ABSTRACT:
This study aims to investigate the roles of strategic knowledge leaders in a Malaysian semiconductor manufacturing organization, with a special emphasis on engineering performance. This study provides a concept that amalgamates the use of knowledge management (KM) and strategic leadership to solve engineering problems in the particular organization. After collecting 226 survey responses, hypotheses tests were done using reliability, linear regression and multiple linear regression analysis. It was found that both KM and strategic leadership have a positive and significant effect on engineering performance in this particular organization. Although the correlation between KM and engineering performance is strong, it is still important for this organization to carry out a leadership style that combines KM and strategic leadership for further improved engineering performance. The results of this study serve as an exemplary guideline for most Malaysian semiconductor organizations who intend to refine their underlying strengths in KM practices or leadership.
Keywords: Knowledge management, Strategic leadership, Engineering performance
1. Introduction
Today’s competitive
survival of manufacturing organizations can be linked with their ability to
produce products that meet or exceed customers’ expectations (Ugboro and Obeng, 2000). The underlying thrust and motivation for an organization to
succeed is through its strategic leadership potentials and proven successful
management practices (Idris and Ali, 2008).
Hence, organizations have been emphasizing on developing methods and training
programmes to help their leaders and management improve product quality and
exceed consumer needs (Ugboro and Obeng, 2000).
For many years, leaders have been strategically proposing ideas to enhance
organizational performance, staff commitment and welfare (Puffer
and McCarthy, 1996). Leaders of multinational corporations also face a
predominant challenge in managing employees from a variety of cultures (Howell et al., 2003). The increasing number of
multinational corporations today justifies the need for studies to be done on
the importance of international strategic leadership in the multicultural
aspects of these organizations (Howell et al.,
2003).
However, according to Zaccaro and Horn (2003), the leadership theory
has not fully lived up to its promise of helping practitioners resolve
challenges that occur in organizations. They argue that many theories on
leadership do not appear to be contextualized, nor do the critical factors
facing leaders drive their construction.
Porter and McLaughlin (2006) argue that if a relative void still exists in the literature on the impact of the organizational context on strategic leadership, the situation would seem to be like the weather: many talking about it, but very few doing much about it insofar as empirical research is concerned. The abovementioned issues in our current industrial practices show that there is a need to identify a novel and integrated leadership style that encompasses all the fundamental areas of importance in an organization.
Hence, the purpose of this study is to determine the roles of strategic knowledge leaders in a Malaysian semiconductor manufacturing organization, with a special emphasis on engineering performance. This study provides a concept that amalgamates the use of knowledge sharing and strategic leadership to solve engineering problems in the particular organization. The study employs the use of surveys for data collection. Linear regression and multiple linear regression analyses are used for this study to determine the relationship between strategic knowledge leadership and engineering performance.
This study provides empirical evidence that explains the impactful roles of strategic knowledge leaders who combine the use of strategic leadership and knowledge management concepts for improved engineering performance in a Malaysian semiconductor organization. The results can be potentially used to develop training programmes, guidelines and industrial policies for the practice and research concerning strategic leadership and knowledge management.
2. Knowledge
Management (KM)
KM is a practice that involves building on previous experiences and creating new mechanisms for exchanging knowledge (Boyle et al., 2006). It is a process that creates significance with the indefinable advantages of an organization and combines areas such as information technology, industrial engineering and organizational performance (Liebowitz, 1999). KM focuses on enabling rather than the management of knowledge (Zhengfeng et al., 2007 ).
According to Gold et al. (2001), an organization that can competently maintain the performance of its KM processes is capable of integrating various concepts and technologies more efficiently. Lee and Chang (2006) suggest that KM acts as a foundation for continuous improvement, especially in the new product development (NPD) processes.
It is said that Japanese organizations are successful not due to their production ability or relationships with customers, but due to their ability in organizational knowledge creation (Nonaka and Takeuchi, 1995). Organizations that have competitive KM initiatives have more superior NPD performance since the constant knowledge creation allows them to improve processes (Liu et al., 2005).
When implementing a KM approach in manufacturing organizations, engineers need to be fast in summarizing detailed information from different areas for the understanding of other departmental counterparts (Faniel and Majchrzak, 2007). This allows them to develop their cross-functional knowledge and reuse the value-added information they created for future existing processes.
