Knowledge Sharing Through Social Network IJCIT 2013

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International Journal of Computer and Information Technology (ISSN: 2279 – 0764)Volume 02 – Issue 05, September 2013

www.ijcit.com 911

Significant Factors that Contributes to Knowledge

Sharing Through Social Network

Shamsul Kamal Wan Fakeh*

Faculty of Information ManagementUniversiti Teknologi MARA (UiTM)

Shah Alam, MalaysiaEmail: *shamsul {at} salam.uitm.edu.my

Izzatil Husna Arshad, Mohd Sazili Shahibi, MuhamadKhairulnizam Zaini, Wan Ab Kadir Wan Dollah,

Zaharuddin Ibrahim, Mohd Jailani Paiman, Juwahir

Ali

Faculty of Information ManagementUniversiti Teknologi MARA (UiTM)

Shah Alam, Malaysia

Abstract — As t h e tec hno l og y c on ti nu e s t o gro w , so ci a l

n etw or k s i te s has been mad e a v a i l ab le and become

c ommon to da y . Us i ng so ci a l network s , people convey

and exc hang e i n f orma ti on wit h p e op le , norma lly wit h

o t h e rs w ho shar e sam e i n te r e s t . Th i s i s ho w t h e

pro ce ss o f shar i ng c ou l d happ e n. Thus, t h i s s t ud y

has c ondu cte d t o ex am i n e t h e su cce ss f a ct ors t ha t

c ou l d e n c ourag e t o t h e process of sharing information on

knowledge through social n etw or k s. Th e f a ct ors ma i n ly

c ons i s t o f i nd ivi dua l or c ommun ity f a ct or , c on te n t

f a ct or , and tec hno l og ic a l f a ct or . I nd ivi dua l and

c ommun ity f a ct or i s f a ct or w h ic h e mb e dd e d wi t h i n

i nd ivi dua l or c ommun ity w h ic h c ou l d e n c ourag e p e op le

t o shar e . C on te n t f a ct or is f a ct or w h ic h pro vi d e d b y

so ci a l n etw or k s i te s, i n cl ud i ng w ha t i n f orma ti on c an

b e shar e d, ho w d e p t h p e op le c ou l d shar e , etc .

T ec hno l og ic a l f a ct or c on ce rns wi t h fe a t ur e s o f t h eso ci a l n etw or k s i te s, including the tools , links , navigation , etc .

Re su l t f rom t h i s s t ud y c ou l d ass i s t organ i za ti ons,

e sp eci a lly e du c a ti ona l i ns ti t u ti ons (as t h e r e spond e n t s

ar e s t ud e n t s) t o ev a l ua te t h ei r usag e o f so ci a l n etw or k s

s i te s and u t i l i z e i t f or k no wle dg e shar i ng, as well as t o

suppor t te a c h i ng and le arn i ng pro ce ss. Th i s s t ud y c ou l d

a l so h el p t o bu i l d a w ar e n e ss among p e op le o f w ha t

exte n t t h ey ha ve a l r e ad y shar e d, and w ha t i n f orma ti on

shou l d t h ey i mpro ve t o shar e i n f u t ur e .

Keywords- i nd ivi dua l and c ommun ity f a ct ors, c on te n tf a ct ors, tec hno l og ic a l f a ct ors, so ci a l n etw or k , k no wle dg e

shar i ng.

I. INTRODUCTION

Technology has been consider ed a signif icantr equir ement in our daily lif e r ecently. The r a pid growth of the technology tools and a pplications has made thetechnology an im por tant medium to tr ansf er and distr i bute infor mation. Thus, it can be consider ed asena bler for knowledge shar ing, wher e by it provides

infr astructur e for shar ing even though it might not be themotivation for people to shar e their knowledge (Shahr inaz

Ismail, 2010). Today, we can see a lot of technology

tools that have been made availa ble to f acilitate shar ing, which include electronic bulletin boards, data bases, forums, e-mails, blogs, as well as social network ing sites. This study attem pts to investigate the f actors that could encour ageknowledge shar ing through social networks. Socialnetwork has been chosen as the medium to be studied

because it is widely used all over the wor ld today. Ther efor e, this study is conducted with the aim to discover what f actors

that might lead to knowledge shar ing through socialnetworks and how de pth social networks being used to

f acilitate the process.

