46gssr10.31703/gssrGlobal Social Sciences Reviewgssr2520-03482616-793XHumanity PublicationsPakistan39054910.31703/gssr.2018(III-III).1110181.0articleDimensions of Social Capital and Innovation Capabilities of Firms The Performance of Information Technology as a Mediator.BashirMohsin BashirConceptualizationWriting – original draftBariMuhammad WaseemWriting – review & editingRazaSyed HassanWriting – review & editingLyallpur Business School, Government College University, FaisalabadAssistant ProfessorPunjabPakistanChairman, Department of Business Administration, Allama Iqbal Open University, IslamabadPunjabPakistanCorresponding Author: Mohsin Bashir, Assistant Professor, Lyallpur Business School, Government College University, Faisalabad, Punjab, Pakistan.. Contact: 0
The authors declare that they have no conflicts of interest.
This study did not require formal ethics approval.
Data sharing is not applicable to this article.
300920180920181602202233Summer17519216022022
The authors received no specific funding for this work.
2018Humanity PublicationsThis is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License.
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This paper empirically inquire the relation of social capital dimensions (relational social capital, structural social capital, and cognitive social capital), organization innovation capabilities, and the performance of information technology (IT) as a mediator in the said relationships. A total of 263 workers of different management cadres from software SMEs (Zhongguancun Software Park, Beijing, China) were randomly selected. However, 143 respondents submitted the complete response. Thus, the response rate was 54%. For the empirical investigation, the present paper uses Partial Least Squares, Structural Equation Modeling (PLS-SEM) and Importance-Performance Matrix Analysis (IPMA) techniques to analyze the survey data. The direct and indirect relationship between dimensions of social capital and organizational innovation capabilities is significant. However, IT generates a partial mediation effect. IPMA highlights the importance of relational and structural social capital to innovation capabilities, however, IT is indicated as the key driver that trigger the effect of social capital on organization innovation capabilities. Future studies guidelines and limitations are explained at the end of this paper.
Social CapitalInnovation CapabilitiesSocial Exchange TheoryInformation TechnologyPLS-SEMIPMAviews3061downloads67html-views0google-scholar-citations0crossref-citations0Structural Social Capital and Innovation Capabilities
The patterns and strength of ties among the participants of a social network refer to the structural dimension of SC (Camps & Marques, 2014). The structural SC offers channels and platforms for information and resource stream and provides specific advantages to network members (Nahapiet & Ghoshal, 1998). In a new social network at the individual or organizational level, members first do interaction and share experiences then start to develop and share mutual trust, distinctiveness, and norms, and finally develop a common vision and aim. This pattern of social network indicates that structural SC provides a base for relational and cognitive SC (Al-Tabbaa & Ankrah, 2016; Nahapiet & Ghoshal, 1998). Frequent social interactions help employees to know each other, share rich information, and build a common understanding. However, it depends on types of relationships and their worth within the network (Sanchez-Famoso et al., 2014). Above discussion indicate that structural dimension of SC provides a foundation to knowledge workers for knowledge creation and sharing and contribute to the ICs development (Camps & Marques, 2014; Landry, Amara, & Lamari, 2002; Nahapiet & Ghoshal, 1998; Pérez-Luño et al., 2011; Sanchez-Famoso et al., 2014). Thus, it is proposed that,
H1(b) The organization structural SC has a positive relationship with ICs.
Cognitive Social Capital and Innovation Capabilities
The cognitive SC refers to the scope of a common shared vision between its participants that links them for a mutual purpose (Akram et al., 2016). The cognitive SC denotes to the shared language and framework within a specific structure. It helps in intra-organization and inter-organizations to share and integrate resources, reduce conflicts, and achieve common objectives (Sanchez-Famoso et al., 2014). With reference to social exchange theory, shared goals in the network motivate members and develop common perceptions and behaviors (Cook et al., 2013; Nonaka, 1994). The cognitive dimension of organizational SC targets resources like mutual interests and understanding the participants of the network and these resources assist in interaction and recombination among members of the network (Al-Tabbaa & Ankrah, 2016). Common goals of intra-organization and inter-organizations also support to accomplish the benefits of knowledge transfer and exchange, which leads to organizational ICs (Inkpen & Tsang, 2005). Several scholars confirmed that the organization cognitive SC is helpful in organizational ICs development (Akçomak & Ter Weel, 2009; Camps & Marques, 2014; Landry et al., 2002; Sanchez-Famoso et al., 2014). Thus, it is hypothesized that,
H1(c) The organization cognitive SC has a positive relationship with ICs.
