International Journal of Management, Knowledge and Learning, 3(1), 3–22
How the Organizational Goals Affect
Knowledge Management
Wei-Shong Lin
National Taiwan University of Science and Technology, Taiwan
Jui-Ling Huang
National Taiwan University of Science and Technology, Taiwan
Margaret L. Sheng
National Taiwan University of Science and Technology, Taiwan
How to enhance customer satisfaction and technology innovation have been
topics of discussion for some time; however, few studies have explored the
two issues by applying the knowledge creation theory, and analyzed their dif-
ferences in knowledge creation activities. The present study aims to explore
how the firm’s organizational goal affects its knowledge creation process.
Based on Nonaka’s knowledge creation theory, questionnaires were devel-
oped and sent to Taiwanese firms in various industries, including the manu-
facturing and service industries. These questionnaires were collected either
by mail or interview. Our findings suggest that externalization and combina-
tion activities should be emphasized when the organizational goal is innova-
tion, whereas internalization activity should be emphasized when the organi-
zational goal is customer satisfaction.
Keywords: knowledge management; knowledge creation process; organiza-
tional goal; innovation; customer satisfaction; SECI
Introduction
Nonaka and von Krogh (2009) pointed out that the knowledge creation the-
ory defines knowledge in three parts: (a) Knowledge is justfied true belief;
(b) Knowledge is (i) the actuality of skillful action and/or (ii) the potentiality
of defining a situation so as to permit action; and (c) Knowledge is explicit
and tacit along a continuum. Since Nonaka (1991) introduced the ‘knowl-
edge creation spiral’ to characterize the tacit-explicit knowledge transfor-
mation process in knowledge-intensive firms, the knowledge creation the-
ory shed light on organizational creativity, learning, innovation, and change
in organizations (Nonaka and von Krogh, 2009; Rasmussen and Nielsen,
2011).
Over the past two decades, numerous empirical studies have followed
their concept, and verified the sequential knowledge creation activities in
the process-from socialization (S) to externalization (E), combination, (C)
internalization (I), and then back to socialization (S) (hereafter referred to
www.issbs.si/press/ISSN/2232-5697/3_3-22.pdf
4 Wei-Shong Lin, Jui-Ling Huang, and Margaret L. Sheng
as the SECI model) (Dyck, Starke, Mischke, & Mauws, 2005; Sabherwal
and Becerra-Fernandez, 2003).
The past empirical studies of the SECI model made several discoveries.
Becerra-Fernandez and Sabherwal (2001) discussed that knowledge man-
agement (KM) processes would impact perceived knowledge effectiveness.
They found that the combination and externalization processes, and not
the internalization and socialization process, would affect knowledge sat-
isfaction. Sabherwal and Becerra-Fernandez (2003) further analyzed that
different KM processes would influence perceived knowledge effectiveness
at different organizational levels. For instance, internalization and external-
ization processes influence perceived effectiveness at the individual level,
while socialization and combination processes exert influence at the group
and organizational levels. Schulze and Hoegl (2008) also depicted that the
novelty of product ideas generated is influenced both by the positive effect
of socialization and internalization processes, as well as the negative effect
of externalization and combination processes.
Notwithstanding the rich empirical studies of Nonaka’s knowledge cre-
ation theory during the past two decades, the main question proposed by
Baloh, Uthicke, and Moon (2008) still exists, that is, ‘Which knowledge pro-
cess do they need to nurture?’ (p. 436). Empirical studies such as that
of Schulze and Hoegl (2006) depicted the crucial processes when facing
different product development phases (i.e., concept development, product
development). However, their studies focused on a single industry (manu-
facturing) with a single organizational goal (new product success). In this
study, we attempt to put all the industries together to identify the key knowl-
edge activities when facing different organizational goals.
