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E-İşte Sürdürülebilir Bağlantılığı İzlemek için Ağ Tabanlı Teorinin Kullanımı

Year 2020, Volume: 13 Issue: 2, 145 - 155, 30.04.2020

Ziya Nazım Perdahçı , Mehmet Nafiz Aydın

https://doi.org/10.17671/gazibtd.647396

Abstract

Çevrimiçi Etkileşimli Platformlar, yüz binlerce birbirine bağlı kullanıcı oluşturabilen iletişim, bilgi alışverişi ve bağlantılar gibi çeşitli hizmetler sunar. Son çalışmalar, platfom kullanıcılarının birbirine bağlılığının, yani kullanıcıların birbirine bağlılığa yönelik ortak eğiliminin büyümesinin ve sürdürülebilirliğinin, bu tür e-İşletmelerde başarılı olmak için iki temel bileşen olduğunu göstermektedir. Bu makale, çevrimiçi etkileşimli platformların yöneticilerine, kullanıcıların birbirine bağlılığının büyümesini ve sürdürülebilirliğini izlemek için analitik bir araç sağlamak amacıyla e-İş bağlamında “ağ korelasyonu” kavramını araştırmayı amaçlamaktadır. Hem statik hem de zamana bağlı ağ korelasyon analizi yapmak için Doktorsitesi.com'u dijital iz verisi kaynağı ve kullanıcı meta verileri (rol, cinsiyet) kaynağı olarak kullanarak kullanıcıların birbirine ne ölçüde bağlı olduğuna ilişkin ağ korelasyonlarının ve kullanıcı meta verilerine ilişkin ağ korelasyonlarının, kullanıcıların birbirine bağlı büyümesini nicel olarak sürdürmenin yollarını izlememize ve önerebilmemize yardımcı olabileceğini gösteriyoruz. Birlikte ele alındığında, sonuçlarımız ağ korelasyon analizlerinin, çevrimiçi etkileşimli platformlarda sürdürülebilir kullanıcı bağlantılığını daha iyi tanımlamak, tavsiye etmek ve ön görmek için analitik sunabildiğini göstermektedir. Buna göre, platform yöneticileri, yeni kullanıcıların, birbirine bağlılık analitiğine dayalı olarak birbirine bağlılıklarını artırmalarına yardımcı olan bir platform özelliği eklemeyi düşünebilir.

