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Bahria University Journal of Management & Technology: Vol.2, No. 2 pp. 24-37
24
Analysis of the Combined and Split up Effects of Strategic Human
Resource Practices on Knowledge Management Capacity and Firm-
Innovation
Rab Nawaz Lodhi1, Shahbaz Sharif2, Ayesha Naeem3 1,2Department of Economics and Business Management, University of Veterinary and Animal Sciences, UVAS Business
School, Lahore, Pakistan 3Institute of Business and Management, University of Engineering and Technology, Lahore, Pakistan
ABSTRACT
This study was organized to test the combined and split effects of Strategic Human resource
(SHR) practices on organizational innovation and on knowledge management capacity. Data was
collected through survey questionnaires from 300 private bank sector employees including bank
officers, administrative officers, middle and upper management and then applied partial least square
(PLS) technique to elaborate the relationships between independent and dependent variables. The
findings of this study showed that each specific SHR practice has a positive effect on knowledge
management capacity and organizational innovation except (i) “selective staffing” and (ii) “training”
dimensions of SHR. They have week but positive impact on knowledge management capacity and
organizational. In addition, the combined effect of SHR practices on knowledge management capacity
and organizational innovation was also significant and positive. This study has contributed to
managerial and theoretical implications and lastly, limitations and future research were also discussed.
Keywords: SHR practices, Firm Innovation, Knowledge-management capacity, PLS
Introduction
Strategic human resource practices are the key element to improve firm performance and
innovation as they are a crucial resource to translate firm needs and employees behavior (Sánchez,
Marín, & Morales, 2015). Hence, HR managers need to manage their employee's compensation,
staffing, training, participation and performance-based appraisals as this engage their employees in
developing a new idea or sharing an existing idea, method, and policy which increases organizational
innovation. SHR practices and firm innovation had been investigated in the wide range of all places in
the world (Beugelsdijk, 2008; Camelo-Ordaz, Garcia-Cruz, Sousa-Ginel, & Valle-Cabrera, 2011;
Chang, Gong, Way, & Jia, 2013; Diaz-Fernandez, Bornay-Barrachina, & Lopez-Cabrales, 2017;
Eriksson, Qin, & Wang, 2014). However, the existing literature had utilized the innovation
measurement as product innovation (Lopez‐Cabrales, Pérez‐Luño, & Cabrera, 2009; Shipton, Fay,
West, Patterson, & Birdi, 2005), IT, administrative and process innovation (Bondarouk & Kees Looise,
2005; Jimenez-Jimenez & Sanz-Valle, 2005; Verburg, Den Hartog, & Koopman, 2007). The
measurement of organizational innovation in form of exploitation and exploration has rarely been
identified in the existing literature (Fındıklı, Yozgat, & Rofcanin, 2015), particularly unexplored in
Pakistan banking sector.
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In addition, HRM practices and knowledge management had also been recognized in many
contexts while rare has been recognized with knowledge management capacity. The objective of the
present study was (1) to analyze the relationship between SHR practices and firm innovation; (2) Too
much extent SHR affect the knowledge sharing, acquisition, and application. The contribution of this
study can be summarized to check the impact of SHR practices on the knowledge management capacity.
SHR practices also contributed to firm innovation in the form of exploration and exploitation. The term
strategy used to analyze the employee’s knowledge at a firm place and firm performance.
Literature Review/Theoretical Framework
Strategic Human Resource practices (SHRPs)
The idea which utilizes employees in an effective and efficient manner to achieve competitive
edge is the strategic human resource practices. Strategic human resource management creates
relationship between employees and firm innovation. HRM practices are designed to formulate firm
innovation and knowledge sharing, acquisition and application. Today, many companies involved in
innovation process to increase their productivity level and market demand (Afacan Fındıklı, Yozgat, &
Rofcanin, 2015). Exploitation and exploration are two important strategies which are used to sustain
and improve firm innovation. Exploitation means to share existing knowledge among organizational
employees and management while exploration means to develop a new idea, theory or method (Curado,
2008). Strategic human resource practices such as training, staffing, performance appraisal, active
participation, and compensation have a positive effect on firm performance (Sánchez et al., 2015). SHR
management is important to the development of human capital to achieve firm goals and targets (Collins
and Clark, 2003; Jackson et al., 2014). Knowledge management as a tool to promote productivity and
flexibility of firm to meet competencies (Davenport and Volpel., 2001; Sveiby et al., 2005).
SHR practices and Firm Innovation - Exploration and Exploitation
Organizations should use their resources in an effective way to ensure profit in short and long
run, they maintain new skills and new market situations which demanded by new customers.
