Strategic group analysis: A methodological approach for exploring the industry level impact of information technology

Omega, Int. J. Mgmt Sci. Vol.22, No. I, pp. 13-34,1994 Copyright © 1994Elsevier Science Ltd Printed in Great Britain. All rights reserved

Pergamon

0305-0483/94$6.00+ 0.00

Strategic Group Analysis: A Methodological Approach for Exploring the Industry Level Impact of Information Technology AH SEGARS The Wallace E. Carroll School of Management, Boston College, USA

V GROVER College of Business Administration, University of South Carolina, USA (Received 7 June 1993; in revised form 22 September 1993) The goal of this study is to present a viable technique for exploring the firm and industry level factors which may influence the outcomes of information technology for competitive advantage (ITCA). An examination of research in the area of ITCA reveals an absence of empirically derived models of industry behavior critical to the assessment of the impacts of these information initiatives. Strategic group analysis (SGA), a widely used methodology in the areas of organizational economics and strategic management, is described and suggested as a potential technique to segment and assess industries. 2"his technique utilizes objective measures of strategic orientation in order to identify sets of homogenous firms (strategic groups) within a given industry. Thus, researchers and decision makers can build dynamic pictures of industry positioning and more objectively analyze the past and potential impacts of competitive IT initiatives. We mustrate SGA through analysis of the wholesale drag distribution imlu~ry. The subject of many research efforts in the area of ITCA, this industry is segmented according to strategic thrust and examined during the deployment of a widely studied strategic system. The final segment of this study suggests specific uses of SGA in the exploration of the more prominent issues currently dominating both academic and practitioner dialogue regarding ITCA. Key words--information technologyfor competitiveadvantage, industry impacts of technology, strategic group analysis, cluster analysis

INTRODUCTION THE EMERGENCE

OF theoretical

to evolve from a reactive 'organizational handyman' to a proactive corporate resource capable of distinguishing a firm within its industry. While IT will continue to support routinized efficiency oriented tasks such as payroll, t r a n s a c t i o n processing, and process control; information managers are constantly s e e k i n g n e w ways to employ this resource for more competitive versus operational objectives. In such instances, the focus of IT planning and deployment broadens to include

literature

a n d case studies which explore the 'competitive' use o f i n f o r m a t i o n and information technology provides c o n v i n c i n g e v i d e n c e of the critical role information resources can play in the realization o f c o m p e t i t i v e a d v a n t a g e

[7, 12, 39, 47, 55]. Lower costs, flexibility and enhanced capabilities afforded by today's information technology (IT) has caused it 13

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Segars, Grover--Strategic Group Analysis

both organizational efficiency and effectiveness. In other words, the relevant managerial concern becomes: 'How can information resources contribute to the long term effectiveness of the firm in relation to other industry participants?' For researchers, the emergence of information resources as viable tools for both formulation and implementation of strategic corporate objectives has raised a number of difficult research design issues. Like managers in earlier stages of information systems (IS) development, scholars in the field have typically concentrated on firm specific issues such as user satisfaction [5, 20, 38], implementation [26, 43] and DP related costs/ controls [52]. This state of affairs indicates that past managerial and academic emphasis has been primarily focused on 'within firm' considerations. In contrast to this traditional view of IT, information resources which are deployed to gain competitive advantage clearly have 'between firm' implications. In other words, the impact of these systems crosses firm boundaries and can change the very nature of the firm's industry structure [47]. Therefore, research designed to explore this phenomenon must capture the dynamic and complex nature of markets, industry structures, and top level managerial planning processes. Bakos and Treacy [4] in their discussion of research in the area of information technology for competitive advantage (ITCA) note: "Literature in the area abounds with a number of frameworks for identifying and categorizing opportunities. There has been a notable absence, however, of testable models based on relevant theory. As this area of research matures, there is an increasing need to move beyond frameworks and toward exploratory models of the underlying phenomena." Alternatively stated, the changing nature of IT use requires broader models and methodological approaches which are capable of identifying both past and potential changes in industry structure as a result of technological initiatives. Such methodological approaches are markedly different from those so heavily relied upon in studies of 'within finn' impact. Clearly, the imperatives, risks and desired outcomes of competitive systems are quite different from the traditional transaction and decision support systems commonly studied by IS researchers. Specific issues raised in recent ITCA literature include:

• How can managers assess the sustainability of applications designed for competitive advantage? How long before a response? Which competitors can respond? How effective will the response be? [24] • When is adoption of IT a competitive necessity? [10] # W h a t enables a particular company to succeed with IT based strategy while others in the sector fail? [23] • What factors facilitate the use of IT for competitive advantage? [10, 23] • What are the competitive risks of developing strategic information systems? [67] • When can an IT based strategy confer competitive advantage? [14] A common theme among these issues is their implicit reference to industry characteristics and their influence on ITCA outcomes. Somewhat analogous to organizational environments which promote or discourage IT innovation, usage, learning etc.; the industry environment in terms of structure and composition has been cited in each of these works as a major factor in the success or failure (in terms of quantifiable 'bottom line' measures) of competitive IT initiatives. Thus, early theoretical work in this area suggests that any empirical exploration and subsequent theory formulation concerning the actual impact of these systems must incorporate valid operationalizations of aggregate (bottom line) performance measures, firm resource characteristics, and structural characteristics of the industry. Importantly, these characteristics must also be modeled in dynamic fashion. That is, changes in industry structure as a result of competitive IT becomes the basis of exploration rather than static, single firm pictures. The goal of this study is to present a viable technique for exploring the industry level factors which may influence the outcome of ITCA. In doing so, we hope to underscore the potential of industry modeling in the exploration of the many new issues which currently face IS researchers interested in ITCA. The discussion will proceed in four parts. First, past research efforts in the area of ITCA are outlined in order to explore its evolutionary roots and assemble a picture of current research perspectives. Second, 'strategic group analysis' (SGA), a widely used methodology developed in the areas of organizational economics and

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strategic management, is presented. This technique utilizes objective measures of strategic orientation in order to determine sets of homogenous firms (strategic groups) within a given industry. Advantages of industry segmentation along strategic dimensions are the identification of firm position within the industry structure and the evaluation of performance differences among groups. Thus, it is possible to identify 'nearest neighbors' with respect to strategic orientation and resources as well as pinpoint the strategic characteristics of the industry's best financial performers. Third, SGA is illustrated through analysis of the wholesale drug distribution industry. The subject of several academic as well as popular articles in the area of strategic IT and competitive advantage [11, 13, 17, 30, 58]; SGA is employed to: partition the industry according to strategic thrust, determine the orientation of the best performing strategic group(s), and explore changes in industry structure during the deployment of a well known and widely studied competitive information system. The final segment of this study suggests specific uses of SGA in the exploration of the more prominent issues currently dominating both academic and practitioner dialogue regarding ITCA. INFORMATION TECHNOLOGY FOR COMPETITIVE

ADVANTAGE

All business entities possess a set of 'competitive tools' which define the relevant domain for formulating and implementing corporate strategy. In other words, organizational missions, goals and objectives are constrained by the availability of competitive resources such as capital, skilled labor, technological expertise, informational resources and interorganizational alliances. Only in oligepolistic industry structures will the competitive tools of all industry participants be equal. In most instances, differences in competitive resources provide the basis for competitive advantage. In its early stages, information resource deployment was relegated to such internal organizational tasks as payroll, accounting systems and data processing. While these processes were, and still are, critical to the successful operation of the firm, they have generally been thought of as activities from which a competitive advantage cannot be garnered [39]. The availability of technology

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and the structured orientation of these tasks seemingly offered little hope for any long term gain in competitive positioning. Thus, the early orientation of management towards its collection of information tools was efficiency based. Information resources provided operational support but strategic factors critical to long term competitive advantage were often thought to lie in the firm's other competitive resources [41, 42, 561. Currently, there is general agreement in both the practitioner and academic communities that information resources can become the basis of competitive strategy as well as being supportive of strategic initiatives [39, 41, 42, 55, 68]. Pyburn [56] cites three events responsible for the recognition of information resources as strategic: (1) the issue of efficiency which dictated the planning and implementation of so many early systems' efforts is now a central theme in many firm's corporate strategy set, (2) information resources have become a major vehicle for implementation of corporate strategy (particularly in the case of interorganizational links) and (3) information and information technology have become part of the products and services of many firms. Ives and Learmonth [39] include lower costs of supporting information technologies, the emergence of global competition and government deregulation as factors heightening the importance of information technology in the organization's strategic plan. A subtle but important observation concerning the recognition of IT as a strategic resource is the focus on technology use versus capabilities. That is, the backbone of ITCA does not rely so much on technological advances as it does on the effective identification and exploitation of IT based opportunities within the industry. As noted by Keen [40], technology is available to all, it is the management of the technology which makes it 'strategic'. Thus, earlier forms of internal transaction processing which, as noted earlier, could be considered operational necessities, can suddenly be transformed into competitive weapons when innovatively linked to customers, suppliers or even other competitors. It is these notions that form the early foundation of ITCA literature. Stated as research questions, these issues are: 'How can competitive opportunities within the industry be identified?' 'How have other firms used ITCA?' 'How can firms assess,

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Segars, Grover--Strategic Group Analysis

develop, and improve their IT capabilities within the context of their respective industry?', and 'How can top managers be made aware of IT's potential contribution to effective competition?'