Keywords

sürdürülebilir e-iş ağ teorisi ağ korelasyonu bağlantılılık

References

  • R. Benbunan-Fich, M. Koufaris, “An Empirical Examination of the Sustainability of Social Bookmarking Websites”, Information Systems and e-Business Management, 8(2), 131–148, 2010.
  • D. K. Liou, W. H. Chih, L. C. Hsu, C. Y. Huang, “Investigating Information Sharing Behavior: The Mediating Roles of the Desire to Share Information in Virtual Communities”, Information Systems and e-Business Management, 14(2), 187–216, 2016.
  • B. K. Samanthula, W. Jiang, “Interest-driven Private Friend Recommendation”, Knowledge and Information Systems, 42(3), 663–687, 2015.
  • M. Chau, J. Xu, “Business Intelligence in Blogs: Understanding Consumer Interactions and Communities”, MIS Quarterly, 36(4), 1189–1216, 2012.
  • S. Kisilevich, C. S. Ang, M. Last, “Large-scale analysis of self-disclosure patterns among online social networks users: a Russian context”, Knowledge and information systems, 32(3), 609–628, 2012.
  • M. K. Foster, A. Francescucci, B. C. West, “Why Users Participate in Online Social Networks”, International Journal of e-Business Management, 4(1), 3–19, 2010.
  • C. Ridings, M. Wasko, “Online Discussion Group Sustainability: Investigating the Interplay Between Structural Dynamics and Social Dynamics Over Time”, Journal of the Association for Information Systems, 11(2), 95–121, 2010.
  • A. Iriberri, G. Leroy, “A Life-cycle Perspective on Online Community Success”, ACM Computing Surveys (CSUR), 41(2), 11, 1–29, 2009.
  • H. Chen, R. H. Chiang, V. C. Storey, “Business Intelligence and Analytics: From Big Data to Big Impact”, MIS Quarterly, 36(4), 1165–1188, 2012.
  • T. W. Wang, Y. Verbitskiy, W. Yeoh, “Depicting Data Quality Issues in Business Intelligence Environment through a Metadata Framework”, International Journal of Business Intelligence Research (IJBIR), 7(2), 20–31, 2016.
  • D. J. Watts, S. H. Strogatz, “Collective Dynamics of ‘small-world’ Networks”, Nature, 393(6684), 440–442, 1998.
  • M. N. Aydin, N. Z. Perdahci, “Dynamic Network Analysis of Online Interactive Platform”, Inf Syst Front, 21(2), 229–240, 2019.
  • G. F. Khan, “Social Media Analytics”, Social Media for Government, Springer, Singapore, https://doi.org/10.1007/978-981-10-2942-4_6, 2017.
  • F. D. Malliaros, V. Megalooikonomou, C. Faloutsos, “Estimating Robustness in Large Social Graphs”, Knowledge and Information Systems, 45(3), 645–678, 2015.
  • J. Cao, K. A. Basoglu, H. Sheng, P. B. Lowry, “A Systematic Review of Social Networking Research in Information Systems”, Communications of the Association for Information Systems, 36(37), 727–758, 2015.
  • A. Vespignani, “Twenty Years of Network Science”, Nature, 558, 528–529, 2018.
  • S. Milgram, “The Small World Problem”, Psychology Today, 2(1), 60–67, 1967.
  • S. Wasserman, Social Network Analysis: Methods and Applications, Vol. 8, Cambridge University press, 1994.
  • L. Freeman, The Development of Social Network Analysis. A Study in the Sociology of Science, BookSurge, LLC North Charleston, A.B.D., 2004.
  • S. P. Borgatti, D. S. Halgin, “On Network Theory”, Organization Science, 22(5), 1168–1181, 2011.
  • A. L. Barabási, Network Science, Cambridge University Press, Cambridge, B.K., 2016.
  • T. van Mierlo, “The 1% rule in four digital health social networks: An observational study”, Journal of medical Internet research, 16(2), 2014
  • A. L. Barabási, R. Albert, “Emergence of Scaling in Random Networks”, Science, 286(5439), 509–512, 1999.
  • P. Erdős, A. Rényi, “On the Evolution of Random Graphs”, Publ. Math. Inst. Hung. Acad. Sci, 5(1), 17–60, 1960.
  • M., McPherson, L. Smith-Lovin, J. M. Cook, “Birds of a Feather: Homophily in Social Networks”, Annual Review of Sociology, 27(1), 415–444, 2001.
  • K. Zhang, D. Lo, E. P. Lim, P. K. Prasetyo, “Mining Indirect Antagonistic Communities from Social Interactions”, Knowledge and Information Systems, 35(3), 553–583, 2013.
  • M. E. Newman, “Assortative Mixing in Networks”, Physical Review Letters, 89(20), 208701-1–208701-4, 2002.
  • M. E. Newman, “Mixing Patterns in Networks”, Physical Review E, 67(2), 026126-1–026126-13, 2003.
  • A. L. Barabási, “Scale-free Networks: A Decade and Beyond”, Science, 325(5939), 412–413, 2009.
  • M. N. Aydin, N. Z. Perdahci, “Network Analysis of an Interactive Health Network”, Journal of Internet Social Networking & Virtual Communities, 2016(2016), 1–17, 2016.
  • J. Leskovec, J. Kleinberg, C. Faloutsos, “Graph Evolution: Densification and Shrinking Diameters”, ACM Transactions on Knowledge Discovery from Data (TKDD), Cilt: 1, Editör: Jiawei Han, ACM New York, NY, A.B.D., 1–41, 2007.
  • G. T. Bosslet, A. M. Torke, S. E. Hickman, C. L. Terry, P. R. Helft, “The Patient–doctor Relationship and Online Social Networks: Results of a National Survey”, Journal of General Internal Medicine, 26(10), 1168–1174, 2011.
  • R. Kumar, J. Novak, A. Tomkins, “Structure and Evolution of Online Social Networks.”, Link mining: models, algorithms, and applications, Cilt: 1, Editör: P. Yu, J. Han, C. Faloutsos, Springer, New York, NY, A.B.D., 337–357, 2010.
  • G. Ünel, “Information Passing in Heathcare Socical Networks”, Bilişim Teknolojileri Dergisi, 10(1), 99–104., 2017
  • J. Howison, A. Wiggins, K. Crowston, “Validity Issues in the Use of Social Network Analysis with Digital Trace Data”, Journal of the Association for Information Systems, 12(12), 767–797, 2011.
  • G. Csardi, T. Nepusz, “The iGraph Software Package for Complex Network Research”, Inter Journal, Complex Systems, 1695(5), 1–9, 2006
  • D. A. Schult, P. Swart, “Exploring Network Structure, Dynamics, and Function Using NetworkX”, The 7th Python in Science Conferences (SciPy 2008), Pasadena, Kaliforniya, 11–16, 19-24 Ağustos, 2008.
  • X. Chen, C.-Z. Yang, “Visualization of Social Networks”, Handbook of Social Network Technologies and Applications, Cilt 1, Editör: Furht, B., Springer, New York, NY, A.B.D., 585–610, 2010.
  • T. M. Fruchterman, E. M. Reingold, “Graph Drawing by Force‐Directed Placement.”, Software: Practice and experience, 21(11), 1129–1164, 1991
  • G. C. Kane, M. Alavi, G. Labianca, S. P. Borgatti, “What’s Different About Social Media Networks? A Framework and Research Agenda”, MIS Quarterly, 38(1), 275–304, 2014
  • V. Ö. Budak, E. Kartal, S. Gülseçen, “Site-içi Aramalar ve Apriori Algoritması Kullanılarak Web Sitesi Ziyaretçilerinin İhtiyaç Tespitine Yönelik Bir Örnek Olay İncelemesi”, Bilişim Teknolojileri Dergisi, 11(2), 211-222, 2018.
  • S. Koushik, J. Birkinshaw, S. Crainer, “Using Web 2.0 to Create Management 2.0”, Business Strategy Review, 20(2), 20–23, 2009
  • İ. Birol, A. Hacinliyan, “Approximately Conserved Quantity in the Hénon-Heiles Problem”, Physical Review E, 52(5), 4750–4753, 1995
  • N. Z., Perdahçı, A. Hacınlıyan, “Normal Forms and Nonlocal Chaotic Behavior in Sprott Systems”, International Journal of Engineering Science, 41(10), 1085-1108, 2003
  • H. B. Hu, X. F. Wang, “Disassortative Mixing in Online Social Networks”, EPL (Europhysics Letters), 86(1), 18003–18009, 2009
  • L. Peel, D. B. Larremore, A. Clauset, “The Ground Truth About Metadata and Community Detection in Networks”, Science Advances, 3(5), e1602548, 1–8, 2017
  • S. Stieglitz, L. Dang-Xuan, A. Bruns, C. Neuberger, “Social Media Analytics”, Bus Inf Syst Eng, 6(2), 89–96, 2014
  • B. Karaöz, U. T. Gürsoy, “Adaptif Öğrenme Sözlüğü Temelli Duygu Analiz Algoritması Önerisi”, Bilişim Teknolojileri Dergisi, 11(3), 245–253, 2018.