Exploration and exploitation strategies are used to reduce the internal tension and manages firm
management control system (Schuler RS and Jackson SE., 1987). Some SHR practices were associated
with firm innovation and knowledge sharing, acquisition and implication (Afacan Fındıklı et al., 2015).
Firm innovation may be different from each other for example exploration declared as experiments to
develop the new idea while exploitation is the extension of existing technologies and idea or method
(Zhao et al., 2014).
Innovativeness is conceptualized to encourage innovation and has closely related to sharing
knowledge and knowledge application to attain firm’s goal (Ferraresi et al., 2012). The literature shows
that this study examines the relationship between SHR practices and firm innovation and knowledge
management capacity. SHR practices are used to develop the skills of workers and employees to
increase the productivity level and firm flexibility (Swart and Kinnie, 2009).
Exploration and exploitation process was increased by SHR practices. SHR practices help to
promote exploration and exploitation for the accurate use of firm capital and resources (Afacan Fındıklı
et al., 2015). Another study shows that firm innovation is positively and significantly influenced by
SHR practices (Nasurdin, 2011). A firm cannot transfer knowledge among employees easily without
SHR management (Chung et al., 2007). Without the implementation of SHR practices, the knowledge
Bahria University Journal of Management & Technology: Vol.2, No. 2 pp. 24-37
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management has no direct effect on firm performance (Sánchez et al., 2015). After depth discussion,
the hypothesis was generated:
H1: SHR practices have a positive and significant effect on exploration and exploitation as a
firm innovation
SHR practices and Knowledge management capacity
Knowledge management capacity is the form of sharing, acquiring and application of
knowledge which can be enhanced through the use of strategic human resource practices (Afacan
Fındıklı et al., 2015). There was a positive and significant relationship between knowledge management
and firm performance (Shehu Aliyu, Bello Rogo, & Mahmood, 2015).
SHR practices provide meaning to the application of shared knowledge and acquire more
knowledge to develop the firm innovativeness (Ferraresi et al., 2012). Knowledge management capacity
was significantly and positively influenced by the implementation of performance appraisal and
compensation (Ferraresi et al., 2012). It is important that organizational changes based on the
development of competencies of human available resources. SHR management is used to in order to
increase competencies to manage knowledge (Chourides et al., 2003). If organization enables
employees to share, acquire and apply knowledge in firm innovation then firm financial performance,
productivity can be increased. SHR management is organized to make employees competencies by
sharing knowledge, acquiring knowledge and application in a systematic manner.
SHR practices are established to achieve the knowledge management (Nonaka and Takeuchi,
1995; Hall, 2001; Davenport and Volpel, 2001; Jelenic, 2010). Effective knowledge of employees must
be evaluated by SHR practices such as staffing and performance appraisal (Wang and Noe, 2010; Chen
and Huang, 2009). SHR practices are used to allow employees to improve their behaviors to achieve
firm goals (Scarbrough, 2003; Collins and Clark, 2003; Von Krogh, 1998). The behavior of knowledge
sharing and knowledge application can be derived from performance appraisal and compensation
benefits (Gold et al., 2001; Chen and Huang, 2009).
H2: SHR practices have positive and significant effect on knowledge- economy i.e. sharing,
acquiring and application
Research methodology
Sample and procedure
This study covered private banking sector operating in Pakistan. The participants were full-time
employees including bank officers, administrative officers, middle and upper management. 430 survey
questionnaires were distributed among employees and out of 430, 327 questionnaires were collected
but 300 of them were answered completely. Hence, the participant’s response rate was 69.77%. The
participants participated in survey research were 72% males and 28% females. The 68% participants
were below 30 years of age, 26% were between the age of 30-39, 5% participants were between 40-49
and only 1% above 50 year of age. The participants also categorized into different designations such as
51% were bank officers, 25% were administrative personnel, 22% were middle management and only
2% were upper management.
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Construct Measurements
All construct measurements were adopted from the prior literature and measured on 5 point
Likert scale, representing 1= strongly disagree to 5= strongly agree.
SHR Practices
The SHR practices construct included 5 dimensions i.e. training, compensation, performance-
based appraisal, staffing and active participation and was measured by adopting 15 items scale from
Collins and Clark (2003) and Chen and Haung (2009). Each dimension contained 3 items and the
Cronbach alpha coefficient for the combined SHR practices was 0.889. While, the alpha coefficient for
training was 0.74, compensation was 0.714, performance-based appraisal was 0.711, staffing was 0.734
and participation was 0.76.
