Araştırma Makalesi
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Estimation With Artificial Neural Network on Electronic Word of Mouth: Opinion Searching

Yıl 2020, Cilt 18, Sayı 35, 183 - 207, 31.01.2020
https://doi.org/10.35408/comuybd.604645

Öz

Today's consumers see social media as a reliable source of information and make electronic word-of-mouth (e-WOM) on this platform by talking about products and services. In social media, e-WOM is used in three different ways: “opinions searching”(being the most common), “opinion giving”, and “opinion forwarding”. Identifying factors that motivate consumers for opinion searching can make a significant contribution to the achievement of marketing objectives of corporations. For this reason, e-WOM has been discussed in the recent literature with various motivation factors and analysis methods. This study differs from other research by combining motivation factors and detailing e-WOM behavior as well as using artificial neural networks. Facebook, most widely used social media site, was used for this study. Motivation factors were estimated by artificial neural network method. Bayesian Regulation method was used for the analysis. As a result showed that the performance values were acceptable and the success rate was 90%.

Kaynakça

  • Anderson, E. W. (1998) ‘Customer Satisfaction and Word of Mouth’, Journal of Service Research, pp. 5–17. doi: 10.1177/109467059800100102.
  • Aşkın, D., İskender, İ. and Mamızadeh, A. (2011) ‘Farklı Yapay Sinir Ağları Yöntemlerini Kullanarak Kuru Tip Transformatör Sargısının Termal Analizi’, 26(4), pp. 905–913.
  • Atsalakis, G. S., Atsalaki, I. G. and Zopounidis, C. (2018) ‘Forecasting the success of a new tourism service by a neuro-fuzzy technique’, European Journal of Operational Research. Elsevier B.V., 268(2), pp. 716–727. doi: 10.1016/j.ejor.2018.01.044.
  • Bauer, H. H., Mark, G. and Mark, L. (2002) ‘Customer Relations Through the Internet, Journal of Relationship Marketing’, Journal of Relationship Marketing, 1(2), pp. 39–55. doi: 10.1300/J366v01n02.
  • Chu, S. C. and Kim, Y. (2011) ‘Determinants of consumer engagement in electronic Word-Of-Mouth (eWOM) in social networking sites’, International Journal of Advertising, 30(1). doi: 10.2501/IJA-30-1-047-075.
  • Çuhadar, M. and Kayacan, C. (2005) ‘Yapay Sinir Ağları Kullanılarak Konaklama İşletmelerinde Doluluk Oranı Tahmini : Türkiye ’ deki Konaklama İşletmeleri Üzerine Bir Deneme’, pp. 24–30.
  • Cvijikj, I. P. and Michahelles, F. (2013) ‘Online engagement factors on Facebook brand pages’, Social Network Analysis and Mining, 3(4), pp. 843–861. doi: 10.1007/s13278-013-0098-8.
  • Dasgupta, C. G., Dispensa, G. S. and Ghose, S. (1994) ‘Comparing the predictive performance of a neural network model with some traditional market response models’, International Journal of Forecasting, 10(2), pp. 235–244. doi: 10.1016/0169-2070(94)90004-3.
  • Dichter, E. (1966) ‘How word-of-mouth advertising works’, Harvard business review, 44(6), pp. 147–160.
  • Douglas R., P. and Terry G., V. (2004) ‘Controlling the grapevine: how to measure and manage word-of-mouth’, Marketing management, 13(4), pp. 24–30. Available at: http://eprints.lancs.ac.uk/29662/.
  • Efe, M. Ö. and Kaynak, O. (2004) Yapay Sinir Ağları ve Uygulamaları. Bo{\u{g}}aziçi Üniversitesi.Ellison, N. B. and Boyd, D. (2007) ‘Social Network Sites: Definition, History, and Scholarship’, Journal of Computer-Mediated Communication, pp. 210–230. doi: 10.1111/j.1083-6101.2007.00393.x.
  • Fish, K. E., Barnes, J. H. and Aiken, M. W. (1995) ‘A New Methodology for Industrial Market Segmentation’, Industrial Marketing Management, 8501(95), pp. 431–438. doi: 10.1038/ncomms2499.
  • Fuchs, C. et al. (2010) ‘Theoretical Foundations of the Web: Cognition, Communication, and Co-Operation. Towards an Understanding of Web 1.0, 2.0, 3.0’, Future Internet, 2(1), pp. 41–59. doi: 10.3390/fi2010041.
  • Funk, T. (2010) Advanced social media marketing: How to lead, launch, and manage a successful social media program, Press.
  • Garson, G. D. (1991) ‘Interpreting neural-network connection weights’, AI expert. Miller Freeman, Inc., 6(4), pp. 46–51.
  • Hoffman, D. L. and Novak, T. P. (1996) ‘Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations’, Journal of Marketing, 60(3), p. 50. doi: 10.2307/1251841.
  • Jayalakshmi, T. and Santhakumaran, A. (2011) ‘Statistical Normalization and Back Propagationfor Classification’, International Journal of Computer Theory and Engineering, 3(1), pp. 89–93. doi: 10.7763/IJCTE.2011.V3.288.
