ISSN 1411 - 7835

ISSN 1411 - 7835

Vol. 12 - No. 4 April 2013

JURNAL MANAJEMEN INDONESIA

JURNAL MANAJEMEN INDONESIA Vol. 12 - No. 4 April 2013 Terbit enam kali per volume setiap bulan April, Agustus, dan Desember. Berisi tulisan yang diangkat dari hasil penelitian dan kajian analitis-kritis di bidang manajemen. ISSN: 1411-7835 Pelindung

: Ir. Husni Amani, MSc, M.B.A (Rektor IM Telkom)

Penanggung Jawab

: Ir. Yusuf Budiana, M.B.A (Dekan SMTM) Drs. Palti Sitorus, MM (Dekan SPS)

Ketua Penyunting

: Herry Irawan, ST, MM

Anggota

: Astrie Krisnawati, S.Sos., M.Si. Yuhana Astuti, S.T., M.T.Magr.

Penyunting Ahli

: Dr. Maya Ariyanti : Prof. Hiro Tugiman Dr. Jafar Sembiring Dr. Riko Hendrawan Dr. Gadang Ramantoko Dr. Yudi Pramudiana Dra. Ade Irma Susanty, MM. Ir. Dodie Tricahyono, MM.

Sekretariat & Sirkulasi

: Andiyannita Krishandiri Mifta Rahma Sufiana

Alamat Penyunting dan Tata Usaha: Institut Manajemen Telkom, Kompleks Telkom Learning Center (TLC), Jalan Gegerkalong Hilir no. 47, Bandung-40152, Telepon (022) 2011384,85,88, Fax. (022) 2011387, http://www.imtelkom.ac.id JURNAL MANAJEMEN INDONESIA diterbitkan mulai bulan Januari 2001 oleh Institut Manajemen Telkom d.h. Sekolah Tinggi Manajemen Bisnis Telkom; Mulai edisi Januari 2011, JMI diterbitkan oleh Program Studi Manajemen Bisnis Telekomunikasi dan Informatika (Sekolah Manajemen Telekomunikasi dan Media) dan Program Studi Magister Manajemen (Sekolah Pasca Sarjana) Institut Manajemen Telkom. Penyunting menerima sumbangan tulisan yang belum pernah diterbitkan dalam media lain. Naskah diketik dengan format seperti tercantum pada halaman kulit dalam belakang (Persyaratan naskah untuk JMI). Naskah yang masuk dievaluasi dan disunting untuk keseragaman format, istilah, dan tata cara lainnya. Naskah dapat dikirim dalam bentuk CD atau bisa dikirim melalui email: [email protected]

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DAFTARISI PEMETAANSTRUKTUR,PERILAKU,DANKINERJAPADAINDUSTRISEMEN INDONESIA—249-264 —RisrisRismayaniSuwarma&YudiPramudiana PENGUKURANBENEFITINVESTASITEKNOLOGIINFORMASIMENGGUNAKAN METODEINFORMATIONECONOMICS:(StudiKasusdiPT.Telekomunikasi Indonesia,Tbk.)—265-282 —ChrisnaJulia&PaltiMT.Sitorus VALUATIONOFCOMPANIESLISTEDINJAKARTAISLAMICINDEXDURINGTWO ECONOMICCRISIS;SUBPRIMEMORTGAGE2008ANDEUROZONESOVEREIGNTY DEBTCRISIS2011—283–296 —RikoHendrawan&SyauqiMujahidRobbani

ANALISISTRANSPARANSIDANAKUNTABILITASPEMERINTAHDAERAH MELALUIPENGUNGKAPANINFORMASIPADAWEBSITE:(StudipadaKota/ KabupatenseluruhIndonesia)—297–308 —Al iraSo ia&BagusHusen THEROLEOFSOCIALNETWORKANALYSISFORKNOWLEDGE MANAGEMENT—309–314 —AndryAlamsyah ANALISISSEGMENTASIPENGGUNATELKOMSPEEDY DIKOTABANDUNG—315–328 —LusianaKartika&Re iRifaldiWindyaGiri HUBUNGANANTARAJUMLAHUANGBEREDAR,NILAITUKAR,DANINDEKS HARGASAHAMGABUNGAN:(PendekatanVectorAutoregressiondan Kointegrasi)—329-343 —RizkiVidyamukti

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Vol. 12 - No. 4 April 2013

Andry Alamsyah1 E-mail: [email protected]

