Strategic management as an applied science, but not as we ...

Strategic management as an applied science, but not as we (academics) know it1 Authors Paula Jarzabkowski* and Monica Giulietti Aston Business School and Advanced Institute of Management (AIM) Aston University Birmingham B4 7ET UK * Contact author: [email protected] Abstract This paper reports on the results of a survey on the adoption of strategy tools by domestic and international alumni from a sample of UK business schools. Results show that strategic management tools are indeed relevant in practice, suggesting that strategic management is an applied science. However, in order to understand strategy as an applied science, we need to understand how and why practitioners use strategy tools rather than holding academic preconceptions about the theoretical bases and utility of those tools. This paper explains why we should look at the use of strategic management theory in practice as a matter of ‘tools’ rather than ‘theory’. It then explains the research design and methods of a mapping study of strategy tools adoption conducted with a sample of domestic and international alumni in nine UK business schools in 2007. The findings from this survey are presented in terms of strategy tool awareness, rank ordering of tools within different stages of the strategy process by volume of use and by perceived value, and some individual, organizational and educational influences on strategy tool adoption. These results are interpreted and discussed. The paper contributes to the relevance debate by providing a body of evidence on what strategic management tools are used, by whom, in what context and for what tasks. Such evidence is essential to inform the ongoing relevance debate in two ways. First, it confirms that strategic management theory is relevant to practitioners, albeit not necessarily in the ways that academics conceptualize relevance. Second, it extends the notion of relevance beyond primarily instrumental considerations, as the results indicate that this is not the only consideration influencing practitioner’ selection of tools

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We wish to thank the Advanced Institute of Management (AIM) and Aston Business School for the financial support that enabled us to conduct this study and the business schools that participated in the data collection. We also thank ABS doctoral student, Qin Zhou, for her assistance with survey design and data collection, management and coding.

Strategic management as an applied science, but not as we (academics) know it Recurrent academic debates highlight the problem that management theory is not relevant to practice (e.g. Academy of Management Conference, 2004; Academy of Management Journal, 2001; Administrative Science Quarterly, 1982; British Journal of Management, 2001). This problem is extended to the strategy field with concerns about the relevance of strategy theory to strategy practice (Baldridge, Floyd and Markoczy, 2004; Bettis, 1991; Ghoshal, 2005; Ghoshal and Moran, 1996; Lowendahl and Revang, 1998; Prahalad and Hamel, 1994). Such academic debates are also a matter of policy concern. For example, in reporting on the skills and training of UK managers Keep and Westwood (2003) note that little is known of how, or indeed whether, managers use the theoretical resources gained from management education. However, despite academic and policy concerns, there has been, with some exceptions (e.g. Balridge et al, 2004; Haspeslagh, 1982; Hodgkinson and Wright, 2002; Miner, 2003), little empirical examination of what theory is used in practice and even less of how it is used (Jarzabkowski, 2004; Whittington, 2003).

There are two main problems with analyzing the uses of academic theory in practice that make the debate difficult to further theoretically or empirically. First, as there is little consensus on what constitutes that management theory which is not relevant to practice, it is difficult to operationalize a body of management theory for investigation. Second, the concepts of practical relevance that underpin the debate are ill-defined and also difficult to operationalize (Baldridge et al, 2004). This paper addresses these problems by conceptualizing theory-in-practice as the use of management tools, and reporting on an empirical, survey-based mapping study of how strategy tools are used in practice.

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It is necessary to define what constitutes the theory that might be used by practitioners. Strategy theory typically goes through a process of dissociation from its theoretical foundations, deriving a set of concepts, tools and techniques (Jarzabkowski and Wilson, 2006; Weick, 1995; Worren et al, 2002), here referred to as ‘tools’, which are commonly disseminated into practice through textbooks, classrooms, popular media and consultants (Abrahamson, 1996; Mazza and Alvarez, 2000). For example, Porter’s Five Forces is a simplified tool for analyzing industry structure that may be dissociated from the structureconduct-performance paradigm that underpins it. This simplification makes the Five Forces and other such tools easier for practitioners to adopt (Argyres and McGahan, 2000; Miller, 1956; Worren et al, 2002). By contrast, the curious fact that many studies purporting to examine the practical relevance of theory have used academics to grade this relevance (e.g. Dunn, 1980; Miner, 2003; Shrivastava, 1987), may well be grounded in an empirical problem; it is difficult to ask practitioners about the relevance of, for example, ‘transaction cost economics’ if they do not use this theoretical term. In order empirically to analyse strategy theory-in-practice, we propose that it is important to study the use of strategy tools rather than strategy theories, as this more accurately reflects the dissemination of theory to practice.