In previous research, organizational goals of innovation and customer
satisfaction were respectively applied to discussions about the high-tech
industry (Ho, 2011; Tsai & Wang, 2009, Tsai, Chen, & Chin, 2010) and the
service industry (Chen & Li, 2006; Lin, Su, & Chen, 2006; Shahina & Zairi,
2009). However, innovation is not limited to the high-tech industry; it also
exists in the service industry (Drejer, 2004; Hipp & Grupp, 2005; Castel-
lacci, 2008). As most empirical studies show, technology innovations such
as the auto teller machine (ATM) equipment (Jaw, Lo, & Lin, 2010), logistics
tracking (Chang, 2003), and e-business (Ramsey, Ibbotson, Bell, & McCole,
2005) play an important role in the service industry. Application of these
innovations improved operating procedures in the industry and reduced its
service costs. Therefore, nowadays innovation is not a group of segmented
factors applicable to the manufacturing industry, the high-tech industry, or
the service industry, as in the case of prior studies; on the contrary, in-
novation has become a catalyst for a firm’s success. In addition, although
various studies describe how an organization’s overall learning efficiency af-
International Journal of Management, Knowledge and Learning
How the Organizational Goals Affect Knowledge Management 5
fects its innovation performance (Clifton, Keast, Pickernell, & Senior, 2010;
Ho, 2011; Li, Huang, & Tsai, 2009), few studies have analyzed the indi-
vidual effect of each activity on innovation performance in the knowledge
creation process (KCP). Therefore, the primary purpose of our research is
to find out which activities in the KCP have a significant effect on a firm’s
innovation performance.
Customer satisfaction, on the other hand, has been a key factor in mea-
suring the success of a market segmentation strategy (Athanassopoulos,
2000) or employees’ service quality in the field of market research. Various
studies have explored the relationship between learning and customer sat-
isfaction (Caemmere & Wilson, 2010; Chang & Ku, 2009; Lin et al., 2006);
however, few studies have analyzed the individual effect of each activity in
the KCP in terms of customer satisfaction performance. Therefore, the sec-
ond purpose of the current research is to identify which activities in the KCP
have a significant effect on a firm’s performance measured by customer sat-
isfaction. To summarize, the aim of this study is to identify the activities in
the KCP that firms should focus on when they have different organizational
goals.
The rest of this paper is organized as follows: the second section reviews
prior literature to summarize the development of the knowledge creation the-
ory; the third section explains our research method, and the fourth section
discusses our research findings. Future research directions are presented
in the final section.
Literature Review
Knowledge Value
Originating from creativity, individual experiences, and organizational learn-
ing, knowledge exists in written documents, as well as in routines, tasks,
processes, practices, rules, and values that shape an organization (Bhagat,
Kedia, Harveston, & Triandis, 2002). Knowledge results from the interaction
between individuals and organizations, and is specific to a context defined
by particular time and place (Nonaka, von Kroghh, & Voelpel, 2000). There-
fore, knowledge management is a type of organization memory, which en-
compasses a wide range of clear processes, methods, rules, and data, and
thus enabes people to search for important knowledge among different KM
operations (Liu, Chen, & Tsai, 2005).
Organization knowledge is path-dependent, hard for third parties to ap-
propriate, difficult to imitate, and causally ambiguous (Cabrera & Cabrera,
2002); hence, every organization is enabled to create its unique competi-
tive advantage and sustainability (Grant, 1996, Tsai & Li, 2007). In other
words, a firm will enjoy more efficiency when its advantage is built on its
daily processes and routines, and is created by individuals and groups who
Volume 3, Issue 1, 2014
6 Wei-Shong Lin, Jui-Ling Huang, and Margaret L. Sheng
could act autonomously as knowledge creators and transmitters (Nonaka,
Toyama, & Konno, 2000; Sabherwal & Becerra-Fernandez, 2003). KM has
become a fundamental task of an organization and the main challenge of
its members (Pertusa-Ortega, Zaragoza-Sáez, & Claver-Cortés, 2010).
The model proposed by Nonaka (1994) has several characteristics. First,
the model includes two important elements for knowledge creation: tacit
knowledge and explicit knowledge. Tacit knowledge is highly subjective, id-
iosyncratic, and deeply rooted in personal experiences. Explicit knowledge,
on the other hand, is rational, theoretical, and so-called scientific knowl-
edge, which can be documented (Martín-de-Castro, López-Sáez, & Navas-
López, 2008).
Second, in Nonaka’s SECI model, tacit knowledge and explicit knowl-
edge are complementary processes. They should not be seen as sepa-
rate entities, since they are based on the same continuum (Nonaka & von
Krogh, 2009). Third, knowledge transformation occurs during its transit,
that is, knowledge is deepened and widened not only in the transforma-
tion between tacit knowledge and explicit knowledge, but also in the transit
between individuals and groups. Sabherwal and Becerra-Fernandez (2003)
clearly pointed out that the efficiency of the KCP is determined by the level
of effectiveness at individual, group, and organizational levels, which inter-
act continually when the KCP exists.
