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A social software strategy for knowledge management and organization culture

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In the information era, social software recently emerges as an effective tool for knowledge workers to collaborate and share knowledge. This article presents an analytical model in which a social software strategy is applied to complement an extant knowledge base to improve an organizational culture fit and achieve maximal profit. Capturing the mutual effects among social software, knowledge base and organizational culture fit, we determine the optimal level of social software and investigate how it changes with the crucial factors including the volume of knowledge base and initial organizational culture fit. This research provides valuable insights for practitioners to implement social software for knowledge management.
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Original Article
A social software strategy for
knowledge management and
organization culture
Zuopeng (Justin) Zhang
School of Business and Economics, State University of New York
at Plattsburgh, Plattsburgh, New York 12901, USA.
E-mail: zzhan001@plattsburgh.edu
Abstract In the information era, social software recently emerges
as an effective tool for knowledge workers to collaborate and share
knowledge. This article presents an analytical model in which a social
software strategy is applied to complement an extant knowledge base
to improve an organizational culture fit and achieve maximal profit.
Capturing the mutual effects among social software, knowledge base
and organizational culture fit, we determine the optimal level of social
software and investigate how it changes with the crucial factors includ-
ing the volume of knowledge base and initial organizational culture fit.
This research provides valuable insights for practitioners to implement
social software for knowledge management.
OR Insight (2012) 25, 60–79. doi:10.1057/ori.2011.14;
published online 20 July 2011
Keywords: knowledge management; organizational culture; social
software; strategy
Received 10 November 2010; accepted 23 May 2011 after two revisions
Introduction
The development of the latest information technologies witnessed the
emergence of Web 2.0 that was first proposed by O’Reilly (2005) who believed
that the transition from Web 1.0 to Web 2.0 demonstrates that the web has
changed its focus from publication to participation. Web 2.0 technologies
facilitate the development of social software, referred as the ‘software which
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www.palgrave-journals.com/ori/
supports, extends or derives added value from human social behaviour,
message-boards, musical taste-sharing, photo-sharing, instant messaging,
mailing lists and social networking (Coates, 2005)’. The major types of social
software include weblogs, wikis and various kinds of social network services,
such as social tagging, bookmark sharing and so on. Social software provides
fundamental support for direct interactions between people or groups, for
social feedback, and for social interactions (Boyd 2006).
Firms have recently started to implement social software in exploiting and
managing organizational knowledge assets that are identified as the main
source of organizational competitive advantage (Kogut and Zander, 1992;
Nonaka, 1994). Integrating social software on their current knowledge man-
agement (KM) platforms, firms can create virtual communities of interests for
employees to interact with each other to share information and knowledge
so as to improve productivity. For instance, British Telecom (BT), one of the
strongest proponents of enterprise social software, has adopted a series
of social software tools, including a huge wikipedia-style database called
BTpedia, a central blogging tool, a podcasting tool, project collaboration soft-
ware and an enterprise social networking platform. Using these social software
technologies has enabled the company to break through traditional boundaries
of communication and gain global commercial opportunities (Hill, 2008).
Viewing social software as a valuable tool for organizational KM, vendors are
competing for the market of enterprise social software used for collaboration
and knowledge sharing. For instance, IBM launched Lotus Connections
to compete with Microsoft’s SharePoint (Lynch, 2008). Jive allows firms to
customize their social software applications to meet their specific needs
and business objectives and provides a flexible pricing schema for firms with
demands for different levels of social software applications.
1
Despite the important role of social software and its practical applications
in KM, the fundamental relationship between KM and social software is not
well understood. In particular, the economic benefits that social software
can bring to firms through better managing organizational knowledge assets
and promoting organizational culture have not been studied in prior research.
Our research addresses this gap by investigating the strategies of social
software for firms to manage organizational knowledge assets to promote
organizational culture and seek maximal profits. Akin to the third level of
culture definition identified by Leidner and Kayworth (2006), we view organi-
zational culture as the essential knowledge or skills that a knowledge worker
should possess within an organization in order to be an effective revenue
producer. Thus, culturally unfit knowledge workers will become fit if they can
obtain the necessary knowledge through various channels. We study how
social software can serve as a major instrument in facilitating knowledge
Social software strategy for knowledge management
61&2012 Operational Research Society Ltd 0953-5543 OR Insight Vol. 25, 2, 60–79
sharing and learning so as to transform workers to become culturally fit in
organizations. Specifically, our research makes significant contribution by
answering the following research questions.