Using the Network-Based Theory for Monitoring Sustainable Interconnectedness on e-Business

Year 2020, Volume: 13 Issue: 2, 145 - 155, 30.04.2020

Ziya Nazım Perdahçı , Mehmet Nafiz Aydın

https://doi.org/10.17671/gazibtd.647396

Abstract

Online Interactive Platforms offer a variety of services including communication, information exchange, and connections that can create hundreds of thousands of interconnected users. Recent studies suggest that growth and sustainability of platfom users’ interconnectedness, that is, users' collective tendency to interconnectedness, are the two essential ingredients to succeed in such e-Businesses. This paper aims to explore the notion of “network correlation” in e-Business context in an attempt to provide managers of online interactive platforms with an analytical tool for monitoring growth and sustainability of users’ interconnectedness. We leverage Doktorsitesi.com as a digital trace data source and user metadata (role, gender) source to conduct both static and temporal network correlation analysis, and show that network correlations regarding the extent to which users are interconnected and network correlations regarding user metadata can help us to monitor and to suggest ways to quantitatively sustain growth of users’ interconnectedness. Taken together, our results suggest that network correlation analyses is capable of offering analytics to better describe, prescribe, and predict sustainable users’ interconnectedness on online interactive platforms. Accordinly, platform managers may consider the addition of a platform feature that helps newcomers with growing their interconnectedness based on the interconnectedness analytics.

