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IMDS
119,9 Social media usage and
employee’s job performance
The moderating role of social media rules
1908 Rana Muhammad Sohail Jafar
College of Management, Shenzhen University, Shenzhen, China
Received 18 February 2019
Revised 12 July 2019 Shuang Geng, Wasim Ahmad and Ben Niu
Accepted 27 August 2019 Shenzhen University, Shenzhen, China, and
Felix T.S. Chan
Department of Industrial and Systems Engineering,
Hong Kong Polytechnic University, Kowloon, Hong Kong

Abstract
Purpose – This era is an era of social media (SM); thus, it is an essential tool for communication among
individuals and organizations. The excessive use of SM by employees has raised many questions about their
job performance. Therefore, there is a dire need to investigate the effects of SM use on an employee’s job
performance mediated by knowledge exchange. Furthermore, the purpose of this paper is to examine how the
organization’s SM rules can moderate the relationship between personal and work-related use of SM with
information sharing and obtaining information.
Design/methodology/approach – Quantitative methodology was used and randomly 1,200 questionnaires
data were collected physically from the employees of the public and private sectors in Pakistan. To examine
the hypothesized relationships, partial least squares (PLS), rather than covariance-based structural equation
modeling, was used to analyze the data. For this reason, multivariate technique, Smart PLS-3.2.1, was used for
data analysis.
Findings – The findings of this study demonstrated that personal and work-related use of SM could enhance
employees’ job performance through knowledge exchange, and SM rules have adverse impacts on the
relationships between SM use and knowledge exchange.
Originality/value – This study provides a novel model for the investigation of whether SM use affects
employees’ job performance. Furthermore, it will help the policy makers and researchers regarding the
management of SM use at work.
Keywords Information sharing, Social media, Obtaining information, Social media rules,
Employee’s performance
Paper type Research paper

1. Introduction
Social media (SM) is a tool that provides a facility for social and commercial
communication (Parveen et al., 2015) that was built on the technological foundations of
Web 2.0 (Habibi et al., 2016). Recently, SM sites such as WeChat, Facebook, Twitter,
Instagram and YouTube have become popular around the globe for sharing and obtaining
information (Berthon et al., 2012). Individuals and organizations are using SM for their
social and commercial purposes by creating, sharing and exchanging information with
their friends, family, colleagues and customers (Shi et al., 2013). Organizations can take the
advantage of the potential of SM to effectively engage with their employees, customers
and other patrons for business value creation and collaboration (Naudé et al., 2014).
Industrial Management & Data
Systems This work is partially supported by The National Natural Science Foundation of China (Grants
Vol. 119 No. 9, 2019
pp. 1908-1925 Nos 71971143, 71571120), Project Supported by Guangdong Province Higher Vocational Colleges &
© Emerald Publishing Limited Schools Pearl River Scholar Funded Scheme 2016, and Project supported by Innovation and
0263-5577
DOI 10.1108/IMDS-02-2019-0090 Entrepreneurship Research Center of Guandong University Student (2018A073825).
Therefore, the effective use of SM can enhance employees’ capabilities associated with Social media
employees’ job performance and organizations (Nisar and Prabhakar, 2018). SM platforms usage
are becoming more collaborative and interactive for users because of the increasing trends
of use. Therefore, these applications can be accessed on the web and through mobile
technology (Siamagka et al., 2015).
Nowadays, individuals can share their experiences and check other posts, and these
processes can be performed with minimal effort and time (Osatuyi, 2013). Usually, 1909
employees share and obtain information from friends and colleagues to maintain a sense of
social relationship, and this is how knowledge sharing or obtaining phenomenon positively
influences job performance and learning capabilities (Eid and Al-Jabri, 2016). The first
important measure of an employee’s progress in the workplace is work performance, that is,
whether an employee is working efficiently. Similar to other emerging technologies, SM use
has also been contentious in the workplace. Studies (Tajvidi and Karami, 2017; Bennett et al.,
2010) have claimed that SM use in the workplace improves employee work performance.
In contrast, other studies (Meshi et al., 2013; Shepherd, 2011) have reported that SM use is a
waste of time and reduces employee’s productivity because of personal messaging.
However, most studies have focused on the impact of using enterprise SM to measure the
overall work performance, and these studies have been conducted using college and
university students’ data (Clark and Roberts, 2010); thus, their results might not be
generalizable to business firms and organizations.
Keeping the existing research in mind, we found significant research gaps in the literature
that most studies explored the general perspective of SM use or just focused on work-related
use of SM that did not demonstrate the mechanism for employee’s performance. Notably, there
has been little knowledge regarding the belongings of SM use in organizations about
employees’ job performance. Additionally, there is also a lack of research that describes the
mediating and moderating factors that influence the motivations of employee’s SM use and
their job performance. Specifically, existing literature has not considered how SM use can
intensify employee’s job performance via knowledge exchange behaviors and how such causal
relationships are moderated by the perceptions of employees about SM rules. Based on these
research gaps identified, we assert that an investigation of the underlying mechanisms for the
effects of SM use on employees’ job performance and potential moderators for such effects is
necessary. Recently, organizational implementation of SM policy has been increasing (O’Connor
et al., 2016), and one fundamental question is how well do employees know and appreciate their
employer’s rules, and how this knowledge can be useful for organizations. Therefore, the key
objectives of this study are to investigate the underlying knowledge exchange mechanisms that
mediate the relationship between employee’s personal and work-related use of SM and job
performance. Additionally, this study investigates the moderating role of SM rules, that is, how
it influences the relationships between employee’s SM use and knowledge exchange behaviors.
In summary, to address the research question regarding what factors influence
employees’ performance when using SM in organizations, we put forward an innovative
approach that integrates social exchange theory (David Gefen, 2002). Based on how SM
applications or social networking sites (SNS) are used, this study proposes two categories of
SM use (i.e. personal and work-related) and two types of knowledge exchange
(i.e. information sharing and obtaining information). We then examined how the two
categories of SM use affect employee’s job performance through the two types of knowledge
exchange, and how such effects are moderated by employees’ perception of organizational
SM rules. To sum up, this study aims to answer the following questions:
RQ1. How do the personal use of SM and work-related use of SM affect employee’s
performance via knowledge exchange behaviors?
RQ2. How do the perceptions of organizational SM rules moderate the effects?
IMDS The paper is organized as follows: first, we extensively review the literature on SM use,
119,9 information sharing, obtaining information and employees’ job performance. Second, we
discuss the theoretical foundations of this study on the bases of the social exchange theory
that helped us to perform hypotheses development. Third, we describe the methodology and
data analysis results of the study. Finally, the implications, limitations and visions for future
research are discussed.
1910
2. Theoretical background
In Sections 2 and 3, we comprehensively review the past studies and describe the theoretical
foundations of this study. For the development of our research model, we used a widely
acknowledged social exchange theory.