First, how does social software relate with KM to improve organizational
culture fit?
Second, what is the best level of social software that firms should
implement?
Third, what are the critical factors that affect the optimal levels
of social software?How do they impact the optimal levels of social software?
The rest of the article proceeds as follows. The next section reviews related
literature, focusing on the interconnections among social software, KM and
organization culture. The subsequent section presents our model of
implementing social software as a KM strategy to improve the organizational
culture fit. The penultimate section details the analysis on the best level of
social software and how it changes with some crucial factors. The final section
concludes the entire article with limitations and future extensions.
Related Literature
In this section, we first review prior literature by focusing on two streams
of research in KM: (a) KM and organizational culture and (b) role of social
software in KM, and then highlight the contribution of our research to the
current literature.
Many studies have examined the implications and effects of organizational
culture on various aspects of KM. For instance, Park et al (2004) empirically
verify the correlation between organizational culture and KM and confirm that
some critical organizational attributes facilitate knowledge sharing and help
implement KM technologies. Applying a web-based knowledge base for tacit
knowledge transfer, Lemken et al (2000) argue that developing an organiza-
tional culture that promotes knowledge sharing enables organizations to
sustain their KM practice to adapt to changing environments. Donate and
Guadamillas (2010) study how organizational culture affects KM processes and
find that organizational culture has different moderating effects when firms
adopt different KM initiatives on storing and transferring organizational
knowledge. Analysing four types of organizational cultures (clan, adhocracy,
market and hierarchy), Fong and Kwok (2009) investigate their effects on the
knowledge flow and the success of knowledge management systems (KMSs) in
contracting organizations. They claim that clan culture is the most popular at
the project and organizational levels, whereas hierarchy culture is the least
favored. Focusing on two major KM approaches (organizing communities,
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62 &2012 Operational Research Society Ltd 0953-5543 OR Insight Vol. 25, 2, 60–79
KM processes), Leidner et al (2006) examine how organizational culture
influences these approaches and suggest that knowledge initiatives may
evolve into the formation of either an information repository or electronic
communities. Extending the model of organizational cultural fit (Carrillo and
Gromb, 1999) that depicts the congruence of workers with the given organi-
zational culture, Zhang (2009) constructs a KM framework and studies
the relationship between personal KM and organizational KM. Although there
exists abundant research on how organizational culture influences KM practice
and strategies, few studies have investigated the effect of KM on organiza-
tional culture, which is the focus of our research.
Recent studies in KM have also shown increasing interest in the relationship
between social software and KM, exploring the special role of social software
in transferring and creating knowledge. Focusing on ‘personal knowledge
repositories and learning journals or networking instruments’, Efimova (2005)
investigates how to use social software in managing personal knowledge
assets. Avram (2006) discusses the application of social software in regulating
five core KM activities, including knowledge scan, creation, codification,
transfer and reuse. Evaluating current organizational social bookmarking
solutions, Greenfield (2007) claims that social bookmarking applications can
be a very effective KM platform. Kim et al (2006) establish a conceptual
framework by integrating social network activities with knowledge processes
through social software agents. Their prototype enables knowledge workers
to collaborate and share knowledge via a human or software agent in a
wiki-based environment. Applying a participatory action research methods,
Jackson (2010) establishes a theoretically framework for knowledge capture
and maintenance with social software technologies. Classifying customer KM
based on two dimensions: knowledge and process, Zhang (2011) study how
to implement social software to effectively manage customer knowledge
assets so as to facilitate online transactions. However, none of these studies
have specified social software as a useful strategy in supplementing normal KM
practice in promoting organizational culture, which we investigate in this research.
In summary, despite the growing awareness of the critical role of social
software in KM, none of the prior research has explicitly studied how to im-
plement social software for managing knowledge assets in organizations. In
addition, the mutual effects among social software, knowledge base and or-
ganizational culture have not been explored yet. We address these important
issues in our research. In particular, adopting the idea of cultural fit (Carrillo
and Gromb, 1999), we consider that KM practices can improve a firm’s cultural
fit. Thus, the firm seeks the best KM strategy implemented with both social
software and a KM repository to leverage knowledge assets within organiza-
tions to increase organizational cultural fit, maximizing organizational profit.
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ModelofSocialSoftwareStrategy
In this section, we present a model of social software strategy for KM and
organizational culture. Beginning with the discussion of the five mutual effects
among the three components in our model, we then describe our model’s
business setting and the organizational decision problem.
