Araştırma Makalesi
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Analysis of the Extended Technology Acceptance Model in Online Travel Products

Yıl 2017, Cilt: 8 Sayı: 2, 45 - 61, 30.12.2017
https://doi.org/10.5505/iuyd.2017.03522

Öz

This study integrates perceived enjoyment and perceived
trust into a technology acceptance model (TAM) to
understand consumer’s acceptance of online travel
products. The data were collected by e-mail questionnaire
technique. Furthermore, partial least squares structural
equation modelling was applied for data analysis because of
the data were non-normally distributed and sample size
was small. Structural equation model reveals that
perceived ease of use, perceived enjoyment and perceived
trust influence consumers’s attitudes toward online
shopping. Perceived enjoyment has strong effect on
perceived usefulness. Moreover, perceived usefulness has a
stronger influence on behavioral intention than on
attitudes toward online shopping

Kaynakça

  • Ajzen, I., & Fishbein, M. (1980), Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs, NJ.
  • Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
  • Ali, F., Amin, M., & Cobanoglu C. (2006). An Integrated Model of Service Experience, Emotions, Satisfaction, and Price Acceptance: An Empirical Analysis in the Chinese Hospitality Industry. Journal of Hospitality Marketing & Management, 25(4), 449-475.
  • Altan, S. (2017). 2016’da Online Alışveriş Harcamaları, %24,6 Artış Göstererek 69 Milyar TL’ye Ulaştı. Download Date:14 November 2017. URL: http://www.pazarlamasyon.com/e-ticaret/2016da-online-alisveris-harcamalari-6-artisgostererek-69-milyar-tlye-ulasti/.
  • Amaro, S., & Duarte, P. (2015). An Integrative Model of Consumers’ Intentions to Purchase Travel Online. Tourism Management, 46, 64-79.
  • Anderson. J., & Gerbing, D. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin, 103(3), 411-423.
  • Bagozzi, R. P., & Yi, Y. (1988). On The Evaluation of Structural Equation Models. Journal of the. Academy of Marketing Science, 16(1), 74-94.
  • Bruner II, G. C., & Kumar, A. (2005). Applying T.A.M. to Consumer Usage of Handheld Internet Devices. Journal of Business Research, 58, 553-558.
  • Burton-Jones, A., & Hubona, G. S. (2005). Individual Differences and Usage Behavior: Revisiting a Technology Acceptance Model Assumption. ACM SIGMIS Database Archive, 36(2), 58-77.
  • Chau, P. Y. K., Hu, P. J. H., Lee, B. L. P., & Au, A. K. K. (2006). Examining Customers’ Trust in Onlinevendors and Dropouts: An Empirical Study. Electronic Commerce Research and Applications, 6(2), 172-83.
  • Chen, L. D., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing Online Consumers: An Extended Technology Acceptance Perspective. Information & Management, 39(8), 705- 709.
  • Chen, L. D., Gillenson, M. L., & Sherrell, D. L. (2004). Consumer Acceptance of Virtual Stores: A Theoretical Model and Critical Success Factors for Virtual Stores. Database for Advances in Information Systems, 35(2), 8-31.
  • Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and Utilitarian Motivations for Online Retail Shopping Behaviour. Journal of Retailing, 77(4), 511-535.
  • Chin, W. W. (2010). How to write up and report PLS analyses V.E. Vinzi, W.W. Chin, J. Henseler, H. Wang (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications in Marketing and Related Fields, Springer.
  • Chin, W. W., Peterson, R. A., & Brown, S. P. (2008). Structural Equation Modeling in Marketing: Some Practical Reminders. Journal of Marketing Theory and Practice, 16(4), 287‐298.
  • Chircu, A. M. Davis, G. B., & Kauffman R. J. (2000). Trust, Expertise and Ecommerce Intermediary Adoption J. DeGross (Ed.), Proceedings of the sixth Americas conference on information systems, ACM, New York.
  • Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13, 319-39.
  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. Journal of Applied Social Psychology, 22, 1111–1132.
  • Diamantopoulos, A., & Siguaw, J. A. (2006). Formative versus Reflective Indicators in Organizational Measure Development: A Comparison and Empirical Illustration. British Journal of Management, 17(4), 263‐282.
  • e-marketer. (2017). Mobile drives growth of online travel bookings. Download Date:15 November 2017. URL: https://www.emarketer.com/Article/Mobile-Drives-Growth-ofOnline-Travel-Bookings/1016053.
  • Fornell, C. & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50.
  • Ha, S., & Stoel, L. (2009). Consumer E-Shopping Acceptance: Antecedents in a Technology Acceptance Model. Journal of Business Research, 62, 565–571.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, Mirror on the Wall: A Comparative Evaluation of Composite-Based Structural Equation Modeling Methods. Journal of the Academy of Marketing Science, 45(5): 616-632.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M. & Sarstedt, M. A. (2013). Primer on Partial Least Squares Structural Equation Modeling. Sage, Thousand Oaks.