2.1 SM use and job performance

Employee performance can be affected by conditions such as job satisfaction, working
environment, motivation and stress (Kakkos et al., 2010). Koo et al. (2011) asserted that social
communication technologies influenced employees’ job performance and social factors moderate
the degree of such relationships. Social technologies can enhance an employee’s job performance.
Employee’s effective use of such SM technologies is positively linked to task performance and
building and maintaining social ties with colleagues and friends over SM platforms (Parveen et al.,
2015). Moreover, Ali-Hassan et al. (2015) indicated that the use of SNS strengthens employees’
skills to create, share and obtain knowledge that conclusively increases job performance.
Therefore, Sujatha and Krishnaveni (2018) advocated the importance of knowledge contribution
to work performance. It is essential for an organization to enhance internal consistency and to
smoothen the communication process among the employees by taking advantage of SM.
A recent study on the advancement of artificial intelligence describes that the rapid
growth of artificial intelligence and robots revolution will significantly replace the labor
force in the organizations (Berg et al., 2018). Alternatively, new technologies open up more
opportunities for technically skilled workers (Van Roy et al., 2018). Technological
innovations are labor friendly and have positive and significant impact over high-tech and
medium-tech manufacturing sectors.
Similarly, Frey and Osborne’s (2017) findings showed that non-technical staff will be
diverted to assignments that are not susceptible to computerization. The organizational
staff will need to acquire creative and social intelligence skills in order to save their
jobs. Moreover, Decanio (2016) described that artificial intelligence technologies will
cause decline in the aggregate wages of industrial workers as robots proliferate.
Hence, employees should be conscious and update their skills with the rapid advancement
of new technologies.

2.2 SM use and organization’s rules

Many organizations have implemented an SM use policy for their employees (Hodzic et al.,
2018). SM policy should be clear and collectively cover all the aspects of employees; SM use
intentions to provide them with a friendly and cooperative working environment. Patterson et al.
(2005) defined working environment as a representation of employees’ perceptions regarding
organizational policies, practices and procedures as well as successive patterns of interactions
and behaviors that support creativity, innovation, safety or service in the organization.
O’Connor et al. (2016) suggested that organizations should provide sufficient training to
employees before the implementation of newer policies or rules; otherwise, such regulations
create distress among the employees and lower their job performance. Subsequently, blocking
SNS in the workplace is not a suitable option for organizations to prevent employees from using
SM because this would decrease employees’ productivity and performance (Tulu, 2017).
Therefore, most of the studies have recommended that organizations should provide adequate Social media
training and guidance to employees for the appropriate use of SM. usage
2.3 Social exchange theory
Social exchange theory deals with the knowledge exchange process among individuals,
groups (Kankanhalli et al., 2005) and online communities (Wasko and Faraj, 2005). Social
exchange theory defines the behaviors of individuals that can maximize their benefits from 1911
social interaction (David Gefen, 2002). The individual’s capability to develop and maintain
social connections with others is necessary for social exchange (He et al., 2009). Moreover,
individuals like to share their knowledge when they aim to establish a professional identity
and reputation in relevant communities (Hsu and Lin, 2008).

2.4 Knowledge exchange

SM provides benefits to business organizations by connecting them to end-consumers directly
and facilitating various areas of marketing and public relations (Kaplan and Haenlein, 2010).
The findings of Nisar and Prabhakar (2018) provided evidence regarding the effects of SM
knowledge management discussion groups on organizational performance through
knowledge exchange and social communication. Parveen et al. (2015) revealed that SM use
has improved information sharing and accessibility for organizations. Besides, Eid and
Al-Jabri (2016) suggested that educational institutions should contemplate the use of SM tools
in their design of courses to promote knowledge sharing and learning. Osatuyi (2013) explored
information sharing among individuals and concluded that the behavioral act of sharing
information is commonly supposed to be benefit oriented. Kuzu and Özilhan (2014) asserted
that knowledge sharing is a significant activity that enriches an individual’s competency
regarding learning, problem solving and self-improvement. Companies must create open
environments and incentive–reward systems to motivate members to share their knowledge
positively and voluntarily (Kankanhalli et al., 2005). Aldieri and Vinci’s (2018) study describes
that knowledge diffusion plays a vital role in the employment effects of sustainable
development investments for large worldwide businesses.
All the studies have emphasized the importance of knowledge contributions because
valid information sharing and obtaining information are linked to the success of employees
and organizations.

3. Research model and hypotheses

After analyzing the literature, we proposed a conceptual model of the study. In the
research model, we divided employee’s SM use into two components, i.e., “personal use
of SM” and “work-related use of SM.” Similarly, knowledge exchange has been categorized
into “information sharing” and “obtaining information” that are subsequently associated
with employee’s job performance. Additionally, SM rules are used as a moderator among
SM use and knowledge exchange. According to Pi et al. (2013), multiple factors are
attached to the use of SM that can promote employees’ knowledge exchange and job
performance (Leftheriotis and Giannakos, 2014). Table I provides the formal definitions of
all the constructs.

3.1 SM use
3.1.1 Personal use of SM. The personal use of SM describes the individual’s interest and
purpose regarding using SM tools: when an individual wants to share, seek and contact
friends, colleagues or family because of a personal need and no external factors are the
impetus for this action. Cheng et al. (2017) asserted that interpersonal communication is
point-to-point interactions between two separate individuals.
IMDS Construct (abbreviation) Definitions
119,9
Personal use of SM Use of SM to contact friends, colleagues and family or for entertainment purposes
(PUSM)
Work-related use of SM An activity to maintain and strengthen professional links with friends, colleagues
(WRSM) and customers for the purpose of information sharing and obtaining information
(Cao et al., 2012a; Skeels and Grudin, 2009)
1912 Information sharing (IS) The extent of an individual’s share of useful information on SM to help other
members of the SM community (Ma and Agarwal, 2007)
Obtaining information The individual’s ability to obtain knowledge from the SM community to enhance
(IO) work performance (Ma and Agarwal, 2007; Dholakia et al., 2004)
Employee job The degree to which employees meet their job requirements or level of satisfaction
Table I. performance (EJP) according to their manager (Groen et al., 2017)
Formal definitions SM rules The degree to which an organization has implemented specific policies regarding
of constructs SM use (Demek et al., 2018)