ITCA frameworks Noting that most early literature in the realm of ITCA consists of frameworks for analysis, Earl [23], develops a 'framework of frameworks' as a way of overviewing and synthesizing these important research efforts. In general, Earl suggests that three classes of frameworks can be identified: awareness, opportunity and positioning. Each of these classes differs in purpose, scope and use. The purpose of awareness frameworks is executive appreciation and understanding of the strategic potential and impact of IT. In short, the usefulness of these paradigms is primarily educational. Examples can be found in works by Porter and Millar [55] as well as Wiseman and MacMillan [68]. In each, broad competitive factors are defined and associated with uses and potential impacts of competitive IT. Opportunity frameworks can be conceptualized as analytical tools which uncover firm specific strategic opportunities and/or clarify business strategies in order to better identify viable options for competitive IT development. Porter's value chain [54], along with Ives and Learmonth's [39] customer resource life cycle (CRLC) are perhaps the best known paradigms for discovering these opportunities. Each model focuses on the value adding processes of the firm, or customer in the case of CRLC, and attempts to target those activities which can be enhanced through information resources. Positioning frameworks are concerned with improving executive's understanding of how IT should be managed within their particular organization. Specific concerns addressed are the assessment, development, and improvement of IT capabilities within the organization. McFarlan's [47] strategic grid as well as Nolan's [52] stage model are examples. Each of these frameworks focuses on managerial actions necessary to effectively guide the organization's technological resources. Case analysis Another dominant form of early ITCA research, which closely parallels the aforementioned awareness and opportunities frame-

works, consists of case studies. Descriptions of strategic systems such as Merrill Lynch's CMA, McKesson's Economost, American Airline's SABRE and Digital Equipment's XCON have become legendary examples of IS deployment for competitive advantage [13, 21, 60]. A common pattern in these studies is the identification of system attributes, a description of the firm's business process or distribution networks, and an account of how the system leveraged the firm's resources for competitive advantage. The primary intent being to create both managerial and academic awareness of the IT's strategic potential. Although these studies provide interesting insights into competitive uses of IT, a significant shortcoming is their static and descriptive nature. In other words, these studies fail to explore industry impacts over time in any scientific or empirical vein. Thus, practitioners know how individual firms have exploited IT for competitive advantage but are left without a generalizable paradigm for identifying strategic opportunities within their respective industry structures. While frameworks and case studies have served an important definitional role in the study and use of ITCA, they are, perhaps, limited by the 'general' nature which makes them so intuitively appealing. Once opportunities are identified, awareness built, and portfolios of systems assessed: 'what then?', 'Should other considerations such as competitor, customer, or supplier reaction figure into the decision to invest?', and if so, 'How? What are the chances of failure?' 'How long can a competitive edge based on IT be sustained?' Clearly, these concerns are far more complex than those cited earlier and require far deeper analysis than that provided by early literature. These questions are based on industry structural characteristics in terms of current and future time frames, and all too often, the answers are not easily attained. Yet, only the most risk averse firms would dare chance the financial, technical and human resources necessary for the building of these IT based initiatives without some assessment of these variables.

Sustainability of ITCA: the role of structural characteristics Largely as a result of these concerns, academic as well as practitioner attention has recently focused on the conditions and

Omega, Vol. 22, No. I

associated risks surrounding the deployment of ITCA. This current perspective suggests that attaining competitive advantage from IT may be more difficult than suggested by previous research and that 'true' competitive advantage must be defined in terms of the 'sustainability' of competitive gains [10, 11, 13, 15,67]. In essence, the overriding concern of these efforts is assessment of factors which influence the riskiness of competitive IT initiatives in terms of competitor duplication/improvement or other adverse industry shifts. As noted by Clemons and Kimbrough [11], the deployment of IT may become a 'strategic necessity' rather than a 'strategic advantage' if the resource can be easily replicated by other competitors. In such an instance, the copying firm often enjoys the advantages of newer and better technology, learns from the experience of the innovator, and subsequently improves on the original system. Vitale et al. [67] state that strategic IT decisions can present risks by changing the basis of competition. Short term competitive advantage can soon become 'obligatory for continued competitive viability' resulting in the same industry competitive situation at an increased level of cost. Clemoi{s and Row [14] explain the likely outcome of strategic IT initiatives through analyzable traits of industry members and the specific application. In essence, it is-suggested that the relative positions of competitors and potential entrants in terms leverageable 'complementary resources' are important in determining the sustainability of gains realized through strategic uses of IT. These resource differences are broadly described as: (1) differences in degree of vertical integration, (2) differences in diversification and (3) differences in resource quality and organization. In outlining areas for future research these authors suggest: " . . . Expanding current theory to encompass shifts in firm boundaries and the structure and inter-relationships of industries. This is critical in understanding opportunities for interorganizational cooperation and outsourcing..." and " . . . Developing empirical tests for this theoretical approach...". Clearly different from earlier research in the area, this perspective raises the level of analysis from intra-firm planning processes to evaluation of dynamic industry interaction as a result of

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technological initiatives. In addition, this view perhaps best addresses the questions currently being asked about ITCA in both practitioner and academic communities. Yet, little research has been attempted, and few methodologies proposed, to empirically model these important relationships. Future research directions

The importance of information and technology as viable strategic resources calls for a broadening of existing research efforts. Methodological approaches which empirically investigate the industry impact of strategic information technology must be developed in order to provide practitioners with tangible results so important in tactical planning for these systems. The strategic planning process of firms which view information resources as tools for strategy formulation and/or implementation is highly dependent upon industry characteristics. Yet, there is a noticeable absence of empirically derived models of industry behavior in the arena of IS research. Whether supporting corporate objectives with information initiatives or attempting to gain competitive advantage, the high risks and capital investment associated with these systems places a premium on top management's ability to correctly assess the competitive characteristics of the industry. Therefore, IS research must take the 'next step' and develop research designs that are longitudinal in nature and empirically driven. Only then can abstract constructs such as 'resource differences', 'strategic uses of IT', and 'sustainability' be measured and explained within the context of relevant industry characteristics. A primary objective of this study is the introduction of 'strategic group analysis' (SGA) and a demonstration of its use within the domain of ITCA research. The longitudinal nature of this technique along with its use in capturing the strategic orientation of industry groups make it a promising methodology for studying the presence or absence of sustainability factors as well as the nature of structural characteristics within industries and between firms. Obviously, the development and subsequent use of a single technique is not the panacea for all the methodological issues previously outlined. However, it does provide a necessary shift from

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Segars, Grooer--Strategic Group Analysis

study of firm attributes to industry dynamics within the realm of strategic uses of information resources. STRATEGIC GROUP ANALYSIS

