Social capital and job performance the roles of knowledge sharing knowledge acquisition in social media communities

This study focuses on social interaction ties, trust, and shared language of the social capital theory to explore which motivations make people to share and acquire knowledge in social m

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LE DANG PHUONG

SOCIAL CAPITAL AND JOB

PERFORMANCE: THE ROLES OF KNOWLEDGE SHARING, KNOWLEDE ACQUISITION IN SOCIAL MEDIA

COMMUNITIES

ID: 22120102

MASTER OF BUSINESS (Honours)

SUPERVISOR: Dr.LE NHAT HANH

Ho Chi Minh City – Year 2014

ACKNOWLEDGEMENTS

I would like to express my sincere thankfulness to my research advisor, Dr Le Nhat Hanh for her time and enthusiasm in guiding me to complete this thesis She has spent all her time in Saturday morning during our preparation for this thesis to help, support and solve hard problems for us I am sure that the thesis would not have

finished without her support

I also give many thanks to Prof Nguyen Dinh Tho and Dr Nguyen Thi Mai

Trang for carefully guiding me in Research and Design subject to help me have enough knowledge to finish this thesis

I would like to thank all ISB lecturers and staffs who facilitate necessary knowledge and materials for us to study and finish this course

Finally, I would like to give my special thanks for my wife and family for supporting me during my study

ABSTRACT

Social media professional groups have been developing in recent years Many people come there for seeking for knowledge to solve problems at work However, seeking knowledge on social media professional groups can be difficult because people tend to hoard rather than exchange knowledge to other people This study focuses on social interaction ties, trust, and shared language of the social capital

theory to explore which motivations make people to share and acquire knowledge in social media professional groups Besides that, this research go further to investigate how these knowledge affect on job performance

Data collected from 207 members of social media professional groups in Ho Chi Minh city of Viet Nam support for the proposed model The results indicate social interaction ties have positive impact on knowledge acquisition Trust and shared language have positive impact on knowledge sharing and knowledge acquisition Besides that the finding also shows that knowledge acquisition has a positive relationship with job performance

Keywords: Social capital, social media professional groups, knowledge sharing, knowledge acquisition, Job performance

TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION 7

1 1 Background 7

1.2 Research Questions and Objectives 10

1.3 Research Delimitation 11

1.4 Research contribution 11

1.5 Thesis Structure 11

CHAPTER 2: LITERATURE REVIEW 12

2.1 Knowledge, Knowledge sharing and Knowledge acquisition 12

2 2 Social capital theory and knowledge sharing and knowledge acquisition 13

2.3 Social interaction ties 14

2.4 Trust 15

2.5 Shared language 16

2.6 The relationship between knowledge sharing, knowledge acquisition and job performance 17

CHAPTER 3: RESEARCH METHODOLOGY 20

3.1 Research processes 20

3.2 The unit of observation 21

3.3 Sample: 21

3.4 Measures of the constructs 21

3.4.1 Social interaction ties. 21

3.4.2 Trust. 22

3.4.3 Shared language. 23

3.4.4 Knowledge sharing. 23

3.4.5 Knowledge acquisition. 24

3.4.6 Job performance 25

3.5 Qualitative research 25

3.6 Quantitative research 26

3.7 Data collection 27

3.8 Data coding 27

CHAPTER 4: DATA ANALYSIS 29

4.1 Respondents’ demographics 29

4.2 Reliability Analysis 31

4.3 Exploratory Factor Analysis (EFA) 34

4.4 Regression Analysis 37

4.4.1 The relationship between social interaction ties, trust, shared language and Knowledge sharing 38

4.4.2 The relationship between social interaction ties, trust, shared language and knowledge acquisition 40

4.4.3 The relationship between knowledge acquisition, knowledge sharing and Job performance 42

CHAPTER 5: CONCLUSION, IMPLICATIONS, AND LIMITATIONS 46

5.1 Conclusion 46

5.2 Implication 47

5.3 Limitations and future research 48

REFERENCES 50

TABLE OF FIGUIRES

Figure 2.7: Research model 19

Figure 3.4: Research processes 20

Figure F1: Histogram of knowledge sharing 74

Figure F2: Normal P-P Plot of knowledge sharing 74

Figure F4: Histogram of knowledge acquisition 75

Figure F5: P-P Plot of knowledge acquisition 75

Figure F6: P-P Scatter Plot of knowledge acquisition 75

Figure F7: Histogram of Job performance 76

Figure F8: Normal P-P Plot of job performance 76

Figure F9: Scatter plot job performance 76

LIST OF TABLES Table 3.1: Scales of Social Interaction Ties 21

Table 3.2: Scales of Trust 22

Table 3.3 : Scales of Shared language 23

Table 3.4: Scales of Knowledge sharing 23

Table 3.5: Scales of acquisition 24

Table 3.6: Scales of Job performance 25

Table 4.1: Respondents’ demographics 30

Table 4.2: Cronbach’s Alpha 32

Table 4.3: KMO and Bartlett's Test 34

Table 4 5 Rotated Component Matrix 36

Table 4.6: Results of Pearson correlations 38

Table 4.7: Model Summary of social interaction ties, trust and shared language to knowledge sharing 38

Table 4.8: ANOVA of social interaction ties, trust and shared language to knowledge sharing 39

Table 4.9: Coefficients of social interaction ties, trust and shared language to knowledge sharing 39

Table 4.10: Model Summary of social interaction ties, trust and shared language to knowledge acquisition 41

Table 4.11: ANOVA of social interaction ties, trust and shared language to knowledge acquisition 41

Table 4.12: Coefficients of social interaction ties, trust and shared language to knowledge acquisition 41

Table 4.13: Model Summary of knowledge sharing and knowledge acquisition to job performance 43

Table 4.14: ANOVA of knowledge sharing and knowledge acquisiton to job performance 43

Table 4.15: Coefficients of knowledge sharing and knowledge acquisiton to job performance 43

Table 4.16: Summary of hypotheses testing 45

communication which transcend time and distance limitations” (North, 2010, p 192)

Many people use SNSs for entertainment or meet other people Kuss and Griffiths (2011) report that Social Networking Sites (SNSs) are social networks where

users can “create individual public profiles, interact with real-life friends, and meet other people based on shared interests” (p 3528) People take part in social networks,

especially in social media professional groups for accessing knowledge, experiences, and skills to find solution for work (Chiu, Hsu, and Wang, 2006) Hof, Browder, and Elstrom (1997) say that 42% of social media professional groups members state it relate to their profession Massari (2010) claims that SNSs such as: Facebook, Flickr, MySpace, Orkut, YouTube, LinkedIn can involve in both business and personal

environments, and help members to improve business and decline costs, to support

learning, to connect with friends and interact with each other Furthermore, Bennett,

Owers, Pitt, and Tucker (2010) report that the benefits of SNSs usage in the workplace can improve collective knowledge, and increase effectiveness

SNSs are also very popular in Viet Nam Pham (2013) emphasizes that Viet Nam is the leading country in using internet in ASEAN area Facebook, Zing Me, and YouTube, LinkedIn are the most popular ones Social media professional groups are also established as a trend of development of the SNSs They are related to careers and specific benefit of one’s groups Social media professional groups allow creating and

developing company’s profile, advertising products or disclosing helpful information about companies Marketers know how to connect with customers and get feedback

from customers to create wonderful achievements for themselves in social media professional groups (“Building strategic online marketing,” 2013) 36% of Viet Nam internet users are members of SNSs (Cimigo, 2011) People use SNSs for many different purposes According to InfoQ Viet Nam (2013), people use SNSs for keeping contact with friends (97%), updating information (75%), expanding new relationship (56%), solving problem at workplace (47%) According to “Surprised

advantage of SNSs,” (2013), Social media professional groups can help people to be better in many fields For example, YouTube can teach people how to cook, make clothes, or offer basic knowledge in many areas that people concern Moreover, they are channels to access information in order to serve for working and learning

Moreover, according to “Advantage of SNSs” (2014), people use SNSs to introductions to business people whom known to their contacts Cuong Nguyen Cao, a

lecturer of University of Social Sciences, and Humanities, says that he uses SNSs to share his material and lectures and this receives the good feedback from students He emphasizes that it is very useful for students to interact and learn from each other Tham Trinh Thi, a student of University of Social Sciences, and Humanities,

comments that SNSs are very helpful for students, they can share knowledge and learn from classmates In addition, comments of other students help to create many interesting ideas to solve problems