Lastly, knowledge creation is a spiral system. As Nonaka (1994) stated,
‘This spiral illustrates the creation of a new concept in terms of a continual
dialogue between tacit and explicit knowledge. As the concept resonates
around an expanding community of individuals, it is developed and clarified.
Gradually, concepts, which are thought to be of value, obtain a wider cur-
rency and become crystallized’ (p. 15). Therefore, Nonaka thinks of the KCP
as a spiral process, where new knowledge will be continuously created from
previous findings.
In Nonaka’s SECI model, the transition process is composed of four dis-
tinctive activities (Figure 1). The first is socialization, where tacit knowledge
is transferred among members through mentoring and peer discussion.
Therefore, the members share one mentality, technical skills, and experi-
ence. This is generally an intermingled multi-level trial-and-error process full
of peer discussions, which is conducive to the next process-externalization.
In the externalization process, tacit knowledge is attempted to be articu-
lated into explicit concepts, usually via metaphors, continuous dialogues,
and collective actions, in the hope that concrete concepts can be formed
and group solutions can be found. The next is combination, which aims
at combining different entities of explicit knowledge to bring about or even
document new ideas and solutions. Lastly, internalization is the process
whereby explicit knowledge is turned into tacit knowledge through learning-
International Journal of Management, Knowledge and Learning
How the Organizational Goals Affect Knowledge Management 7
Socialization Externalization
CombinationInternalization
Tacit knowledge
Explicit knowledge
Ta
ci
t
kn
ow
le
dg
e
Ex
pl
ic
it
kn
ow
le
dg
e
Figure 1 Knowledge Creation Model (adapted from Nonaka and Takeuchi, 1995)
by-doing, a process during which new experiences accumulate (Choi & Lee,
2002; Nonaka & von Krogh, 2009).
KCP and Organizational Goal
Gilbert and Cordey-Hayes (1996) argued that knowledge application is the
facilitator of successful innovation. Thus, technology innovation in pro-
cesses, products, and services helps industries enhance the efficiency of
their production activities (Hung, Kao, & Chu, 2008; Miles, 2005; Sirilli
& Evangelista, 1998). Ideas for technology innovation grow from visions,
metaphors, or any language form into explicit knowledge. Take Matsushita’s
bread-maker, for example. A manager of the company got ideas about how
to make bread from a hotel baker’s implicit experience. The manager then
translated these ideas into explicit language for their engineers and con-
sequently, the development team combined the engineers’ scientific knowl-
edge with the manager’s ideas to develop the bread-maker (Nonaka, 1991).
Innovation often first involves concept developments, followed by the
organization of these concepts into a product structure (Hall & Andriani,
2003). Matsushita’s example illustrates two key activities in an innovation
process, namely externalization and combination. The translation of the
manager’s implicit ideas into explicit language is an externalization activity.
The development of the bread-maker based on the engineers’ scientific
knowledge and the manager’s explicit language is a combination activity.
Nonaka et al. (2000) further indicated that concept creation in new product
development is a process of externalization and converting explicit concepts
into more complex and systematic sets of product knowledge is a process
of combination. Hence, externalization and combination are the two key
activities in the innovation process.
Therefore, the present study proposes the following hypotheses:
H1a When a firm’s organizational goal is innovation, increasing the focus
Volume 3, Issue 1, 2014
8 Wei-Shong Lin, Jui-Ling Huang, and Margaret L. Sheng
on externalization activity in the KCP will increase the firm’s perfor-
mance.
H1b When a firm’s organizational goal is innovation, increasing the focus
on combination activity in the KCP will increase the firm’s perfor-
mance.
As Peter Drucker (1954) argued, creating a satisfied customer is the only
valid definition of business purpose (also cited in Mohr-Jackson, 1998, p.
109). Cater and Cater (2009) also asserted that customer satisfaction is
more affected by personal interaction than by price holds. Therefore, cus-
tomer satisfaction is the main factor of service quality, which involves phys-
ical equipment and personnel interaction (Thomas, 1978). Prior research
shows that if a firm’s customer satisfaction or customer loyalty can be im-
proved, the firm will enjoy significantly positive outcomes in terms of its
service quality (Liao & Chuang, 2004; Tracey, Tannenbaum, & Kavanagh,
1995).
