Examining the Effect of Knowledge
Management Strategic Alignment on
Business Performance
Yue-Yang Chen
Department of Business Administration
I-Shou University
1
OVERVIEW
™KM
™Strategic alignment (fit)
™Why focus on KM
™What is strategic alignment
™Why coalignment among
KM, HRM, and IT in KM field
1. different conceptualizations,
verbalizations, and
perspectives of analysis of
alignment are necessary
and complementary for
research methodologies.
2. strategic alignment of KM
strategy, IT strategy, and
HRM strategy has a real
potential to enhance
business performance.
™MIS strategic alignment research and model
™KM strategic alignment research and model
Problem
Literature
Findings
Model
Data
Analysis
Research model
Research
Method
Survey methodology
Source: collected from manufacturing, banking, and service industry in Taiwan
Data Analysis methodology: fit as covariation, (V.S. direct effects model),
fit as profile deviation, and fit as matching
2
1. Introduction
™Drucker (1993) argues that knowledge is a
significant resource, more important than other
assets
™ In the complicating and rapidly changing
business environment, Knowledge management
(KM) is one of the growing issues in
contemporary business
™the implementation of KM projects compliant
with various KM strategies provide organizations
the capabilities of improving knowledge quality
and quantity and consolidating the value and
practicability of knowledge (Hansen et al., 1999;
Hoffman et al., 2005; Keskin, 2005; Kogut &
Zander, 1992; Spender & Grant, 1996)
3
1. Introduction (cont.)
™Recently, both researchers and
practitioners have started to realize the
importance of the information technology
(IT) for effective KM activities
}
}
}
KM + IT ÆKM performance
effective KM project alone can’t lead to
success without the support of IT, vice versa
Accordingly, their co-alignment with other
resources or strategies used in managing
business activities must be considered for
business performance (Asoh, 2004; Zack,
2002)
4
1. Introduction (cont.)
™However, the high-high fit of KM and IT is
not always yield positive results (Asoh,
2004, Zack, 2002)
}
human resource management strategy and
knowledge strategy are interdependent (Shih
and Chiang, 2005)
™Thus a linkage of effective IT and KM
activities that are consistent with HRM
policies is the key to reduce costs, which
in turn, a higher performance achieved
(Davenport and Prusak, 1998; Tiwana,
2002)
5
1. Introduction (cont.)
™Current research on knowledge
management covers:
}
}
}
}
understanding what differences among data,
information, knowledge
conceptual issues and managerial themes
issues for KMS implementation design,
analysis, verification of the KM models
survey-based studies examining issues that
contribution to KMS success
6
1. Introduction (cont.)
™research regarding the integrated
investigation of various strategies of the
organization is not sufficient
™In the practical terms, the basic alignment
mechanism is “strategy” (Miles & Snow
1984)
™it is though that a match between strategy
and organization is the key driven to
effectiveness at realizing intended
strategies (Gupta and Govindarajan, 1984)
7
1. Introduction (cont.)
™therefore, we focused on 3 types of
strategy:
}
}
}
human resource management strategy
knowledge management strategy
and information technology strategy
™We posit that organizational performance
is a result of the strategic alignment
among these 3 strategies
8
Why strategic alignment
™The issue of alignment of alternative
resources is one of the top concerns of
executives and senior management in
general since the mid-1980s (Luftman et
al., 1996; Watson et al., 1997)
™Doty et al. (1993) state that the increased
organizational effectiveness is driven by
the internal consistency or fit among the
patterns of relevant contextual, structural,
and strategic factors.
9
Why strategic alignment (cont.)
™The importance of strategic alignment of
IT/IS is also being acknowledged
(Henderson & Venkatraman 1993; Reich &
Benbasat 2000)
™A recent research proposed by Lee et al.
(2004), stating that contingency, and fit
(alignment) theory is the top five frequently
used
10
Why strategic alignment (cont.)
™Alignment
}
}
a great contribution to potential capabilities of an
organizational IT
a significant positive direct effect on organizational
overall performance (Azab, 2005; Xia and King, 2002)
™Misalignment
}
}
a redundancy and inefficiency in IT functions and in
an increase in costs and delays (Gold et al., 2001)
it can be one of the critical reasons in organizational
performance lessened (Chan et al., 1997; Luftman
and Brier, 1999)
™Thus, strategic alignment issue is important to
firms
11
1.2. Research motivation and purpose
™Research purposes:
}
}
}
First, it intends to provide further insights into
performance implications within the broad
conceptualization of strategic alignment among KM
strategy, IT strategy, and HRM strategy.
Second, it examines KM strategic alignment by using
the multiple perspectives of fit as covariation, fit as
profile deviation, and fit as matching.
Finally, we are devoted to shedding more light on KM
strategic alignment research to reflect the holistic and
bivariate pattern of interlinkages between KM strategy
and other strategies that influence KM activities.
12
Types of alignment
™Van de Ven and Drazin (1985) define fit as
three approaches: selection, interaction,
and systems approaches
™Venkatraman (1989) defined six different
perspectives of fit: matching, moderation,
mediation, gestalts, covariation, and profile
deviation
13
Types of alignment (cont.)
™Adapted from Venkatraman (1989)
Holistic
(system)
Reductionistic
(bi-variate)
14
Types of alignment (cont.)
™Adapted from Venkatraman (1989)
15
Types of alignment (cont.)
™Adapted from Venkatraman (1989)
16
Strategic alignment model 1 of
our research (cont.)
IT environment
scanning
Information
technology
strategy
Growth
Strategic use
of IT
System
Human
Knowledge
management
strategy
HR flow
Work systems
Strategic
alignment
H1
Business
performance
Profitability
Human resource
management
strategy
Reward systems
Research Model 1: a holistic perspective of strategic
alignment (strategic alignment model)
【back1】
【back】
17
3. Research model and hypotheses
™The holistic perspective of strategic
alignment in KM strategy, IT strategy,
and HRM strategy (H1)
}
}
Numerous studies argue that proper IT solutions can
enhance the speed of knowledge exploration and
exploitation, from individuals to organizational
members (e.g., Ruiz-Mercader et al., 2006; CecezKecmanovic, 2004; Sher and Lee, 2004; Pan and
Leidner, 2003; Tippins and Sohi, 2003; Gottschalk,
2001; Scott, 2000).