Firm Innovation
Firm innovation means any change in firm performance made through employees by using firm
resources and practices. Firm innovation construct included two dimensions; exploration and
exploitation and adopted from 12 items scale of Zi-Lin He and Poh-Kam Wong (2001) and Jansen et
al., (2009). Each dimension contained 6 items and the Cronbach alpha coefficient of exploitation was
0.890 and exploration was 0.856. While, the Cronbach alpha of combined firm innovation construct
was 0.909.
Knowledge management capacity
The three dimensions of knowledge management capacity including knowledge acquisition,
sharing and application were evaluated with eight items scale developed by Lin HF & Lee GG, (2005),
Chen and Huang (2009b) & Gold AH, Malhotra (2001). Knowledge sharing and knowledge acquisition
contained 3 items and knowledge application contained 2 items. Cronbach alpha coefficient for
knowledge acquisition was 0.844, knowledge sharing was 0.838 and knowledge application was 0.826.
While, the Cronbach alpha for the whole construct was 0.891.
Data analysis
Testing measurement model
Smart PLS-SEM 3 was applied to test the validity and reliability of measurement constructs
and the nature of relationships among them. The rationale for using this software is its advanced
reporting features and it’s user-friendly. PLS algorithm was applied to evaluate the convergent and
discriminant validity known as construct validity. Researchers advocated that (1) factor loadings must
be greater than 0.70, (2) AVE must be greater than 0.5 and (3) Composite reliability must be greater
than 0.70 for convergent validity assessment (Joseph F. Hair, 2013; Joseph F. Hair, 2014 & Coltman,
2008). We have found that all items of SHR practices, firm innovation and knowledge management
capacity had factor loadings greater than 0.7 (Table.1) except STAF1 (λ=0.658) and TRAIN3 (λ=0.543)
and hence removed from the model. Moreover, the average variance extracted of all constructs were
also greater than 0.50, hence, it could be said that convergent validity of all constructs was valid in both
construct wise & item wise. Fornell & Larcker., 1891 typology was used to assess the discriminant
validity of constructs. The most recommended criteria for evaluating discriminant validity is “Fornell
and Larcker., 1981” which is a multiple trait evaluation techniques of constructs. This method makes
the comparison of each construct’s AVE value with square inter-correlation of another construct in the
PLS model (Joseph F. Hair, 2013; Andrew M Farrell, 2009). The value of all constructs was greater
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than 0.7 and also greater than the value of another construct (Table.2). These results supported our
measurement model discriminant validity.
Table 1. Factor loadings/Average variance extracted/Cronbach alpha
Variable/Items Loading Reliability(α) AVE
Compensation 0.714 0.636
COMP1 0.817
COMP2 0.818
COMP3 0.756
Selective Staffing 0.734 0.713
STAF2 0.886
STAF3 0.823
Training 0.740 0.634
TRAIN1 0.828
TRAIN2 0.763
Active Participation 0.760 0.608
AP1 0.702
AP2 0.850
AP3 0.780
Performance appraisal 0.711 0.634
PA1 0.766
PA2 0.794
PA3 0.828
Exploration 0.856 0.581
EXP1 0.739
EXP2 0.730
EXP3 0.778
EXP4 0.802
EXP5 0.758
EXP6 0.765
Exploitation 0.890 0.646
EXT1 0.753
EXT2 0.814
EXT3 0.795
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EXT4 0.862
EXT5 0.837
EXT6 0.753
Knowledge sharing 0.838 0.756
KS1 0.810
KS2 0.910
KS3 0.885
Knowledge acquisition 0.844 0.762
KAC1 0.862
KAC2 0.879
KAC3 0.877
Knowledge application 0.826 0.851
KAPP1 0.925
KAPP2 0.920
Table 2: Fornell and Larcker. (1981)
COMP EXT EXP FI KAPP KAC KS KMC AP PA SHRP STAF TRA
IN
Compensation 0.798
Exploitation 0.548 0.803
Exploration 0.504 0.637 0.762
Firm innovation 0.583 0.916 0.893 0.709
Knowledge
Application 0.5 0.581 0.538 0.62 0.923
Knowledge
acquiring 0.728 0.558 0.533 0.604 0.503 0.873
Knowledge
Sharing
0.516 0.608 0.52 0.626 0.784 0.51 0.869
Knowledge
management
capacity
0.684 0.682 0.62 0.721 0.872 0.794 0.9 0.76
Active Participation 0.621 0.552 0.555 0.612 0.501 0.625 0.484 0.63 0.78
Performance
appraisal 0.537 0.583 0.546 0.625 0.491 0.578 0.523 0.62 0.66 1.0
SHRP 0.824 0.646 0.636 0.709 0.581 0.745 0.592 0.75 0.86 1.0 0.656
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Selective Staffing 0.568 0.473 0.502 0.538 0.414 0.54 0.445 0.55 0.65 1.0 0.804 0.845