In discussions regarding the characteristics of successful programs or
how to foster customer satisfaction in organizational activities, Li, Yang,
and Wu (2008) pointed out that improved service quality relies on certain
critical factors, including a service system (which involves a standardized
service process and physical environment) and HR practices (which influ-
ence employees’ job attitudes and service ability). In short, sharing implicit
knowledge among employees through social interaction, followed by knowl-
edge internalization through learning-by-doing, is the main way to enhance
customer satisfaction. Therefore, we propose the following hypotheses:
H2a When a firm’s organizational goal is customer satisfaction, increased
focus on socialization activities in the KCP will increase the firm’s
performance.
H2b When a firm’s organizational goal is customer satisfaction, increased
focus on internalization activities in the KCP will increase the firm’s
performance.
Research Method
Sampling
Each sample represents one firm’s knowledge creation activity. Given that
our study attempts to identify how KCP works in firms with different organiza-
tional goals, we gathered data from firms that come from various Taiwanese
industries, including manufacturing and service industries.
Every sampled firm was sent a copy of the questionnaire. Our target re-
spondents were middle managers who are acquainted with the whole KCP
in their firm (Lee & Choi, 2003). Middle managers were chosen because
International Journal of Management, Knowledge and Learning
How the Organizational Goals Affect Knowledge Management 9
Table 1 Sample Distribution
Industry Category Frequency
Semiconductor and optical
industry
IC Design house 2 1.50
Manufacturing 5 3.76
Equipment 2 1.50
Optical 2 1.50
Others 2 1.50
Sub-total 13 9.77
High-Tech Industry Cars 3 2.26
Software 7 5.26
Electronic 9 6.77
Computer 9 6.77
Peripheral equipment 8 6.77
Others 3 2.26
Sub-total 40 30.08
Communication and equipment
industry
Communication equipment 6 4.51
Web equipment 18 13.53
Communicating products 1 0.75
Others 1 0.75
Sub-total 26 19.55
Food processing, house
appliances and construction
industry
Food processing 11 8.27
Construction 1 0.75
appliances 9 6.77
Others 3 2.26
Sub-total 24 18.05
Service industry Medical 3 2.26
Insurance 4 3.01
Bank 8 6.02
Beverage 4 3.01
Communication 2 1.50
Others 9 6.77
Sub-total 30 22.56
Total 132 100.00
Continued on the next page
they are an important trigger for new instructions, which they make by coor-
dinating information gathered from employees following the request of the
top manager (Nonaka et al., 2006). The data was collected either by mail or
interview. Of the 100 copies of questionnaires mailed, this study received
55 copies and thirteen of them were incomplete. The other 90 copies were
fulfilled via face-to-face interviews. Using the two described approaches, we
collected data from 132 firms. An ANOVA was then conducted to analyze
whether the firms came from the same population by testing the mean dif-
Volume 3, Issue 1, 2014
10 Wei-Shong Lin, Jui-Ling Huang, and Margaret L. Sheng
Table 1 Continued from the previous page
Industry Category Frequency
Sex Male 99 75.00
Female 33 25.00
Total 132 100.00
Years of service
(at the current company)
<5 years 34 25.76
<10 years 35 26.52
<15 years 31 23.48
<20 years 19 14.39
≥20 years 13 9.85
Total 132 100.00
Firm Size <100 40 30.30
<200 17 12.88
<500 15 11.36
<1000 11 8.33
≥1000 49 37.12
Total 132 100.00
ferences among the firms; the results were all non-significant differences,
which eventually proved to be true. The average tenure of the respondents
is 9.9 years, and the ratio of males to females is 3:1. The detailed distribu-
tion of the firms is listed in Table 1.
Variables
Independent Variables. Drawing on other researchers’ work (Boiral, 2002;
Chou et al., 2005; Jordan & Jones, 1997), our questionnaire items regard-
ing a firm’s KCP were mainly developed based on the ideas of Nonaka et
al. (2000). However, the questionnaire veered away from traditional surveys
that ask respondents about the presence of the four knowledge creation ac-
tivities in their organization; instead, we asked them how much their organi-
zation values each of the activities. We ran a pilot test on 26 firms to make
sure the construction of this questionnaire was appropriate for all firms.
The final version of the questionnaire contained nine questions regarding
socialization, three questions regarding externalization, seven questions re-
garding combination, and three questions regarding internalization. Table 2
details the reliability of the pilot test and the revised items.
