101
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Two-stage hybrid sem-neural network approach and Van city residents’ perception of brand

, &
Pages 585-616 | Received 01 Sep 2020, Published online: 20 Sep 2021

References

  • Yuan, K. and P.M. Bentler, A Unified Approach to Multigroup Structural Equation Modeling With Nonstandard Samples, in New Developments and Techniques in Structural Equation Modeling G.A. Marcoulides and R.E. Schumacker, Editors. 2009, Lawrence Erlbaum Associates.: Mahwah, New Jersey London p. 35-57.
  • Wan, T.T.H., Evidence-Based Health Care Management: Multivariate Modeling Approaches. 2002, Boston Kluwer Academic Publishers.
  • Nachtigall, C., et al., (Why) Should We Use SEM? Pros and Cons of Structural Equation Modeling. Methods of Psychological Research Online, 2003. 8(22): p. 1-22.
  • Hew, J.-J., et al., Mobile social tourism shopping: A dual-stage analysis of a multi-mediation model. Tourism Management, 2018. 66: p. 121-139.
  • Schumacker, R.E. and R.G. Lomax, A Beginner’s Guide to Structural Equation Modeling. Lawrence Erlbaum Associates, Inc., Publishers, 2004: p. 1-498.
  • Scott, J.E. and S. Walczak, Cognitive engagement with a multimedia ERP training tool: Assessing computer self-efficacy and technology acceptance. Information & Management, 2009. 46(4): p. 221-232.
  • Kline, R.B., Principles and Practice of Structural Equation Modeling. 3th ed, ed. T.D. Little. 2011, New York: Guilford Press.
  • Leong, L.-Y., et al., Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications, 2013. 40(14): p. 5604-5620.
  • Tan, G.W.-H., et al., Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-Neural Networks approach. Computers in Human Behavior, 2014. 36: p. 198-213.
  • Sternad Zabukovšek, S., et al., SEM–ANN based research of factors’ impact on extended use of ERP systems. Central European Journal of Operations Research, 2018. 27(3): p. 703-735.
  • Chan, F.T.S. and A.Y.L. Chong, A SEM–neural network approach for understanding determinants of interorganizational system standard adoption and performances. Decision Support Systems, 2012. 54(1): p. 621-630.
  • Dumitru, C. and V. Maria, Advantages and Disadvantages of Using Neural Networks for Predictions. Ovidius University Annals, Economic Sciences Series, 2013. 13(1): p. 444-449.
  • Leong, L.-Y., et al., An SEM–artificial-neural-network analysis of the relationships between SERVPERF, customer satisfaction and loyalty among low-cost and full-service airline. Expert Systems with Applications, 2015. 42(19): p. 6620-6634.
  • Chong, A.Y.-L., A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Systems with Applications, 2013. 40(4): p. 1240-1247.
  • Hew, J.-J., et al., The age of mobile social commerce: An Artificial Neural Network analysis on its resistances. Technological Forecasting and Social Change, 2019. 144: p. 311-324.
  • Talukder, S., et al., A two-stage structural equation modeling-neural network approach for understanding and predicting the determinants of m-government service adoption. Journal of Systems and Information Technology, 2019. 21(4): p. 419-438.
  • Sharma, S.K., et al., Structural equation model (SEM)-neural network (NN) model for predicting quality determinants of e-learning management systems. Behaviour & Information Technology, 2017. 36(10): p. 1053-1066.
  • Basheer, I.A. and M. Hajmeer, Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 2000. 43: p. 3-31.
  • Gorr, W.L., D. Nagin, and J. Szczypula, Comparative study of artificial neural network and statistical models for predicting student grade point averages. International Journal of Forecasting, 1994. 10(1): p. 17-34.
  • Sharma, S.K., Integrating cognitive antecedents into TAM to explain mobile banking behavioral intention: A SEM-neural network modeling. Information Systems Frontiers, 2017.
  • Sohaib, O., et al., A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption. IEEE Access, 2020. 8: p. 13138-13150.
  • Paliwal, M. and U.A. Kumar, Neural networks and statistical techniques: A review of applications. Expert Systems with Applications, 2009. 36: p. 2-17.
  • Benitez, J.M., J.L. Castro, and I. Requena, Are Artificial Neural Networks Black Boxes? IEEE Transactions on Neural Networks 1997. 8(5): p. 1156-1164.
  • Garson, G.D., Neural Network: An Introductory Guide for Social Scientists. 1998, London: Sage Publications.
