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How does organizational
structure influence performance
through learning and innovation
in Austria and China
Qingmin Hao
School of Management, Tianjin University, Tianjin, China, and
Helmut Kasper and Juergen Muehlbacher
Institute for Change Management and Management Development,
Vienna University of Economic and Business, Vienna, Austria
Abstract
Purpose – The purpose of this paper is to investigate the relationship between organizational
structure and performance, especially through organizational learning and innovation, based on
evidence from Austria and China.
Design/methodology/approach – Based on the literature and hypothesis, a theoretical, conceptual
and structural equation model is set up through a questionnaire survey and sample of about
90 Austrian and 71 Chinese samples. Partial least squares were used in the analysis and the results are
tested by bootstrap methods.
Findings – The findings reinforce the important infrastructure position of organizational structure
on performance. First, organizational structure has more effects on organizational learning than on
innovation, organizational learning has an indirect effect on performance through innovation, except
the direct effect of structure on performance. Second, managers in Austria think structure has a more
important effect on performance; both managerial and technical innovation influence performance,
managerial innovation is not significant in China. Austrian companies prefer structural-oriented
innovation whereas Chinese prefer learning-oriented innovation. Third, in a hi-technology or
knowledge intensive industry, organizational structures affect organizational performance mainly
through innovation and organizational learning. But in traditional industry, such as labor- or
capital-intensive industry, organizational structure impacts organizational performance mainly
through innovation. Fourth, for younger firms, learning is important in the relationship of
organizational structure with performance, but in older firms, innovation is the mediator for structure
on performance. Finally, senior managers think organizational structure improves performance
directly and through innovation. But the middle and junior managers think organizational learning
has an important mediating effect on performance.
Originality/value – The paper shows that innovation is a more important mediating variable in the
influence of organizational structure on performance. Innovation needs to be encouraged at the
strategy level instead of at the implication level.
Keywords Austria, China, Organizational performance, Organizational structure,
Organizational learning, Organizational innovation, Innovation capability, Information management
Paper type Research paper
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1750-614X.htm
The authors thank: the National Natural Science Foundation of China (71071108); the work team
of the Institute for Change Management and Management Development, Vienna University of
Economics and Business, for their help with the empirical questionnaire survey; and
Eurasia-Pacific-Uninet for providing funding for the scientific research.
CMS
6,1
36
Chinese Management Studies
Vol. 6 No. 1, 2012
pp. 36-52
qEmerald Group Publishing Limited
1750-614X
DOI 10.1108/17506141211213717
1. Introduction
During the 1920s Chandler (1962) proposed that organizations should highly value
fit-to-market and fit-to-strategy tests. Businesses have constantly shifted organizational
structures to attempt to achieve better performance. In the practical field, many
companies want to follow the successful firms through adopting their organizational
structures, learning from advanced experiences, encouraging innovation and so on. In
the research field, scholars try to find an effective path leading to high performance
through research in knowledge and technology transfer, organizational and cultural
learning, innovation capability, etc. But, it is easy to imitate these forms of the
organizational structure, but difficult to dig out the mechanism of the different
organizational learning and innovation behavior resulting in the different performance.
The related topics have received significant attention over the last few years: some
organizational structures appear to perform better in specific sectors, but there is no
one best way of organizing (Meijaard et al., 2005). Different types of organizational
structures perform equally well; stable organization has not found many opportunities
to increase profit than changing organization (Kosova et al., 2010). On the contrary,
Leitao and Franco (2008) give strong evidence about the relationship between the
structure and performance, efficient organizational structure impacts positively on
both types of performance: non-economic and economic.
For the detailed context among structure on performance through learning or
innovation: organizational learning has a positive influence on relationship orientation
as well as on the improvement of effectiveness and firm performance (Panayides, 2007).
Knowledge sharing and learning behaviors are positively associated with business
process improvement and product and service offerings and they in turn are positively
related to organizational performance (Law and Ngai, 2008). Organizational learning
influences organizational performance positively, both directly and indirectly through
organizational innovation and organizational innovation influences organizational
performance positively (Morales et al., 2007).