Sociological scholars have advocated that an individual’s frequent online interpersonal

communication transforms weedy connections into durable connections. Every user has
different objectives for SM use: some are sharing information with their coworkers, friends
and online community, and others are seeking information for themselves (Leidner et al.,
2010). Personal use of SM by employees helps them share various information or feelings
with their workmates as well as obtaining various types of information from their peers,
which could potentially be beneficial to their jobs. Based on this understanding, we
hypothesize the following:
H1a. Personal use of SM positively affects information sharing.
H1b. Personal use of SM positively affects obtaining information.
3.1.2 Work-related use of SM. As aforementioned, work-related use of SM increases external
professional links that can be helpful when experts share knowledge about managing
customers’ feedback and other stakeholders (Skeels and Grudin, 2009). According to Pi et al.
(2013), numerous factors influence the attitude toward knowledge sharing in the SM group, for
example, reputation, expected relationship, sense of self-worth and subjective norm.
Alternatively, employees choose to meet new individuals instead of only connecting with those
they already know. Hence, work-related use of SM strengthens existing ties and provides
opportunities to form new social relationships. In addition, Yardi et al. (2009) demonstrated that
employees expected to receive attention when they contributed to blogs. Many companies have
launched internal SM sites to encourage employees to share professional and personal
information, for example, “IBM, Microsoft, and HP” (Leftheriotis and Giannakos, 2014).
Work-related use of SM by employees allow them to connect with their workmates or
customers, thereby facilitating the sharing and gaining of useful information for performance
enhancement. Based on this understanding, we hypothesize the following:
H2a. Work-related use of SM positively influences information sharing.
H2b. Work-related use of SM positively influences obtaining information.

3.2 Knowledge exchange

3.2.1 Information sharing. In organizations, employees work as a team and learn from their
team members’ experiences. Therefore, positive information sharing and collaboration
helps employees form social relationships with their team members and improves the
performance of individuals and organizations (Kim et al., 2012). Information sharing with
SM enhances the coordination and trust among organization staff, which positively Social media
influences team performance and helps improve decision making (Srivastava et al., 2006). usage
In an analysis of local government officials, Cross and Cummings (2004) specified that
knowledge sharing increases the job performance of an organization’s members. Thus, we
conclude that knowledge-sharing activities with the use of SM improve job performance.
Based on these considerations, we introduce the following hypothesis:
H3. Information sharing positively influences employee job performance. 1913
3.2.2 Obtaining information. Obtaining information is an individual’s attitude or desire to
fulfill the mindful needs of acquiring knowledge (Dholakia et al., 2004). Obtaining
information helps individuals develop a sense of understanding the opinion of others in
online SM communities (Flanagin and Metzger, 2001). According to social exchange theory,
if individuals perceive meaningful information from knowledge communities, they regard
the knowledge community as positive (Ma and Agarwal, 2007). When individuals’ desire for
information is fulfilled, they are pleased with the community and their job performance
improves. Thus, we proposed the following:
H4. Obtaining information positively influences employee job performance.

3.3 Employee job performance

Employee job or work performance is a concern for organizations. Several studies have been
conducted to understand the relationship between knowledge exchange and employee
performance (Zack et al., 2009; Akroush and Al-Mohammad, 2010). Basically, job
performance is the measurement of an employee’s behaviors, in other words, how well
employees fulfill their job tasks in organizations (Groen et al., 2017). Factors that affect
employee performance include job satisfaction, working environment, motivation and stress
(Kakkos et al., 2010). Alternatively, Du et al. (2018) indicated that job strain drains an
employee’s mental and physical ability to perform in their workplace. Thus, an assessment
of the effects of the use of SNS on job performance would help organizations understand the
relationship between SM and employee performance.

3.4 Organizational SM use rules

Demek et al. (2018) proposed that SM policy reflects the degree to which an organization has
implemented appropriate strategies regarding the use of SM. Several organizations have
implemented an SM use policy that restricts the employees’ use of SM in the workplace.
Some organizations have even banned the use of SM websites at work. An SM policy
outlines how an organization and its employees should interact through online platforms.
Therefore, Haimes (2012) suggested that organizations should establish new policies
regarding employee use of SM. The analysis of Adzovie et al. (2017) revealed that SM use by
employees increases their productivity. In this study, we applied SM use policies and rules
of organizations as a moderator because organizational regulations can affect the usage
intensity of the SM users regarding intentions to exchange knowledge with their peers.
When an organization has strict rules against the use of SM at the workplace, employees
cannot get access to SM, which would adversely influence their knowledge exchange
behaviors. Therefore, we hypothesize the following:
H5a. Organizational rules for SM use negatively moderates the relationship between
personal use of SM and information sharing.
H5b. Organizational rules for SM use negatively moderates the relationship between
personal use of SM and obtaining information.
IMDS H5c. Organizational rules for SM use negatively moderates the relationship between
119,9 work-related use of SM and information sharing.
H5d. Organizational rules for SM use negatively moderates the relationship between
work-related use of SM and obtaining information.
Based on the above discussion, this paper puts forward the research model as shown
1914 in Figure 1.

4. Research design
4.1 Instruments development
To develop the instrument, first, the survey was pilot tested by some skilled staff of the
Shenzhen University in China. The questionnaires were circulated to seven candidates.
Pilot participators filled up the instruments and provided their expert opinions regarding
length, wording and instructions. Three of the participants cross-examined the survey
against the feedback attained from the others, and they suggested minor modifications to
the survey design. Most of the constructs in our theoretical model were latent variables,
which are the best choice for this type of survey approach (Nunnally and Bernstein, 1994).
We modified the instruments of Kankanhalli et al.’s (2005) for work-related use of SM and
Zhang et al.’s (2018) instruments for personal use of SM. The measures of Ye et al.’s (2015)
are used for obtaining information and Deng et al.’s (2017) for sharing information.
In addition, we borrowed the instruments for job performance from Pitafi et al. (2018), and
self-developed instruments measured SM rules. A five-point Likert scale was used to
measure the instruments and was anchored from “strongly disagree” to “strongly agree.”

4.2 Sample and data collection

The data were collected by personal physical survey, and we approached the employees of
banks, hospitals, hotels, digital media (TV ) channels, colleges and universities, and
telecommunication companies in Pakistan who have SM use involvement as our samples.
The motivation for the data collection was to represent individuals who are usually active
users of SM (Lavrakas, 2008). The survey questionnaire was divided into two sections.
In the first section, we asked the employee about their gender, age, education, experience,
income, position and SM use. If the employee did not use any type of SM or did not have
knowledge of SM use, we requested them to stop the interview. Otherwise, they could
complete the survey.

Knowledge Contribution

Personal Use of SM Information

Sharing

Employee Job
Performance

Work-Related Use Obtaining

of SM Information

Figure 1. Organization’s Rules

Conceptual model for SM Use
We randomly distributed 1,400 questionnaires to the employees of the indicated Social media
organizations; the duration of the data collection process was one month. We collected usage
1,002 completed questionnaires from the organizations. Based on our survey requirements,
833 were valid completed questionnaires, and all the reported organization’s employees had
a strong focus on SM regarding work-related and personal use. The ratio of SM uses is
reported in Table II from highest to lowest among all organizations.
Moreover, we also included age, gender, education, income, use and purpose of using SM 1915
for the online knowledge sharing and obtaining knowledge in the conceptual model as
control variables that may influence employee job performance. Table III shows the
respondents’ demographic details.