Training 0.57 0.433 0.479 0.503 0.446 0.531 0.422 0.55 0.52 1.0 0.742 0.542 0.796
Note: Diagonal values represent discriminant validity and other correlation coefficients**
COMP=Compensation, EXT=exploitation, EXP=exploration, FI=firm innovation, KAPP=knowledge
application, KAC=knowledge acquiring, KS=knowledge sharing, KMC=knowledge management capacity,
AP=active participation, PA=performance appraisal, SHRP=strategic human resource management,
STAF=selective staffing and TRAIN=training
Regression analysis
1st order measurement constructs relationships
Smart-PLS generated T-statistics to test the inner & outer model significance level by applied
a technique, called bootstrapping. The procedure followed the 500 subsamples from the original sample
312 (maximum iterations with stop value-7) with replacement to standard errors in bootstrap which give
output in form of T-statistics values for significance testing of the Path model. Structural equation
modeling (SEM) was used to test the effects of SHR practices on the knowledge management capacity
and firm innovation (see table 3). The two-tail test is applied at 5% significant level (P<0.05) standard
error. A t-statistics value must be greater than 1.96, then it can be say that there is a significant
association (Wong, 2013).
The compensation factor of SHR practices showed positive and significant effect on firm
innovation (β=0.206***, t=17.431) and knowledge management capacity (β=0.218***, t=16.425). The
second factor of SHR practices, active participation also showed significant and positive effect on firm
innovation (β=0.197***, t=18.362) and knowledge management capacity (β=0.209***, t=21.463). The
third factor performance appraisal revealed significant and positive impact on firm innovation
(β=0.204***, t=19.454) and knowledge management capacity (β=0.216***, t=21.121). Selective
staffing had a positive and significant effect on firm innovation (β=0.140***, t=17.257) and knowledge
management capacity (β=0.148***, t=18.157). The training factor had a significant and positive impact
on firm innovation (β=0.117***, t=13.862) and knowledge management capacity (β=0.124***,
t=14.075). Present study proved that all the latent factors of SHR practices influence both knowledge
management capacity i.e. knowledge sharing, acquiring and application and firm innovation i.e.
exploitation and exploration.
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Model 1. The effects of compensation, selective training, training, active participation and
performance appraisal on knowledge-economy and firm innovation
2nd order measurement constructs relationships
This study had further elaborated the combined effect of SHR practices on knowledge
management capacity (β=0.751***, t=24.949) and on firm innovation (β=0.709***, t=24.515). These
significant and positive effects showed that employees developed existing or new idea when they were
exercised by SHR practices in the banking sector. These results of SHR practice’s opportunities are
consistent with other literature studies that have been examined the combined and latent effects of SHR
practices on knowledge management capacity and firm innovation.
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Model 2. The combined effects of SHR practices on knowledge-economy and firm innovation
Model fitness
The coefficient of determination (R²) is a most important measure to test the structural model
fitness. R² evaluated the combined effect of all latent constructs of the exogenous variable on the
endogenous variable (Wong, 2013). In addition, Adjusted R² values reduced the values of R² by the
number of explaining variables & size of a sample. Present research had good predictive accuracy/
adequacy in terms of R² and adjusted R² values because R² is a measure to test the model predictive
adequacy (Joe F. Hair Jr, 2014). The R² and adjusted R² values must be placed between 0 to 1 with the
higher level the high level of predictive accuracy and vice versa (Joseph F. Hair, 2013; Joe F. Hair Jr,
2014). Researchers argued that R² values vary from 0.75, 0.50 to 0.25 which respectively described as
strong, moderate and weak relationship (Hair et al., 2012; Sarstedt, Ringle, Smith, Reams, & Hair, 2014;
Hair et al., 2013; Hair, Sarstedt, Pieper, & Ringle, 2012 and Joe F. Hair Jr, 2014). Table 3 reveals that
there was good predictive accuracy and adequacy from independent constructs to the dependent
constructs.