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An important question to address in order to inform the relevance debate in strategic management is; what, of the strategy tools that practitioners have been exposed to, do they use? That is, it behoves academics to question whether those tools typically taught in the classroom are actually used by graduates in the workplace. From limited empirical investigation, there is evidence that practitioners do indeed use some strategy tools in practice (e.g. Clark, 1997; Frost, 2003; Grant, 2003; Haspeslagh, 1982; Hodgkinson et al, 2006;

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McCabe and Narayanan, 1991; Rigby, 2001; Stenfors et al, 2004; 2007). While these studies are not easily comparable because of inconsistencies in method, tools analysed and sampling criteria, they provide some insights into potential sources of variation in strategy tool use. Specifically, adoption of strategy tools varies by context (organizational and environmental), hierarchical level of the user, stage of the strategy process, and features of tool design (Spee and Jarzabkowski, 2006). These findings from extant literature are summarized in Table 1. However, there has been little empirical study of the typical set of tools disseminated in strategic management courses and their subsequent adoption by practitioners. A mapping study is therefore indicated, examining the adoption and adaptation of typical strategy tools by those who have had exposure to such tools through strategic management education. This paper addresses this question, presenting the results of a survey of strategy tool use by business school alumni in the UK.

RESEARCH DESIGN A survey method has been used to map the adoption of typical strategy tools by a population of domestic and international alumni from nine of the top 30 UK business schools. Alumni were selected because they meet our first criteria for target population; those who have had at least a foundation course in strategic management, and thus might be expected to use or not use its products on a reasonably informed basis (Priem and Rosenstein, 2001; Westwood and Keep, 2003). Higher-ranked schools were selected because these schools have higher graduate employment, ensuring the target population is employed in positions where they might reasonably have an opportunity or need to use tools. As the study does not query how institutional ranking or quality of the educational experience shapes tool adoption, limiting the study to higher-ranked schools with high graduate employment was felt to control for unintended educational variation effects. The sample population covers both undergraduate

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and postgraduate alumni who have graduated within a 20 year period to allow for career progression effects. The limitations of surveys, for example in terms of self-report biases, are acknowledged and, where possible, have been addressed by the survey design.

In order to establish a list of tools most typically taught in foundation strategic management courses, a survey of 66 strategy academics in the top 30 business schools was conducted, using frequency counts to derive a list of 20 commonly taught tools. We then developed a survey instrument to map tool use according to contextual and individual features, tool characteristics, and processual and socio-political uses. Three pilot studies of this survey were conducted, generating 76 responses in total. Results of each pilot were analysed to further shape the questionnaire and ensure the questions provided robust measurements. The survey was then administered online between February and May 2007 to a population of 20,108 alumni in a sample of nine out of the top 30 UK business schools. These schools were selected pragmatically because they were prepared to email our survey link to their alumni databases at this time period. The alumni population parameters of these schools are consistent with those of UK business schools within their league (top 30), insomuch as these figures are known. The specific response rate from our target population (business school alumni who have done a foundation strategy course) is difficult to ascertain, as we do not have figures for non-responsive email accounts and were not able to isolate the datasets to include only those alumni in our target population but had to email to the alumni databases held by the various business schools. For example, in some schools strategic management is not a required course in some degrees, such as finance, operations research or personnel management, whereas in others it is. However, the response rate from total numbers emailed, without excluding non-responsive emails or non-target population, is 14.2%, suggesting that

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responses from the target population of those alumni who have been taught foundation strategic management is at least 14.2%, from which we gained 1844 usable responses.