The term 'strategic group' was originally coined by Hunt [37] and defined as: "A group of firms within an industry that are highly symmetric.., with respect to cost structure, degree of product diversification, formal organization, control systems, and management rewards and punishments ... (and) the personal views and preferences for various possible outcomes . . . " As noted by Harrigan [31], strategic group mapping can be a useful way of tracking industry dynamics as firms become more similar to or different from each other. The matching of market segment changes with strategic group evolutions provides a useful means of predicting the nature of competition. Scholars in the area of strategic management implicitly accept the definition and usefulness of strategic group methodology despite the variance in the treatment of strategic groups in empirical research settings. A variety of methods and classification schemes have been used in past research as a basis for strategic group formulation. A useful paradigm for classifying the more dominant perspectives in the treatment of strategic groups is offered by Thomas and Venkatraman [64]. These authors categorize strategic group research based on two dimensions: (1) the operationalization of strategy and (2) the approach used for the development of groups. Operationalization of strategy and development

of strategic groups A primary objective of strategic group analysis is to delineate distinct sets of firms that are 'maximally similar' within groups and 'maximally different' between groups with respect to strategic orientation. Therefore, a critical issue in group formulation is the operationalization of strategy. The general issue of operationalizing and measuring strategy has been explored and discussed thoroughly in past research [27, 29, 34, 35, 59, 61,66]. Methodologies for operationalization can be dichotomized into narrow (focusing on one functional area or singular dimension) versus broad (multiple functional areas or dimensions reflecting a complex array of scope and resource

deployment decisions). As noted by Thomas and Venkatraman [64], "The development of strategic groups using a narrow conceptualization of strategy is unlikely to capture the complexity of the strategy construct, thus limiting the usefulness of strategic groups for both descriptive and predictive purposes". Therefore, an important research design consideration is the match between research objective and breadth of strategy operationalization. Because of its importance in capturing an overall strategic orientation of an organization and subsequent use in this study, components of broad strategy operationalization are developed further. The overall (broad) strategic thrusts of the firm consist minimally of two sets of activities: (1) scope commitments and (2) resource deployment decisions [3, 16, 18, 36, 45]. Scope commitments include decisions involving market segmentation, product/service characteristics, and geographic reach of strategy. Resource commitments consist of business level deployments of resources to those functional areas that are key to maintaining a competitive advantage. The combination of resource and scope commitments defines business strategy and provides the basis of competitive advantage within the firm's respective industry [1, 36, 49, 54,]. Thus, firms which belong to the same strategic group would compete within an industry on the basis of similar combinations of scope and resource commitments. Further, differences in these commitments among groups should lead to differences in performance [55]. Therefore, determination of the most profitable groups facilitate identification of those strategic orientations which deliver competitive advantage. From a research perspective, two key ingredients in the formulation of strategic groups are: (I) development of proxies for strategic orientation and (2) choice of scheme used to define homogenous groups. Typically, variables used to identify groups are selected within the context of the industry under study [16, 32, 48]. For example, studies of the pharmaceutical industry by Fiegenbaum et al. [25] as well as Cool and Schendel [16] identified sets of variables which were proxies for firm factors such as size, research and development concentration

Omega, Vol. 22, No. I

and advertising intensity. Content analysis of annual and industry reports as well as interviews with experts provided the basis for factor selection. The factor proxies consisted of financial measures and ratios such as total assets, total sales, R&D expenses as a percentage of sales and advertising expenses as a percentage of sales. Other studies have included primary data in the form of surveys and interviews in the formulation of strategic orientation [19, 50, 62]. Once key strategic variables have been identified, both heuristic (judgmentbased) and statistical techniques can be used in forming clusters of homogenous firms. Predominant grouping schemes include space mapping, heuristic techniques, cluster analysis, regression analysis, MANOVA and factor analysis [31, 64]. Research groups

methodology for identifying strategic

A flow diagram of strategic group formulation is presented in Fig. 1 and discussed in the subsequent paragraphs. In most existing studies, multivariate analysis approaches, particularly cluster analysis, have been utilized in forming strategic groups [31, 64]. Therefore, the methodology illustrated in Fig. 1, and subsequently demonstrated in this study is based on the use of cluster analysis as the requisite grouping technique. Research intent can be deductive or inductive. In the former, the goal is to verify through a scientific process the existence of structures or constructs which are proposed by theory. The latter intent is to determine, through data analysis, the existence of relevant structures and/or relationships. Strategic group analysis is applicable to either a theory building or theory development approach. Therefore, hypothesized industry relationships in terms of informational attributes, technological architecture, strategic orientation or scope characteristics can be tested. In contrast, primary or secondary data sources can be used to discover relevant relationships across various informational and strategic dimensions. The first step illustrated by Fig. 1 involves identification of relevant time frame and industry composition. These considerations are contingent upon the industry involved and the research objective. Typically, standard industrial classification codes (SIC) have been the OME 2 2 / 1 ~ "

19

'industry defining' criteria in strategic management and IO economics [25,64]. However, Thomas and Venkatramen [64] note that it is neither appropriate to be bound to a SIC scheme which reflects product variations, nor to one which limits sample frame to national boundaries. Variations in product, market and technology must also be considered in formulation of industry composition. At step two, the researcher must determine if corporate, business or functional level strategies should be examined and then assess which components (scope, resource deployment) best reflect those strategic decisions. As argued by Porter [54], Cool and Schendel [16], Hofer and Schendel [36] and Fiegenbaum et al. [25], scope and resource deployment decisions reflect the manifestation of the firm's strategic decisions. In turn, competitive advantage and synergy are the direct results of these scope and resource deployment decisions. Step three involves determination of variables which accurately reflect the firm's scope and resource deployment decisions within the competitive context under study. As stated earlier, the operationalization of strategy is a key component in the formulation of strategic groups. Therefore, careful consideration must be given to relevant industry characteristics such as competitive strategies, critical success factors, economies of scale and environmental forces. Operationalization can be facilitated by prior research, industry reports, surveys and/or expert panels. The output of this step should be a set of objective measures which accurately reflect the chosen levels and components of the firm's strategic decisions. Next, periods of homogeneity and similarity in industry strategic behavior must be identified within the chosen time period. Commonly referred to as stable strategic time periods (SSTPs), these periods have generally been defined as stages in which relationships among strategic variables are consistent. Once these periods have been identified, then analysis of strategically similar groups becomes much more meaningful [16, 25]. As proposed by Cool and Schendel [16], it is essential to test the stability of the variance-covariance matrix of the strategic variables in adjacent time periods in order to determine if a strategic change has occurred in the industry. When firms alter

Segars, Grover--Strategic Group Analysis

20

--..

Deductive (confirmatory) [

Step l - Determine relevent time period and industry composition

Step 2 - Determine levels of strategy to be examined. Corporate, business or functional level. Choose relevent component of strategy. Scope and/or resource deployment Step 3 - Identify variables which best capture the firm's scope and resource deployment decisions

Step 4 - Identify periods of homogeneity in strategic behavior within industry

Step 5 - Cluster firms in groups for each stable time period

Step 6 - Descriptive validity. Can resultant clusters be meaningfully labeled?

Inductive

(exploratory) [

/

Choose time

period and sampling frame /

Choose levels and components Components: scope, res. d e p l o y m e n t

Levels: corp., bus., funct.

1 Identify firm's strategic decisions Strategy operationalization

Identify

I-

stable strategic time periods

Cluster firms into strategic groups Heuristics Clustering techniques

Statistical m e t h o d s

Label resulting

clusters

Can clusters be m e a n i n g f u l l y labeled?

I No

ll Yes

Theory construction Step 7 - Does resultant cluster stueture have implications for existing or new theory? Examination of clusters for performance or other significant differences

P e r f o r m a n c e differences Rationale for resultant g r o u p i n g s

groups for different: time, levels, components?

Cluster

Yes

End Fig. 1.