  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An Assessment of the Use of Partial Least Squares Structural Equation Modeling in Marketing Research. Journal of the Academy of Marketing Science, 40(3): 414-433.
  • Hair, J.F. Ringle, C.M. & Sarstedt M. A. (2011). PLS-SEM: indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-151.
  • Hair, J. F., Black, W C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis. 7th Edition, Pearson, New York.
  • Ham S., Kim W. G., & Forsythe H. W. (2008). Restaurant Employees’ Technology Use Intention: Validating Technology Acceptance Model with External Factors. Journal of Hospitality & Leisure Marketing, 17(1/2), 78–98.
  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
  • Henseler, J., & Sarstedt, M. (2013). Goodness-Of-Fit Indices for Partial Least Squares Path Modeling. Computational Statistics, 28(2), 565-580.
  • Karataş, M., & Babür, S. (2013). Gelişen Dünya’da Turizm Sektörünün Yeri. KMÜ Sosyal ve Ekonomi̇k Araştırmalar Dergi̇si, 15(25), 15-24.
  • Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A Trust-Based Consumer Decision-Making Model in Electronic Commerce: The Role of Trust, Perceived Risk, and Their Antecedents. Decission Support System, 44(2), 544-564.
  • Kubaş, A., Yılmaz, R., Güt, A., & Baloğlu, S. (2016). Tekirdağ İlinde bulunan Tüketicilerin İnternet Üzerinden Satın Alma Yaklaşımlarının analizi. Social Sciences Research Journal, 5(4), 12-29.
  • Lim, W. M., & Ting, D. H. (2012). E-Shopping: An Analysis of the Technology Acceptance Model. Modern Applied Science, 6(4), 49-62.
  • Lu, Y., Zhou, T., & Wang, B. (2009). Exploring Chinese Users’ Acceptance of Instant Messaging Using the Theory of Planned Behavior, the Technology Acceptance Model, and the Flow Theory. Computers in Human Behavior, 25, 29-39.
  • Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for A World Wide Web Context. Information & Management, 38, 217-30.
  • Pavlou, P. A. (2003). Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model. International Journal of Electronic Commerce, 7(3), 101-134.
  • Peng, D. X., & Lai, F. (2012). Using Partial Least Squares in Operations Management Research: A Practical Guideline and Summary of Past Research. Journal of Operations Management, 30(6), 467-480.
  • Podsakoff, P. M., MacKenzie, S. B., Podsakoff, N. P., & Lee J. Y. (2003). The Mismeasure of Man(agement) and Its Implications for Leadership Research. The Leadership Quarterly, 14(6), 615-656.
  • Reinartz, W. J., Haenlein, M., & Henseler, J. (2009). An Empirical Comparison of the Efficacy of Covariance-Based and Variance-Based SEM. International Journal of Market Research, 26(4), 332-344.
  • Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not So Different After All: A Cross-Discipline View of Trust. Academy of Management Review, 23, 393-404.
  • Selamat, Z., Jaffar, N., & Ong, B. H. (2009). Technology Acceptance in Malaysian Banking Industry. European Journal of Economics, Finance and Administrative Sciences, 1(17), 143-155.
  • Shih, H. P. (2004). An Empirical Study on Predicting User Acceptance of E-Shopping on the Web. Information & Management, 41(3), 351-368.
  • Simkin, M. G., & McLeod, A. (2010). Why Do College Students Cheat? Journal of Business Ethics, 94, 441-445.
  • Taylor, S., & Todd, P. A. (1995). Understanding Information Technology Usage a Test of Competing Models. Information Systems Research, 6, 144-176.
  • Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y. M., & Lauro, C. (2005). PLS Path Modeling. Computational Statistics & Data Analysis, 48(1), 159-205.
  • Teo, T. S. H. (2001). Demographic and Motivation Variables Associated With Internet Usage Activities. Internet Research, 11(2), 125-137.
  • Türker, A., & Türker, G. Ö. (2013). Turistik Ürün Satın Alma Davranışının Teknoloji Kabul Modeli İle İncelenmesi. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 15(2), 281-312.
  • Venkatesh, V. (2000). Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model. Information Systems Research, 11(4), 342-365.
  • Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204.
  • Venkatesh, M., Morris, G., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward A Unified View. MIS Quarterly, 27(3), 425-78.
  • Wetzels, M., Odekerken-Schröder, G., & van Oppen, C. (2009). Using PLS Path Modeling For Assessing Hierarchical Construct Models: Guidelines and Empirical Illustration. MIS Quarterly, 33(1): 177-195.
  • Yuslihasri, I. A., & Daud, A. K. (2011). Factors That Influence Customers Buying Intention On Shopping Online. International Journal of Marketing Studies, 3(1), 128-143.