As a mapping study, the survey is not guided by hypotheses, but addresses a range of questions, informed by the literature (see Table 1), that might illuminate adoption and variation in use of strategy tools. Specifically, the survey questions address contextual variation in tool use according to: national context of use (Frost, 2003; Guillen, 1994; Whittington et al, 2003); sectoral context (Ferlie, 2002; Haspeslagh, 1982); and organizational context (Clark, 1997; Frost, 2003). Variation by individual characteristics is incorporated through personal demographics such as age, job tenure, education-level and gender, as well as job function, hierarchical position (Hodgkinson et al, 2006) and time since formal strategy education was last undertaken (Priem and Rosenstein, 2000). The survey then questions which tools are typically used, rank ordering the top three for further analysis on tool characteristics such as design, ease of use, suitability to tasks and reasons for use (Stenfors et al, 2007; Worren et al, 2002). The processual and socio-political uses of these tools are also examined, questioning which tools are employed in which stage of the strategy process and how tools are used in social interactions with peers, subordinates and higher-level managers (Grant, 2003). Finally, the survey questions reasons for non-use of tools, establishing criteria for satisfaction or dissatisfaction with tools. This paper presents the initial descriptive findings about the top tools used, weighted frequencies for their use according to some individual, organizational and educational characteristics, and correlations of their volume-of-use to perceived value within different stages in the strategy process.

RESULTS What tools are used?

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INSERT TABLE 2 ABOUT HERE Rank order of tool use: Table 2 addresses respondents’ awareness and use of tools. It rank orders the 20 tools according to highest current use, showing also those tools which have been used but are no longer used, those tools which participants have heard of but do not use and those tools of which participants have not heard. Consistent with other studies, SWOT, is found to have the highest use (e.g. Clark, 1997; Frost, 2003; Stenfors et al, 2007). Various authors have commented on the ubiquity of SWOT, which is perceived as simple, practical and easy to use (e.g. Dyson, 2004; Hill and Westbrook, 1997; Pickton and Wright, 1998). However, other tools show some differences with these other studies. For example, in our study scenario planning and value chain analysis rank fourth and fifth respectively, while they rank seventh and 15th for Stenfors et al (2007). Some care needs to be taken in comparing results, as other authors do not use the same list of tools, which may influence ranking. Interestingly no tools are entirely unused, although the last nine tools have low adoption with 70% or more of respondents either never having used these tools or not being aware of them. PESTLE is an outlier with 30% of respondents claiming unawareness, given the relatively high current use. This maybe because respondents recognize the tool by its various names, such as SLEPT and PEST. This consideration of nomenclature must also be given to other tools, which might not be recognized by their names in this study, albeit that these are common strategy textbook names for tools.

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Tables 3a and 3b present respondents’ rank ordering of their top three tools. From this analysis, we find that there are three categories of tools; high-use, medium-use and low-use tools. Table 3a presents these findings, with the raw data, to illustrate that eight tools

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consistently occur among the top three tools used by respondents, a further five tools occur as medium-use tools and seven tools have low use, although no tool is totally without use. We might thus define the top eight tools as the core strategy ‘toolkit’ (SWOT, KSFs, Value chain, Core competences, Five forces, PESTLE, RBV). The difference between these categories of tools in terms of relative weighting is graphically expressed in Table 3b, which illustrates the high dominance of the top eight tools, compared to the medium and low-use tools.

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Tool use in the strategy process Respondents were asked about what stage of the strategy process they used their top three tools. Consistent with other surveys (e.g. Clark, 1997; Frost, 2003; Stenfors et al, 2007) and with textbook representations of the strategy process (Hendry, 2000; Tsoukas and Knudsen, 2001), we divided the strategy process into four main strategy activities, which we explained: strategy analysis and formulation; strategic choice; strategy implementation and an ‘other’ category in case none of these activities covered respondents’ uses of a tool. Respondents were able to select multiple activities for any tool. The results of this analysis for the top 11 tools used are presented in Tables 4a and 4b. Table 4a illustrates the proportional use of each tool according to different phases of the strategy process. Table 4b illustrates the relative weighting of tools in terms of their use in different stages of the strategy process, where tools tend to follow their rank ordering from Tables 3a and 3b.