ABSTRACT As a part of Social Compu!ng, Social Network Analysis is recognized as a methodology to understand rela!onships between the actors and modeling them into graph theory, while Knowledge Management comprises a range of strategies and prac!ces used in an organiza!on to iden!fy, create, represent, distribute, and enable adop!on of insights and experiences. The organiza!on ability to manage their intellectual capital is crucial for business sustainability. This paper will present the idea on how knowledge is disseminated e$ec!vely by the help of Social Network Analysis Keywords: social network analysis, graph theory, knowledge management, computer science, social compung

INTRODUCTION In tradi!onal social sciences study where research of knowledge management usually found, data collec!on is built using ques!onnaire and asking respondents on details how they perceived their rela!ons with others. The network is constructed based on the responds where node represents individual worker and edge represents rela!ons between them. The main limita!on of this approach is, it can only reveal the explicitly known rela!ons, e.g. the actor a is the colleague of actor b, actor b is at the same department with actor c. We o#en found various types of rela!ons between two individual and many types rela!ons inside a simple network with limited number of nodes. This will be a shortcoming to the e$ort of capturing the overall picture if we can only gathers limited amount of data. We also have problem with limited scale of the approach, typically hundreds actors in one study. The arrival of social medias on the other hand, show us how they provide readily available interac!ons between individual on an unprecedented scale. In the last few years, we have experiencing unprecedented growth usage of online social networking. The popularity of web 2.0 and social media support the development of Social Compung (Wang and Zeng: 2007) and understanding pa$erns of rela!ons from Network Science (Newman: 2010), both are the area in informa!on technology which study human behavior and social rela!ons connected via computer networks. An e$ort to quan!fying rela!ons through some measures and metrics are in form of Social Network Analysis / SNA (Sco$: 2000). The early research of SNA done in the 60’s when it is di%cult to do the experimenta!on in order to analyze and measure using large data simply because the 1

Dosen Tetap Prodi MBTI Ins!tut Manajemen Telkom

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limita!on of compu!ng power. Today by using online social network, we have massive amount of data that makes us possible to model social rela!ons, facilitate informa!on exchange between individuals or between users inside groups or communi!es. Knowledge is a central importance to advanced economies, organiza!onal performance and has become more intensive in a role that many decision makers needs to have. Knowledge Management comprises a range of strategies and prac!ces used in an organiza!on to iden!fy, create, represent, distribute, and enable adop!on of insights and experiences. Growth of interest in both learning in organiza!on and knowledge management occurred at very similar !me. This is to a large extent no accident, and indicated the interrelatedness and interconnectedness of both issues (Hislop: 2005). The new way of acquiring and spreading knowledge, i.e. exchanging ideas, values, informa!on "ow, and behaviors is developing rapidly, by the in"uence from outside the organiza!on such as Internet or social media, and inside the organiza!on. Although, we can say that it is contagious, the more an organiza!on has knowledgeable elements, the be#er their compe!!ve advantage (Porter: 1985). As it is said by Drucker : The basic economy resources is no longer capital nor natural resources, nor labor, but it is and will be knowledge (Drucker: 1993). The $gure 1 below is an illustra!on from (Cross, et.al: 2002) on how di%erent formal organiza!on structure and informal organiza!on structure in a context of informa!on "ow, in an explora!on and produc!on division of a petroleum organiza!on.

(a)

Figure 1 (a). formal organiza!onal structure (b) informal organiza!onal structure of informa!on "ow

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(b)

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A Social Network Analysis (SNA) views social rela!onship in terms of Graph Theory and Network Science consis!ng of nodes and es, which also called edges, links or connecons. Nodes are the individuals or organiza!onal actors within the networks, and !es are the rela!onships between the actors. The resul!ng graph-based structures are o$en complex. There can be many kinds of !es between the nodes. Research in a number of academic #elds has shown social networks operate on many levels, from families up to the level of na!ons, and play a cri!cal role in determining the way problems are solved, organiza!on are run and the degree to which individuals succeed in achieving their goals (Sco$: 2000). Ties have one or more speci#c types of interdependency, e.g. friendship, kinship, common interest, #nancial exchange, dislike, sexual rela!onship, or rela!onship of beliefs, knowledge or pres!ge. SNA has several tools and metrics for mapping important knowledge rela!onship between peoples or departments that to be par!cularly helpful for improving collabora!on knowledge crea!on and knowledge transfer in organiza!onal se%ngs (Cross, et.al: 2002).

