Social media information benefits, knowledge management and smart organizations

This paper examines the growth of social media within organizations and its impact on knowledge sharing in a particular type of KM system - Community of Practice - (CoP) based discussion groups (KMDG). It investigates two intermediate information mechanisms that social media KMDGs are theorized to generate, and quantifies their effects on labor productivity and return on assets.

Contents lists available at ScienceDirect
Journal of Business Research
journal homepage: www.elsevier.com/locate/jbusres
Social media information benefits, knowledge management and smart
organizations
Tahir M. Nisara,⁎, Guru Prabhakarb, Lubica Strakovaa
aSouthampton Business School, University of Southampton, Highfield, Southampton SO17 1BJ, UK
b Faculty of Business & Law, UWE, Bristol BS16 1QY, UK
A R T I C L E I N F O
Keywords:
Knowledge management
Information richness
Social media
Communities of practice
Discussion groups
A B S T R A C T
Social technologies can provide a potent means for organizations to manage their information flows and thu
induce changes in their knowledge management (KM) systems,which can then be linked to performance im-
provements. This paper examines the growth of social media within organizations, considering the impact th
may have upon knowledge sharing in a particular type of KM system - Community of Practice - (CoP) based
discussion groups (KMDG).We focus on this KM toolbecause it provides employees with an opportunity to
strategically reach out to different groups of people within their CoP, and engage in information exchange an
communication.Using a content analysis method,we investigate two intermediate information mechanisms
(information richness and informalcommunication) that socialmedia KMDGs are theorized to generate,and
quantify their effects on labor productivity and return on assets.Our findings provide evidence of KMDG po-
sitively affecting organizational performance through embedded information and social communication.
1. Introduction
The power of knowledge has become an importantresource for
organizations to develop expertise,solve problems,increase organiza-
tional learning, and initiate new situations for both the individual and
the organization now and in the future (Bell, 1973; Grant, 1996). The
amplified velocity and dynamic nature of the new economy, partnered
by substantialadvances in technology,has created an incentive for
many organizations to reconcile and utilize their knowledge in order to
generate value over a sustained period of time. The effective utilization
of a firm's intangible assets has also functioned as a catalyst for creating
a competitive advantage overother organizationsoperating in the
market (Leal-Rodríguez,Roldán, Leal, & Ortega-Gutiérrez,2013).
Knowledge management(KM) is a discipline that promotesan in-
tegrated approach to identifying, capturing, evaluating, retrieving, and
sharing all the enterprise'sinformation assetsincluded databases,
documents and procedures, among others (Leal-Rodríguez et al., 2013).
The speed by which knowledge management has become an integral
business function for many organizations is astounding, as reflected in
the way different KM systems have evolved over the years,including
‘communities of practice’(Levine & Prietula, 2012). It is these barriers
that influence the choice of a KM system to accomplish the access to
and deployment of knowledge in different workplace contexts.As or-
ganizations must consider a wide variety of technical and human issues
when choosing the ‘right’ mix of a KM system in order to lever
knowledge effectively,the firm's energy,organizationalactivity,and
investment can often result in ineffective KM initiatives. Becker (2002,
p.1041) argues that the coordination of knowledge involves more than
just identifying sources of knowledge, as the “dispersedness of knowl-
edge is inextricably linked to the problem of designing communication
structures.” Corso,Martini, Pellegrini,Massa,and Testa (2006) state
that informaland formalchannels,such as the intranet or corporate
portals, should be employed to help access this knowledge. Against this
background,we reflect on the recent surge of Internet-based technol-
ogies that have created a revolution in the way we communicate with
each other.The proliferation of social media usage within society has
permeated organizationsboth formally and informally. A range of
technologies from blogs to social networks have extended the reach of
the digital revolution to the organization,creating challenges and op-
portunities thatare expected to be compounded over time as social
media is further integrated into the organizational landscape.
While previously seen as a platform for establishing a convenient
link with friends and family across the world,today social media has
grown beyond a space for just personalized interactions— it has
transformed into a professionalspace running alongside the personal
space (Gal, Jensen,& Lyytinen, 2014; Jeppesen & Fredricksen,2006).
Interestingly,communication and organizationalspecialists have also
benefited from the opportunities this forum offers.They endeavor to
https://doi.org/10.1016/j.jbusres.2018.05.005
Received 5 August 2017; Received in revised form 1 May 2018; Accepted 5 May 2018
⁎ Corresponding author.
E-mail addresses: [e-mail] (T.M. Nisar), [e-mail] (G. Prabhakar), [e-mail] (L. Strakova).
Journal of Business Research 94 (2019) 264–272
Available online 10 May 2018
0148-2963/ © 2018 Elsevier Inc. All rights reserved.
T

exploit every bit of this space as an alternative path through which to
connect with and thus reach out to individualemployees,and some-
times even groups, and provide them with alternate — sometimes even
more exciting — opportunities againstconventionalmeans to com-
municate and collaborate with each other. Social media is often defined
along the lines of any website or application that enables users to en-
gage in social networking activities such as creating,sharing or inter-
acting with information (Piskorski, Eisenmann,Chen, & Feinstein,
2011). The surge in the developmentof the new technologicalplat-
forms within the ‘socialmedia’space such as search engines,next-
generation mobile communication devices and their correspondingly
sophisticated interfaces,expanded person-to-person communication
spectrums, and a plethora of the next generation of ‘online social net-
working’platforms have all contributed to the construction of a much
more encouraging and engaging space for organizations, affording them
improved and enhanced access to employee-generated content (Faraj,
Jarvenpaa, & Majchrzak, 2011; Gal et al., 2014). These efforts usually
complement how organizations strategize ways of leveraging knowing
and learning (Aral, Brynjolfsson, & Van Alstyne, 2012).