According to a r esear ch that has been conducted

by IDC Resear ch (a body which actively conducts r esear ches in Malaysia), the Internet users in Malaysiahas r eached 16 millions and pr edicted to incr ease to 20.4 millions in 2012. (Syahr ir Mat Ali and Fatin Hasnan, 2009). Due to the active usage of Internet, this

phenomenon has r esulted in social network sites existence. Social networks communities and we bsites have

indicated a signif icant tr end r ecently towards business,

academic communities, as well as to the individuals

within a society. Fr iendster had r e por ted over 17 millions users and MySpace 20 millions users in July 2005 (Shar inazIsmail, 2010). The social network services and we bsites ar e

expanding and produce community knowledge in every

second (Ak iyoshi, 2008).

Social network communities usually r equest users to shar e their personal infor mation (identity). This is thestar ting point for users to build trust among them and f acilitate wider learning environment af ter trust ar e builtstrongly among them. Social networks ar e believed to bea ble to r esolve barr iers to knowledge shar ing, which is lack of open communication (Ramir ez, 2007) as in (Shar inazIsmail, 2010). Trust has been the major f actor that has been

studied by many r esear chers when consider ing shar ing of


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Lastly, technological f actor can be divided into f iveelements; ease of use, navigation, inter active, useful links, and useful tools. Ease of use is a bout how technological

f eatur es of social network could hel p users perfor m their

task f aster. For exam ple, if social networks have the a bility to shar e ar ticles, people might mor e inter ested in shar ing.

Navigation is a bout how the social network ing sites works. A good navigation can be deter mined by easy to understand icon, clear instruction, and organized menus. The availa bility of icon might also hel p. For

exam ple, today, we could easily found Face book icon everywher e on the we bsites. This could f acilitate shar ing,

because users can sim ply click on the icon to shar e ar ticles or videos that they found into Face book. Useful links is def ined by the a bility of the social network to provide any links that may be needed for users. For exam ple, if users sear ch the ter m ‗ politic‘ in the sear ch box through Face book, the ca pa bility of Face book to provide r elated links might be hel pful in serving knowledge shar ing needs.

Lastly, useful tools can be def ined by the availa bility of tools (added value) which could aid users to communicate

with others easier. For exam ple, most of social network ingsites today, especially Face book and Fr iendster has alr eady equi pped with ‗link‘ tool (user could copy and

paste links that they found from other we bsites), events tool(user could set an a ppointment or events), upload video or

pictur e tools, comments (wher e others could providef eedbacks), notes, etc.

Figure 2: Research Model

VI. RESEARCH METHODOLOGY

Research Design

A r esear ch model has been developed for the purpose of this study. The model consists of thr eemain elements, individual or community f actor, content f actor, and technological f actor. The elementsinvolve in research development are research design, themodel, research instruments, data collection method, anddata analysis method.

Population and Sampling

For the purpose of this study, Faculty of Infor mation Management is the population that has been selected. It is located at Puncak Perdana, Shah Alam, and also has four other br anches at other states, including Machang

(K elantan), Merbok (K edah), Segamat (Johor) and KotaSamar ahan (Sar awak). However, for this study, it focused to the r espondents at the f aculty which located at Puncak Perdana, since only Puncak Perdana have com pletenum bersof bachelor degr ee‘s students from all four

progr ams. According to the statistical data which gather ed from the Faculty of Infor mation Management, ther e ar e a total of 2151 students from var ious modes and

progr ams. Ther e ar e 1684 students of bachelor degr eefrom four progr ams, including Infor mation System

Management (IM 221), Records Management (IM 222), Infor mation Resour ce Center (IM 223) and also Li br ary

and Infor mation Management (IM 220). Others ar e consists

of student of Pengajian Luar K am pus (PLK) with 117 students, Masters with 332 students and PHD with 18 students. Gener ally, ther e ar e thr ee types of sam pling method being used, which include grouping, str atif ied and r andom. Grouping is done by selecting one specif icgroup. For exam ple, focus on Records Management‘s

students only. Str atif ied, in the other hand, is done through selecting a f ew people from each group at an aver age.

