The Translation Process of the Research

In this section we introduce and outline the principle elements that characterize the translation process.

1. Concepts and Data

1.1. The theoretical realm

The theoretical realm encompasses all the knowledge, concepts, models and theories available, or in the process of being constructed, in the literature on a subject. With respect to translation, however, researchers are primarily con­cerned with concepts – or to be more precise, with their particular definition of each concept studied. For example, Venkatraman and Grant (1986) found that the concept of strategy is defined in numerous different ways in management research. Rather than being a question of one term referring to multiple con­cepts, it is more a case of a number of different perspectives being grouped together under one label. Zaltman et al. (1973) also make a valuable distinction between a concept and the terms used to designate it. Consequently, although reading through the literature is the starting point of the research design process, conceptual definitions adopted by the researcher will always condition the translation process he or she employs. For this reason, throughout this chapter the term ‘concept’ should be understood as synonymous with ‘concep­tual definition’.

1.2. The empirical realm

The empirical realm encompasses all the data that can be either collected or made use of on the field. This data may include facts (a meeting, the date of an event), opinions, attitudes, observations (of reactions or behaviors) or docu­ments (files, reports). When embarking upon a work of research in manage­ment, researchers delimit, by their interest and their attention, an area of study within this empirical realm. This area can relate to a branch of industry, to a population of organizations or a single company, or to particular groups of actors. Moreover, the fieldworker’s presence may demarcate this area of study in time; thus it may comprise the lifespan of the studied phenomenon – for example, a project, a structural reform or a change of leadership. Mintzberg (1973), in his study of the role of the manager, defined an area within an empirical realm delimited in space (managers and their activities) and in time (daily life).

When researchers base themselves in the empirical realm, they have a cir­cumscribed body – or a ‘closed set’ (De Groot, 1969) – of data (facts, opinions, attitudes, observations and documents) at their disposal. Although this data often approximates conceptual items, empirical elements are never able to represent completely, nor to duplicate, the significance of the underlying theo­retical concepts (Zeller and Carmines, 1980).

2. Moving from One Realm to the Other

Whichever realm (theoretical or empirical) they are working in, researchers have particular elements at their disposal (concepts or data). To move from one realm to the other, these elements have to be construed in the language of the second realm (Zeller and Carmines, 1980). To go from the theoretical to the empirical involves translating a conceptual definition in order to pinpoint ele­ments of the empirical realm that most closely illustrate it. When, on the other hand, a researcher wants to link empirical elements to the theoretical realm, data collected in the field has to be translated into the concepts that underlie it.

As the following example illustrates, a conceptual definition has no objective correspondence in the empirical realm. That is to say, for any given concept – any given conceptual definition – there is no empirical data that corresponds exclusively to it. Similarly, researchers who wish to move from the empirical to the theoretical work from elements (data) that may be understood to be the manifestation of any of a number of potential concepts.

Example: Non-exclusive correspondence

A researcher may choose to consider the concept of high velocity environments either through the annual rate of innovation within a sector, or through the rate of the renewal of skills within companies operating in this sector. However, the empiri­cal element ‘rate of the renewal of skills within companies’ could also be used to consider the concept of resources when looking at the correlation between a com­pany’s resources and its performance.

As Figure 7.1 illustrates, the translation process consists essentially of con­necting a concept to one or more empirical elements (when the researcher has been operating in the theoretical realm) or of connecting one or more empirical elements to a concept (when the researcher has been operating in the empirical realm).

The theoretical and the empirical realms offer the researcher resources of quite different natures (conceptual definitions on the one hand, empirical ele­ments on the other). As we will see, the translation process involves two dis­tinct processes, and the form it takes is closely related to the realm in which the researcher first starts to think about a problem. We call the passage from the theoretical to the empirical ‘measurement’, and the opposite process, which takes us from the empirical towards the theoretical, ‘abstraction’.

2.1. Measurement

Several definitions of ‘measurement’ have been proposed by writers in the social sciences. The definition we will use here, is that of DiRenzo (1966), to whom measurement ‘refers to the procedures by which empirical observations are made in order . . . to represent the conceptualizations that are to be expla­ined’. According to Larzarsfeld (1967), however, measurement must be consi­dered in a broader sense in the social sciences than in fields such as physics or biology. A researcher in the social sciences may take measurements that are not necessarily expressed in numbers, in which case the measurement process comprises three, or perhaps four, principal stages. These stages are outlined below.

2.2. Abstraction

In the above example we considered a situation in which the researcher moved from the theoretical to the empirical realm. However, research work in manage­ment can also begin in the empirical realm, in which case the translation process is no longer a question of taking a measurement, but instead requires the researcher to operate an abstraction. The researcher accumulates a body of data, which he or she then tries to reassemble within a broader framework from which an underlying conceptualization may be established.

For this translation – or abstraction – process, the researcher carries out pro­gressive regroupings of the empirical data collected, so as to draw out more conceptual elements from the facts, observations and documents at hand. The researcher codes the data, formulates indicators (Lazarsfeld, 1967), establishes categories, discovers their properties and, finally, attempts to propose a con­ceptual definition. As we shall see, abstraction may serve either a descriptive or a theoretical purpose.

We have seen that the researcher can operate in either the theoretical or the empirical realm. We have also seen that the translation process entails ques­tioning how we can move from one realm into the other. More precisely, it involves translating the elements researchers have at their disposal initially, into the language of the realm to which they wish to go. In the case of measurement, the translation process consists of establishing indicators that correspond to a given concept. In the case of abstraction, the translation process entails decid­ing how the data that has been gathered is to be categorized.