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Consistent with other studies (e.g. Clark, 1997; Frost, 2003), we find that tools are not specific to a single phase of the strategy process. Indeed, any tool might be used in any stage of the process, which suggests that instrumental (Pelz, 1978) considerations of use in terms of selecting the ‘best’ tool for a task may not guide tool adoption. Rather, practitioners might adopt tools for some other reason (perhaps familiarity, organizational symbolism, or some design characteristics) and adapt it to the tasks they need to perform (Jarzabkowski and Wilson, 2006).

Our findings also suggest that some tools are more typically used in some phases of the strategy process than others. For example, using a cut off of 38% to indicate dominance of a tool in one phase of the strategy process, we find that the top four tools for strategy analysis are, in rank order; PESTLE, Porter’s GSM, Porter’s 5 Forces and SWOT. In strategic choice, there are three top tools; Portfolio Matrices, Porter’s GSM and Scenario Planning. Strategy implementation does not fare well, with most falling below 30% and no tools meeting the 38% cut off, although two tools stand out as being more useful than others; KSFs and Resource-based Analysis. Thus, foundation strategic management courses appear to provide, on a sliding scale, more resources for strategy analysis, some resources for strategic choice and few resources for strategy implementation.

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What has perceived value? Interestingly, some tools which did not feature in the ‘High-Use’ category from Tables 3a and 3b, such as Porter’s GSM and Portfolio Matrices, did score highly in terms of use within one or more phases of the strategy process. Such tools, while they may not be highly popular by volume of use, may be perceived as useful. This finding is

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further validated when we examine responses to the perceived value of tools within different phases of the strategy process, which were scored on a Likert scale of 1 (adds little value) to 5 (adds high value). This analysis indicates that the tools with the highest perceived value in each stage of the strategy process, in rank order, are: Strategy Analysis: Porter’s 5 Forces, Porter’s GSM, PESTLE, SWOT, Core Competences, Portfolio Matrices; Strategic Choice: Porter’s GSM, Scenario Planning, Portfolio Matrices, Core Competences; Strategy Implementation: KSFs, Resource-Based Analysis. Greater insights may be gained by correlating volume of use from Tables 4a-4b, with perceived value of tools, which is presented in Tables 5a-5c.

In these correlations, the outliers, such as SWOT and Porter’s GSM, are particularly interesting. SWOT continues to have high use in all three phases of the strategy process, despite being of decreasing usefulness in each phase, while Porter’s GSM has relatively low use in all three phases but is of high perceived value in strategy analysis and strategic choice. Extant research provides some reasons for the finding on SWOT. Specifically, SWOT is considered easy to use and commonly understood, which explains its high use, but also to be of relatively limited analytic value because it encourages lists rather than providing analytic categories (e.g. Dyson, 2004; Hill and Westbrook, 1997; Pickton and Wright, 1998). The GSM finding is more complex as it appears that some tools that are not widely adopted may have high value (see also Priem and Rosenstein, 2000).

Interestingly, while many tools, including GSM and SWOT, are perceived to have low value for strategy implementation, they continue to be used. These findings further corroborate our proposition that tools are not used for instrumental purposes of getting a job done (Pelz, 1978), as users acknowledge the relatively low value of some tools, even as they continue to

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use them, while other tools which are perceived as of high value when used have relatively little use. Something other than instrumentality, thus, is associated with the use of tools, which we hope to better explain when we correlate use to factors such as organizational familiarity, tool design features and socio-political characteristics of use.

Individual characteristics and use Managerial levels: While previous research has not tended to sample by multiple management levels or function, there is an indication that tools tend to be used more by top managers than lower level managers, presumably because senior managers have more strategic responsibilities (Grant 2003; Hill and Westbrook 1997; Hodgkinson et al, 2006). We queried tool use by job description, which included not only hierarchical level but also professional or technical status. The results for the top seven tools are displayed in Table 6. Of the three main categories, senior/directorate-level managers, middle/line-level managers and professionals, we find a sliding scale weighted towards senior levels in all tools with middle/line managers next and professionals least, with the exception of PESTLE, which has marginally more use by professionals than middle/line managers. However, these differences are not marked. Use by each category is largely similar.

