Business Strategy and IT-enabled Business Capabilities: Fits, Misfits and Firm Performance

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Business Strategy and IT-enabled Business
Capabilities Fits, Misfits and Firm Performance

• Abhay Nath Mishra
• Visiting Assistant Professor
• David A. Tepper School of Business
• Carnegie Mellon University
• Seminar at American University
• March 24, 2008

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Motivation

• IT is the largest single component of capital
  investment in the US.
• Businesses worldwide spend more than 2 trillion
  on IT every year.
• Are organizations making their IT investment
  decisions judiciously?
• Are these investments likely to impact firm
  performance?

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Motivation

• IT Doesnt Matter. (Carr 2003)
• Generic IT may not matter, but firm-specific IT
  capabilities do (Barua et al. 2004, Bhardwaj
  2000, Gurbaxani 2003).
• Only when IT investments are converted into IT
  capabilities and put to use that they add value
  (Soh and Markus 1995, Zhu and Kraemer 2005)
• Are firms making IT investments on capabilities
  that are likely to have the highest impact on
  their performance?

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IT Capabilities

• The importance of IT capabilities widely
  acknowledged
• Implicit assumptions in the literature
• More is better (Bhatt and Grover 2005, Bhardwaj
  2000)
• All IT capabilities are important (Bhatt and
  Grover 2005)
• The significance of firm strategy in IT
  capability development and deployment

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Research Questions

• Are the requirements for IT capabilities likely
  to vary systematically among firms following
  different business strategies?
• How should firms determine the right level of IT
  investment in various IT capabilities?
• Does the fit between the business strategies
  followed by a firm and its IT capabilities impact
  performance?

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Empirical Observations

• Different flavors of strategy and IT capabilities
• Wal-mart
• Kmart
• Zara

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Research Model

• Business Strategy
• Prospector
• Analyzer with Innovation
• Analyzer without Innovation
• Differentiated Defender
• Low-cost Defender

Firm Performance
FIT

• Business-oriented IT Capabilities
• Entrepreneurial
• Operational
• Renewal
• Customer
• Vendor
• Competitor
• Visioning
• Relational

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Business Strategy Archetypes

• The Miles and Snow (1978) typology
• Prospector, Analyzer, Defender and Reactor
• Extensions to the Miles and Snow typology
• Prospector, Analyzer with Innovation, Analyzer
  without Innovation, Low Cost Defender and
  Differentiated Defender
• The need for extension more nuanced strategies
  observed in contemporary firms (Ruekert and
  Walker 1987, Burton and Obel 1998, DeSarbo et al.
  2005)

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IT Capabilities

• Capabilities embedded routinized processes
  that reflect a firms ability to perform
  repeatedly a productive task which relates either
  directly or indirectly to a firms capacity for
  creating value (Grant 1996).
• IT Capabilities the broad ability of firms to
  develop, diffuse, apply, and manage IT
  effectively to achieve firm objectives.
• The focus in this study business-oriented IT
  capabilities

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The Classification of IT Capabilities (1)

• Process-integration capabilities
• Entrepreneurial IT capabilities (ENT)
• Operational IT capabilities (OPR)
• Renewal IT capabilities (REN)

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The Classification of IT Capabilities (2)

• Market orientation capabilities
• Customer orientated IT capabilities (CUS)
• Vendor orientated IT capabilities (VEN)
• Competitor orientated IT capabilities (COM)

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The Classification of IT Capabilities (3)

• Strategy and IT vision alignment capabilities
• Business and IT visioning capabilities (VIS)
• Business and IT relational capabilities (REL)

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Attributes for the Creation of Strategy Profiles

• Based upon and an extension of Venkatramans
  (1989) STROBE instrument
• Retain the five dimensions used in STROBE
• Proactiveness (PR), Aggressiveness (AG), Risk
  orientation (RO), Analysis (AN) and Futurity (FT)
• Disaggregate the defensiveness dimension
• Cost reduction (CR) and Close alliances (CA)
• Introduce a new dimension
• Innovativeness (IN)