So the following questions may arise: first, how does organizational structure influence
organizational performance, directly or indirectly? Which is more important based on
different conditions? Second, what is the relationship between organizational structure
and organizational learning and innovation capability or their effects on performance?
Based on these problems, the main objective is to investigate the relationships
between organizational structure and its conduct on organizational performance
empirically, especially through organizational learning and innovation, and the
different path leading to better performance using evidence from Austria and China.
The discussion is structured as follows. In Section 2, literature is reviewed in order to
set out hypotheses and build up a brief framework. In Section 3, research methodology
designed on the different sample information and empirical data. In Section 4, conclusion
and sensitivity analysis are presented. Finally, implications are discussed for
management and future research.
2. Theoretical foundations and hypotheses
2.1 Organizational structure
Organizational structure is normally described as the way responsibility and power are
allocated, and work procedures are carried out among organizational members
(Nahm et al., 2003).
Organizational
structure
37
Organizational structure includes the nature of layers of hierarchy, centralization of
authority, and horizontal integration. It is a multi-dimensional construct in which
concerns:
.work division especially roles or responsibility including specialization,
differentiation or departmentalization, centralization or decentralization,
complexity; and
.communication or coordination mechanisms including standardization,
formalization and flexibility.
The main feature of new organizational structures is the flexibility and the ability to
acclimatize to the changing environment (Sakalas and Venskus, 2007), to encourage
harmony and collaboration (Gold et al., 2001).
Six items are employed to represent organizational structure characteristics such as:
flexibility (Miesing, 2006), openness and authority (Lotti et al., 2006), communication,
delegation and decentralization (Koontz and Weihrich, 1990) and complexity
(Morales et al., 2007). The questionnaire items about organizational structure in
Table I are adopted from other researchers namely Koontz, Mensing, Miesing and
Morales due to the closer link with our research.
The structures of living things are related to their performance (Baligh, 2006), so the
discussion on organizational performance for this research is based on this and is as follows.
2.2 Organizational performance
Organizational performance is anchored around a multidimensional conceptualization
related predominately to stakeholders, heterogeneous market circumstances, and time
(Richard et al., 2009). Different studies propose different viewpoints on performance
measurement. The financial performance index selected like profit, return of investment
(Panayides, 2007) and non-financial performance index selected like growth, market and
public relationship, five items are used as Table II shows.
The related research on the relationships of organizational structure on
performance as follows.
Organizational structure has relevant and predictable effect on a wide range of
organizations (Csaszar, 2008), organizational form changes have a significant impact
Label Items Characters References
SF Organizational configuration is more flexibility
to suit the uncertain environment
Flexibility Miesing (2006)
SC Our firm is in proper control and good
communication
Delegation Koontz and Weihrich
(1990)
SE Openness to learning best practices and
exchange lessons
Openness Lotti et al. (2006)
SS Suitable empowerment, delegating to tap
subordinates’ full potential
Empowerment Lotti et al. (2006)
SD Encourages decision-making and assumption of
authority and responsibility
Decentralization Koontz and Weihrich
(1990)
SL The firm was a learning organization Complexity Morales et al. (2007)
Note: The question items were adopted from the reference resources, same as the following tables
Table I.
Selected items about
organizational structure
CMS
6,1
38
on service provision and investments, changes in organizational form affect
performance directly (Ciliberto, 2006). The level of centralization or flatness influences
the performance outcomes (Teixeira et al., 2008).
There is a negative relationship of vertical integration decisions on performance in
the short-term, but greater performance gains over the product life cycle with higher
levels of vertical integration in the auto industry (Novak and Stern, 2008).
Based on sample of 1,411 Dutch small firms, Meijaard et al. (2005) find that
organizational structures (centralization, specialization) have different effects in terms
of different outcome or results.
So, based on the discussion and theory of organizational structures on performance
above, the following hypothesis is generated:
H1. Organizational structure plays complex roles on organizational performance.