Keywords

sustainable e-business theory of network network correlation interconnectedness

References

  • R. Benbunan-Fich, M. Koufaris, “An Empirical Examination of the Sustainability of Social Bookmarking Websites”, Information Systems and e-Business Management, 8(2), 131–148, 2010.
  • D. K. Liou, W. H. Chih, L. C. Hsu, C. Y. Huang, “Investigating Information Sharing Behavior: The Mediating Roles of the Desire to Share Information in Virtual Communities”, Information Systems and e-Business Management, 14(2), 187–216, 2016.
  • B. K. Samanthula, W. Jiang, “Interest-driven Private Friend Recommendation”, Knowledge and Information Systems, 42(3), 663–687, 2015.
  • M. Chau, J. Xu, “Business Intelligence in Blogs: Understanding Consumer Interactions and Communities”, MIS Quarterly, 36(4), 1189–1216, 2012.
  • S. Kisilevich, C. S. Ang, M. Last, “Large-scale analysis of self-disclosure patterns among online social networks users: a Russian context”, Knowledge and information systems, 32(3), 609–628, 2012.
  • M. K. Foster, A. Francescucci, B. C. West, “Why Users Participate in Online Social Networks”, International Journal of e-Business Management, 4(1), 3–19, 2010.
  • C. Ridings, M. Wasko, “Online Discussion Group Sustainability: Investigating the Interplay Between Structural Dynamics and Social Dynamics Over Time”, Journal of the Association for Information Systems, 11(2), 95–121, 2010.
  • A. Iriberri, G. Leroy, “A Life-cycle Perspective on Online Community Success”, ACM Computing Surveys (CSUR), 41(2), 11, 1–29, 2009.
  • H. Chen, R. H. Chiang, V. C. Storey, “Business Intelligence and Analytics: From Big Data to Big Impact”, MIS Quarterly, 36(4), 1165–1188, 2012.
  • T. W. Wang, Y. Verbitskiy, W. Yeoh, “Depicting Data Quality Issues in Business Intelligence Environment through a Metadata Framework”, International Journal of Business Intelligence Research (IJBIR), 7(2), 20–31, 2016.
  • D. J. Watts, S. H. Strogatz, “Collective Dynamics of ‘small-world’ Networks”, Nature, 393(6684), 440–442, 1998.
  • M. N. Aydin, N. Z. Perdahci, “Dynamic Network Analysis of Online Interactive Platform”, Inf Syst Front, 21(2), 229–240, 2019.
  • G. F. Khan, “Social Media Analytics”, Social Media for Government, Springer, Singapore, https://doi.org/10.1007/978-981-10-2942-4_6, 2017.
  • F. D. Malliaros, V. Megalooikonomou, C. Faloutsos, “Estimating Robustness in Large Social Graphs”, Knowledge and Information Systems, 45(3), 645–678, 2015.
  • J. Cao, K. A. Basoglu, H. Sheng, P. B. Lowry, “A Systematic Review of Social Networking Research in Information Systems”, Communications of the Association for Information Systems, 36(37), 727–758, 2015.
  • A. Vespignani, “Twenty Years of Network Science”, Nature, 558, 528–529, 2018.
  • S. Milgram, “The Small World Problem”, Psychology Today, 2(1), 60–67, 1967.
  • S. Wasserman, Social Network Analysis: Methods and Applications, Vol. 8, Cambridge University press, 1994.
  • L. Freeman, The Development of Social Network Analysis. A Study in the Sociology of Science, BookSurge, LLC North Charleston, A.B.D., 2004.
  • S. P. Borgatti, D. S. Halgin, “On Network Theory”, Organization Science, 22(5), 1168–1181, 2011.
  • A. L. Barabási, Network Science, Cambridge University Press, Cambridge, B.K., 2016.
  • T. van Mierlo, “The 1% rule in four digital health social networks: An observational study”, Journal of medical Internet research, 16(2), 2014
  • A. L. Barabási, R. Albert, “Emergence of Scaling in Random Networks”, Science, 286(5439), 509–512, 1999.
  • P. Erdős, A. Rényi, “On the Evolution of Random Graphs”, Publ. Math. Inst. Hung. Acad. Sci, 5(1), 17–60, 1960.
  • M., McPherson, L. Smith-Lovin, J. M. Cook, “Birds of a Feather: Homophily in Social Networks”, Annual Review of Sociology, 27(1), 415–444, 2001.
  • K. Zhang, D. Lo, E. P. Lim, P. K. Prasetyo, “Mining Indirect Antagonistic Communities from Social Interactions”, Knowledge and Information Systems, 35(3), 553–583, 2013.
  • M. E. Newman, “Assortative Mixing in Networks”, Physical Review Letters, 89(20), 208701-1–208701-4, 2002.