Table 3. R square and adjusted R square
Constructs R2 T Statistics
(|O/STDEV|)
Adjusted
R2
T Statistics
(|O/STDEV|)
P-values
Exploitation 0.838 50.418 0.838 50.268 0.000
Exploration 0.798 35.107 0.797 34.998 0.000
Firm-innovation 0.502 12.349 0.501 12.288 0.000
Knowledge-application 0.761 35.264 0.760 35.148 0.000
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Multi-collinearity statistics (VIF)
Multicollinearity was assessed by calculating the tolerance (1-R2) and variance inflation factor
(VIF) (Sarstedt, Ringle, Smith, Reams, & Hair, 2014 and Joseph F. Hair, 2014). PLS-SEM explored
multicollinearity for a set of exogenous constructs toward endogenous in the model. VIF calculates as
“1/tolerance” but as a rule of thumb, VIF accepts if its value >=5 then it can be said that the constructs
are highly correlated (Wong, 2013; Sarstedt, Ringle, Smith, Reams, & Hair, 2014 & Coltman, 2008).
R² value facilitates to compute the VIF of constructs (Sarstedt et al., 2014). Table 4 discloses the values
of VIF and tolerance of all constructs under present study, which designates that our data do not contain
any problem of multicollinearity.
Table 4. Collinearity Statistics
Collinearity Statistics
Constructs Tolerance Variance Inflation Factor
VIF
Knowledge-economy 0.435 2.299
Knowledge-sharing 0.191 5.236
Knowledge-acquiring 0.370 2.703
Knowledge-application 0.239 4.184
Firm-innovation 0.498 2.008
Exploitation 0.162 6.173
Exploration 0.202 4.950
Discussion
This study examined the impact of SHR practices on firm innovation and knowledge
management capacity. SHR practices has been considered an important asset for every organization in
order to acquire suitable solution for innovative performance. Literature studies evaluated the positive
and significant relationship between SHR practices and knowledge management capacity (Afouni,
2007; Haesli & Boxall, 2005; Theriou & Chatzonglou, 2008 and Wang et al., 2012). This study came
from the classical review of mediating relationship of SHR practices on knowledge management and
firm performance (Sánchez et al., 2015). The principal goal of this study was to investigate the effects
of SHR practices on firm innovation and knowledge management capacity in private banking system
as a whole in Punjab, Pakistan.
Knowledge-acquiring 0.630 16.321 0.629 16.257 0.000
Knowledge-sharing 0.809 40.681 0.809 40.556 0.000
Knowledge-economy 0.565 12.565 0.563 12.51 0.000
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All hypotheses were accepted on the following grounds: (1) knowledge-sharing, knowledge-
acquiring and knowledge-application are systematically dependent on the SHR practices in the private
banking sector. (2) Firm exploitation and exploration also systematically dependent on the SHR
practices because SHR practices served as a role model for any industry. (3) All dimensions of SHR
practices substantially influence on knowledge management capacity and firm innovation. Moreover,
training, compensation, performance appraisal, selective staffing and active participation are important
industrial substantial factors that develop the employees' behaviors toward knowledge sharing,
acquiring and its application and on another way, firm exploitation and exploration. It is concluded that
if organization executes SHR practices then it can infuse the employee’s feelings towards independence
and success, then there will be an increase in knowledge sharing, acquiring and application and firm
exploitation and exploration (Sánchez et al., 2015).
The hypothesis 1 result showed that SHR practices are positively correlated with knowledge
management capacity. This result is consistent with the findings of Sanchez et al., 2015. In addition,
the hypothesis 2 result showed that SHR practices are positively correlated with firm innovation. This
result is consistent with the findings of Nawaz et al., 2014; (Afacan Fındıklı et al., 2015) and (Nasurdin,
2011). It is also summarized that the training factor is positively correlated with the knowledge
management capacity. For this reason, it could be said that organization gives more importance to the
training of their employees to get better results in the knowledge management capacity and firm
innovation. Compensation is positively correlated with knowledge management capacity and firm
innovation, which concluded that if organization agrees to compensate their employees, will better
results in sharing, acquiring and applying knowledge and develop new or existing idea for innovative
firm. Performance appraisal is positively correlated with knowledge management capacity and firm
innovation which showed that if the organization measures the employee’s performance on authentic
scales then they will better contribute to knowledge management capacity and firm innovation.
Active participation is positively related to knowledge management capacity and firm
innovation this contributes to the fact that if employees take part in decision making, making policies
and work enhancement then they would learn more which in turn, would encourage knowledge
management capacity and firm innovation. Selective staffing is also positively correlated with
knowledge management capacity and firm innovation, which concluded that if organization selects right
personnel for the available post or his/her qualification is consistent with that post then he/she will
contribute more to knowledge management capacity and firm innovation. These results are consistent
with previous studies (Sánchez et al., 2015); Nawaz et al., 2014; (Afacan Fındıklı et al., 2015) and
(Nasurdin, 2011).