Some researchers argue that KM-related or IT-related
variables alone are not sufficient for explaining
organizational performance (March, 1991),
18
3. Research model and hypotheses
(cont.)
}
}
Shih et al. (2005) indicate that strategic alignment
(fit) among KM strategy, corporate strategy, and
HRM strategy are significantly related to better
KM effectiveness
Powell and Dent-Micallef (1997) and Mata et al.
(1995) also contend that ITs alone would not
produced sustainable performance, combining
certain human and business resources with ITs are
the right way to explain significant performance
variance
19
3. Research model and hypotheses
(cont.)
™It is reasonable to contend that enhanced
business performance will be achieved if the
strategic alignments between KM strategy and
HRM strategy (e.g., Shih and Chiang, 2005;
Bierly and Daly, 2002), and between IT strategy
and HRM strategy (e.g., Cabrera and Bonache,
1999; Miles and Snow, 1994) are well
constructed.
™various patterns of KM strategy, IT strategy, and
HRM strategy must be aligned to optimize
organizational outcomes.
20
3. Research model and hypotheses
(cont.)
™Therefore, a strategic alignment model that
incorporates KM strategy, IT strategy, and HRM
strategy is proposed
}
because these aspects reinforce each other, serving
as the basis for business performance, as measured
in terms of growth and profitability.
™H1: Strategic alignment between KM strategy, IT
strategy, and HRM strategy has a positive direct
effect upon business performance, in terms of
growth and profitability.
21
The reductionistic perspective of strategic alignment
in KM strategy, IT strategy, and HRM strategy
IT strategy
•IT environment scanning
•Strategic use of IT
KM strategy
•Human
•System
HRM strategy
•HR flow
•Work systems
•Reward systems
Strategic
alignment
H2-1a, H2-1b, H2-2a, H2-2b
Business performance
•Growth
•Profitability
Strategic
alignment
H3-1a, H3-1b, H3-2a, H3-2b,
H3-3a, H3-3b, H3-4a, H3-4b
H3-5a, H3-5b, H3-6a, H3-6b
【back】
Research Model 2: a reductionistic perspective of strategic alignment
22
The reductionistic perspective of strategic alignment
in KM strategy, IT strategy, and HRM strategy
™ 3.2.1. Strategic alignment between KM strategy and
IT strategy (H2)
}
}
}
strategic IT management has been regarded an enabler in
business performance, when it fits with certain aspects of the KM
context (Alavi and Leidner, 2001).
IT strategies can be classified into two general categories: IT
environment scanning; and strategic use of IT (Bergeron et al.,
2004).
System KM strategy requires IT tools that allow for explicit
knowledge to be formalized and articulated in documents, and
shared electronically through IT infrastructures such as intranets
(Scott, 1998).
ƒ Organizations should invest in an extensive IT system to codify
knowledge.
ƒ A firm’s IT strategy should focus on paying more attentions to
strategic use of IT internally, in order to improve the quality and
quantity of electronic repositories or databases.
23
The reductionistic perspective of strategic alignment
in KM strategy, IT strategy, and HRM strategy
}
Human KM strategy draws upon interpersonal
relationships to exchange and share tacit
knowledge across the organization.
ƒ Firms need a moderate investment in IT to connect
experts in the organization. The technologies may
include an e-mail system, on-line discussion networks,
videoconferencing, and other collaborative tools
(Scheepers et al., 2004).
ƒ A firm’s IT strategy should aim at scanning the
external IT environment, searching for communication
tools and other interactive technologies to support
person-to-person knowledge-sharing.
24
The reductionistic perspective of strategic alignment
in KM strategy, IT strategy, and HRM strategy
™ H2: The strategic alignment between KM strategy and IT
strategy has a positive direct effect on business
performance, as measured in growth and profitability
}
}
}
}
H2-1a: The strategic alignment between human KM strategies
and IT strategies for IT environment scanning has a positive
direct effect on business performance, as measured in growth.
H2-1b: The strategic alignment between human KM strategies
and IT strategies for IT environment scanning has a positive
direct effect on business performance, as measured in
profitability.
H2-2a: The strategic alignment between system KM strategies
and IT strategies for the strategic use of IT has a positive direct
effect on business performance, as measured in growth.
H2-2b: The strategic alignment between system KM strategies
and IT strategies for the strategic use of IT has a positive direct
effect on business performance, as measured in profitability.
25
3.2.2. Strategic alignment between
KM strategy and HRM strategy (H3)
™ According to Hansen et al. (1999), different KM strategies
should reflect different drivers of their human resources.
™ In the system KM strategy, adequate HR policies consist of
hiring persons who are well suited to the reuse of knowledge
and the implementation of solutions, training people in groups
and through computer-based distance learning, and rewarding
people for using and contributing to document databases.
™ With the human KM strategy, suitable HR policies are hiring
persons who like problem-solving and can tolerate ambiguity,
training people via one-on-one mentoring, and rewarding people
for directly sharing knowledge with others.
™ Therefore, both system and human KM strategies highlight the
importance of recruitment and selection of employees (HR flow),
training and development employment security, teams and job
redesign control (work systems), and reward systems.
26
3.2.2. Strategic alignment between
KM strategy and HRM strategy (H3)
™Shih and Chiang (2005) also assert that a
satisfactory HRM strategy, that is the HR flow of
hiring or promoting polities and training
programs, the work systems of tasks and
assignment, and reward systems of wage level
and appraisal, should be compatible with KM
strategy to optimize organizational performance.