S.No. Organizations No. of surveys distributed No. of respondents Percentage

1 Banks 200 157 15.65

2 Colleges and universities 300 221 22.03
3 IT and telecommunication companies 300 218 21.73 Table II.
4 Hospitals 200 190 18.94 Organizations
5 Media channels 200 113 11.26 that participated
6 Hotels 200 104 10.36 in the survey

Total numbers of respondents (n ¼ 1,002) Frequency Percentage

Gender
Male 488 48.7
Female 514 51.3
Age (in years)
18–30 449 44.8
31–40 492 49.1
41–50 42 4.2
51 and above 19 1.9
Education
Primary education or lower 20 2.0
Middle school Education 190 19.0
High school education 279 27.8
Bachelor’s degree 329 32.8
Diploma/ Certificate 164 16.4
Postgraduate degree 20 2.0
Income
Rs0–15,000 163 16.3
Rs15,0001–30,000 561 56.0
Rs30,001–50,000 220 22.0
Rs50,001–80,000 34 3.4
Rs80,001–100,000 14 1.4
More than Rs100,000 10 1.0
Use of SM
No 169 16.9
Yes 833 83.1
Purpose Table III.
Personal 391 46.9 Respondents’
work-related 235 28.2 demographic profile
Commercial 207 24.8 for SM use
IMDS To check for the non-response bias, we conducted the analysis of variance test, and no
119,9 significant differences among all respondents were found in the conceptual model.
This indicates that non-response bias was not a serious concern in this study.
Furthermore, we compared the demographics of the study sample with the evidence
delivered by the six groups’ respondents. The resultant F-statistics were not significant;
thus, the data can be pooled.
1916
5. Data analysis and results
5.1 Techniques
For the data analysis, we used partial least square (PLS) because it is superior over other
types of structural equation modeling. Moreover, it can measure complex models with
multiple relationships (Ma and Agarwal, 2007), because it involves no assumptions
regarding the population or score measurement (Fornell and Bookstein, 1982). PLS
comprises two models: the inner model and outer model. The inner model describes the
relations among latent variables, and the outer model defines the relationship between latent
variables and their observed indicators (Henseler and Sarstedt, 2013). Additionally, PLS
involves the slightest loads on variable distributions. Hence, PLS reduces bias caused by
depending on factor-based covariance techniques through software, for example, LISREL
and AMOS (Chin, 1998). In this study, we also performed the bootstrapping facility to test
the statistical significance of path coefficients. In the tested model, all constructs were
demonstrated as reflective because their measurement items are manifestations of these
constructs. We used SmartPLS (version 3.2.1) for data analysis.

5.2 Measurement model

The most important aspects of the appraisal of the reflective measurement model are its
internal consistency and validity. Both Cronbach’s α (CA) and composite reliability (CR)
values exceeding 0.7 indicate acceptable reliability (Nunnally and Bernstein, 1994), and
average variance extracted (AVE) value should exceed 0.5 (Falk and Kosfeld, 2006). Table IV
presents the factors loading, CA, Dillon–Goldstein’s ρ (rho_A), CR and AVE for all the
reflective constructs. All CA, CR and AVE scores were higher than the recommended values
of 0.70 and 0.5, respectively, indicating that all constructs possessed excellent reliability and
AVE. Table IV describes the loadings of each item that fulfill its criterion. All the attained
values of internal consistency (reliability), CR scores and CA scores for every construct are
well above 0.70, which is the suggested benchmark for acceptable reliability (Hair et al., 2017).
Our study results are supported by social science studies (Kankanhalli et al., 2005; Ma and
Agarwal, 2007; Wasko and Faraj, 2005).
According to Hair et al. (2017), all the items should load highly on their respective latent
variables. The AVE score for all constructs ranged from 0.51 to 0.65, and factor loadings
exceeded 0.70, which satisfies this requirement. Hair et al. (2006) recommended a factor
loading exceeding of 0.6 as a good indicator for validity at the item level. After applying the
PLS method, we observed that some of the variable’s signs received a bit lower loadings
than 0.70, such as, WRSM3, WRSM6, WRSM7, WRSM8, IS3, IO1, IO2 and IO5 (Table VI),
and all are considered acceptable (Shujahat et al., 2019; Henseler and Sarstedt, 2013).

5.3 Discriminant validity

The primary objective of a discriminant validity assessment is to ensure that a reflective
construct has the strongest relationships with its indicators in the PLS path model
(Hair et al., 2017). Table IV demonstrates that all indicators load more strongly on their
corresponding constructs than another construct in the model. The HTMT standard value
should be below 0.90, and as shown in Table V, all constructs have values lower than 0.90.
Items Loadings CA rho_A CR AVE
Social media
usage
EJP1 0.795 0.846 0.846 0.846 0.647
EJP2 0.815
EJP3 0.804
IO1 0.687 0.865 0.867 0.865 0.518
IO2 0.691
IO3 0.782 1917
IO4 0.723
IO5 0.682
IO6 0.747
IS1 0.719 0.871 0.873 0.872 0.531
IS2 0.781
IS3 0.675
IS4 0.719
IS5 0.735
IS6 0.741
PUSM1 0.697 0.907 0.910 0.907 0.551
PUSM2 0.816
PUSM3 0.715
PUSM4 0.674
PUSM5 0.695
PUSM6 0.796
PUSM7 0.801
PUSM8 0.731
SMR1 0.798 0.885 0.886 0.885 0.562
SMR2 0.730
SMR3 0.732
SMR4 0.760
SMR5 0.738
SMR6 0.737
WRSM1 0.846 0.921 0.926 0.921 0.542
WRSM2 0.801
WRSM3 0.648
WRSM4 0.703
WRSM5 0.727
WRSM6 0.629
WRSM7 0.683
WRSM8 0.685
WRSM9 0.856
WRSM10 0.748 Table IV.
Notes: EJP, employee job performance; IO, obtaining information; IS, information sharing; PUSM, personal Factor loadings,
use of SM; WRSM, work-related use of SM; SMR, SM rules AVE, CA and CR

1 2 3 4 5 6 7 8 9 10

1. EJP –
2. IO 0.444
3. IS 0.522 0.626
4. PUSM 0.520 0.397 0.428
5. PUSM × moderator SMRio 0.033 0.202 0.225 0.047
6. PUSM × moderator SMRis 0.033 0.202 0.225 0.047 0.057
7. WUSM 0.568 0.388 0.427 0.305 0.030 0.030
8. WUSM × moderator SMRio 0.054 0.238 0.183 0.021 0.261 0.261 0.063 Table V.
9. WUSM × moderator SMRis 0.054 0.238 0.183 0.021 0.261 0.261 0.063 0.057 Heterotrait–monotrait
moderator SMR 0.250 0.363 0.341 0.281 0.021 0.021 0.252 0.038 0.038 ratio (HTMT)
IMDS Overall, the constructs demonstrate strong discriminant validity. Furthermore, preliminary
119,9 tests, including checking the unidimensionality of constructs, were achieved by the results
contained in the outer model.