Mediating Role of Information Technology:
IT plays a dual role. Firstly, IT applications help in social network development and easy interaction among members. Secondly, for the sake of knowledge management and knowledge sharing, IT provides access to knowledge repositories and helps in knowledge management (Agrawal et al., 2011). Effective and innovative knowledge enhance individual and organization ICs. Thus, it is proposed that,
H2(a) IT mediates the relationship between relational SC and ICs.
H2(b) IT mediates the relationship between structural SC and ICs.
H2(c) IT mediates the relationship between cognitive SC and ICs.
Research Model
Method
Context and Sample
In this study, with "random sampling approach" 263 workers of different management cadres from software SMEs (Zhongguancun Software Park, Beijing, China) were selected as the sampling component. Entire contributors were nominated irrespective of their sex, qualification, position, and capabilities. The instrument was developed in English and Chinese languages. Before data collection, the key subjects of the instrument were also explained to the workers. Individual connections and e-mail based approaches were adopted for data collection. Entire data gathering period was 40 days and 143 employees provided comprehensive forms; thus, the reply percentage was 54 %.
Construct Measurement
A 5 points Likert-type scale ( 1= strongly disagree to 5= strongly agree) is used to measure all items of variables with a survey based instrument, accepted from different earlier papers with some adjustments as per paper objectives. Total 21 items are measured for five constructs. The references to indicators used in this paper are as follows,
Cronbach's alpha for 5 constructs RSC, SSC, CSC, IT, ICs are 0.779, 0.831, 0.794, 0.872 and o.894 correspondingly.
Partial Least Square, Structural Equation Modeling (PLS-SEM)
Partial least square, structural equation modeling (PLS-SEM), a second generation multivariate statistical technique is used to evaluate the direct and mediating and moderation effects of the variables (Hair, Hult, Ringle, & Sarstedt, 2016). Multivariate data examination includes the use of statistical approaches that at the same time analyze numerous factors such as estimations related with people, organizations, occasions, s, circumstances etc. (Hair Jr et al., 2016). SEM is utilized to either investigate or affirm the theory. Exploratory modeling includes creating theory while confirmatory modeling confirms/reject the theory (Hair et al., 2016). SEM-PLS is a good approach to measure the insights and practices of the respondents. Smart PLS-3, software is a latest and friendly user instrument for small data analysis (N=143) (Hair et al., 2016).
Results and Analysis
Employees' Demographic Trends
Table 1 presents the demographic tendencies of the workers in IT firms, Beijing, P.R. China. From total 143 respondents, 76 were males and 67 females, the majority of the respondents' age was between 20 to 35 years. Thirty-two percent of respondents experience was above 10 years. A reasonable ratio of all levels of management (lower, middle, and upper) was included in the sample data. The education level of most of the respondents was university, and college graduates.
Table 1. Demographic Trends, N = 143
Table 1
Trends
Group
Numbers
Ratios
Gender
Man
Women
76
67
53%
47%
Age
20 - 35 Years
36 - 50 Years
84
59
59%
41%
Experience
Less than 10 years
10 to 20 years
97
46
68%
32%
Job Position
Lower
Middle
Upper
50
64
29
35%
45%
30%
Education
School
College
University
15
62
66
10%
43%
47%
Model Assessment
Table 2 explains the internal consistency, reliability and convergent validity of the model. The outer loading values of all variables are higher than 0.70 (Hair Jr et al., 2016). Composite reliability (CR) and Cronbach's Alpha figures of all indicators are also higher than 0.70 that are within the defined boundary (Hair Jr et al., 2016). Researchers indorse the limit of average variance extracted (AVE) 0.5 or higher (Hair Jr et al., 2016). All variables' AVE is above the limit of 0.5 as shown in table 2. R2 figures also indicate a robust model and good association among constructs (Bari, Fanchen, & Baloch, 2016). All variables are measured at 0.05% significant level.
Table 2. Model Assessment
Table 2
Dimensions
Items
OLs
CR
α
AVE
R2
Relational Social Capital
RSC.1
0.753
0.857
0.779
0.601
---
RSC,2
0.762
RSC,3
0.773
RSC,4
0.810
Table Continued on Next Page
Table Continued on Next Page
Structural Social Capital
SSC,1
0.841
0.886
0.831
0.661
---
SSC,2
0.786
SSC,3
0.807
SSC,4
0.818
Cognitive Social Capital
CSC,1
0.730
0.863
0.794
0.614
---
CSC,2
0.712
CSC,3
0.835
CSC,4
0.848
Information Technology
IT,1
0.751
0.907
0.872
0.662
0.597
IT,2
0.810
IT,3
0.854
IT,4
0.833
IT,5
0.817
Innovation Capabilities
ICs,1
0.867
0.926
0.894
0.759
0.684
ICs,2
0.879
ICs,3
0.860
ICs,4
0.878
*Level of significance 0.05%
Discriminant Validity
In Table 3 confirms the discriminant validity of this study. As per Fornell-Lacker method, "the square root of each is equated to the correlation of all constructs down in the same column and established that all square root (values) are higher than the correlation values in each column" (Hair Jr et al., 2016).