We consider a model in which a firm seeks the best social software strategy
implemented with a KMS to leverage knowledge assets within organizations to
increase organizational cultural fit, maximizing organizational profit. The KMS
consists of two major components: a centralized knowledge base stores
organizational information and knowledge and social software enables workers
to interact, socialize and share knowledge. Our model captures the five mutual
effects among the knowledge base, social software and organizational culture
fit (see Figure 1) as follows:
Effect 1: The knowledge base provides useful information (knowledge) for
workers to become culturally fit.
Effect 2: The social software enables workers to obtain relevant information
(knowledge) from other workers so as to become culturally fit.
Effect 3: If there are more culturally fit workers in the firm, the probability will
be higher for a culturally unfit worker to be helped through the social software.
Effect 4: If a worker is helped through the social software, the solution
will be recorded in the knowledge base, augmenting the volume of the
knowledge base.
(2)
(1)
KMS
Knowledge Base
Social Software
Organizational
Culture Fit
(3)
(4) (5)
Figure 1: The model of social software strategy: Mutual effects.
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Effect 5: If a worker can find the relevant information (knowledge) from the
knowledge base, the social software will be less useful for workers to seek
solutions from others.
We next describe the business setting of the model with the above five effects
embedded. The firm hires knowledge workers (measure at 1) from the labour
market to perform a series of tasks for finite nperiods. On the labour market,
only f
0
proportion of the hired workers are ‘fit’ for organization culture A.
In each period, workers generate a revenue R
L
when they are not fit for
culture Aand R
H
¼R
L
þDotherwise. Workers get fixed wage payment win
each period regardless of whether they are fit for culture A. Assume that the
revenue generated from each worker is always greater than the wage
payment, that is, R
L
4w. At the end of each period, each worker voluntarily
quits the firm with probability 1land joins the other firms. Besides, the firm
screens workers at the end of each period to check whether workers are ‘good
fits’ for current culture A. Those who are not fit will be identified and fired with
probability P. The left and fired workforce will be replenished from the labour
market.
On the basis of our proposed KMS platform, the firm applies appropriate KM
strategy to facilitate knowledge sharing and learning among hired workers in
each period so that those unfit workers may become fit after learning either
from the knowledge base or from other culturally fit workers. After the firm’s
KM initiatives in time period t,r
t
proportion of initially unfit workers becomes fit
for culture A. The rest of the proportion 1r
t
remains unfit with culture A.
We define the proportion r
t
as the performance index (PI) of the implemented
KMS in time period t, which measures the proportion of culturally unfit workers
being transformed into fit. In our study, we assume that the knowledge base
already exists in the firm. Hence, the firm’s KM strategy is to determine the
level e
t
(for each time period t) of social software so as to complement its
current knowledge base. We further assume that the total investment c(e
t
)
on social software convexly increases in the level e
t
of social software,
which implies that a higher level of social software demands a larger amount
of investment.
A worker always tries to merge into the current culture by utilizing the
organizational KMS. The worker first searches the knowledge base for the
solution to her questions (Effect 1); the larger the current volume K
t1
of
the knowledge base, the higher the probability g(K
t1
) of the solution available
from the knowledge base (Effect 5). We model the capability of the knowledge
base in providing solutions to workers as a linear two-stage function, akin to
the revenue function in Zhang (2009). As shown in Figure 2, the probability
g(K
t1
) linearly increases in the current volume of the knowledge base K
t1
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until it reaches the threshold level K
ˆ, beyond which g(K
t1
) achieves its
maximal value ^
g. We use the threshold level K
ˆto measure the maturity of the
knowledge base: if the volume of the knowledge base is lower than K
ˆ,
the knowledge base will not be mature yet; and if the volume of the knowledge
base is higher than K
ˆ, the knowledge base will be mature, sustaining a steady
probability ^
gof returning useful information to workers’ queries.