These findings are interesting, given that tools tend towards the strategic analysis and choice functions (see Tables 4a-4b), which are typically considered senior management responsibilities, and less towards strategy implementation, which is considered a middle management responsibility (e.g. Balogun, 2003; Floyd and Lane, 2000; Grant, 2003; Mantere, 2005; Regner, 2003). Thus, either the range of strategy tasks are more distributed across levels than these studies propose (e.g. Barry and Elmes, 1997), or some non-instrumental, social, political or alternative dynamic is necessary to explain tool use.

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It is also interesting to note the significant professional use of strategy tools, as professional labour is often considered to be outside the managerial chain of command, and hence to be governed by alternative principles of strategy-making (e.g. Hinings and Leblebici, 2003; Mintzberg, 1979; Mintzberg, 1998; Podsakoff et al, 1986). Again, further analysis is necessary to understand the additional characteristics associated with professional use of strategy tools.


Boy’s toys? The gender effect: While some 70% of respondents were male, when results are weighted, Table 7 illustrates little gender difference in the use of the top seven tools, with the exception of value chain, which may be explained by other factors. For example, findings not presented in this paper indicate that value chain also has slight dominance in the manufacturing industry, which may have a dominantly male workforce. Tool use thus does not appear to be guided by gender considerations.


Organizational characteristics and tool use Does size matter?: Extant research suggests that large organizations use tools more than small organizations, and that this may be linked to the increased formality of strategic planning processes in large organizations (Frost 2003; Rigby 2001; 2003; and Bilodeau 2005; Tapinos et al, 2005). Frost’s (2003) studies of SME’s suggests that managers in these organizations do use tools but tend to use a more limited subset of tools than those in large organizations. Table

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8 illustrates our findings that size is not a marked differentiator in the use of the top seven tools, which have relatively equal adoption in organizations of less than 50 employees through to organizations in excess of 10,000. Further analyses are necessary to ascertain whether these results are associated with other factors, such as small organization respondents being largely senior managers, which might slightly increase tool use in that category of respondent (see Table 6).


Educational status and tool use


Amnesia effects?: Organizational behaviour and learning theory suggest that those most recently exposed to education are most likely to use educational products, while familiarity and use are expected to decrease the longer and individual is out of formal education; a case of forgetting over time (Hebb, 1949; Priem and Rosenstein, 2000). However, as Table 9 shows, weighted results indicate little difference in the use of the top seven tools between those less than five years out of education and those in excess of 20 years out of education, with the exception of value chain, which has greater use by those in the 10-20 year leaving bracket, which may be driven by other factors such as the stage of career progression at which value chain is typically used or other job or organizational characteristics.

This lack of distinction between different time-since-education brackets may have at least three interpretations. First, these tools may be useful throughout a career, as suggested by the

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relatively small distinction in use between senior and middle manager categories (see Table 6), and hence to be remembered regardless of distance since education because they are in frequent use. Second, these tools may have become part of a general lexicon of strategy, such that even without conscious ‘remembering’ they are used as a matter of course (see also Barry and Elmes, 1997; Jarzabkowski, 2004; Seidl, 2007). Third, while we had only a slight dominance of responses from those finished in under five years to the next two categories of response, we had few responses from those over 20 years out of formal education. These respondents may thus be those who have retained a high interest in the educational process and its products, as indicated by their ongoing interaction in alumni events, such as our survey.


Education level effects: Table 10 presents our final findings for this paper, looking at the relationship between strategic management education level and the tendency to use strategy tools. We find that taught postgraduate/MBA or management development programmes (MDP) tend to increase the use of strategy tools over undergraduate degree level education. As respondents were able to tick multiple options in this question, there may be a cumulative effect that bears further investigation. Alternatively the reason may be based in teaching approach (Knowles, 1990), as postgraduate and MDP teaching tends to use a combination of case method and practical experience and, particularly for MBA and MDP, often requires practical experience as a prerequisite for enrolment (Christensen and Hansen, 1981; Greiner et al, 2003). These characteristics may increase the relevance of tools to the individual at the time of learning (Knowles, M. 1990) and, hence, their retention in the workplace. There may also be career effects here, as postgraduates and MDP students have often selected or been

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selected for further management training as a career move. Hence, the use of tools learned in that training may be part of a career strategy. We suspect that these findings may also be influenced by other educational factors, such as whether a respondent has also engaged in ongoing management training, even if not in strategic management, which will require further analysis in relation to other results.

