Patterns in Strategic IS Planning Decisions: An Inductive Approach

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1 Association for Information Systems AIS Electronic Library (AISeL) AMCIS 2009 Proceedings Americas Conference on Information Systems (AMCIS) 2009 Decisions: An Inductive Approach Prasanna P. Karhade University of Illinois at Urbana-Champaign, Michael J. Shaw University of Illinois at Urbana-Champaign, Ramanath Subramanyam University of Illinois at Urbana-Champaign, Follow this and additional works at: Recommended Citation Karhade, Prasanna P.; Shaw, Michael J.; and Subramanyam, Ramanath, " Decisions: An Inductive Approach" (2009). AMCIS 2009 Proceedings This material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic Library (AISeL). It has been accepted for inclusion in AMCIS 2009 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact

2 Decisions: An Inductive Approach Prasanna P. Karhade Michael J. Shaw University of Illinois at Urbana-Champaign, University of Illinois at Urbana-Champaign, 120 South Sixth Street, Champaign IL South Sixth Street, Champaign IL Ramanath Subramanyam University of Illinois at Urbana-Champaign, 120 South Sixth Street, Champaign IL 120. ABSTRACT Alignment between business strategy and IS strategy is relevant to most large organizations today. We build on the literature on alignment and strategic IS planning to study patterns of antecedents that explain planning decisions. When addressing misalignment risks: Are there systematic differences in the patterns of antecedents that explain decision-making across differing strategic orientations? This research question guides our investigation. By adopting an inductive learning methodology, a unique dataset collected from a multi-business Fortune 10 organization of proposed IS initiatives and the actual planning decisions is analyzed. We present insightful decision models and provide empirical support to suggest that strategic orientation is a key driver explaining the differences in decision-making during strategic IS planning. Keywords: Strategic IS planning, alignment, patterns, inductive learning INTRODUCTION Strategic information systems planning (SISP) and executive decisions on IS initiatives are critical for superior organizational performance (Piccoli and Ives 200). IS initiatives have delivered a variety of benefits (Sabherwal and Chan 2001); changed the competitive landscape; improved enterprise resource planning; enabled enterprises to tap into emerging markets. At the same time, trade press also reports significant losses due to failed IS initiatives (Jeffery and Leliveld 2004). Lack of alignment between business strategies and the mix of IS initiatives approved during SISP has been noted as one key factor explaining this unsatisfactory performance (Sabherwal and Chan 2001). Decision making during SISP is primarily concerned with aligning IS plans with business goals (Lederer and Sethi 19). Managing these misalignment risks during SISP is thus critical. When addressing these misalignment risks during SISP: Are there systematic differences in the patterns of antecedents that explain decision-making across differing business strategies? This question guides our investigation. This paper contributes to the literature in several ways. First, we study misalignment risks during SISP by analyzing actual executive decisions on a large portfolio of proposed IS initiatives. Our approach generates unique insights compared to most prior work on strategic alignment and SISP which has relied on a survey based design (for e.g. Sabherwal and Chan 2001). Secondly, this unique dataset is chosen from three businesses within one organization, which are pursuing different business strategies. Thus, the natural controls in our field setting help us make controlled inferences and attribute the differences in the patterns of antecedents that explain SISP decisions to differences in the business strategy. Thirdly, by analyzing our data set using an inductive learning methodology which is best suited to uncover patterns, we develop insightful decision models (one each for a Defender, Prospector and an Analyzer organization (Miles and Snow 197)) and demonstrate that there exist systematic identifiable differences in the patterns of antecedents that explain SISP decisions. Our work has significant managerial implications. Decision trees are often used to uncover tacit decision-making knowledge. They compactly represent the decision making rationale which can be effectively communicated to various stakeholders and potentially expedite future SISP sessions. BACKGROUND Lederer and Sethi (19) identified two goals of SISP. First, SISP is primarily responsible for identifying and then approving a portfolio of IS initiatives that assist an organization in executing its business goals. Secondly, SISP also entails searching Proceedings of the Fifteenth Americas Conference on Information Systems, San Francisco, California August th -9 th