In summary, people participate in social media professional groups, mainly for entertainment or gaining and exchanging knowledge This paper focuses on the facet

of gaining and sharing knowledge in social media communities Knowledge plays a very important role in operating and developing a company According to Berman, Down, and Hill (2002), knowledge is very important and valuable intangible resource

in acquiring competitive advantage that many organizations try to get it to meet their business needs and goals Knowledge exchanging among members is important to

improve performance (Huang, Liu, & Warden, 2005; Käser & Miles, 2002) However,

an individual may keep knowledge for themselves rather than share to the others because knowledge is valuable and important for them to create competitive advantages with their colleagues (Osterloh & Frey, 2000) According to Chiu et al., (2006), “the biggest challenge in fostering a virtual community is the supply of knowledge, namely the willingness to share knowledge with other members” (p

1873) Therefore, this paper will collect data and use social capital theory to identify factors that affect on people behavior to share and acquire knowledge in social media professional groups and then continue to identify how this knowledge affect on job performance

Nahapiet and Ghoshal (1998) basically define social capital theory with three dimensions: structural, relational, and cognitive The structural dimension of social capital displays through social interaction ties, the relational dimension displays

through trust, norm of reciprocity and identification, and the cognitive dimension displays through shared vision and shared language This study focuses on social interaction ties of the structural dimension, trust of the relational dimension and shared language of the cognitive dimension to explore how these factors impact on people behavior to share and acquire knowledge in social media professional groups

and then how this knowledge affect on job performance The research model is at figure 1.1

Figure 1.1 Research model

1.2 Research Questions and Objectives

This research aims to examine whether the social capital has any impacts on knowledge sharing and knowledge acquisition within social media professional

groups, which in turn affect the job performance Specifically, it investigates:

- The impact of interaction ties, trust, and shared language on knowledge sharing

in social media professional groups

- The impact of interaction ties, trust, and shared language on knowledge acquisition in social media professional groups

- The relationship between knowledge sharing and job performance

- The relationship between knowledge acquisition and job performance

1.3 Research Delimitation

This study focuses on employees who use social media professional groups and work for organizations in Ho Chi Minh City of Viet Nam This study focuses on social interaction ties of the structural dimension, trust of the relational dimension, and shared language of the cognitive dimension to investigate the impact of these factors

on people who share and acquire knowledge in social media professional groups and

then how this knowledge affect on job performance

1.4 Research contribution

Base on the results of this study, the author hope to provide practical contribution to members of social media professional groups who would like to use

social media professional groups to share and acquire knowledge for solving problem

at work A new point of this paper in comparison with previous researches is to combine the use of social capital theory to investigate the motivation employees share knowledge in social media professional groups and the impact of knowledge on job performance

1.5 Thesis Structure

The structure of this thesis is as follows:

- Chapter 1 is the research background, research questions and objectives, research delimitation, significance of the Research and thesis structure

- Chapter 2 presents the literature review and conceptual research model and its hypotheses

- Chapter 3 introduces the methodology used to test the research model

- Chapter 4 presents research results of data analysis

- Chapter 5 summarizes the research results; provide the findings, and recommendations

CHAPTER 2: LITERATURE REVIEW

This chapter is an overview of social capital theory, knowledge sharing, knowledge acquisition, job performance, and their relationships that previous

researchers have conducted Based on these studies, the author builds the conceptual model and develop the hypotheses

2.1 Knowledge, Knowledge sharing and Knowledge acquisition

According to Nonaka (1994), knowledge has two foundation forms, tacit and

explicit Explicit knowledge refers to knowledge that has been articulated and written down It can be “transmittable in formal, systematic language” (p 16) Knowledge published in books, journals, manuals, guidelines, databases, and so on are examples for explicit form (Panahi, Watson & Partridge, 2012) On the other hand, “tacit knowledge has a personal quality, which makes it hard to formalize and communicate” (Nonaka, 1994, p.16) Tacit knowledge refers to “personal knowledge

of an individual” in the forms of experience, skills, know-how, insight, expertise, and

so on Tacit knowledge can be found in discussions in the people lives, “face-to-face informal meetings and reports” (Panahi, Watson & Partridge, 2012, p 1096) Moreover, Akram and Bokhari (2011) (as cited in Davenport and Prusak, 1998) view

“knowledge as an evolving mix of framed experience, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new

experiences and information” ( p 44)

Berman et al (2002) state that tacit knowledge is more meaningful for both the

organization and individual rather than explicit knowledge Yang and Farn (2009) argue that tacit knowledge is the most the important asset for organizational members

includes critical resources that are difficult to imitate and lead to competitive advantages So knowledge used in this study is tacit knowledge