3. Strategic Leadership
Strategic leadership is the
capability to foresee, visualize, sustain flexibility and empower people to
create strategic change (Hitt et al., 2005).
Strategic leadership is important in organizations because without it,
organizations can fail to attain satisfactory, much less, improved performance
when confronting the challenges of the global economy (Hitt and Ireland, 1999).
Strategic leadership
involves the decision-making of an organization’s products/services, executive
selection, resource planning, strategizing and organizational goal-setting (House and Adiya, 1997; Pechlivanidis and Katsimpra, 2003).
It arises as a cooperative and participative process (Hitt et al., 2005).
Strategic leadership can also
be described as an organization’s key leadership skills that present strategies
in nurturing the skills of employees towards achieving organizational goals (Idris and Ali,
2008). Reed et al. (2000) suggest that it
is important for the
commitment of strategic leaders to be established in an instructive and
encouraging manner.
In order to survive
in equivocal market environments, organizations need strategic leaders that are
responsive, adaptable and able to create strategies and control them in an
advantageous way (Yukl, 2008). Chin et al. (2002) point out that these
strategic leaders should also be geared towards enhancing quality-driven
initiatives and dynamically instilling customer focused goals in the middle
management levels. Strategic leaders should be capable of directing the whole quality process, which involves
establishing standards, setting goals and improving systems deliberated to
fulfil customer requirements and improve organizational performance (Fuentes-Fuentes et al., 2006).
4. Engineering
Performance
Engineering performance mainly refers to the general success of
predetermined engineering project goals. The uniqueness of engineering performance
in many studies is in its capability to be succinctly linked with other
variables, characteristics and research models (Cho
et al., 2009).
Engineering
performance involves the management of factors such as time, cost, superiority,
creativity and product development performance. The detailed explanation of
these factors is given in Table 1.
Table
1: Factors Of Engineering Performance
|
Engineering Performance |
Explanation |
References |
|
Time |
When a decision is made, the project execution time must be short
since it is a success factor in engineering performance. |
(Thiry,
2002) |
|
Cost |
Delayed projects cost
money and reduce customer satisfaction. This causes financial support
difficulties and further slippages in project timelines to transpire. |
(Kaliba
et al., 2009),
(Ahsan and Gunawan, 2009) |
|
Superiority |
The crucial project success factor is product
superiority. In manufacturing, product superiority refers to the distribution
of quality products with quality features to customers. |
(Stevens
et al., 1999),
(Cooper, 1996) |
|
Creativity |
Creativity is an important aspect of
engineering performance as it involves creative idea generation and
innovation which are useful for the conceptual stages in manufacturing
projects. |
(Leenders
et al., 2002),
(Garcia and Calantone, 2002) |
|
Product
development performance |
Organizations should manage risks related to developing new products
because there is still a possibility of product failure. Thus, product
development performance is also essential in engineering performance because
it is a critical determinant in the success or failure of a project. |
(Schmidt et al., 2009) |
For this study, the components of KM and strategic leadership will be tested against engineering performance using statistical analysis techniques in order to determine the significance of their relationships. The following hypotheses are proposed for this study:
H1: KM affects engineering performance in a Malaysian semiconductor organization
H2: Strategic leadership affects engineering performance in a Malaysian semiconductor organization
H3: Strategic knowledge leadership affects engineering performance in a Malaysian semiconductor organization
5. Research
Method
The organization chosen for this study was founded in
The choice of this organization for the study can be justified by its
recognition from the Malaysian Government in its 10th Malaysian Plan. According
to the 10th Malaysian plan, this organization is a leading global company that
has established a strong cluster ecosystem among other semiconductor
organizations in
The population of this study is made up of 2100 engineers. The unit of
analysis is the engineers of the organization. 2100 surveys were handed out to
them. Data were collected over a period of six weeks using a seven-point
Likert scale survey instrument. The response rate was 11%.
The
questionnaire includes closed-ended questions from the sources identified in
the literature. Close-ended questions allow respondents to make quick
decisions by selecting from the options available, thereby increasing response
rates (Zikmund, 2003). Using SPSS 18.0, the data collected were
analyzed with reliability, linear regression and multiple linear regression
analyses.