Nevertheless, the knowledge base may not always contain the wanted in-
formation or knowledge even when it is mature. If the desirable solution does
not exist in the knowledge base, the worker will resort to her peer workers for
help through the social software (Effect 2); we assume that the more so-
phisticated the level e
t
of social software, the higher the probability of her
obtaining the solution from other workers. In addition, when there are more
culturally fit workers in the firm, it is more likely that the questions from
the culturally unfit workers will be answered (Effect 3). In this regard, we use
S(e
t
,f
t1
) to denote the overall probability of a culturally unfit worker’s
question being addressed by other workers through social software, where
S(e
t
,f
t1
) concavely increases in eand f
t1
. Hence, the PI, or the proportion of
unfit workers being transformed into fit ones in time period tcan be expressed as
rt¼gðKt1ÞþSðet;ft1Þ½1gðKt1Þ:
During this transformation, if the worker’s question is answered by her peer
workers, the solution will be saved in the knowledge base (Effect 4); therefore,
the volume of the knowledge base in time period twill be augmented through
the knowledge-sharing activities in the current period. On the basis of its
volume in the previous period, the knowledge base’s volume in time period tis
Kt¼Kt1þSðe;ft1Þyð1ft1Þ;
Ki
K
ˆ
ˆ
(Ki)
Figure 2: The graphical representation of function g(K
i
).
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where yrepresents the average amount of knowledge to be stored in the
knowledge base when a worker’s question is answered via the social software
and the term 1f
t1
measures the proportion of culturally unfit workers at
the beginning of time period t(in time period t1).
Consequently, the firm’s decision problem [P] is to determine the best level
of social software in each period to maximize its total payoff at the end of time
period n, which is
max
et
pn¼X
n
t¼1
bi1RLwÞþftDcðetÞg:
In other words, the firm has to decide how to implement social software
to complement its current knowledge base to develop its organization culture,
while maximizing its total profit (see all the notations in Table 1).
Analysis and Discussion
We analyse the firm’s best social software strategy (that is, the optimal level of
social software in each period) to increase organizational cultural fit and
achieve the maximal profit. Beginning with the analysis on the organizational
culture fit, we then investigate the optimal levels of social software for both
single-period and multi-period settings.
Table 1: Summary of notation
Acurrent organization culture in firm X
bdiscount rate
c(e
t
) cost of implementing social software at level e
i
Ddifference of revenues between culturally fit and unfit workers
e
t
level of social software in time period t
g(K
t
) probability of a worker finding her solution in knowledge base
K
t
volume of knowledge base in time period t
lprobability of a worker staying in the firm in each period
Pprobability of a worker being identified as unfit in each period
f
0
probability of a worker being fit with culture Aon the market
f
t
organizational cultural fit in time period t
R
L
revenue from culturally unfit workers
R
H
revenue from culturally fit workers
r
t
PI of KMS in time period t
S(e
t
,f
t1
) probability of a worker being helped through social software
tindex for time periods
wfixed wage payment for workers in each period
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Organizational culture fit
We first study the organizational culture fit in each period and the factors
that influence the cultural fit.
Given our model setting about the KM strategy via social software for
improving organization culture, the firm’s cultural fit in the first period is
f1¼½f0þð1f0Þr1lþfð1f0Þð1r1Þð1lÞþð1f0Þð1r1Þlp
þ½f0þð1f0Þr1ð1lÞgf04f0;
where the term [f
0
þ(1f
0
)r
1
]lstands for the actually fit workers who decide
to stay, including those that have been transformed from culturally unfit into fit
ones, the term (1f
0
)(1r
1
)(1l) denotes the workers who remain unfit
and quit the firm, the term (1f
0
)(1r
1
)lprepresents the workers who
remain unfit and want to stay, but are fired because of being identified as
culturally unfit and the last term [f
0
þ(1f
0
)r
1
](1l) corresponds to the
culturally fit workers who quit the firm.
Generally, the firm’s cultural fit in time period tcan be expressed using a
function of the cultural fit in time period t1as
ft¼½ft1þð1ft1Þrtlþfð1ft1Þð1rtÞð1lÞþð1ft1Þð1rtÞlp
þ½ft1þð1ft1Þrtð1lÞgf0
¼lð1rtÞð1pf0Þft1þ½1lþlpð1rtÞf0þlrt:
We demonstrate that the organizational cultural fit in each time period
increases with several critical factors in Lemma 1.
Lemma 1 The organizational culture fit f
t
in each time period t always
increases in r
t
,land p.
Proof See Appendix A. &
Lemma 1 indicates that the organizational cultural fit in each time period
always increases with the PI of its current period. Apparently, if the firm
exerts more effort in promoting knowledge transfer with the KMS, more
culturally unfit workers will become fit, resulting in a higher level of the
organizational cultural fit in each period. In addition, Lemma 1 also indicates
that organizational culture fit increases in each period when more workers
are willing to stay in the firm or the firm has better ability in identifying
culturally unfit workers.