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Based on Newman and Sco$ (Newman: 2010; Sco$: 2000), we iden!fy several tools and metrics useful for knowledge dissemina!on inside an organiza!on: Centrality: to measure importance or in&uence an individual or group, there are several type based on the approach: Degree Centrality, Betweeness Centrality, Closeness Centrality, Eigenvector Centrality, Alpha Centrality, Katz Centrality, Pageranks, etc. Bridge: an individual or group that connec!ng two dis!nct group or department, without them the two dis!nct group or department will be separated, this approach is operated at the same ideas with Structural Hole. Density and Distance: a topological measure useful for predic!ng the scale of our organiza!on. Tie Strength: to measure how strong the rela!onship between individuals or groups, the stronger !e the be$er environment for collabora!on knowledge crea!on, although weak !es have their own advantage. Another measures which also useful is measuring connec!ons which includes: Homophily: groups of individuals with same interest, job descrip!ons or other factors form a !es together versus dissimilar others, Reciprocity: the extent to which two individuals reciprocate each others friendship or other interac!on leading to the strength of !e, Mutuality, Transivity. Network segmenta!on can also be measured by SNA using the metrics such as: Clustering Coe"cient: a measures of likelihood that two associates of a node are associates, in prac!cal we measure complete mutuality between nodes that we called Clique. There are some other aspect of SNA that we are not going to explain further such as Random Walks and Temporal Networks.

SOCIAL NETWORK ANALYSIS AND KNOWLEDGE Many organiza!ons have discovered the importance of understanding knowledge process, and trying hard to implement several methods of managing it. Organiza!on o$en ini!ates various programs to facilitate knowledge crea!on and the usability, enhanced organiza!onal learning, knowledge transfer, knowledge innova!on, but it is di'cult to understand the impact of such interven!on. SNA allows managers to visualize and understand the details of rela!onship that can either facilitate or obstruct knowledge crea!on and transfer. Several ques!ons that can be resolved with the help of SNA such as How does informa!on &ow within the organiza!on?, To whom people turn for advice?, Have subgroups emerged?, Have individuals or groups are sharing what they know e*ec!vely as they should?. The development of organiza!on management and social network has been closely related, both factors are in&uence each other. The rela!on of both described by Cross et.al. (Cross, et.al: 2002) as follows: 1.

The discovery of the importance of informal structure within an organiza!on. i.e as described in Figure 1

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Table 1 Several cases in a business organiza"on that can be resolved by the help of SNA

3.

The shi! in organiza"onal model that is #a$er, more #exible, team oriented, and more dependent to knowledge asset, more interconnected between each elements across the organiza"on The rapid growth in close coopera"on, rela"onship across organiza"onal boundaries, such as outsourcing, joint ventures, alliances, mul" organiza"onal project work

We iden"fy several cases in where SNA can be a solu"on in helping a business organiza"on handle the numbers of situa"ons in Table 1. as follows:

Case

Questions

SNA Tools

Leader Selection

Who is the central in the trust and respect network?

Degree Centrality

Ranks

How do we rank our top performer individuals in the organization?

Eigenvector Centrality, Pageranks

Task Force Selection

How do we put together a team that maximally connected through out the organization?

Closeness Centrality

Mergers and Acquisition

How to merge separate cultures / networks?

Homophilly, Reciprocity, Mutuality, Transitivity

Competitive Advan- What is the missing links between supply tages and demand?

Structural Holes

Advertising Attach- How strong the impact of our advertisement ment effort?

Tie Strength

M a r ke t S e g m e nta - How segmented our market is? tion

Clustering Coefficient, Clique, Cohesive

Information Dissemi- How is the information / knowledge nation spreading?

Random Walks, Hits Algorithm

Strength out the organization

How to increase redundancy and interconnectedness?

Bridge

Dynamics of Organization

How dynamics our organization is?

Temporal Networks

The extent of which SNA can help organiza"on analyze their knowledge environment is exhaus"ve than we imagine before which previously limited only to the graph visualiza"on and simple metrics. Analysis of SNA diagrams helps determine measure and metrics we men"oned above regardless network condi"ons such as whether it’s a dense or sparse network, there are existed subgroups inside overall organiza"on network. A few details scenario that closely related to SNA (Cross et.al. : 2002): 1. 2. 3.

4.

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5. 6.

Bo$lenecks / Overload: Central nodes that provide the only connec"on between parts of network Number of Links: Insu%cient or excessive links between departments that must coordinate e&ec"vely. Average Distance: Degrees of separa"on connec"ng all pairs of nodes in the groups. Short distance transmits informa"on accurately and in "mely way, while long distances transmit slowly and can distort the informa"on. Isola"on: People that are not integrated well into group and therefore, represent both untapped skills and a high likelihood of turnovers. Highly Expert People: We need to make sure that are being u"lized properly Organiza"onal Subgroups / Cliques: Can develop their own subcultures and nega"ve a'tudes toward other groups.