Research methodology for identifying strategic groups.

their commitments along strategic variables, the covariances between these variables should reflect this strategic repositioning. Thus, a SSTP can be defined as a period in which the variance--eovariance matrix formed from the strategic variables is more stable within versus across the considered time period. Bartlett's

procedure [28] is commonly used to test the equivalence of variance--covariance matrices. Once SSTPs have been identified, firms can then be clustered into strategic groups. As noted in Fig. 1, several multivariate statistical as well as judgmental approaches can be employed to develop industry groupings. Although each may

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be more appropriate under differing circum- ~ology is the identification of strategic thrust stances, only cluster analysis is considered here. and determination of competitive differences An excellent review of clustering algorithms, between resultant groups. Then, it becomes decision rules and methodological concerns is possible to develop empirically based theory presented by Punj and Stewart [57]. In general, regarding the existence of competitive factors these authors recommend the use of Ward's such as scope, asset utilization and deployment minimum variance criterion as it outperformed of corporate resources. Thus, a dynamic picture other clustering algorithms in simulated data of industry structure emerges in which factors analysis. Although no formal criteria exists for relevant to planning, impact and opportunity determining the number of appropriate clusters, assessment can be more clearly identified and a commonly used rule of thumb in selecting modeled. The thesis of this paper is that SGA can be a the number of clusters involves examination valuable research methodology in furthering the of 'tightness' of the clusters as the algorithm progressively combines groups and observations understanding of structural characteristics and [16, 31]. This quantity is referred to as pseudo their influence on ITCA. In the next section we F and is a ratio of between-group distance formally apply this technique within the domain to within-group distance as measured by the of ITCA research. The purpose of doing so is clustering variables. Validity of the clustering two-fold. First, through illustration, we can solution may also be assessed through multiple more fully develop each step of the methodoanalysis of variance (MANOVA) [2]. logical model presented in Fig. 1. Second, we Once clusters have been identified across hope to underscore the usefulness of (and in stable time periods, it becomes the researcher's a sense validate) SGA by adding empirical responsibility to accurately label the differing findings to existing narratives within the context strategic orientations of the resultant groups. of competitive uses of IT. Importantly, our As shown in the flow diagram, the ability to view is that SGA provides a complimentary identify strategic orientation is directly related source of evidence to descriptive narratives. to the prior step of strategy operationalization. Complete identification and explanation of To facilitate identification, summary statistics industry changes resulting from technological should be developed for each cluster. Difficulties imperatives is probably beyond the reach of in identifying strategic orientation should be either empirical techniques or case studies cause for skepticism regarding strategy opera- in isolation. However, the synergies created tionalization. As noted earlier, strategic groups through the combination of these techniques consist of firms which resemble each other more may help provide many of the answers to the closely than firms in other groups along the questions currently asked regarding the use and chosen strategic dimension. These criteria can sustainability of ITCA. be guaranteed by the chosen clustering algorithm. In other words, no matter what criteria AN ILLUSTRATION OF STRATEGIC are used to form groups, the algorithm will GROUP ANALYSIS: THE WHOLESALE always yield sets of homogenous clusters. ThereDRUG INDUSTRY fore, in the absence of a theoretical underpinning or prevailing strategic orientation these There are a limited number of heralded cases groupings may represent numerical anomalies regarding the effective deployment of inforrather than scientific significance. mation resources for competitive advantage. Finally, theory construction and research Thus, these few, well-known, instances form implications can be formed. Most early studies the basis of many academic and practitioner in the area of strategic group analysis have, as articles on the subject. Perhaps the most cited their primary goal, the establishment of hetero- of these cases is McKesson Drug Company's geneity along strategic dimensions within the 'Economost', an electronic system for direct industry of interest [64]. While the notion of order entry. As noted by Clemons and Row generic strategies is grounded in this view [50], [13], McKesson's strategic IT initiative drastithe mere existence of groups is of little academic cally changed the competitive structure of interest in the absence of extant theory. Clearly, the drug distribution industry. The theorized the next step in the evolution of this method- nature of these impacts include improved

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Segars, Grover--Strategic Group Analysis

competitive position of the innovating firm in terms of market share and profitability, changing industry structure in terms of profitability and concentration, and altered supplier/customer relationships. While intuitively appealing and highly relevant for understanding of the complexities surrounding ITCA, no empirical work has attempted to model the structural changes described above. Yet, as described earlier, there are theoretical frameworks and methodological tools available for modeling the dynamics of industry level relationships. Analysis of industry level repositioning based on innovative technology can be valuable in sensitivity analysis of strategic IT plans as well as determination of critical sustainability factors necessary for long term competitive advantage. The aim of this illustration is to track the dynamics of the drug distribution industry from the launch of McKesson's Economost thereby adding empirically based findings to the existing narratives surrounding this proclaimed use of information technology. Specifically, strategic group analysis will be used to detect the presence, timing and nature of significant structural changes in the industry. M c K e s s o n ' s Economost

Clemons and Row [13] provide an excellent overview of McKesson's Economost. In general, this system allows retail pharmacies to order products with a hand-held electronic order entry device. When the order has been entered, the customer transmits the information via McKesson's 800 W line to the firm's national distribution center. Within a day, McKesson delivers the items ordered in cartons that match the aisle arrangement and major departments of the drug store. The high reliability of McKesson's deliveries (99%) eliminates the need for safety stocks and excess inventory. McKesson also provides the retail pharmacist with valuable management control information in the form of monthly purchase reports. Although McKesson's clientele realize obvious financial benefits such as reduced transaction and inventory holding costs, most customers cite the lower wholesale prices and working relationships with McKesson's sales force as reasons for system adoption. Thus, true to the nature of 'strategic systems', McKesson's Economost is able to

effectively leverage existing firm competencies for competitive advantage. Prior to the introduction of electronic order entry, productivity was low and costs high within the drug distribution industry. Filling customer orders was slow and labor intensive with a great deal of wasted time and motion. In most instances, purchasing and order entry staff were duplicated at each warehouse. Therefore, potential benefits of automated order entry technology include reduction in clerical and sales personnel, increased productivity of distribution process, solidification of customer loyalty and lower costs of selling. In short, the 1975 rollout of Economost had industry changing potential. Additionally, such an innovation should be expected to deliver above average returns to its originator. Yet, commenting on the state of the industry in the aftermath of Economost, Ciemons and Row [13] note "... while the profitability of the entire industry has improved dramatically,it is not obvious that any player has obtained 'competitive advantage', that is, persistent high profitability relative to competitors. McKesson does not appear to enjoy substantially higher profitability than other large national and regional competitors, nor do the major competitors in the industry seem to enjoy significantly greater profitability than do smaller players ...".

Issues for research Clearly, the introduction of this strategic system altered the playing field of the industry. Cost of participation in terms of technology grew substantially higher suggesting that scope and economies of scale would become key factors for successful industry competition. Yet, as suggested by Clemons and Row [13] no clear advantages in the form of sustained profitability seemed to accrue to these larger players. Instead, it has been proposed that rapid adoption of order entry technology by other competitors may have caused these economic profits to be transferred to the drug wholesaler's customers in terms of lower prices and improved service. Such a proposition is in direct contrast to much popular and academic literature regarding this system [17, 58]. Even today, Economost is touted in classrooms as a prime example of a strategic IT innovation which delivered a competitive

Omega, VoL 22, No. 1

advantage to McKesson. Thus, narratives surrounding this technology appear consistent in the belief that it radically changed the industry but inconsistent in the amount of purported benefits transferred to its innovator. McKesson's technological initiative was not proprietary or easily defensible from imitation by competitors. Therefore, measurement of the strategic reactions of competitors and changes in industry structure during the deployment and adoption of this technology provides a rare opportunity to objectively explore many of the issues regarding the sustainability of ITCA and the potential role of both firm and industry structural characteristics. Specific issues of interest are: 'What is the basis of competition within the industry? How did the industry change during the deployment of this technology?' and 'Were innovators and/or adopters of the technology more profitable than other industry participants?' We now explore these questions utilizing the SGA methodology developed earlier. Sample and data sources: Step 1

Perhaps the most difficult initial consideration in SGA is definition of sample frame. Specifically stated, this initial concern is: 'What are the boundaries of industry participation?' As noted earlier, many firms may participate in multiple industries through diversity of product offerings, wholly owned subsidiaries or joint partnerships. Thus, industry definition may not be as straightforward as simple examination of SIC codes. Accordingly, in the majority of studies examined, SIC codes are used only as a beginning point in industry definition [22, 64]. Typically, if ambiguities in classification arise; analysis of industry surveys, examination of past literature and/or solicitation of expert opinion are used as a basis for improved categorization. Within the context of this study, composition of relevant industry participants was determined by multiple measures including SIC code, industry/analyst surveys (Value Line 1975-1982 [65]; Standard and Poor's Industry Surveys, 1975-1982 [63]), annual reports and previous literature [13, 17, 58]. Close attention was paid to: (1) the types of products/services offered by potential industry members and (2) consistency of classification across independent studies, reports and/or classification schemes.