  • Kallas, P. (2013) Top 10 Social Networking Sites by Market Share of Visits [August 2012], Dreamgrow. Available at: http://www.dreamgrow.com/top-10-social-networking-sites-by-market-share-of-visits-august-2012/.
  • Kalpaklioglu, N. U. and Toros, N. (2011) ‘Viral Marketing Tecniques Within Online Social Network’, Journal of Yasar University, 6(24), pp. 112–129. Available at: http://web.b.ebscohost.com/ehost/detail/detail?sid=cfd12124-d638-4a2f-82c9-bb06f26bd9e6%40sessionmgr120&vid=0&hid=125&bdata=Jmxhbmc9ZXMmc2l0ZT1laG9zdC1saXZl#AN=69910047&db=a9h.
  • Kaplan, A. M. and Haenlein, M. (2010) ‘Users of the world, unite! The challenges and opportunities of Social Media’, Business Horizons, 53(1), pp. 59–68. doi: 10.1016/j.bushor.2009.09.003.
  • Kemp, S. (2016) Digital in 2016. Available at: http://wearesocial.com/sg/special-reports/digital-2016%5Cnhttp://www.slideshare.net/wearesocialsg/digital-in-2016.
  • Kietzmann, J. H. et al. (2011) ‘Social media? Get serious! Understanding the functional building blocks of social media’, Business Horizons. ‘Kelley School of Business, Indiana University’, 54(3), pp. 241–251. doi: 10.1016/j.bushor.2011.01.005.
  • King, M. A., Abrahams, A. S. and Ragsdale, C. T. (2014) ‘Ensemble methods for advanced skier days prediction’, Expert Systems with Applications. Elsevier Ltd, 41(4 PART 1), pp. 1176–1188. doi: 10.1016/j.eswa.2013.08.002.
  • MacKay, D. J. C. (1992) ‘A Practical Bayesian Framework for Backpropagation Networks’, EFSA Journal, 4, pp. 448–472. doi: 10.2903/j.efsa.2018.5430.
  • Mangold, W. G. and Faulds, D. J. (2009) ‘Social media: The new hybrid element of the promotion mix’, Business Horizons, 52(4), pp. 357–365. doi: 10.1016/j.bushor.2009.03.002.
  • Moon, Y. B. and Janowski, R. (1993) ‘A neural network approach for smoothing and categorizing noisy data’, Micron, 67, pp. 81–89. doi: 10.1016/j.micron.2014.06.009.
  • Nielsen (2013) Under The Influence: Consumer Trust in Advertising. Available at: www.nielsen.com/us/en/insights/news/2013/under-the-influence-consumer-trust-in-advertising.html.
  • Park, M. (2018) ‘Facebook - Facebook Reports First Quarter 2018 Results’, Facebook Investor Relations, pp. 1–11. doi: 10.1080/15389580290129062.
  • Payal, A., Rai, C. S. and Reddy, B. V. R. (2015) ‘Analysis of Some Feedforward Artificial Neural Network Training Algorithms for Developing Localization Framework in Wireless Sensor Networks’, Wireless Personal Communications. Springer US, 82(4), pp. 2519–2536. doi: 10.1007/s11277-015-2362-x.
  • Smith, K. A. and Gupta, J. N. D. (2000) ‘Neural networks in business: Techniques and applications for the operations researcher’, Computers and Operations Research, 27(11–12), pp. 1023–1044. doi: 10.1016/j.jplph.2014.08.017.
  • Ticknor, J. L. (2013) ‘A Bayesian regularized artificial neural network for stock market forecasting’, Expert Systems with Applications. Elsevier Ltd, 40(14), pp. 5501–5506. doi: 10.1016/j.eswa.2013.04.013.
  • Torlak, Ö. and Ay, U. (2012) ‘Facebook ’ ta Bulunma Amacı ve Facebook Reklamlarına Duyulan İlgi Arasındaki İlişki The Relationship Between Purpose of Using Facebook and Interest with Facebook Ads Öz Giriş’, pp. 83–94.
  • Vellido, A. (1999) ‘Neural networks in business: a survey of applications (1992–1998)’, Expert Systems with Applications, 17(1), pp. 51–70. doi: 10.1016/S0957-4174(99)00016-0.
  • De Vries, L., Gensler, S. and Leeflang, P. S. H. (2012) ‘Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing’, Journal of Interactive Marketing, 26(2), pp. 83–91. doi: 10.1016/j.intmar.2012.01.003.
  • Wong, B. K., Bodnovich, T. A. and Selvi, Y. (1996) ‘Neural network applications in business: A review and analysis of the literature (1988-95)’, 19, pp. 301–320. doi: 10.1016/S0167-9236(96)00070-X.
  • Yadav, M. S. et al. (2013) ‘Social commerce: A contingency framework for assessing marketing potential’, Journal of Interactive Marketing. Elsevier B.V., 27(4), pp. 311–323. doi: 10.1016/j.intmar.2013.09.001.
  • Zahavi, J. and Levin, N. (1995) ‘Issues and Problems in Applying Neural Computing to Target Marketing’, 9(3), pp. 33–45.
  • Zhang, H. C. and Huang, S. H. (1995) ‘Applications of neural networks in manufacturing: a state-of-the-art survey’, The Internatioal Journal of Production Research, 33(3), pp. 705–728.
  • Zhang, Z. (2018) Multivariate Time Series Analysis in Climate and Environmental Research. doi: 10.1007/978-3-319-67340-0.

