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Poslovna ekonomija
2016, vol. 10, br. 2, str. 206-223
jezik rada: srpski
vrsta rada: izvorni naučni članak
doi:10.5937/poseko10-12417
Određivanje relativnog uticaja pojedinih faktora na prihvatanje mobilne trgovine primenom neuronskih mreža
Univerzitet u Kragujevcu, Ekonomski fakultet

e-adresa: zkalinic@kg.ac.rs, vmarinkovic@kg.ac.rs

Projekat

Inteligentni sistemi za razvoj softverskih proizvoda i podršku poslovanja zasnovani na modelima (MPNTR - 44010)

Sažetak

Široka rasprostranjenost mobilnih uređaja dovela je do 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.

Ključne reči

mobilna trgovina; TAM model; neuronske mreže; društveni uticaj; inovativnost; kastomizacija; mobilnost; korisnost; jednostavnost upotrebe; namera korišćenja

Reference

Agarwal, R., Prasad, J. (1998) A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology. Information Systems Research, 9(2): 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, 16(2): 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. (2013) Analysis of the determinants of consumers' m‐commerce usage activities. Online Information Review, 37(3): 443-461
Cho, Y.C. (2011) Assessing User Attitudes Toward Mobile Commerce In The U.S. Vs. Korea: Implications For M-Commerce CRM. Journal of Business & Economics Research (JBER), 6 (2), str. 91-102
Chong, A.Y. (2013) Predicting m-commerce adoption determinants: A neural network approach. Expert Systems with Applications, 40(4): 1240-1247
Chong, A.Y. (2013) A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Systems with Applications, 40(4): 1240-1247
Chong, A.Y., Chan, F.T.S., Ooi, K. (2012) Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia. Decision Support Systems, 53(1): 34-43
Chong, A.Y., Liu, M.J., Luo, J., Keng-Boon, O. (2015) Predicting RFID adoption in healthcare supply chain from the perspectives of users. International Journal of Production Economics, 159: 66-75
Citrin, A.V., Sprott, D.E., Silverman, S.N., Stem, D.E. (2000) Adoption of Internet shopping: the role of consumer innovativeness. Industrial Management & Data Systems, 100(7): 294-300
Dai, H., Palvi, P.C. (2009) Mobile commerce adoption in China and the United States. ACM SIGMIS Database, 40(4): 43
Davis, F.D. (1989) Perceived ease of use and user acceptance of information technology. MIS quarterly, 13 Nov, str. 319-340
Ecommerce Europe (2015) Global B2C Ecommerce light report 2015. Brussels: Ecommerce Europe, Preuzeto 06. 03. sa https: //www. ecommerce-europe. eu/facts-figures/free-light-reports
eMarketer (2015) Mobile commerce roundup: eMarketer Report. Preuzeto 06. 03. 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. 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. 3. sa http://www.gartner.com/newsroom/id/3215217
Goodhue, D., Thompson, R. (1995) Task-technology fit and individual performance. MIS Quarterly, vol. 19, br. 2, str. 213-236
Grgar, D., Radnović, B. (2012) Digitalni marketing u funkciji razvoja preduzetništva. Poslovna ekonomija, vol. 6, br. 2, str. 63-78
Haykin, S.S. (2001) Neural networks: A comprehensive foundation. Englewood Cliffs, NJ, itd: Prentice Hall
Huang, J. (2010) Remote health monitoring adoption model based on artificial neural networks. Expert Systems with Applications, 37(1): 307-314
International Telecommunication Union (2016) ICT Facts & Figures: The World in 2015. Geneva, Preuzeto 06. 03. sa http://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2015.pdf
Kalinic, Z., Marinkovic, V. (2015) Determinants of users’ intention to adopt m-commerce: an empirical analysis. Information Systems and e-Business Management, 14(2): 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): 310-322
Kuo, Y., Yen, S. (2009) Towards an understanding of the behavioral intention to use 3G mobile value-added services. Computers in Human Behavior, 25(1): 103-110
Leong, L., Hew, T., Tan, G.W., Ooi, K. (2013) Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications, 40(14): 5604-5620
Liébana-Cabanillas, F.J., Sánchez-Fernández, J., Muñoz-Leiva, F. (2014) Role of gender on acceptance of mobile payment. Industrial Management & Data Systems, 114(2): 220-240
Lu, J. (2014) Are personal innovativeness and social influence critical to continue with mobile commerce?. Internet Research, 24(2): 134-159
Mallat, N., Rossi, M., Tuunainen, V.K., Öörni, A. (2008) An empirical investigation of mobile ticketing service adoption in public transportation. Personal and Ubiquitous Computing, 12 (1), 57-65
Mallat, N., Rossi, M., Tuunainen, V.K., Öörni, A. (2009) The impact of use context on mobile services acceptance: The case of mobile ticketing. Information & Management, 46(3): 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, 26(2): 246-271
Negnevitsky, M. (2011) Artificial intelligence: a guide to intelligent systems. Essex, England: Pearson Education, 3rd edition
Park, E., Kim, K.J. (2013) User acceptance of long‐term evolution (LTE) services. Program, 47(2): 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. sa http://www.ratel.rs/upload/documents/Pregled_trzista/rate-pregled-trzista-za-2014-web.pdf
Rogers, E.M. (1995) Diffusion of innovations. New York: Free Press, 5th Edition
Schierz, P.G., Schilke, O., Wirtz, B.W. (2010) Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications, 9(3): 209-216
Shih, Y., Chen, C. (2011) The study of behavioral intention for mobile commerce: via integrated model of TAM and TTF. Quality & Quantity, 47(2): 1009-1020
Sim, J., Tan, G.W., Wong, J.C.J., Ooi, K., Hew, T. (2014) Understanding and predicting the motivators of mobile music acceptance – A multi-stage MRA-artificial neural network approach. Telematics and Informatics, 31(4): 569-584
Tan, G.W., Ooi, K., Leong, L., Lin, B. (2014) Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-Neural Networks approach. Computers in Human Behavior, 36: 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., Li, H. (2012) Factors influencing mobile services adoption: a brand‐equity perspective. Internet Research, 22(2): 142-179
Wei, T.T., Marthandan, G., Chong, A.Y., Ooi, K., Arumugam, S. (2009) What drives Malaysian m‐commerce adoption? An empirical analysis. Industrial Management & Data Systems, 109(3): 370-388
Wong, Y.K., Hsu, C.J. (2006) A confidence-based framework for business to consumer (B2C) mobile commerce adoption. Personal and Ubiquitous Computing, 12(1): 77-84
Wu, J., Wang, S. (2005) What drives mobile commerce?. Information & Management, 42(5): 719-729
Yao, J., Tan, C.L., Poh, H. (1999) Neural networks for technical analysis: a study on KLCI. International Journal of Theoretical and Applied Finance, 02(02): 221-241
Yeh, Y.S., Li, Y. (2009) Building trust in m‐commerce: contributions from quality and satisfaction. Online Information Review, 33(6): 1066-1086
Zarmpou, T., Saprikis, V., Markos, A., Vlachopoulou, M. (2012) Modeling users’ acceptance of mobile services. Electronic Commerce Research, 12(2): 225-248
Zhang, L., Zhu, J., Liu, Q. (2012) A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behavior, 28(5): 1902-1911