CONCLUSIONS AND IMPLICATIONS

This paper presents the preliminary results of a survey into the use of those strategy tools typically taught in foundation strategic management courses by a sample of domestic and international alumni from nine of the top 30 UK business schools. Alumni were chosen as the target population because they have been exposed to strategic management education and thus might be expected to use its products on a reasonably informed basis (Priem and Rosenstein, 2000). Findings on strategy tools awareness, volume of use and perceived usefulness in different stages of the strategy process were presented, followed by results on some individual, organizational and educational characteristics of use. Implications of the specific results and their contributions to existing research into strategy tool adoption are discussed in the body of the text

These results inform the ongoing relevance debate in management in two ways. First, they indicate that strategic management tools are used by practitioners who have been exposed to them through education. Furthermore, some tools are perceived by alumni as adding value to their strategy processes. This suggests that strategic management tools meet some practitioner criteria of relevance, answering at least some of the academy’s ongoing soul-searching over whether we have relevance (e.g. Academy of Management Conference, 2004; Academy of

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Management Journal, 2001; Administrative Science Quarterly, 1982; British Journal of Management, 2001). Some of the artefacts or ‘products’ arising from research, which have been distilled to the extent that they now form typical strategic management teaching tools, techniques and frameworks have relevance. While we may not regard this as particularly theoretical, it is important to understand that academics rarely disseminate theory in its ‘theoretical state’ into practice. Rather, we disseminate through a range of media the products of theory, such as frameworks, concepts and techniques (Abrahamson, 1996; Jarzabkowski and Wilson, 2006; Mazza and Alvarez, 2000; Worren et al, 2002).

Second, our results inform the problematic concept of relevance (Balrdridge et al, 2004). Pelz (1978) suggests three ways of understanding relevance, based around the different ways that practitioners use social science theory: instrumental, meaning direct application of theory to practice; conceptual, meaning using theory to enlighten practice; and symbolic, meaning the ceremonial adoption of theory with little significant alteration of practice. Any of these uses might be relevant to practitioners, in terms of fulfilling their needs in adopting a theoretical framework. However, the academy has tended to regard only the first of these, instrumental use, as indicating relevance (Tsoukas and Knudsen, 2002). Alternative or non-instrumental uses of theory may be regarded as a matter of deviance (Beyer and Trice, 1978; Merton, 1938), deliberate distortion (Weiss, 1979), or corruption (Lozeau et al, 2002). That is, any use of strategy knowledge other than direct instrumental application is regarded as either a failure of management, who are unable or unwilling to use the tool adequately, or a failure of the knowledge since it is not able to have the desired effect (Lozeau et al, 2002). However, our results, particularly those on the volume and perceived value of tools to different stages of the strategy process, indicate that tools are applied, albeit not always in the stages of the strategy process we might consider most applicable, and that they continue to have high volume of

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use, even where their value to the tasks of the process may not be highly regarded. It thus appears that instrumental use is only one, and perhaps not the most important, reason why practitioners use tools. We have speculated on some of these reasons in interpreting the results, including individual, educational and career path considerations. However, social, political and symbolic factors also need to be considered.

Further analysis is needed to make more of the results presented in this paper, particularly in examining what other factors drive tool selection and use. However, we suggest that these results inform the relevance debate by indicating that strategic management theory, as disseminated through typical classroom tools, is relevant to practice. Furthermore, the results inform the relevance debate by indicating that instrumental applications of strategy tools in order to complete tasks does appear to guide selection. However, it is not the only reason why practitioners use tools, indicating that multiple interpretations of relevance are necessary. We emphasize that all of these interpretations of relevance, if they guide practitioner selection and application of tools, should be considered important evidence of academic relevance.