As r
t
¼g(K
t1
)þS(e
t
,f
t1
)[1g(K
t1
)], the PI r
t
in time period tincreases
with the volume of the knowledge base at the beginning of time period tand
the level of the social software e
t
in time period t. Therefore, the organizational
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68 &2012 Operational Research Society Ltd 0953-5543 OR Insight Vol. 25, 2, 60–79
cultural fit in each time period increases with the volume of the knowledge
base at the beginning of the time period and the level of the social software
implemented in the current period.
Optimal levels of social software
We detail our analysis on the optimal levels of social software in this sub-
section. We first study the optimal level of social software in a single-period
setting and examine the impact of various factors on the optimal level, and
then proceed with the investigation of the multi-period setting.
Single period
We first show how the firm should determine the level of social software in one
period in the following proposition, providing useful insights for our further
analysis.
Proposition 1 The optimal level e*of social software in a single-period
setting can be derived from the following equation:
lf0ð1f0Þð1pf0Þ½1gðK0ÞD¼c0ðeÞ
s0ðeÞ:ð1Þ
Proof See Appendix B. &
Proposition 1 characterizes the optimal level of social software in a single
period, from which some valuable insights can be inferred. First, when the
volume of knowledge base is sufficiently large (when g(K
0
) approaches 1),
there will be no solution to e* which implies that if the current knowledge base
is already good enough to cover all the topics for workers to become culturally
fit, the firm should not implement social software at all. Second, Proposition 1
indicates that the optimal level of social software depends on some crucial
factors, which are summarized in the next proposition.
Proposition 2 In a single-period setting, the optimal level of social software
increases with l, decreases with p and K
0
.
Proof See Appendix C. &
Proposition 2 demonstrates that when workers are more willing to remain in
the firm, the firm should increase the optimal level of social software.
Intuitively, when there are more workers staying in the firm, the organizational
cultural fit tends to be lower; hence, the firm needs to implement more
sophisticated social software. Proposition 2 also implies that when the firm can
better monitor workers to identify culturally unfit workers, or when the initial
volume of knowledge base is larger, the firm should implement the social
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software at a lower level. In addition to these parameters (l,pand K
0
), the
optimal level of social software also changes with the organization’s initial
cultural fit f
0
, which is shown in the next proposition.
Proposition 3 In a single-period setting, there exists a threshold level
fL¼ðð1þpÞ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1pþp2
pÞ=3p such that when f
0
A(0, f
L
),
the optimal level of social software increases with f
0
, when
f
0
A(f
L
,1], the optimal level of social software decreases
with f
0
.
Proof See Appendix D. &
Proposition 3 shows that the optimal social software level does not mono-
tonically change with the initial organizational cultural fit f
0
, but first increases
and then decreases with f
0
. The turning point happens at a threshold level f
L
of the organizational cultural fit. The threshold level depends on the firm’s
ability of monitoring culturally unfit workers. Our Corollary 1 demonstrates
that the threshold level has both an upper bound and a lower bound. Hence, it
is certain that the optimal level of social software will increase and decrease in
two ranges of f
0
, independent of the threshold level.
Corollary 1 In a single-period setting, the threshold level f
L
belongs to the
range (1/3, 2/3) and the optimal level of social software
increases with f
0
when f
0
A[0, 1/3) and decreases with f
0
when f
0
A(2/3, 0].
Proof See Appendix E. &
Corollary 1 demonstrates the boundaries of the threshold level f
L
and
implies the existence of three ranges of the organizational cultural fit (see
Figure 3). As the threshold level resides in the middle rage, the optimal level
e*
12/31/3
00
L
Figure 3: The changing pattern of the optimal level of social software with respect to
f
0
in a single period.
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of social software always increases with the organization’s initial cultural fit
when it is in the lower range and decreases with the fit when it is in the upper
range. This result provides useful insights for managers to adjust the level of
social software based on their organization’s cultural fit.
Multiple periods
Following on our analysis on the single-period setting, we continue to
investigate the optimal levels of social software in multiple periods.
Proposition 4 In a multi-period setting, the optimal level e
t
,8t¼1, 2,
3, y,n,of social software in each period of time can be
obtained from solving the following simultaneous equations:
lft1ð1ft1Þð1pf0Þ½1gðKt1ÞD¼c0ðetÞ
s0ðetÞ;
8t¼1;2; :::; n:
ð2Þ
Proof See Appendix F. &
Proposition 4 demonstrates the existence of optimal social software level in
each time period. Equations (2) and (1) share a similar pattern. Hence, we
can obtain similar insights. When the volume of the knowledge base K
t1
at
the beginning of time period tis sufficiently large, workers may retrieve all the
culture-related questions from the knowledge base. Therefore, social software
will not serve as a necessary component for the firm’s KMSs. In addition,
the optimal level of social software e
t
also relates with several crucial factors,
which is summarized in the next proposition.
