TRABAJABILIDAD

Pepper (1961) describes contextualism as a synthetic and dispersive theory Synthetic in that it takes as its subject patterns of historical events, rather than discrete facts, and dispersive in that it assumes that these patterns are loosely structured, rather than connected by fundamental laws or algorithms (Tsoukas 1994). Events are intrinsically complex and composed o f interconnected patterns of activities, which are constantly changing over time (Pepper 1961). Although, the reality of the structure of these events is ‘vigorously asserted’

The structure of events can be described in terms of their ‘quality and texture’ (Pepper 1961) Quality refers to the overall meaning or ‘character of the event’, while texture is the ‘details and relationships that make up that character’ including its context Tsoukas (1994) suggests that these features are ‘two sides o f the same coin’ He argues that when we perceive the wholeness of an event, we suppress its detail, and when we analyse the detail, we ignore the overall pattern Contextualist researchers

therefore face the difficulty of capturing both the quality and texture of the patterns of events being studied.

Pettigrew (1985, 1990 and 1997) suggests contextualist research must aim to develop holistic explanations. He argues that this requires phenomena to be examined at multiple vertical and horizontal levels of analysis and the interconnections between those levels through time recognised. The vertical levels of analysis refer to the social and economic, industrial and internal contexts in which events and processes are embedded The horizontal level o f analysis refers to the ‘sequential interconnectedness of phenomena’ in time (Pettigrew 1990). Managers’ actions will be influenced by the past, the present and expectations about the future.

Methodology

Our epistemology, together with our ontological assumptions, had significant implications for the research methods we adopted We needed to:

collect data using multiple methods - acquire data from multiple levels o f analysis

acquire longitudinal data and maintain the temporal component of this data during analysis

describe events in time and explore the influence of context and management action on those events.

Chapter 3 - Methodology

Multiple Methods and Multiple Levels of Analysis

The post positivist view that different aspects of reality are revealed by the use of different methods was central to the design of this project. Our research questions required not only the identification of phenomena at an industry level but also an explanation of the firm level features that cause such phenomena. Pettigrew (1990) describes these types o f questions as the ‘what, why and how’ of organisational research. To these we should perhaps add ‘when’ to emphasis the importance o f time to our study. Given such a broad set of research questions it would be unrealistic to expect one method to meet all our requirements.

Traditionally, researchers adopting a contextual approach have used the comparative case study methodology (Pettigrew 1990 and Eisenhardt 1989). This qualitative method has considerable strengths It is particularly useful for revealing and understanding the micro processes within a firm and how these processes interact with the firm’s internal and external contexts However, it also has its weaknesses In particular, the method is limited in its ability to ‘see the wider terrain’ (Pettigrew 1997). So, while the case study method was ideal for addressing our questions on firm level phenomena, it is not suited to detecting patterns at an industry level

It was apparent that a multiple method approach was required for our study. There is considerable debate within the methodological literature about the appropriateness of such a research design (Jick 1979, Greene et al 1989 and Creswell 1994). This focuses on whether qualitative and quantitative methods should be mixed. Crotty (1998) suggests that there has been a ‘great divide’ portrayed in ‘most research textbooks’,

with qualitative and quantitative research being seen as polar opposites Typically, quantitative methods would be deemed appropriate for positivist research while qualitative methods would be deemed appropriate for phenomenology and other subjectivist epistemologies.

Greene et al (1989) suggests there are three groups in the debate - purists, pragmatists and those who adopt a middle ground position. Purists (e g. Guba and Lincoln 1998) argue that particular methods are intrinsically related to a particular ontology and should never be mixed with methods associated with other views of reality. Creswell (1994) argues that academic journals are intrinsically purist due to the limitations of space in which researchers can adequately detail the use of multiple methods Pragmatists take the opposite view (Miles and Huberman 1998, Jick 1979 and Mathison 1988). Methods are independent of ontological and epistemological positions (Crotty 1998). For instance a case study based on interviews may be designed in a positivistic manner to test or prove false hypotheses o r in a phenomenological way to build a narrative of the situation being studied (Remenyi et al 1998). As a result, methods can be mixed and matched to meet research requirements Researchers who adopt the middle ground position suggest that the epistemological stance of the research will determine the main research tool that is used (Creswell 1994). However, other methods can be introduced to prompt new ideas, corroborate findings or through elaboration provide richness and detail (Greene et al 1989).

In line with the post positivist view we agree with the pragmatists over the issue of mixing methods. Like Remenyi et al (1998) and Crotty (1998) we believe it is not the

Chapter 3 - Methodology

method itself, but how that method and the results obtained are used that is dependent upon the ontological and epistemological position. So, we are free to pick and choose methods to explore different aspects o f reality providing we pay due regard to the validity and credibility o f our results (Denzin and Lincoln 1998).