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Theoretical Underpinning Configurational Theory

• Basic tenets of Configurational theory
• Organizational configurations are
  multidimensional constellation of distinct
  attributes that commonly occur together (Ketchen
  et al. 1993, Sabherwal and Chan 2001)
• Configurational approach takes a step beyond the
  contingency approach by adopting a holistic stand
• The identification of ideal profile types is
  central to the configurational theory
• The closer a firm is to an ideal profile type and
  the better is the fit between different
  attributes, the better is firm performance (Doty
  et al. 1993, Meyer et al. 1993)
• The concept of equifinality (Drazin and Van de
  Ven 1985)

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Inductive and Deductive Approaches to
Configuration Analysis

• Inductive approach
• Uses multivariate data analysis techniques to
  uncover patterns for top performing firms
• These patterns are chosen as ideal profile types
• Deductive approach
• Uses theoretical perspectives to define ideal
  profile types and hypothesizes the relationship
  between configurations and performance
• The extant literature has used the inductive
  approach predominantly

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The Strategy Typology
Ideal Strategy Types CR CA PR AG RO IN AN FT
Prospector (PRO) -1 -1 1 1 1 1 -1 -1
Analyzer with innovation (AWI) 0 0 1 0 0 1 1 0
Differentiated defender (DD) -1 1 0 -1 -1 0 0 1
Analyzer without innovation (AOI) 0 0 0 0 0 -1 1 0
Low cost defender (LCD) 1 1 -1 -1 -1 -1 0 1
-1 Low 0 Medium 1High
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Ideal IT Capability Profiles of Strategy
Archetypes
Ideal Strategy Types ENT OPR REN CUS VEN COM VIS REL
Prospector (PRO) 1 -1 0 1 -1 1 1 -1
Analyzer with innovation (AWI) 1 0 1 -1 -1 1 1 -1
Differentiated defender (DD) 0 0 -1 1 0 0 0 1
Analyzer without innovation (AOI) 0 0 1 0 0 -1 -1 0
Low cost defender (LCD) -1 1 -1 1 1 -1 -1 1
-1 Low 0 Medium 1High
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Research Model

• Business Strategy
• Prospector
• Analyzer with Innovation
• Analyzer without Innovation
• Differentiated Defender
• Low-cost Defender

Firm Performance
FIT

• Business-oriented IT Capabilities
• Entrepreneurial
• Operational
• Renewal
• Customer
• Vendor
• Competitor
• Visioning
• Relational

Control Variables Firm size, industry,
diversification, other variables
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Hypotheses

• The equifinality hypothesis (Drazin and Van de
  Ven 1985, Miles and Snow 1978)
• H1 Firms classified as Prospectors, Analyzers
  with Innovation, Analyzers without Innovation,
  Differentiated Defenders, and Low Cost Defenders
  perform equally well.

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Hypotheses

• The strategic fit hypothesis (Venkatraman 1989,
  Sabherwal and Chan 2001, Vorhies and Morgan 2005)
• H2 The greater the fit between a firms realized
  business strategy profile and that of its
  corresponding ideal type, the better its
  performance.

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Hypotheses

• The IT capability fit hypothesis
• H3 The greater the fit between a firms realized
  business-oriented IT capability profile and the
  theoretically determined ideal profile for
  business-oriented IT capabilities corresponding
  to its strategy archetype, the better its
  performance.

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Hypotheses

• The IT capability fit hypotheses
• H4The greater the fit between a firms realized
  business-oriented IT capability profile and the
  empirically derived ideal profile for
  business-oriented IT capabilities corresponding
  to its strategy archetype, the better its
  performance.

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Hypotheses

• The total fit hypotheses
• H5aThe greater the total fit of a firms
  strategy profile and its business-oriented IT
  capability profile with its theoretically
  determined ideal archetype, the better its
  performance.
• H5b The greater the total fit of a firms
  strategy profile and its business-oriented IT
  capability profile with its empirically derived
  ideal archetype, the better its performance.