5.4 Structural model

Figure 2 demonstrates the path coefficients and explicated variances for the structural
1918 model. We maintain the demographic variables (i.e. gender, age, education) and the income
variable as controls for employee job performance in the analysis. None of the control
variables was significant.
All the main hypotheses of the study were supported. Additionally, moderator SM use
rules (SMR) had an adverse impact on PUSM and WRSM with respect to IS and OI.
Consistent with our hypotheses, Table VI summarizes the results of each corresponding
hypothesis tests.
Similar to Preacher and Hayes (2008), a 97.5% confidence interval of the indirect effects
was obtained with 1,000 bootstrap re-samples. Results of the mediation analysis established
the full mediating role of information sharing and obtaining information in the relationship
between personal and work-related use of SM and an employee’s job performance. Table VII
describes the PLS bootstrapping results.

Knowledge Exchange
R2 = 0.399
Personal Use of SM 0.297*** Information
Sharing R2 = 0.294
0.401***
0.261***
Employee Job
Performance
2
0.299*** R = 0.379
0.193***
Work-Related Use Obtaining
of SM 0.267*** Information
–0.224***
–0.231***
–0.172***
–0.155***

Figure 2. Organization’s
Results of the Rules for SM Use
PLS analysis
Note: ***p < 0.05

Hypothesis Proposed paths Path estimates p-levels Hypothesis tests

H1a PUSM → IS 0.297 o 0.01 Supported

H1b PUSM → IO 0.261 o 0.01 Supported
H2a WRSM → IS 0.299 o 0.01 Supported
H2b WRSM → IO 0.267 o 0.01 Supported
H3 IS → EJP 0.401 o 0.01 Supported
H4 IO → EJP 0.193 o 0.01 Supported
H5a SMR → PUM → IS −0.224 o 0.01 Supported
Table VI. H5b SMR→PUSM→IO −0.172 o 0.01 Supported
Tests of the research H5c SMR → WRSM → IS −0.155 o 0.01 Supported
hypotheses H5d SMR → WRSM → IO −0.231 o 0.01 Supported
 Confidence
Social media
interval usage
Original Sample t-statistics Mediation
sample (O) mean (M) SD (|O/SD|) p-values 2.5% 97.5% existence

Direct effect
PUSM → EJP 0.169 0.170 0.019 9.019*** 0.000 0.130 0.203
IO → EJP 0.193 0.191 0.046 4.165*** 0.000 0.098 0.281 1919
IS → EJP 0.401 0.403 0.047 8.457*** 0.000 0.310 0.492
PUSM → IO 0.261 0.261 0.032 8.194*** 0.000 0.200 0.323
PUSM → IS 0.297 0.296 0.032 9.253*** 0.000 0.233 0.361
WRSM → EJP 0.171 0.173 0.021 8.209*** 0.000 0.128 0.211
WRSM → IO 0.267 0.268 0.034 7.748*** 0.001 0.203 0.334
WRSM → IS 0.299 0.301 0.031 9.516*** 0.001 0.242 0.361
Indirect effect
PUSM → IO → EJP 0.050 0.050 0.014 3.475*** 0.001 0.025 0.082 Full
WRSM → IO → EJP 0.052 0.051 0.015 3.381*** 0.001 0.026 0.085 Full
PUSM → IS → EJP 0.119 0.120 0.021 5.721*** 0.000 0.079 0.159 Full Table VII.
WRSM → IS → EJP 0.120 0.121 0.021 5.699*** 0.000 0.083 0.164 Full PLS bootstrapping
Note: ***p o0.05 results

6. Discussion and implications

The findings in this study enrich the theory of social exchange. An investigation of the SM
use among the employees of the public and private sectors of Pakistan was the objective of
this research. Currently, SM platforms support many features for interactive communication
that are easily enumerated at the individual and organizational levels (Hua and Haughton,
2012). This study aimed to obtain a complete understanding of the antecedents of
information sharing and obtaining information. SM has become a popular platform for
individuals to contribute knowledge and gain knowledge (Wasko and Faraj, 2000; Ma and
Agarwal, 2007). Taking this information into consideration, we aimed to improve the
understanding of employees’ SM use relationship with their job performance through
knowledge exchange. The results support our hypotheses: information sharing and
obtaining information provide exclusive contributions to the explanation of an employee’s
job performance.
The study findings show that the personal use of SM and work-related use of SM have
a positive and significant effect on information sharing and obtaining information.
Additionally, SM rules have adverse moderation effects on personal and work-related use
of SM for information sharing and obtaining information. Information sharing
and obtaining information have a positive and significant impact on an employee’s
job performance. Therefore, if organizations facilitate SM use at work, employees
can get benefits of knowledge exchange that would be better for their job as well as
organizational performance.
Therefore, the mediating role of knowledge exchange between SM use and job
performance is positive and significant. Similarly, when employees face restrictions
against SM use they feel stressed and deprived from knowledge exchange process which
ultimately declines information sharing and obtaining information. Hence, SM rules
negatively moderate the relationship among SM use (i.e. personal and work-related
use of SM) and knowledge exchange (i.e. information sharing and obtaining information).
Hence, we can say that an employee’s social behavior motivates them to use SM for
information sharing and obtaining information that can provide benefits related to their
job or organizations.
IMDS 6.1 Theoretical implications
119,9 This research has resulted in substantial theoretical contributions. First, the findings
strongly advocate for the social exchange theory, which asserts that individuals perform in
a manner that can enhance their benefits (Molm, 1997). Second, this study is unique
compared with the literature (Leftheriotis and Giannakos, 2014) because of its exclusive and
innovative approach regarding variable selection, data collection and methods. For the
1920 comprehensive analysis of SM use, we proposed a unique conceptual model for this study
and we separated SM use into two types, i.e., personal use of SM and work-related use
of SM. The past studies explored the direct effects of SM use, which have not differentiated
SM usage as we did in our study.
Third, our research model used knowledge exchange as a mediator between SM use and
job performance. Existing studies have not focused on this perspective that how SM use can
enhance the job performance through knowledge exchange behaviors. Therefore, we used
knowledge exchange as a mediator and it has been further isolated into two dimensions,
e.g. information sharing and obtaining information. Our study results affirm the literature
regarding SM, knowledge exchange and employee job performance akin to several past
studies (Leftheriotis and Giannakos, 2014; Shujahat et al., 2019; Ali-Hassan et al., 2015;
Nisar and Prabhakar, 2018) by finding that personal and work-related use of SM contribute
to knowledge exchange that can enhance an employee’s job performance.
Last but not least, to analyze the impact of organizational SM rules, we introduced the
SM rules as a moderator between SM use and knowledge exchange. SM rules negatively
moderate the relationship between SM use and knowledge exchange behaviors. Some of the
organizations implemented an SM use policy to control their employees. Similarly, Wu and
Wang’s (2006) study findings support our results that SM rules in organizations have
adverse impacts on employee’s job performance. The information exchanging increases an
employee’s feelings of connection with their company and creates a sense of trust and
confidence that leads to improved performance. Conversely, when employees face
restriction against the use of SM they missed a lot of valuable information from friends and
colleagues who could help them enhance their job satisfaction and performance.
The findings from this study suggest that SM use can enhance knowledge exchange and
help employees improve workstation performance. Our study contributed to the literature
related to knowledge exchange and explored whether the antecedent of information sharing
and obtaining information improve employees’ performance.