3 for innovative IS which can change the basis of competition. Successful execution of IS initiatives approved during SISP can lead to advantages and a significant body of literature has studied the sustainability of these advantages (Piccoli and Ives 200). Boynton and Zmud (197) emphasized that one of the central items on the SISP agenda is risk analysis. But, a systematic emphasis on risk management during SISP continues to be a relatively understudied area. Investing in a mix of IS initiatives not aligned with the business goals of an organization is a key source of risk that needs to be managed with effective decision making during SISP. SISP decisions that manage these misalignment risks are guided by a complex interaction of at least three sets of antecedents; the organization s business strategy; types of benefits associated with IS initiatives and risks/risk mitigation mechanisms associated with IS initiatives (Das et al. 1991). The Miles and Snow (197) Defender-Prospector-Analyzer typology suggests that the fundamental difference in these three business strategies is the rate of change preferred in the organizational domain. Building on the Miles and Snow (197) typology, Sabherwal and Chan (2001) argued that a different mix of IS initiatives will be in alignment with these different business strategies. Defenders are risk averse; stress efficiency of operations; emphasize a narrow domain by aggressively controlling secure niches in their industry; and engage in little or no new product development (Miles and Snow 197). Since Defenders operate in a narrow stable domain, to manage misalignment risks, their decision making during SISP is largely expected to focus on approving IS initiatives designed for improving internal efficiencies (Sabherwal and Chan 2001). Defenders are expected to prefer a higher proportion of investments geared towards improvements. Prospectors are risk takers; constantly explore emerging opportunities; stress new product development (Miles and Snow 197). Prospectors thrive on product uniqueness. Thus, to manage misalignment risks during SISP, Prospector decision making is expected to prefer a mix of IS initiatives that encourage flexibility and growth. Sabherwal and Chan (2001) argue that Prospectors will place a lesser emphasis on IOS initiatives implying certain proposed IOS initiatives are likely to be rejected by Prospectors. Defenders and Prospectors represent extreme ends of the spectrum and Analyzers exhibit characteristics of both Defenders and Prospectors (Miles and Snow 197). Like Defenders, Analyzers are risk averse. Analyzers are expected to prefer a balanced mix comprising of all the four types of initiatives described in Table 1. Defender Prospector Analyzer Mix of Recommended IS Initiatives Operational Support Systems (OSS) Initiatives High Low Medium Marketing Information Systems (MIS) Initiatives Low High High Inter-organizational Systems (IOS) Initiatives High Medium High Strategic Decision Support Systems (SDSS) Initiatives High High High Risk Taking Tendencies Low High Low Table 1. Mix of initiatives recommended for strategic alignment (Sabherwal and Chan 2001) Research Question: When addressing misalignment risks during SISP: Are there systematic differences in the patterns of antecedents that explain decision-making across differing business strategies? METHODOLOGY We choose a multi-business subsidiary of a Fortune 10 organization for this study. This subsidiary is in the manufacturing industry and participates in several different businesses. Data Data collection was a two step process. (1) Ascertaining the business strategy: Qualitative data was gathered via various mechanisms to ascertain the business strategy of a chosen business. Data was collected based on interaction with six key informants within this subsidiary (Vice President and Chief-CIO of the subsidiary, and senior executives in the CIO team). For effective triangulation, data was collected by the following methods; content analysis of information presented in the annual reports; face-to-face semistructured interviews with all key informants spanning 20 hours; unobtrusive participation in a SISP session lasting two hours; conference calls with all informants spanning twenty hours; and exchange of several confidential documents between the researchers and the key informants. Based on the qualitative data collected over the course of this investigation, the three Proceedings of the Fifteenth Americas Conference on Information Systems, San Francisco, California August th -9 th

4 business units within this subsidiary were chosen for this study as they were ascertained to be pursuing differing strategies. One business was classified as a Defender, another as a Prospector and the third business was classified as an Analyzer (Miles and Snow 197). Appendix I describes three mini-cases on these businesses. (2) IS portfolio data: This dataset contains 11 proposed initiatives across these 3 businesses and the associated planning decisions 1. Table 2 summarizes the portfolio data used in this study which was analyzed using an inductive methodology to study patterns in related decision making. The main theme of the decision model for each business was used to infer if there is alignment between business and IS strategy, i.e. evidence to suggest that misalignment risks were managed. Measure Development Characterizing Risks We adopt McFarlan (191) s approach for assessing the risk of IS-dependent initiatives 2. Initiative Size: This attribute was measured based on the estimated investment size in dollars. Risk associated with an initiative increases with its size (McFarlan 