For exam ple, 50 students Infor mation System Management progr am, 50 from Records Management progr am, etc. Thethird type is by r andom. This method is done by r andomly selecting the r espondent r egardless of how many of themhave to be selected from each progr am.

For this study, the str atif ied r andom sam pling is being used. Str atif ied is in this case is means by the 160

r espondents that wer e selected from f inal semester of bachelor degr ee students, from all four progr ams of

Faculty of Infor mation Management. These 160respondents ar e actually r e pr esenting 43.48% of f inalsemester students (the total num ber of f inal semester

students ar e 368). From the total of 160 r espondents, ther e wer e r andomly selected to answer the questions. The str atif ied r andom sam pling was used because thediff iculty to f ilter and to r each all of the total r espondents in a limited time (368 r espondents).

Research Instrument (Questionnaire)

From the r esear ch model and r esear ch questions,

a set of questionnair e has been developed. The r esear ch model divided the main f actors that could contr i bute to

knowledge shar ing through social networks into thr ee main categor ies; individual or community f actors, content f actors, and technological f actors. Resear ch questions for this study can be viewed as following;


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What ar e the f actors (technology, content, individual or community) that encour age

knowledge shar ing through social network ?

Why people choose to use social network sites?

What types of infor mation that being shar ed and

being used over the social network ?

The questionnair e consists of f ive sections;demogr a phic data (Par t A), the usage of socialnetwork a pplication (Par t B), individual or community f actors (Par t C), content f actors (Par tD), and technological f actors (Par t E). Thedivision of each section could be viewed as

follows;

i.Part A – Demographic Data

Age, Gender, Course, Secondary Education Level, Work ing Exper ience.

ii.Part B – The Usage of Social Network

Application

a. Fr equency of access (never open af ter

r egistr ation, 1-5 times week ly, 6-10 timesweek ly, 11-15 times week ly, mor e than 15

times week ly).

b. Social network sites usage Face book, Twitter, Blogger, MySpace, Fr iendster, hi5, Tagged (with Lik er t scale 1 to 4, ―I contr i bute‖, ―I

only use‖, ―I know it but do not use‖, ―I do notknow it‖).

c. Reason to use social network sites (To k ee p in touch, to f ind and exchange infor mation -

knowledge shar ing, to shar e inter ests, to infor m/ being infor med, to meet new people, to

adver tise exper tise/ service / business/ products).

iii.Part C – Individual and Community Factors

Consist of seventeen (17) questions which cover elements of trust, f airness, community cultur e, individual attitude, en joyment, and r elationshi p.

iv.Part D – Content Factors

Consist of sixteen (16) questions which cover

elements of usefulness (with additional questions

of types of infor mation), detail, openness, r e putation, and benef its.

v.Part E – Technological Factors

Consist of seven (7) questions which cover elements of ease of use, navigation, inter active, useful links, and useful tools.

Data Collection Method

This study used questionnair e method, which is

the most f avora ble method being chosen in order to havestandardize f eedbacks from users. The questionnair e thathas been developed wer e tested for validation (5 students from Masters of Science in Infor mation Management, IM 770 wer e selected) to ensur e r espondents could clear ly understand the questions befor e it can be distr i buted to theactual r espondents. The questionnair e was also being sentto an exper t (lectur er /supervisor) for r evision. Af ter the

pilot test and r evision, some corr ections have been done to im prove and f inally the questionnair e was distr i buted to

160 r espondents. In order to ensur e the num ber ofr espondents r each the total of 160, f eedbacks fromr espondents wer e collected as soon as they com plete thequestionnair e. Other than questionnair e, online observation was also conducted. This is to observe the tr end of whatk ind of infor mation that is commonly being shar ed by users, how fr equent they shar e, as well as how de pth theshar ing occur r ed