™That is, a proper HRM strategy significantly
facilitates the successful implementation of a KM
strategy (Shih and Chiang, 2005).
27
3.2.2. Strategic alignment between
KM strategy and HRM strategy (H3)
™ H3: The strategic alignment between KM strategy and
HRM strategy has a positive direct effect on business
performance, as measured in growth and profitability.
}
}
}
}
}
}
H3-1a: The strategic alignment between human KM strategies and
HRM strategies for HR flow has a positive direct effect on business
performance, as measured in growth
H3-1b: The strategic alignment between human KM strategies and
HRM strategies for HR flow has a positive direct effect on business
performance, as measured in profitability.
H3-2a: The strategic alignment between human KM strategies and
HRM strategies for work systems has a positive direct effect on
business performance, as measured in growth.
H3-2b: The strategic alignment between human KM strategies and
HRM strategies for work systems has a positive direct effect on
business performance, as measured in profitability.
H3-3a: The strategic alignment between human KM strategies and
HRM strategies for reward systems has a positive direct effect on
business performance, as measured in growth.
H3-3b: The strategic alignment between human KM strategies and
HRM strategies for reward systems has a positive direct effect on
business performance, as measured in profitability.
28
3.2.2. Strategic alignment between
KM strategy and HRM strategy (H3)
}
}
}
}
}
}
H3-4a: The strategic alignment between system KM strategies
and HRM strategies for HR flow has a positive direct effect on
business performance, as measured in growth.
H3-4b: The strategic alignment between system KM strategies
and HRM strategies for HR flow has a positive direct effect on
business performance, as measured in profitability.
H3-5a: The strategic alignment between system KM strategies
and HRM strategies for work systems has a positive direct effect
on business performance, as measured in growth.
H3-5b: The strategic alignment between system KM strategies
and HRM strategies for work systems has a positive direct effect
on business performance, as measured in profitability.
H3-6a: The strategic alignment between system KM strategies
and HRM strategies for reward systems has a positive direct
effect on business performance, as measured in growth.
H3-6b: The strategic alignment between system KM strategies
and HRM strategies for reward systems has a positive direct
effect on business performance, as measured in profitability.
29
3.3. General perspectives in KM strategy,
IT strategy, and HRM strategy
IT
environment
scanning
Strategic use
of IT
Information
technology
strategy
H5
Growth
System
Human
Knowledge
management
strategy
H6
HR flow
Work
systems
H4
Business
performance
Profitability
Human
resource
management
strategy
Reward
systems
Research Model 3: a general perspective direct effects model
【back】
30
3.3. General perspectives in KM strategy,
IT strategy, and HRM strategy (cont.)
™3.3.1. KM strategy (H4)
}
}
Choi and Lee (2003) classified KM methods into four
styles: labeled dynamic, system-oriented, humanoriented, and passive. After empirical evaluation of 54
Korean firms in the manufacturing, service, and
financial industries, these authors indicated that a
dynamic style, integrating explicit-oriented with tacitoriented methods has a significant impact on
business performance.
Ｍuch evidence exists to support the claim that
developing a KM strategy provides a valuable
opportunity to obtain a greater understanding of the
way a business operates to foster success in its KM
practices (Shih & Chiang, 2005; Garavelli et al., 2004;
Robertson, 2004).
H4: KM strategy has a significant positive direct
effect on business performance.
31
3.3. General perspectives in KM strategy, IT
strategy, and HRM strategy (cont.)
™3.3.2. IT strategy (H5)
}
}
While many researchers have indicated that IT has a
significant positive direct effect on organizational
outcome, enough exceptions exist to contest this
(Barua and Lee, 1997; Markus and Soh, 1993; Quinn
and Baily, 1994; Clemons and Row, 1991).
IT strategy should be aligned with business strategy
(or KM strategy) or other meaningful activities; thus,
the optimal effectiveness can be achieved for an
organization
H5: IT strategy has a significant positive direct
effect on business performance.
32
3.3. General perspectives in KM strategy,
IT strategy, and HRM strategy (cont.)
™3.3.3. Human resource management
strategy (H6)
}
}
The positive relationship between HRM and
organizational performance has been proven (Pfeffer,
1998; Huselid, 1995).
From a resource-based view of the firm (Barney,
1995; 1991; Lado & Wilson, 1994), human resources
also are regarded to be key sources of competitive
advantage, because they comprise the skills,
behaviors and values of staff that are paramount to
sustaining high performance (Pfeffer, 1998).
H6: HRM strategy has a significant positive
direct effect on business performance.
33
4. Research design and
methodology (cont.)
™4.5. Statistical Techniques
}
}
For assessing strategic alignment, one must consider
using multiple approaches to test coalignment (Van
de Ven and Drazin, 1985; Venkatraman, 1990) and to
avoid the mixed research results that can arise from
imprecise specification of the functional form of
contingency or alignment (Schoonhoven, 1981; Joyce
et al., 1982; Drazin & Van de Ven, 1985; Van de Ven
& Drazin, 1985; Venkatraman, 1990).
In this vein, the performance impact of strategic
alignment in our study was examined using multiple
approaches.
34
4.5. Statistical Techniques
}
}
According to Bergeron et al. (2001), the
profile deviation and covariation approaches
of alignment are applicable to theory testing;
thus, these two methodologies were
employed for the ‘system’ perspective of
alignment.
The purpose of using the covariation and
profile deviation perspectives was to prove
that a holistic strategic alignment relationship
exists between KM strategy, IT strategy, and
HRM strategy.
35
4.5. Statistical Techniques (cont.)
}
}
Meanwhile, the matching perspective was adopted to
demonstrate which pattern of alignment, related to
these three related variables, is significant to
performance.
Consequently, a systems approach, the covariation
perspective and the profile deviation perspective all
involve considering all the KM strategy-IT strategyHRM strategy dimensions simultaneously, whereas a
bivariate matching model involves examining the
relationship between the individual strategic
alignments of KM strategy-IT strategy and KM
strategy-HRM strategy.