191). This variable was assigned three values: Low (size < $100,000), Medium (size > $100,000 and < $1 Million) and High (size > $1 Million). Initiative Structure: Some initiatives, by their very definition, are well-defined, in terms of their inputs and outputs. The organizational tasks required to convert inputs to the desirable outputs, are relatively straightforward. Initiatives of high structure (McFarlan 191) are less risky when compared to initiatives with low structure. Initiatives where the expected outputs are vulnerable to change are low structured and inherently risky. This variable was assigned two values: high (welldefined objectives for the initiative) and low (objectives of the initiative are fluid) Prior Experience: As the familiarity of an organization with a technology increases, the likelihood of encountering technical problems reduces. Higher the prior experience with technologies, lower the risks associated with those initiative (McFarlan 191). This variable was assigned three values: low (new application development with emerging technologies); medium (non-trivial improvements to standard technologies); high (relatively simple applications of standard technologies). Characterizing Benefits Initiative Type: Investments in IS can provide benefits to organization in many ways 3. Based on the various types of benefits that can be extracted from IS initiatives, detailed descriptive information on proposed initiatives was used assign this variable, the following values (Sabherwal and Chan 2001): inter-organizational systems (IOS) initiative and/or marketing information systems (MIS) initiative and/or strategic decision support systems (SDSS) initiative, and/or operational support systems (OSS) initiative. Process Benefits: IS-dependent initiatives can enable process improvements (Broadbent et al. 1999). This variable was assigned a value of 1 when the initiative enabled business process improvements and a value of 0 otherwise. 1 The initial size of the portfolio collected from this subsidiary was larger and included IT projects, IS initiatives, mandatory SOX-compliance projects and investments in IT infrastructure. Guided by our research question we retained only IS initiatives pertaining to business applications and IS strategy. Mandatory SOX-related proposals were eliminated as the decision making for such proposals is not guided by the chosen business strategy. Proposals from a business unit strictly pertaining to IT infrastructure investments intended to be shared across all businesses within this subsidiary were also eliminated. Proposals from another business unit which strictly related to IT hosting services applicable to the entire subsidiary were eliminated. Low priority initiatives were eliminated given their low substantive significance. By focusing only on just these 11 IS initiatives, we eliminate alternative explanations for observed differences in decision making (differences in industry type, different types of investments in IT infrastructure) and can attribute differences in decision making to differences in business strategies adopted by individual businesses. 2 We adopt McFarlan s (191) risk assessment for two reasons: (1) Their model is geared towards the analysis of portfolios and easily lends itself to our research objectives (2) Portfolio decision-making require planners to compare risks associated with initiatives within a portfolio. McFarlan (191) abstracts risk items to a simple rank order risk metric which allows for easy comparison between initiatives within a portfolio. 3 A rigorous quantification of benefits associated with initiatives (such as a ROI) would be desirable. But often arriving at quantification like this is extremely difficult and unrealistic especially given the planning paradox. 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5 Strategic Orientation of Business Unit Benefits Associated With Initiatives Risks Associated with Initiatives SISP Decisions Defender s Proposed Portfolio of IS-Dependent Initiatives (n=72) Initiative Type OSS Initiative (2%) (1%) IOS Initiative (0%) SDSS Initiative (24%) Process Impact (93%) Initiative Structure (Low Structure = 2%, High Structure = 74%) Initiative Size (Low = 2%, Medium = 1%, High = 11%) Prior Experience (High = 7%, Medium = 3%, Low = 7%) BPR Work Done (Yes = 4%) BPR Resources Committed (Yes = 39%) Reject IS-Dependent Initiatives (%) Fully Fund IS-Dependent Initiatives (92%) Prospector s Proposed Portfolio of IS-Dependent Initiatives (n=32) Initiative Type OSS Initiative (%) (1%) IOS Initiative (0%) SDSS Initiative (34%) Process Impact (7%) Initiative Structure (Low Structure = 0%, High Structure = 0%) Initiative Size (Low = 37%, Medium = 9%, High=3%) Prior Experience (Low=1%, Medium=19%) BPR Work Done (Yes = 12%) BPR Resources Committed (Yes = 3%) Reject IS-Dependent Initiatives (0%) Fully Fund IS-Dependent Initiatives (0%) Analyzer s Proposed Portfolio of IS-Dependent Initiatives (n=7) Initiative Type OSS Initiative (79%) (3%) IOS Initiative (49%) SDSS Initiative (32%) Process Impact (2%) Initiative Structure (Low Structure = 32%, High Structure = %) Initiative Size (Low=23%, Medium =1%, High=1%) Prior Experience (Low=7%, M=23%, High=10%) BPR Work Done (Yes = 1%) BPR Resources Committed (Yes = 23%) Reject IS-Dependent Initiatives (2%) Partially Fund IS-Dependent Initiatives (30%) Fully Fund IS-Dependent Initiatives (4%) Total Proposed Portfolio of IS-Dependent Initiatives (n=11) Initiative Type OSS Initiative (7%) (49%) IOS Initiative (4%) SDSS Initiative (29%) Process Impact (%) Initiative Structure (Low Structure = 33%, High Structure = 7%) Initiative Size (Low = 2%, Medium = 1%, High = 11%) Prior Experience (Low = %, Medium = 2%, High = 7%) BPR Work Done (Yes = 10%) BPR Resources Committed (Yes = 3%) Reject IS-Dependent Initiatives (22%) Partially Fund IS-Dependent Initiatives (11%) Fully Fund IS-Dependent Initiatives (7%) Table 2: Data Summary: Portfolio of IS Initiatives Proceedings of the Fifteenth Americas Conference on Information Systems, San Francisco, California August th -9 th