6.2 Practical implications

This study has significant practical implications for employees and organizations. Both the
personal use and work-related use of SM motivate employees to share information related to
their work experience, collect and provide feedback to customers and their colleagues. Such
types of information sharing and obtaining information help them to be up-to-date with the
company’s performance and policies. Therefore, knowledge sharing and obtaining knowledge
helps them improve job performance. Employees of all these sectors (i.e. industry and services)
must be up-to-date regarding the situations of markets and products because, to some extent,
customers rely on their knowledge. Therefore, information plays a vital role in an employee’s
job performance. Because SM allows for information exchange, they help employees improve
knowledge transfer and enhance their knowledge regarding products and services.
As aforementioned, information sharing and obtaining information are ultimate factors that
affect employees’ work performance through SM technologies (Leftheriotis and Giannakos,
2014). Therefore, organizations should promote both the personal and work-related use of SM,
especially for exchanging information regarding the job.
The moderator SM rules demonstrate an adverse impact on the relationship between SM
and knowledge contributions. The adverse effect is only because of the poor policies of
organizations that are not significantly beneficial for users and organizations. SM use Social media
motivates employees to share and obtain information. Hence, this knowledge exchange usage
behavior improves workplace performance. Alternatively, in a constrained environment,
employees cannot perform well at the workplace. Thus, we assert that there is a dire need
for employee training regarding SM use instead of restricting SM use. In conclusion, to
evade potential legitimate issues, employers should educate and train workers regarding
company SM use regulations. Today, work life and online life are inextricably linked; thus, 1921
organizations must build and communicate such strategies to ensure employees
understand. Last but not least, the findings of this research contribute to the literature on
online knowledge sharing, obtaining knowledge and employee job performance.

6.3 Conclusion, limitations, and further directions

To perform a clear and comprehensive investigation of SM use by employees, this
study divided SM into two categories: personal and work-related use of SM. Additionally,
this study aimed to fill the following gaps in the literature: at what intensity can
employees use SM toward knowledge exchange and how can knowledge exchange
influence their job performance. According to the social exchange theory, if an employee’s
motivations are fulfilled, they feel a sense of satisfaction at work; hence, they improve
their workplace performance. Thus, based on this phenomenon, organizations should
provide a flexible environment to their employees instead of restricting the use of SM
because organizations can benefit from considering employees’ needs and interests
associated with SM use.
Like all studies, this study has limitations. First, organizations remain confused as to
whether they should ban SM. Our findings only provide clues for organizations that strict
SM rules will harm employees’ knowledge exchange and job performance. However, how
strict the SM rules should be and how to devise SM rules to facilitate knowledge exchange
while protecting security information and intellectual properties remain unknown. Future
research could work on these issues by adopting different research methods (e.g. Modeling
or simulation). Second, we examined individuals’ personal and work-related use of SM and
knowledge exchange behaviors. However, this study used a sample only from Pakistan.
Thus, the results of this research would be more robust if questionnaires were gathered
from a variety of countries because of the differences in SM use by country. Therefore, we
plan to investigate this option to perform larger-scale research in cooperation with other
institutions or organizations. Finally, this study investigated the impacts of SM use on
employee job performance. We assert that we should have examined the other perspectives
of SM use on an employee’s job, such as employee well-being, organizational commitment
and social capital. Therefore, future studies should focus on these directions and explore
how SM can help an employee improve organizational citizenship behavior and
organization performance. Our findings clearly assert that the adaptation of the social
exchange theory is essential to improve the understanding of SM use and knowledge
exchange in relation to employee job performance. We are confident that our proposed
conceptual model can serve as a solid foundation for future work.