3. Translation Methods

Several resources are available to the researcher when establishing connections between concepts and data. We will look first at measuring instruments, and then we will consider abstraction processes.

3.1. Measurement instruments

Understanding the nature of indicators The object of measurement is to estab­lish indicators that correspond to a given concept. These indicators make it pos­sible to associate a value or a symbol with part of the concept, which is why we refer to them as measurement instruments. Either a single indicator alone or a group of indicators can constitute a measurement instrument. Boyd (1990) uses a number of indicators to measure the complexity of an environment, including geographical concentration, the number of firms in the industry and the distri­bution of market shares. At the same time, however, he uses a single indicator – the rate of sales growth – to measure the environment’s dynamism. Indicators can also help the researcher to determine the type of data to collect. In measuring the technological intensity of intercorporate alliances by the ratio of research and development budget to sales, for example, Osborn and Baughn (1990) were led to compile a precise type of information on the firms they studied – putting together a resister of their average research and development budgets and sales figures.

Measurement instruments can be qualitative or quantitative. In their research into the relationship between strategy-making and environment, Miller and Friesen (1983) used a variable broken down into seven items to represent changes that might occur in a company’s external environment. A seven-point Likert scale was associated with each of these items. For example, actors were asked to give the following sentence a rating from 1 to 7 (from complete agree­ment to total disagreement): ‘The tastes and preferences of your customers in your principal industry are becoming more stable and more foreseeable’. The measures used were metric and the indicator quantitative – as they were in Osborn and Baughn’s research, where the instrument used to measure the technological intensity of intercorporate alliances was a ratio of numerical data.

Controlling the number of indicators Several indicators can usually be found for a given concept. This means that a researcher working on environmental dynamism will find various types of indicators used in the literature. In Miller and Friesen (1983), for example, this concept is measured by the actors’ per­ceptions of the amount and the predictability of change occurring within an environment (in areas of consumer tastes, production techniques and ways in which companies competed). Boyd (1990), however, measures the concept of environmental dynamism purely on the rate of sales growth.

Pre-existent correspondences between concepts and indicators are available to researchers in the form of proxies. Used often in research in management, a proxy is an indirect measurement of a concept. Performance, for instance, can be measured by the proxy: ‘share prices’. Similarly, the turbulence of a branch of industry can be measured by the proxy: ‘number of companies entering and leaving the sector’. There are also instruments for which the number of indica­tors is preset, as illustrated by the work of Miller and Friesen (1983). When researchers use this type of instrument they are typically led to calculate an index: for example, the average of the ratings given to each of the seven items.

In this way, the researcher – as Lazarsfeld (1967) recommends – can define indicators using measuring instruments other than graded scales. Such indica­tors are particular combinations of indicators that can synthesize a part of a concept. The researcher must take care, however, not to misrepresent or distort the relationship that each of the indicators included have to this concept. For example, a researcher may decide to measure performance by constructing an index expressed by the ratio of turnover to profits. In this case he or she must ensure that variations in the two indicators are reflected by an equally significant variation in the index. The researcher who expects increased performance to be reflected in an increase in turnover and profits, also expects an increase in this index. But where are we then? The increase in the numerator is compensated by that of the denominator: the index remains stable, and the measure becomes inoperative.

3.2. Abstraction methods

If researchers decide to begin their work in the empirical realm, they start out with a body of related empirical data which they then have to interpret. This process entails questioning the level of abstraction to which they wish to subject these empirical elements. They may try to propose a concept or a body of inter­related concepts, or even to establish a model or a theory. The level of abstrac­tion initially envisaged by the researcher has an influence on the level of sophistication of the processes and methods that will be used to carry out this abstraction.

In the abstraction process, researchers are confronted with the problem of coding the empirical elements they have collected. Strauss and Corbin (1990) identify three methods of coding data: open, axial and selective.

Open coding Open coding basically consists of naming and categorizing phenomena by thoroughly examining the data collected in relation to it. By collating this data and classifying it into categories – sets and subsets – the researcher can progressively reduce the number of units that are to be mani­pulated. The researcher then has to try to label these categories. While in some cases existing conceptual definitions can be compared with these new cate­gories and found to be appropriate, it is generally advisable to try to propose original definitions, drawn from fieldwork. Glaser (1978) refers to such defini­tions as ‘in vivo’. Once established, these categories should be refined by high­lighting their intrinsic properties and the continuum along which they may fluctuate.

Axial coding A researcher can make the abstraction process more sophisticated by using axial coding. Based on the same principle as open coding, axial cod­ing goes further to specify each category in terms of causality, context, actions and interactions, and their consequences.

Selective coding The principle of selective coding consists in going beyond simple description and towards conceptualization. This involves theoretical integration or construction (Strauss and Corbin, 1990). Selective coding aims at defining a central category, to which the researcher tries to link up all of the properties of the categories that have been established previously. Strongly con­nected to this type of abstraction process is the idea of identifying what Schatzman and Strauss (1973) call a ‘key linkage’. This can refer to a metaphor, a model, a general outline or a guiding principle that researchers can use to group their data.

Key linkage serves as a basis for grouping not only data, but also the cate­gories themselves (through similarities in properties and dimensions). After carrying out this abstraction, the researcher obtains central categories that are connected not only on a broad conceptual level, but also to each specific pro­perty of the other categories.

Source: Thietart Raymond-Alain et al. (2001), Doing Management Research: A Comprehensive Guide, SAGE Publications Ltd; 1 edition.