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Data

• Survey data from 2000 largest firms in the US
• Surveys answered by a top business manager
• 13 industries represented in the sample
• Both public and private firms represented in the
  sample
• Performance data obtained from secondary sources

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Data Issues

• Response rate 3.35
• Reasons for low response rate
• Long questionnaire
• Involved questions
• Sample profile over-surveyed
• Final sample size 67
• See Rajagopalan (1997), Droge et al. (2004) and
  Anderson et al. (1994)

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Non-response Bias Across Different Sampling
Rounds
Measures Round 1 Mailing Round 2 Mailing Round 3 Online Anova Pooled sample Mean (s.d.)
N() 33 (49.3) 17 (25.4) 17 (25.4) 67
Total sales in 2003 (M) 501 (751) 895 (176) 1350 (3390) F (2,64) 1.05 817 (1980)
Total sales in 2004 (M) 542 (701) 998 (1550) 1610 (3390) F (2, 64) 1.74 929 (1950)
Employees (2003) 1745 (1710) 3389 (5887) 5856 (14173) F (2,64) 1.40 3205.39 (7836.08)
Employees (2004) 1946 (2170) 3685 (6351) 6147 (15314) F (2,64) 1.58 3453.58 (8481.73)
Market share in primary 4-SIC () 0.03 (0.05) 0.07 (0.23) 0.10 (0.14) F (2,64) 1.30 0.06 (0.14)
Age (years in 2004) 45.76 (39.29) 55.76 (45.14) 43.12 (36.56) F(2, 64) 0.49 47.63 (39.88)
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Non-response Bias Respondents and
Non-respondents
Measures Respondents Non-respondents Between group variance Within group variance Statistic to test difference between groups
N 67 78 - - -
Sales 2003 (M) 817 (1982) 929 (1687) 0.45 478.48 F(1, 143) 0.14
Employees 2003 3205.38 (7836.08) 4352.33 (7054.62) 29.11E6 8580.00E6 F(1, 143) 0.49
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Sanity Checks on the Data

• Adequate reliability and validity
• PCA items loaded as expected
• Variance extracted estimate gt0.5 for every
  construct

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Data Analysis Determination of Ideal Profile
Types

• Step 1 Cluster analysis to uncover strategy
  configurations - Two-step approach (Punj and
  Stewart 1983)
• Hierarchical clustering using Wards algorithm
• Two stopping rules Calinski and Harabasz
  pseudo-F index and Duda Hart rule
• Partitional clustering using K-means algorithm
• Use centroid values from hierarchical clustering
  as seeds in the iterative K-means algorithm
• Distance measures
• Euclidean distance (v ? (xi yi)2)
• Manhattan or city block distance (? (xi
  yi))
• Chebychev distance Max(xi yi)
• Power distance (? (xi yi)p)1/r

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Data Analysis Classification of Firms into Ideal
Profile Types

• Step 2 assessing deviation from ideal strategy
  profiles
• Deviation v ? (xsj Xij)2
• xsj the mean score for cluster s in the study
  sample on the jth strategy dimension (j 1 to
  8), and Xij the score for the ideal profile for
  strategy archetype i (i 1 to 5) on the jth
  strategy dimension

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Data Analysis Calculation of Strategy and IT
Capability Fit/Misfit

• Step 3 Calculating strategy profile deviations
  from the theoretical ideal profiles
• Deviation v ? (xsj Xij)2
• where xsj the score for a firm s within cluster
  i on the jth strategy dimension, and Xij the
  theoretically determined score on the jth
  strategy dimension for the strategy archetype
  that cluster i is classified as
• Step 4 Calculating IT capability profile
  deviations from the theoretical/empirical ideal
  profiles
• Deviation v ? (xsj Xij)2
• where xsj the score for a firm s within cluster
  i on the jth IT capability dimension, and Xij
  the theoretically/empirically determined score on
  the jth IT capability dimension for the strategy
  archetype that cluster i represents

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Data Analysis

• Step 4 Regression Analysis
• Regress performance on strategy deviation,
  capability deviation, sum of strategy capability
  deviations and control variables
• Assumptions regarding normality of residuals,
  multicollinearity and homoscedasticity of error
  terms not violated