36
Research model
Data
analysis
methodology
Covariation
Covariation
(second-order
(second-order model)
model)
Profile
Profile deviation
deviation
Matching
Matching
comparison
Direct
Direct effects
effects model
model
(first-order
(first-order model)
model)
Legend:
Holistic
Holistic view
view
Reductionistic
Reductionistic
view
view
The research model is
validated as holistic
view by fit as
covariation and fit as
profile deviation
After the research model is
validated as holistic view
by fit as covariation and fit
as profile deviation, it is
analyzed using fit as
matching to test the
bivariate alignment
37
5. Data analysis and results
™5.1. Sample
characteristics
Variable
1. Industry
Manufacturing
Service
Finance/Banking
2. Number of Employees
100 or less
100 - 499
500 - 999
1000 or more
3. Job Title
Top manager
Middle manager
First-line manager
others
4. Years in firm
less than 1 year
1 -2 years
3 - 5 years
6 - 10 years
10 years or more
5. Educational level
High school degree
Junior college degree
Undergraduate degree
Graduate school degree
6. Sex
Female
Male
7. Age
20 or less
21-30
31-40
41-50
51 or more
Number
Frequency (%)
92
53
16
57.1 %
32.9 %
10.0 %
16
61
35
49
9.9 %
37.9 %
21.7 %
30.5 %
19
78
60
4
11.8 %
48.4 %
37.3 %
2.5 %
13
18
33
39
58
8.1 %
11.2 %
20.5 %
24.2 %
36.0 %
0
29
76
56
0.0 %
18.0 %
47.2 %
34.8 %
40
121
24.8 %
75.2 %
0
27
64
58
12
0.0 %
16.8 %
39.8 %
36.0 %
7.4 %
38
5.2 Descriptive statistics
™Table 8 Descriptive statistics and
correlations
Variable
Mean S.D.
1
2
3
4
1. System
4.80 0.93
2. Human
4.70 0.83 0.66
3. IT environment scanning 5.01 0.89 0.59 0.61
4. Strategic use of IT
5.14 0.88 0.64 0.61 0.72
5. HR Flow
4.62 1.11 0.43 0.50 0.41 0.40
6. Work systems
4.54 0.96 0.51 0.51 0.46 0.49
7. Reward systems
4.68 0.98 0.46 0.50 0.50 0.57
8. Growth
4.49 1.06 0.38 0.44 0.33 0.38
9. Profitability
4.43 1.04 0.40 0.44 0.31 0.39
Note: 1. N=161.
2. All correlations are significant at the p < 0.01 level (2-tailed).
5
6
7
8
0.66
0.66 0.62
0.49 0.40 0.41
0.55 0.43 0.43 0.87
39
5.3. Assessment of construct
validity
Kss1
Kss2
Kss3
.87
.75
.74
System
(α=0.91,ρ=0.87)
.79***
Knowledge
management
strategy
.79
kss4
.85
ksp1
.86
ksp2
.69
(α=0.91,ρ=0.89)
Human
(α=0.84,ρ=0.84)
ksp3
ksp4
iye1
iye2
.71
iye4
.68
.91
.88
iye5
.92
iye3
iyu1
iyu2
iyu3
iyu4
iyu5
iyu6
.85
.88
.85
.79
χ2 = 7.30 (d.f. = 3, p = 0.063)
χ2/d.f. = 2.43
AGFI = 0.89
CFI = 0.99
NFI = 0.99
NNFI = 0.96
SRMSR = 0.019
***: p<.001
IT environment
scanning
.97***
(α=0.91,ρ=0.91)
Information
technology
strategy
.83
.76
.99***
.80***
Strategic use
of IT
(α=0.94,ρ=0.93)
(α=0.95,ρ=0.88)
χ2 =24.45 (d.f. = 22, p = 0.32)
χ2/d.f. = 1.11
AGFI = 0.92
CFI = 0.99
NFI = 0.98
NNFI = 0.99
SRMSR = 0.02
***: p<.001
40
5.3. Assessment of construct
validity
hrf1
hrf2
hrf3
hrw1
.62
HR flow
.79
(α=0.66,ρ=0.67)
.98***
Work systems
.99***
.72
hrw2
hrw3
.64
(α=0.87,ρ=0.98)
hrr1
.67
hrr2
.77
opg1
opg2
opg3
opp1
opp2
opp3
opp4
opp5
.94
.98
.90
Growth
.96***
Reward systems
(α=0.73,ρ=0.77)
hrr3
hrr4
(α=0.64,ρ=0.63)
Human resource
management
strategy
.74
.98***
χ2 =10.60 (d.f. = 4, p = 0.03)
χ2/d.f. = 2.65
AGFI = 0.86
CFI = 0.99
NFI = 0.98
NNFI = 0.92
SRMSR = 0.036
***: p<.001
(α=0.96,ρ=0.85)
Business
performance
.91
.95
.95
Profitability
.96
.76
(α=0.96,ρ=0.96)
.89***
(α=0.97,ρ=0.93)
χ2 =12.71 (d.f. = 8, p = 0.12)
χ2/d.f. = 1.59
AGFI = 0.91
CFI = 0.99
NFI = 0.99
NNFI = 0.99
SRMSR = 0.011
***: p<.001
41
Table 9: Scale properties for the
measurement model
™p.59~p.60
Construct
indicators
KM strategy
System
kss1
kss2
Mean
S.D.