Dependent Variables. Two types of organizational goal-innovation and cus-
tomer satisfaction-were used in this study, where innovation refers to pro-
cess improvement (Law & Ngai, 2008), introduction of a new product, or a
new marketing project. Innovation was assessed using a single item, and
respondents were asked to indicate their performance relative to their com-
petitors. Similar to innovation, customer satisfaction was also assessed
International Journal of Management, Knowledge and Learning
How the Organizational Goals Affect Knowledge Management 11
Table 2 Survey Items
Construct Items Source
Socialization
(.732)
(1) Experience-sharing with customers Nonaka et al., 2000
(2) Experience-sharing with suppliers Nonaka et al., 2000;
Walter et al., 2007
(3) Dialogue with competitors Nonaka et al., 2000
(4) Information-gathering from the sales force and
production sites
Nonaka et al., 2000;
Walter et al., 2007
(5) Informal meetings with competitors outside
the firm
Nonaka et al., 2000;
Boiral, 2002
(6) Idea-generation for corporate strategy from
daily social life and interaction with external
experts
(7) Information contact within the department Boiral, 2002; Nonaka
et al., 2000
(8) Contact between departments Nonaka et al., 2000
(9) Expertise demonstrated by a master and
acquired through practice
Externalization
(.650)
(1) Concepts created via the Internet
(2) Training evaluation (involvement of industrial
designers in project teams)
Nonaka et al., 2000;
Chou, Chang, Tsai,
and Cheng, 2005
(3) Improvement proposal (adductive thinking) Nonaka et al., 2000
Combination
(.742)
(1) Market information gathering
(2) Information gathering from all over the
company (e.g., manufacturing and marketing)
(3) Construction of manuals, documents, and
databases on products and services
(4) Planning and holding presentations to transmit
newly created concepts
(5) Quality and quantity of new patents Jordan and Jones 1997;
Nonaka et al. 2000
(6) The number of new products Nonaka et al. 2000
(7) Quality of documents ( e.g., marketing
analysis, strategic analysis, and so on)
Internalization
(.653)
(1) Employees’ job attitude (‘enactive liaising’
activities with functional departments through
cross-functional development teams and
overlapping product development)
(2) Employees’ job accuracy (experiments
conducted and results shared with the entire
department)
(3) The performance of inter-firm competition
(encouraging engagement in facilitation,
prototyping, and benchmarking, as well as a
challenging spirit within the organization)
Volume 3, Issue 1, 2014
12 Wei-Shong Lin, Jui-Ling Huang, and Margaret L. Sheng
Table 3 Construct Validity – Convergent and Discriminant
Indicators (1) (2) S E C I
S 1. Sharing with customers 0.39 4.24
2. Sharing with suppliers 0.26 2.75
3. Dialogue with competitors 0.59 6.77
4. Sales force and production sites 0.58 6.60
5. Informal meetings 0.54 6.06
6. Interaction with external experts 0.45 4.91
7. Contact within the department 0.62 7.23
8. Contact between departments 0.69 8.21
9. Demonstrated by a master 0.49 5.48
E 10. Concepts created 0.56 6.15 10.45*
11. Training evaluation 0.65 7.40
12. Improvement proposal 0.64 7.26
Continued on the next page
using a single item on a five-point Likert Scale, wherein 1 equaled much
worse and 5 equaled much better.
Control Variables. We adopted turnover rate (Cater & Cater, 2009) as the
first control variable, which asked the firms to compare themselves to their
competitors in terms of their turnover rate on a five-point Likert scale,
wherein 1 equaled much lower and 5 equaled much higher. The employ-
ees’ rotation rate was then used as the second control variable, which also
asked respondents about the firm’s rotation rate compared to that of their
competitors on a five-point Likert scale, wherein 1 equaled much lower and
5 equaled much higher. Firm size was used as the third control variable,
which was also based on a five-point scale. Finally, employees’ university
degree was used as the last control variable. The firms were asked to spec-
ify how important a university degree is in recruiting their staff. A five-point
Likert scale, wherein 1 equaled unimportant and 5 equaled very important,
was also used for this control variable.
