The Study of Computer Industry Company's Performance: The Roles of Technology Strategy and External Network
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
The Study of Computer Industry Company’s Performance: The
Roles of Technology Strategy and External Network
Fen-Hui Lin Hsing-Ya Chang
National Sun Yat-Sen University, 70 National Sun Yat-Sen University, 70
Lien-hai Rd. Kaohsiung 804, Taiwan Lien-hai Rd. Kaohsiung 804, Taiwan
fhlin@mis.nsysu.edu.tw p924020007@student.nsysu.edu.tw
Abstract
Collaborate Commerce becomes gradually important in these years, when the
technology divides into various segments so that the firms are enforced to collaborate
with external network linkage. This is a survey research to study the influential factors
of the company performance in computer industry in Taiwan. The two independent
factors are technology strategy and external network. By using the random sampling
method, the questionnaire was mailed to members of Taiwan Electrical and Electronic
Manufactures Association. We collected one-hundred-and-forty-four valid responds
that were used for the statistic analysis. The empirical results show that the external
network does not directly affect the company’s performance, but through the
technology strategy. In addition, because the model fit computed by the LISREL is not
satisfactory, several adjusted models are added for the result comparison.
Keywords: Technology Strategy, External Network, Survey Research, Structural
Equation Modeling (SEM)
1. Introduction
The small-and-medium enterprises (SMEs) have been very successful in Taiwan and
created a great amount of fortune for the country. Technology strategy is counted as one of
the most important attributes for the achievement. The entrepreneurs are innovative to
introduce novel products to the markets. In addition, they proactively seek new business
opportunities not only inside the country but also around the world. While most of the
companies start from family business, the growing organizations are able to keep the
flexibility to adapt to the changes both in the market and industry environments.
With the development of technological innovation, Taiwan has become the most
important computer manufacturer in the world. The technology transfer began with the
computer OEM of IBM compatible computers around 1980’s. Nowadays, the customers
include those world-top computer corporations such as Dell, HP and IBM. The Hsin-Chu
1542SIPA (Science-Based Industrial Park) well known as the Asia Silicon Valley has generated a
significant clustering effect for high-tech companies. Computer industry has developed a
complete and sophisticated value chain from very up-stream of raw materials and components
to the down-stream with various types of computers. A few companies have built their brand
names in the world, such as Acer and Asus. Others would focus on their core competence of
manufacturing by OEM (Original Equipment Manufacturer) and ODM (Original Design
Manufacturer). As the technology advances rapidly and the business competition becomes
global, we would like to find out how companies approach their technology strategies and
those effects towards the company performance as well.
As mentioned in the precedent, the high-tech companies clustered in the Tsin-Chu SIPA
and have created great synergy for the production value chain of computers. Because of the
short life cycle of computers and high dynamics in the industry, the close relationships with
external network outside the companies can help them maintain sensibility to the changes in
this field. Those external networks can be the research institutes or universities, the
competitive companies, the suppliers or customers, and some financial institutions.
This research proposed two influential factors about the company performance in
Taiwanese computer industry. The two factors are the technology strategies and external
networks. Those constructs frame a structural equation model that would be tested by using
the LISREL software.
The following session is a brief literature review for the three constructs. The hypotheses
are included and explained. The third session is the research methodology that presents the
research design and the data collection process. The fourth session is the empirical results and
the practical implication.
2. Literature Review
2.1 Technology Strategy
Technology strategy has been considered one of the most important issues in the
business strategy especially in dynamic environment such as computer industry. There are a
number of definitions for technology strategy; it can be the desired competencies, technology
sources, timing for different technologies, or potential use (Mitchell 1990; Porter 1985). In
the reference (Zahra & Covin 1993), it proposed the interface between business strategy and
technological policy would influence company performance. They commented that
technology had become increasingly prominent in strengthening the competitive position of
companies. A company can use technology to create a competitive advantage by building
entry barriers, introducing novel products or processes, or changing the rules of competition
in the industry (Golder & Tellis 1993; Zahra 1996).