Knowledge sharing is the action of transferring tacit knowledge from people to

people on social media communities While Knowledge acquisition is the action to

acquire tacit knowledge shared by other people in social media communities

2 2 Social capital theory and knowledge sharing and knowledge acquisition

Many researchers have proved that social capital plays an important role in knowledge exchange Tsai and Ghoshal (1998) claim that social capital facilitates resource exchange and production innovation within the organization Renko, Autio, and Sapienza discover the effects of social capital on knowledge acquisition in young technology-based firms

Wasko and Faraj (2005) find that social capital is very important in knowledge exchange and the authors claim that social capital is useful in knowledge contribution According to Coleman (1988), social capital theory is the relationships among people and these relationships can be helpful resources for actors Nahapiet and Ghoshal (1998) define social capital theory as “the sum of the actual and potential resources embedded within, available through, and derived from the network of relationships

possessed by an individual or social unit” (p 243) Putnam (1995) states that social capital creates coordination and cooperation for getting mutual benefit Individuals can share a lot of knowledge through a network though they may not know each other

in the real life (Wasko & Faraj, 2005) (as cited in Brown and Duguid, 2000) Nonaka (1994) suggest that knowledge sharing is subject to social interaction

However, Chiu et al., (2006) consider that knowledge transfers in social media professional groups are different from organizational settings because interaction among social media professional groups’ members is mainly through online network Therefore, whether the impact of social capital on resource exchange and knowledge management activities found in the organizational settings could be applied to social

media professional groups is still unclear Chiu et al., (2006) claim that

Members in social media communities differ from general internet users in that shared interests, goals, needs, or practices bring virtual community members

together This begs the key question — whether the social capital developed in virtual communities is strong enough to stimulate members to overcome the

barriers of complex knowledge sharing process, and then share valuable knowledge, especially when no extrinsic reward is provided (p 1875)

2.3 Social interaction ties

According to Tsai and Ghoshal (1998, p 467), social interaction ties are

“channels for information and resource flows” An actor may “gain access to other actors’ resources through social interactions” Nahapiet and Ghoshal (1998) state “The fundamental proposition of the social capital theory is that network ties provide access

to resources Network ties influence both access to parties for combining and exchanging knowledge and anticipation of value through such exchange” (p 252)

Social interaction ties among members of social media professional groups can help people to achieve a lot of knowledge sources with low cost (Chiu et al 2006) Wasko and Faraj (2005) claim that the willingness of contributing knowledge depends on

individual’s position in the network Moreover, Marques, Cardoso, and Zappala (2008, p 167) (as cited in Lee, 2002) suggest that

measuring social interaction is a possible and good way to measure tacit

knowledge sharing because in the context of these interactions, the exchange transcends the simple channeling of knowledge, being complemented by non-verbal communication, tips, advice, and all sorts of elements that can facilitate this transfer

In addition, Yang and Farn (2009) also mention tacit knowledge exchanging as

a process of social interaction Panahi, Watson, and Partridge (2012) (as cited in Zheng, Li, and Zeng, 2010) claim that social media supports for social interaction, and knowledge sharing” (p 1098) Social interactions and tacit knowledge sharing have a close relationship in social media (Panahi et al., 2006) Nonaka (1994) suggests that knowledge sharing is related to the social interaction between individuals who exchange and develop knowledge Therefore, the hypotheses are as follow:

H1a Members' social interaction ties are positively related with knowledge

and use tacit knowledge” (p 1099) Chiu et al (2006) says that trust has an important role in knowledge sharing in social media professional groups Nonaka (1994) believes that trust is important for employees to exchange knowledge so the author emphasizes that it is necessary to build trust among people Moreover, Trust plays an important role in online transactions in social media communities (Ridings, Gefen &

Arinze, 2002)

In addition, Yang and Farn (2009) imply that members can easier get

knowledge from the others when trust exists between them in the social network They find that there is a positively relationship between trust and an individual’s tacit knowledge acquisition Building trust in social media is as important as face-to-face communication for knowledge exchanging (Panahi et al., 2012) Thus, the hypotheses are:

H2a Trust is positively associated with the knowledge sharing

H2b Trust is positively associated with the knowledge acquisition

2.5 Shared language

Nahapiet and Ghoshal (1998) state that shared language create the conditions for the combination and exchange of intellectual capitals in many ways First,

language plays an important function in social relation, shared language creates ability

of people to get information from the others Second, shared language facilitates a

common conceptual equipments for evaluating the likely benefits of exchange and

combination Finally, shared language increases the capability of combining the

knowledge and information that members achieve from social exchange Moreover, Chiu et al., (2006) report that:

Shared language is essential to learning in virtual communities It provides an

avenue in which participants understand each other and build common vocabulary in their domains In this regard, shared language not only helps share ideas but also enhances the efficiency of communication between people with similar background or practical experience

In addition, Wasko and Faraj (2005, p.43) report that “Language is the means

by which individuals engage in communication It provides a frame of reference for interpreting the environment and its mastery is typically indicated by an individual's level of expertise”

Accordingly, shared language also plays an important role in knowledge

sharing and acquisition It will motivate people to involve in knowledge exchange activities Therefore, the hypotheses are:

H3a Shared language is positively associated with knowledge sharing

H3b Shared language is positively associated with knowledge acquisition

2.6 The relationship between knowledge sharing, knowledge acquisition and job performance

Marques et al (2008) finds that individuals who exchange knowledge likely get higher job performance and individuals who have better performance usually exchange their knowledge They emphasize that the relationship between knowledge sharing and individual performance score is extremely important, because it gives

strong proofs to confirm their expectations and beliefs that emerged from previous researches and it contributes to this scientific field Nahapiet and Ghoshal (1998, p 247) (as cited in Coke & Yanow, 1993) “Knowledge workers are key factors in the organization’s performance, particularly in the contexts where the performance of individual is crucial” Burt (1992) claims that information networks improve problems

in knowledge exchanging because they provide more related information and rise efficiency performance in workplace

Furthermore, Du, Ai, and Ren (2007) report that the way to get better

innovative and performance is to share experience Berman et al., (2002) finds the

positive relationship between knowledge exchanging and performance Quinones,

Ford, and Teachout (1995) discover the strong relationship between knowledge

exchanging to get experience and job performance

Moreover, Ingram, and Simons (2002) state that job performance is directly related to knowledge exchanging Panahi et al., (2012, p 1095) claim that the knowledge is the a key element in enhancing performance and it is considered an

important asset to ameliorate the quality of work and task performance

In addition, Akram and Bokhari (2011) report that knowledge exchanging impact on improvement of individual performance Therefore, the hypothesis are:

H4a: Knowledge sharing is positively related to individual job performance H4b: Knowledge acquisition is positively related to individual job

performance

H2a Trust is positively associated with the knowledge sharing

H2b Trust is positively associated with the knowledge acquisition

H3a Shared language is positively associated with knowledge sharing

H3b Shared language is positively associated with knowledge acquisition

H4a: Knowledge sharing is positively related to individual job performance

H4b: Knowledge acquisition is positively related to individual job performance

In conclusion, this chapter go through the literature to find the definition and the relationship among variables and then use the deduction method to develop the hypotheses and build the conceptual model

CHAPTER 3: RESEARCH METHODOLOGY

In this chapter, the author reported the methodology that the author used to do the research

3.1 Research processes

Figure 3.1: Research processes

3.2 The unit of observation

The objective of the study was to investigate the motivation of knowledge sharing and knowledge acquisition of professional groups in social media community

so that the professional groups members in social media community was the unit of

observation

3.3 Sample:

The author conducted the survey in Ho Chi Minh City of Viet Nam, focused on employees who joined in professional groups in social media Then the process of research methodology would conduct to test hypothesizes

3.4 Measures of the constructs

The scale of the study was established based on the theory and was used the scale that previous researchers have used for their studies For all measurement scales,

a seven-point Likert scale was adapted with anchors ranging from strongly disagree (1) to strongly agree (7)

3.4.1 Social interaction ties

These scales adapted from Chiu et al., (2006)