  • Hew, T.-S., et al., Predicting Drivers of Mobile Entertainment Adoption: A Two-Stage SEM-Artificial-Neural-Network Analysis. Journal of Computer Information Systems, 2016. 56(4): p. 352-370.
  • Gallinari, P., et al., On The Relations Between Discriminant Analysis and Multilayer Perceptrons. Neural Networks, 1991. 4(3): p. 349-360.
  • Cheng, B. and D.M. Titterington, Neural Networks: A Review from a Statistical Perspective. Statistical Science, 1994. 9(1): p. 2-30.
  • Detienne, K.B., D.H. Detienne, and S.A. Joshi, Neural Networks as Statistical Tools for Business Researchers. Organizational Research Methods, 2003. 6(2): p. 236-265.
  • Fukumizu, K., Statistical active learning in multilayer perceptrons. IEEE Trans Neural Netw, 2000. 11(1): p. 17-26.
  • Morris, S.A., et al., Prediction of CASE adoption: a neural network approach. Industrial Management & Data Systems, 2004. 104(2): p. 129-135.
  • Sharma, S.K., S.M. Govindaluri, and S.M. Al Balushi, Predicting determinants of Internet banking adoption. Management Research Review, 2015. 38(7): p. 750-766.
  • Liébana-Cabanillas, F., et al., Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach. Technological Forecasting and Social Change, 2018. 129: p. 117-130.
  • Sarle, W.S. Neural Networks and Statistical Models. in Nineteenth Annual SAS Users Group International Conference, . 1994. NC: SAS Institute.
  • Davies, F., et al., LISREL and neural network modelling: two comparison studies. Journal of Retailing and Consumer Services, 1999. 6: p. 249-261.
  • Hoyle, R.H., The structural equation modeling approach: Basic concepts and fundamental issues., in Structural equation modeling: Concepts, issues, and applications, R.H. Hoyle, Editor. 1995, Sage Publications. p. 1-15.
  • Bollen, K.A., Structural Equations with Latent Variables, K.A. Bollen, Editor. 1989, John Wiley & Sons. p. 1-9.
  • Hair, J.F., et al., Multivariate Data Analysis 7th ed. 2010: Prentice Hall, Upper Saddle River, New Jersey.
  • Bowen, N.K. and S. Guo, Introducion, in Structural Equation Modeling, N.K. Bowen and S. Guo, Editors. 2011, Oxford Scholarship Online: Oxford. p. 240.
  • Garson, G.D., Structural Equation Modeling. 2015: Statistical Associats Publishing.
  • Yuan, Q., Model design of influence of rural financial demand based on structural equation. Journal of Discrete Mathematical Sciences and Cryptography, 2018. 21(2): p. 271-276.
  • Thanoon, T.Y., R. Adnan, and M.A.B. Md Jedi, Model comparison of Bayesian structural equation models with mixed ordered categorical and dichotomous data. Journal of Statistics and Management Systems, 2017. 20(1): p. 113-131.
  • Hair, J.F., et al., A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 2014, Los Angeles,CA: SAGE Publications.
  • Wall, M.M. and Y. Amemiya, 15 Nonlinear Structural Equation Modeling as a Statistical Method, in Handbook of Latent Variable and Related Models, S.-Y. Lee, Editor. 2007, Elsevier: Amsterdam, Netherlands. p. 321-343.
  • Moosbrugger, H., et al., Testing Multiple Nonlinear Effects in Structural Equation Modeling: A Comparison of Alternative Estimation Approaches, in Structural Equation Modelling in Educational Research: Concepts and Applications, T. T. and K. M. S., Editors. 2009, Sense Publishers: Rotterdam, NL.
  • Lodder, P., et al., Modeling Interactions Between Latent Variables in Research on Type D Personality: A Monte Carlo Simulation and Clinical Study of Depression and Anxiety. Multivariate Behav Res, 2019. 54(5): p. 637-665.
  • Klein, A.G. and H. Moosbrugger, Maximum likelihood estimation of latent interaction effects with the LMS method. Psychometrika, 2000. 65: p. 457–474.
  • Klein, A.G. and B.O. Muthén, Quasi maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects. Multivariate Behavioral Research, 2007. 42: p. 647–673.
  • Jöreskog, K.G. and F. Yang, Nonlinear structural equation models: The Kenny-Judd model with interaction effects., in Advanced Structural Equation Modeling, M. G. and S. R., Editors. 1996, Lawrence Erlbaum Associates: Mahwah, NJ. p. 57-87.
  • Kenny, D.A. and C.M. Judd, Estimating the nonlinear and interactive effects of latent variables. Psychological Bulletin, 1984. 96: p. 201-210.