In this paper,we examine the extent to which these socialmedia
knowledge and learning benefitsaffect organizationalperformance.
The study focuses on one particular KM system, that is, communities of
practice- (CoP) based discussion groups (DGs) (Thompson,2005).We
measure the degree to which CoP-based DGs can exploit the potential of
social media and further develop the organization's knowledge base.
CoP-based DGs can now benefit greatly from social media-based two-
way communication channels that are much more effective and per-
sonal. Both the instantaneity of two-way social media communications
and the directionality of CoP discussions make an organization's DGs an
ideal candidate for investigating KM systems.Our conceptualframe-
work essentially builds on the notion oftwo-way socialmedia com-
munications,where both employees and organizations gain from the
new emerging technologicallandscape as when they engage in more
frequentand direct communications.Consequently,employee-gener-
ated content amplifies the organizationalknowledge base and,along
with firm-generated content,it potentially createsopportunitiesfor
improvements in organizationalperformance.We further refine these
knowledge exchange processes in terms of two new categories of in-
formation and knowledge management. If social media communication
enables superior work outcomes by helping individuals build informa-
tion-rich KM systems,it should also produce the same intermediate
information benefits that a KM system is theorized to provide. We thus
investigate the question ofwhether socialmedia-induced knowledge
management systems or KMDGs generate information richness and in-
formal and social communication. Rather than assuming that KM systems
can be understood as providing one type ofinformation benefit,we
unbundle information and knowledge mechanisms into different types
(i.e. information richness and informal communication). Consequently,
we can not only quantify the actualbenefits of both KM systems and
technology,but also discern the more relevanttype of information
mechanism that affects organizational performance (Goh, Heng, & Lin,
2013; Wu, 2013).
Our conceptualization of user-generated content-based KM systems
as characterized by information richness and informal communication
sheds light on the nature of evolving organizational technologies that
draw on multi-sided communication platforms employed by different
sets of users (e.g., employers-employees). We therefore fill an important
research gap in the literature on socialmedia and knowledge man-
agement systems as prior research has mainly focused on narrow clas-
sifications of knowledge and information transformation mechanisms
(Baird & Parasnis,2011).We define information richness as the het-
erogeneity ofinformation content in an individual's posts on KMDG.
Earlier access to a variety of information sources allows an individual to
gather increasingly diverse information, which can be instrumental to
productivity.Informal communication measures how much ofan in-
dividual's communication is related to socializing and informalsocial
activities.Guzman and Trivelato (2008,p.255) emphasize the im-
portance ofsocialization,as “only through spending time together…
can experience be shared.” We then examine the extent to which social
media-based information and knowledge mechanismsare positively
related to organizationalperformance.Our findings will likely show
that socialmedia has the potentialto transform and change existing
organizational structures by making KMDGs an integral KM system that
increases company knowledge. This transformation can have important
economic consequences,such as improving worker productivity and
firm profitability.We could then suggest that the growth of Internet-
based high-competition markets means that organizations can respond
faster and are able to resolve more complex problems through social
collaboration and knowledge sharing.
The paper is organized as follows.In the first section,research on
knowledge management, social media and consumer-generated content
is reviewed and amalgamated to piece together the bigger picture of the
role and effects of communities of practice and discussion groups within
an organization's knowledge management system.The following sec-
tion provides a review of the study's methodology and introduces our
data.We then present our research findings.In the final section,we
examine the implications of our results for future research and discuss
the study's limitations.
2. Research hypotheses
2.1. KM systems and information benefits
Our next question is whattype of UGC social media do KMDGs
produce? This question is pertinentbecause,as Fernandez(1991)
contends, some information benefits may facilitate organizational pro-
cesses and may even have competing performance implications.This
means that it is important to measure and classify various types of in-
formation benefits in order to understand how they affectwork and
organizationaloutcomes (Goh et al.,2013; Wu, 2013).Similar argu-
ments can be advanced in relation to a KM system because itwas
previously difficultto observe and classify the type ofinformation
generated by a DG. Old communication systems had limited ability to
precisely capture the content of people's communications. Using social
media tools,one can now record and process employee-generated in-
formation and use it to quantify various aspects of information benefits.
Aral and van Alstyne's (2011) study of email traffic shows that brokers
are more likely to deal with heterogeneous information. KM systems are
also likely to improve the knowledge contentof human interactions
that take place within a CoP.Chen and Xie (2008) identify how en-
ormously diverse conversations can take place on socialmedia.Con-
sequently,it becomes importantto fully understand the type ofin-
formation that is being transferred between individualsinside an
organization.In addition, social media-based KM systems may allow
individuals to make socialcontacts with each other,thus increasing
social communication within the system.There is a large body of
marketing literature thattreatsinformal and social communication
different from other types of user-generated content(Chen & Xie,
2008). For example, social communication is distinct from information
richness in that it captures the intensity of one type of information that
helps build stronger personalrelationships(Goh et al., 2013; Wu,
2013). In our present context,we can thus distinguish between in-
formation richness and informal and social communication as two distinc
types of KMDG-generated information mechanisms.Such mechanisms
are arguably vitalin supporting KM systems fortwo clear reasons.