23

The culmination of these efforts was a set of firms (or industry) as homogenous as possible in terms of the collection of products/services offered. Upon development of industry definition, attention must then turn to the relevant time period. Given that the specific objective of this analysis is examination of industry changes after the initial deployment of order entry technology; a time frame of 1976-1982 was chosen because it reflects a relatively stable regulatory environment and encompasses a significant period of time beyond Economost's late 1975 introduction from which changes in the overall industry structure can be gauged. Again, industry reports (Value Line, 1975-1982 [65]; Standard and Poor's Industry Surveys, 1975-1982 [63]) as well as past literature were relied upon to help determine the appropriate time period. In this portion of the analysis, close attention was paid to uncontrollable environmental factors (regulatory changes, lawsuits, general economic characteristics etc.) during the desired time period of analysis which might cause significant changes in industry structure. Such factors can undermine the internal validity of even the most carefully designed study. Thus, the desired (and realized in the instant case) outcome of this analysis was a time frame relevant to the research question and, as important, stable with respect to major environmental factors which might confound inferences drawn from the data.

Strategy operationalization: Steps 2 and 3 As noted by Mintzberg [51], financial measures contain elements of realized strategy and therefore provide useful proxies of the firm's scope and resource deployment decisions. Measures utilized in this study as proxies of strategic decisions are shown in Table 1 and encompass both scope and resource deployment activities. Selection of these strategic variables is a union of those used to capture strategic behavior in the strategic management literature [16, 25, 32, 33, 53] and those identified by industry analysts (Standard and Poor's Industry Surveys, various years [63]; Value Line Investment Surveys, various years [65]) as key elements of competitive strategy in the drug wholesale distribution industry.

24

Segars, G r o v e r - - S t r a t e g i c Group Analysis

Table 1. Strategic variables and measures Scope commitments: "domain" commitments. Market presence of industry participants in terms of size or market reach. Ability

of firm to generate both internal and external capital for reinvestment Dimension

Variable(s)

Measure(s)

Citation,s)

Size

Assets Sales Inventory

Gross value of assets Firm's total sales Firm's total inventory level

Fiegenbaum et al. [25] Cool and Schendel [16] Hatten et al. [33] Pant [531

Cashflow Working capital

Firm cashflow/investment Working capital/sales

Chakravarthy [8] Bourgeois [6] Douglas and Rhee [22]

Slack resources

Resource deployment decisions: financing decisions. Business-level deployments of resources (cash, human, materials, etc.) to those functional areas that are key to obtaining and maintaining a competitive advantage in target product-market segments

Dimension Finance

Production

Marketing

Variable(s)

Measure(s)

Citation(s)

Current ratio Quick ratio Times interest earned Equity to debt

Current assets/current liabilities Cash/current liabilities Operating income/interest expense Owners equity/debt

Fiegenbaum et al. [25] Chakravarthy [8]

Inventory e f f i c i e n c y Employee efficiency Cost intensity

Sales/inventory dollars Sales/employees Sales/cost of goods sold

Receivables intensity

Scope commitments

Industry scope can be conceptualized as the size and cash flow generating ability of member firms [16, 25]. As shown in Table 1, measures of size include the gross value of assets, sales and inventory. Segmentation along this dimension provides a first glimpse of industry participants framed on the basis of market strength and breadth of asset base. Studies conducted on both intra-industry [16, 19,32,33] and interindustry firms [22] utilize similar or exact operationalizations for this factor as those found in Table 1. Scope of operations is perhaps the most prominently and consistently mentioned strategic variable in strategy operationalization literature [16, 62]. Recent IS literature has also noted the importance of scope in initiating and sustaining competitive advantage with IT [14, 24]. Scale advantages may make it difficult for all members of the industry to imitate the actions or initiatives of the largest participants. However, size is an advantage only if there are compelling economies to being large. As noted by Pant [53], small firms may be able to react to changes in the marketplace sooner than larger firms. A small firm may have a less complex organizational structure, making organizational change easier to implement. Another important dimension of scope is the ability of the firm to generate slack resources. In essence, levels of organizational slack refer

Sales/receivables

Standard and Poor's Sur~'eys Value Line Sur~'ey

Fiegenbaum et al. [25] Hatten et al. [33] Standard and Poor's Surveys Value Line Surcey

Cool and Schendel [16] Hatten et al. [33]

to the firm's 'internal capital' or the ability to generate cash flow for purposes of reinvestment. The measures adopted above are grounded in past studies of slack resources and their influence on firm performance. An excellent overview of this research is provided by Chakravarthy [8]. Slack can be invested by the firm for: (1) enhanced managerial or technical capabilities [49], (2) expanded organizational capabilities [9], or (3) to reduce its resource dependencies [44]. Thus, the absence or presence of slack resources significantly influences the number of viable strategic alternatives available to the firm [8]. With regard to ITCA, the presence of slack resources may facilitate both the development and enhancement of, as well as the ability to respond to, competitive initiatives. As noted by Vitale et al. [67] as well as Clemons and Row [14], these systems require rather large outlays of capital and may expose the firm to high levels of competitive as well as financial risk. Thus, the ability of industry participants to generate slack resources may be an important structural characteristic in the maintainability of unique system features, and subsequently, the sustainability of realized competitive gains.

Resource deployment decisions The second component of strategic orientation, resource deployment, refers to financing

Omega, Vol. 22, No. I

decisions and distribution of important corporate resources to those functional areas key in obtaining competitive advantage in targetmarket segments [16]. Based on review of past literature along with critical success factors uncovered through analysis of industry surveys, this component of strategic orientation was operationalized along three dimensions; finance, production and marketing. Firms may alter their commitments along any of these dimensions in order to cope with new or changing industry characteristics [16, 64]. However, it is not likely that all firms will be identical in their chosen pattern of competition. As duly noted by Porter [54], firms may choose to focus on cost leadership, differentiation or target marketing. Each of these strategies is viable in a given industry and each would likely result in varying commitments to these strategic dimensions [16]. Both past literature as well as industry surveys emphasize the role of financial risk as an important determinant of resource deployment. In short, risk disposition is concerned with management's willingness to utilize financial or operating leverage in pursuit of competitive goals. Similar to studies by Fiegenbaum et al. [25] as well as Cool and Schendel [16], multiple ratios are utilized in the measurement of this strategic dimension. Importantly, both short term operating risk (current and quick ratios) as well as long term structural risk (times interest earned and equity/debt) are captured through the selected financial proxies. As argued by these authors, these operationalizations can yield insight into management's complexion concerning risk taking; not only in terms of utilizing financial leverage but also in terms of investing in riskier projects. In many industries production efficiencies are an important component of competitive survival. Our analysis of relevant literature suggests such a case in the drug distribution industry. Past studies in both the brewing [33] and pharmaceutical industry [16, 25] have developed well-grounded measures of this important strategic dimension. As shown in Table 1, these efficiencies are measured along levels of cost, inventory and human resources. Given the previously described nature of McKesson's Economost, we would expect this dimension to be heavily impacted by its deployment. Thus, in contrast to the previously defined

25

antecedent dimensions of size, slack resources and risk-taking posture this dimension may be more of an outcome measure within this particular example. Clearly, within this industry it is a critical dimension of competition and one in which Economost was specifically designed to enhance. The final operationalized dimension of this set is marketing. In many studies this characteristic is operationalized through measurement of advertising expenditures. However, given the nature of the industry under study (virtually no expenses are recorded for advertising), a more appropriate measure is receivables management. Although this is similar to the efficiency measures developed earlier, its separate treatment here is consistent with past studies [16, 25] as well as analysts' reports. The rationale being that management of this resource is not production related; rather, it is related to policy regarding the selling of product/services to the firm's customers. Here also, the nature of order entry technology is likely to have a noticeable impact. Actual data for these strategic variables is collected from quarterly and yearly tapes of the Compustat II financial database. Strategic grouping along these dimensions will result in an objectively derived industry structure consistent with key competitive characteristics commonly cited by industry experts/analysts. Thus, a complex competitive picture is made simpler through identification of firms similar in terms of scope and resource deployment. In a competitive sense, managers and researchers can then identify sub-competition within the industry, examine changes in structure over time and identify avenues for competitive advantage through IT or other firm resources. In a defensive sense, the likely impact of innovative IT can be assessed providing valuable input into IT investment and prioritization decisions. In sum, mapping of these strategic dimensions provides valuable input for top-level as well as informational resource planning for industry innovators as well as followers. Thus far, relevant time frame, industry composition and strategic operationalization have been established. In the sections that follow, identification of stable strategic time periods and determination of strategic groupings across these stages will yield a dynamic competitive picture of the wholesale drug distribution