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Table 1. Variation in strategy tool adoption according to extant literature (Taken from Spee and Jarzabkowski, 2006: 28) Context – Organization Hierarchical level & Environment - Larger firms use more - Top management uses tools more extensively tools than small ones than middle managers (Frost 2003; Rigby (Dyson 2004; Grant 2001b; 2003; and 2003; Hill and Bilodeau 2005) Westbrook 1997; - Strategy tool adoption Hodgkinson et al. 2006) differs by industry - Tool adoption depends (Haspeslagh 1982; on CEO support Rigby 2001a) and (Hodgkinson and country (Clark 1997; Rigby 2001b; 2003; and Wright 2002) - Tools are used in Bilodeau 2005) individual and/or during -Variation of tool use group activities (Dyson according to 2004; Hill and environmental conditions (Grant 2003; Westbrook 1997; Hodgkinson and Wright Koufopoulos and 2002) Chryssochoidis 2000)

Strategy process

Tool design

- High tool use in situation assessment and strategic analysis phase, while low tool use in strategy implementation phase (Clark 1997; Grant 2003; Hodgkinson et al. 2006) - One tool may support different strategic tasks (Clark 1997; Webster et al. 1989) - A strategic task may be supported by several strategy tools (Clark 1997; Frost 2003) - Companies use more than one tool (Rigby 2001b; Stenfors et al. 2004)

- Tools producing quantitative data tend to be used more for medium-term planning (Grant 2003) - Tools producing qualitative data tend to be used more for longterm planning (Grant 2003) - Simple and transparent tools are preferred to complex ones (Clark 1997; Stenfors et al. 2004) - Mixed results for using IT to support strategy tools (Rigby and Bilodeau 2005; Stenfors et al. 2004)

21

Table 2: Tool rank order according to awareness of tool and its use (Underline indicates outlier; Dividing line with italics below, indicates tools where last 2 columns make up some 70% of respondents) Tools SWOT Key Success Factors Core Competences analysis Scenario Planning Value Chain Porter’s Five Forces Resource-Based Analysis Industry Life Cycle PESTLE Analysis Portfolio Matrices, e.g: BCG or McKinsey Porter’s Generic Strategy Model Strategic Groups Analysis Ansoff’s Product/Market Matrix Porter’s Diamond Merger and Acquisition Matrices Dynamic Capabilities Analysis Globalisation Matrices Methods of Expansion Matrices Corporate Parenting Matrices Bowman’s Strategy Clock

Currently used % 76% 58% 47% 45% 41% 39% 38% 36% 33% 29% 23% 18% 15% 12% 8% 8% 6% 4% 4% 3%

Used but not now % 13% 13% 19% 19% 20% 25% 14% 21% 14% 20% 19%

Heard of, not used % 10% 21% 25% 29% 34% 30% 33% 33% 38% 40% 36%

Never heard of % 1% 8% 9% 7% 5% 6% 18% 10% 30% 13% 18%

12% 14% 18% 7% 6% 7% 7% 5% 6%

35% 42% 40% 28% 24% 26% 26% 26% 26%

34% 36% 28% 45% 59% 53% 63% 65% 65%

22

Table 3a: Results from rank order of top three tools (Cut off lines represent high-use tools, medium-use tools and low-use tools) Top 1

Top 2

SWOT Scenario Planning

444 204

Key Success Factors Value Chain Core Competences analysis Porter’s Five Forces PESTLE Analysis Resource-Based Analysis

167 126

Portfolio Matrices, e.g: BCG or McKinsey Industry Life Cycle Strategic Groups Analysis Porter’s Generic Strategy Model Ansoff’s Product/Market Matrix Merger and Acquisition Matrices Bowman’s Strategy Clock Dynamic Capabilities Analysis

91 84 73

273 220

207 173

134 121

SWOT Key Success Factors Core Competences analysis Scenario Planning

Scenario Planning Porter’s Five Forces Value Chain Resource-Based Analysis

102 94 91

Porter’s Five Forces Value Chain Resource-Based Analysis

102 90 81

90

PESTLE Analysis

78

Industry Life Cycle Portfolio Matrices, e.g: BCG or McKinsey

61

Industry Life Cycle Portfolio Matrices, e.g: BCG or McKinsey

71 52

Strategic Groups Analysis Ansoff’s Product/Market Matrix Porter’s Generic Strategy Model