Firstly, discussing issues raised within the official records, through so-
cial media mechanisms,helps people to understand the tacitdimen-
sions of knowledge embedded within the records (Roberts, 2001). This
is important for many organizations, as the parties may not be experts
in some fields,yet they may need a good understanding ofall per-
spectives to make commercially viable decisions.Secondly,it may be
that such socialmedia communications stimulate further knowledge-
T.M. Nisar et al. Journal of Business Research 94 (2019) 264–272
265

sharing among parties as relationships develop.Such observations de-
monstrate the importance of explicit knowledge-sharing considerations
within a KM system. We therefore present our first set of hypotheses.
Hypothesis 1A. Social media KMDGs increase information richness
within a KM system.
Hypothesis 1B. Social media KMDGs increase informaland social
communication within a KM system.
2.2. Information richness
Social media KMDGs allow users to interact with a diverse group of
people,with expertise across a range ofdifferentoperationalareas.
Information gathered from these work-related interactions with col-
leagues and friends is the primary purpose that KMDGs can serve. Such
serendipitous events may generate important research ideas, and help
organizations get feedback and analyze users'interactions in order to
improve their work processes and outcomes. Social media allows users
to come together online and exchange, discuss, communicate and par-
ticipate in various forms of social interaction. In addition, there may be
some specific reasons for employees to engage in socialmedia-based
KMDG communication.These may consist of prior knowledge and ex-
perience, social ties, and learning and developmental goals (Dellarocas,
2003). For example,employees would often look foradditional in-
formation when dealing with a new client, operating new technology,
or adapting to a new organizational system.Furthermore,co-workers'
judgments on these and other related matters can shed important light
on workplace changes that happen quite frequently.Whereas KMDGs
compensate for the inadequate knowledge and experience of some of
the employees,they also provide opportunities for dialogue and in-
formation-sharing. Given that insufficient knowledge and experience is
one of the motivations ofemployees to engage in socialmedia,it is
likely that a diverse range of topics will crop up in KMDGs. It is likely
that the resulting communication will enable a KM system to generate
superior return by various performance measures.Moreover,KM lit-
erature provides extensive arguments for why KM systems'informa-
tiveness may positively impact productivity (Adler,2001;Fernandez,
1991; Wu, 2013). Such systems can be viewed within the framework of
instrumentalunderstanding as they generate task-related information
and advice (Garicano & Wu, 2012). We can therefore take information
richnessas a proxy for instrumentalKMDG relationships.They are
crucial to higher work performance through their effect on knowledge
sharing. On the other hand, there is an issue of organizational control of
employee-generated content (such as to prevent knowledge leakage);
this is likely to affect knowledge generation in an organization.How-
ever,since the direction of the relationship between KM systems and
organizational performance is still likely to be positive, we hypothesize
the following.
Hypothesis 2. Information richness associated with KMDGs improves
organizational performance measured as labor productivity.
2.3. Informal and social communication
Informal and social communication focuses on how much ofan
actor's communication is related to socializing and informal social ac-
tivities,and thus it measures the intensity of one type of communica-
tion. Socializing informally with a diverse group of people opens the
way for individual employees to learn about new expert developments
and technology changes.They may even acquire both innovative and
conventionalsolutions to everyday production problems (Goh etal.,
2013; Wu, 2013). Not only can they getto know each other better
through these socializing activities, but they can also develop a diverse
circle of friends.It is important to consider the role ofinformal and
social communication within the context of traditional KM system tools
such as DGs. Second, social ties may lead online users to consider other
people's opinions (Burke,Kraut, & Marlow, 2011). The sense ofbe-
longing to the online community and altruism increases their awareness
of other users. Social ties may encourage people to share their knowl-
edge and expertise on the Internet (Chow & Chan, 2008); furthermore,
Chen and Xie (2008) report that the desire for social interaction and the
concern for other users are some of the reasons for writing online re-
views. In the presentKMDG context,online users mightshare their
views so as to develop their ability and persuasiveness,and they may
also wish to enhance their prestige and self-image in the virtual com-
munity.Because ofthese motivations,employees are likely to be in-
terested in interacting with and feeling part of such a community. Po-
sitive feelings are likely to encourage greater understanding of others'
behaviors and opinions, and people may take these feelings into a team
or group situation where collaboration is important. Informal and social
communication may thus encourage team play and cooperation and, if
more people can generate positive feelings about each other's skills and
efforts,they would be more willing to contribute to such initiatives
(Wu, 2013; Wu & Wang, 2006). We thus hypothesize thatinformal
communication resultsin improved levels of organizationalpro-
ductivity. In light of this discussion, we suggest the following hypoth-
esis.
Hypothesis 3. Informal and social communication associated with
KMDGs improves organizationalperformancemeasuredas labor
productivity.