Standardized
loadings1
IIR2
4.8
4.65
4.92
0.93
0.99
1.04
0.79
0.87
0.75
0.62
0.76
0.56
kss3
4.75
1.09
0.74
0.55
kss4
Human
ksp1
ksp2
4.88
1.05
4.70
4.75
4.51
0.83
0.94
1.04
0.79
0.99
0.85
0.86
0.62
0.98
0.72
0.74
ksp3
IT strategy
IT environment scanning
iye1
iye2
4.84
0.87
0.69
0.48
5.01
5.27
4.80
0.89
1.01
1.03
0.97
0.71
0.68
0.94
0.50
0.46
5.07
5.00
1.04
1.03
0.91
0.88
0.83
0.77
4.93
1.08
5.14
5.21
5.11
0.88
1.02
1.01
0.92
0.80
0.83
0.76
0.85
0.64
0.69
0.58
iyu3
5.29
0.92
0.85
0.72
iyu4
4.91
1.05
0.88
0.77
iyu5
5.17
0.96
0.85
0.72
iyu6
5.16
1.04
0.79
0.62
iye3
iye4
iye5
Strategic use of IT
iyu1
iyu2
CR3
AVE4
0.89
0.87
0.62
0.84
0.65
0.88
0.91
0.67
0.93
0.69
42
Table 9: Scale properties for the
measurement model
Construct
indicators
HRM strategy
HR Flow
hrf2
hrf3
Work systems
hrw1
hrw3
Reward systems
hrr1
hrr2
hrr4
Business performance
Growth
opg1
opg2
Standardized
loadings1
IIR2
0.96
1.23
1.00
0.98
0.62
0.79
0.99
0.72
0.64
0.96
0.38
0.62
0.98
0.52
0.41
4.54
4.80
0.98
1.12
0.96
0.67
0.92
0.45
4.71
1.12
0.77
0.59
4.54
1.19
0.74
0.55
Mean
S.D.
4.62
4.61
4.63
1.11
1.36
1.21
4.54
4.34
4.75
4.49
4.54
4.46
1.06
1.10
1.11
0.98
0.94
0.98
0.96
0.88
0.96
4.48
1.10
4.43
4.45
4.39
1.10
1.13
1.14
0.90
0.89
0.91
0.95
0.81
0.79
0.83
0.90
opp3
4.29
1.14
0.95
0.90
opp4
4.35
1.11
0.96
0.92
opg3
Profitability
opp1
opp2
4.65
1.12
opp5
0.79
0.58
Note:
1
All item loadings (λ) are significant at p < 0.05 level
2
Individual item reliability (IIR) = (Standardized loadings)2
3
Composite reliability (CR) = (ΣLi)2/((ΣLi)2+ΣVar(Ei))
4
Average variance extracted (AVE) = ΣLi2/(ΣLi2+ΣVar(Ei))
CR3
AVE4
0.98
0.67
0.50
0.63
0.46
0.77
0.53
0.93
0.85
0.88
0.96
0.83
43
Table 10 Intercorrelations and AVEs
Construct
kss
ksp
iye
iyu
hrf
hrw
hrr
opg
opp
kss
0.62
0.43
0.35
0.40
0.19
0.26
0.21
0.15
0.16
ksp
iye
iyu
hrf
hrw
hrr
opg
opp
0.65
0.37
0.37
0.25
0.26
0.25
0.19
0.19
0.67
0.52
0.17
0.21
0.25
0.11
0.09
0.69
0.16
0.24
0.33
0.14
0.15
0.50
0.44
0.43
0.24
0.30
0.46
0.38
0.16
0.19
0.53
0.17
0.19
0.88
0.76
0.83
1.kss: system; ksp: human; iye: IT environment scanning; iyu: strategic use of IT; hrf:
HR flow; hrw: work systems; hrr: reward systems; opg: growth; opp: profitability.
2.Diagonal elements (in bold) represented the Average Variance Extracted
(=ΣLi2/(ΣLi2+ΣVar(Ei))), while off-diagonal elements were represented by the square
of correlation among constructs. For discriminant validity, diagonal elements should
be larger than off-diagonal elements.
44
5.4. Hypothesis testing
™According to Venkatraman (1989a), even though
the covariation approach can be modeled as
exploratory factor analysis (EFA) and
confirmatory factor analysis (CFA), CFA is
preferred as a tool to model fit as covariation.
™In this vein, a direct effects model (i.e., first-order
factor model) must compete against the
covariation model (second-order factor model).
45
5.4. Hypothesis testing (cont.)
™Following the methodology proposed by
Venkatraman (1989a), we used three
criteria to assess the preferred model
between the first-order factor and secondorder factor model.
46
Three criteria to assess the preferred
model
™1. Comparing the coefficients of determination of
the two models (analogous to R2)
}
the goodness of fit of second-order model can never
be better than that of the first-order model
™2. Calculate the target coefficient (T )
™3. Assessing the statistical significance of the
loadings on the second-order factor of strategic
alignment
47
5.4. Hypothesis testing (cont.)
™ 1. Comparing the coefficients of determination of the two
models (analogous to R2).
}
}
As can be seen in Figures 15 and 16, the direct effects model
explains 4% less variance in business performance (R2=0.40
versus 0.44).
Furthermore, according to Marsh and Hocever (1985), a secondorder factor model is just a parsimonious explanation of the
covariation among the first-order factors. Therefore, even when
the second-order model effectively explains the covariation
among the first-order factors, the goodness of fit can never be
better than that of the first-order model (Venkatraman, 1989a;
1990). The fit indices of the second-order factor model in this
study are slightly lower than those of the first-order model
(depicted in Figures 15 and 16), suggesting acceptance of the
coalignment over the main effects model.
48
5.4. Hypothesis testing (cont.)
™2. Calculate the target coefficient (T)
}
The ratio of the chi-square of the first-order model
to the chi-square of the second-order model, as
defined by Marsh and Hocevar (1985).~
χ12階
}
}
χ
2
2階
A value close to 1 will support using the covariation model
versus the main-effects model (Venkatraman, 1989a)
This coefficient has an upper bound of 1.00, and
can be interpreted similar to Bentler and Bonett’s
(1980) delta index.
In the present research, the T-coefficient has a
value of 0.99 (21.76/21.996) which additionally
supports the superiority of the second-order model.