2.3 Organizational learning
Organizational learning is the firm’s capability to improve performance based on
experience (Morales et al., 2007). Mostly, organizational learning can be divided into
broad categories focused on the types (single/double loop, cognitive/behaviorial), the
levels (individual, team, organization, and network) and the process architecture
(acquisition, conversion, application and protection). These factors are essential
organizational capabilities or preconditions for effective knowledge management or at
organizational level (Panayides, 2007).
Mainly based on the knowledge management system, the focus is on seven questions
depending on the following characters: commitment to learning, shared vision,
open-mindedness, system perspective, process architecture and so on. The
organizational learning questions derived from the related researches (Gomez et al.,
2005; Vijande et al., 2005; Panayides, 2007) are listed in Table III.
2.3.1 Organizational structure and learning. As organizational structure is an
important factor in knowledge management, learning organization is impossible to
realize without respective organization management structures (Sakalas and Venskus,
2007). Some scholars simulate the influence of interpersonal network structure on
organizational-level learning: modest amounts of cross-group linking were associated
with higher equilibrium performance levels (Fang et al., 2007). Organizational structure
complexity is a significant determinant in the development of realized absorptive
capacity, centralization and formalization weak related to realized absorptive capacity
(Fernandez et al., 2004). The relationship between organization structure and the
characteristics of organization learning listed in Table IV.
Label Items Characters References
PP High profitability compared to industry average Profit Panayides (2007)
PR Moderate return on investment compared to industry
average
Efficiency Panayides (2007)
PS Sales grow stably compared to industry average Growth Panayides (2007)
PM Market share increased compared to major
competitor in main products and markets
Market Panayides (2007)
PD The degree of customer satisfaction is in ideal level Relationship Morales et al. (2007)
Table II.
Selected items about
performance
Organizational
structure
39
As we saw from the above, the different organizational structures are related to the
different organizational learning styles, so we specify the hypothesis:
H2. Organizational structure impacts on organizational learning.
2.3.2 Organizational learning and performance. Different kinds of organizational
learning relate to performance, the interactions between the practice of
intra-organizational learning and the engagement in inter-organizational learning
have moderating effects on performance of the contracting organizations (Wong and
Cheung, 2008). Social and market learning moderates the relationship between
internationalization and multinational enterprises’ performance (Hsua and Pereirab,
2008). Relationship learning is positively related to relationship performance (Lai et al.,
2009). Organizational learning in the strategic purchasing units positively influences
the cycle time performance in the purchasing process (Hult et al., 2002). Customer- and
learning-oriented organizational value system is more likely to develop and improve
firm performance (Yilmaz et al., 2005). Learning orientation stimulates the
market-oriented behavior and that it also positively affects the establishment of
long-term relationships with strategic clients (Vijande et al., 2005).
Temporarily, the hypothesis is concluded as follows:
H3a. Organizational learning has indirect effect on performance.
Label Items Characters References
LC Organizational culture fosters the constant innovation in
procedures
Open mindedness Leticia (2005)
LK Employee learning capability is considered as a key
factor
Commit to learning Gomez et al.
(2005)
LP Promotes innovation as a way of improving the work
processes
Openness
experimentation
Gomez et al.
(2005)
LE Learning is necessary for improvement and efficiency is
shared at all levels
Share vision Leticia (2005)
LO The employees’ contribution of original ideas is valued Open mindedness Leticia (2005)
LG All employees have generalized knowledge regarding
this firm’s objectives
System perspective Gomez et al.
(2005)
LT Suitable capacity for acquiring, transmitting and using
new knowledge
Process Morales et al.
(2007)
Table III.
Selected items about
organizational learning
Organization
structure Characters of structure Characters of organizational learning
Order-control Specialization, hierarchy,
centralization
High knowledge sharing and communication
Work team Low-specialization High share and innovation but low store and
memory
Ring-form Authority share, double-chain Better transfer and share, store, normal acquire
Network Flat and flexibility, authority
share
High innovation and responsibility to market,
normal store
Source: Zheng (2003)
Table IV.