Proposition 5 In a multi-period setting, the optimal level of social software
e
t
in time period t increases in l, decreases in p and f
0
.
Proof See Appendix G. &
Proposition 5 suggests that in a multi-period setting, the optimal level of
social software in each time period increases when more workers are willing to
stay in the firm, and decreases when the firm has a better ability of identifying
culturally unfit workers or there exists a higher level of organizational culture
fit at the beginning, which is consistent with that in a single-period setting.
However, in a multi-period setting, the optimal level of social software
demonstrates different patterns under different conditions when the time
periods unfold. We discuss these patterns in the next proposition.
Proposition 6 In a multi-period setting, when f
0
X1/2, e
t
keeps decreasing,
8t¼1, 2, y,n;when k
0
XK
ˆand f
n
o1/2,e
t
keeps increasing,
8t¼1, 2, y,n.
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Proof See Appendix H. &
Figure 4 summarizes the results of Proposition 6. As evidenced from equation
(2), the optimal level of social software in time period tdepends on two driving
forces: the organizational culture fit and the volume of the knowledge base at
the beginning of time period t. The continuously increasing volume of the
knowledge base enables workers to obtain solutions simply from the knowl-
edge base, so the level of social software should decrease accordingly. The
organizational culture fit has a mixed effect on the level of social software: (a)
when the majority of workers are culturally fit, social software will not
be utilized so much, so social software can be less advanced; (b) when the
majority of workers are culturally unfit, the firm needs more sophisticated
social software applications to facilitate workers’ transformation. Combining
the effects of both organizational culture fit and volume of the knowledge base
on the level of social software, we find that if the knowledge base at the
beginning is already mature (that is, K
0
XK
ˆ), then the knowledge base will not
have any direct effect on the social software; hence, the level of social software
keeps decreasing if the majority workers are culturally fit at the beginning
(that is, f
0
X(1/2)) and the level keeps increasing if the majority workers are
still culturally unfit at the end (that is, f
n
o(1/2)). In addition, if the organi-
zational culturally fit is sufficiently large at the beginning (that is, f
0
X1/2),
then the knowledge base will have the similar effect on the levels of social
software as the organizational culture fit does, resulting in a continuously
decreasing level of social software for each time period. Finally, when the
knowledge base and the organizational culture fit have opposite effects on the
social software (that is, f
n
o(1/2) and K
n
oK
ˆ), the optimal level of social
software does not change monotonically; the firm has to evaluate which effect
is dominant so as to determine the changing pattern of social software levels.
Optimal levels of social
software in each period
e1,e2,...,en
Organizational Culture Fit
At the end
Volume of
Knowledge Base
At the
beginning
keep increasing
At the
end
Undecided
1
2
1
2
<
keep decreasing
keep decreasing
At the beginning
Ko K
.
.
Kn< K
0n
Figure 4: The changing pattern of the optimal levels of social software in a
multi-period setting.
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Conclusion
KM has recently received much attention with the development of social
software technologies that enable knowledge workers to collaborate, socialize
and share knowledge in an unprecedented manner. However, there lacks
research on the mutual relationship among KM, social software and organi-
zational culture. Exploring the social software approach as a KM strategy in
improving organizational culture fit and achieving maximal profit, our article
addresses the gap.
Specifically, our research makes the following original contributions. First,
we present a model of social software strategy that captures the mutual effects
among social software, KM and organizational culture fit. We view social
software as a beneficial supplement for firms’ central knowledge base to
enable knowledge workers to acquire relevant information and knowledge
so as to become culturally fit. Second, on the basis our proposed framework,
we analytically confirm that organizational KM initiatives enhance organiza-
tional culture fit; as time goes on, more knowledge workers will become
culturally fit due to knowledge sharing and learning promoted by firms’ KM
practice. Third, we demonstrate the existence of the optimal levels of social
software for both single-period and multi-period settings, which provide useful
guidance for firms to implement social software under different scenarios.
