MOBİL ALIŞVERİŞTE, AĞIZDAN AĞZA PAZARLAMANIN VE TÜKETİCİ MEMNUNİYETİNİN YAŞA GÖRE ETKİSİ

Yıl 2019, Cilt: 2 Sayı: 6, 260 - 272, 31.07.2019

Ayşe Ece Ak

https://doi.org/10.33723/rs.573671

Cited By: 1

Öz

Türkiye’de
1990’lı yıllarda çok sınırlı alanda kullanılan İnternet zaman içerisinde
yaygınlaşmaya başlamış, günümüzde ise günlük hayatın bir parçası haline
gelmiştir. Önceki dönemlerde internete erişmek için kişisel dijital
yardımcılar, masaüstü ve dizüstü bilgisayarlar kullanılırken günümüzde akıllı
telefonlar ve tabletler üzerinden de internete erişim sağlanmaktadır. Bunun
yanı sıra, internetin yeniçağı şeklinde tanımlanan Web 2.0, tüketiciler için
onların yeni içerikler üretebilecekleri platformlar sunmuştur. Tüketiciler bu
platformlarda çeşitli konularla ilgili bilgi paylaşarak, birbiriyle etkileşimde
bulunmaya başlamışlardır. Böylece ağızdan ağza pazarlama (Word of Mouth
Marketing-WOMM) online platformlara taşınmış; tüketicilerin ürünler ve markalarla
ilgili konuşup, tecrübelerini paylaştıkları bir mecra meydana gelmiştir. Bu
çalışmada mobil alışverişte, ağızdan ağza pazarlamanın ve tüketici
memnuniyetinin yaşa göre etkisi incelenmiştir. Çalışmada girişten sonra ilk
kısımda, literatür çalışmasına ve metodolojiye yer verilmiş, teorik altyapı
incelenmiştir. İkinci kısımda, mobil alışverişte, ağızdan ağza pazarlamanın ve
tüketici memnuniyetinin yaşa göre etkisinin incelendiği araştırmanın
bulgularına yer verilmiştir. Sonuç bölümünde ise bulgular tartışılmıştır. Mobil
alışverişte, ağızdan ağza pazarlama ve memnuniyet arasındaki ilişki ve ilişkiyi
etkileyen faktörler tanımlanmıştır. Değişkenler arasındaki ilişkiyi ölçerken
örnek alışveriş sitesi olarak Türkiye’de en çok tercih edilen ve güvenilir olan
5 alışveriş sitesi ‘Hepsiburada.com, Trendyol, Morhipo, AliExpress, n11.com’
seçilmiştir (https://www.twentify.com). Çalışmanın sonucunda bulguların
değerlendirilmesiyle birlikte; mobil alışverişte, ağızdan ağza pazarlamanın ve
tüketici memnuniyetinin etkisinin yaşa göre farklılık gösterdiği sonucuna
ulaşılmıştır

Anahtar Kelimeler

Mobil Alışveriş, Tüketici Memnuniyeti, Ağızdan Ağza Pazarlama, Tüketici Memnuniyeti

The Effect of Word of Mouth Marketing (Womm) and Consumer Satisfaction by Age on Mobile Shopping

Öz

At the beginning of the
1990s, when the Internet is used in a very limited area in Turkey, it has
spread rapidly while today it has become a part of everyday life. Personal
digital assistants, desktops and laptops have been used to access the Internet
in the previous periods, while access to the internet is now available through
smart phones and tablets. In addition, Web 2.0, defined as the new era of the
internet, provided platforms for consumers to produce new content for them.
Consumers have begun to interact with each other by sharing information on
various issues in these platforms. Thus, mouth to mouth marketing (Word of
Mouth Marketing-WOMM) has been moved
to online platforms; a channel where consumers talk about products and brands
and share their experiences. In this study, the effect of mobile shopping,
mouth to mouth marketing and consumer satisfaction by age is examined. In the
first part of the study, literature study and methodology are given and the
theoretical background is examined. In the second part, the findings of the
study, which examines the effect of age on mobile shopping, mouth to mouth
marketing and consumer satisfaction by age. In the conclusion section, the
findings were discussed. The relationship between marketing and satisfaction
and the factors affecting the relationship are defined. The relationship
between the variables measuring sample shopping site as the most preferred and
reliable shopping site in Turkey 5 'Hepsiburada.com, Trendyol, Morhipo,
AliExpress, n11.com' is selected (https://www.twentify.com). As a result of the
study; It was concluded that the effect of mobile shopping, mouth to mouth
marketing and consumer satisfaction varied according to age.