26

Segars, Grover--Strategic Group Analysis Table 2. Test of variance-covariance differences between adjacent years 1976-1977 1977-1978 1978-1979 1979--1980 1980-1981 1981-1982 Bartlett's chi square

101.4

87.7

I 11.6

201.6"

101.5

92.2

*Significant at the 0.01 level.

industry. In essence, the following steps determine if and when the industry changed with respect to strategic orientation and the specific nature of these shifts in terms of strategic group membership. Stabifity o f strategic time periods: Step 4

Step 4 of Fig. 1 involves identification of periods of strategic homogeneity within the industry. As argued by Cool and Schendel [16], and recently by Fiegenbaum et al. [25], shifts of individual firms among industry groups may be random occurrences in the absence of evidence indicating fundamental industry change. More succinctly stated, the first objective of the researcher should be to determine when an overall shift in the industry occurs then determine the nature of group membership between these time periods. Bartlett's test [28] is typically used for identifying significant differences in the variance-covariance matrix of strategic variables between adjacent time periods. The goal of this analysis is to identify changes in interrelationships among strategic variables over time. A significant difference in Bartlett's chi square indicates that a fundamental shift in the allocation of industry resources to strategic variables has taken place. In other words, a shift in industry deployment of strategic resources is identified. As argued earlier, clustering within these stable periods facilitates examination of differences across industry stages. The results of this procedure for the drug distribution industry are presented in

Table 2. Note that two stable strategic time periods (SSTPs) exist between the years 1976--1979 and 1980-1982. The significant finding of this analysis is that no immediate impact in terms of change in strategic orientation among industry members resulted directly after the introduction of order entry technology. In fact, the above result would seem to suggest that the industry remained homogenous with respect to strategic orientation from 1976 through 1979, a full four years after the introduction of Economost. However, a change along these dimensions did occur in 1980 suggesting perhaps a delayed impact of this technology. To substantiate this supposition an analysis of industry and annual reports for the years 1979 and 1980 was undertaken. Although no formal content analysis was employed, an obvious pattern in these documents was the mention of industry consolidation and change due to order entry technology. Specifically, the consensus of both managerial participants and observer analysts was that cost-based competition was beginning to drive the industry and that order entry technology had made the services of industry participants more 'generic'. As noted by one analyst (Standard and Poors 1980 [63]): ... Through consolidation and cost reducing strategies, firms have repositioned themselves for competition in lieu of the advances in technology. . . .

Thus, in a quantifiable sense, SGA seems to be adequate in identifying the timing of IThe specific narratives analyzed include Standard and fundamental industry changes. Although not Poor's Industry Surveys [63], Value Line Investment enough in isolation to infer causality due to Surveys [65],annual reports for various members of the technological imperatives, the narratives ~ anaindustry (McKesson,Bergen,Allou, Ketcbum,National Intergroup), and relevant practitioner and academic lyzed seem to suggest that technology was literature [13, 17, 58]. a major driver in the transition of this indusStandard and Poor's provides comprehensive analysis of try. What has been added here is a method events and trends across a multitude of industries. It is published yearlyand is widelyused by both managerand to empirically support these suppositions analyst to monitor firms and industries. Value Line also across a range of operationalizations and provides a wealth of information concerning industry research questions. Given the identification of trends. Although more investmentorientedthan S&P,its quarterly publication provides unique insight into key this significant shift in industry orientation, industry/firm trends and events. cluster analysis can now be employed to further

Omega, Vol. 22, No. !

identify the nature of resulting strategic groups and their changes over time.

Development of strategic groups: Step 5 Formulation of strategic groups, Step 5 of Fig. I, was accomplished via Ward's minimum variance clustering algorithm. The clustering criteria of this technique is minimization of total within-group sums of squares. Alternatively stated, clusters formed at each step have minimum within-group sums of squares. The determination of cluster retention (number of strategic groups) involved evaluation of the statistic pseudo F. This statistic is defined as the mean square between-groups divided by mean square within-groups. For each year, the various clustering solutions were plotted against pseudo F. Jumps or 'elbows' in the plot were used to identify likely clustering solutions. Examination of other statistical criterion, specifically root mean square and semipartial R square, were used to confirm visual conclusions. In each year examined, large 'elbows' in the plots of pseudo F were observed. Thus, a definite 'structure' of firms with differing strategic thrusts seems evident within this industry during the time periods examined. Some researchers have questioned the validity of resulting cluster solutions due to the heuristic nature of clustering algorithms. Thus, a frequently used technique for cluster validation is multivariate analysis of variance (MANOVA) [2, 46]. In each comparison among emergent groups (1976-1982), observed F statistics revealed differences significant at the 0.01 level. We therefore conclude that firms within emergent groups are as homogenous as possible with respect to the operationalized strategic characteristics and that the groups themselves are statistically different from each other along these collective dimensions. Consistent with the findings regarding SSTPs, cluster analysis of sample firms revealed relative consistency in strategic groups for the years 1976-1979 with a fundamental shift in groupings occurring in 1980. In the SSTP of 1976-1979, the statistical stopping rules suggest a five cluster solution. Analysis of clustering solutions beyond this time period (1980-1982) suggest a three group structure implying a consolidation of previously distinct strategic orientations. As noted earlier, this is consistent

27

with industry and annual reports of the time. However, knowledge of distinct groups of competitors is of little use in the absence of meaningful descriptions of their unique competitive dimensions. As outlined in Steps 6 and 7 of Fig. 1, meaningful descriptions and identification of performance differences greatly add to understanding concerning the nature of industry structure.

Analysis of strategic orientation: Step 6 To facilitate identification of differences among emergent groups, means of strategic variables were calculated for each cluster across the two SSTPs. These means were then used to rank (1 = highest value- 5 = lowest value) strategic groups across competitive variables. Tables 3 and 4 present the ranks of each strategic group for the time period 1976-1979 (SSTP 1) and 1980-1982 (SSTP 2), respectively. These rankings along with annual reports and industry surveys were used in the formulation of the strategic map illustrated in Fig. 2. Names of strategic groups were judiciously chosen to reflect the prevailing strategic thrust of member firms along the dimensions of size and markets served. As shown in Fig. 2, the fundamental change across time periods within this industry is caused by consolidation of larger competitors which were previously distinct in terms of strategic orientation. We now briefly elaborate on the specific differences in resource allocations among these emergent strategic groups and their changes in membership and orientation across the two time periods. Industry Kings. Kings are so named because they represent the largest of this industry's competitors in terms of size and ability to generate slack resources. In the first observed SSTP, specific firms within this collection include McKesson, National Intergroup and AMFAC. Interestingly, during this time each of the industry Kings competed along different strategic orientations. Particularly noteworthy are differences in operating risk (current and quick ratios); inventory, employee and cost efficiencies; as well as receivables' intensity. McKesson's resource deployment decisions suggest a moderate posture towards operating and structural risk with a distinct emphasis on inventory, employee and cost efficiencies. Although not the largest of these firms in terms of size, McKesson does

28

Segars, Grover--Strategic Group Analysis Table 3. R a n k of strategic groups across measures SSTP I (1976-1979) Strategic groups King I: King 2: McKesson A M F A C

King 3: Nat. Intergroup

Squatters

Explorers

5

4 4 4

2 2 2

3 3 3

I 1 I

5

4 4

I 1

2 2

3 3

2 2 I I

4 4 3 3

3 3 2 2

I 1 4 4

5 5 5 5

5 4 4

2

I

3

4

2

I

5

3

2

I

3

5

3

2

5

I

4

Scope Size (I = large-5 = small)

Assets Sales Inventory

5 5

Slack resources (1 = large-5 = small)

Working capital Firm cashflow

5

Resource deployment Finance (I = risk averse-5 = risky)

Current ratio Quick ratio Times interest earned Equity to debt

Production (I = efficient-5 = inefficient)

Inventory efficiency Employee efficiency Cost intensity

Marketing (I = efficient-5 = inefficient)

Receivables intensity

distinguish itself in terms of its ability to generate slack resources. Other industry Kings are distinguishable in terms of focus on cost

Table 4. R a n k of strategic groups across measures SSTP 2 (1980-1982)

Squatters

Kings: previous menders Explorers plus ~rgen

Scope Size (1 = large-5 = small)

Assets Sales Inventory

3 3 3

2 2 2

I I I

3 3

2 2

I I

2 2 I I

I I 3 3

3 3 2 2

3 3 3

2 2 I

I I 2

3

2

I

Slack resources (I = large-5 = small)