6. Results
In this study, Cronbach’s alpha was used to assess the internal consistency
of the survey items. Normally, the alpha value ranges from 0 to 1. An alpha
coefficient that is above 0.7 signifies high reliability (Cronbach and Shavelson, 2004; Nunnally and Bernstein,
1994). The reliability analysis shows that the Cronbach’s alphas for
both strategic leadership and KM are acceptably above 0.7. In addition, the
overall Cronbach’s alpha is also above 0.7 (See Table 2).
Table
2: Reliability Analysis For Strategic Knowledge Leadership
|
Variable |
Sub-variable |
Cronbach’s alpha, α |
Overall cronbach’s alpha |
|
Strategic knowledge leadership |
KM |
0.949 |
0.831 |
|
Strategic leadership |
0.921 |
Linear regression analysis
was used to evaluate the hypotheses H1 and H2. Table 3 presents the results of the
linear regression analysis for H1. An R2
of 0.487 is reported with
this regression analysis, indicating that 48.7% of the variance in engineering performance can be
explained by KM. This relationship is considered to be moderately correlated (R=0.698). KM also establishes an
importance towards engineering performance with a reported β of 0.571. In addition to that, the model is significant as
indicated by the ANOVA results of F (1,
225) = 212.946, p<0.001.
Therefore, the effect of KM on engineering performance in this
organization is positive and
significant, and H1 is not rejected.
Table 3: Linear Regression Analysis
For KM – Engineering Performance
|
Predictor |
β |
Std. Error |
t |
F |
R |
R2 |
|
(Constant) |
1.856 |
0.158 |
11.740 |
212.946*** |
0.698 |
0.487 |
|
KM |
0.571 |
0.039 |
14.593*** |
(Notes: *** p<0.001; N=226; Durbin Watson = 1.702)
Table 4 presents the
results of the linear regression for H2. An R2
of 0.334 is reported with
this regression analysis, indicating that 33.4% of the variance in engineering performance can be
explained by strategic leadership. The strength of the relationship is
considered to be moderately correlated (R=0.578).
Strategic leadership also establishes an importance towards engineering performance
with a reported β of 0.375.
Additionally, the model is significant as indicated by the ANOVA results of F (1, 225) = 112.250, p<0.001. Therefore, the effect of
strategic leadership on engineering performance in this organization is positive and significant. Hence,
H2 is not rejected.
Table 4: Linear Regression Analysis
For Strategic Leadership – Engineering Performance
|
Predictor |
β |
Std. Error |
t |
F |
R |
R2 |
|
(Constant) |
2.387 |
0.167 |
14.320 |
112.250*** |
0.578 |
0.334 |
|
Strategic leadership |
0.375 |
0.035 |
10.595*** |
(Notes: *** p<0.001; N=226; Durbin Watson = 1.605)
A stepwise multiple linear regression analysis was conducted to evaluate H3. Table 5 presents the results of the analysis. The R2 of 0.497 indicates that up to 49.7% of the variance in engineering performance is explained by strategic knowledge leadership. A strong correlation coefficient (R=0.705) was also obtained for this relationship. In addition to that, the model is significant as indicated by the ANOVA results of F (2, 224) = 110.306, p<0.001.
The results show that the effect of strategic knowledge leadership on
engineering performance in this organization is positive and significant. Thus,
H3 is not rejected. It is also important to note that the when individually
tested, the correlation of KM with engineering performance appears to be higher
than that of strategic leadership’s correlation. However, when the two
components are combined, the correlation increases.
Table
5: Multiple Linear Regression Analysis For Strategic Knowledge Leadership –
Engineering Performance
|
Strategic
Knowledge Leadership |
β |
Std. Error |
t |
F |
R |
R2 |
|
(Constant) |
1.769 |
0.162 |
10.898*** |
110.306*** |
0.705 |
0.497 |
|
KM |
0.484 |
0.057 |
8.516*** |
|||
|
Strategic leadership |
0.095 |
0.045 |
2.102* |
(Notes: * p<0.05; ** p<0.01; *** p<0.001; N=226; Durbin Watson = 1.721)
7. Discussion
Based on the analysis of H1
and H2, it is evident that both KM and strategic leadership have a positive and
significant effect on engineering performance. It is therefore evident that
both KM and strategic leadership have a potential to help improve engineering
performance and generate significant value to organizations through indefinable
advantages (Liebowitz, 1999; Shea and Howell,
1999; Thamhain, 2004).