Table 3. Fornell-Lacker Criteria
Table 3
Constructs
CSC
ICs
IT
RSC
SSC
CSC
0.783
ICs
0.692
0.871
IT
0.709
0.776
0.814
RSC
0.706
0.694
0.677
0.775
SSC
0.675
0.685
0.663
0.645
0.813
*Level of significance 0.05%
Table 4, the second method heterotrait-monotrait ratio (HTMT) test is also performed to confirm the validity of the model. In all scenarios, HTMT ratios are within the range of 0.85 or 0.90 (Hair Jr et al., 2016). All the above checks established that the present paper model is consistent and effective for further investigations.
Table 4. HTMT Ratios
Table 4
Constructs
CSC
ICs
IT
RSC
ICs
0.800
IT
0.820
0.876
RSC
0.886
0.826
0.816
SSC
0.785
0.779
0.757
0.783
*Level of significance 0.05%
Furthermore, every set of the indicators in the study is verified for any potential collinearity in the data for reliable and improved outcomes. All VIF outcomes are under the limit of 5.00 (Hair Jr et al., 2016). It refers that there is no problem of collinearity among all variables.
Direct Relationship
Through smart PLS-SEM-3 software, the bootstrapping method is employed to evaluate the degree of significance, Table 5, clarifies that all independent variables RSC (β=0.193, t-value=5.007, f2=0.049), SSC (β=0.194, t-value=4.848, f2=0.054) and CSC (β=0.117, t-value= 2.691, f2=0.016) have significant direct relationship with endogenous construct ICs. With the support of direct significant associations among variables, the hypotheses H1 (a), H1(b) and H1(c) are accepted at 0.05% level of Significance.
Table 5. Direct Relationship
Table 5
Direct Effect
Path Coefficient (t-value)
Effect size (f2)
Confidence Interval
(95 %)
(p-Value)
0.05%
Outcomes
RSC ICs
0.193 (5.007)
0.049
(0.118-0.270)
0.000
Accepted (H1-a)
SSC ICs
0.194 (4.848)
0.054
(0.118-0.275)
0.000
Accepted (H1-b)
CSC ICs
0.117 (2.691)
0.016
(0.034-0.205)
0.007
Accepted (H1-c)
RSC IT
0.266 (5.389)
0.079
(0.171-0.366)
0.000
SSC IT
0.256 (5.687)
0.079
(0.168-0.343)
0.000
CSC IT
0.349 (6.536)
0.126
(0.241-0.449)
0.000
IT ICs
0.434 (8.959)
0.240
(0.339-0.530)
0.000
*Level of significance 0.05%
MediationRelationship
With smart PLS-SEM,3, the bootstrapping technique, with replacement, five thousand randomly drawn samples at 0.05% degree of significance are used. To measure the effect of mediation variance accounted for (VAF) method is applied. The VAF > 80% designates full mediation, ≥ 20% and ≤ 80% VAF depicts partial mediation, while < 20% VAF specifies no mediation effect (Ali & Park, 2016; Bari, Fanchen, & Baloch, 2016). Table-6 elucidates that IT partially mediates the effects of all independent variables (RSC, SSC, and CSC) on dependent variables (ICs). Thus, hypotheses H2(a), H2(b) and H2-(c) are accepted. However, CSC has a highest indirect effect (VAF=56.13%) on organization ICs.
Table 6. Indirect Relationship
Table 6
Mediation Relationship
Direct Relationship (t-value)
Indirect Relationship (t-value)
Total Relationship
VAF
(%)
Level of Mediation
Decision
RSC→IT→ ICs
0.193
(5.007)
0.115
(4.456)
0.308
37.33
Partial
Mediation
Accepted (H2-a)
SSC→IT→ ICs
0.194
(4.848)
0.111
(4.681)
0.305
36.39
Partial
Mediation
Accepted (H2-b)
CSC→IT→ ICs
0.117
(2.691)
0.151
(5.520)
0.269
56.13
Partial
Mediation
Accepted (H2-c)
*Level of Significance 0.05
Importance-Performance Matrix Analysis (IPMA)
Importance-performance matrix analysis (IPMA) is an advanced technique presented in PLS-SEM investigation (Hair Jr et al., 2016).
IPMA enhances the traditional PLS-SEM results revealing of path coefficient assessments, considering the normal estimations of the latent variable scores. (Hair Jr et al., 2016; Hock, Ringle, & Sarstedt, 2010; Kristensen, Martensen, & Gronholdt, 2000). PLS-SEM, IPMA enlighten the structural path model aggregate effects on a specific target construct (ICs). Bari & Fanchen, (2017) explains that the total effect represent the exogenous constructs' importance for the target construct, and their average latent construct scores represent their performance (Hair Jr et al., 2016).