6 Characterizing Risk Mitigating Factors BPR (Business Process Reengineering) Work Done: Performing BPR before starting initiatives is critical to minimizing process risks (Broadbent et al. 1999) related to the execution of initiatives. This variable was assigned a value of 1 when initiative related BPR work was completed and a value of 0 otherwise. BPR Resources Committed: IS initiatives can either constrain or facilitate BPR initiatives (Broadbent et al. 1999). Vice-aversa, BPR activities can constrain or facilitate IS initiatives. Committing resources for undertaking BPR before starting IS initiatives can be a risk mitigation factor. This variable was assigned a value of 1 when resources were assigned to IS initiatives for conducting BPR and a value of 0 otherwise. Portfolio Decisions Decisions for each proposed IS-dependent initiative belonged to one of the following three classes: initiatives were (1) rejected; (2) or partially approved and supported with partial funding; (3) or fully approved and funded. Inductive Learning Methodology Decision trees have been used to study organizational decision-making (Quinlan 1990). In its general form, the inductive learning process contains three phases: (1) instance space; (2) algorithm used for learning; (3) output describing the target concept. The instance space is an n-dimensional space where each instance is described by n attributes and a classification concept. For every run of the learning algorithm the instance space is represented by a training sample. In our case the target concept is a description of the induced managerial decision-making process. The purpose of induction is to discover the most precise approximation of the target concept. From an instance space, an approximation of the target concept, called hypothesis is induced. Each such approximation forms an instance in the hypothesis space. Each hypothesis represents a more or less credible approximation of the target concept. The decision tree representation which has been previously applied to discover decision-making processes, describes the target concept using a set of conjunctives (Quinlan 19). Trees create an ordering among the attributes characterizing examples that belong to a particular class and the ones that do not. The treebased inductive learning approach discovers and represents the knowledge contained in a decision process in a comprehensive and structured way (Shaw and Gentry 19). Decision trees possess predictive validity comparable to other statistical classifiers. Given this high descriptive validity, decision trees offer unique advantages over statistical classifiers. An interpretation of the paths in the decision tree provides insights concerning the underlying structure of the data, which highlights a collection of attributes used in the classification procedure. The number of examples classified on a particular decision path guides us in the discovery of patterns in the decision-making process (Tessmer et al. 1993). The length and the width of the decision trees capture the complexity of the decision process. A randomly drawn sample of 70% of the portfolio was used for training and the prediction accuracy of the induced model was tested on a disjoint randomly drawn sample of 30% of the total portfolio. Every random sample was selected such that all the classes of the decision were represented; ensuring purity of induced trees. Trees were randomized ten times at this stage to study structural stability and to compare the prediction accuracy of the induced models. To further improve the validity of the findings, the same analysis was conducted using an 0% training sample and a 20% testing sample. All the decision trees were generated by using the C4. learning algorithm (Quinlan 19). A decision tree with high structural stability and high prediction accuracy was selected as the best approximation of the underlying decision process. Steps taken to choose the best representative are presented in Appendix II. RESULTS Interpretation of the paths in the decision models led to the discovery of patterns. These patterns help us construct a rich understanding of how misalignment risks were mitigated by businesses with differing strategic orientations. Given that the decision models are significantly different (ignoring paths that classified only one initiative, the three best representative decision models across the three business strategy archetypes had no attributes in common) we empirically demonstrate that strategic orientation of a business is a key driver