2.4. Complementarities between information richness and informal
communication
Our current focus on knowledge,particularly for KM,is often ex-
plicitly oriented toward commercialeffectiveness.However,some re-
search claims that,in order to achieve the levelof effective behavior
required for competitive excellence, organizations must first overcome
various social,human and cognitive barriers before considering the
technological factors that enable effective knowledge sharing. It is not
always possible to simply use technology to seed the development of a
knowledge-sharing community (Brazelton & Gorry,2003). Thomas,
Kellogg, and Erickson (2001) recognize that organizational knowledge
is “inextricably bound up with human cognition, and the management
of knowledge takes place within an intricately structured socialcon-
text”.Hsu, Chen, Chiu, and Ju (2007) suggestthat strong levels of
employee interaction are crucial for organizations to remain competi-
tive, although a reliance on virtual knowledge sharing without neces-
sary incentives could reduce the motivation to share expertise across
the firm. An aspect of a social media KMDG is that it freely allows in-
formal communication and helps with building personal relationships.
It is in this context that information richness and informalcommu-
nication may reinforce each other's impact so as to take advantage of
both types of information and knowledge benefits in achieving desired
organizationaloutcomes.In this way, they could also overcome the
constraints that hamper the use of knowledge-sharing technologies. To
understand their joint effects on organizational outcomes, we examine
how information richness and informal communication together affect
organizational productivity. Information richness may help improve an
individual's work productivity,whereas informaland social commu-
nication can also play an important role in developing knowledge-based
communities by enhancing interpersonal relationships (Garicano & Wu,
2012).Informal activities and sharing of information are essential for
creating a contextof trust and confidence (Dixon,2000). Informal
communication enables higher levels of collaborative performance both
globally and locally and allowsbetterdecision making (Guzman &
Trivelato, 2008). The notion of complementarity refers to a variety of
effects (e.g., one variable reinforces the other; the effects are reciprocal;
one variable moderates the other (Ichniowski & Shaw, 2003)). In some
situations,therefore,the relationship between information richness
T.M. Nisar et al. Journal of Business Research 94 (2019) 264–272
266

(the cognitive) and informal and social communication may not sit well
in a community of practice context. From a CoP perspective, it is par-
ticipation in social processes that is front and centre, involving a social
process whereby a person travels from a peripheral point to become a
central member, and has nothing to do with information richness. Our
hypothesis below also reflects these concerns.
Hypothesis 4. Information richness and informal and social
communication complementeach other to the extentthat there are
reciprocal benefits for their impact on organizational performance.
3. Empirical methods
3.1. Study context
Social media platforms now enable many features for observable,
interpersonalcommunication,which one can easily quantify atthe
dyadic individuallevel and investigate their impact on economic and
organizationalmeasuresof performance (Farajet al., 2011; Hua &
Haughton, 2012; Jeppesen & Fredricksen, 2006). These features mainly
relate to the instantaneity oftwo-way communications and the con-
sequent directionality of discussions that take place on these platforms.
These developments have enormously improved the KMDGs'function-
ality. For example,previous studies highlighted the poor presentation
of DGs and the difficulty in finding relevant information,even when
they used online platforms such as company's intranet.Cluttered re-
sponses usually made it difficult to locate knowledge and tricky to ac-
cess via these platforms.Ardichvili, Page,and Wentling (2003) sug-
gested that a message board-type format would improve the DG display,
providing a ‘cleaner’interface within which to search for expertise. In
such non-social media DG settings, all responses that impede discussion
visibility are presented individually, and employees are forced to open
individual responses separately.A social media DG platform,on the
other hand, collates responses under one title, making all inputs visible
to the DG ‘visitor’.Furthermore,it reduces the repetition ofsimilar
answers.
Posts and commentsare usually the two content methodsthat
KMDG users employ to interact with each other. Importantly, all such
communication is two-way,which means that KMDG members share
their experiences all the time, reaching an influential audience of col-
leagues, peers and consultants. KMDG members can download the ap-
plication (‘app’) to their mobile phones and can read allcommunica-
tions sent out by the company to its employees.They can view other
employees'posts and comments and make and post any comments in
response. By engaging in a strong access strategy this potentially opti-
mizes communications within the CoP and increases the efficiency in
organizational communication channels. The app drives individuals to
comment on and respond to new skill learning opportunities in specific
CoP areas,thus building communities and relationships around them
(see Hua and Haughton (2012) for other similar examples). Moreover,
by successfully implementing this strategy the organization can gain
employee insight on new policies and project ideas. The mobile/tablet
app that we study here was new and proprietary and exclusively de-
signed for the focal company, providing better ‘out of office’ access. The
focal company is a global project management business which designs
and implements large engineering projects and also helps other com-
panies design, enable, manage and secure their project environments by
using their processknowledge,technicalexpertise,and engineering
capabilities. Previously, staffcould access the company's Intranet from
home via a computer or smart phone.This led to an increase in con-
tributions from ‘tech-savvy’employees who found the new technology
more appealing. However, many others showed a reluctance to use the
technology for their information needs. The alternative technology (the
app) improved the longevity of DGs by adopting social media-specific
elements, increasing the use among younger employees as well as older
employees.
3.2. Content analysis
Content analysis is defined as “the systematic,objective,quantita-
tive analysis of message characteristics” (Neuendorf, 2002, p.1). In our
case, it is a quantitative analysis of the content of the KMDG posts and
the responses. A common disadvantage of using content analysis is that
the information needed is limited or incomplete.However,this dis-
advantage is overcome by analyzing social media posts because of the
time-line nature of the KMDG app, which allows an app visitor to scroll
back in time to the beginning of the KMDG page,gaining access to a
vast amount of posts.We measure information richness and informal
and social communication using these postsand commentsin the
KMDG app. Several studies have already employed electronic commu-
nication data to explore organizationalproblems(Wu, Huberman,
Adamic, & Tyler, 2004). When analyzing the textual or qualitative data
for quantitative analysis,it is common to use text mining techniques.