Online seyahat ürünlerinde genişletilmiş teknoloji kabul modelinin analizi

Yıl 2017, Cilt: 8 Sayı: 2, 45 - 61, 30.12.2017
https://doi.org/10.5505/iuyd.2017.03522

Öz

Bu çalışmada tüketicilerin online seyahat ürünleri
kabulunun anlaşılmasında, teknoloji kabul modeli (TAM)
algılanan eğlence ve algılanan güvenlik boyutları ile
genişletilmiştir. Çalışmanın verileri e-mail yoluyla anket
tekniği ile toplanmıştır. Verilerin normal dağılmaması ve
örneklem boyutunun küçük olması nedeniyle verilerin
analizi için kısmi en küçük kareler tekniği kullanılmıştır.
Yapısal eşitlik modeli ile, algılanan kullanım kolaylığı,
algılanan eğlence ve algılanan güvenin tüketicilerin online
alışverişe yönelik tutumlarını etkilediği ortaya
çıkarılmıştır. Algılanan eğlence, algılanan kullanışlılık
üzerinde güçlü bir etkiye sahiptir. Ayrıca, algılanan
kullanışlılık, online alışverişe karşı tutuma göre
davranışsal niyet üzerinde daha güçlü bir etkiye sahiptir

Kaynakça

  • Ajzen, I., & Fishbein, M. (1980), Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs, NJ.
  • Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
  • Ali, F., Amin, M., & Cobanoglu C. (2006). An Integrated Model of Service Experience, Emotions, Satisfaction, and Price Acceptance: An Empirical Analysis in the Chinese Hospitality Industry. Journal of Hospitality Marketing & Management, 25(4), 449-475.
  • Altan, S. (2017). 2016’da Online Alışveriş Harcamaları, %24,6 Artış Göstererek 69 Milyar TL’ye Ulaştı. Download Date:14 November 2017. URL: http://www.pazarlamasyon.com/e-ticaret/2016da-online-alisveris-harcamalari-6-artisgostererek-69-milyar-tlye-ulasti/.
  • Amaro, S., & Duarte, P. (2015). An Integrative Model of Consumers’ Intentions to Purchase Travel Online. Tourism Management, 46, 64-79.
  • Anderson. J., & Gerbing, D. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin, 103(3), 411-423.
  • Bagozzi, R. P., & Yi, Y. (1988). On The Evaluation of Structural Equation Models. Journal of the. Academy of Marketing Science, 16(1), 74-94.
  • Bruner II, G. C., & Kumar, A. (2005). Applying T.A.M. to Consumer Usage of Handheld Internet Devices. Journal of Business Research, 58, 553-558.
  • Burton-Jones, A., & Hubona, G. S. (2005). Individual Differences and Usage Behavior: Revisiting a Technology Acceptance Model Assumption. ACM SIGMIS Database Archive, 36(2), 58-77.
  • Chau, P. Y. K., Hu, P. J. H., Lee, B. L. P., & Au, A. K. K. (2006). Examining Customers’ Trust in Onlinevendors and Dropouts: An Empirical Study. Electronic Commerce Research and Applications, 6(2), 172-83.
  • Chen, L. D., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing Online Consumers: An Extended Technology Acceptance Perspective. Information & Management, 39(8), 705- 709.
  • Chen, L. D., Gillenson, M. L., & Sherrell, D. L. (2004). Consumer Acceptance of Virtual Stores: A Theoretical Model and Critical Success Factors for Virtual Stores. Database for Advances in Information Systems, 35(2), 8-31.
  • Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and Utilitarian Motivations for Online Retail Shopping Behaviour. Journal of Retailing, 77(4), 511-535.
  • Chin, W. W. (2010). How to write up and report PLS analyses V.E. Vinzi, W.W. Chin, J. Henseler, H. Wang (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications in Marketing and Related Fields, Springer.