Many researchers have provided several dimensions for the discussion of technology,
including a firm’s technological resources, types of R&D programs (Foster 1986), R&D
1543spending (Schoonhoven 1984), internal vs. external sources of technology (Ford 1988), and
organizational policies for the development and use of technology (Camillus 1984). In the
reference (Hambrick et al. 1983), the authors did an empirical test and then concluded that
Prospectors emphasized product innovation more than Defenders. Later, six dimensions were
defined (Maidique & Patch 1988): type of technology desired level of competence, internal vs.
external sources of technology, R&D investment, timing of technology introductions, and
R&D organization. The relationships between technology and business strategy were
examined by using three constructs: production methods, rate of innovation, and product
sophistication (Miller 1988).
Furthermore, a study for the effect of the technology strategy to the business
performance adopted six dimensions (Zagra 1996b): (1) the pioneer-follower posture; (2) the
content of its portfolio (the mix of product and process technologies); (3) the portfolio’s
breadth; (4) R&D spending on basic and applied research; (5) external technology sources;
and (6) forecasting. In another case, seven dimensions were proposed to study the enterprise
technology strategy (Hambrick, et al., 1983): (1) technology posture; (2) scope of R&D; (3)
technology options; (4) technology portfolio; (5) intellectual property rights; (6) R&D
spending; (7) technology executives. With those literature reviews, we summarized four
dimensions of technology strategy: R&D emphasis, IP emphasis, Technology aggressive and
Technology forecasting. The first hypothesis of this study is:
H1: The technology strategy has a positive effect towards the company performance.
2.2 External Network
A newly established organization needs network relationship to obtain business
opportunity for the business survival as well as the growth (Aldrich & Zimmer, 1986). The
networks can help them acquire valuable resources; in addition, new business ideas can be
testified through the business connection. Furthermore, Jarillo (1989) suggests the long-term
relationship is beneficial for firms to grow.
The external networks comprise various linkages, such as accountants, lawyers and
consultants. They might bring great influence to the firms (Lipparini & Sobrero, 1994;
Ostgaard & Birley, 1996). Lee, et al. (2001) considered that both the internal capability and
the external networks significantly affect the company performance. The external network
contains two aspects: the corporation linkages (professional aid, business chain, committee),
and the assistance linkages (finance institution, academy, government).
The term “external network” discussed in the precedent is referred to the wstern
literature. The meaning and interpretation are close to the Chinese term “Guan-Xi.”
The literal translation of Guan-Xi is the “relationships.” It contains many forms of
relationship that connect family members, couples, friends, business stakeholders, and
possible social links. In the Chinese business world, however, Guan-Xi is understood
as the network of relationships among various parties that cooperate together and
1544support one another. It has been a pervasive part of the Chinese business world for the last
few centuries and considered as a determinant factor for the business performance (Luo,
2000). Firms are bound into a social and business web. For the past decades, the Chinese
around the world has demonstrated the effectiveness of Guan-Xi, especially in high tech
industry. Since the external network is an important factor for the company performance, we
adopt the operation definition associated with the measures discussed by Lee, et al (2001) to
test the following hypothesis.
H2: The external network has a positive effect towards the company performance.
Shaw (1993) mentioned, based on network theory, technology development is created
by the interaction among the members of industrial relationship. Therefore, the technology
progress depended on the linkage and interaction. Thomas (1994) explained the technology
within different organizations, there are sufficient evidences show that only very few firms
can develop new product, process or potential technology by itself. Thus, increase
outsourcings and emphasis on their core competences become the main applications these
years.
Ford & Thomas (1997) thought the values of technology are evaluated by the industrial
members and users. There are no firm can hold all of the technology, thus the importance of
the firm is base on the value of the firm’s resource and the technology in the industrial field.
Thus, the network’s position provides a method to analyze or evaluates the usage of
technology, and also can check the other members’ capability in the networks.
H3: The external network has a positive effect towards the technology strategy.
2.3 Performance
Before we discuss the performance of the firms, the meaning of the performance should
be considered in the view of the past literatures. They measure the firms’ performance in
several ways.
Covin & Slevin (1989) concluded the previous scholars’ opinions to adapt the subjective
measure factors. There were at least two reasons: first, the small firms are usually unwilling to
provide the exact finance data; second, we can’t check the accuracy of the finance data. Thus,
even we get the data from public; it is hard to explain the small firms’ phenomenon.