Relationship between
organizational structure
and learning
CMS
6,1
40
2.4 Innovation
From an organizational perspective, innovation is generally understood as the
successful introduction of a new thing or method or embodiment, combination,
or synthesis of knowledge in original, relevant, valued new products, processes, or
services (Luecke and Katz, 2003). Innovation is also considering and acting on insights
leading to significant organizational improvements in terms of improved or new
business products, services, or internal processes (Davila et al., 2006). The interactive
nature of the innovation process calls for organizational structures and mechanisms to
ensure the appropriate interactions among the various institutions that makes up spatial
systems of innovation (Fischer, 2006).
Innovation may occur in every aspect of an organization’s operations and can
therefore be classified by organizational function (Yang et al., 2009): managerial
innovation, technological innovation, process-, product- and market-innovation, etc.
these dimensions were adopted in Table V in line with the objectives of our research,
and the items are also drawn from the other scholars (Alegre and Chivab, 2008; Wang
and Ahmed, 2004; Morales et al., 2007; Yang et al., 2009).
2.4.1 Organizational structure and innovation. Kasper et al. (2008) give a detailed
classification of the four kinds of innovation related to the knowledge management
system: A high degree of innovation is linked withdecentralized knowledge management
systems, it is implied that the different innovation related to different organizational
structures from the bureaucratic to the highly flexible and dynamic.
Machine bureaucracy that fits complex integrated processes depends on
specialist-individuals for innovation. Project-type of organization that is team-based
and provides flexibility, which best fits new product or process development.
A mission-oriented structure is based on shared vision and articulated mission can
provide successful innovation (Ezra, 2005). For using formal (centralization and
formalization) and informal (connectedness) coordination mechanisms, centralization
negatively affects exploratory innovation, whereas formalization positively influences
exploitative innovation. And connectedness is an important antecedent of both
exploratory and exploitative innovation ( Jansen et al., 2006).
Therefore, the following hypothesis can be specified:
Label Items Characters References
IE Extension of product range and development of
environment-friendly products
Product Alegre and Chivab (2008)
IP Product innovations and process improvement
have been increased
Process Weerawardena et al.
(2006)
IO Opening of new markets abroad or new domestic
target groups
Markets Yang et al. (2009)
IA Innovation development time and cost is suitable
for our company
Efficiency Alegre and Chivab (2008)
IM Managerial innovations and improvements is
reasonable
Managerial Weerawardena et al.
(2006)
IT Our technology level is often first class to market Technological Wang and Ahmed (2004)
IR The rate of introduction of new products or
services into the firm has grown
Dynamic Morales et al. (2007)
Table V.
Selected items about
innovation
Organizational
structure
41
H4. Organizational structure is high related to innovation.
2.4.2 Innovation and organizational performance. From a resource based view, unique
organizational resources are important to the firm’s performance, innovation leads to
higher performance (Hyva
¨rinen, 1990), Market orientation, market sensing and
innovativeness (among other knowledge-related resources) have an effect on superior
performance (Olavarrieta and Friedmann, 2008).
Based on this literature review, a firm’s product innovation, influenced by emotional
and learning capability, significantly impacts the performance (Akguna et al., 2007).
Domestic ownership structure is more important than foreign capital to explain the
effect of innovation on organizational performance (Kuo and Wu, 2007).
Therefore, in the innovation and organizational performance context, it could be
hypothesized that:
H5. Innovation directly improve the organizational performance.
2.4.3 Organizational learning and innovation. Therin (2002) discusses the difference
between learning and innovation: learning is to integrate new knowledge or mix
existing knowledge in different way, learning leads to innovation, innovation will be
the by-product of the learning organization.
For innovation capability to come to the fore in firms, a high degree of effective
learning capability is required (Park and Kim, 2006). A firm committed to learning
increased its innovation capability. Learning has been recognized as an important
process for innovation (Lampela, 2009), organizational learning capability affects
innovation performance (Alegre and Chivab, 2008). Therefore, the hypothesis below:
H3b. Organizational learning affects innovation.