  • Chin, W. W., Peterson, R. A., & Brown, S. P. (2008). Structural Equation Modeling in Marketing: Some Practical Reminders. Journal of Marketing Theory and Practice, 16(4), 287‐298.
  • Chircu, A. M. Davis, G. B., & Kauffman R. J. (2000). Trust, Expertise and Ecommerce Intermediary Adoption J. DeGross (Ed.), Proceedings of the sixth Americas conference on information systems, ACM, New York.
  • Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13, 319-39.
  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. Journal of Applied Social Psychology, 22, 1111–1132.
  • Diamantopoulos, A., & Siguaw, J. A. (2006). Formative versus Reflective Indicators in Organizational Measure Development: A Comparison and Empirical Illustration. British Journal of Management, 17(4), 263‐282.
  • e-marketer. (2017). Mobile drives growth of online travel bookings. Download Date:15 November 2017. URL: https://www.emarketer.com/Article/Mobile-Drives-Growth-ofOnline-Travel-Bookings/1016053.
  • Fornell, C. & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50.
  • Ha, S., & Stoel, L. (2009). Consumer E-Shopping Acceptance: Antecedents in a Technology Acceptance Model. Journal of Business Research, 62, 565–571.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, Mirror on the Wall: A Comparative Evaluation of Composite-Based Structural Equation Modeling Methods. Journal of the Academy of Marketing Science, 45(5): 616-632.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M. & Sarstedt, M. A. (2013). Primer on Partial Least Squares Structural Equation Modeling. Sage, Thousand Oaks.
  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An Assessment of the Use of Partial Least Squares Structural Equation Modeling in Marketing Research. Journal of the Academy of Marketing Science, 40(3): 414-433.
  • Hair, J.F. Ringle, C.M. & Sarstedt M. A. (2011). PLS-SEM: indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-151.
  • Hair, J. F., Black, W C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis. 7th Edition, Pearson, New York.
  • Ham S., Kim W. G., & Forsythe H. W. (2008). Restaurant Employees’ Technology Use Intention: Validating Technology Acceptance Model with External Factors. Journal of Hospitality & Leisure Marketing, 17(1/2), 78–98.
  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
  • Henseler, J., & Sarstedt, M. (2013). Goodness-Of-Fit Indices for Partial Least Squares Path Modeling. Computational Statistics, 28(2), 565-580.
  • Karataş, M., & Babür, S. (2013). Gelişen Dünya’da Turizm Sektörünün Yeri. KMÜ Sosyal ve Ekonomi̇k Araştırmalar Dergi̇si, 15(25), 15-24.
  • Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A Trust-Based Consumer Decision-Making Model in Electronic Commerce: The Role of Trust, Perceived Risk, and Their Antecedents. Decission Support System, 44(2), 544-564.
  • Kubaş, A., Yılmaz, R., Güt, A., & Baloğlu, S. (2016). Tekirdağ İlinde bulunan Tüketicilerin İnternet Üzerinden Satın Alma Yaklaşımlarının analizi. Social Sciences Research Journal, 5(4), 12-29.
  • Lim, W. M., & Ting, D. H. (2012). E-Shopping: An Analysis of the Technology Acceptance Model. Modern Applied Science, 6(4), 49-62.
  • Lu, Y., Zhou, T., & Wang, B. (2009). Exploring Chinese Users’ Acceptance of Instant Messaging Using the Theory of Planned Behavior, the Technology Acceptance Model, and the Flow Theory. Computers in Human Behavior, 25, 29-39.
  • Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for A World Wide Web Context. Information & Management, 38, 217-30.
  • Pavlou, P. A. (2003). Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model. International Journal of Electronic Commerce, 7(3), 101-134.
  • Peng, D. X., & Lai, F. (2012). Using Partial Least Squares in Operations Management Research: A Practical Guideline and Summary of Past Research. Journal of Operations Management, 30(6), 467-480.
  • Podsakoff, P. M., MacKenzie, S. B., Podsakoff, N. P., & Lee J. Y. (2003). The Mismeasure of Man(agement) and Its Implications for Leadership Research. The Leadership Quarterly, 14(6), 615-656.
  • Reinartz, W. J., Haenlein, M., & Henseler, J. (2009). An Empirical Comparison of the Efficacy of Covariance-Based and Variance-Based SEM. International Journal of Market Research, 26(4), 332-344.
  • Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not So Different After All: A Cross-Discipline View of Trust. Academy of Management Review, 23, 393-404.
  • Selamat, Z., Jaffar, N., & Ong, B. H. (2009). Technology Acceptance in Malaysian Banking Industry. European Journal of Economics, Finance and Administrative Sciences, 1(17), 143-155.
  • Shih, H. P. (2004). An Empirical Study on Predicting User Acceptance of E-Shopping on the Web. Information & Management, 41(3), 351-368.
  • Simkin, M. G., & McLeod, A. (2010). Why Do College Students Cheat? Journal of Business Ethics, 94, 441-445.
  • Taylor, S., & Todd, P. A. (1995). Understanding Information Technology Usage a Test of Competing Models. Information Systems Research, 6, 144-176.
  • Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y. M., & Lauro, C. (2005). PLS Path Modeling. Computational Statistics & Data Analysis, 48(1), 159-205.
  • Teo, T. S. H. (2001). Demographic and Motivation Variables Associated With Internet Usage Activities. Internet Research, 11(2), 125-137.
  • Türker, A., & Türker, G. Ö. (2013). Turistik Ürün Satın Alma Davranışının Teknoloji Kabul Modeli İle İncelenmesi. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 15(2), 281-312.
  • Venkatesh, V. (2000). Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model. Information Systems Research, 11(4), 342-365.
  • Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204.
  • Venkatesh, M., Morris, G., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward A Unified View. MIS Quarterly, 27(3), 425-78.
  • Wetzels, M., Odekerken-Schröder, G., & van Oppen, C. (2009). Using PLS Path Modeling For Assessing Hierarchical Construct Models: Guidelines and Empirical Illustration. MIS Quarterly, 33(1): 177-195.
  • Yuslihasri, I. A., & Daud, A. K. (2011). Factors That Influence Customers Buying Intention On Shopping Online. International Journal of Marketing Studies, 3(1), 128-143.
Toplam 53 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ekonomi
Bölüm Araştırma Makalesi
Yazarlar

Nurdan Sevim

Deniz Yüncü Bu kişi benim

Elif Eroğlu Hall

Yayımlanma Tarihi 30 Aralık 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 8 Sayı: 2

Kaynak Göster

APA Sevim, N., Yüncü, D., & Eroğlu Hall, E. (2017). Analysis of the Extended Technology Acceptance Model in Online Travel Products. İnternet Uygulamaları Ve Yönetimi Dergisi, 8(2), 45-61. https://doi.org/10.5505/iuyd.2017.03522

Cited By