This study has empirical evidence that success of every organization depends upon effective
SHR practices and in turn, employees introduce multiple products throughout their career enhancement
and development. In addition, the employees improve the existing products if they feel any deficiency
of banking product or feel further modifications in existing products. The employees develop an
effective control system that influences the customers, colleagues, and officials. Hence, they facilitate
the existing employees with content to launch a new policy that encourages outsiders to contribute their
personal habits within a bank.
Limitations and Future research
The present study was conducted from empirical evidence by using cross-sectional study
method. So, it is expecting to conduct a longitudinal study to draw continuous fluctuations of different
factors on firm innovation and knowledge management capacity. This study was narrow down and only
depends upon previous literature shreds of evidence, hence, there is a need of more attention to studying
Bahria University Journal of Management & Technology: Vol.2, No. 2 pp. 24-37
35
the hidden factors to affect the firm innovation and knowledge management capacity. Future research
may conduct to explore the substantial factors like management strategy, employee organizational
behaviors, information technologies, organization commitment that provide managerial support to the
knowledge management capacity and firm innovation in a different context. Examination of this study
leads to more investigation because every firm has different strategies. So, we can say that this study
needs to explore more ideas and concepts that help the audience and add to our understanding of firms
in general.
Implications
Implications of the present study are providing to be more effective in the managerial
perspective. SHR practices proved the satisfactory effect on management with a little bit factors but in
future, it may different perspective to achieve the firm goals and objectives. The present study was
extended to SHR practices that acted as a role model to firm innovation, improve management sharing
and performing capabilities. SHR practices like performance appraisal, compensation & merit base
selection provided evidence that industrial system always depends upon managerial practices system,
seminars conduction etc. SHR practices should be implemented in every organization to stable
employee intention. SHR practices used to share and interpret existing or new ideas in the organization
with the collaboration of workers. Sometime SHR practices are used as mediation that has the firm main
focus on SHR practices in the form of the knowledge-economy. Knowledge economy enables
employees in the industry to communicate with each other that are not felt hesitation from requiring
anything from top management. SHR practices also increase organization commitment of employees
and providing evidence for not firing just hiring.
References
Afacan Fındıklı, M., Yozgat, U., & Rofcanin, Y. (2015). Examining Organizational Innovation and Knowledge
Management Capacity The Central Role of Strategic Human Resources Practices (SHRPs). Procedia - Social and
Behavioral Sciences, 181, 377-387. doi:10.1016/j.sbspro.2015.04.900
Afiouni, F. (2007). Human resource management and knowledge management: A road map toward improving
organizational performance. Journal of American Academy of Business, 11(2): 124-130.
Abdul-Rashid, I., Bassioni, H., and Bawazeer, F. (2007). Factors affecting safety performance in large
construction contractors in Egypt. Proc., 23rd Annual ARCOM, 661–670
Aibinu, A. A. and Al Lawati, A. M. (2010). Using PLS- SEM technique to model construction organization’s
willingness to participate in bedding. Automation in Construction, 19, (6): 714–724
Anderson, J.C. and Gerbing, D.W. (1988). Structural Equation Modeling in Practice: A Review and
Recommended Two-Step Approach. Psychological Bulletin, 103(3), 411-42
Aliyu, M. S., et al. (2015). Knowledge Management, Entrepreneurial Orientation and Firm Performance: The
Role of Organizational Culture. Asian Social Science, 11(23): 140-164
Beugelsdijk, S. (2008). Strategic human resource practices and product innovation. Organization Studies, 29(6),
821-847.
Bondarouk, T., & Kees Looise, J. (2005). HR contribution to IT innovation implementation: Results of three case
studies. Creativity and Innovation Management, 14(2), 160-168.
Camelo-Ordaz, C., Garcia-Cruz, J., Sousa-Ginel, E., & Valle-Cabrera, R. (2011). The influence of human resource
management on knowledge sharing and innovation in Spain: the mediating role of affective commitment. The
International Journal of Human Resource Management, 22(07), 1442-1463.
Chang, S., Gong, Y., Way, S. A., & Jia, L. (2013). Flexibility-oriented HRM systems, absorptive capacity, and
market responsiveness and firm innovativeness. Journal of Management, 39(7), 1924-1951.
Bahria University Journal of Management & Technology: Vol.2, No. 2 pp. 24-37
36
Chen, C. J. and J. W. Huang, (2007). How organizational climate and structure affect knowledge management.