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NETWORK VIEW OF KNOWLEDGE RELATIONSHIPS We argue that the importance of social network means more than just communica!on map or informa!on "ow perspec!ve. E#ec!ve interven!ons for improving speci$c networks of people o%en have more to do with helping groups to know what the others know and ensuring safety and easy access to informa!on among people. Based on this fact, we began to focus less on communica!on and more on knowledge based dimensions of rela!onships that make useful in sharing and crea!ng knowledge. A study from IBM (Cross, et.al.: 2002) about key rela!onship, they found four dimensions tended to be cri!cal for rela!onship to be e#ec!ve, in terms of knowledge crea!on and use: 1.

2.

3.

4.

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Knowing what someone knows, this is related to analyzing their understanding of each other’s knowledge, skills, and abili!es to evaluate the overall cohesion of the group. It is called the “know” network. Gaining !mely access to speci$c person, this is related to iden!fying the central people in a speci$c network, of which their skills and knowledge are the most in"uen!al in terms of knowledge crea!on and use. Crea!ng viable knowledge through cogni!ve engagement, this is related to assess those who are not well connected in the network, this people probably represent underu!lized asset. Learning from trust rela!onship, this is related to analyzing the network to highlight !es between people who we will trust in knowledge sharing environment.

Figure 2 Network representa!on of knowledge transfer inside an organiza!on with 34 employees

By applying these dimensions to important groups of people inside an organiza!on, we can be&er analyze and intervene in cri!cal points of knowledge crea!on and sharing. The four key dimensions can be viewed separately to illustrate di#erent aspect of an organiza!onal network, but they can also examined together and cumula!vely. For the illustra!on we have data rela!onship about knowledge transi!on inside a network of 34 employees regarding issues about IT subject, who should they turn to ask for knowledge / informa!on among their colleagues. The network representa!on is in Figure 2. We can see individuals no. 1, 33 and 34 emerged as central of this group, which means they are the most likely peoples to turn for advice regarding any subject about IT. We also found that there is a subgroup that contain of 6 peoples on the right side of the network, which means without the rela!onship to No. 1 and 18, they would disconnected from the main network. The existence of subgroups implies ine'ciency in knowledge u!liza!on; members of the subgroup are not u!lizing exper!se of the main groups. Strengthening out !es between individuals is probably one solu!on to broader the informa!on access. Trust Network and

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Recommender System (Victor et.al.: 2011) also play an important roles in mapping who we trust, who we recommend and how our recommenda!on accepted and accessible to the rest of the network.

CONCLUSION We present the idea on how Social Network Analysis helps Knowledge Management to perform e$ec!vely in knowledge dissemina!on inside an organiza!on. Many organiza!ons put their e$ort largely on hiring quality individual for their day-to-day opera!onal. However their a#en!on on a#ract, develop and retain highly skilled individual alone is not su$cient. There is a li#le e"ort into systema!c ways of providing knowledge that embedded in people and rela!onships, the signi%cant shortcomings is the facts that people rely on their knowledge and the knowledge of their colleague to solve the problem. Social Network Analysis allow us to understand be#er how an organiza!on create and share knowledge, with this approach we will be#er equipped to move beyond this approach alone. Given the vast &exibility of Social Network Analysis, There are many possibili!es to to implement this idea on other subject in management.

REFERENCES Cross. R, Parker. A, and Borga'. S. (2002) A Bird Eye’s View: Using Social Network Analysis to Improve Knowledge Crea!on and Sharing. IBM Instute of Business Value, IBM Corpora!on. Drucker, P. F. (1993) Postcapitalist Society. New York: HerperCollins Publishers. Easley. D, and Kleinberg. J. (2010) Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press. Hislop, D. (2005) Knowledge Management in Organizaons. Oxford University Press. Newman. M.E.J.(2010) Networks: an Introducon. Oxford University Press. Porter, M. (1985) Compeve Advantage: Creang and Sustaining Superior Performance. The Free Press. Sco#, J. (2000) Social Network Analysis Theory and Applicaons. Sage Publica!ons. Victor. P, Cornelis. C, and De Cock. M. (2011) Trust Networks for Recommender Systems. Atlan!s Press. 2011. Wang. F, Zeng. D. (2007) Social Compu!ng From Social Informa!cs to Social Intelligence. Journal of Intelligent System, IEEE.

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Vol. 12 No. 4 April 2013

ISSN 1411 - 7835

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