Data Anal ysi s M et hod

Data analysis involves tr ansf err ing infor mation

gather ed from questionnair e into com puter ized data, and to be processed by cer tain sof twar e. Dur ing this process, data ar e examined and validated for errors, and later being tr ansfor med into useful infor mation. For the purpose of this study, data processing was done using commer cialstatistical sof twar e, called SPSS (version 16). SPSS is awidely used sof twar e for data analysis by students,

academician and prof essionals (Zamalia, 2009). This study

used sever al a pproaches to process the data into meaningful infor mation. Firstly, descr i ptive analysis – sim ple ta bulation was used. It was a bout calculating thenum ber of diff er ent r esponses and arr anged the datainto an organized manner to infor m the r esear chers

a bout the r esponses occur. The exam ple can be viewed as in Ta ble 5. The analysis also used fr equency distr i bution

a pproach. A ta ble was pr e par ed to display the counting of r esponses for each category (the fr equency of occurr ence). The exam ple can be viewed as in Ta ble 5.Mor eover, cross ta bulation also being used. This means that a ta ble was pr e par ed to organize data by group or classes. It is a joint fr equency distr i bution of two or mor esets of var ia bles. The exam ple can be viewed as in Ta ble 3.

In order to analyze the f actors which contr i bute to knowledge shar ing, data was organized and processed

using descr i ptive statistic analysis. A ta ble being produced


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and each f actor wer e descr i bed by mean and standard deviation. From the Lik er t Scale, r ank ing 1 to 5 (―Strongly Disagr ee, ―Disagr ee‖, ― Neutr al‖, ―Agr ee‖, ―Strongly Agr ee‖), the aver age num ber will be calculated. If themean is less than 3, it is a negative r esult, while if it mor ethan 3, it indicates positive r esult. The exam ple can be

viewed as in Ta ble 7. The fur ther descr i ption of Lik er tScale that being used in this study is explained in as following;

Number

Representation

Statement Description

1 StrongDisagree

Indicates that respondentshave strong disagreement with

the question (negative

feedback)

2 Disagree Indicates that respondents

have moderate disagreement

with the question.(negative

feedback)3 Neutral Indicates that respondents do

not have any idea or opinion or

feeling unrelated towards the

question. (neutral feedback)

4 Agree Indicates that respondents

have moderate agreementwith the question. (positive

feedback)

5 Strongly

Agree

Indicates that respondents

have strong agreement withthe question (positive

feedback)

Table 1: Likert Scale

VII. DATA ANALYSIS AND FINDINGS

This cha pter provides the analysis and f indings fromthe questionnair e that have been distr i buted. It includes thedemogr a phic data, social network usage, fr equency of usage, f actors, etc. Fur ther discussion will be pr esented in cha pter 8 (discussion).

Demographic Data

The questionnair e was sent out to 160 of

r espondents from f inal semester students of bachelor degr ee progr ams from Faculty of Infor mation Management, UiTM.

From Ta ble 2, it was found out that major ity r espondents ar e f emale with 80.62% (129 r espondents), while male is only 19.38% (31 r espondents). This major diff er ence maybecaused by the diff er ent r atio of f emale students to malestudents. The total num ber of f inal semester f emalestudents is 293 students, while f inal semester male students ar e only 75 students. The highest group par tici pation camefrom IM 220 progr am (Li br ary and Infor mationManagement), with 36.87% (59 r espondents), followed by