for explaining patterns in decision making. The Defender s model (Figure 1) compactly represents their emphasis on improvements. A large majority of the attributes that emerged in this model (3 of the total 4 decision attributes) pertained to the measurement of risks associated with initiatives. The interpretation of the Defender s decision model reveals two key patterns: (1) The Defender is focused on efficiency improvements. As demonstrated by the main path in the model (Main Path D1), we find that a large majority of IS initiatives were approved if they could deliver process improvements. Interpretation of this main path (the main decision making theme) provides evidence to suggest that misalignment risks are being systematically managed by the Defender given that potential for process improvements is considered a sufficient antecedent for funding initiatives. (2) A large majority of IS Proceedings of the Fifteenth Americas Conference on Information Systems, San Francisco, California August th -9 th 2009

7 initiatives are being approved even when the BPR work for such initiatives was ongoing. Ideally, when managers exert such effort (for e.g. complete related BPR tasks) before starting IS-dependent initiatives, such initiatives are more likely to be successful (Lambert 19, Broadbent et al. 1999). Given that very few initiatives are classified on path D2 as opposed D1 in the Defender s tree, we find that the Defender is bearing more risk than expected given their risk averse tendencies. Figure 1. Comparing Decision Models ( Defender Vs Prospector) of the total 9 decision attributes that emerged in the decision model for the Prospector (Figure 1) characterized the benefits associated with initiatives. This suggests that Prospector decision making thrives on risk-taking and is focused more on understanding the benefits that can be extracted from IS initiatives. The interpretation of the Prospector s model reveals two patterns: (1) For achieving strategic alignment, Prospectors are expected to approve IS initiatives that enable them to explore new opportunities. Given that MIS initiatives is the top classification attribute (Path P2), we find strong evidence suggesting that this Prospector is clearly managing misalignment risks during planning. (2) Prospectors value flexibility. We find that IOS initiatives that create restrictive electronic links with suppliers and customers are being rejected as demonstrated by the main path in the Prospector s decision tree (P1). IOS initiatives being rejected are highly structured (i.e. inputs/outputs of such projects are rigidly defined). These highly structured initiatives, given their rigid definitions, arguably stifle flexibility. This interpretation of the Prospector s tree suggests that misalignment risks can be mitigated by approving initiatives that support growth and simultaneously rejecting IS initiatives that stifle flexibility. Both Defender-like and Prospector-like behavior is evident from the Analyzer s decision model (Figure 2) which places equal emphasis on exploring benefits and mitigating risks given that of the 14 attributes in the final model half pertained to characterizing benefits and the other half pertained to mitigating risks. The interpretation of the Analyzer s decision tree reveals two patterns: (1) The Analyzer emphasizes efficiency improvements and simultaneously exploring emerging opportunities. Balancing these conflicting goals is challenging. As expected given these conflicting goals, and as should be evident based on the length and width of the decision tree, we empirically demonstrate that the Analyzer s decision model is most complicated. The Analyzer had a more complex decision space, initiatives are not only being rejected or approved; some promising initiatives are also partially funded. Proceedings of the Fifteenth Americas Conference on Information Systems, San Francisco, California August th -9 th 2009

8 Figure 2. Analyzer s Decision Model The main path in this model indicates that this Analyzer is partially funding strategic IS initiatives with the objective of keeping these initiatives alive and reevaluating choices as uncertainties resolve themselves over time. Additional funding can be awarded to these initiatives based on more information obtained in the future. (2) Defender-like and Prospector-like behavior is strongly evident in this decision model. Some decision paths in the Prospector and Defender decision trees reappeared in the Analyzer s model providing strong evidence of the Analyzer behavior which aims to achieve a mix of often conflicting business goals by blending both Defender and Prospector like tendencies. CONCLUDING COMMENTS Are there systematic differences in the patterns of antecedents that explain SISP decision-making across differing strategic orientations for mitigating misalignment risks? This research question guided our investigation. Our work provides empirical evidence to suggest that the strategic orientation of a business is a key driver of the patterns in decision making. Executives from businesses pursuing different strategic orientations are likely to mitigate misalignment risks differently. Defenders are likely to prefer initiatives that support improvements. The main pattern or the main decision making theme in the Defender s