Zahra (1996a) referred to Covin’s, et al. (1991) observation; the performance can be
reflected by the expectation in the business operation. Chandler & Hanks (1994) suspected
the external validity of the type to measure any firms’ performance, but another scholar
(Brush & Vanderwerf 1992) found that it is significant to measure in this way. Besides that,
Covin & Slevin (1994) study the past researches and found there is no statistic significance
between the subjective and objective measurements. Thus, it is considered reasonable to
substitute the subjective point with the objective point.
We consider the subjective measurements and conclude into four sections: sales increase,
1545market-share, return of investment, net profit. The structure model of the research is showed
in Figure 1.
3. Research Method
We adopted those measures and questions discussed in the session of literature review and
translated into Chinese. The translated questionnaires then were tested by several graduate
students in the author’s teaching department. Some of students are professionals and
managers in the computer industry around Kaohsiung city, the second largest city in Taiwan.
We adopted some of those testers’ opinions and correct the writing and wording for each
question in order to minimize possible misunderstanding and answer biases of the
respondents.
After the pretest process, the questionnaires were mailed to the member companies of
Taiwan Electrical and Electronic Manufacturers Association (TEEMA) on February 2003.
Because the research questions were more about the company business policies and strategies,
we asked for the company chief executive officers (CEO) or high-ranking managers who are
presumed to own the best insight of their companies to answer the questionnaires. If those
companies did not reply in two weeks, the research assistants would call to remind for
responses. After waiting for two months, the replies were still slow and fewer than one
hundred. Then, we asked for the executive graduate students in the information management
department whose companies or friends were in the TEEMA list to help to answer the
questionnaires. After this, the replies have added to one hundred and fifty questionnaires by
June 2003. The valid responses turned out to be 144 because there were six incomplete
questionnaires. Table 1 lists the capitals and employee numbers of the sample companies. The
median of the firms’ capital is sixty-five million NT dollars. It implies that more than half of
the sample companies are small and medium enterprises (SME’s). The mean values of each
index for the technology strategy and external networks were shown in Table 2.
The internal consistency of each dimension was assessed by examining estimates of
composite reliability (Hair et al. 1998). Composite reliability reflects the degree to which the
construct is represented by the indicators. All results, as reported in Table 2, exceed the
recommended value of 0.7 for composite reliability (Hair et al. 1998).
With each dimension exhibiting properties of good reliability and validity, the fit of this
revised model now be assessed. The model, which now includes 14 items, is satisfactory and
shows good and improved model parameters. All the items, expect two, have satisfactory
standardized factor loading (Figure 2). One item in the “R&D emphasize” and another in the
“Aggressive” measures are slightly below the desired level, but still in an acceptable range,
i.e., above the 0.6 threshold suggested by Chin (1998).
The role of technology strategy as second-order factor is to explain the covariance
between the four first-order factors. This second-order factor introduces new regressions of
the first-order factors on the second-order factor. Convergent validity of the second-order
1546factor model is well supported by the results. The dimension “IP” has a factor loading of0.65,
slightly below the recommended value of 0.70 (Chin 1998). All other dimensions are well
above this threshold value, ranging from 0.71 to 0.85 (figure 2). This shows that the
second-order factor is connected to the first-order ones with strong paths.
We concluded the literatures of technology strategy and considered the industry we focus
on. The original model was trimmed into the revised model (Figure 2). Table 3 is the values
of the goodness-of-fit between two models. The χ2 statistics, Good-of-Fit Index (GFI) and
Root Mean Square Residual statistic (RMSR) are absolute indices representing the ability of
the model to reproduce the actual covariance matrix. We can compare each numbers of the
index, the revised model is better than the initial model. The GFI number (0.88>0.82), shows
that revised model’s fit is better, even though the value is still below the recommended values
of 0.90 (Gefen et al. 2000). The standardized RMSR characterizes the residual variance of the
observed variables; as high values suggest high residual variance; smaller values are better
(Gefen et al. 2000).