The text mining toolfirst decomposes the textualcontent into words
and phrases based in its large library.It then performs extraction of
concepts,where the number of concepts can indicate the richness of
information contained therein.1 Our measuresof KMDG factors are
directly derived from these text mining results.We measure informa-
tion richness as the number of concepts extracted.By finding distinct
topics in each person's KMDG posts,we can capture the information
heterogeneity across individuals.We also measure the frequency of
social communications and informalactivities in a person's electronic
posts. We define information richnessas the heterogeneity ofin-
formation contentin an individual's posts on KMDG,whereas social
communication measures how much of an individual's communication
is related to socializing and informalsocial activities.How intense a
certain type of information — such as social communication — is can
also be beneficial, particularly in situations where team and group work
is important.
We take two distinct steps to classify LDA topics. In the first phase,
we search the entire topic space using every document in a corpus so as
to classify words into topics.We use this method to classify 75 topics
using the entire corpus of electronic communications. Examples include
topics such as ‘Research’, ‘Leading’, ‘Problem’, ‘Building’, and ‘Project’.
We calculate information richness for each person in every month as the
average cosine dissimilarity of the topic space in the person's DG con-
tributions. We then asked four employees who had extensive experience
at the firm for many years to verify thatthe DG-based information
exchanges revolve around these topics generally.
3.3. Control variables
To obtain robust estimates of the effect of focal UGC constructs, we
control for potentially confounding factors at the individual employee
level. Our control variables include individual employees'demo-
graphics (age and gender), managerial roles, monthly income, and job
ranks. Male is a dummy indicator for male gender (1: male, 0: female)
and monthly income is the levelof employee i's monthly income (1:
lowest,5: highest).If the effect of UGC constructs on work and orga-
nizational outcomes indeed derives from individual employees'demo-
graphics,the effectof UGC should disappear once differences in in-
dividual employees'demographicsare controlled for. We create a
dummy variable for the managerial role indicating whether the person
is a project manager. Job ranks take an ordinal value ranging from 5 to
10: level 5 is the junior manager and level 10 is a vice president.We
have a dummy variable for each business division to controlfor the
differences across various divisions. The other important aspect of UGC
(employee-generated content) is the totalposting volume (volume at
1 Marketing researchershave earlier operationalized information richnessas the
number of concepts (e.g., price, quality) communicated by advertisements (e.g., Healey &
Kassarjian, 1983).
T.M. Nisar et al. Journal of Business Research 94 (2019) 264–272
267

period t) and, to accountfor potentialselection bias atthe content
generation level,we include an employee user's own posting volume
(i.e. total volume of content generated by employee i in his/her CoP at
period t).
We examine empirically whether the use of social media can induce
performance improvements after controlling for seasonality, individual
characteristics, and past performance.
∑
∑ ∑
∑ ∑
= +
+
+ + + +
+ +
+ + +
Performance α β volume
β labproduct
β gender β age β mgr β income
β jobrank β division
β ownpost β month ε .
i t i t
i t
i i i w w w
j j j d d d
o o o t t t i t
, 1 ,
2 ,
3 4 5
, (1)
This is followed by our examination of whether KMDGs generate the
two types of information and knowledge benefits — i.e.-,information
richness and informal communication — that we envisage as improving
work and organizationalperformance.2 To carry out these investiga-
tions, we first estimate a fixed-effects (FE) model. Subsequently, we run
a random-effects (RE) model to conduct the analysis of the relationships
between a KM system's informativeness and information richness, a KM
system's informativeness and social communication, and a KM system's
informativeness and work and organizationalperformance.We mea-
sure organizationalproductivity using labor productivity as another
performance measure,which we calculate as earnings before interest
and taxes (EBIT) over total labor costs. The use of total labor costs as
denominator allows us to account for variations among the firm's salary
structure.We also use Return on Assets as a financialperformance
measure; this is calculated as net profit divided by revenues. We control
for the differences in individual characteristics as individuals may have
different propensities to engage in social media.We incorporate attri-
butes such as gender,demographics and job roles thatare likely to
affect both information benefits and organizational performance.
Business organizations are coming to view knowledge as their most
valuable and strategic resource.It is therefore vitally important for a
firm to create an integrated knowledge infrastructure thatis well
regulated and supported by all. Engineering and consultancy companies
operate in an industry where the speed of innovation determines the
success of the company. As one industry reports suggests, “the average
interaction worker spends an estimated 28% of the workweek mana-
ging e-mail and nearly 20% looking for internal information or tracking
down colleagues who can help with specific tasks” (McKinsey,2012).
Social media has completely changed these patterns of communication
in the workplace:a message now takes the form of“content”.Em-
ployeesspend lesstime searching fortask-related information,sig-
nificantly reducing the time spent on a searchable record of knowledge.
Knowledge sharing leads to better decision making as faster access to
more experts or relevant documents increases the chance that better
decisionsare made. Consequently,workers are likely to seize any
productivity improvementopportunity by accessing information ex-
peditiously. Thus, if a social media platform is to produce informational
benefits,it should also have a strong effecton organizationaland
worker productivity. Table 1 shows the summary statistics of managers'
demographics, job roles, and network characteristics.
