Case studies
Case study research is a research method based on empirical data (obtained from a …eld study) which study a phenomenon while taking into account the context in which the phenomenon occurs, since the borders separating the phenomenon and the context are not clearly de…ned. By means of case studies new theories can be generated and already existing theories can be tested or re…ned by using an inductive process based on diverse document and reference sources. These sources can be of a qualitative or quantitative nature: direct observation, interviews, questionnaires, …les, economic series, etc.
There are various major works in some way related to case study research. One of them is the work by Glaser and Strauss (1967) on grounded theory ("teoría fundamentada" in Spanish, see Andreu Abela et al., 2007), which is a methodological approach oriented towards theory building (conceptualising) by taking qualitative data analyses, where data are systematically gathered and analysed. At the beginning of the 80s, Yin (1984) made a signi…cant contribution by clarifying the methodological basis of case studies. He analysed the multiple weaknesses and stereotypes applied at that time to case study research in social sciences and put forward some strategies, which right from the design stage of research, enable consistency, internal validity and external validity to be increased as well as the reliability of the results obtained through case studies. He also suggested a typology for designing case studies, established what analytical generalisation and replication logic consisted of, and detailed the kind of research that was most suited to the use of case studies.
We can also highlight the works of Miles and Huberman (1984), who proposed various techniques for structuring and observing relationships in quantitative data analysis, and the renowned paper by Eisenhardt (1989), which consists of a synthesis of the previous works and contributes a multistage process for generating new theories from case studies. Finally, the contributions made by Ellram (1996) and Voss et al. (2002) are also relevant within an Operations Management focus. The …rst contribution show how case study method can be used in business research and more speci…cally, in business logistics research. The second contribution falls within the context of the methodology training seminars for PhD students that have been held annually since 1998 as part of the annual EurOMA conference.
Action-Research
The term action-research, coined in the 40s by the director of the "Center for Group Dynamics" at the MIT, Kurt Lewin, denotes a research method that combines a process of participation on the reality being researched with the generation of new knowledge. To achieve this, he con- scientiously and deliberately used a cyclical process, a “spiral of steps”(Figure 3-2), consisting in: planning, acting, evaluating the action, start planning again and so on, successively.
Lewin (1946) argues in favour of the need to use this methodological approach based on the idea that everything connected with the human and social factor, when it becomes an object of
Recogida de datos Retroalimentación datos Análisis de datos Planificación de la acción Implementación Evaluación Control Recogida de datos Retroalimentación datos Análisis de datos Planificación de la acción Implementación Evaluación Control Ciclo 1 Ciclo 2
Figure 3-2: Action-Research cycles. Adapted from Coughlan and Coughlan (2002)
study, exhibits a series of special characteristics (interiority, non-determinism and uniqueness of each human being) that require a di¤erent methodology from that traditionally adhered to in the “hard” sciences. In the research construction process, action-research must involve both the researchers and the actors taking part in the experience.
Lewin’s work had a large in‡uence on the founders of the Tavistock Institute and, particu- larly, on Eric Trist. For this institution, the action-research approach was a core component of the work method they used with industry and business. Trist’s works and the 1961 Norwegian Industrial Democracy Project jointly provided the seed for the founding of a network of research centres to promote action-research in various European countries. This was a major contribu- tion to the spread of action-research approaches and experiences, some of the most marked being the works of Trist and Bamforth (1951), Emery and Trist (1965 and 1972), Emery and Thorsrud (1976), Hill (1976), Herbst (1974) and Davis and Cherns (1975).
Combining the generation of new knowledge with the participation in a given reality, on the one hand, requires quality criteria that are di¤erent from those traditionally used in a positivist approach to science (Susman and Evered, 1978). On the other hand, researchers need to become aware of their dual role in action-research. Apart from generating new knowledge, the
researcher acts as an agent of change by facilitating action and re‡ection from the inside of the organisation. As a consequence of this dual focus, action-research has sometimes been labelled as "consultancy dressed up as research". Gummesson (2000) presents various characteristics that di¤erentiate action-research from consultancy, such as the rigour of the research process and its documentation, the use of theoretical evidence (in consultancy empirical evidence is su¢ cient) or the cyclical process of action-research (in consultancy the process is usually linear: approach the problem, take action and …nish).
Survey research
This method of research basically consists in gathering information from a speci…c sample in a population and submitting the information obtained to appropriate statistical processing with the purpose of contributing to knowledge on a particular area of interest (typically, by generalizing by statistical inference the results observed in the sample to the population as a whole). Gathering information implies that it is necessary to make contact with a set of individuals through postal or electronic questionnaires, telephone calls, personal interviews, etc, and formulating a set of questions about themselves or about the organisations of which they are part
Forza (2002) describes the steps to be followed during a research project that uses surveys as a base method. As a prior step to this design, a theoretical model or conceptual framework on the phenomenon under study is required. The …rst step should consist in transferring concepts and proposals from the conceptual framework to the survey design. The second step is to design the research. This includes drawing up the questionnaire and choosing the sampling method to be used. When the tools required to conduct the research have been designed (sample questionnaire), it is advisable to …ne-tune this design using a pilot test, which, in addition, will let the procedures be set up for managing the questionnaires submitted. Once the samples and …nal questionnaire are available, the next step is to collect the de…nitive data, then analyse the data and write the report to publicise the …ndings.