Cashflow Working capital

Resource deployment Finance (I ffi risk averse-5 = risky)

Current ratio Quick ratio Times interest earned Equity to debt

Production (1 = efficient-5 = inefficient)

Inventory efficiency Employee efficiency Cost intensity

Marketing (I = efficient-5 = inefficient)

Receivables intensity

efficiencies (National intergroup inefficient with respect to cost, AMFAC moderately efficient), receivables' focus (AMFAC very efficient), as well as levels of operating risk (AMFAC moderately conservative, National Intergroup extremely risky). Explorers. Although smaller than industry Kings, Explorers are distinguishable in SSTP 1 by their rather high levels of production and marketing efficiencies. Only McKesson demonstrated higher levels of production efficiencies than this collection of firms. This finding is somewhat surprising given the scope of economies which would be expected to accrue to the industry's larger competitors. Explorers are also distinguishable by their somewhat liberal use of working capital (current and quick ratios) for internal financing. Thus, it would seem that these firms are more risk tolerant in terms of operating leverage than the majority of industry Kings and smaller Squatters (discussed later). We label this group Explorers based on their size and strategic intent as manifested in industry/analyst and annual reports (Value Line Investment Surveys, Standard and Poor's, 1975-1982 [63, 65]). Specifically, the competitive orientation of these firms during this time was to explore strategic avenues (consolidation and/or innovation) in order to compete on a

Omega, Vol. 22, No. 1

29

Kings ~ -

Explorers

Bergen

~-

McKesson

SSTP 1: High production efficiencies Abundant slack resources

SSTP 1: Focus on production, marketing' efficiencies Moderate risk in financing decisions SSTP 2: Less efficient than consolidated Kings. Conservative j financial posture

~,, SSTP 1: High marketing efficiencics ~ Low operating risk p o s y

J

SSTP 2: The three previously distinct Kings are consolidated along dimensions of scope and resource deployment. Bergen, an Explorer in SSTP1, shifts to King AS a whole Kings adopt a more liberal p o s t u r e / towards risk and lead the industry in most efficiency measures

/

Fig. 2. Strategicgrouping of drug wholesaleindustry 1976-1982.

scale even with Kings. In essence, production and cost efficiencies as well as strategic acquisition were cited by the management of many of these firms as the needed route to join the existing group of industry Kings. Squatters. Squatters, consisting of numerous small firms, compose the final emergent strategic group. Due to their size and specialized services, this collection of firms is not typically concerned with production efficiencies. However, as shown in Table 3 these firms are more efficient in terms of marketing activities than two of the industry Kings. Squatters also seem to employ lower levels of debt in their capital structures suggesting perhaps a more conservative posture towards risk taking in comparison to other industry groups. Analogous to 'land squatters', these firms tend to operate in niche or specialty markets and, in some instances, sell value-added (complimentary) services to those offered by larger competitors. These markets are within the domain of all groups but are conceded in the interest of pursuing more profitable market segments. Changes between SSTPs. Interestingly, the

structure described above held for four years after the initial introduction of order entry technology. However, in the second SSTP (1980-1982) the three previously strategically distinct Kings merged into one strategic group (Table 4). In addition, a previous Explorer, Bergen, shifted strategic orientation and joined the existing Kings. Thus, between these time periods previous differences among 'Kings' along dimensions of size, risk disposition, as well as production and marketing efficiencies were consolidated. As a collection, Kings seem to outperform other industry groups in SSTP 2 in all efficiency related measures except cost. In contrast, Explorers outperformed most members of Kings along the dimensions of production and marketing efficiency in the first SSTP. Kings also seem to have adopted more risk tolerant disposition in terms of operating leverage as evidenced by current and quick ratios. With the aforementioned exception of Bergen, the composition of Explorers remained stable between time periods. However, an interesting strategic change among this collection of

Segars, Grover--Strategic Group Analysis

30

firms is observed in operating leverage. As noted earlier, Explorers were somewhat risk-taking in employment of working capital in SSTP 1. In SSTP 2 this disposition is clearly risk-averse perhaps suggesting adoption of a more conservative operating posture as a result of changing industry conditions. As noted earlier, previously realized efficiencies by this group in production and marketing in relation to other strategic groups (particularly Kings) seem to have been competed away between time periods. This perhaps suggests a recognition by larger firms that production efficiencies were becoming the basis for industry competition. In essence, the adoption of order-entry technology by larger, direct competitors of McKesson as a means to compete in this new climate may have erased any advantages previously known by Explorers.

Determination of performance differences:Step 7 Given the identification of the competitive nature of this industry and changes across time, the final step of Fig. 1, determination of performance differences across strategic groups can be applied. Table 5 presents the results of analysis of variance (ANOVA) tests across measures of group profitability for each SSTP identified. These measures of performance are adopted from past studies of strategic group performance [16, 25, 53] and meta-analytic work in the area of industrial organization [8]. As shown in Table 5, significant differences among groups are found in terms of the profitability measures return on assets (ROA) and return on sales (ROS) across the first SSTP. Pairwise comparisons conducted at an experiment wise error rate of 0.05 reveal significant differences in all measures between McKesson and other Kings. In each instance McKesson's measures tested significantly higher. These measures were also significantly higher for Table 5. Evaluation of performance differences between strategic groups 1976-1982

ANOVA Profitability measures (SSTP 1, 1976-1979) Measure F P > f Conclusion ROA ROS

5.01 5.14

0.01** 0.01"*

Difference among groups Difference among groups

Profitability measures (SSTP 2, 1980-1982) Measure F P >f Conclusion ROA ROS

2.16 0.89

0.1191 0.6654

No difference among groups No difference among groups

McKesson in comparison to industry Explorers and Squatters. A significantly higher difference across these profitability measures is also observed for AMFAC in comparison to National Intergroup and industry Squatters. In sum, these findings seem to suggest that after the initial introduction of Economost some competitive advantage in terms of improved profitability was transferred to McKesson. Interestingly, no differences in performance among emergent groups are observed for the second SSTP. Thus, merged groups of industry Kings are not significantly different from industry Explorers or Squatters in terms of these profitability measures. This result seems to analytically confirm the notion that swift adjustment to this technology by larger firms prohibited any sustained competitive gains in terms of improved profitability or strategic differentiation [13]. Thus, it would seem that no strategic thrust in terms of scope or resource commitments was superior in leveraging this particular technology for long term asymmetric profitability distribution within this industry. Instead, this analysis along with other narratives suggests the emergence of a more concentrated, competitive industry as a possible result of this technology. Through merger and technology adoption, Bergen found the required avenue to compete on a scale and along similar strategic dimensions as Kings (Value Line Investment Surveys [65]). In addition, adoption of order entry technologies by Explorers and other Kings may have eliminated any initial gains realized by McKesson. In essence, this technology may have made the services offered by industry participants so generic that economic profits were indeed transferred to customers in terms of lower prices and improved service. This implies that McKesson may have unwittingly increased competition within its strategic domain with the deployment of Economost.

Concluding thoughts Prior research efforts in the area of competitive uses of IT have taken a narrative approach to describing the phenomena empirically modeled in this illustration. As innovator, McKesson effectively changed the playing rules of the industry with its innovative use of IT. However, this analysis supports recently raised doubts regarding the level of sustained

Omega, Vol. 22, No. I

competitive advantage realized by the firm. Because of the simplicity, bounded functionality and portability of this technology, an industry innovator was unable to 'build in' the necessary search and switching costs essential for sustained competitive advantage. Thus, firms competing within the current strategic domain of McKesson (Kings), and those searching for entry avenues (Explorers), rapidly adopted the technology resulting in a more competitive industry environment. These findings seem to support Clemons and Row's [13] contention regarding the 'strategic necessity' of this technology. That is, a new way of conducting business was defined by this system and competitors rapidly adjusted thus eliminating any competitive asymmetry realized by McKesson. Importantly, some cautions and limitations to the above analysis should be noted. First, although great care was taken to screen out uncontrollable factors such as economic, environmental and regulatory concerns, there is always the possibility that these uncontrollables contribute to shifts in industry structure. Although the US economy was headed into a recession in the early eighties, analysts' reports suggest that this industry was well insulated from these effects (we may also argue that such factors would impact all participants equally). However, this may not be true for all industries or research situations. Therefore, caution is advised in the formulation of future research designs. Second, in isolation, this technique is probably not adequate to completely infer causality due to the introduction of technological imperatives. We constantly found ourselves referencing industry and annual reports for the meaning behind the numbers and patterns we were seeing. Perhaps future designs in other research contexts can improve upon our modest attempt to better explain shifts due specifically to ITCA. Finally, the choice of strategic variables within this analysis is specific to this industry. Analysis of other industry settings may require operationalizations quite different from those utilized in this analysis. Nonetheless, careful attention to research design in terms of problem studied and competitive characteristics key to the specific industry studied can make this technique applicable to multiple settings and research questions. In sum, the contribution of this study is evidence that compliments the

31

rather large amount of narratives surrounding

this widely studied strategic system. We now close this paper with specific suggestions for potential areas of future research.