With the constant environment, one-degreerise of the exogenous construct' (RSC, SSC, CSC, and TI) performance raises the performance of the target construct (ICs) by the magnitude of the exogenous' unstandardized aggregate effect(Hair Jr et al., 2016).Table7, explains that RSC has the best performance and importance (among three dimensions of SC) to ICs. However, IT with low performance has thehighest importance to ICs. It indicates, theorganization should do more focus on IT to enhance the ICs.
Table 7. Importance-Performance Matrix Analysis
Table 7
Constructs
Direct Relationship
Indirect Relationship
Total Effect/
Importance
Performance
RSC
0.194
0.116
0.310
30.880
SSC
0.197
0.112
0.309
22.932
CSC
0.127
0.163
0.290
30.572
IT
0.445
----
0.445
29.271
*Level of significance 0.05%
Importance-Performance Map for ICs:
Figure 2, shows the (unstandardized) total effects/ importance of RSC, SSC, and CSC on x-axis and y-axis represents the average unstandardized and rescaled latent construct (ICs) scores (performance). As figure 2, depicts that lower right side of the importance-performance (ICs) map have high importance and lowest performance area where RSC is placed. Therefore, the little change in RSC performance can create more effect on ICs than SSC and CSC (Hair Jr et al., 2016).
Figure 2. IPMA, Constructs Map
Figure 2. IPMA, Constructs Map
Discussion
The target of this paper was to examine the relationship between dimensions of organization SC and organization ICs, mediating role of IT between the relationship of SC dimensions and organizational ICs in the situation of the IT industry, China. The outcomes of this paper approve the positive and significant association between RSC, SSC, and CSC and ICs. These outcomes are also linked with the earlier studies (Camps & Marques, 2014; Landry et al., 2002; Pérez-Luño et al., 2011; Sanchez-Famoso et al., 2014; Svetlik et al., 2007).
Figure 3. Developed Model
Figure 3. Developed Model
These results also confirm the application of social exchange theory and knowledge sharing and transformation theory in the situation of the IT industry, China (Cook et al., 2013; Nonaka, 1994). The full model is explained in Figure 3 with important figures. IT complementary mediates the association between RSC, SSC, and CSC and organization ICs (product, process, strategic, and services) (Agrawal et al., 2011; Davison et al., 2013; Dibrell, Davis, & Craig, 2008)(M. K. Chang, Cheung, & Tang, 2013). However, IT highly mediates the effect of CSC among three dimensions of SC on organizational ICs. CSC helps in intra-organization and inter-organizations to share and integrate resources, reduce conflicts, and achieve common objectives and IT increases the strength of CSC (Agrawal et al., 2011).
An important contribution of the present study is highlighting the most important dimension of SC to increase the firm ICs in the background of the IT industry (software SMEs) in China. IPMA explains that the relational dimension of SC highly performs and creates the highest effect on ICs. On the other side, the structural dimension of SC has considerably low performance than relational SC but almost the equal effect on ICs. Therefore, the structural dimension of SC is equally important as relational SC. However, IT has the highest importance/ total effect on ICs.
Study Boundaries and Future Investigations
Similar to other research papers, this paper also has certain limitations. First, this study evaluates the organizational ICs collectively (incremental and radical) with four dimensions (product, process, strategic, and services). In the future, a separate investigation of radical and incremental organizational ICs is recommended. Second, the cross-sectional data is collected and used in this paper. In future studies, time lag or longitudinal approaches are recommended for data collection. Third, the results and managerial implications of the present study are drawn from the data collected from IT firms, Beijing, China. The application of the present study model may produce different results in other industries and contexts. Therefore, the present study model application in other countries and industries are recommended. Fourth, the application of IT highly depends on the education level of employees, therefore, respondents' education as a moderator on ICs can be investigated in future studies.
Conclusion
The pioneers the use of PLS-SEM and IPMA techniques to examine the mediating role of IT for ICs of software firms in Beijing, China. The results describes that RSC has the greatest performance and effect (among three dimensions of SC) to ICs. However, IT with low performance has the highest importance to ICs. It indicates thatfirms should do more focus on IT to effective utilization of SC and increase the level of ICs.In short, IT performs the critical role to enhance the effect of SC dimensions on organization ICs. The researchers recommend respondents' education as a moderator on ICs in future studies.
Conflict of Interest
The authors declare that they have no conflicts of interest.
Ethics Statement
This study did not require formal ethics approval.
Data Availability
Data sharing is not applicable to this article.
Supplementary Materials
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