49
5.4. Hypothesis testing (cont.)
™ 3. Assessing the statistical significance of the loadings on the
second-order factor of strategic alignment.
}
}
}
The ML estimates (shown in Figure 15) indicate that the three
parameters are significant, meaning that the three first-order factors
consistently contribute to the second-order factor, termed strategic
alignment, whereas 2 of the 3 parameters demonstrate insignificant
path loadings of the first-order factor model (shown in Figure 16).
Furthermore, the fourth parameter (from strategic alignment to business
performance) in the second-order factor model represents the impact of
strategic alignment on business performance, in terms of growth and
profitability, providing strong support to the performance implications of
strategic alignment.
Overall, the fit statistics indicate a good fit of both models with the data
collected from the validated measures (Strategy alignment model: χ152
= 21.996; χ2/d.f. = 1.47, p<.001; AGFI = 0.91; CFI = 0.99; NFI = 0.98;
NNFI = 0.98, SRMSR = 0.035; direct effects model: χ162 = 21.76;
χ2/d.f. = 1.36, p<.001; AGFI = 0.92; CFI = 0.99; NFI = 0.98; NNFI =
0.99, SRMSR = 0.028).
50
5.4. Hypothesis testing (cont.)
IT
environment
scanning
.82***
.88***
Strategic use
of IT
Information
technology
strategy
Growth
.90***
.92***
System
.80***
.82***
Human
Knowledge
management
strategy
.96***
Strategic
alignment
.56***
.95***
.84***
HR flow
Work
systems
Profitability
.74***
.78***
.80***
Reward
systems
Business
performance
(R2=0.44)
Human
resource
management
strategy
χ2 =21.996 (d.f. = 15, p = 0.11)
χ2/d.f. = 1.47
AGFI = 0.91
CFI = 0.99
NFI = 0.98
NNFI = 0.98
SRMSR = 0.035
***: p<.001
Figure 15: The strategic alignment model
51
5.4. Hypothesis testing (cont.)
IT
environment
scanning
Strategic use
of IT
.83***
Information
technology
strategy
.86***
-.19
Growth
.91***
System
.80***
.82***
Human
Knowledge
management
strategy
.38
Business
performance
(R2=0.40)
.96***
Profitability
.45***
HR flow
.83***
Work system
.79***
.79***
Reward
systems
Human
resource
management
strategy
χ2 =21.76 (d.f. = 16, p = 0.15)
χ2/d.f. = 1.36
AGFI = 0.92
CFI = 0.99
NFI = 0.98
NNFI = 0.99
SRMSR = 0.028
***: p<.001
Figure 16: The direct effects model
52
Figure 15: The strategic alignment model
Figure 16: The direct effects model
53
NKFUST
5.4. Hypothesis testing (cont.)
™5.4.2. Perspective two: profile deviation
approach
}
}
}
The contingency model hypothesizes that, if the
distance between an organizational profile and the
‘ideal profile’ increases, organizational performance
will decrease.
To operationalize these deviations from an ideal
profile, the Euclidean distance score is calculated
(Drazin and Van de Ven, 1985), which, in effect,
represents the degree of fit.
The underlying notion is that the extent to which the
distance scores in the pattern from an ideal profile are
negatively and significantly correlated to performance
measures determines the strength of support for the
presence of a strategic alignment relationship.
54
5.4.2. Perspective two: profile
deviation approach
}
The Euclidean Distance or Misalignment =
7
∑(X
j =1
ij
− X ij )
X ij = the score for the unit in the study sample along the jth variable
Where X ij = the mean for the calibration sample along the jth variable
j = 1, 2, 3, 4, 5, 6, 7 (the seven variables in this study)
55
5.4.2. Perspective two: profile
deviation approach (cont.)
}
}
In accordance with the research of Venkatraman
and Prescott (1990) and Bergeron et al. (2001), the
top 10 percent of the sampled firms (a more severe
criterion than the 15 or 20 percent they sampled) in
terms of growth and profitability were used as the
ideal or calibration sample (n=19 for growth and
n=16 for profitability performance assessment).
the mean scores for the seven variables (system,
human, IT environment scanning, strategic use of
IT, HR flow, work systems, reward systems) of
each calibration sample were calculated to specify
the ‘ideal’ profile empirically.
56
5.4.2. Perspective two: profile
deviation approach (cont.)
As in Drazin and Van de Ven (1985), strategic
alignment (or more appropriately ‘misalignment’)
was measured for the remaining subgroup
(n=142 for growth and n=145 for profitability
performance) as the Euclidean distance metric
from the individual pattern of scores to the ideal
pattern, for the seven variables.
}Result:
}
Table 11: Relationship between misalignment and performance
Performance measure
Growth
Profitability
misalignment
-0.36*** (n=142)
-0.40*** (n=145)
*** p<0.001
57
5.4.2. Perspective two: profile
deviation approach (cont.)
™Table 12: A schematic representation of fit as
profile deviation for business performance in
terms of growth
Variable
Mean score1,2
1
2
3
4
5
System (kss)
Xr
Human (ksp)
Xr
Xc
Xc
Xr
IT environment scanning (iye)
Xc
Xr
Strategic use of IT (iyu)
HR Flow (hrf)
Xr
Work system (hrw)
Xr
Reward systems (hrr)
6
Xr
7
(0.98)
(0.90)
(0.98)
Xc
Xc
Xc
Xc
(1.10)
(1.09)
(1.12)
(1.02)
1
Xc: calibration samples are the top 10% in business performance re: growth (n=18; Xkss=5.66;
Xksp=5.49; Xiye=5.88; Xiyu=6.11; Xhrf=5.58; Xhrw=5.53; Xhrr=5.58)
2
Xr: remaining sample (n=143, Xkss=4.68; Xksp=4.59; Xiye=4.90; Xiyu=5.01; Xhrf=4.49; Xhrw=4.41;
Xhrr=4.56)
58
5.4.2. Perspective two: profile
deviation approach (cont.)