In Operations Management, many of the di¢ culties that arise from survey research are due to the sampling process (selecting the population elements that will be part of the sample), either because not enough elements have been selected (sample size), or because care has not
been taken to take a su¢ ciently representative sample (randomness). An unsuitable sample design limits the possibility of generalising results (Kotzab, 2005). Obtaining a su¢ cient sample size to ensure the required level of statistical signi…cance is often a question of increasing the questionnaire’s response rate. Frohlich (2002) presents several strategies for this purpose, such as sponsorship by professional associations or the sending of periodic reminders. Distributing the questionnaires via email and the use of Web questionnaires makes it easier to complete the questionnaire and avoids manual data having to be entered for their subsequent automatic processing. To ensure that the sample is representative there are various probabilistic and non- probabilistic sampling techniques, like simple random sampling, systematic sampling, strati…ed sampling and convenience sampling. Only probabilistic sampling techniques allow extending (through statistical inference) the results observed in the sample to the population as a whole. Survey research has become increasingly important in the area of Operations Management. On the one hand, this is taken as a positive symptom for the area for two reasons. Firstly, because the availability of di¤erent research methodologies and particularly …eld-based research methodologies (cases and surveys), traditionally less widely used in the area of Operations Management than mathematical models and simulation, is an indicator of the discipline having reached maturity (Malhorta and Grover, 1998). Secondly, because one of the main applications of survey research is to test the validity of pre-existing theory models with empirical data. Although Operations Management is a relatively new discipline and nowadays there is a greater need to construct new theories than to test already existing ones, the growth of survey-based research is indicative of the extent to which theory has moved forward in the area (Filippini, 1997). However, it should also be pointed out that badly planned and badly implemented survey research contributes little or is even a handicap to the advancement of knowledge in this area. Therefore, it is would appear essential to enhance the quality of this kind of research in the area of Operations Management. Many authors consulted coincide in the opinion that, in general, there is room for improvement in the application of survey research methods.
Quantitative models
The use of analytical approaches or models for solving problems related to managerial decision- making form the basis of the most longstanding research method in the area of Operations
Management. These models are representations or idealisations of reality which enable us to predict and explain phenomena with a high degree of accuracy, but by performing in a way that is less complex and less di¢ cult to control than reality (Acko¤ and Sasieni, 1968). In order to make them tractable from a mathematical point of view, the models are necessarily simpler than reality. They are made up of abstractions or idealisations of reality that highlight how the major variables of the problem are related from the viewpoint of the researcher construct- ing the model (Acko¤, 1971; Sagasti and Mitro¤, 1973). Hax (2007) emphasises the impact that operational research has (and still has) on the design and control of the most operative processes. This is because operational research techniques adapt particularly well to the context of "programmable" decisions, that is to say, decisions where the problem presents a high level of structuring and the in‡uence of the human factor is negligible, such as production planning and control or resource allocation.
Depending on their ultimate purpose, quantitative models have traditionally been classi…ed as normative models and descriptive models. Normative models allow the partial capture of the decision-making problems that arise in actual production and logistic processes. They consist of techniques that select the best or the preferred solution from the di¤erent alternatives (which may be in…nite) by considering a set of criteria elected by the decision-maker. The normative models are so-called because this type of technique gives the manager a norm or standard on which to base decision-making, a rule for choosing a particular policy. Included in this category are mathematical programming models, either deterministic (Kantorovich, 1939; Dantzig, 1963) or stochastic models, decision-theory models (Raifa, 1968; Schlaifer, 1969), multicriteria decision techniques (Zeleny, 1975) and goal programming (Ignizio, 1976).
Descriptive models characterise and explain the structure of a speci…c phenomenon. Their interest lies in the fact that if the causes behind a speci…c phenomenon are understood its future behaviour can be predicted. Descriptive models are also used to choose the most advisable solution from a set of alternatives. However, instead of it being the model itself that points to the choice in accordance with some prede…ned criteria, with descriptive models it is the decision- maker who chooses the best alternative according to the predictions made by the the descriptive model. The category of descriptive models includes demand forecasting techniques based on time-series (Box and Jenkins, 1976), system dynamics models (Forrester, 1961), queueing theory
and discrete event simulation models (Law and Kelton, 1991).
3.3.2 Comparative analysis of research methods in Operations Management: