ODREĐIVANJE RELATIVNOG UTICAJA POJEDINIH FAKTORA NA PRIHVATANJE MOBILNE TRGOVINE PRIMENOM NEURONSKIH MREŽA

  • Zoran S Kalinić Univerzitet u Kragujevcu, Ekonomski fakultet
  • Veljko Marinković Univerzitet u Kragujevcu, Ekonomski fakultet

Sažetak


Široka rasprostranjenost mobilnih uređaja dovela je razvoja niza aplikacija i usluga komercijalne prirode, i danas sve više ljudi koristi svoj mobilni telefon i za kupovinu robe i usluga ili mobilna plaćanja. Prilikom uvođenja svake nove tehnologije veoma je važno utvrditi koji su to faktori koji značajno utiču na odluku potrošača da počne da je koristi. U ovom radu izvršeno je određivanje relativnog uticaja faktora na prihvatanje mobilne trgovine u našoj zemlji. Pri tome su korišćeni prošireni TAM model i veštačke neuronske mreže, koje omogućavaju i modeliranje nelinearnih relacija između promenljivih. Kao najuticajniji faktor na nameru korišćenja mobilne trgovine identifikovana je njena korisnost, dok je kao najznačajniji uticajni faktor na korisnost identifikovana kastomizacija. Konačno, istraživanje je pokazalo da na percepciju jednostavnosti korišćenja mobilne trgovine od strane potrošača najveći uticaj ima faktor mobilnost, a zatim kastomizacija.

Biografije autora

Zoran S Kalinić, Univerzitet u Kragujevcu, Ekonomski fakultet

Docent

Veljko Marinković, Univerzitet u Kragujevcu, Ekonomski fakultet
vanredni profesor

Reference

Agarwal, R., Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, Vol. 9, No. 2, str. 204–215.

Alkhunaizan, A., Love, S. (2012). What drives mobile commerce? An empirical evaluation of the revised UTAUT model. International Journal of Management and Marketing Academy, Vol. 2, No. 1, str. 82–99.

Anderson, E. W., Fornell, C., Rust, R. T. (1997). Customer satisfaction, productivity, and profitability: differences between goods and services. Marketing Science, Vol. 16, No. 2, str. 129–145.

Bhatti, T. (2007). Exploring Factors Influencing the Adoption of Mobile Commerce. Journal of Internet Banking and Commerce, Vol. 12, No. 3, str. 1–13.

Chan, F. T. S., Chong A.Y. L. (2013). Analysis of the determinants of consumers’ m-commerce usage activities. Online Information Review, Vol. 37, No. 3, str. 443–461.

Cho, Y. C. (2008). Assessing User Attitudes toward Mobile Commerce in the U. S. vs. Korea: Implications for M-commerce CRM. Journal of Business & Economic Research, 6 (2), str. 91–102.

Chong, A. Y. L. (2013a). Predicting m-commerce adoption determinants: A neural network approach. Expert Systems with Applications, Vol. 40, No. 4, str. 1240–1247.

Chong, A. Y. L. (2013b). A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Systems with Applications, Vol. 40, No. 4, str. 1240–1247.

Chong, A.Y. L., Chan, F. T. S., Ooi, K-B. (2012). Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia. Decision Support Systems, 53 (1), str. 34–43.

Chong, A. Y. L., Liu, M. J., Luo, J., Ooi, K–B. (2015). Predicting RFID adoption in healthcare supply chain from the perspective of users, International Journal of Production Economics, Vol. 159, str. 66–75.

Citrin, A., Sprott, E., Silverman, N., Stem, E. (2000). Adoption of Internet shopping: The role of consumer innovativeness. Industrial Management & Data Systems, 100(7), str. 294–300.

Dai, H., Palvia, P. C. (2009). Mobile Commerce Adoption in China and the United States: A Cross-Cultural Study. The DATA BASE for Advances in Information Systems, Vol. 40. No. 4, str. 43–61.

Davis, F. D. (1989). Perceived usefulness, perceived ease-of-use, and user acceptance of information technologies. MIS Quarterly, 13 (3), str. 319–340.

Ecommerce Europe (2015). Global B2C Ecommerce light report 2015, Ecommerce Europe, Brussels. Preuzeto 06. 03. 2016. sa https://www.ecommerce-europe.eu/facts-figures/free-light-reports.

eMarketer (2015). Mobile commerce roundup, eMarketer Report, Preuzeto 06. 03. 2016. sa https://www.emarketer.com/public_media/docs/eMarketer_Mobile_Commerce_Roundup.pdf.

Eurostat (2016). Mobile communications – subscriptions and penetration, EUROSTAT Database, preuzeto 06. 03. 2016. sa http://ec.europa.eu/eurostat/data/database.

Gartner, (2016). Gartner Says Worldwide Smartphone Sales Grew 9.7 Percent in Fourth Quarter of 2015. Gartner Press Release. Preuzeto 06. 03. 2016. sa http://www.gartner.com/newsroom/id/3215217.

Goodhue, D. L., Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, Vol. 19, No. 2, str. 213–236.

Grgar, D., Radnović, B. (2012). Digitalni marketing u funkciji razvoja preduzetništva. Poslovna ekonomija, Godina VI, broj 2, vol. XI, str. 63–78.