model reveals that initiatives that enable process improvements are readily approved. Prospectors are likely to encourage flexibility by rejecting restrictive initiatives and approving initiatives that help them to tap into emerging markets. The main pattern or the main decision making theme in the Prospector s model reveals that restrictive initiatives are being rejected. Analyzers will have to adopt a complex model to manage conflicting goals associated with both Defender-like and Prospector-like behavior. The main pattern or the main decision making theme in the Analyzer s decision model reveals that some significantly large key strategic initiatives deserve a decision that goes beyond the yes/no typology. This Analyzer partially approved and funded such initiativees. Balancing Defender-like and Prospector-like behavior arguably requires the Analyzer to partially fund certain initiatives. Future decisions on such initiatives can be taken as uncertainties unfold themselves over time. Proceedings of the Fifteenth Americas Conference on Information Systems, San Francisco, California August th -9 th

9 Implications for Research Patterns uncovered here suggest that benefit and risk attributes characterizing proposed IS initiatives are incorporated in the decision making differently by executives pursuing different strategic orientations. Defenders given their risk averse nature tend to consume more risk attributes during decision making. Prospectors tend to decide on IS initiatives largely based on the attributes that characterize their benefits. Analyzer decision making is significantly more complex, as the Analyzer behavior tries to blend Defender and Prospector-like tendencies. Our results suggest that there exist systematically different patterns that compactly explain SISP decisions based on differing strategic orientation of businesses. By analyzing actual portfolio decisions to generate insightful patterns of explanatory antecedents, our work complements existing work on strategic alignment which largely relies on a matched-pair survey design relying on a Euclidian distance based measure of alignment (Sabherwal and Chan 2001). Every study suffers from some limitations. Our data were collected from one organization and our results suffer from limited generalizeability. Moving forward, research that explores the performance implications of decision rules would be beneficial. Identifying which of the initiatives performed successfully and correlating them with rules used during planning is likely to help us understand the efficacy of the rules presented in our work. Implications for managers Trees have been used to study organizational decision making (Quinlan 19, 1990). Decision trees cluster decision making attributes along paths or rules in meaningful ways, compactly explaining different organizational decisions. Executives can use these rules to clearly communicate their policies to various stakeholders. All our key informants validated the decision trees presented here; and found them to be extremely effective tools for facilitating communication across groups within their organization. The patterns we uncover compactly describe executive decision-making rationale behind seemingly complex decisions. Decision trees give planners the vocabulary to potentially expedite future planning sessions. REFERENCES 1. Boynton, A.C., and Zmud, R.W. (197) Information Technology Planning in the 1990's: Directions for Practice and Research, MIS Quarterly, 11, 1, Broadbent, M., Weill, P., Clair, D.S., and Kearney, A.T. (1999) Implications of Information Technology Infrastructure for Business Process Redesign, MIS Quarterly, 23, 2, Das, S.R., Zahra, S.A., and Warkentin, M.E. (1991) Integrating the Content and Process of Strategic MIS Planning with Competitive Strategy, Decision Sciences, 22,, Jeffery, M., and Leliveld, I. (2004) Best Practices in IT Portfolio Management, MIT Sloan Management Review, 4, 3, Lambert, R.A. (19) Executive effort and selection of risky projects, RAND Journal of Economics, 17, 1, Lederer, A.L., and Sethi, V. (19) The Implementation of Strategic Information Systems Planning Methodologies, MIS Quarterly, 12, 3, Maizlish, B., and Handler, R. (200) IT Portfolio Management: Unlocking the Business Value of Technology, John Wiley and Sons, Hoboken, New Jersey.. McFarlan, F.W. (191) Portfolio approach to information systems, Harvard Business Review, 9,, Miles, R., and Snow, C. (197) Organizational Strategy, Structure and Process, McGraw-Hill, New York. 10. Piccoli, G., and Ives, B. (200) IT-Dependent Strategic Initiatives and Sustained Competitive Advantage: A Review and Synthesis of the Literature, MIS Quarterly, 29, 4, Quinlan, J.R. (19) Induction of decision trees, Machine Learning, 1, 1, Quinlan, J.R. (1990) Decision trees and decision-making, IEEE Transactions on Systems, Man, and Cybernetics, 20, 2, Sabherwal, R., and Chan, Y.E. (2001) Alignment between Business and IS Strategies: A Study of Prospectors, Analyzers, and Defenders, Information Systems Research, 12, 1, Shaw, M. J. and Gentry, J.A. (19) Using an Expert System with Inductive Learning to Evaluate Business Loans. FM: The Journal of the Financial Management Association, 17, 3, Shaw, M.J., and Gentry, J.A. (1990) Inductive learning for risk classification, IEEE Expert, 47-3.