IM 223 (Infor mation Resour ce Center Management) with

24.38% (39 r espondents), IM 221 (Infor mation System Management) with 20% (32 r espondents) and IM 222 (Records Management) with 18.75% (30 r espondents).As what have been descr i bed in cha pter 6 (r esear ch methodology), r espondents wer e r andomly selected from the160 targeted r espondents. Ther efor e the r esults have shown

slight diff er ences among total num bers of r espondents for

GENDER / COURSE CROSS TABULATION

Course

IM 220 IM 221 IM222 IM223TOTAL

7 11 9 4 31

Gender

Male

4.38% 6.87% 5.62% 2.5% 19.38%

52 21 21 35 129

Female

32.50% 13.13% 13.13% 21.86% 80.62%

59 32 30 39 160

Total 36.8% 20.00% 18.75% 24.38% 100.00%

Table 2: Demographic Data

The Social Network Sites Usage

Respondents wer e ask ed a bout their usage of social

network sites to investigate which social network sites thatar e mostly used by r espondents. Using Lik er t Scale, from 1

to 4 (―I do not know it‖, ―I know it, but do not use‖, ―I only use it‖, ―I contr i bute to it‖), r espondents wer e r equir ed to

indicate their level of par tici pation with par ticular socialnetwork ing sites, namely Face book, Fr iendster, Blogger, Twitter, MySpace, hi5, and Tagged. Users ar e allowed to choose mor e than one answer, as they might utilize mor ethan one social network sites. From Ta ble 3 , it has

shown that the most popular social network site amongr espondents is Face book, wher e by every r espondent know

its existence (Statement ―I do not know it‖ = 0% of r espondents). 50.6% of r espondents (81 r espondents) contr i bute to Face book. This mean that they ar e actively update their status / shout out, posting videos or links, as well as involve in group discussion. This num ber is followed by 46.9% of r espondents (75 r espondents) who

only use Face book without contr i bution. This means thatthey ar e passively par tici pate in using Face book tools.

They just use Face book to view other s‘ prof ile, to communicate with fr iends, etc. without actively contr i bute to it. This phenomenon might caused by availa bility of

Face book links everywher e. Users can easily access Face book from Yahoo! site, as well as any other sites which

ena ble user to log on into Face book to shar e infor mation they found. In addition, users could also logon into Face book through mobile phones (most of latest model of mobile

phones ar e equi pped with Face book icon). The social


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network sites usage is followed by Blogger, Fr iendster, Twitter, MySpace, Tagged and hi5. This indicates that someusers own their own blogs to shar e their personalexper iences or academic/work r elated infor mation.

Social Network Sites UsageStatement Social Network Sites

Facebook Twitter MySpace Friendster Blogger Hi5 Tagged

I do notknow it

0 4 5 2 6 38 26

0.00% 2.5% 3.1% 1.2% 3.8% 23.8% 16.2%I, know it, but do notuse

4 102 98 67 97 99 82

2.5% 63.8% 61.2% 41.9% 60.6% 61.9% 51.2%

I only use it 75 32 42 64 26 15 38

46.9% 20.0% 26.2% 40.0% 16.2% 9.4% 23.8%I

contributionto it

81 22 15 27 31 8 14

50.6% 13.0% 9.4% 16.9% 19.4% 5.0% 8.8%

TOTAL 160 160 160 160 160 160 160

Table 3: The Social Network Sites Usage

Frequency of Social Network Sites Usage

In order to measur e fr equency of usage of socialnetwork sites, r espondents wer e ask ed to choose their fr equency of usage according to the option given (― Never open it af ter r egistr ation‖, ―1-5 times per week ‖, ―6-10 times per week ‖, ―11-15 times per week ‖, ―Mor e than 15 times per week ‖). Ta ble 3 de picted the r esults der ived fromthe f eedbacks provided by the r espondents. From Ta ble 4, itwas found out that most user access social network sites mor e than 15 times per week, with a total num ber of

33.13% of r espondents (53 r espondents). As well, only four (4) r espondents who never use their socialnetwork accounts af ter the f irst r egistr ation. This was

r e pr esented by 2.5% of r espondents. This r esultindicates that r espondents ar e actively using social network sites.

Gender/Frequency of Accessing to Social Network Sites

Cross Tabulation

How many times do you log into your social network site in a week?