Not all learning leads to improved performance, costs of learning may be more visible
in organizations than its performance benefits, and time delays between learning and
performance may obscure or even undermine evidence of a clear causal relationship
(Henderson et al., 2008). Organizational learning and culture have a positive effect on
performance ( Jimenez et al., 2007), but indirect (Skerlavaj et al., 2007). Organizational
learning positively affects performance, but mainly through innovation (Correa et al.,
2007). Learning orientation is an important antecedent of firm’s innovativeness, which
in turn influences firm’s performance, product- and technical-innovation is often
considered a major consequence of good organizational learning and knowledge
management practices (Calantone et al., 2002).
There is an intermediate role of innovation in the relationships between
organizational learning, market orientation and organizational performance
(Weerawardena and O’Cass, 2004). As we emphasize on the mediating effect of
learning in H3a. So, we modify the H3b as follow H3:
H3. Organizational learning affects performance through innovation.
2.5 The conceptualized framework
A conceptualized framework in which all the main constructs are shown together with
the relationships and a set of hypothesis discussed above. The construct includes
organizational structure, organizational learning and innovation capability and
organization performance as shown in Figure 1.
CMS
6,1
42
2.6 Control variables
As firm’s size and age have significant influence on their innovation and performance
(Rothaermel and Deeds, 2004), so firm’s size and age were used as control variables.
Age was assessed by asking the number of years since the firm was founded
(Gulati and Higgins, 2003) and firm size was indicated by the number of employees.
The European Union definition categorizes companies with fewer than 50 employees
as “small” and those others with fewer than 250 as medium-sized entrepreneur
company (Morales et al., 2007).
As the relationship is stronger when the partners are based on the same industry
and weaker across industries ( Jiang and Li, 2008), firm’s factors exert a much stronger
impact on performance than industry in both SMEs and large enterprises
(Caloghirou et al., 2004). The more detail about the classification, the smaller
subsamples that have to be investigated, so hi-technology or knowledge intensive, labor-
and capital-intensive industry were used to analyze industry problems.
Different positions of the manager in the hierarchy of the organizational structure is
employed for analysis, the respondent position like senior or junior manager is used for
analysis of hierarchical problems, which may also be harnessed to test working
experiences.
3. Research design
The instruments for all constructs in Figure 1 are measured by using multiple-item
scales which is adapted from literature and are revised to fit our research context.
Questionnaires were designed based on the theoretical analysis and hypothesis from
Table I to Table V except Table IV. We used about 25 items and several open questions
to investigate. Items associated with these constructs employ five-point Likert-type
scales ranging from 1 to 5 (delegate the “strong disagree” to “strong agree” or “rarely
use” to “often use” or “not suitable” to “more suitable”) capture the related information
about this research.
3.1 Data and sample
Data and information were collected through e-mail survey and through questionnaire
initially. Questionnaires comprised of e-mail which states the research objective. About
270 questionnaires were e-mailed in Austria. Starting during the last week of June 2009,
data collection lasted for about eight weeks. During this period, of the 270 questionnaires
e-mailed, about 70 respondents were received with a 26 percent response rate. After
scrutinizing returned questionnaires, the remaining 68 with completed and reasonable
Figure 1.
The conceptualized
research model
Organizational learning:
Commit to learning, open
mindedness, share version
Organizational structure:
Decentralization, delegation,
formalization, flexibility,
complexity
Performance:
Profits, efficiency, growth,
market, Public relationship
Innovation:
Managerial-, Technology-
Product-, Market-, process-
Control variable:
Firm (size, age)
Industry
H1
H4
H2
H3 H5
Organizational
structure
43
answers were used. The response rate of usable returns is 97 percent. And then there are
about 22 Austrian samples added in January, 2011.
Chinese data is collected from 2009 to 2010; there are about 70 samples mainly from
EMBA and MBA students.
Preliminary analyses were conducted to provide information about the
characteristics of sample firms in Table VI, including the type of company, firm
size, age and employees and so on.
3.2 Measures
The data analysis was conducted in following steps: first, in order to reduce the
measurement error, a pretest is adopted to refine the measurement items in
the questionnaire design. Design of the is items based on the reviews of literature for
the related study in which Tables I-V listed. Then, after excluding some items with high
cross-loadings, all other items were found to have factor loadings higher than 0.5. The
average variance extracted (AVE) is computed for each variable, all the values that were
obtained in Table VII were higher than the AVE cut-off value of 0.5 and composite
reliability (CR) exceeds 0.8 advised by Wetzels et al. (2009), average of AVE values of
constructs is greater than the correlation between constructs. It indicated that the
measurement had sufficient convergent validity and good discriminate validity
(Wang et al., 2009).