Statistics
Confirmatory Factor Analysis (CFA). This study used CFA to examine the
convergent and discriminant validity of the scale. The measurement model
included 22 items describing four constructs: socialization (S), externaliza-
tion (E), combination (C), and internalization (I). Table 3 shows the validity
results. The items of factor loadings were significant (i.e., t> 1.96) to their
corresponding construct (Bagozzi, Yi, & Phillips, 1991), with the lowest t-
value being 2.75. Moreover, the discriminant validity was tested by different
chi-square between constructs (Bagozzi et al., 1991), and most of them
International Journal of Management, Knowledge and Learning
How the Organizational Goals Affect Knowledge Management 13
Table 3 Continued from the previous page
Indicators (1) (2) S E C I
C 13. External information gathering 0.53 5.95 29.15* 3.32†
14. Infor. gathering from company 0.67 7.92
15. Construction of manuals,
documents and databases
0.59 6.76
16. Planning and holding present. 0.59 6.82
17. New patents 0.43 4.71
18. New products 0.39 4.25
19. Quality of documents 0.51 5.69
I 20. Job attitude 0.85 9.14 53.85* 39.95* 50.99*
21. Job accuracy 0.76 8.20
22. Inter-firm competition 0.35 3.70
Notes (1) factor loadings; (2) t-value (1.96); p*<0.05, χ2 = 3.841; p† <0.1, χ2 = 2.706.
Table 4 Means, Standard Deviations, Pearson Correlations and Cronbach’s Alpha
Item Mean SD 1 2 3 4 5 6
1. Satisfaction 3.38 0.71
2. Innovation 3.33 0.88 0.358**
3. Socialization 3.55 0.69 0.214* 0.163† 0.648a
4. Externalization 3.09 0.80 0.184* 0.381** 0.585** 0.650a
5. Combination 3.50 0.68 0.186* 0.394** 0.465** 0.597** 0.733a
6. Internalization 3.89 0.71 0.350** 0.151† 0.358** 0.366** 0.401** 0.625a
Notes a Cronbach’s alpha for each activity on the diagonal and all scale’s is 0.886. Corre-
lation is significant at the p**<0.01, p*<0.05, p† <0.1 (2-tailed).
had a significant effect (p< .05). Therefore, the scale has convergent valid-
ity and discriminant validity in this study.
Reliability. Cronbach’s alphas were used to measure the reliability of the
multi-item scale for each dimension. The reliability of the whole instrument
was .886, and the reliability of each SECI activity ranged from .625 to .7337
(Table 4). An adequate alpha is one that is higher than .5, although Nunnally
(1978) recommended reliability higher than .6. Hence, the measurement in-
strument is reliable. The same argument of reliability is acceptable accord-
ing to the standard of Bagozzi and Yi (1988, p. 80) and Baker, Parasuraman,
Grewal, and Voss (2002, p. 130).
Pearson Correlation. Pearson correlation was conducted to check the cor-
relation among activities. As shown in Table 4, most correlation variables
are significant (p< .05).
Common Method Bias. Harman’s one-factor test. A principal factor analysis
of all measurement items yielded seven factors with eigenvalues larger than
Volume 3, Issue 1, 2014
14 Wei-Shong Lin, Jui-Ling Huang, and Margaret L. Sheng
one. These factors accounted for 67.644 percent of the variance. Consid-
ering that no single factor emerged as dominant, common method variance
is unlikely to be a serious problem in our data (Podsakoff, & Organ, 1986).
Results and Findings
To compare the KCP of the better-performing group (i.e., survey participants
who consider their innovation performance ‘better’ or ‘much better’ than
their competitors) to that of the poor-performing group (i.e., survey partici-
pants who consider their innovation performance has ‘no difference’ or is
‘worse’ or ‘much worse’) when innovation is the dependent variable, ANOVA
was run to test the mean differences in a firm’s emphasis on each SECI
activity of the KCP. As shown in Table 5, the mean differences of KCP in a
firm’s emphasis on both externalization and combination activities between
the two groups are significant (p < .01). However, no significant difference
regarding either socialization or internalization between the two groups is
presented (p > .05). Furthermore, we ran a regression test to find which
SECI activities have a significant effect on a firm’s innovation performance.
The results are presented in Table 6. Both externalization and combina-
tion activities have a significant positive effect on innovation performance
(p< .01). In sum, H1a and H1b cannot be rejected.
To compare the KCP of the better-performing group (i.e., survey partic-
ipants who consider their customer satisfaction ‘better’ or ‘much better’
than their competitors) to that of the poor-performing group (i.e., survey
participants who consider that their customer satisfaction has ‘no differ-
ence’ or is ‘worse’ or ‘much worse’) when customer satisfaction is the
dependent variable, ANOVA was also run to test the mean differences in a
firm’s emphasis on each SECI activity of the KCP. Also shown in Table 5, the
mean differences of the KCP in a firm’s emphasis on both socialization and
internalization activities between the two groups are significant (p < .01).