Never openit after

registration

1-5times

perweek

6-10 times per week

11-15 times per week

More than15 times

per week

Total

Male 2 7 1 7 14 31

Female

2 45 24 19 39 129

Total 4 52 25 26 53 160

Table 4: Frequency of Social Network Sites Usage

Reason of Social Network Usage

This section would answer one of the r esear ch ob jectives, which is to identify why people choose to usesocial network sites. Respondents wer e given six statements and wer e r equir ed to indicate their agr eement for each

statement. The statements ar e ―To k ee p in touch‖, ―To f ind and exchange infor mation – knowledge shar ing‖, ―To shar einter est with others‖, ―To infor m / be infor med a boutcontacts, events, a ppointments‖, ―To meet new people‖, and ―To adver tise exper tise, service / business / product‖. Fromthe six statement, two of them ar e or iented towards mor e

specif ic knowledge shar ing (academic or work-r elated), which ar e ―To f ind and exchange infor mation – knowledge shar ing‖, and ――To adver tise exper tise, service /

business / product‖. Another four statements could beconsider ed as or iented towards gener al knowledge or shar ing infor mation casually. The diff er ences between statements ar e

purposely done in order to identify the level of involvement of r espondents in shar ing their knowledge or infor mation. To answer the question, r espondents wer e given a Lik er t Scale, from 1 to 5 (―Strongly Disagr ee‖, ―Disagr ee‖, ― Neutr al‖,

―Agr ee‖, and ―Strongly Agr ee‖) and they ar e r equir ed tor espond according to the scale given. From Ta ble 5 , it was found out that most r espondents par tici pate in social

network ing site because they would lik e to k ee p in touch with fr iends and f amilies, which r e pr esented by a total of 86.9% r espondents (139 r espondents). This num ber was der ived froma total sum of ―Agr ee‖ and ―Strongly Agr ee‖. Other r easons that followed the main r eason (to k ee p in touch) ar e to infor m or be infor med (contacts, events, a ppointments) with 86.2% (138 r espondents), to shar e inter est with others with

82.5% (132 r espondents), to f ind & exchange infor mation (k-shar ing) with 73.1% (117 r espondents), to meet new peoplewith 63.1% (101 r espondents), and to adver tise ser vice,

products, exper tise with 59.4% (95 r espondents).

Reason To Use Social Network Sites

Strongly

Disagree

Disagree Neutral Agree Strongly

Agree

Total

Keep in touch 2

1.2%

1

0.6%

18

11.2%

63

39.4%

76

47.5%

160

100%

Find & exchange

information

(knowledge sharing)

2

1.2%

2

1.2%

39

24.4%

72

45.0%

45

28.1%

160

100%

Share interests with

others

4

2.5%

0

0.0%

24

15.%

80

50.%

52

32.5%

160

100%

Inform / Be informed

about contacts,

events, appointments

3

1.9%

2

1.2%

17

10.6%

77

48.1%

61

38.1%

160

100%

Meet new people 4

2.5%

4

2.5%

51

31.9%

64

40.0%

37

23.1%

160

100%

Advertise expertise /

service / business /

product

8

5.0%

6

3.8%

51

31.9%

64

40.0%

31

19.4%

160

100%

Table 5: R eason of Social Network Sites Usage


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F act ors t hat C ont r ibute t o Knowledg e Shar ing t hrough Social N et work

Ind ividual or C ommunity F act or

Ta ble 6 pr esented the r esults of individual or

community f actors that could contr i bute towards knowledgeshar ing through social network. Each element within theindividual and community f actor was displayed. It has

been shown that the highest f actor is user‘s willingness to shar e (with mean = 4.03). However, these f actors wer e then grouped according to r esear ch model (attitude, en joyment, r elationshi p, cultur e, trust, and f airness). The f actors wer eagain being calculated in order to r e pr esent each elements of the f actor as in the r esear ch model. The r esult can beviewed as in Ta ble 7.

Individual / Community Factors Mean Std.