33

24

10

Strategic Groups Analysis Ansoff’s Product/Market Matrix Porter’s Generic Strategy Model

9

Merger and Acquisition Matrices

8

Globalisation Matrices

61 46 46 16 15 13

Top 3

SWOT Key Success Factors Core Competences analysis PESTLE Analysis

45

25

7

Merger and Acquisition Matrices Dynamic Capabilities Analysis

3

Porter’s Diamond

6

Porter’s Diamond Methods of Expansion Matrices Corporate Parenting Matrices

2

Bowman’s Strategy Clock Corporate Parenting Matrices

4

3

Globalisation Matrices

0

Globalisation Matrices Methods of Expansion Matrices

Porter’s Diamond Corporate Parenting Matrices Dynamic Capabilities Analysis Methods of Expansion Matrices

1

Bowman’s Strategy Clock

8

1 0

1407

3

1333

137 108

HIGH USE

MED USE

19 18 11 8 8 3

LOW USE

3 3 1 1197

23

Table 3b: Relative weighting of high-, medium- and low-use categories of tools Top 3 tools: intensity of use 1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 Tool 1

Tool 2 HIGH USE TOOLS

MEDIUM USE TOOLS

Tool 3 LOW USE TOOLS

24

Table 4a: Summary of top three tool use in stages of the strategy process percentage use in strategy process 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% SWOT

KSF

scen plan

core comp

value chain

Porter's PESTLE 5Fs

analysis

choice

Res based

implement

industry portfolio life-cycle matrices

Porter's GSM

average use

other

Table 4b: Relative weighting in strategy process by volume of use tool use in strategy process 0.30

0.25

0.20

0.15

0.10

0.05

analysis

choice

SM r's

G

es

ol io po

rtf

ry

Po rte

elif

es in du st

R

implementation

m at ric

cy cl e

ed ba s

PE ST LE

5F s r's

Po rte

ch ai n

va lu e

co

re

co m p

pl an sc en

KS F

SW O

T

0.00

other

25

Table 5a: Use to perceived value in Strategic Analysis phase of strategy process Use and value in strategy analysis (correlation 0.11) 700

SWOT

600

number of users

500

400

300 KSF SCPLAN

200

100

RESBAS

PESTLE

CCOMP

VCHAIN

P5F

PORTMAT

LIFEC

PORTGSM

0 3

3.2

3.4

3.6

3.8

4

4.2

value of tool

Table 5b: Use to perceived value in Strategic Choice phase of strategy process Use and value in strategy choice (correlation -0.15) 600

SWOT

500

number of users

400

300

KSF SCPLAN

200

CCOMP VCHAIN P5F PESTLE

100

PORTMAT

RESBAS LIFEC

PORTGSM

0 3.2

3.3

3.4

3.5

3.6

3.7

3.8

3.9

4

4.1

value of tool

26

Table 5c: Use to perceived value in Strategy Implementation phase of strategy process Use and value in strategy implementation (correlation 0.42) 400

350

SWOT

KSF

number of users

300

250

200 SCPLAN CCOMP VCHAIN

150

RESBAS

100

P5F PESTLE

50

LIFEC PORTMAT

PORTGSM

0 2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

value of tool

27

Table 6: Use by job description Top 7 tools by job descriptions 100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0% SWOT

Scenario Planning

Key success factors

Senior/Directorate

Value Chain

Line/Middle managers

Core competences analysis Professional

Porter's five forces

PESTLE

Others

28

Table 7: Boy’s Toys? Use by gender Top 7 tools by gender 100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0% SWOT

Scenario Planning

Key success factors

Value Chain

Female

Core competences analysis


PESTLE

Male

29

Table 8: Does size matter? Use by organizational size Top 7 tools by organisation size 100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0% SWOT

Scenario Planning

Key success factors

20yrs

31

Table 10: Educational level association with use Top 7 tools use by strat man education 100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0% SWOT

Scenario Planning

Key success factors

Value Chain

Mgmt dev

UG

Core competences analysis


PESTLE

PG-MBA

32