However, no significant difference regarding either externalization or combi-
nation between the two groups is presented (p> .05). Furthermore, we ran
a regression test to find which SECI activities have a significant effect on
a firm’s customer satisfaction performance. The results are also presented
in Table 6. Only internalization activity has a significant positive effect on
satisfaction performance (p< .01), while socialization activity has no effect.
In sum, H2a is not supported, and H2b cannot be rejected.
Discussion and Conclusion
This study analyzed the SECI activities when firms face different organiza-
tional goals. Few firms succeed in improving their KM performance despite
substantial resources devoted to it. In their research, Pfeffer and Sutton
(1999) examined what leaders do, how they spend their time, and how they
International Journal of Management, Knowledge and Learning
How the Organizational Goals Affect Knowledge Management 15
Table 5 The Anova Analysis for Innovation Orientation and Customer Satisfaction
Dummy N Mean SD F Sig.
In
no
va
tio
n
or
ie
nt
at
io
n S 0 70 3.456 0.559 3.010† 0.085
1 62 3.661 0.795
Total 132 3.552 0.685
E 0 70 2.838 0.678 17.051** 0.000
1 62 3.382 0.834
Total 132 3.093 0.800
C 0 70 3.304 0.667 13.265** 0.000
1 62 3.715 0.623
Total 132 3.497 0.676
I 0 70 3.810 0.722 1.890 0.172
1 62 3.979 0.688
Total 132 3.889 0.708
C
us
to
m
er
sa
tis
fa
ct
io
n S 0 73 3.443 0.521 4.255* 0.041
1 59 3.687 0.831
Total 132 3.552 0.685
E 0 73 2.977 0.681 3.529† 0.063
1 59 3.237 0.912
Total 132 3.093 0.800
C 0 73 3.410 0.631 2.742† 0.100
1 59 3.605 0.719
Total 132 3.497 0.676
I 0 73 3.712 0.721 10.937** 0.001
1 59 4.108 0.633
Total 132 3.889 0.708
Table 6 The Regression Analysis of Hypothesis
Item Innovation Customer Satisfaction
Model 1 Model 2 Model 3 Model 4
Socialization –0.135 –0.142 0.091 0.064
Externalization 0.296** 0.328** 0.013 0.086
Combination 0.288** 0.279* 0.017 –0.007
Internalization –0.023 –0.048 0.307** 0.301**
Turnover rate –0.128 –0.013
Rotation rate 0.101 –0.031
Firm size –0.051 –0.075
Degree 0.070 0.008
R2 00.201 0.239 00.132 0.138
F-value 7.926** 4.403** 4.807** 2.238*
Sig. F change 0.001 0.000 0.001 0.030
Notes p**<0.01, p*<0.05, p† <0.1.
Volume 3, Issue 1, 2014
16 Wei-Shong Lin, Jui-Ling Huang, and Margaret L. Sheng
allocate resources to close the ‘knowing-doing gap’ condition. The present
study offers important guidelines for firms regarding the question of how
to prioritize knowledge creation activities for different organizational goals
when facing limited resources and time.
Previous literature has shown that the knowledge creation process is im-
portant for new product development (Schulze, & Hoegl, 2006). An empirical
study by Lawson, Petersen, Cousins, and Handfield (2009) demonstrated
the importance of externalization activities during the development of new
products. In addition, total quality management (TQM) should be another
type of innovation, as it aims to improve the existing operation processes to
become more efficient (Hung, Lien, Fang, & McLean, 2010). Therefore, TQM
is also a kind of knowledge creation activity, and its innovation processes
also emphasize externalization and combination activities (Hackman and
Wageman, 1995; Martínez-Costa & Jiménez-Jiménez, 2009). In the service
industry, Leiponen (2006) showed that business service improvements and
new service introductions are significantly associated with collectively held
knowledge, such as codifying service solutions or team-based competences
and procedures. In sum, when a firm’s main object is innovation, it should
pay more attention to externalization and combination activities. That is,
a firm should provide channels to increase intra-organizational interaction
and discussion so that more ideas and problem-solving methods can be
generated (Nonaka & Takeuchi, 1995).