  • Bui, D.-K., et al., A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete. Construction and Building Materials, 2018. 180: p. 320-333.
  • Nguyen, T., et al., Deep neural network with high-order neuron for the prediction of foamed concrete strength. Computer-Aided Civil and Infrastructure Engineering, 2019. 34(4): p. 316-332.
  • Lippman, R.P., An introduction to Computing with Neural Nets. IEEE ASP Magazine, 1987. 4: p. 4-22.
  • Kohonen, T., An introduction to neural computing. Neural Networks, 1988. 1(1): p. 3-16.
  • Jain, A.K., M. Jianchang, and K.M. Mohiuddin, Artificial neural networks: a tutorial. Computer, 1996. 29(3): p. 31-44.
  • Yazıcı, A.C., et al., Yapay Sinir Ağlarına Genel Bakış. Turkiye Klinikleri J MedSci 2007. 27(1): p. 65-71.
  • Alwosheel, A., S. van Cranenburgh, and C.G. Chorus, Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis. Journal of Choice Modelling, 2018. 28: p. 167-182.
  • Kalogirou, S.A., Artificial intelligence for the modeling and control of combustion processes: a review. Progress in Energy and Combustion Science, 2003. 29(6): p. 515-566.
  • Karataş, C., A. Sozen, and E. Dulek, Modelling of residual stresses in the shot peened material C-1020 by artificial neural network. Expert Systems with Applications, 2009. 36(2): p. 3514-3521.
  • Neseli, S., S. Tasdemir, and S. Yaldız, Estimation of surface roughness on turning with Artificıal Neural Network. Journal of Engineering and Architecture Faculty of Eskisehir Osmangazi University, 2009. XXII(3): p. 65-75.
  • Fındık, T., Ş. Taşdemir, and I. Şahin, The use of artificial neural network for prediction of grain size of 17-4 pH stainless steel powders. Scientific Research and Essays, 2010. 5(11).
  • Askin, D., I. Iskender, and A. Mamizadehi, Dry type transformer winding thermal analysis using different neural network methods. Journal of the Faculty of Engineering and Architecture of Gazi University, 2011. 26(4): p. 905-913.
  • Chong, A.Y.-L. and R. Bai, Predicting open IOS adoption in SMEs: An integrated SEM-neural network approach. Expert Systems with Applications, 2014. 41(1): p. 221-229.
  • Ahani, A., N.Z.A. Rahim, and M. Nilashi, Forecasting social CRM adoption in SMEs: A combined SEM-neural network method. Computers in Human Behavior, 2017. 75: p. 560-578.
  • Hackl, P. and A.H. Westlund, On structural equation modelling for customer satisfaction measurement. Total Quality Management, 2000. 11(4-6): p. 820-825.
  • Asadi, S., et al., An Integrated SEM-Neural Network Approach for Predicting Determinants of Adoption of Wearable Healthcare Devices. Mobile Information Systems, 2019.
  • Kalinic, Z., et al., A multi-analytical approach to peer-to-peer mobile payment acceptance prediction. Journal of Retailing and Consumer Services, 2019. 49: p. 143-153.
  • Talukder, M.S., et al., Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach. Technological Forecasting and Social Change, 2020. 150.
  • Merrilees, B., D. Miller, and C. Herington, Antecedents of residents’ city brand attitudes. Journal of Business Research, 2009. 62(3): p. 362-367.
  • Jansson, J., et al., The image of the city: Urban branding as constructed capabilities in Nordic city Regions, J. Jansson and D. Power, Editors. 2006, Nordic Innovation Centre: Oslo. p. 40.
  • Insch, A. and M. Florek, Place satisfaction of city residents: Findings and implications for city branding, in Towards effective place brand management: Branding European cities and regions G. Ashworth and M. Kavaratzis, Editors. 2010, Edward Elgar: Cheltenham, UK. p. 191-204.
  • Insch, A., Branding the City as an Attractive Place to Live, in City Branding :Theory and Cases K. Dinnie, Editor. 2011, Palgrave Macmillan: New York. p. 8-14.
  • Dolnicar, S., K. Grabler, and J.A. Mazanec, Analyzing Destination Images: A Perceptual Charting Approach. Journal of Travel & Tourism Marketing, 2008. 8(4): p. 43-57.
  • Lin, C.-J., H.-F. Chen, and T.-S. Lee, Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines:Evidence from Taiwan. International Journal of Business Administration, International Journal of Business Administration, Sciedu Press, 2011. 2(2).
  • Silva, E.S., et al., Forecasting tourism demand with denoised neural networks. Annals of Tourism Research, 2019. 74: p. 134-154.
