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Factors Affecting the Use of Mobile Recommendation Systems: a Structural Equation Model

Year 2019, Volume: 21 Issue: 1, 37 - 55, 30.04.2019

Abstract

The main purpose of this study is to investigate
attitudes and behaviors of university students towards mobile recommender
systems using a proposed structural equation model (SEM). In the proposed
model, expected recommendation quality of mobile recommender systems was
defined as the exogenous latent variable, while perceived recommendation
quality, enjoyment, collectivism, and attitude were defined as the mediating
endogenous latent variables, and behavior was defined as the endogenous latent
variable. To this end, the survey developed based on the literature was
administered to 416 students from various faculties. The fitness of the
proposed structural model was investigated based on various fitness criteria
and the model was found to be within acceptable limits.  Data analysis showed that expected
recommendation quality and perceived recommendation quality were closely
related and perceived recommendation quality, user enjoyment, and collectivism
positively affected attitudes of students towards mobile recommender systems.
Also, it was found that an increase of one unit in students’ positive attitudes
towards recommender systems led to an increase of 0.38 units in mobile
recommender system use behavior. 

References

  • Al-Gahtani, S.S., Hubona,G.S., and Wang, J.(2007). Information technology (IT) in Saudi Arabia: culture and the acceptance and use of IT. Information & Management, 44, 681–691.
  • Al-Natour, S., Benbasat, I., and Cenfetelli, R.T. (2008). The effects of process and outcome similarity on users' evaluations of decision aids. Decision Sciences, 39, 175–211.
  • Bandyopadhyay, K., and Fraccastoro, K.A. (2007). The effect of culture on user acceptance of information technology. Communications of the Association for Information Systems, 19, 522–543.
  • Baum, D., and Spann, M. (2014). The interplay between online consumer reviews and recommender systems: an experimental analysis. International Journal of Electronic Commerce, 19(1), 129-162.
  • Choeh J.Y., and Lee, H.J. (2008). Mobile push: personalization and user experience. AI Communications, 21, (2008) 185–193.
  • Choi, J., Lee, H.J., Sajjad, F., and Lee, H. (2014). The influence of national culture on the attitude towards mobile recommender systems. Technological Forecasting & Social Change, 86, 65-79.
  • Davis, F.D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Q., 13, 319–340.
  • Demirkıran, E.T. (2016). International Conference on Research in Education and Science (ICRES),377-381, May 19-22, 2016, Bodrum, Turkey.
  • Fornell, C., and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1):39–50.
  • Hair, J. F., Anderson, R. E., Tatham R. L., and Black, W. C. (1998). Multivariate Data Analysis, 5th Edition,New Jersey Prentice-Hall International.
  • Herlocker, J., Konstan, J.A., Terveen, L.G., and Riedl, J.T. (2004). Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems, 22(1), 1-53.
  • Hofstede, G. (2001). Culture's Consequences, 2nd ed. Sage Publications, Thousand Oaks, CA.
  • Hong, S.J., and Tam, K.Y. (2006). Understanding the adoption of multipurpose information appliances: the case of mobile data services. Information Systems Research, 17, 162–179.
  • Huang, S. (2011). Designing utility-based recommender systems for e-commerce: Evaluation of preference-elicitation methods. Electronic Commerce Research and Applications,10,398-407.
  • Kumar, N., and Benbasat, I. (2006). The influence of recommendations and consumer reviews on evaluations of websites. Information Systems Research. 17, 425–429.
  • Lee, B., Choi, B., Kim, J., and Hong, S. (2007). Culture–technology Fit: effects of cultural characteristics on the post-adoption beliefs of mobile internet users. International Journal of Electronic Commerce. 11, 11–51.
  • Martin, R., and Hewstone, M. (2003). Social-influence Processes of Control and Change: Conformity, Obedience to Authority and Innovation, Sage, London.
  • Raykov, T. and Marcoulides, G.A. (2006). A first course in structural equation modelling, Mahwah, NJ: Lawrance Erlbaum Associates, 238.
  • Schermelleh- Engel, K., and Moosbrugger, H. (2003). Evaluating the fit of structural equation models: Test of significance and descriptive goodness of-fit measures. Methods of Psychological Research- Online, 8(2), 23-74.
  • Tam, K.Y., and Ho, S.Y. (2005). Web personalization as a persuasion strategy: an elaboration likelihood model perspective. Information Systems Research, 16, 271–291.
  • Turel, O., Serenko, A., Detlor, B., Collan, M., and Nam, I. J. (2006). Puhakainen, Investigating the determinants of satisfaction and usage of mobile IT services in four countries. Journal of Global Information Technology Management, 9, 6–25.
  • Xu, X., Dutta, K., and Ge, C. (2018). Do adjective features from user reviews address sparsity and transparency in recommender systems? Electronic Commerce Research and Applications,29, 113-123.
  • V. Venkatesh (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11, 342–365.
  • Xiao, B., and Benbasat, I. (2007). E-commerce product recommendation agents: use, characteristics, and impact. MIS Quarterly, 31, 137–209.
  • Xu, D.J. (2006). The influence of personalization in affecting consumer attitudes toward mobile advertising in China. Journal of Computer Information Systems, 47, 9–19.