The reliability of the scales was assessed using Cronbach’s
a
which need acceptable
(
a
.0.6). All indicators in Table VII satisfied the standards. The results implied that
our measurement had good reliability and highly internal consistency. Table VII
shows the related statistics indicators.
AVE CR R
2
a
Communality Redundancy S L I P
S 0.564 0.885 0.844 0.564 1
L 0.575 0.904 0.659 0.876 0.575 0.377 0.812 1
I 0.541 0.892 0.527 0.859 0.541 0.217 0.685 0.695 1
P 0.616 0.889 0.360 0.844 0.616 0.150 0.551 0.812 0.550 1
Notes: AVE – average variance extracted; CR – composite reliability; S – organizational structure;
L – organizational learning; I – innovation; P – performance
Table VII.
Composite reliabilities,
AVE,
a
and correlations
Items Samples Items Samples
Type of
company
Hi-tech and knowledge 62 Age of firms #24 73
Labor or capital-intensive 99 .24 88
Country Austria 90 Working experience ,10 73
China 71 $10 88
Employee
number
,250 60 Position Senior manager 60
$250 101 Middle-junior 101
Table VI.
Characters of
sample firms
CMS
6,1
44
3.3 Results
Collected data is analyzed through partial least squares (PLS) to test path models
involving latent constructs indirectly observed by multiple indicators. As it is known,
PLS is a multivariate technique based structured equation modeling technique and is
identified as a form of soft modeling. PLS also assisted in avoiding the necessity
of a large sample size and is not sensitive to the assumptions of normality, PLS is
the suitable approach to fit the research data or sample. PLS regression and analyze the
significance of the hypotheses by calculate 500 bootstrapping resamples with the
construct level changed, and the results showed in Table VIII.
Above all, the results indicate that organizational structure explains the 65.9 percent
of variance (R
2
¼0.659) in the learning capability, learning capability and structure
together explain the 52.7 percent of variance in innovation. All independent variables
explain the 36 percent of variance in performance (Figure 2).
4. Further findings and conclusion
The relationship between organizational structure, organizational learning and
innovation, and their effects on firm’s performance are tested and the results listed as
follows:
(1) From Figure 2, the findings reinforce the important infrastructure position of
organizational structure on performance (support H1), not only directly but also
indirectly. The direct effect is more significant than the indirect effect in almost
Items Estimate T-stat. Items Estimate T-stat. Items Estimate T-stat.
SF 0.653 12.7 LE 0.819 33.7 IT 0.715 17.6
SC 0.742 22.4 LO 0.814 31.9 IR 0.752 20.3
SE 0.817 30.1 LG 0.701 15.3 PP 0.809 25.1
SS 0.733 19.1 LT 0.728 20.3 PR 0.715 19.3
SD 0.790 27.6 IE 0.708 17.5 PS 0.798 25.0
SL 0.761 23.2 IP 0.780 27.1 PM 0.835 32.2
LC 0.743 20.6 IO 0.766 21.9 PD 0.700 16.4
LK 0.756 20.6 IA 0.690 15.0
LP 0.738 21.5 IM 0.735 16.9
Table VIII.
Measurement modal
(loading) – bootstrap
Figure 2.
Main results
90 Samples from Austria 71 Samples from China161 Samples
IP
L
0.330
(4.1)
0.322
(3.6)
0.812
(38.6)
0.412
(4.9)
0.351
(4.2)
R2 = 0.527R2 = 0.659 R2 = 0.36
S
IP
L
0.448
(6.7)
0.329
(4.8)
0.811
(42.0)
0.248
(2.9)
0.446
(4.8)
R2 = 0.439 R2 = 0.499
S
IP
L
0.203
(2.2)
0.359
(3.4)
0.791
(39.6)
0.578
(8.1)
0.225
(3.1)
R2 = 0.591R2 = 0.625 R2 = 0.269
S
R2 = 0.657
Organizational
structure
45
different cases (soundly support H1). The change of organizational structure
also has more powerful effects on the organizational learning (support H2) than
on innovation (support H4), Innovation has more effective impact on
performance in most of the subsample (support H5), and the learning effects
on innovation is significant (support H3). The calculated results showed in
Figure 2 and Table IX.