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Regression Analysis Controls

• Firm size ( employees)
• Lagged sales
• Number of industries count
• Age
• Industry size
• Industry competition ( of other firms in the
  4-digit SIC segment)
• Industry concentration (C8, C4, C20, C50)

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Results

• Equifinality hypothesis tested by ANOVA ((F4,59)
  0.56 p0.70))
• The fit hypotheses are tested by OLS results

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OLS Results
Variables M1 Deviations from theoretical ideal profile (N64) M1A M1B M1 Deviations from theoretical ideal profile (N64) M1A M1B M2 Deviations from empirical ideal profile (N 54) M2A M2B M2 Deviations from empirical ideal profile (N 54) M2A M2B
Size (employees) 0.144 (0.155) 0.304 (0.155) 0.293 (0.155) 0.295 (0.153)
Lagged sales 0.468 (0.154) 0.354 (0.161) 0.212 (0.189) 0.212 (0.187)
No. of industries (4-SIC) count 0.212 (0.082) 0.295 (0.081) 0.342 (0.081) 0.342 (0.080)
Share of sales in prim 4-SIC ind. 1.298 (0.385) 0.989 (0.389) 0.310 (0.410) 0.318 (0.402)
Age (in years) 0.026 (0.147) -0.071 (0.148) 0.089 (0.154) 0.084 (0.149)
Industry size (Total empl.) -0.232 (0.114) -0.311 (0.113) -0.189 (0.100) -0.186 (0.097)
Industry Sales -0.329 (0.076) -0.276 (0.081) -0.267 (0.094) -0.267 (0.093)
Industry competition 0.068 (0.120) 0.008 (0.124) -0.215 (0.096) -0.218 (0.093)
Industry concentration (C8) 0.934 (0.700) 0.970 (0.681) 0.261 (0.746) 0.279 (0.728)
Strategy deviations -0.461 (0.204) -0.308 (0.172)
IT Capability deviations -0.308 (0.155) -0.338 (0.179)
Sum of deviations -0.387 (0.100) -0.322 (0.140)
Constant -4.027 (3.025) -2.470 (3.147) -3.250 (3.422) -3.282 (3.375)
Model Fit F(11,52) 19.76 R2 0.81 adj. R2 0.77 F(10,53) 20.76 R2 0.80 adj. R2 0.76 F(11,42) 11.38 R2 0.75 adj. R2 0.69 F(10,43) 12.81 R2 0.75 adj. R2 0.69
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Robustness Checks

• Three sets of models estimated
• Across strategy top 1 overall performer
• Across strategy top 5 overall performers
• 5 random performers
• Deviations from non-ideal calibration profiles do
  not impact performance significantly
• Provides a power analysis for hypothesis testing

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Limitations

• Modest sample size
• Cross sectional data
• Dimensions of strategy and IT capability weighed
  equally
• Lack of generalizability

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Theoretical Contributions

• Conceptualization of a key and multi-dimensional
  set of IT capabilities to support business
  strategies
• Demonstration of the nuanced alignment of
  business strategy and IT capabilities
• Examination of the alignment between business
  needs with business capabilities fostered by IT
• Extension of Venkatramans STROBE instrument
• Development and empirical validation of the
  extended Miles and Davis typology
• The use of both deductive and inductive
  approaches to study profile deviations

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Managerial Implications

• The development and deployment of IT capabilities
  and IT investments should be contingent upon the
  business strategy
• Select a few critical areas and invest in IT
  selectively to build capabilities
• Focus on the profile of the strategy leader and
  not the industry leader benchmark IT
  capabilities accordingly
• Pay close attention to IT capabilities at the
  disaggregated level

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Future Research in IT Strategy

• Complementarity and substitutability of IT
  resources, capabilities and investments
• Do certain IT resources, capabilities and
  investments provide higher benefits in
  association with other resources, capabilities
  and investments? How and why?
• Can certain IT resources, capabilities and
  investments substitute for other resources,
  capabilities and investments? How and why?

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Thanks!!