Because it is possible to obtain a better-fitting model by estimating more parameters, we
use the parsimonious fit indices to evaluate the fit of the model relative to the number of
estimated coefficients (or, conversely, the degree of freedom) needed to achieve that level of
fit. Among those indices are the normed χ2 (χ2/df), which adjusts the χ2 by the degree of
freedom, and the Root Mean Square Error of Approximation statistic (RMSEA), a measure of
discrepancy per degree of freedom. Appropriate values for the normed χ2 should exceed one
and should be less than two or three in a conservative test, or five in a more liberal test (Hair
et al. 1998). The initial model has an acceptable normed χ2 of 1.836. The RMSEA value of
0.076 is also within the acceptable range of 0.05 to 0.08 (Hair et al. 1998).
Based on these results, with only the parsimonious fit indices suggesting an acceptable fit,
we concluded that the fit of the initial first-order factor model is not satisfactory. To improve
the overall fit, we assessed measurement properties of each dimension and undertook
modifications. As described in Sethi and King (1994), the objective of this approach is to
isolate and locate the misspecifications in each dimension. Once each dimension meets the
reliability and validity criteria, the revised full model can be retested. In a complex model,
this “piecewise model fitting” approach helps to identify the part of the model with a poor fit
(Bollen 1989).
To summarize, the model of Performance representing Technology Strategy and External
Network as second-order factors shows satisfactory results. The statistical significance of the
loadings (Figure 1) and overall fit indices support the model.
1547Capital Employees Company age
(Million NT Dollar) (Years)
Mean 748 1200 17
Min ~ Max 1 ~ 44,300 2 ~ 120,000 1 ~ 76
Percentile
25th 15 29 9
50th 65 61 16
75th 303 200 24
Table 1. The capitals and employee numbers of the sample companies
Dimension Subdimensions #items Mean value Alpha
Technology Strategy R&D emphasize 5 3.28 0.8908
IP 4 3.07 0.9331
Technology aggressive 2 3.55 0.7068
Technology Forecasting 3 3.75 0.8513
External Network Professional Aid 3 3.09 0.8399
Business Chain 3 3.33 0.8153
Committee 3 3.01 0.7432
Finance Institution 3 2.85 0.8226
Academy 3 2.92 0.8874
Government 3 2.63 0.8967
Table2. Mean values and Reliability Estimates
Initial Model Revised Model Desired Levels
Total No. of items 21 14
2
χ 337.82 150.32 smaller
df 184 86 --
2
χ /df 1.836 1.748 0.9
AGFI 0.77 0.83 >0.8
Standardized RMR 0.092 0.064 0.9
CFI 0.92 0.95 >0.9
Table 3. Goodness-of-Fit Indices for the Technology Strategy Measurement Model
1548Technology Strategy
0.24*
0.55*** Performance
External Network
0.18
* p-value < 0.05 ; ** p-value4. Data Analysis and Conclusion
We used the LISREL software to perform the statistical computation for the proposed structure
equation model. The computation contains two steps: EFA (exploratory factor analysis) and
CFA(confirmatory factor analysis). EFA is used to test the reliability and validity for each constructs
or factors associated with their measures for the variables. At this step, researchers delete those
variables that are not significantly related to its constructs. We would skip the explanation of our
operation and manipulation of EFA that is counted as the stage of data cleaning. In this session, the
results of various structural equation models would be presented and discussed.
SEM is a sophisticated statistic tool to study the relationships among latent variables that are
technology strategy, external network, and company performance. Because the main purpose of this
study is to explore how the company performance is affected by other factors. The company
performance is the first endogenous variable. Table 4 listed the LISREL results of the five models that
are abbreviated as M1 through M5. The symbol “” indicated the regression effect. M1 and M2 are
simple SEMs that have only one exogenous variable and one endogenous variable. The regression
coefficients are all significant. It implies that both of the exogenous variables can explain the
company performance to certain degree. M3 is a confirmatory SME that we want to check the
relationship between the strategy and external networks. The regression coefficient is significant. It
implies that the external network can explain the technology strategy to certain degree.