  • Wen, L., C. Liu, and H.Y. Song, Forecasting tourism demand using search query data: A hybrid modelling approach. Tourism Economics, 2019. 25(3): p. 309-329.
  • Ekinci, Y. and S. Hosany, Destination Personality: An Application of Brand Personality to Tourism Destinations. Journal of Travel Research, 2006. 45: p. 27-139
  • Kanibir, H., R. Saydan, and S. Nart, Şehir Pazarlamasında Marka Kişiliğinin Etkisi: Algılanan Marka Kişiliği-Turistlerin Tavsiye Etme Davranışı İlişkisi. Pazarlama ve Pazarlama Araştırmaları Dergisi 2010. 3: p. 53-84.
  • Yavuz, M.C., et al., Kars Algısı, İmajı ve Marka Kimliği Araştırması. 2014, Kars, Türkiye: Serhat Kalkınma Ajansı.
  • Sehribanoglu, S., et al., Kent, Kimlik ve İmaj : Van İli Örneği. YYU The Journal of Social Sciences Institute 2017. 4: p. 566-574.
  • Aaker, J.L., Dimensions of Brand Personality. Dimensions of Brand Personality, 1997. 34: p. 347-356.
  • Büyüköztürk, Ş., Faktör Analizi: Temel Kavramlar ve Ölçek Geliştirmede Kullanımı. Kuram ve Uygulamada Eğitim Yönetimi Dergisi 2002. 32: p. 470-483.
  • Bagozzi, R.P., Y. Yi, and L.W. Phillips, Assessing construct validity in organizational research. Administrative Science Quarterly, 1991. 36: p. 421-458.
  • Schermelleh-Engel, K., H. Moosbrugger, and H. Müler, Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. Methods of Psychological Research Online, 2003. 8(2): p. 23-74.
  • Bentler, P.M. and D.C. Bonnet, Significance Tests and Goodness of Fit in the Analysis of Covariance Structures. Psychological Bulletin, 1980. 88(3): p. 588-606.
  • Jöreskog, K. and D. Sörbom, LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language. Chicago, IL: Scientific Software International Inc. , 1993.
  • Hooper, D., J. Coughlan, and M.R. Mullen, Structural Equation Modelling: Guidelines for Determining Model Fit Electronic Journal of Business Research Methods, 2008. 6(1): p. 53-60.
  • Marsh, H.W. and D. Hocevar, Application of confirmatory factor analysis to the study of self-concept: First- and higher-order factor models and their invariance across groups. Psychological Bulletin, 1985. 97: p. 562-582.
  • TIHV. Curfews in Turkey Between the Dates 16 August 2015 – 1 January 2019. TIHV-Human Rights Foundation of Turkey 2015 [cited 2020 April,10]; Available from: https://en.tihv.org.tr/curfews-in-turkey-between-the-dates-16-august-2015-1-january-2019/.
  • AA. Anti-terror operation ends in Cizre, southeast Turkey. 2016 [cited 2020 April,10]; Available from: https://www.aa.com.tr/en/turkey/anti-terror-operation-ends-in-cizre-southeast-turkey/519705.
  • AA. Counter-terrorism action ends in Sur, southeast Turkey. 2016 [cited 2020 April,10]; Available from: https://www.aa.com.tr/en/tur-key/counter-terrorism-action-ends-in-sur-southeast-turkey/534397.
  • Kingsley, P. and G. Abdul-Ahad, Military coup attempted in Turkey against Erdoğan government. 2016, The Guardian: United Kingdom.
  • Taecharungroj, V., City ambassadorship and citizenship behaviours. Journal of Place Management and Development, 2016. 9(3): p. 331-350.
  • GuinnessWorldRecords. Largest full breakfast (attendance). 2014 [cited 2020 April,10]; Available from: https://www.guinnessworl-drecords.com/world-records/largest-full-breakfast-(attendance).
  • HürriyetDailyNews. Turkey’s morning meal capital Van sets Guinness world record for most crowded breakfast table. 2014 [cited 2020 April,10]; Available from: https://www.hurriyetdailynews.com/turkeys-morning-meal-capital-van-sets-guinness-world-record-for-most-crowded-breakfast-table-67242.
  • Westland, J.C., Frontiers in Latent Variable Analysis, in Structural equation models: From paths to networks. 2015, Springer International Publishing: Cham, Switzerland. p. 127-134.
  • Westland, J.C., Data Collection, Control, and Sample Size, in Structural equation models: From paths to networks. 2015, Springer International Publishing: Cham, Switzerland. p. 67-89.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.