References
Adzovie, D.E., Nyieku, I.E. and keku, J.A. (2017), “Influence of Facebook usage on employee
productivity: a case of university of cape coast staff”, African Journal of Business Management,
Vol. 11 No. 6, pp. 110-116.
Akroush, M.N. and AL-Mohammad, S.M. (2010), “The effect of marketing knowledge management on
organizational performance: an empirical investigation of the telecommunications organizations
in Jordan”, International Journal of Emerging Markets, Vol. 5 No. 1, pp. 38-77.
IMDS Aldieri, L. and Vinci, C.P. (2018), “Green economy and sustainable development: the economic impact of
119,9 innovation on employment”, Sustainability, Vol. 1 No. 10, pp. 35-41.
Ali-Hassan, H., Nevo, D. and Wade, M. (2015), “Linking dimensions of social media use to job
performance: the role of social capital”, The Journal of Strategic Information Systems, Vol. 24
No. 2, pp. 65-89.
Bennett, J., Owers, M., Pitt, M. and Tucker, M.J.P.M. (2010), “Workplace impact of social networking”,
Property Management, Vol. 28 No. 3, pp. 138-148.
1922
Berg, A., Buffie, E.F. and Zanna, L.F. (2018), “Should we fear the robot revolution? (The correct answer
is yes)”, Journal of Monetary Economics, Vol. 97 No. 1, pp. 117-148.
Berthon, P.R., Pitt, L.F., Plangger, K. and Shapiro, D. (2012), “Marketing meets Web 2.0, social media,
and creative consumers: implications for international marketing strategy”, Business Horizons,
Vol. 55 No. 3, pp. 261-271.
Cheng, X., FU, S. and DE Vreede, G.-J. (2017), “Understanding trust influencing factors in social media
communication: a qualitative study”, International Journal of Information Management, Vol. 37
No. 2, pp. 25-35.
Chin, W.W. (1998), “The partial least squares approach to structural equation modeling”, Modern
Methods for Business Research, Vol. 295 No. 2, pp. 295-336.
Clark, L.A. and Roberts, S.J. (2010), “Employer’s use of social networking sites: a socially irresponsible
practice”, Journal of Business Ethics, Vol. 95 No. 4, pp. 507-525.
Cross, R. and Cummings, J.N. (2004), “Tie and network correlates of individual performance in
knowledge-intensive work”, Academy of Management Journal, Vol. 47 No. 6, pp. 928-937.
David Gefen, C.M.R. (2002), “Implementation team responsiveness and user evaluation of customer
relationship management: a quasi-experimental design study of social exchange theory”, Journal
of Management Information Systems, Vol. 19 No. 1, pp. 47-69.
Decanio, S.J. (2016), “Robots and humans - complements or substitutes?”, Journal of Macroeconomics,
Vol. 49 No. 1, pp. 280-291.
Demek, K.C., Raschke, R.L., Janvrin, D.J. and Dilla, W.N. (2018), “Do organizations use a formalized risk
management process to address social media risk?”, International Journal of Accounting
Information Systems, Vol. 28 No. 1, pp. 31-44.
Deng, S., Lin, Y., Liu, Y., Chen, X. and Li, H. (2017), “How do personality traits shape information-
sharing behaviour in social media? Exploring the mediating effect of generalized trust”,
Information Research: An International Electronic Journal, Vol. 22 No. 3, pp. 1-37.
Dholakia, U.M., Bagozzi, R.P. and Pearo, L.K. (2004), “A social influence model of consumer
participation in network-and small-group-based virtual communities”, International Journal of
Research in Marketing, Vol. 21 No. 3, pp. 241-263.
Du, Y., Zhang, L. and Tekleab, A.G. (2018), “Job strains, job control, and POS on employee performance:
an interactionist perspective”, Journal of Business Research, Vol. 82 No. 1, pp. 213-219.
Eid, M.I. and Al-Jabri, I.M. (2016), “Social networking, knowledge sharing, and student learning: the
case of university students”, Computers & Education, Vol. 99 No. 1, pp. 14-27.
Falk, A. and Kosfeld, M. (2006), “The hidden costs of control”, American Economic Review, Vol. 96
No. 5, pp. 1611-1630.
Flanagin, A.J. and Metzger, M.J. (2001), “Internet use in the contemporary media environment”, Human
Communication Research, Vol. 27 No. 1, pp. 153-181.
Fornell, C. and Bookstein, F.L. (1982), “Two structural equation models: LISREL and PLS applied to
consumer exit-voice theory”, Journal of Marketing Research, Vol. 19 No. 4, pp. 440-452.
Frey, C.B. and Osborne, M.A. (2017), “The future of employment: how susceptible are jobs to
computerization?”, Technological Forecasting and Social Change, Vol. 114 No. 1, pp. 254-280.
Groen, B.A., Wouters, M.J. and Wilderom, C.P. (2017), “Employee participation, performance metrics,
and job performance: a survey study based on self-determination theory”, Management
Accounting Research, Vol. 36 No. 3, pp. 51-66.
Habibi, M.R., Laroche, M. and Richard, M.-O. (2016), “Testing an extended model of consumer behavior Social media
in the context of social media-based brand communities”, Computers in Human Behavior, Vol. 62 usage
No. 9, pp. 292-302.
Haimes, Y.Y. (2012), “Systems-based guiding principles for risk modeling, planning, assessment,
management, and communication”, Risk Analysis: An International Journal, Vol. 32 No. 9,
pp. 1451-1467.
Hair, J.F. Jr, Hult, G.T.M., Ringle, C. and Sarstedt, M. (2017), A Primer on Partial Least Squares 1923
Structural Equation Modeling (PLS-SEM), Sage Publications, Thousand Oaks, CA, pp. 1-84.
Hair, J.F. Jr, Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (2006), Multivariate Data
Analysis, Pearson Prentice Hall, Upper Saddle River, NJ, p. 2.
He, W., Qiao, Q. and Wei, K.-K. (2009), “Social relationship and its role in knowledge management
systems usage”, Information & Management, Vol. 46 No. 3, pp. 175-180.
Henseler, J. and Sarstedt, M. (2013), “Goodness-of-fit indices for partial least squares path modeling”,
Computational Statistics, Vol. 28 No. 2, pp. 565-580.
Hodzic, A., Sajnic, A., Erdelja, K., Podnar, A., Barisic, I., Pauker, K., Zovko, T., Rezic, S. and Iricanin, Z.P.
(2018), “Relationship of anxiety and depression with sociodemographic characteristics in
hospitalized patients with chronic obstructive pulmonary disease”, European Respiratory Journal,
Vol. 52 No. 62, pp. 15-16.
Hsu, C.-L. and Lin, J.C.-C. (2008), “Acceptance of blog usage: the roles of technology acceptance, social
influence and knowledge sharing motivation”, Information & Management, Vol. 45 No. 1, pp. 65-74.
Hua, G. and Haughton, D. (2012), “A network analysis of an online expertise sharing community”,
Social Network Analysis and Mining, Vol. 2 No. 4, pp. 291-303.
Kakkos, N., Trivellas, P. and Fillipou, K. (2010), “Exploring the link between job motivation, work
stress and job satisfaction: a case study in the banking industry”, Proceedings of the
7th ICESAL, Rhodes, June, pp. 28-29.
Kankanhalli, A., Tan, B.C. and Wei, K.-K. (2005), “Contributing knowledge to electronic knowledge
repositories: an empirical investigation”, MIS Quarterly, Vol. 29 No. 1, pp. 113-143.
Kaplan, A.M. and Haenlein, M. (2010), “Users of the world, unite! the challenges and opportunities of
social media”, Business Horizons, Vol. 53 No. 1, pp. 59-68.
Kim, C., Lee, S.-G. and Kang, M. (2012), “I became an attractive person in the virtual world: users’
identification with virtual communities and avatars”, Computers in Human Behavior, Vol. 28
No. 5, pp. 1663-1669.
Koo, C., Wati, Y. and Jung, J.J. (2011), “Examination of how social aspects moderate the relationship
between task characteristics and usage of social communication technologies (SCTs) in