Haykin, S. (2001). Neural networks: A comprehensive foundation. Englewood Cliffs, NJ: Prentice Hall.

Huang, J-C. (2010). Remote health monitoring adoption model based on artificial neural networks. Expert Systems with Applications, Vol. 37, str. 307–314.

International Telecommunication Union (2016). ICT Facts & Figures: the World in 2015, International Telecommunication Union, Geneva, Preuzeto 06. 03. 2016. sa http://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2015.pdf.

Autor, (2016). Determinants of users’ intention to adopt m-commerce: an empirical analysis. Information Systems and E-Business Management, Vol. 14, Issue 2, str. 367–387.

Kim, C., Mirusmonov, M., Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), str. 310–322.

Kuo, Y., Yen, S. (2009). Towards an understanding of behavior intention to use 3G mobile value-added services. Computers in Human Behavior, Vol. 25, No. 1, str. 103–110.

Leong, L-Y., Hew, T-S., Tan, G.W-H., Ooi, K. B. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural network approach. Expert Systems with Applications, Vol. 40, str. 5604–5620.

Liebana-Cabanillas, F.J., Sanchez-Fernandez, J., Munoz-Leiva, F. (2014). Role of gender on acceptance of mobile payment. Industrial Management & Data Systems, 114 (2), str. 220–240.

Lu, J. (2014). Are personal innovativeness and social influence critical to continue with mobile commerce? Internet Research, Vol. 24, No. 2, str. 134–159.

Mallat, N., Rossi, M., Tuunainen, V. K., Oorni, A. (2008). An empirical investigation of mobile ticketing service adoption in public transportation. Personal and Ubiquitous Computing, Vol. 12, No. 1, str. 57–65.

Mallat, N., Rossi, M., Tuunainen, V. K., Oorni, A. (2009). The impact of use context on mobile services acceptance: The case of mobile ticketing. Information & Management, Vol. 46, str. 190–195.

Morosan, C. (2014). Toward an integrated model of adoption of mobile phones for purchasing ancillary services in air travel. International Journal of Contemporary Hospitality Management, Vol. 26, No. 2, str. 246–271.

Negnevitsky, M. (2011). Artificial intelligence: a guide to intelligent systems, 3rd edition, Pearson Education, Essex, England.

Park, E., Kim, K. J. (2013). User acceptance of long-term evolution (LTE) services: An application of extended technology acceptance model. Program: electronic library and information systems, Vol. 47, No. 2, str. 188–205.

RATEL, (2015). Pregled tržišta telekomunikacija i poštanskih usluga u Republici Srbiji u 2014. godini, Regulatorna agencija za elektronske komunikacije i poštanske usluge, Preuzeto 01. 03. 2016. sa http://www.ratel.rs/upload/documents/Pregled_trzista/rate-pregled-trzista-za-2014-web.pdf.

Rogers, E. M. (1995). Diffusion of Innovations, Free Press, New York.

Schierz, P. G., Schilke, O., Wirtz, B. W. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications, Vol. 9, No. 3, str. 209–216.

Shih, Y-Y., Chen, C-Y. (2013). The study of behavioral intention for mobile commerce: via integrated model of TAM and TTF. Quality & Quantity, 47 (2), str. 1009–1020.

Sim, J. J., Tan, G. W-H., Wong, J. C. J., Ooi, K-B., Hew, T-S. (2014). Understanding and predicting the motivators of mobile music acceptance – A multi stage MRA-Artificial neural network approach, Telematics and Informatics, Vol. 31, str. 569–584.

Tan, G.W-H., Ooi, K-B., Leong, L-Y., Lin, B. (2014). Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-Neural Networks approach. Computers in Human Behavior, Vol. 36, str. 198–213.

Venkatesh, V., Morris, M. G., Davis, G. B., Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, Vol. 27, No. 3, str. 425–478.

Wang, W-T., Li, H-M. (2012). Factors influencing mobile services adoption: a brand-equity perspective. Internet Research, Vol. 22, No. 2, str. 142–179.

Wei, T. T., Marthandan, G., Chong, A. Y-L., Ooi, K-B., Arumugam, S. (2009). What drives Malaysian m-commerce? An empirical analysis, Industrial Management & Data Systems, 109 (3), str. 370–388.

Wong, Y. K., Hsu, C. J. (2008). A confidence-based framework for business to commerce (B2C) mobile commerce adoption. Personal and Ubiquitous Computing, 12 (1), str. 77–84.

Wu, J-H., Wang, S-C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management, 42 (5), str. 719–729.

Yao, J., Tan, C. L., Poh, H. L. (1999). Neural Networks for Technical Analysis: A Study on KLCI. International Journal of Theoretical and Applied Finance, Vol. 2, No. 2, str. 221–241.

Yeh, Y. S., Li, Y-M. (2009). Building trust in m-commerce: contributions from quality and satisfaction. Online Information Review, Vol. 33, No. 6, str. 1066–1086.

Zarmpou, T., Saprikis, V., Markos, A., Vlachopoulou, M. (2012). Modeling users’ acceptance of mobile services, Electronic Commerce Research, Vol. 12, No. 2, str. 225–248.

Zhang, L., Zhu, J., Liu Q. (2012). A meta-analysis of mobile commerce and the moderating effect of culture, Computers in Human Behavior, Vol. 28, No. 5, str. 1902–1911.

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