Fourth, we show that for both the single- and multi-period settings, the optimal
level of social software (in each time period) increases when more workers are
willing to remain in the firm, and decreases when the firm has a better ability of
identifying culturally unfit workers or a higher level of initial organizational
culture fit. Finally, we identify the changing patterns of optimal social software
levels in a multi-period setting under various conditions. For instance, when
the initial organizational culture fit level is relatively high (41
2), the best level of
social software implemented in each time period should keep decreasing;
when the organizational knowledge base has reached its mature level and the
initial organizational culture fit level is relatively low (o1
2), the best level of
social software should increase in each time period.
Our study is the first one analytically investigating the effects of social
software on KM and organizational culture. However, the proposed framework
and our derived results are based on some simplified assumptions for tract-
ability purposes. For instance, we assume that knowledge workers will become
congruent with the organizational culture if they can obtain the answers to
their questions from either the knowledge base or other fellow workers via the
social software. This assumption essentially results in a higher pace of trans-
forming workers to become culturally fit than the real-world scenarios. In
addition, we assume that the probability of workers obtaining solutions from
Social software strategy for knowledge management
73&2012 Operational Research Society Ltd 0953-5543 OR Insight Vol. 25, 2, 60–79
the knowledge base as a two-stage linear function, which is also critical for our
model and analytical results. Future research may relax these assumptions to
allow for more realistic modelling of organizational settings. In addition, future
extensions may further the analysis by empirically verifying the analytical
results in this research and studying the implementing issues of the optimal
social software strategy, such as how to apply KM techniques, achieve inter-
operability at different levels and satisfy security and privacy requirements,
which all affect the effective adoption of collaborative and social-oriented
framework proposed in this research.
This article provides valuable insights for managers to understand the
fundamental relationship between social software, KM and organizational
culture, and provides practical guidance to implement appropriate social
software strategy.
About the Author
Zuopeng (Justin) Zhang is an associate professor of Management Information
Systems at the School of Business and Economics in State University of New
York at Plattsburgh. He obtained his PhD in Business Administration from
Pennsylvania State University. His research interests include economics of
information systems, knowledge management, electronic business, workflow
systems and web services. His research has been recently published or will
appear at Knowledge Management Research & Practice,Journal of the
Operational Research Society,Business Process Management Journal,
Information Systems Management,Knowledge and Process Management,
Online Information Review,International Journal of E-Business Research and
other journals.
Note
1 See details at http://www.jivesoftware.com/products/how-to-buy.
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Appendices
Appendix A
Proof of Lemma 1
Proof By examining the relationship between the cultural fit and PI of the
KMS in the same period, we can clearly see that cultural fit can always
be improved by increasing PI because
qft
qrt
¼lð1ft1Þð1pf0Þ40:
In addition, the first-order derivative of f
t
with respect to lis
qft
ql¼ð1rtÞð1pf0Þft1f0þpð1rtÞf0
þrtþlð1rtÞð1pf0Þqft1
ql
¼ð1rtÞft1pð1rtÞf0ft1f0
þpð1rtÞf0þrtþlð1rtÞð1pf0Þqft1
ql
¼ðft1f0Þþrtð1ft1Þþpð1rtÞf0ð1ft1Þ
þlð1rtÞð1pf0Þqft1
ql40;
and the first-order derivative of f
t
with respect to pis
qft
qp¼lð1rtÞf0ð1ft1Þþlð1rtÞð1pf0Þqft1
qp40:
Therefore, f
t
increases with land p.&
Appendix B
Proof of Proposition 1
Proof The organizational culture fit at the end of a single period is
f1¼½f0þð1f0Þr1lþfð1f0Þð1r1Þð1lÞþð1f0Þð1r1Þlp
þ½f0þð1f0Þr1ð1lÞgf0;
where
r1¼gðK0ÞþsðeÞf0½1gðK0Þ
and
K1¼K0þsðeÞf0yð1f0Þ:
Zhang
76 &2012 Operational Research Society Ltd 0953-5543 OR Insight Vol. 25, 2, 60–79