In addition, when customer satisfaction is the primary organizational
goal, internalization activity should be the main process; the reason is that
internalization activity helps explicit knowledge to become the tacit knowl-
edge of individuals. Griffith and Sawyer (2010) considered that information
processes are passed face to face, thus efficient transfer of tacit knowledge
will help improve customer satisfaction. Some researchers have discovered
that service firms are more oriented toward external sourcing of knowledge,
inter-organizational collaboration, and customer interaction and networking
(Howells, 2010; Mansury & Love, 2008). On the other hand, the experi-
ential knowledge of salespeople is gained through serving customers, an
individual and psychological process that matches the characteristics of in-
ternalization activities in Nonaka and von Krogh’s study (2009). In high-tech
companies, Reychav and Weisberg (2009) showed that sharing tacit knowl-
edge has a positive effect on enhancing employees’ customer goal. More-
over, Howells (2010) declared that customer service is always intangible
and perishable. Given these traits, what accumulates from customer ser-
vice experience is tacit knowledge. The sharing of tacit knowledge among
employees is the internalization activity in the KCP. That is, firms should offer
HR practices, such as apprenticeship, social interactions, model learning,
and simulation training, to enhance knowledge sharing and improve cus-
tomer satisfaction (Li et al., 2008), as well as employees’ work attitudes.
International Journal of Management, Knowledge and Learning
How the Organizational Goals Affect Knowledge Management 17
In addition, firms can also create an environment that embodies a learning
climate and a training system for employees to increase the efficiency of
a firm’s internalization activities (Castrogiovanni, Urbano, & Loras, 2011;
Lambert & Vero, 2013).
Limitations and Suggestions
The results of this study clarify the differences in the KCP for different orga-
nizational goals and build up the guidelines for the KCP in different firms so
that they are enabled to modify their processes to match their organizational
goals. Following our findings, a firm will be able to enjoy more competitive
advantages compared to rivals who do not mark out the activities they need
most.
However, this study still has some limitations. First, the sample size is
small, which is a common problem in the empirical literature about firm
performance. Second, the research is based on self-reported data, which
might incur the possibility of common method bias; additionally, the relia-
bility of the sample is not higher than .9. However, the tests of common
method variance did not find a serious problem in this study. The reliability
is about .88. The reason is that the objective of this study includes the
manufacturing and service industries; hence, some questionnaire items in
this study are fit for the manufacturing industry, but not for the service in-
dustry, whereas some are suitable for the service industry but not for the
manufacturing industry. This would be the reason why the validity of this
questionnaire is relatively low. However, this study also used multiple as-
sessments to detect validity, such as convergent validity and discriminant
validity. Future research might use a project-base for KM processes in dif-
ferent industries to strengthen the research design.
As for future research suggestions, this study did not take into account
the empowerment of employees when they perform specific knowledge ac-
tivities. A manager of a learning organization should make it an ‘execution-
as-learning’ organization, where the employees are enabled to collaborate,
by making information available, routinely capturing process data, and find-
ing ways to improve (Edmondson, 2008). To achieve this goal, the organi-
zation’s KM must involve empowerment. Therefore, we suggest that future
studies consider the effects of empowerment on the efficacy of KM.
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Wei-Shong Lin is Associate Professor at the Department of Business Admin-
istration at National Taiwan University of Science and Technology. He holds a
PhD in Economics from the University of Kansas, USA. He teaches MBA and
PhD classes in Strategic and Organization Management. His research inter-
ests relate to Strategic and Organizational Design. wlin@mail.ntust.edu.tw
Jui-Ling Huang is Doctoral candidate at the Department of Business Adminis-
tration at National Taiwan University of Science and Technology. Her research
interests relate Strategic and Organizational Design.
D9408102@mail.ntust.edu.tw
Margaret L. Sheng is Associate Professor at the Department of Business Ad-
ministration at National Taiwan University of Science and Technology. She
holds a PhD from the University of Minnesota, USA. She teaches MBA
and PhD classes in Marketing and creativity management. Her research
interests include Innovation Adoption and Diffusion, E-commerce, Knowl-
edge Management, International Marketing, Creativity and Entrepreneurship.
msheng@mail.ntust.edu.tw
This paper is published under the terms of the Attribution-
NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
License (http://creativecommons.org/licenses/by-nc-nd/4.0/).
International Journal of Management, Knowledge and Learning