™Table 13: A schematic representation of fit as
profile deviation in business performance in
terms of profitability
Variable
Mean score1,2
1
2
3
4
5
System (kss)
Human (ksp)
Xr
Xr
Xr
IT environment scanning (iye)
Strategic use of IT (iyu)
HR Flow (hrf)
Work system (hrw)
Reward systems (hrr)
6
Xc
Xr
(1.00)
Xc
(0.82)
Xc
(0.72)
Xr Xc
(0.79)
Xc
(1.32)
Xr
Xr
7
Xc
Xc
(1.03)
(1.10)
1
Xc: calibration samples are the top 10% in business performance re: profitability (n=16; Xkss=5.70;
Xksp=5.44; Xiye=5.66; Xiyu=5.85; Xhrf=5.81; Xhrw=5.47; Xhrr=5.67)
2
Xr: remaining sample (n=145, Xkss=4.70; Xksp=4.62; Xiye=4.94; Xiyu=5.06; Xhrf=4.49; Xhrw=4.44;
Xhrr=4.57)
59
5.4.3. Perspective three:
matching approach
™The most common technique used when
adopting the matching perspective is the
deviation score model (Hoffman et al., 1992),
which has been used with great success by
many researchers (e.g., David et al., 1989;
Keller, 1994; Lai, 1999).
™According to Venkatraman (1989a), the
underlying premise of deviation score analysis is
that the “absolute difference between the
standardized scores of two variables indicates a
lack of fit” (p. 431).
}
the closer the fit (i.e., the smaller the absolute
difference score) of two matching variables, the
greater the performance about the dependent
variable.
60
5.4.3. Perspective three: matching
approach (cont.)
™To minimize bias in the scaling of questionnaire
items, the item scores first were standardized
using Z scores prior to computation of the
difference scores.
™SPSS was used to run linear hierarchical
regression analyses.
™The usefulness of an antecedent variable in
explaining variance in performance was
determined by the increment in squared multiple
correlations (R2), after each given variable was
added to the regression equation (Tabachnick &
Fidell, 2001).
Y = α 0 + α1 X + α 2 Z + α 3 (| X − Z |) + e
61
Table 14: Results of hierarchical
regression analysis (N=161)
Dependent variable: Business performance
Growth (Model 1)
Profitability (Model 2)
Step 2
Step 3
Step 4
Step 1
Step 2
Step 3
Independent variables
Step 1
KM strategy
System
0.17+
0.11
0.06
-0.02
0.20*
Human
0.33***
0.28**
0.17+
0.25*
0.31***
IT strategy
IT environment scanning
-0.01
-0.04
-0.11
Strategic use of IT
0.14
0.11
0.08
HRM strategy
HR Flow
0.32**
0.26
Work systems
0.02
0.08
Reward systems
0.03
0.15
Strategic alignment
System-HR flow fit
-0.33**
System-work systems fit
-0.19+
System-reward systems fit
-0.27**
Human-HR flow fit
-0.13
Human-work systems fit
-0.12
Human-reward systems fit
-0.23*
System-IT environment scanning fit
0.05
System-strategic use of IT fit
-0.20*
Human-IT environment scanning fit
-0.17*
Human-strategic use of IT fit
-0.03
R2
0.21
0.22
0.30
0.41
0.21
2
0.01
0.08
0.11
ΔR
20.76***
10.88***
9.57***
5.85*** 21.53***
F
0.99
6.34***
2.56**
ΔF
2
0.20
0.20
0.27
0.34
0.20
Adjusted R
2.14
D.W.
11.84
C.I.
Notes: Standardized regression coefficients are shown. + p<.1; *p<.05; **p<.01; ***p<.001
0.16
0.28**
-0.08
0.18
0.10
0.14
-0.13
0.15
0.40***
0.02
0.02
0.23
0.02
11.46***
1.30
0.21
0.31
0.08
12.07***
9.19***
0.32
Step 4
0.04
0.21*
-0.18
0.13
0.36***
0.06
0.11
-0.20+
-0.18+
-0.12
-0.13
-0.03
-0.15
0.07
-0.18*
-0.12
-0.04
0.41
0.10
5.93***
2.41**
0.34
2.05
11.84
62
6. Discussion and
Conclusions
Hypotheses
H1: The strategic alignment between KM strategy, IT strategy, and HRM strategy
has a positive direct effect on business performance, as measured in growth
and profitability.
H2: The strategic alignment between KM strategy and IT strategy has a positive
direct effect on business performance, as measured in growth and profitability.
H2-1a: The strategic alignment between human KM strategies and IT strategies
for IT environment scanning has a positive direct effect on business
performance, as measured in growth.
H2-1b: The strategic alignment between human KM strategies and IT strategies
for IT environment scanning has a positive direct effect on business
performance, as measured in profitability.
H2-2a: The strategic alignment between system KM strategies and IT strategies
for the strategic use of IT has a positive direct effect on business
performance, as measured in growth.
H2-2b: The strategic alignment between system KM strategies and IT strategies
for the strategic use of IT has a positive direct effect on business
performance, as measured in profitability.
H3: The strategic alignment between KM strategy and HRM strategy has a positive
direct effect on business performance, as measured in growth and
profitability.
H3-1a: The strategic alignment between human KM strategies and HRM
strategies for HR flow has a positive direct effect on business
performance, as measured in growth.
H3-1b: The strategic alignment between human KM strategies and HRM
strategies for HR flow has a positive direct effect on business
performance, as measured in profitability.
H3-2a: The strategic alignment between human KM strategies and HRM
strategies for work systems has a positive direct effect on business
performance, as measured in growth.
H3-2b: The strategic alignment between human KM strategies and HRM
strategies for work systems has a positive direct effect on business
performance, as measured in profitability.
Results
Y
Partial
Y
N
Y
Y
Partial
N
N
N
N
63
6. Discussion and Conclusions
(cont.)