Estimation With Artificial Neural Network on Electronic Word of Mouth: Opinion Searching

Yıl 2020, Cilt 18, Sayı 35, 183 - 207, 31.01.2020
https://doi.org/10.35408/comuybd.604645

Öz

Günümüz tüketicileri sosyal medyayı güvenilir bir bilgi kaynağı olarak görmekte ve bu platformda ürün ve hizmetler hakkında konuşarak elektronik ağızdan ağıza iletişim (E-AAİ) gerçekleştirmektedirler. Tüketiciler sosyal medyada e-AAİ’yi “görüş arama”, “görüş iletme” ve “görüş belirtme” olarak üç farklı şekilde yapmaktadırlar. Bu davranışlardan en yaygın olanı ise görüş aramadır. Tüketicileri görüş aramaya motive eden faktörlerin belirlenmesi işletmelerin pazarlama amaçlarına ulaşılmasına önemli katkı sağlayabilir. Bu nedenle E-AAİ, literatürde çeşitli motivasyon faktörleri ve analiz yöntemleriyle ele alınmıştır. Bu çalışma motivasyon faktörlerini bir araya getirmesi, e-aai davranışını detaylandırması ve yapay sinir ağlarını kullanması ile diğerlerinden farklılaşmaktadır. Çalışma en yaygın kullanıma sahip sosyal medya sitesi Facebook temelinde yapılmıştır.  Motivasyon faktörleri yapay sinir ağları yöntemiyle tahmin edilmeye çalışılmıştır. Bayesian regülasyon geri yayma ile analizi yapılmıştır. Analizler neticesinde performans değerlerinin kabul edilebilir değerde ve başarı oranının %90 olduğu görülmüştür. 