The social interaction perspective. International Journal of Information Management, 27(2): 104-118.
Chen, C.-J., & Huang, J.-W. (2009a). Strategic human resource practices and innovation performance—The
mediating role of knowledge management capacity. Journal of business research, 62(1), 104-114.
Chen, C.-J., & Huang, J.-W. (2009b). Strategic human resource practices and innovation performance — The
mediating role of knowledge management capacity. Journal of Business Research, 62(1), 104-114.
doi:10.1016/j.jbusres.2007.11.016
Collins, C. J., & Smith, K. G. (2006). Knowledge exchange and combination: The role of human resource practices
in the performance of high-technology firms. Academy of management journal, 49(3), 544-560.
Curado, C. (2008). Perceptions of knowledge management and intellectual capital in the banking industry. Journal
of Knowledge Management, 12(3): 141-155.
Chin, W. W. (1998). The partial least squares approach for structural equation modelling. Modern Methods for
Business Research, Lawrence Erlbaum Associates
Chin, W. W., and Newsted, P. R. (1999). Structural Equation Modeling analysis with Small Samples Using Partial
Least Squares. Statistical Strategies for Small Sample Research, Sage Publications
Coltman, T. M. D. F. Midgley, S. Venaik., (2008). Formative versus reflective measurement models’: Two
applications
Davenport, T. H. and S. C. Völpel, (2001). The rise of knowledge towards attention management Journal of
Knowledge Management, 5(3): 212-222.
Diaz-Fernandez, M., Bornay-Barrachina, M., & Lopez-Cabrales, A. (2017). HRM practices and innovation
performance: a panel-data approach. International Journal of Manpower, 38(3), 354-372.
Efron, B. and Gong, G. (1983). A leisurely look at the Bootstrap, the Jackknife, and Cross Validation. The
American Statistician, 37(1): 36-48.
Eriksson, T., Qin, Z., & Wang, W. (2014). Firm-level innovation activity, employee turnover and HRM
practices—Evidence from Chinese firms. China Economic Review, 30, 583-597.
Ferraresi, A. A., Quandt, C. O., dos Santos, S. A., & Frega, J. R. (2012). Knowledge management and strategic
orientation: leveraging innovativeness and performance. Journal of Knowledge Management, 16(5), 688-701.
doi:10.1108/13673271211262754
Fındıklı, M. A., Yozgat, U., & Rofcanin, Y. (2015). Examining organizational innovation and knowledge
management capacity the central role of strategic human resources practices (SHRPs). Procedia-Social and
Behavioral Sciences, 181, 377-387.
Fornell, C., Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and
measurement error”, Journal of Marketing Research, 18 (1), 39-50.
Haesli, A. and P. Boxall (2005). When knowledge management meets HR strategy: An exploration of
personalization-retention and codification-recruitment configurations. The International Journal of Human
Resource Management, 16(11): 1955-1975
Hair, J. F., Sarstedt, M., Pieper, T. M. & Ringle, C. M. (2012), "The Use of Partial Least Squares Structural
Equation Modeling in Strategic Management Research: A Review of Past Practices and Recommendations for
Future Applications", Long Range Planning, (45), 320-340.
Hair, J. F., Ringle, C. M. & Sarstedt., (2013). Partial Least Squares Structural Equation Modeling: Rigorous
Applications, Better Results and Higher Acceptance, Long Range Planning, (46): 1-12.
Heffernan, M., Dundon, T., Cafferkey, K., & Harney, B. (2009). Exploring the relationship between HRM,
creativity climate and organisational performance: Evidence from Ireland.
Hu, L.T. and Bentler, P.M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional
Criteria versus New Alternatives. Structural Equation Modeling: A Multidisciplinary Journal, (6), 1-55.
Jansen, J.J., Vera, D. and Crossan, M. (2009). Strategic Leadership for Exploration and Exploitation: The
Moderating Role of Environmental Dynamism. The Leadership Quarterly, (20)5–18.
Jiang, J., Wang, S., & Zhao, S. (2012). Does HRM facilitate employee creativity and organizational innovation?
A study of Chinese firms. The International Journal of Human Resource Management, 23(19), 4025-4047.
Jimenez-Jimenez, D., & Sanz-Valle, R. (2005). Innovation and human resource management fit: an empirical
study. International journal of Manpower, 26(4), 364-381.