10 1. Tessmer, A. C., Shaw, M.J. and Gentry, J.A. (1993) Inductive Learning for International Financial Analysis: A Layered Approach, Journal of Management Information Systems, 9, 4, APPENDIX I: ASSESSING STRATEGIC ORIENTATION: THREE MINI CASES The Defender Business This business had 3,000 full time employees and annual revenues were more than $19 billion. At the time this study was being conducted, an interview with the Vice President and CIO of the subsidiary revealed that in spite of being an extremely large business, over 0% of the revenues of this particular business unit were generated primarily in the US by one product based on a stable, proven technology. Nevertheless, within this one narrow stable domain, aggressive strategies adopted by this business had made industry leaders. Annual reports revealed that over a ten year time horizon, the business had plans to invest in diverse technologies. One senior executive did not prefer the connotation of the Defender descriptor; another executive agreed that this business strategy archetype seemed appropriate to characterize the strategic orientation of this business. The Prospector Business This business had ten thousand employees and annual revenues of four billion U.S. dollars. One senior executive on the CIO staff we interviewed, having worked for this particular business for over five years, indicated that this business was a small, lean and relative agile business. A high emphasis was continually placed on seeking and exploring new opportunities to grow this business. This business was open to undertaking risks with untried new products. Annual reports revealed that new products and new emerging markets were being constantly explored by this business. The classification of this business unit as a prospector was validated by the senior executive we interviewed from this business who said that we had done a good job of capturing the business stereotype. The Analyzer Business This large business within the subsidiary we studied had over thirty eight thousand employees and had annual revenues of over thirteen billion U.S. dollars. This business operates on a very complex business model, especially when compared to the other two businesses within the subsidiary we studied. All the key information we interviewed validated that this business showed traits of focusing on improvements and efficiency balancing the exploration of new opportunities. Given the nature of the industry in which it participates, this business places a heavy emphasis on analysis of factors external/internal sources of uncertainties. This business, in the past has produced innovations that have fundamentally changed their industry and yet now are choosing to explore new opportunities with caution. Key informants had worked within this business and were intimately familiar with the operations of this business. Our Analyzer assessment of this business was unanimously validated by our informants. 9

11 APPENDIX II: SELECTING THE BEST REPRESENTATIVE DECISION MODEL BPR Work Done was the top classification attribute in the following induced trees (T13, T14, T1, and T19). The prediction accuracy was low and inconsistent (error rates swinging from 0 to.7%). Level two attributes and the structure of the overall tree was also not stable. Similarly, trees induced with Prior Experience as the top classification attribute suffered from low prediction accuracy with error rates ranging from 1.7% to 0. Trees with as the top classification attribute (T12, T1) were consistently stable with regards to level two attributes (and overall tree structure) and prediction accuracy. Trees with as the top classification attribute were chosen to faithfully represent the Prospector s underlying unknown decision making process owing to their stability (same level two attributes, consistently high prediction accuracy and overall structural stability of the tree). T1 was chosen as the best representative tree as its length is smaller than that of T12 (tree of smaller length and width.) # L W Level Two Attributes Top Classification Attribute Error Rate in Prediction PANEL 1: (Training set = 70% of original portfolio Testing set = 30% of original portfolio) T1 T2 T3 T4 T T T7 T T9 T Prior Experience, Prior Experience, Initiative Structure Initiative Structure OSS Initiative, Process Impact IOS Initiative, Initiative Structure, Initiative Structure Prior Experience, IOS Initiative IOS Initiative Process Impact PANEL 2: (Training set = 0% of original portfolio Testing set = 20% of original portfolio) T11 T12 T13 T14 T1 T1 T17 T1 T19 T , IOS Initiative IOS Initiative IOS Initiative, SDSS Initiative, IOS Initiative IOS Initiative IOS Initiative Prior Experience Prior Experience Prior Experience L = Length of the induced tree, W = Width of the induced tree Table 3: Choosing the Best Representative Decision Model