Hypotheses
H3-3a: The strategic alignment between human KM strategies and HRM
strategies for reward systems has a positive direct effect on business
performance, as measured in growth.
H3-3b: The strategic alignment between human KM strategies and HRM
strategies for reward systems has a positive direct effect on business
performance, as measured in profitability.
H3-4a: The strategic alignment between system KM strategies and HRM
strategies for HR flow has a positive direct effect on business
performance, as measured in growth.
H3-4b: The strategic alignment between system KM strategies and HRM
strategies for HR flow has a positive direct effect on business
performance, as measured in profitability.
H3-5a: The strategic alignment between system KM strategies and HRM
strategies for work systems has a positive direct effect on business
performance, as measured in growth.
H3-5b: The strategic alignment between system KM strategies and HRM
strategies for work systems has a positive direct effect on business
performance, as measured in profitability.
H3-6a: The strategic alignment between system KM strategies and HRM
strategies for reward systems has a positive direct effect on business
performance, as measured in growth.
H3-6b: The strategic alignment between system KM strategies and HRM
strategies for reward systems has a positive direct effect on business
performance, as measured in profitability.
H4: KM strategy has a significant positive direct effect on business performance.
H5: IT strategy has a significant positive direct effect on business performance.
H6: HRM strategy has a significant positive direct effect on business performance.
Legend: Y = Supported; N = Not supported; Partial = Partially supported
Results
Y
N
Y
Y
Y
Y
Y
N
N
N
Y
64
6. Discussion and Conclusions
(cont.)
™In summary, these results show significant
support for a holistic perspective of strategic
alignment between KM strategy, IT strategy, and
HRM strategy, when using fit as covariation and
profile deviation as assessment approaches.
™Additionally, the reductionistic perspective, using
the approach of fit as matching, definitely
recognizes the bivariate patterns of impact upon
business performance.
™That is, the results from both holistic approaches
taken together strongly support the theoretical
propositions of the performance impact of KM-,
IT-, and HRM strategic alignment.
65
6. Discussion and Conclusions
(cont.)
™These bivariate strategic alignment
patterns that have been found to have a
significant impact on business
performance are
}
}
}
}
}
}
human-IT environment scanning fit (KM-IT fit)
system-strategic use of IT fit (KM-IT fit)
human-reward systems fit (KM-HRM fit)
system-HR flow fit (KM-HRM fit)
system-work systems fit (KM-HRM fit)
system-reward systems fit (KM-HRM fit).
66
6. Discussion and Conclusions
(cont.)
™ Successful firms with a system-oriented (codification)
KM strategy utilize:
}
}
}
}
}
}
}
extensive selection and training procedures and have relatively
high job security in their HR flow practices;
compensation and promotion decisions tend to be tightly
connected to employees’ work performance;
these companies generally use broadly defined jobs with
enriched design;
utilize team-based work organization;
rotate jobs among employees to familiarize them with their
colleagues’ work.
All this is done to ensure that the reused codified knowledge can
store abundant expertise derived from different employees.
focus their IT strategies on strategic use of IT , meaning that they
not only collect operational knowledge to connect people with
reusable codified knowledge, they also focus on generating large
overall revenues.
67
6. Discussion and Conclusions
(cont.)
™Successful firms that use human-oriented
(personalization) KM strategies
}
}
}
have reward systems that encourage workers to
share knowledge directly with others;
instead of providing intensive training within the
company, employees are encouraged to develop
social networks, so that tacit knowledge can be
shared.
Such companies focus on ‘maintaining’ not ‘creating’
high profit margins, and on external IT environment
scanning, supporting the latest technologies, so as to
facilitate person-to-person conversations and
knowledge exchange.
68
6. Discussion and Conclusions
(cont.)
™Neither human-HR flow fit nor human-work
systems fit have found to have a significant
impact on performance
}
}
}
One possible explanation may be that the strategy a
firm used on knowledge sharing in human KM
strategy is mainly by members’ face-to-face
conversation in private.
The informal dialogues between organizational
members are just encouraged through appraisal and
compensation systems related to tacit knowledge
sharing, accumulation, and creation.
No matter how much training about the jobs a firm
offered to their employees, or how often the
employees rotated to another jobs, the person-toperson social network for linking people to facilitate
conversations and exchange of knowledge would
never be diminished.
69
6.2.1 Implications for other
researchers
™the analysis and design of organizations using a
holistic perspective is important
™cogitating and integrating various factors related
to the KM area are considered by researchers to
be most important tasks
™one must take into account the realities of
strategic alignment in the KM field
™not only to examine alignment or fit issues, but
also to use multiple perspectives to test the
performance implications of strategic alignment
70
6.2.2 Implications for practitioners
™ Selecting and managing IT and human resources
effectively in KM projects is the way to success.
™ If firms try to develop social networks to promote the
sharing of knowledge person-to-person, there must be a:
}
}
reward system encouraging this
the company must scan the external IT environment and support
the latest IT in order to enhance person-to-person
communication
™ Companies that want to develop high-quality and reliable
information systems to codify, store, disseminate, and
reuse knowledge, they must:
}
}
}
}
provide extensive training to employees
have clear, definite job definitions,
tightly link compensation to work performance
must use IT strategically to connect people with reusable
codified knowledge.
™ All of the above benefits require that CEOs or managers
take an active role in seeking KM strategic alignment.
71
6.3 Limitations of the research
and future directions
™this study measured IT strategy and HRM
strategy with their original variables
}
‘buy-bureaucratic’ and ‘make-organic’ HRM strategies,
and ‘high’ and ‘low’ use of IT
™Other factors could influence strategic alignment
and business performance
}
Business strategy, KM structure…etc.
™According to Sabherwal et al. (2001), strategic
alignment is a dynamic process
}
punctuated equilibrium model
72
THANK YOU
FOR YOUR
ATTENTION
73