Joe F. Hair JR, M. S., Lucas Hopkins, Volker G. Kuppel Wieser (2014). Partial least squares structural equation
modeling (PLS-SEM): An emerging tool in business research”, European Business Review, 26(2):106-121
Bahria University Journal of Management & Technology: Vol.2, No. 2 pp. 24-37
37
Joseph F. Hair, J., G.Tomas M. Hult Christian M. Ringle, Marko Sarstedt., (2013), “A Primer on Partial Least
Squares Structure Equation Modeling (PLS-SEM)”, SAGE Publications Inc.
Lau, C. M., & Ngo, H. Y. (2004). The HR system, organizational culture, and product innovation. International
business review, 13(6), 685-703.
Lin HF, Lee GG, (2005). Impact of organizational learning and knowledge management factors on e-business
adoption. Manage Decision, 43(2):171–88.
Liu, H., et al. (2014). Knowledge Management Capability and Firm Performance: The Mediating Role of
Organizational Agility, PACIS.
Lopez‐Cabrales, A., Pérez‐Luño, A., & Cabrera, R. V. (2009). Knowledge as a mediator between HRM practices
and innovative activity. Human Resource Management, 48(4), 485-503.
MacDuffie, J. P. (1995). Human resource bundles and manufacturing performance: Organizational logic and
flexible production systems in the world auto industry. Industrial & labor relations review, 48(2): 197-221.
Mine Afacan Find1kh1, U. y., Yasin Rofcanin, (2015). Examining organizational innovation and knowledge-
management capacity: The central role of strategic human resource practices (SHRPs). Procedia: Social and
Behavioral sciences, 377-787.
Nasurdin, C. L. T. a. A. M. (2011). Human Resource Management Practices and Organizational Innovation:
Assessing the Mediating Role of Knowledge Management Effectiveness. The Electronic Journal of Knowledge
Management, 9(2), 155-167.
Nonaka, I. and H. Takeuchi, (1995). The knowledge-creating company: How Japanese companies create the
dynamics of innovation, Oxford university press.
Salojärvi, S., et al. (2005). Knowledge management and growth in Finnish SMEs. Journal of Knowledge
Management, 9(2): 103-122.
Sánchez, A. A., Marín, G. S., & Morales, A. M. (2015). The mediating effect of strategic human resource practices
on knowledge management and firm performance. Revista Europea de Dirección y Economía de la Empresa,
24(3), 138-148. doi:10.1016/j.redee.2015.03.003
Sarstedt, M., Ringle, C. M., Smith, D., Reams, R. & Hair, J. F. (2014), “Partial least squares structural equation
modeling (PLS-SEM): A useful tool for family business researchers”, Journal of Family Business Strategy, (5),
105-115.
Scarbrough, H. (2003). Knowledge management, HRM and the innovation process. International journal of
manpower 24(5): 501-516.
Shehu Aliyu, M., Bello Rogo, H., & Mahmood, R. (2015). Knowledge Management, Entrepreneurial Orientation
and Firm Performance: The Role of Organizational Culture. Asian Social Science, 11(23).
doi:10.5539/ass.v11n23p140
Shipton, H., Fay, D., West, M., Patterson, M., & Birdi, K. (2005). Managing people to promote innovation.
Creativity and Innovation Management, 14(2), 118-128.
Swart, J. and N. Kinnie, (2010). Organizational learning, knowledge assets and HR practices in professional
service firms. Human Resource Management Journal, 20(1): 64-79.
Tan, C. L. and A. M. Nasurdin (2011). "Human resource management practices and organizational innovation:
assessing the mediating role of knowledge management effectiveness." Electronic Journal of Knowledge
Management, 9(2): 155-167.
Takeuchi, Riki, Kazuo Takeuchi, and David P. Lepak, (2007). An Empirical Examination of the Mechanisms of
Mediating Between High-Performance Work Systems and the Performance of Japanese Organizations. Journal
of Applied Psychology, 92, 1069-1083.
Ringle, C.M., Sarstedt, M., Straub, D.W., (2012), “A critical look at the use of PLS-SEM”, MIS Quarterly, 36 (1).
Verburg, R. M., Den Hartog, D. N., & Koopman, P. L. (2007). Configurations of human resource management
practices: a model and test of internal fit. The International Journal of Human Resource Management, 18(2), 184-
208.
Wong, K. K.-K. (2013). Partial Least Squares Structural Equation Modeling (PLS-SEM) Techniques Using
SmartPLS, (24), 1-32.
Zi-Lin He and Poh-Kam Wong, (2001). Exploration vs. Exploitation: An Empirical Test of the Impact of
Innovation Strategy on Firm Performance, Business Link
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