Kaynakça

  • Anderson, E. W. (1998) ‘Customer Satisfaction and Word of Mouth’, Journal of Service Research, pp. 5–17. doi: 10.1177/109467059800100102.
  • Aşkın, D., İskender, İ. and Mamızadeh, A. (2011) ‘Farklı Yapay Sinir Ağları Yöntemlerini Kullanarak Kuru Tip Transformatör Sargısının Termal Analizi’, 26(4), pp. 905–913.
  • Atsalakis, G. S., Atsalaki, I. G. and Zopounidis, C. (2018) ‘Forecasting the success of a new tourism service by a neuro-fuzzy technique’, European Journal of Operational Research. Elsevier B.V., 268(2), pp. 716–727. doi: 10.1016/j.ejor.2018.01.044.
  • Bauer, H. H., Mark, G. and Mark, L. (2002) ‘Customer Relations Through the Internet, Journal of Relationship Marketing’, Journal of Relationship Marketing, 1(2), pp. 39–55. doi: 10.1300/J366v01n02.
  • Chu, S. C. and Kim, Y. (2011) ‘Determinants of consumer engagement in electronic Word-Of-Mouth (eWOM) in social networking sites’, International Journal of Advertising, 30(1). doi: 10.2501/IJA-30-1-047-075.
  • Çuhadar, M. and Kayacan, C. (2005) ‘Yapay Sinir Ağları Kullanılarak Konaklama İşletmelerinde Doluluk Oranı Tahmini : Türkiye ’ deki Konaklama İşletmeleri Üzerine Bir Deneme’, pp. 24–30.
  • Cvijikj, I. P. and Michahelles, F. (2013) ‘Online engagement factors on Facebook brand pages’, Social Network Analysis and Mining, 3(4), pp. 843–861. doi: 10.1007/s13278-013-0098-8.
  • Dasgupta, C. G., Dispensa, G. S. and Ghose, S. (1994) ‘Comparing the predictive performance of a neural network model with some traditional market response models’, International Journal of Forecasting, 10(2), pp. 235–244. doi: 10.1016/0169-2070(94)90004-3.
  • Dichter, E. (1966) ‘How word-of-mouth advertising works’, Harvard business review, 44(6), pp. 147–160.
  • Douglas R., P. and Terry G., V. (2004) ‘Controlling the grapevine: how to measure and manage word-of-mouth’, Marketing management, 13(4), pp. 24–30. Available at: http://eprints.lancs.ac.uk/29662/.
  • Efe, M. Ö. and Kaynak, O. (2004) Yapay Sinir Ağları ve Uygulamaları. Bo{\u{g}}aziçi Üniversitesi.Ellison, N. B. and Boyd, D. (2007) ‘Social Network Sites: Definition, History, and Scholarship’, Journal of Computer-Mediated Communication, pp. 210–230. doi: 10.1111/j.1083-6101.2007.00393.x.
  • Fish, K. E., Barnes, J. H. and Aiken, M. W. (1995) ‘A New Methodology for Industrial Market Segmentation’, Industrial Marketing Management, 8501(95), pp. 431–438. doi: 10.1038/ncomms2499.
  • Fuchs, C. et al. (2010) ‘Theoretical Foundations of the Web: Cognition, Communication, and Co-Operation. Towards an Understanding of Web 1.0, 2.0, 3.0’, Future Internet, 2(1), pp. 41–59. doi: 10.3390/fi2010041.
  • Funk, T. (2010) Advanced social media marketing: How to lead, launch, and manage a successful social media program, Press.
  • Garson, G. D. (1991) ‘Interpreting neural-network connection weights’, AI expert. Miller Freeman, Inc., 6(4), pp. 46–51.
  • Hoffman, D. L. and Novak, T. P. (1996) ‘Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations’, Journal of Marketing, 60(3), p. 50. doi: 10.2307/1251841.
  • Jayalakshmi, T. and Santhakumaran, A. (2011) ‘Statistical Normalization and Back Propagationfor Classification’, International Journal of Computer Theory and Engineering, 3(1), pp. 89–93. doi: 10.7763/IJCTE.2011.V3.288.
  • Kallas, P. (2013) Top 10 Social Networking Sites by Market Share of Visits [August 2012], Dreamgrow. Available at: http://www.dreamgrow.com/top-10-social-networking-sites-by-market-share-of-visits-august-2012/.
  • Kalpaklioglu, N. U. and Toros, N. (2011) ‘Viral Marketing Tecniques Within Online Social Network’, Journal of Yasar University, 6(24), pp. 112–129. Available at: http://web.b.ebscohost.com/ehost/detail/detail?sid=cfd12124-d638-4a2f-82c9-bb06f26bd9e6%40sessionmgr120&vid=0&hid=125&bdata=Jmxhbmc9ZXMmc2l0ZT1laG9zdC1saXZl#AN=69910047&db=a9h.
  • Kaplan, A. M. and Haenlein, M. (2010) ‘Users of the world, unite! The challenges and opportunities of Social Media’, Business Horizons, 53(1), pp. 59–68. doi: 10.1016/j.bushor.2009.09.003.