One Factor Performance Multi-factors Performance Desired Levels
with interrelations
M1 M2 M3 M4 M5
TS P 0.33*** 0.24* 0.24*
EN P 0.31*** 0.19 0.19
EN TS 0.5*** 0.5***
χ2 value 28.63 39.93 79.08 125.31 125.31 Smaller
df 18 31 32 71 71 --
χ2 /df 1.59 1.288 2.471 1.765 1.765 0.9
AGFI 0.9 0.91 0.83 0.84 0.84 >0.8
RMR 0.063 0.055 0.084 0.079 0.079 0.9
Model AIC 64.63 87.93 125.08 193.31 193.31 Smaller
CFI 0.98 0.99 0.93 0.95 0.95 >0.9
* p-value < 0.05 ; ** p-valueModel M4 and M5 are more complex model assumptions. It is obvious that external network
has insignificant regression coefficients to the company performance in those models. The M4 has
two exogenous independent variables, while M5 lets technology strategy to be the intervening
variable between external network and performance. In M4 and M5, the external network has
insignificant coefficients to the performance. However, the coefficient value from the external
network to technology strategy is very high.
Obviously, the technology strategy has generated major impact on the company performance;
from the results showed in Table 4, which supported the direct effect that the impact of technology
strategy on the performance would work and the external network would affect the performance
through the technology strategy. Bollen (1989) has well discussed the phenomena that change the
interrelations among those latent variables or measure variables to generate such results. The
implication of the statistics results for those different models as shown in Table 4 needs further
discussion of why it happened in the business practices. The authors will keep working on the subject
after this conference paper submission.
According to Table 1, at least three-fourths of the sample companies are medium and small
enterprises (MSEs). Most of MSEs are followers in the industry. They don’t have sufficient resources
to be the market leaders or technology pioneers. Therefore, the empirical results as shown in Table 4,
which the coefficients of “TS P” is only 0.05 significant levels, provide evidence to our reasons in
the precedence.
In this article, we have completed two tasks: the first is to trim the measure indices by
performing the exploratory factor analysis (EFA) using the LISREL. The latent factor of external
R&D is removed because the coefficient is not statistically significant. We think the result is rational
to the industry phenomenon in Taiwan. The expectation of a firm to pay for the external R&D is to
completely transfer the technology into the firm. Obtaining the know-how let SMEs earn profits in a
short term. For a long-term consideration, the company needs to build the capability of know-why.
However, with very limited resources, most of SMEs can not afford the time and cost to explore the
implicit or solid knowledge within the external R&D.
The second task is that we have confirmed the associations between the technology strategy and
the external network. The influence of external network to the performance is interceded by the
technology strategy. The interpretation is vague to us at this time. The immediate conclusion is that
the good external network can help company to enforce its technology strategy and further to affect its
performance.
Although the empirical results show that the contribution of external network to performance
cannot be neglected, we found that the external network is closely to affect the technology strategy
and through the technology to affect performance. As we know, there are still other factors to affect
the firm’s performance. In this study, we just want to clarify the roles of technology strategy and
external network.
1551Acknowledgment
The research was supported in part by National Science Council (project no. NSC 91-2416-
H-110-011). The authors thank Miss Tasi who participate the front part of this project.
References
Aldrich, H. E. and Zimmer. E. R. Entrepreneurship through social network, In the Art and
Science of Entrepreneurship, Ballinger, Cambridge MA, 1986.
Bollen, K.A. Structural Equations with Latent Variables, Wiley, New York, 1989.
Brush, C. and Vanderwerf, P. “Comparison of methods and resources for obtaining estimates
of new venture performance,” Journal of Business Venturing(7), 1992, pp. 157-170.
Camillus, J. “Technology and market-driven life-cycles: implications for multinational
corporate strategy,” Columbia. J. World. Bus., 19, 1984, pp. 56-60.
Chandler, G. And Hanks, S. “Market attractiveness, resource-based capabilities, venture
strategies, and venture performance,” Journal of Business Venturing(9), 1994, pp.
331-349.
Chin, W. W. “Issues and opinions on structural equation modeling,” MIS Quart.(21:1), 1998,
pp. vii-xvi.
Covin, J.G. and Slevin, D.P. “Strategic Management of Small Firms in Hostile and Benign
Environments,” Strategic management Journal(10), 1989, pp. 75-87.