(2) In Austrian companies: managers pay more attention to the organizational
structure, the affect path of S !P and S !I is more powerful than in China.
And the path of S !I is more important than L !I in Austria, managers think
structure and innovation is the main factor for better performance, like Figure 2
shown. But in Chinese company: the innovation and structure only explained
the performance in lower level. Different from Austria, the path L !Iismore
important than S !I:
.(2.1) If the emphasis is on the different effects on innovation from structure
and learning, Austrian companies tend to like the structural-oriented
innovation (the power of relation S !I in Austrian companies is
greater than in China) and Chinese companies like the learning-oriented
innovation (the coefficient of path L !I in China is bigger than in Austria in
Table IX).
.(2.2) If we divided the innovation into managerial innovation (abbreviate MI
and include IP, IO, IA, IM items) and technological innovation (abbreviate TI
and include IE, IT, IR items), the effects of innovation on performance
recalculate as Figure 3 shows.
In Austria, the mediated effect of managerial innovation (0.695*0.423) are almost equal
with the technological innovation (0.240) in performance, there are no significant
difference. But in China, the effect of technological innovation is bigger than
managerial innovation, managerial innovation on performance is not significant, that
implies that Chinese managers need to pay more attention to their management level
H1 H2 H3 H4 H5
Samples S !PS!LL!IS!II!P
All level-estimate 161 0.330 (3.5) 0.812 (34.3) 0.412 (4.3) 0.351 (3.7) 0.322 (3.3)
Austria 90 0.448 (5.7) 0.811 (37.2) 0.248 (2.7) 0.446 (4.6) 0.329 (4.4)
China 71 0.203 (1.9) 0.791 (32.2) 0.578 (7.2) 0.225 (2.6) 0.359 (3.4)
Hi-technology 62 0.642 (10.) 0.859 (52.2) 0.514 (5.4) 0.296 (3.3) 0.200 (2.5)
Traditional 99 0.169 (1.6) 0.766 (27.9) 0.360 (3.6) 0.351 (3.4) 0.376 (3.7)
Young firms 73 0.259 (2.8) 0.818 (39.7) 0.544 (6.3) 0.281 (3.1) 0.494 (5.1)
Older firms 88 0.372 (4.3) 0.813 (35.3) 0.313 (3.1) 0.397 (4.2) 0.212 (2.4)
Senior manager 60 0.363 (4.8) 0.806 (35.4) 0.447 (5.7) 0.357 (3.9) 0.454 (6.3)
Junior manager 101 0.310 (3.2) 0.788 (34.2) 0.376 (4.0) 0.356 (4.0) 0.218 (1.9)
Medium-small size 60 0.346 (3.7) 0.825 (42.6) 0.604 (6.8) 0.172 (1.8) 0.432 (4.2)
Large company 101 0.339 (3.9) 0.827 (33.9) 0.331 (3.6) 0.439 (5.0) 0.259 (2.5)
Note: All the T-statistics in parentheses which calculated through bootstrap methods are almost
bigger than 2, show the significant of the path coefficients
Table IX.
Results summarize
(161 samples)
CMS
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to improve the performance, not only introducing but also digesting and absorbing the
technological innovation, and need to improve their managerial level to integrate the
innovation as in Austrian companies:
(3) Specially, except for the direct effect of organizational structure on performance,
in hi-technology or knowledge intensive industry, organizational structure
affects organizational performance mainly through organizational learning and
organizational learning on performance through innovation (support H3 and
H5), this result is test by Therin (2002): the presence of organizational learning
is related to innovativeness among high-tech small firms. So, the path
S!L!I!P is more suitable. But in traditional industry, such as labor- or
capital-intensive industry, organizational structure impact organizational
performance mainly through innovation, path S !I!P is more suitable, the
organizational learning effect is smaller on performance than in hi-tech
intensive industry. Both the effect of structure on organizational learning is
significant.