  • Kemp, S. (2016) Digital in 2016. Available at: http://wearesocial.com/sg/special-reports/digital-2016%5Cnhttp://www.slideshare.net/wearesocialsg/digital-in-2016.
  • Kietzmann, J. H. et al. (2011) ‘Social media? Get serious! Understanding the functional building blocks of social media’, Business Horizons. ‘Kelley School of Business, Indiana University’, 54(3), pp. 241–251. doi: 10.1016/j.bushor.2011.01.005.
  • King, M. A., Abrahams, A. S. and Ragsdale, C. T. (2014) ‘Ensemble methods for advanced skier days prediction’, Expert Systems with Applications. Elsevier Ltd, 41(4 PART 1), pp. 1176–1188. doi: 10.1016/j.eswa.2013.08.002.
  • MacKay, D. J. C. (1992) ‘A Practical Bayesian Framework for Backpropagation Networks’, EFSA Journal, 4, pp. 448–472. doi: 10.2903/j.efsa.2018.5430.
  • Mangold, W. G. and Faulds, D. J. (2009) ‘Social media: The new hybrid element of the promotion mix’, Business Horizons, 52(4), pp. 357–365. doi: 10.1016/j.bushor.2009.03.002.
  • Moon, Y. B. and Janowski, R. (1993) ‘A neural network approach for smoothing and categorizing noisy data’, Micron, 67, pp. 81–89. doi: 10.1016/j.micron.2014.06.009.
  • Nielsen (2013) Under The Influence: Consumer Trust in Advertising. Available at: www.nielsen.com/us/en/insights/news/2013/under-the-influence-consumer-trust-in-advertising.html.
  • Park, M. (2018) ‘Facebook - Facebook Reports First Quarter 2018 Results’, Facebook Investor Relations, pp. 1–11. doi: 10.1080/15389580290129062.
  • Payal, A., Rai, C. S. and Reddy, B. V. R. (2015) ‘Analysis of Some Feedforward Artificial Neural Network Training Algorithms for Developing Localization Framework in Wireless Sensor Networks’, Wireless Personal Communications. Springer US, 82(4), pp. 2519–2536. doi: 10.1007/s11277-015-2362-x.
  • Smith, K. A. and Gupta, J. N. D. (2000) ‘Neural networks in business: Techniques and applications for the operations researcher’, Computers and Operations Research, 27(11–12), pp. 1023–1044. doi: 10.1016/j.jplph.2014.08.017.
  • Ticknor, J. L. (2013) ‘A Bayesian regularized artificial neural network for stock market forecasting’, Expert Systems with Applications. Elsevier Ltd, 40(14), pp. 5501–5506. doi: 10.1016/j.eswa.2013.04.013.
  • Torlak, Ö. and Ay, U. (2012) ‘Facebook ’ ta Bulunma Amacı ve Facebook Reklamlarına Duyulan İlgi Arasındaki İlişki The Relationship Between Purpose of Using Facebook and Interest with Facebook Ads Öz Giriş’, pp. 83–94.
  • Vellido, A. (1999) ‘Neural networks in business: a survey of applications (1992–1998)’, Expert Systems with Applications, 17(1), pp. 51–70. doi: 10.1016/S0957-4174(99)00016-0.
  • De Vries, L., Gensler, S. and Leeflang, P. S. H. (2012) ‘Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing’, Journal of Interactive Marketing, 26(2), pp. 83–91. doi: 10.1016/j.intmar.2012.01.003.
  • Wong, B. K., Bodnovich, T. A. and Selvi, Y. (1996) ‘Neural network applications in business: A review and analysis of the literature (1988-95)’, 19, pp. 301–320. doi: 10.1016/S0167-9236(96)00070-X.
  • Yadav, M. S. et al. (2013) ‘Social commerce: A contingency framework for assessing marketing potential’, Journal of Interactive Marketing. Elsevier B.V., 27(4), pp. 311–323. doi: 10.1016/j.intmar.2013.09.001.
  • Zahavi, J. and Levin, N. (1995) ‘Issues and Problems in Applying Neural Computing to Target Marketing’, 9(3), pp. 33–45.
  • Zhang, H. C. and Huang, S. H. (1995) ‘Applications of neural networks in manufacturing: a state-of-the-art survey’, The Internatioal Journal of Production Research, 33(3), pp. 705–728.
  • Zhang, Z. (2018) Multivariate Time Series Analysis in Climate and Environmental Research. doi: 10.1007/978-3-319-67340-0.

Ayrıntılar

Birincil Dil İngilizce
Konular Sosyal
Bölüm Araştırma Makalesi
Yazarlar

İbrahim TOPAL>
MİLLİ SAVUNMA ÜNİVERSİTESİ
0000-0002-7119-9470
Türkiye

Yayımlanma Tarihi 31 Ocak 2020
Başvuru Tarihi 9 Ağustos 2019
Yayınlandığı Sayı Yıl 2020, Cilt 18, Sayı 35

Kaynak Göster

APA Topal, İ. (2020). Estimation With Artificial Neural Network on Electronic Word of Mouth: Opinion Searching . Yönetim Bilimleri Dergisi , 18 (35) , 183-207 . DOI: 10.35408/comuybd.604645