2 I spend a lot of time interacting with some members in the social media professional groups

3 I know some members in the social media professional groups on a personal level

4 I have frequent communication with some members in social media professional

3 Members in the social media professional groups would knowingly do

anything to disrupt the conversation

4 Members in the social media professional groups behave in a consistent

manner

5 Members in the social media professional groups will not take advantage of

others even when the opportunity arises

2 Members in the social media professional groups use understandable

communication pattern during the discussion

3 Members in the social media professional groups use understandable narrative

forms to post messages or articles

1 The knowledge shared by members in the social media professional groups is

relevant to the topics

2 The knowledge shared by members in the social media professional groups is

1 I can always acquire working experience or know-how from other group

members in social media professional groups

2 I can always acquire the ways to solve problems from other group members

at my request in social media professional groups

3 Other group members always try to share their expertise from their education

or training with me in a more effective way in social media professional groups

1 I believe I am an effective employee

2 I am happy with the quality of my work output

3 My manager believes I am an efficient worker

4 My colleagues believe I am a very productive employee

3.5 Qualitative research

Based on previous research about the role of social capital in knowledge sharing and knowledge acquisition and its affect on job performance, the author established four hypotheses of the study After that, the author built the model and

provided the preliminary the scales for questionnaire of the study

After having the original scales, the author made deeply interviews with 5 members of social media professional groups to check whether they got the meaning

of the scales or not Besides, the author expected to get some advice from the respondents to make the improvement for the scales (See Appendix A)

Finally, the qualitative research gave the meaningful result that all scales were

clearly understood by the respondents

3.6 Quantitative research

Based on the qualitative research, the author adjusted the questionnaire again to

help the respondents understand questions easily The official research was conducted

in widely aspects The processes of the quantitative research were taken as following

Firstly, The author established the questionnaire for the research: Questionnaire

was designed in English and Vietnamese.(See Appendix B & C)

Secondly, The author identified the sample size of the research: according to

many researchers, the size of the sample depends on the method of estimate the sample However, most of researchers agree that reasonable size of sample is five samples for each scale The minimum sample for appropriate use for statistical analysis is equal to or greater than five times of number of variables (Hair, Black, Babin & Anderson, 2010) The model in this study consist twenty – five scales so that the necessary, sample size should be: 25*5=125 observations

For exploratory factor analysis, it is suitable when its sample size is larger than

100 and the sample is at least five times for each scale (Hair et al., 2010) Thus, the minimum sample size required by EFA in this research is: n=5*25= 125 observations

For standard multiple regression analysis, the sample size must satisfy: n > 50 + 8m ( m: number of independent variables) (Tabachnick & Fidell, 2007) There were

5 independent variables in this research Therefore, the minimum sample required to run multiple regression in this study should be: n > 50 + 8 * 5= 90 observations

Summarily, with 25 variables 5 independent variables, this research needed 125 observations at least for running EFA and regression

After one week, 250 responses were collected, the response rate was approximate 83 percent After checking, there only 207 responses were qualified for data analysis process 10 responses were deleted because respondents indicated that they did not join in professional groups in social media, 33 questionnaires was

eliminated because they were invalid (respondents just chose one option for all questions) Finally, 207 questionnaires were used as valid data for this research In comparison with minimum sample size, this value was satisfactory

3.8 Data coding

Data coding was presented at the Appendix D

In summary, this chapter described the choice of selecting sample size, measurement

scale construction, and research method employed to process the collected data A paper-based questionnaire was developed for data collection; it was distributed directly and indirectly to respondents This research was designed into two phases: first was qualitative phase (in-depth interview), second was quantitative phase (main survey) The in-depth interview was conducted to modify the measurement scale

After the qualitative phase, the questionnaire was adjusted slightly before the official survey Main survey had sample size which included total 207 valid questionnaires that were used for data analysis with regression method The next chapter will present data analysis results of main survey

CHAPTER 4: DATA ANALYSIS

The collected data was processed by SPSS software version 20 using enter method Various statistical tests were extracted using SPSS First, the validity and

reliability of the scale instrument were checked in the current study using Cronbach’s Alpha analysis and Exploratory Factor Analysis (EFA) The items which were not

meet the conditions of reliability and validity would be deleted before running regression Then multiple regression was used to identify the correlation and measure the impact level of each independent variable to predicted variable

4.1 Respondents’ demographics

This part aimed to provide the general information of respondents The results

of the demographics analysis were summarized in table 4.1 Initial analysis of data indicated that gender was not equally between female and male Female was dominant with 62.3% of respondents while male account for 37.7% of respondents More than half of the respondents who took part in this study were young people from 24 to 30

years old with 53.6% of total sample 2.9% respondents were under 24 years old, 40.6% people were from 31 to 40 years old, 2.9% of the respondents from 41 to 50 years old, and no one was older than 50 years old Experience of participating in social media professional groups was divided into three groups Over 3 years experience to take part in social media professional groups accounted for 47% 1 – 3

years experience was 37.7%, under 1 year was 15.5% 49.8% of respondents had over

5 years working experience, 27.7% of respondents had over 3 – 5 years working

experience, 22.2% of respondents had 1 – 3 years working experience, and 5.3% had under 1 year working experience