Therefore
qr1
qe¼s0ðeÞf0½1gðK0Þ:
In addition, we know that
qf1
qr1
¼lð1f0Þð1pf0Þqr1
qe:
Hence, the first-order condition yields that
lf0ð1f0Þð1pf0Þ½1gðK0ÞD¼c0ðeÞ
s0ðeÞ
In addition, the second-order derivative of the firm’s payoff with respect to e
is negative. Hence, the optimal level e* exists. &
Appendix C
Proof of Proposition 2
Proof The left-hand side of equation (1) increases with l, decreases with p
and K
0
, so the optimal level of social software increases with l,
decreases with pand K
0
.&
Appendix D
Proof of Proposition 3
Proof Solving the quadratic function 3pf
0
2
2(1 þp)f
0
þ1¼0 by regarding p
as the parameter and f
0
as the variable, we obtain that
fL;H¼2ð1þpÞ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
4ð1þpÞ212p
q6p¼ð1þpÞ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1pþp2
p
3p:
Because 3p(1p)X0, therefore 1pþp
2
X4p
2
4pþ1. It follows that
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1pþp2
pX3pð1þpÞ, which is fH¼1þpðÞþ
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1pþp2
p=3pX1:
Because 1 þpXffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1pþp2
p,f
L
40. Therefore, when 0of
0
of
L
,Y40,
and when f
L
of
0
p1, Yo0. &
Appendix E
Proof of Corollary 1
Proof We use Yto denote the derivative of the left-hand side of equation (1)
with respect to f
0
, which is
Y¼l½1gðK0ÞD½ð1f0Þð1pf0Þf0ð1pf0Þpf0ð1f0Þ
¼l½1gðK0ÞD½12f02pf0þ3pf2
0:
Social software strategy for knowledge management
77&2012 Operational Research Society Ltd 0953-5543 OR Insight Vol. 25, 2, 60–79
Therefore, YX0if
pp12f0
2f03f2
0
and f00;2
3Þ;ðE:1Þ
or
pX12f0
2f03f2
0
and f0
2
3;1:ðE:2Þ
Yp0if
pX12f0
2f03f2
0
and f00;2
3Þ;ðE:3Þ
or
pp12f0
2f03f2
0
and f0
2
3;1:ðE:4Þ
In addition, we know that when f
0
A(0, 1/3) and (2/3, 1]
12f0
2f03f2
0
X1;
when f
0
A(1/3, 2/3)
12f0
2f03f2
0
p1;
which, when combined with equations (E.1), (E.2), (E.3) and (E.4), indicates
that Yp0 when f
0
A(0, 1/3) and Yp0 when f
0
A(2/3, 1). &
Appendix F
Proof of Proposition 4
Proof In a multi-period setting
ft¼lð1rtÞð1pf0Þft1þ½1lþlpð1rtÞf0þlrt;
where
rt¼gðKt1ÞþsðetÞft1½1gðKt1Þ:
The first-order derivative of f
t
with respect to e
t
indicates that
qft
qet
¼lð1pf0Þð1ft1Þqrt
qet
þlð1pf0Þð1rtÞqft1
qet
¼lð1pf0Þð1ft1Þqrt
qet
:
Zhang
78 &2012 Operational Research Society Ltd 0953-5543 OR Insight Vol. 25, 2, 60–79
As
qrt
qet
¼s0ðetÞft1½1gðKt1Þ;
the first-order condition of the firm’s total profit with respect to e
t
yields that,
8t¼1, 2, y,n
lft1ð1ft1Þð1pf0Þ½1gðKt1ÞD¼c0ðetÞ
s0ðetÞ:
In addition, the second-order derivative of the firm’s payoff with respect to e
t
is negative. Hence, the optimal level e
t
* in each time period tcan be
obtained. &
Appendix G
Proof of Proposition 5
Proof The left-hand side of equation (2) increases with l, decreases
with pand f
0
, so the optimal level of social software e
t
in time period
tincreases with l, decreases with pand f
0
.&
Appendix H
Proof of Proposition 6
Proof By examining equation (2), we find that when f
0
X1/2, the right-hand
side of equation (2) for the time period tis always less than that for the
time period t1. In addition, when f
n
o1/2 and K
0
XK
ˆ, the right-hand
side of equation (2) for the time period tis always greater than that for
the time period t1. &
Social software strategy for knowledge management
79&2012 Operational Research Society Ltd 0953-5543 OR Insight Vol. 25, 2, 60–79
... Within a particular organization, incorporating social software onto current knowledge management platforms, organizations can create virtual communities of interests for employees to interact with each other to share the knowledge and information that improves productivity (Zhang, 2012). For instance, British Telecom, a proponent of enterprise social software, has adopted a series of social software applications, including a Wikipediastyle database named BTpedia, a central blogging tool, a podcasting tool, project management software, and an enterprise social networking platform (Hill, 2008). ...
... Social media also enables employees to shift knowledge sharing from centralized to decentralized processes or from an intermittent to a continuous workflow (Ellison & Boyd, 2013). Organizations can also create virtual communities of interests, which empower employees in information sharing and knowledge transfer, thereby amplifying productivity levels (Zhang, 2012). ...
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