SUGGESTIONS FOR FUTURE RESEARCH

In this study, we have sought to heighten academic and managerial awareness regarding the importance and feasibility of industry modeling in the realm of strategic IT planning and deployment. Specifically, we contend that industry modelling can be employed to examine the past and future impact of IT on industry structures subsequently providing managers with valuable inputs into IT investment and deployment decisions. In addition, the mapping of industries based on strategic orientation can provide researchers with crucial information in the development of constructs important in theory building and modeling regarding the very important and ever elusive constructs of 'IT based strategy' and ITCA 'sustainability'. As argued earlier, thought concerning ITCA has evolved from development of frameworks for analysis and case studies to concrete firm and industry structural characteristics important in explaining the likely outcomes of IT based strategic initiatives. As such, research in the area must develop concrete theories concerning the nature of these resource characteristics, their importance in developing ITCA and their influence on maintaining realized competitive gains in the long term.

Sustainability: the role of structural characteristics Perhaps the most promising new theories in the arena of ITCA are offered by Clemons and Row [14] and Feeny and Ires [24]. These works focus on industry characteristics in the former case and likely competitor reactions in the latter as a basis for determining when IT-based initiatives can deliver sustainable competitive advantage. Many of the key resource differences and firm characteristics mentioned in these works are readily operationalizable. For example, measures of vertical and horizontal integration, geographic scope and variables related to IT investment are available within the PIMS database. Although access to this dataset is limited, its use seems well suited to modeling

Segars, Grover--Strategic Group Analysis

32

the variables prominently mentioned within the writings of these authors. We would suggest industry modeling for instances of both known IT successes and failures utilizing these measures. Perhaps industry structures and resource characteristics of firms are different between these groups. Other analytic techniques such as discriminant analysis may also be useful in meaningfully differentiating successful from non-successful IT based strategies based on these key resource characteristics. Another fruitful line of research in this domain may be longitudinal studies. Much of Feeny and Ives' [24] model of sustainability is based on the amount of time before competitors can mount a successful competitive response. Specific questions posed by these authors are: 'Who can respond?' 'When can they respond?' and, 'How successful will the response be?' Theories built on industry modeling techniques may provide very specific answers for these questions within various industry settings. Clearly, not all industries are equal. Some may be more predisposed to competitive IT initiatives than others. A formidable task for researchers in this area is to identify the types of industries most favorable and, as importantly, most hostile to various IT based strategies. The results of such studies would provide useful information to strategic planners contemplating not only if, but, when to invest in ITCA.

Measuring IT

based

strategies

Much current literature is based on the concept of IT as an enabler of strategic plans. Yet, there seems to be little research which actually identifies the strategic orientations supported by IT. Stated as research questions 'What is the strategic nature of innovative IT users?' 'Are most IT based strategies founded on cost reduction?' 'Market Growth?' 'Differentiation?' 'Vertical integration?' SGA is a much proven technique in identifying the specific strategic orientation of industry members. Perhaps some strategic orientations and IT based strategies are more sustainable than others. In the case examined within this research the cost-based strategy of McKesson seemed to fall short in terms of delivering sustainable competitive advantage. Perhaps in other industry environments this might not hold. We would suggest the use of both primary and secondary

sources of data as a basis for strategic grouping in this research context. Well designed surveys can elicit much information from management concerning the role of IT in competitive positioning, the linking of IT to strategic planning, as well as a host of other IT related variables. Strategic grouping utilizing these measures in single or multiple industry settings can provide valuable insight into the nature of IT based strategy across varying competitive environments. CONCLUSIONS Only in rare instances will technology itself be adequate to insure competitive advantage. Management's ability to: (1) formulate a strategic picture of core business drivers within the industry, (2) identify unique firm resources, (3) identify corresponding value adding IT initiatives and (4) develop tactical plans for deployment, determine the effectiveness and sustainability of IT deployed for competitive advantage. It should also be remembered that innovators do not necessarily have the 'last word' regarding competitive advantage. Effective industry scanning by technology followers can lead to competitive disadvantage for innovators. These firms learn from the innovator's experience and can, depending on the forecasting and planning processes in place, have greater levels of competitive information (customer, supplier, competitor reactions) available to them at launch time. In sum, it is clear that IT used in support of corporate objectives is 'strategic' in that a great deal of top-level and tactical planning is required in order to realize improved competitive positioning. The wonders of technology are available to all. However, effective managerial planning and forecasting are unique firm resources which in the longrun determine the outcome of IT investment decisions. We contend that future research efforts into competitive uses of IT must evolve beyond case studies and theoretical frameworks into dynamic modeling of industry level positioning. Only then can theory be formulated to build cumulative research tradition and provide interested practitioners with practical inputs into ditficuit managerial decisions. As technology and managerial awareness of its strategic potential have evolved, so have the metrics for

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establishing IT opportunities and subsequent effectiveness. The search for competitive asymmetry must broaden from automation of internal operations into identification of leverageable differences in structural/resource characteristics among industry participants. Once these opportunities are identified, traditional metrics of system effectiveness such as user satisfaction and usage must be supplemented with dynamic mappings of competitive positioning as a result of strategic IT initiatives. As researchers, our task will be to develop both planning and evaluation criteria which are capable of capturing the dynamics of IT's impact on prevailing industry structures. REFERENCES 1. Abell D (1980) Defining the Business. Prentice--Hall, Englewood Cliffs, NJ. 2. Aldenderfer MS and Blashfield RJ Cluster Analysis. Sage, Beverly Hills, Calif. 3. Ansoff HI (1965) Corporate Strategy. McGraw-Hill, New York. 4. Bakos JY and Treacy ME (1986) Information technology and corporate strategy: a research perspective. MIS Q. 10(2), 107-119. 5. Baily JE and Pearson SW (1983) Development of a tool for measuring and analyzing computer user satisfaction. Mgmt Sci. 29(5), 530-545. 6. Bourgeois LJ (1981) On measurement of organizational slack. Acad. Mgmt Rev. 6, 29-40. 7. Cash JI and Konsynski BR (1985) IS redraws competitive boundaries. Harvard Bus. Rev. 63(2), 134-142. 8. Chakravarthy BS (1986) Measuring strategic performance. Strat. Mgmt J. 7, 437-458. 9. Christenson CJ (1973) The contingency theory of organization: a methodological analysis. Harvard University, Graduate School of Business Administration Working Paper 36-73. 10. Clemons EK (1986) Information systems for sustainable competitive advantage. Inform. Mgmt 9(3), 131-136. I1. Clemons EK and Kimbrough S (1986) Information systems, telecommunications, and their effects on industrial organization. In Proceedings of the Seventh International Conference on Information Systems (Edited by Maggi L, Zmud R and Wetherbe J), pp. 99-108. ACM Press, San Diego, Calif. 12. Clemons EK and McFarlan WF (1986) Teleeom: hook up or lose out. Harvard Bus. Rev. 64(4), 91-97. 13. Clemons EK and Row M (1988) McKesson drug company: a case study of Economost--a strategic information system. J. Mgmt Inform. Syst. 50), 36-50. 14. Clemons EK and Row M (1991) Sustaining IT advantage: lhe role of structural differences. MIS Q. 15(3), 275-292. 15. Clemons EK and Weber BW (1990) Strategic information technology investments: guidelines for decision making. J. Mgmt ln/brm. Syst. 7(2), 9 28. 16. Cool KO and Schendel D (1987) Strategic group formation and performance: the case of the U.S. pharmaceutical industry, 1963-1982. Mgmt Sci. 33,

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