results is often low. Then a justification of the solution or hypotheses to be tested have to be given, since no related proof can be generated. After these justifications, the model can be developed. The next step is the set-up of the experimental design. One of the last steps is to perform a statistical analysis of the results. The last step is the interpretation of the results. Model-based empirical research is concerned with either testing the (construct) validity of the scientific models used in quantitative theoretical research, or with testing the usability and performance of the problem solutions obtained from quantitative theoretical research, in real life operational processes. The first step in model-based empirical research is the identification of the basic assumptions regarding the operational process underlying the theoretical models or problems. The second step is to identify the type of operational process and the type of decision problem regarding this operational process to which the basic assumption are assumed to apply. The third step is that operational, objective criteria must be developed for deciding whether or not a real-life operational process belongs to the class of operational processes considered and for identifying the decision system is the operational process that represents the decision problem considered. The fourth step is to derive, from the basic assumptions, hypotheses regarding the behaviour of the operational process. The fifth step is to develop an objective way to do measurement or to make the observations. The sixth step consists of applying the measurement and observation systems, collecting and documenting the resulting data. The seventh step is the processing of the data. The last step is to interpret the results. 18