(4) For younger firms, innovation is important in the intermediary relationship of
organizational structure on performance. Small businesses in sectors with high
knowledge-intensity levels are more likely to use more frequently information
technology tools and organizational learning practices (Mercader et al., 2006).
And the directly relation of structure on performance become smaller power
than older one, path S !L!I!P is more suitable. And for older firms,
innovation is the moderate capability for structure on performance.
Organizational learning has the effect on performance through innovation
(support H3). The same results can be derived from the large company between
media-small sized companies, as Table IX listed.
(5) Managers from different positions hold different ideas about the context, except
for the direct effect of structure on performance. senior managers think
organizational structure improve performance mainly through innovation,
and innovation has a more important effects than learning on performance, the
path S !I!P is more better. But middle- and junior-manager think
organizational learning have a mediating effect on performance through
innovation, there are different ideas between the senior manager and junior
manager about the organizational learning problems, the path S !L!I!P
is more suitable. So the related innovation question is usually considered in
senior managerial level instead of junior level, it is imply that the innovation
Figure 3.
Different innovation effect
on performance
71 Samples from China
P
TI
0.081
(0.8)
0.459
(4.7)
R2 = 0.561
R2 = 0.273
MI
0.749
(25.3)
90 Samples from Austria
P
TI
0.423
(4.9)
0.24
(2.9)
R2 = 0.483
R2 = 0.378
MI
0.695
(16.2)
Organizational
structure
47
should considered mainly through strategy level instead of implication level.
And the learning improvement should mainly implicate in junior level not for
strategy level.
The above results is summarized in Table X.
Organizational structure affects organizational performance directly (Support H1),
and organizational structure always has effect on organizational learning in almost all
research levels. These results tested by Fang et al. (2007): when they extend classic
model of exploration and exploitation by allowing for direct inter-organizational
structure influences organizational learning (Support H2).
From the results in Table IX, it is found the coefficient of H2 (S !L) are bigger than
H4 (S !I) in almost all level, the organizational structure is strong related to learning
than innovation, so, it is thought that “learning organization” are more suitable than
“innovative organization” in our further research. Innovative organizations are those
that are flexible, adoptive, learning, characterized by “organic culture”, with
capabilities of networking and team-working ( Joe et al., 2005).
Organizational learning does not directly impact on performance, but mainly
through innovation. Especially, in older firms, larger firms and senior manager. And
this is also similar to the results from (Weerawardena et al., 2006): firms in competitive
industry tend to pursue innovative ways of performing value-creating activities, which
requires the development of learning capabilities, learning influence innovation and
that innovation influences a brand performance.
Innovation is the critically mediate path of Organizational structure on
performance. Usually, in almost all research level, the innovation is important factor
on performance that proved by Johnston (2003): strategy innovation is aimed at
growing your top line, achieving new levels of performance and success. The
managerial innovation and technological innovation are different in their effect on
performance.
5. Implication
The findings reinforce the position of structure on performance, and organizational
structure directly affects the organizational learning and innovation. Organizational
learning has an effect on performance through innovation and depends on the firms
and industrial character. Hi-technology or knowledge intensive industry need improve
organizational learning, as well traditional firms need improve innovation, and
new firms enforce innovation elder firms takes more time for organizational learning.
The senior manager should always communicate with the middle and junior managers
in order to get the almost similar and clear idea about the working goal. All firms
need pay attention to the suitable organizational structure to encourage learning
and innovation, innovation is more important than learning in the effect on
performance.
Innovation Country Industry Age Position
Structural-oriented innovation (S !I!P) Austria Traditional intensive Elder Senior
Learning-oriented innovation (L !I!P) China Hi-tech intensive Young Junior
Table X.
The sample characters
are related to different
innovation type
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Corresponding author
Qingmin Hao can be contacted at: haoqm@tju.edu.cn
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