Deviation

Willing to share (attitude) 4.03 .70888Approachable, enjoyment (enjoyment) 3.00 .57732

Know each other very well (relationship) 3.99 .67755

Prefer to work in group (relationship) 3.98 .73947

Keep each other updated (culture) 3.97 .74751

Trust (trust) 3.96 .95751

Supportive learning culture (culture) 3.93 .70533

Regardless of seniority (fairness) 3.86 .66812

Encouraged to give opinion (fairness) 3.83 .74582

Seniors commitment (culture) 3.78 .88275

Only share knowledge if it is important to other (attitude) 3.76 .84487

Feel sorry if SNS are shut down (attitude) 3.74 .89364

Have online discussion platform to exchange study-related ideas

3.73 .86838

Prefer people to approach rather than be volunteerattitude)

3.68 .78947

Involvement with knowledge sharing regardless of people(enjoyment)

3.59 .77206

Feel out of touch when haven‘t logged onto socialnetwork for a while (relationship)

3.58 1.00031

Proud to be social network user (relationship) 3.57 .90853

Table 6: Individual or Community Factor Findings

From Ta ble 7, it has shown that trust is thedominant f actors which contr i bute to knowledge shar ing through social networks, with 3.96 (mean). It is followed by

community cultur e and f airness with 3.85 (mean). Relationshi p is the least and has the lower scor e of theindividual or community f actors. However, 3.78 can still beconsider ed as positive r esult. (from the Lik er t scale 1 to 5,

wher e 1 is strongly disagr ee, and 5 is strongly agr ee, theaver age mean should be 3. If r esult is mor e than 3, it

could be consider ed as positive r esult). In over all, the

Individual or Community Factors had scor ed 3.84 mean, which consider ed as signif icant and positive r esult.

Table 7: Individual or Community Factors

Content Factor

Ta ble 8 displays the r esults of the content f actors

which could contr i bute towards knowledge shar ing through social network. The highest f actor from content f actor is theopen communication, wher e by infor mation is a ble to f low fr eely through social network. This f actor scor e 4.02 (mean). However, these f actors wer e not yet r e pr esent theactual f actors as what being pr esented in the r esear ch model. Ther efor e, items in Ta ble 8 wer e grouped into

elements which being pr esented in the r esear ch model. Theelements wer e later calculated again to produce scor e

which r e pr esent each element in content f actors. Ther esults can be viewed as Table 9.

Content Factors Mean Std.

DeviationOpen communication (Openness) 4.02 .69565

Free disseminate information (Openness) 3.95 .68036

Current issue discussion (Usefulness) 3.88 .66340

Information about other societies/states/countries(Usefulness)

3.83 .70533

Relevant/used information (Usefulness) 3.82 .72573

Increase ‗network‘ (connection with people(Reputation)

3.78 .63118

Personal (non academic) benefits (Benefit) 3.78 .67918Personal experience (Detail) 3.74 .71364

Academic / work benefits ( Benefit) 3.74 .64902

Reputation (Reputation) 3.70 .74226

Information about society (e.g. 1 Malaysia, etc)(Usefulness)

3.68 .77338

Information on shopping (Usefulness) 3.67 .72443

Academic experience (Detail) 3.63 .67990

Income (Usefulness) 3.59 .76387

Government information (Usefulness) 3.54 .78418

Political Information (Usefulness) 3.36 .82681

Table 8: Content Factor Findings

Ta ble 9 r e pr esent the elements of content f actor thatcould contr i bute to knowledge shar ing through social

network. Openness of infor mation still has the highest scor e, with 3.99 (mean), followed by benef it with scor e 3.76 (mean). The lowest and the least scor e is usefulness of infor mation with scor e 3.67. Even though it the lowest, itcan still be consider ed as positive r esult. Content Factor had scor ed 3.77 mean, which can be consider ed assignif icant f actor and positive r esult.