Also, it was found that the
relationship between KM and engineering performance is stronger (R=0.698) compared to the relationship
between strategic leadership and engineering performance (R=0.578). These findings indicate that this company practices
extensive and systematic documentation of their standards and processes so that
they can be embodied easily into trainings, workshops and projects (Li and Hsieh, 2009; Linderman et al., 2004).
However, these findings do not mean that strategic leadership is unimportant to
the organization. It may be plausible that the organization is geared towards a
more flexible and participative form of leadership along with the strategic
planning from top management. Participative and flexible leadership styles are
still important in team environments where members require constant motivation
and affirmation (Drath et al., 2008; Kanji,
2008; Shea and Howell, 1999; Wang et al., 2005; Yukl, 2008).
The results for H3’s
evaluation showed that a combination of strategic leadership and KM can enhance
the strength of the correlation with engineering performance (R=0.705). This somewhat proves that
leaders of manufacturing organizations cannot simply rely on only the elements
of strategy and vision to manage employees, but should also focus on knowledge
sharing, teamwork and creativity (Fuentes-Fuentes
et al., 2006; Garcia and Calantone, 2002; Leenders et al., 2002). With
the combined emphasis of all the aforementioned factors, this semiconductor
manufacturing organization can look forward to an improved engineering performance even when
faced with the challenges of the global economy (Hitt
et al., 2005).
8. Conclusion
The findings of this study suggest that the strategic knowledge leaders play a significant role in the engineering performance of a Malaysian semiconductor manufacturing organization. Although the literature survey in this paper conceptually indicated that leadership plays an important role in organizational performance, a factor that can lead to its collapse may be the lack of support from the employees of the organization. However, by including elements of KM in the area of strategic leadership, it is hoped that the improvement in engineering performance in manufacturing organizations can be raised to an even higher level.
It is noted that there are some limitations in this study. For example, a
simultaneous modeling analysis was not carried out since the concepts developed
in such a way that the components were not able to be simultaneously tested
against each other. This limits the possibility of discovering more relations
and effects among the dependent and independent variables. Also, a study of one
organization would normally limit the generality of the results. In this case,
it is still uncertain if this study would have reasonable applicability outside
One of the suggestions to improve this study is to conduct in-depth qualitative studies in every technology cluster of this organization to further understand its organizational context. Also, observational techniques could be employed to shed more light on this phenomenon. In addition to that, instead of using respondent-reported scales, it would be good if researchers are able to use empirical data from the organization’s records e.g. sales performance, customer satisfaction, development cost etc. An empirical study across several manufacturing organizations would also improve the generalizability of this study.
Lastly, a structural equation modeling (SEM) approach using a combination of statistical data and qualitative causal assumptions can be used in order to test and estimate causal relationships. One of the available software that can be utilized for this analysis is called AMOS. Using this approach, the components for this study are able to be tested simultaneously altogether instead of the conventional method where they are linearly tested with only one component against another.
Although much remains to be
learned about the developed hypotheses and relationships in this study, it is
believed that a preliminary understanding of strategic knowledge leadership is now
within sight, due in part of an improved understanding on how their
capabilities and influence can transform the engineering performance of various
manufacturing organizations in the world.
9. References
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Chin, K. S., Tummala, V.
M. R. and Chan, K. M. (2002), Quality Management Practices Based on Seven Core
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Hitt, M. A. and
Hitt, M. A.,
Idris, F. and Ali, K. A.
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Performances: Empirical Evidence from Business Firms in
Kaliba, C., Muya, M. and
Mumba, K. (2009), Cost Escalation and Schedule Delays in Road Construction
Projects in
Nonaka, I. and Takeuchi,
H. (1995), The Knowledge-Creating Company: How Japanese Companies Create the
Dynamics of Innovation,
Nunnally, J. and
Bernstein, I. (1994), Psychometric Theory, McGraw-Hill Publication,
Ugboro,
Zikmund, W. G. (2003),
Business Research Methods, McGraw-Hill Publication,
About the Authors:
Poh Kiat Ng, MEng is a Senior
Lecturer at the Faculty of Engineering and Technology,
Kian Siong Jee, MSc is a Senior
Lecturer and PhD candidate at the Faculty of Engineering and Technology,