4. Results
We first examine whether the adoption of KMDG is correlated with
organizational performance as specified in the reduced-form regression
(see Appendix 1).As we find, KMDG is positively associated with
organizationalproductivity,as measured by labor productivity.The
adoption of KMDG generatesan additional $126.27 in labor pro-
ductivity (β = 126.27, p < 0.01), while we control for temporal shocks
and individual fixed effects. The greater the ability of KMDG to improve
a KM system's informativeness is,the greater its impact on organiza-
tional productivity is. In other words, with the mediating factor being
the ability to improve the informativeness of the KM system, the use of
social media has a significant positive impact on labor productivity and
firm profitability. Social media is thus a relevant technological change
that has the capacity to influence the internal organizational processes
of an organization through its effect on the informativeness of a KM
system.These changes are then linked to improvements in work and
organizational outcomes.
Our above findings suggest that socialmedia enables superior or-
ganizational outcomes by helping individuals develop a more nuanced
understanding of the company's goals, strategy, purposes and processes
as encapsulated in its KM system.Social media shapes a knowledge-
based organizational architecture by helping build strong foundations
for its KM systems. Columns 1 and 2 in Table 2 present the results. In
each one of the regressions, dependent variables are centered to have a
mean of 0 and a standard deviation of 1.As we find,the adoption of
KMDG is positively correlated with information richness (β = 0.539,
p < 0.01). As KMDG allows individuals to provide comments on in-
dividual posts,it encourages employees working in a CoP to acquire
and share new knowledge (or information that they were not previously
exposed to).Hence,the net effect of these changes is that the system
generates increased levels ofinformation richness.We find a similar
trend in relation to informalcommunication (β = 0.148,p < 0.05).
However, the coefficient estimate of informal communication is much
smaller than the coefficient estimate of information richness, suggesting
that the operations of KMDGs are more strongly associated with gen-
erating information richness than informal communication is. Thus, we
can plausibly claim that information richness is the primary benefit of
hosting a KMDG. It is intuitive that the adoption of social media KM has
a bigger effect on information richness than informal communication as
the system is mainly intended to enhance the process of gathering and
storing task-related information (Wu, 2013; Wu & Wang, 2006).
4.1. KM system informativeness
We now examine the extentto which a social media KM system
produces the same intermediate information and knowledge benefits
that an information-rich KM system is theorized to provide (Wu, 2013;
Wu & Wang, 2006). Table 3 shows the relationships between an orga-
nization's KM system and information richness,and between an orga-
nization's KM system and informal communication. In general, KMDG is
positively related to both information richness and informalcommu-
nication. We first used a fixed-effect model, as shown in Column 1. As
can be seen,KMDG is positively correlated with an increase in in-
formation richness(β = 3.436,p < 0.01). With regard to the RE
model,as presented in Column 2,the outcome is notvery different
(β = 5.625,p < 0.01). Moreover,when using a fixed-effectmodel,
Table 1
Summary statistics.
Variable Mean Std. dev. Min Max Obs
Volume (posts) 63.594 58.473 0.000 138.000 19,234
Ownpost 0.023 0.094 0.000 9.000 19,234
Return on assets 0.034 0.042 0.013 0.055 19,234
Labor productivity 17.367 11.928 9.874 26.427 19,234
Gender (0-male) 0.165 0.274 0.000 1.000 19,234
Age 31.398 5.647 21.445 63.274 19,234
Managers 0.173 0.289 0.000 1.000 19,234
Income 2.684 0.736 1.000 5.000 19,234
Job ranks 6.594 1.376 4.000 11.000 19,234
2 However, taking information heterogeneity as representing information richness may
be risky;too much information/knowledge heterogeneity,without appropriate integra-
tion mechanisms, may cause chaos rather than generate information richness.
T.M. Nisar et al. Journal of Business Research 94 (2019) 264–272
268

having a more information rich KM system is positively correlated with
an increase in social communication (β = 0.148,p < 0.01). With the
other estimation approach (RE model),the effect continues to be po-
sitive (β = 0.337,p < 0.05). These results establish the proposition
that an information-rich KM system have both types ofinformation
benefits,supporting Hypotheses1A and 1B. As can be seen,these
counter-intuitive results emphasize the need for having both work and
social elements in a KM system.
4.2. Information benefits and organizational performance
Our results thus far suggest that both information richness and in-
formal communication constituteimportant componentsof an in-
formation-rich KM system. We now investigate the effects of informa-
tion richness and informal communication on labor productivity.
Table 4 presents the results. We use normal controls (i.e. demographics,
job ranks, ownpost, and businessdivisions) in all regressions.In
Column 1, we find a positive relationship between information richness
and labor productivity (β = 0.257,p < 0.5). Hypothesis2 is con-
firmed. Per the result presented in Column 2, informal communication
is also positively correlated with labor productivity (β = 0.114,
p < 0.5), supporting Hypothesis 3.When both information richness
and informal communication are jointly used in the model,we find a
similar association (see Column 3). As we find, the interaction effect of
information richness and informal communication is positive and sta-
tistically significant (β = 0.148, p < 0.01), indicating a plausible
complementary relationship.Hypothesis 4 is thus supported.This is a
counter-intuitive result as it is generally believed that socialcommu-
nication in the workplace results in higher monitoring cost.More im-
portant,this result drives interesting insightsinto the effect of in-
troducing social media-type technologies in the workplace.