organizations”, International Journal of Information Management, Vol. 31 No. 5, pp. 445-459.
Kuzu, Ö.H. and Özilhan, D. (2014), “The effect of employee relationships and knowledge sharing on
employees’ performance: an empirical research on service industry”, Procedia-Social and
Behavioral Sciences, Vol. 109 No. 1, pp. 1370-1374.
Lavrakas, P.J. (2008), Encyclopedia of Survey Research Methods, Sage Publications, Thousand Oaks, CA,
p. 509.
Leftheriotis, I. and Giannakos, M.N. (2014), “Using social media for work: losing your time or improving
your work?”, Computers in Human Behavior, Vol. 31 No. 2, pp. 134-142.
Leidner, D., Koch, H. and Gonzalez, E. (2010), “Assimilating generation Y IT new hires into USAA’s
workforce: the role of an enterprise 2.0 system”, MIS Quarterly Executive, Vol. 9 No. 4, pp. 40-60.
Ma, M. and Agarwal, R. (2007), “Through a glass darkly: Information technology design, identity
verification, and knowledge contribution in online communities”, Information Systems Research,
Vol. 18 No. 1, pp. 42-67.
Meshi, D., Morawetz, C. and Heekeren, H.R. (2013), “Nucleus accumbens response to gains in reputation
for the self-relative to gains for others predicts social media use”, Frontiers in Human
Neuroscience, Vol. 7 No. 439, pp. 1-11.
IMDS Molm, L.D. (1997), Coercive Power in Social Exchange, Cambridge University Press, Cambridge, p. 293.
119,9 Naudé, P., Zaefarian, G., Tavani, Z.N., Neghabi, S. and Zaefarian, R. (2014), “The influence of network
effects on SME performance”, Industrial Marketing Management, Vol. 43 No. 4, pp. 630-641.
Nisar, T.M. and Prabhakar, G. (2018), “Trains and Twitter: firm generated content, consumer
relationship management and message framing”, Transportation Research Part A: Policy and
Practice, Vol. 113 No. 7, pp. 318-334.
1924 Nunnally, J.C. and Bernstein, I. (1994), Psychometric Theory, McGraw-Hill Series in Psychology,
McGraw-Hill, New York, NY, p. 205.
O’Connor, K.W., Schmidt, G.B. and Drouin, M. (2016), “Helping workers understand and follow social
media policies”, Business Horizons, Vol. 59 No. 2, pp. 205-211.
Osatuyi, B. (2013), “Information sharing on social media sites”, Computers in Human Behavior, Vol. 29
No. 6, pp. 2622-2631.
Parveen, F., Jaafar, N.I. and Ainin, S. (2015), “Social media usage and organizational performance:
reflections of Malaysian social media managers”, Telematics and Informatics, Vol. 32 No. 1,
pp. 67-78.
Patterson, M.G., West, M.A., Shackleton, V.J., Dawson, J.F., Lawthom, R., Maitlis, S., Robinson, D.L. and
Wallace, A.M. (2005), “Validating the organizational climate measure: links to managerial
practices, productivity and innovation”, Journal of Organizational Behavior, Vol. 26 No. 4,
pp. 379-408.
Pi, S.-M., Chou, C.-H. and Liao, H.-L. (2013), “A study of Facebook groups members’ knowledge
sharing”, Computers in Human Behavior, Vol. 29 No. 5, pp. 1971-1979.
Pitafi, A.H., Kanwal, S., Ali, A., Khan, A.N. and Ameen, M.W. (2018), “Moderating roles of IT
competency and work cooperation on employee work performance in an ESM environment”,
Technology in Society, Vol. 55 No. 4, pp. 199-208.
Preacher, K.J. and Hayes, A.F. (2008), “Asymptotic and resampling strategies for assessing and
comparing indirect effects in multiple mediator models”, Behavior Research Methods, Vol. 40
No. 3, pp. 879-891.
Shepherd, C.J.S.D. (2011), “Does social media have a place in workplace learning?”, Strategic Direction,
Vol. 27 No. 2, pp. 3-4.
Shi, P., Xu, H. and Chen, Y. (2013), “Using contextual integrity to examine interpersonal information
boundary on social network sites”, Proceedings of the SIGCHI Conference on Human Factors in
Computing Systems, pp. 35-38.
Shujahat, M., Sousa, M.J., Hussain, S., Nawaz, F., Wang, M. and Umer, M. (2019), “Translating the
impact of knowledge management processes into knowledge-based innovation: the neglected
and mediating role of knowledge-worker productivity”, Journal of Business Research, Vol. 94
No. 1, pp. 442-450.
Siamagka, N.-T., Christodoulides, G., Michaelidou, N. and Valvi, A. (2015), “Determinants of social
media adoption by B2B organizations”, Industrial Marketing Management, Vol. 51 No. 8,
pp. 89-99.
Skeels, M.M. and Grudin, J. (2009), “When social networks cross boundaries: a case study of workplace
use of Facebook and Linkedin”, Proceedings of the International Conference on Supporting Group
Work, Sanibel Island, FL, pp. 95-104.
Srivastava, A., Bartol, K.M. and Locke, E.A. (2006), “Empowering leadership in management teams:
effects on knowledge sharing, efficacy, and performance”, Academy of Management Journal,
Vol. 49 No. 6, pp. 1239-1251.
Sujatha, R. and Krishnaveni, R. (2018), “Knowledge creating ba as a determinant of work performance
of employees: an empirical analysis among pump manufacturing firms in South India”,
Asia Pacific Management Review, Vol. 23 No. 1, pp. 45-52.
Tajvidi, R. and Karami, A. (2017), “The effect of social media on firm performance”, Computers in
Human Behavior, Vol. 74 No. 9, pp. 1-10.
Tulu, D.T. (2017), “Should online social Medias (OSMs) be banned at work? The impact of social Medias Social media
on employee productivity in Ambo University, a case study”, Research in International Business usage
and Finance, Vol. 42 No. 4, pp. 1096-1102.
Van Roy, V., Vértesy, D. and Vivarelli, M. (2018), “Technology and employment: mass unemployment
or job creation? Empirical evidence from European patenting firms”, Research Policy, Vol. 47
No. 6, pp. 1762-1776.
Wasko, M.M. and Faraj, S. (2000), “It is what one does: why people participate and help others in
electronic communities of practice”, The Journal of Strategic Information Systems, Vol. 9 Nos 2–3,
1925
pp. 155-173.
Wasko, M.M. and Faraj, S. (2005), “Why should I share? Examining social capital and knowledge
contribution in electronic networks of practice”, MIS Quarterly, Vol. 29 No. 1, pp. 35-57.
Wu, J.-H. and Wang, Y.-M. (2006), “Measuring KMS success: a respecification of the DeLone and
McLean’s model”, Information & Management, Vol. 43 No. 6, pp. 728-739.
Yardi, S., Golder, S.A. and Brzozowski, M.J. (2009), “Blogging at work and the corporate attention
economy”, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems,
pp. 2071-2080.
Ye, H.J., Feng, Y. and Choi, B.C. (2015), “Understanding knowledge contribution in online knowledge
communities: a model of community support and forum leader support”, Electronic Commerce
Research and Applications, Vol. 14 No. 1, pp. 34-45.
Zack, M., Mckeen, J. and Singh, S. (2009), “Knowledge management and organizational performance: an
exploratory analysis”, Journal of Knowledge Management, Vol. 13 No. 6, pp. 392-409.
Zhang, X., Ma, L., Xu, B. and Xu, F. (2018), “How social media usage affects employees’ job satisfaction
and turnover intention: an empirical study in China”, Information & Management, Vol. 55 No. 8,
pp. 1-12.

Further reading
Chou, W.-Y.S., Hunt, Y.M., Beckjord, E.B., Moser, R.P. and Hesse, B.W. (2009), “Social media use in the
United States: implications for health communication”, Journal of Medical Internet Research,
Vol. 11 No. 4, pp. 1-12.
Patel, N. and Jasani, H. (2010), “Social media security policies: guidelines for organizations”, Issues in
Information Systems, Vol. 11 No. 1, pp. 628-634.

Corresponding author
Ben Niu can be contacted at: drniuben@gmail.com

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