Anahtar Kelimeler

Mobile Shopping, Word of Mouth Marketing, Consumer Satisfaction

Kaynakça

• Ahuja, V., & K.hazanchi, D. (2016). Creation of a conceptual model for adoptionof mobile apps far shopping from e-coınrnerce sites-an Indian context. Procedia ComputerScience, 91, 609-616.
• Alam, S., Baker, Z., Ismail, H., Ahsan, N., 2008. Young consumers online shopping: an empirical study. J. Internet Bus. 5, 81-98.
• Altunışık, Remzi; Recai Coşkun, Serkan Bayraktaroğlu ve Engin Yıldırım; (2007), Sosyal Bilimlerde Araştırma Yöntemleri SPSS Uygulamalı, Sakarya Yayıncılık, 127s.
• Ansari, M.S., Channar, Z.A., Syed, A., 2012. Mobile phone adoption and appropria¬tion among the young generation. Procedia 41, 265-272.
• Bhatnagar, A., & Ghose, S. (2004). Segmenting consumers based on the benefits and risks of Internet shopping. Journal of Business Research, 57(12), 1352-1360.
• Brown Jo, Broderick Amanda J., Lee Nick (2007). Word of Mouth Communication within online communities: Conceptualizing the Online Social Network. Journal of Interactive Marketing, 21:3, pp.2-20.
• Buttle Francis A. (1998). Word of mouth: understading and managing referral marketing. Journal of Strategic Marketing. 6, pp.241-254.
• Budak, B. (2010). E-ticaret İnternet Ortamında Ticaret, 1. Baskı, Etap Yayınevi, İstanbul, ss. 17.
• Bauer, R. A. (1960). Consumer behavior as risk taking. In Proceedings of the 43rd Nati6nal Conference of the American Marketing Assocation, June - 15, 16, 17, Chicago, Illinois, 1960. American Marketing Association.
• Bushry, M. (2005). E-commerce, Firewall Media, New Delhi, ss.181.
• Choi, J., Seol, H., Lee, S., Cho, H., Park, Y., 2008. Customer satisfaction factors of mobile commerce in Korea. Internet Res. 18 (3), 313-335.
• Cox, D .. F., & Rich; S. U. (1964). Perceived risk and consumer decision making: The case of telephone shopping. Journal of marketing research, 32-39.
• Davis, F.D., Bagozzi, R.P., Warshaw, P.R., 1992. Extrinsic and intrinsic motivation to use computers in the workplace. J. Appl. Soc. Psychol. 22, 1111-1132.
• Drennan, J., Sullivan, G., & Previte, J. (2006). Privacy, risk perception, and expert online behavior: An exploratory study of household end users. Joumal of Organizational and End User Computing (JOEUC), 18(1), 1-22.
• Eggert, A. (2006). Intangibility and perceived risk in online environrnents. Journal of Marketing Management, 22(5-6), 553-572.
• Ennew Christine T., Banerjee Ashish K., Li Derek (2000). Managing word of mouth communication: empirical evidence from India. International Journal of Bank Marketing. 18:2, pp.75-83.
• Erkan, M. (2012). E-ticaret Çağı, Optimist Yayın Dağıtım, İstanbul, ss.155.
• Garbarino, E., & Strahilevitz, M. (2004). Gender differences in the perceived risk of buyiııg online and the effects of receiving a site recommendation. Journal of Business Research, 57(7), 768-775.
• Gildin Suzana (2003). Z.Understanding the Power of Word of Mouth.n.1, pp.91-106.
• Godes David, Mayzlin Dins (2004). Using Online Conversations to Study Word of Mouth Communication. Marketing Science.23:4, pp.545-560.
• Harris, P., Rettie, R., Kwan, C.C., 2005. Adoption and usage of m-commerce: a cross- cultural comparison. J. Electron. Commer. Res. 6 (3), 210–224.
• Hausman, A. (2000). A multi-method investigation of consumer motivations in impulse buying behavior. Journal of consumer marketing, 17(5), 403-426.
• Hill, W.W., Beatty, S.E., 2011. A model of adolescents' online consumer self-efficacy (OCSE). J. Bus. Res. 64, 1025–1033.
• Howe, N., Strauss, W., 2003. Millennials Go to College: Strategies for a New Generation on Campus. AACRAO and Life Course Associates, Washington.