  • M. E. Newman, “Mixing Patterns in Networks”, Physical Review E, 67(2), 026126-1–026126-13, 2003.
  • A. L. Barabási, “Scale-free Networks: A Decade and Beyond”, Science, 325(5939), 412–413, 2009.
  • M. N. Aydin, N. Z. Perdahci, “Network Analysis of an Interactive Health Network”, Journal of Internet Social Networking & Virtual Communities, 2016(2016), 1–17, 2016.
  • J. Leskovec, J. Kleinberg, C. Faloutsos, “Graph Evolution: Densification and Shrinking Diameters”, ACM Transactions on Knowledge Discovery from Data (TKDD), Cilt: 1, Editör: Jiawei Han, ACM New York, NY, A.B.D., 1–41, 2007.
  • G. T. Bosslet, A. M. Torke, S. E. Hickman, C. L. Terry, P. R. Helft, “The Patient–doctor Relationship and Online Social Networks: Results of a National Survey”, Journal of General Internal Medicine, 26(10), 1168–1174, 2011.
  • R. Kumar, J. Novak, A. Tomkins, “Structure and Evolution of Online Social Networks.”, Link mining: models, algorithms, and applications, Cilt: 1, Editör: P. Yu, J. Han, C. Faloutsos, Springer, New York, NY, A.B.D., 337–357, 2010.
  • G. Ünel, “Information Passing in Heathcare Socical Networks”, Bilişim Teknolojileri Dergisi, 10(1), 99–104., 2017
  • J. Howison, A. Wiggins, K. Crowston, “Validity Issues in the Use of Social Network Analysis with Digital Trace Data”, Journal of the Association for Information Systems, 12(12), 767–797, 2011.
  • G. Csardi, T. Nepusz, “The iGraph Software Package for Complex Network Research”, Inter Journal, Complex Systems, 1695(5), 1–9, 2006
  • D. A. Schult, P. Swart, “Exploring Network Structure, Dynamics, and Function Using NetworkX”, The 7th Python in Science Conferences (SciPy 2008), Pasadena, Kaliforniya, 11–16, 19-24 Ağustos, 2008.
  • X. Chen, C.-Z. Yang, “Visualization of Social Networks”, Handbook of Social Network Technologies and Applications, Cilt 1, Editör: Furht, B., Springer, New York, NY, A.B.D., 585–610, 2010.
  • T. M. Fruchterman, E. M. Reingold, “Graph Drawing by Force‐Directed Placement.”, Software: Practice and experience, 21(11), 1129–1164, 1991
  • G. C. Kane, M. Alavi, G. Labianca, S. P. Borgatti, “What’s Different About Social Media Networks? A Framework and Research Agenda”, MIS Quarterly, 38(1), 275–304, 2014
  • V. Ö. Budak, E. Kartal, S. Gülseçen, “Site-içi Aramalar ve Apriori Algoritması Kullanılarak Web Sitesi Ziyaretçilerinin İhtiyaç Tespitine Yönelik Bir Örnek Olay İncelemesi”, Bilişim Teknolojileri Dergisi, 11(2), 211-222, 2018.
  • S. Koushik, J. Birkinshaw, S. Crainer, “Using Web 2.0 to Create Management 2.0”, Business Strategy Review, 20(2), 20–23, 2009
  • İ. Birol, A. Hacinliyan, “Approximately Conserved Quantity in the Hénon-Heiles Problem”, Physical Review E, 52(5), 4750–4753, 1995
  • N. Z., Perdahçı, A. Hacınlıyan, “Normal Forms and Nonlocal Chaotic Behavior in Sprott Systems”, International Journal of Engineering Science, 41(10), 1085-1108, 2003
  • H. B. Hu, X. F. Wang, “Disassortative Mixing in Online Social Networks”, EPL (Europhysics Letters), 86(1), 18003–18009, 2009
  • L. Peel, D. B. Larremore, A. Clauset, “The Ground Truth About Metadata and Community Detection in Networks”, Science Advances, 3(5), e1602548, 1–8, 2017
  • S. Stieglitz, L. Dang-Xuan, A. Bruns, C. Neuberger, “Social Media Analytics”, Bus Inf Syst Eng, 6(2), 89–96, 2014
  • B. Karaöz, U. T. Gürsoy, “Adaptif Öğrenme Sözlüğü Temelli Duygu Analiz Algoritması Önerisi”, Bilişim Teknolojileri Dergisi, 11(3), 245–253, 2018.
There are 48 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Articles
Authors

Ziya Nazım Perdahçı

Mehmet Nafiz Aydın This is me 0000-0002-3995-6566

Publication Date April 30, 2020
Submission Date November 15, 2019
Published in Issue Year 2020 Volume: 13 Issue: 2

Cite

APA Perdahçı, Z. N., & Aydın, M. N. (2020). Using the Network-Based Theory for Monitoring Sustainable Interconnectedness on e-Business. Bilişim Teknolojileri Dergisi, 13(2), 145-155. https://doi.org/10.17671/gazibtd.647396
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