Longitudinal Research Methods

Management science, including research on strategy, has been dominated by cross sectional methods that collect and analyse data from a single point in time (Monge 1990 and Huber and Van de Ven 1995). Pettigrew (1985) makes the same point suggesting that the majority of such cross sectional studies are ahistoric and aprocessual.

While far less common, qualitative longitudinal methodologies are now well established within management research. The case study method increasingly forms part of studies on the process o f strategy (e g Pettigrew and Whipp 1991 and Brown and Eisenhardt 1998) However, due to the heavy commitment o f research time and funding required, normally only a small number of cases are undertaken In addition, as we discussed above, the case study method is of limited use when the objective is to explore patterns at an industry or societal level

As we saw in Chapter 2, the use of quantitative longitudinal methods is rare within management and particularly within strategy research (also see Huber and Van de Ven 1995). Even where researchers have assumed that organisations and their environments change over time they have often used analytical techniques that assume

stability or that equilibrium has been reached (Teece 1984, Boeker 1991 and Coleman 1981). Techniques such as regression analysis and standard correlation techniques assume relationships between variables are unchanging (Turna and Hannan 1984 and Delacroix and Swaminathan 1991). Monge (1990) has suggested that, as a result, organisational research has suffered from methodological determinism, i.e that the methods used by researchers introduce a static view into their research questions and the theories they develop Similar concerns have been expressed within sociological research (Turna and Hannan 1984).

Patterns of change can be described using longitudinal quantitative data in three ways - numerically, visually or graphically, and mathematically (including statistically) (Menard 1991). Numerical methods typically involve the use of event counts over defined periods of time, e g. number of changes in a year Visual methods generally involve the plotting of variables on a graph where the horizontal axis is time Where data is categorical or dichotomous, mapping may provide a more informative presentation Whether seen as the initial or final step in an analysis, the visual display of data is essential for detecting general patterns or problems within a data set (Morris 1989, Mason and Lind 1990 and Pettigrew 1990). However, few authors provide a comprehensive discussion on the appropriate forms o f visual display The researcher is left to innovate and find the approach which best reveals the information contained within their data

Turna and Hannan (1984) suggest that there are three types of mathematical analysis of longitudinal data which should be used more often in sociological research - panel analysis, event history analysis and time series analysis. Panel analysis is conducted

Chapter 3 - Methodology

by undertaking two or more cross sectional analyses at different points in time and then comparing the results of each analysis. While this type o f analysis can provide significant insight (see Whittington et al 1999), the actual timing of changes is ignored (Tuma and Hannan 1984). Event history analysis is used to study the probability o f an event occurring during a given period (Yamaguchi 1991). Hazard rates, the risk of an event occurring during a given period, are calculated and then compared or linked to explanatory variables (Yamaguchi 1991, Menard 1991 and Allison 1984). Event history analysis is a valuable tool for social scientists and has been used widely by population ecologists. However, rather than analysing the overall pattern of events, incidents are analysed one at a time and their aggregate properties determined across a group o f cases (i.e. companies or other entities) (Abbott 1990).

The third mathematical approach suggested by Tuma and Hannan (1984) is time series analysis. Although much o f the literature on the use of this method focuses on the analysis of a single time series, techniques are available to compare sets of time series. These techniques use modified correlation and regression analysis Typically methods that deal with the analysis of a single time series are prefixed by the term ‘auto’ - e g. ‘autocorrelation’ - while those that deal with the comparison o f multiple time series are prefixed with the term ‘cross’ - eg . ‘cross correlation’. While little used in sociological inquiry these techniques have been extensively adopted in economic research and signal processing. They allow patterns of events found in one case (i.e. a firm) to be compared to patterns observed in another, although as with all regression and correlation techniques causality is not necessarily implied

Earlier Longitudinal Quantitative Research on Strategy Content

As we discussed in Chapter 2, four earlier studies have taken a dynamic view of strategy content. Three of these have used quantitative methods to collect and analyse longitudinal data (Miller and Friesen 1984, Smith et al 1992 and Meyer et al 1993. The fourth (Pettigrew and Whipp 1991) has relied mainly on qualitative data collection and analysis in the form o f the comparative case study method

All three o f the quantitative studies used a combination of archival searches and questionnaires to build time series o f the events that occurred during the period under study. However, the approach each used to analyse the data differed considerably. Miller and Friesen (1984) calculated the differences in a range of variables between two points in time and then used a form of cluster analysis to identify types of transitions. Their approach can be likened to the panel analysis described by Turna and Hannan (1984). Smith et al (1992) and Meyer et al (1993) generated counts of events during periods of time and average order rankings These representative statistics were then linked to organisational variables using graphical and standard regression techniques

In each of these studies the data collection method provided detailed time series o f the events that occurred during the study period However, the data analyses undertaken do have their problems. In particular, the use of counts and difference scores removes the temporal element of the data. This suggests that these approaches would not be suited for this study.

Chapter 3 - Methodology