The results of the effectsof information richnessand informal
communication on productivity,as measured by return on assets,are
presented in Table 5. Both information richness and informal commu-
nication are centered to have a mean of 0 and a standard deviation of 1.
This allows us to directly compare the two information benefits. As we
find, information richnesshas positive relationship with the firm's
profitability ratio (see Column 1). However, as the coefficient estimate
in Column 2 shows, the same is not true with informal communication
as it is not statistically significantly correlated with firm profitability.
We further show that information richness is positively correlated with
firm profitability when treating both information richness and informal
communication as independent variables in the same model(Column
3). These results show that the informativeness of a KM system enabled
by socialmedia can generate both information richness and informal
communication, although they differ in terms of their individual effects
(Goh et al., 2013; Wu, 2013; Wu & Wang, 2006). To examine the
question of whether information richness and social communication are
complements in how they affect organizationalproductivity,we add
the interaction between information richnessand informal commu-
nication in the model. As we find, there is a positive interaction effect,
although only statistically significant at the p < 0.1 level (see Column
4).
5. Conclusion
Knowledge management is theorized to provide information bene-
fits, as it builds on the informational nature of the knowledge economy
(Bell, 1973). Many large companies must maintain the effective transfer
of knowledge across divisions and regions in order to remain compe-
titive in their markets that increasingly rely on the rapid dissemination
of intangible assets.Naturally,knowledge is dispersed throughout an
organization,leading to knowledge asymmetriesbetween people
(Becker, 2002). The coordination of knowledge is therefore important;
this enables appropriate knowledge to be sourced,supporting the ex-
ecution of organizational tasks. However, this process requires the de-
sign of appropriate communication structures(Becker,2002). This
study examines the factors that affect and contribute toward an effec-
tive strategy for generating tangible KM-based employee engagement
through social media. Corporate social media can provide information
and knowledge benefits by enhancing the capacity ofindividuals to
share and communicate criticalpersonal and business information on
their desktop and remotely (Chow & Chan,2008). Our study is sig-
nificant because it reassesses the role ofKM as a social media-based
information-sharing system and sheds light on how organizations can
Table 2
Effects of KMDG adoption on firm profitability, labor productivity, information
richness and informal communication.
(1) (2)
Information richness
(standardized)
Informal communication
(standardized)
KMDG adoption 0.539⁎⁎⁎
(0.267)
0.148⁎⁎
(0.047)
Income 0.235
(0.006)
0.725
(0.017)
Gender −0.187
(0.018)
−0.469
(0.052)
Age −0.783
(0.056)
−0.528
(0.034)
Managers 0.195
(0.014)
0.396
(0.145)
Job ranks 0.126
(0.025)
0.248
(0.032)
Work divisions 0.327
(0.182)
0.163
(0.017)
Ownpost 0.184
(0.112)
0.195
(0.043)
Individual fixed effect Yes Yes
Month dummies Yes Yes
Observations 19,234 19,234
Clustered standard error.
⁎⁎ p < 0.05.
⁎⁎⁎ p < 0.01.
Table 3
Relationshipsamong socialmedia KM, information richnessand informal
communication.
(1) (2) (3) (4)
Information
richness
Information
richness
Informal
communication
Informal
communication
FE RE FE RE
Volume (posts) 3.436⁎⁎⁎
(1.274)
5.625⁎⁎⁎
(2.257)
0.148⁎⁎⁎
(0.109)
0.337⁎⁎
(0.164)
Income 0.278
(0.113)
0.184
(0.165)
0.196
(0.142)
0.243
(0.182)
Gender 0.135
(0.121)
0.249
(0.145)
0.263
(0.134)
0.364
(0.124)
Age 0.282
(0.143)
−0.173
(0.158)
0.289
(0.197)
0.258
(0.243)
Managers 0.267
(0.174)
0.251
(0.228)
0.271
(0.223)
0.131
(0.086)
Job ranks 0.001
(0.000)
0.273
(0.143)
0.165
(0.134)
0.271
(0.165)
Work divisions 0.052
(0.016)
0.176
(0.134)
0.343
(0.268)
0.178
(0.123)
Ownpost 0.354
(0.198)
0.278
(0.223)
0.187
(0.145)
0.143
(0.168)
Observations 15,582 15,582 18,753 18,753
R-squared 0.035 – 0.058 –
Number of people1767 1767 1767 1767
Clustered standard error.
⁎⁎ p < 0.05.
⁎⁎⁎ p < 0.01.
T.M. Nisar et al. Journal of Business Research 94 (2019) 264–272
269

use social media system toolssuch as KMDGs as mechanismsfor
creating a long-term competitive advantage.
Building on prior research (Goh et al.,2013;Wu, 2013),we theo-
retically examine socialmedia KM by conceptualizing two particular
types of information and knowledge benefits that characterize a KMDG
system — information richness and informal communication. Our study
of UGC in this manner brings to the fore the idea that KMDG contents
affect organizational performance through embedded information and
informal and social communication. The study focuses on using virtual
discussion groupsand investigateshow information and knowledge
benefits generated by these groups overcome the technical and human
barriers of sharing knowledge through KMDGs. As we find, KMDGs are
successful at facilitating knowledge sharing within the organization. We
also examine whetherthese KMDG outcomesaffect organizational
performance and, if so, which is most effective – information richness or
informal and social communication. We thus provide specific results on
the degree to which hosting a social media tool within an organization
can change a knowledge management system over time,and whether
there are any economic benefits associated with such a change.Our
findings show that engagement in socialmedia conversations carried
out on KMDGs leads to a positive increase in organizationalperfor-
mance. We also establish the general information and knowledge ben-
efits of using KMDGs, particularly the role of informal and social
communication in KMDG system tools. It will be interesting to discover
whether the social media-induced information and knowledge benefits
found in this research are demonstrated in similar studies on other KM
systems. As we show, KMDGs represent a feasible step that can be taken
to construct an enhanced knowledge-sharing environment.