Gender

Male Total

Correlation was also very significant Normally, if the correlation of each specific item with total of the other items in the scale was quite high or higher than 0.4, “then

the item was made a good component of a summated rating scale” On the other hand,

if the Corrected item – Total correlation of any item was negative or too low (less than 0.4), it was necessary to delete them (Leech, Barrett & Morgan, P 66, 2005) The results of reliability test for each construct in the model were summarized in the table below (Table 4.2)

As shown in Table 4.2, the results indicated significantly high or very high internal reliability for most tested item scales Cronbach’s Alpha of all items were higher than minimum acceptance criterion Cronbach’s Alpha of social interaction ties was 0.78, trust was 0.82, shared language was 0.75, knowledge sharing was 0.87,

knowledge acquisition was 0.85, job performance was 0.88 (detail showed in the table 4.2)

Corrected Item-Total Correlation of all items were over minimum criterion (over 0.4) Social interaction ties was from 0.52 to 0.67, trust ranked from 0.63 to

0.67, shared language ranked from 0.54 to 0.60, knowledge sharing was from 0.64 to 0.79, knowledge acquisition was from 0.71 to 0.73, job performance was from 0.72 to 7.7

At the first time running cronbach’s Alpha, Corrected Item-Total Correlation of item 5 of trust variable and item 6 of knowledge sharing variable were under

minimum criterion (under 0.4), so the author eliminated them (See Appendix E) Table 4.2:

Knowledge sharing (KS): Cronbach's Alpha = 0.87

In summary, all six measurement scales were reliable in measuring the research

concepts because they had the Cronbach’s Alpha greater than 0.7 The Corrected item- Total correlation of all scale items was also satisfied the standard (higher than 0.4), this indicated that all scales met the requirements for reliability As a result, these measures were used in establishing the main survey to test the research hypotheses

For the next step, the author conducted the exploratory factor analysis (EFA) to clarify the validity of measurement scales.

4.3 Exploratory Factor Analysis (EFA)

Exploratory factor analysis (EFA) had been undertaken for confirming the construct validity Exploratory factor analysis could be described as orderly simplification of interrelated measures The main aim of EFA was to investigate a large number of relationships among interval variables (Leech et al., 2005) By performing EFA, the researcher could see how a large set of items group together

under a cluster (Leech et al., 2005) For this study, exploratory factor analysis would

be conducted with Varimax rotation was employed in order to take out the items on the same scale but exposed low loadings on the construct This research followed a strict criterion to delete factors that their loadings were lower than 0.5

Table 4.3:

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .896

condition about the factorability of the correlation matrix was suitable for factor analysis

Factor loading is an indicator ensuring practical significance for EFA method,

if it is equal or higher than 0.4, it is consider practical significance (Hair et al., 2010) The Rotated Component Matrix (see Table 4.5) showed the items and factor loadings for factors after rotation, with the acceptable criteria of factor loadings was more than 0.5 The total 23 items of 6 variables grouped into these six components defined by

high loadings The items in the same construct after rotated also arrange in the same component with strong loadings and not related to any other components This

showed that the items of each construct were well conceptualized

4.4 Regression Analysis

Regression analysis was the next step after testing the reliability and validity of collected data in order to identify the relationship among factors in the research model Regression analysis was conducted though three steps:

- Step 1: The relationship between social interaction ties, trust, shared

language and knowledge sharing

- Step 2: The relationship between social interaction ties, trust, shared

language and knowledge acquisition

- Step 3: The relationship between knowledge acquisition, knowledge

sharing and job performance

Before testing hypotheses, it is necessary to check some important assumptions According to Leech et al (2005), the assumptions for multiple regression include the following: that the relationship between each independent

variable and the dependent variable is linear and that the error, or residual, is normal distributed and uncorrelated with the predictor

According to the diagram in Appendix F, the assumptions were well supported they meet the requirement of the regression (See Appendix F)

In addition, the correlation testing was necessary to check whether the multicollinearity existed or not because this problem could lead to inaccurate results

Hair et al., (2010) report that it is considered if the correlation is higher than 07 The Pearson test showed the value lower than 0.7 and had significant value (Sig<5%), indicated that the explanatory variables were not correlated with each other (see table 4.6)