Covin, J.G. and Slevin, D.P. “A Conceptual Model of Entrepreneurship as Firm Behavior,”
Entrepreneurship: Theory and Practice(16), 1991, pp. 7-24.
Covin, J.G. and Slevin, D.P. “Corporate Entrepreneurship in High and Low Technology
Industries: A Comparison of Strategic Variables, Strategy Patterns and Performance in
Global Markets,” Journal of Euro-Marketing, 1994, pp. 99-127.
Ford, D. “Develop your technology strategy,” Long. Range. Plann.(21), 1988, pp. 85-95.
Ford, D. and Thomas, R. “Technology Strategy in Networks,” International Journal of
Technology Management(14:6), 1997, pp. 596-612.
Foster, R. Innovation: The Attacker’s Advantage. New York: Summit Books, 1986.
Gefen, D., Straub D.W. and Boudreau, M.C. “Structural equation modeling and regression:
Guidelines for research practice,” Comm. Assoc. Inform. Systems(4:7), 2000.
Golder, P. and Tellis, G.. “Pioneer advantage: marketing logic or marketing legend?” J.
Marketing Res.(30), 1993, pp. 158-170.
Hair, J.F., Anderson, R.E. and Tatham, R.L. Multivariate Data Analysis, 5th ed. Prentice
Hall, Englewood Cliffs, NJ, 1998.
Hambrick, D., MacMillan, I. and Barbosa, R. “Business unit strategy and changes in the
product RandD budget,” Manage. Sci.(29), 1983, pp. 757-760.
Jarillo, J.C. “Entrepreneurship and Growth: The Strategic Use of External Resources,”
Journal of Business Venturing(4), 1989, pp. 133-147.
1552Lee, C., Lee, K. and Pennings, J.M. “Internal Capabilities, External networks, and
Performance: A Study on Technology-based Ventures,” Strategic Management Journal, 22,
2001, pp. 615-640.
Lipparini, A. and Sobrero, M. “The Glue and The Pieces: Entrepreneurship and Innovation in
Small-Firm Networks,” Journal of Business Venturing(9), 1994, pp. 125-140.
Luo, Yadong Guan-Xi and Business. World scientific, New York, 2000.
Maidique, M. and Patch, P. Corporate strategy and technological policy. In Readings in the
Management of Innocation. 2nd Edition. M. Tushman and W. Moore, Eds., Ballinger,
Cambridge, MA, 1988.
Miller, D. “Relating Porter’s business strategies to environment and structure: Analysis and
performance implications,” Acad. manage. J.(31), 1988, pp. 280-308.
Mitchell, G. “Alternative frameworks for technology strategy,” Eur. J. Oper. Res.(47), 1990,
pp. 153-161.
Osrgaard, T.A. and Birley, S. “New Venture Growth and Personal Networks,” J. Bus.
Research(36), 1996, pp. 37-50.
Porter, M. Competitive Advantage. New York: Free Press, 1985.
Schoonhoven, C. “High technology firms: where strategy really pays off,” Columbia J. World.
Bus. , 1984, pp. 5-16.
Sethi, V. and King, W.R. “Development of measures to assess the extent to which an
information technology application provides competitive advantage,” Management
Sci.(40:12), 1994, pp. 1601-1627.
Shaw, B. “Formal and Informal Network in the UK Medical Equipment Industry,”
Technovation(13:6), 1993, pp. 349-365.
Thomas, R. “Purchasing and Technological Change,” European Journal of Purchasing and
Supply Management(1:3), 1994, pp. 161-168.
Zahra, S.A. “Technology strategy and financial performance: examining the moderating role
of the firm’s competitive environment,” Journal of Business Venturing(11), 1996a , pp.
189-219.
Zahra, S.A. “Technology strategy and new venture performance: a study of
corporate-sponsored and independent biotechnology ventures,” Journal of Business
Venturing(11), 1996b, pp. 289-321.
Zahra, S.A. and Covin, J.G. “Business strategy, technology policy and firm performance,”
Strategic Manage J.(14), 1993, pp. 451-478.
1553You can also read