Our findings have important managerialimplications.Wu and
Wang's (2006) research argues that system use had no significant po-
sitive effect on user-perceived KM system benefits. However, our results
illustrate thatdiscussion groupsoffer multi-faceted advantages,pri-
marily acknowledging the technical benefits associated with discussion
groups. That is, there are significantinformation richnessbenefits
generated by the organizations'DGs (e.g., DGs can enable ‘quicker
problem solving’ or provide ‘best-practices’).Social benefits(e.g.,
‘better communication’ and ‘camaraderie with peers’ across the group)
are recognized as well,although the technicalbenefits outweigh the
social benefits. These results emphasize that social media KM systems
facilitate knowledge sharing,but it is a collaborative organizational
Table 4
Social media KM and labor productivity.
(1) (2) (3) (4)
Labor productivity Labor productivity Labor productivity Labor productivity
FE IV FE IV
Information richness 0.257⁎⁎
(0.135)
0.234⁎⁎
(0.103)
0.209⁎⁎
(0.117)
Informal communication 0.114⁎⁎
(0.066)
0.123⁎
(0.075)
0.156⁎
(0.093)
Information richness × informal communication 0.148⁎⁎⁎
(0.027)
Income 0.529
(0.344)
Gender 0.026
(0.015)
Age 0.431
(0.183)
Managers 0.387
Job ranks (0.212)
Work divisions 0.229
(0.165)
Ownpost 0.321
(0.224)
Observations 19,234 19,234 19,234 19,234
Number of people 1767 1767 1767 1767
Clustered standard error.
⁎ p < 0.1.
⁎⁎ p < 0.05.
⁎⁎⁎ p < 0.01.
Table 5
Social media KM and financial performance.
(1) (2) (3) (4)
Return on
Assets
Return
on Assets
Return on
Assets
Return on
Assets
FE FE FE FE
Information richness
(standardized)
0.176⁎⁎⁎
(0.016)
0.137⁎⁎⁎
(0.023)
0.137⁎⁎
(0.098)
Informal communication
(standardized)
0.138
(0.119)
0.147⁎
(0.112)
0.134.
(0.125)
Information richness × informal
communication
0.183⁎
(0.123)
Income 0.264
(0.244)
Gender 0.486
(0.327)
Age 0.294
(0.243)
Managers 0.262
(0.227)
Job ranks 0.425
(0.343)
Work divisions 0.139
(0.125)
Ownpost 0.183
(0.162)
Observations 19,234 19,234 19,234 19,234
Number of people 1767 1767 1767 1767
Clustered standard error.
⁎ p < 0.1.
⁎⁎ p < 0.05.
⁎⁎⁎ p < 0.01.
T.M. Nisar et al. Journal of Business Research 94 (2019) 264–272
270

culture that enables it to be exploited. Knowledge sharing through so-
cial technologies leads to more visible recognition (Garicano & Wu,
2012). When people share their knowledge, this increases the feeling of
connection to the company and helps develop a performance culture
based on trust and confidence. However, as discussed above, it is also
importantto acknowledge thatan increased knowledge base cannot
necessarily be directly linked to organizational benefits.There can be
situations where teams create knowledge (output) without a resulting
improvement in performance (outcome).
Appendix 1. Effects of KMDG adoption on firm profitability, labor productivity, information richness and informal communication
(1) (2)
Labor productivity Firm profitability
KMDG adoption 126.274⁎⁎⁎
(119.137)
0.372⁎⁎
(0.184)
Income 0.483
(0.016)
0.274
(0.118)
Gender −0.247
(0.007)
0.068
(0.052)
Age −0.396
(0.032)
0.285
(0.224)
Managers 0.260
(0.021)
0.137
(0.115)
Job ranks 0.001
(0.000)
0.173
(0.164)
Work divisions 0.053
(0.007)
0.328
(0.267)
Ownpost 0.254
(0.135)
0.281
(0.226)
Individual fixed effect No Yes
Month dummies No Yes
Observations 19,234 19,234
Clustered standard error.
⁎⁎ p < 0.05.
⁎⁎⁎ p < 0.01.
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Tahir M Nisar (Southampton Business School,University ofSouthampton,Highfield,
Southampton,SO17 1BJ, UK < [e-mail] >)is associateprofessor.He
works on the following issues: crowdsourcing, attribution modeling and online consumer
behavior.
Guru Prabhakar (Faculty of Business& Law, UWE, Bristol BS16 1QY UK guru.
[e-mail]) is senior lecturer.He works on the following issues:knowledge
management, project management and social media.
Lubica Strakova (Southampton Business School, University of Southampton, Highfield,
Southampton SO17 1BJ, UK < [e-mail] >) is senior teaching fellow. She
works on the following issues: project management, knowledge management and gender
diversity.
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