Individual/Community Factors Mean

Trust 3.96

Social (Community) culture 3.85

Fairness 3.85

Individual attitude 3.80

Enjoyment 3.79

Relationship 3.78

Individual / Community Factors Mean 3.84


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Content Factors Mean

Openness 3.99

Benefit 3.76

Reputation 3.74

Detail 3.69

Usefulness 3.67

Content Factors Mean 3.77

Table 9: Content Factor

T echnol og ical F act or

Ta ble 10 pr esents the f indings from technologicalf actors. It was found out that the highest f actor fromtechnological f actor that could encour age users to shar eknowledge over social network is the a bility of social

network to give user f eedbacks from their fr iends, withscor e 3.84 (mean). However, these elements wer e not yetr e pr esent the actual elements, as what have been descr i bed in r esear ch model. The items wer e later being grouped into the actual elements according to r esear ch model to r e pr esent the elements of technological f actor. Thetechnological f actor can be viewed as in Ta ble 11.

Technologies Mean Std.

Deviation

Feedback (Interactive) 3.84 .81300

Navigations and interface (Navigation) 3.84 .69034

Interactiveness (Interactive) 3.83 .82072

Useful tools (Tools) 3.82 .73435

Online sources is preferable (Ease of use) 3.75 .80876

Useful links (Links) 3.69 .82414Easier rather than face-to-face (Ease of use) 3.61 .89011

Table 10: Technological Factor Findings

Ta ble 11 r e pr esent the actual elements (according to the r esear ch model) which r e pr esent the technologicalf actors that could contr i bute towards knowledge shar ing through social network. The highest element is theinter active and navigation of the social networks, which scor e 3.84 (mean). The lowest or the least element is theease of use, which scor e 3.68 (mean). Even though it is the lowest element, it is still consider ed as signif icant and

provides positive r esult. The Technological Factor scor ed

3.77 (mean), which indicate that technological f actor can beconsider ed as signif icant f actors that contr i bute to knowledge shar ing through social network.

Technological Factors Mean

Interactive 3.84

Navigation 3.84

Tool 3.82

Links 3.69

Ease of use 3.68

Technological Factor Mean 3.77

Table 11: Technological Factor

Infor mation / Knowledg e being Shar ed t hrough Social N et work

This section pr esents the f indings of whatinfor mation that consider ed im por tant (as per ceived by users) to be distr i buted or shar ed through social network.

The r esults indicate what types of infor mation that users fr equently sear ch for or tr ansf er through social network. Ther esults can be viewed as in Ta ble 12. From the r esult, ithas shown that infor mation that fr equently being distr i buted through social network by users is curr ent issuediscussion (gener al knowledge), which might involve thenews or and curr ent event ha ppened within theorganization or country. This scor e 3.88 (mean). User r ank ed infor mation a bout other countr ies or society as thesecond im por tant infor mation, which scor e 3.83.

Surpr isingly, political infor mation has the lowest scor e, with 3.36 (mean).

Information / Knowledge Being Shared MeanCurrent issue discussion 3.88

Information about other societies/states/countries 3.83

Personal (non academic) benefits 3.78

Personal experience 3.74

Academic / work benefits 3.74

Information about society (e.g 1 MALAYSIA, etc) 3.68

Information on shopping 3.67

Academic experience 3.63

Government information 3.54

Political information 3.36

Table 12: Information / K nowledge Being Shared Through Social Network

VIII. DISCUSSION & CONCLUSION

This cha pter is the discussion of the f indings and analysis der ived from the study. The discussions will answer and explain each of the r esear ch ob jectives of the study.

F act ors t hat C ont r ibute t o Knowledg e Shar ing t hrough Social N et work

Re s e ar c h Ob jecti ve : To i n ve s ti ga te f a ct ors t ha t

e n c ourag e o f k no wle dg e shar i ng t hrough so ci a l n etw or k

From Ta ble 7, it has shown that trust is the

dominant f actors of Individual or Community Factor which contr i bute to knowledge shar ing through social networks, with 3.96 (mean). This r esult is in line with other pr evious author which also identif ied that trust is among the mostim por tant f actors to cultivate knowledge shar ing behavior.

This means that users per ceive that trust is im por tant befor e they can actually distr i bute and shar e knowledge

through social network. In over all, the Individual orCommunity Factors had scor ed 3.84 mean, which consider ed as signif icant and positive r esult. Mor eover, fromTa ble 9, openness of infor mation has the highest scor e with 3.99 (mean) of Content Factor. This means that users


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