Mobil Öneri Sistemleri Kullanımını Etkileyen Faktörler: Bir Yapısal Eşitlik Modeli

Year 2019, Volume: 21 Issue: 1, 37 - 55, 30.04.2019

Abstract

Bu çalışmanın temel amacı üniversite öğrencilerin
mobil öneri sistemlerine yönelik tutum ve davranışları önerilen bir yapısal
eşitlik modellemesiyle (YEM) araştırmaktır. Önerilen modelde, mobil öneri
sistemlerinin beklenen öneri kalitesi, dışsal gizli değişken olarak
tanımlanırken, algılanan öneri kalitesi, zevk, paylaşma, tutum aracı içsel
gizil değişkenler ve davranış da içsel gizli değişken olarak tanımlanmıştır. Bu
amaçla, çeşitli fakültelerinde öğretim gören 416 öğrenciye, literatürden
yararlanılarak geliştirilen bir anket uygulanmıştır. Önerilen yapısal model
çeşitli uyum ölçütlerine dayanarak uygunluğu araştırılmış ve sonuçta modelin
kabul edilebilir sınırlar içinde kaldığı görülmüştür.  Verilerin analizi sonucunda, beklenen ve
algılanan öneri kalitesinin yüksek düzeyde ilişkili olduğu, algılanan mobil öneri kalitesinin, kullanıcı
zevki ve paylaşmanın öğrencilerin
mobil öneri sistemlerine yönelik tutumlarını olumlu yönde etkilediği belirlenmiştir.
Ayrıca, öğrencilerin mobil öneri sistemlerini yönelik olumlu
tutumlarındaki bir birimlik artışın, öğrencilerin öneri sistemleri kullanma
davranışlarını 0,38 birim arttırdığı da tespit edilmiştir. 

References

  • Al-Gahtani, S.S., Hubona,G.S., and Wang, J.(2007). Information technology (IT) in Saudi Arabia: culture and the acceptance and use of IT. Information & Management, 44, 681–691.
  • Al-Natour, S., Benbasat, I., and Cenfetelli, R.T. (2008). The effects of process and outcome similarity on users' evaluations of decision aids. Decision Sciences, 39, 175–211.
  • Bandyopadhyay, K., and Fraccastoro, K.A. (2007). The effect of culture on user acceptance of information technology. Communications of the Association for Information Systems, 19, 522–543.
  • Baum, D., and Spann, M. (2014). The interplay between online consumer reviews and recommender systems: an experimental analysis. International Journal of Electronic Commerce, 19(1), 129-162.
  • Choeh J.Y., and Lee, H.J. (2008). Mobile push: personalization and user experience. AI Communications, 21, (2008) 185–193.
  • Choi, J., Lee, H.J., Sajjad, F., and Lee, H. (2014). The influence of national culture on the attitude towards mobile recommender systems. Technological Forecasting & Social Change, 86, 65-79.
  • Davis, F.D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Q., 13, 319–340.
  • Demirkıran, E.T. (2016). International Conference on Research in Education and Science (ICRES),377-381, May 19-22, 2016, Bodrum, Turkey.
  • Fornell, C., and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1):39–50.
  • Hair, J. F., Anderson, R. E., Tatham R. L., and Black, W. C. (1998). Multivariate Data Analysis, 5th Edition,New Jersey Prentice-Hall International.
  • Herlocker, J., Konstan, J.A., Terveen, L.G., and Riedl, J.T. (2004). Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems, 22(1), 1-53.
  • Hofstede, G. (2001). Culture's Consequences, 2nd ed. Sage Publications, Thousand Oaks, CA.
  • Hong, S.J., and Tam, K.Y. (2006). Understanding the adoption of multipurpose information appliances: the case of mobile data services. Information Systems Research, 17, 162–179.
  • Huang, S. (2011). Designing utility-based recommender systems for e-commerce: Evaluation of preference-elicitation methods. Electronic Commerce Research and Applications,10,398-407.
  • Kumar, N., and Benbasat, I. (2006). The influence of recommendations and consumer reviews on evaluations of websites. Information Systems Research. 17, 425–429.
  • Lee, B., Choi, B., Kim, J., and Hong, S. (2007). Culture–technology Fit: effects of cultural characteristics on the post-adoption beliefs of mobile internet users. International Journal of Electronic Commerce. 11, 11–51.
  • Martin, R., and Hewstone, M. (2003). Social-influence Processes of Control and Change: Conformity, Obedience to Authority and Innovation, Sage, London.
  • Raykov, T. and Marcoulides, G.A. (2006). A first course in structural equation modelling, Mahwah, NJ: Lawrance Erlbaum Associates, 238.
  • Schermelleh- Engel, K., and Moosbrugger, H. (2003). Evaluating the fit of structural equation models: Test of significance and descriptive goodness of-fit measures. Methods of Psychological Research- Online, 8(2), 23-74.
  • Tam, K.Y., and Ho, S.Y. (2005). Web personalization as a persuasion strategy: an elaboration likelihood model perspective. Information Systems Research, 16, 271–291.
  • Turel, O., Serenko, A., Detlor, B., Collan, M., and Nam, I. J. (2006). Puhakainen, Investigating the determinants of satisfaction and usage of mobile IT services in four countries. Journal of Global Information Technology Management, 9, 6–25.
  • Xu, X., Dutta, K., and Ge, C. (2018). Do adjective features from user reviews address sparsity and transparency in recommender systems? Electronic Commerce Research and Applications,29, 113-123.
  • V. Venkatesh (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11, 342–365.
  • Xiao, B., and Benbasat, I. (2007). E-commerce product recommendation agents: use, characteristics, and impact. MIS Quarterly, 31, 137–209.
  • Xu, D.J. (2006). The influence of personalization in affecting consumer attitudes toward mobile advertising in China. Journal of Computer Information Systems, 47, 9–19.
There are 25 citations in total.

Details

Primary Language Turkish
Journal Section Main Section
Authors

Erkan Arı 0000-0001-6012-0619

Veysel Yılmaz 0000-0001-5147-5047

Publication Date April 30, 2019
Published in Issue Year 2019 Volume: 21 Issue: 1

Cite

APA Arı, E., & Yılmaz, V. (2019). Mobil Öneri Sistemleri Kullanımını Etkileyen Faktörler: Bir Yapısal Eşitlik Modeli. Ankara Hacı Bayram Veli Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 21(1), 37-55.