• Jansen Bernard J., Zhang Mimi, Sobel Kate, Chowdury Abdur (2009). Twitter Power: Tweets as Electronic Word of Mouth.Journal of American Society for Information Science and Technology. 60:11, pp.2169-2188.
• Kassim, N.M., Abdullah, N., 2008. Customer loyalty in ecommerce settings: an empirical study. Electron. Mark. 18 (3), 275-290.
• Katz, E., Blumler, J., Gurevitch, M., 1974. Utilization of mass communication by the individual. In: Blumler, J., Katz, E. (Eds.), The Uses of Mass Communications: Current Perspectives on Gratifications Research, 25. Sage, Beverly Hills, pp. 19-32.
• Kim, C., Galliers, R. D., Sbin, N., Ryoo, J. H., & Kim, J. (2012). Factors influencing Intemet shopping value an customer repurchase intention. Electronic • Commerce Research and Applications, 11(4), 374-387.
• Kim, S., & Eastin, M. S. (2011). Hedonic tendencies and the online consuıner: an irtvestigation of the online shopping process. Journal of Jnternet Commerce, 10(1), 68-90.
• Lau Geok Theng, Ng Sophia. (2001). Individual and situational factors influencing negative word of mouth behavior. Canadian Journal of Administrative Sciences. 18:3, pp.163-178.
• Laukkanen, T., Sinkkonen, S., Kivijärvi, M., Laukkanen, P., 2007. Innovation resis- tance among mature consumers. J. Consum. Mark. 24 (7), 419–427.
• Lee Mira, Youn Seounmi(2009). Electronic word of mouth(eWom): How eWOM platforms influence consumer product judgement. International Journal of Advertising. 28:3, pp.473-499.
• Li, Y.M., Yeh, Y.S., 2010. Increasing trust in mobile commerce through design aesthetics. Comput. Human Behav. 26 (4), 673-684.
• Litvin Stephen W.,Goldsmith Ronald E.,Pan Bing (2005).Electronic Word of Mouth in hospitality and tourism management. Tourism Management, pp.1-30.
• Lu, H. P ., Hsu, C. L., & Hsu, H. Y. (2005). An empirical study of the effect of perceived risk upon intention to use online applications. Information Management & Computer Security, 13(2), 106-120.
• Manzoor, A. (2010). E-commerce An Introduction. Lambert Academic Publishing, Berlin, ISBN 978-3-8433-7030-1 pp.346.
• Mitchell, V. W. (1999). Consumer perceived risk: conceptualisations and models. European Journal of marketing, 33(1/2), 163-195.
• Oblinger, D.G., Oblinger, J.L., 2005. Educating the Net Generation. EDUCAUSE, Washington.
• Oumlil, A., Williams, A., 2000. Consumer education programs for mature consu- mers. J. Serv. Mark. 14 (3), 232–243.
• Okazaki, S., 2005. New perspectives on m-commerce research. J. Electron. Commer. Res. 6 (3), 160-164.
• Okazaki, S., 2009. Social influence model and electronic word of mouth: PC versus mobile internet. Int. J. Advert. 28 (3), 439-472.
• Okazaki, S. and Navarro Bailon, M.D.L.A., 2010. The impact of ubiquitous context on information privacy concerns in a mobile-based promotion. In: Proceedings of the EMAC Conference, Denmark, pp. 1-7.
• Park, J., Stoel, L., 2005. Effect of brand familiarity experience and information on online apparel purchase. Int. J. Retail Distrib. Manag. 33, 48-160.
• Ranaweera, C., McDougall, G., Bansao, H.A., 2005. A model of online customer behaviour during the initial transaction. Moderating effects of customer characteristics. Mark. Theory 5 (1), 51-74.
• Sadeh, N. (2002). M-commerce. Wiley Computer Publishing, Canada, pp.5.
• Sorce, P., Perotti, V., Widrick, S., 2005. Attitude and age differences in online buying. Int. J. Retail Distrib. Manag. 33 (2), 122-132.
• Sun Thao, Youn Seounmi, Wu Guohua&Kuntaraporn Mana (2006). Online word of mouth: An exploration of its antecedents and consequences. Journal of Computer-Mediated Communication 11, pp.1104-1127.
• Sun, J., Wang, X., 2007. Personal global connectivity and consumer behavior. J. Int. Consum. Mark. 19 (3), 103–119.
• Wolfinbarger, M., & Gilly, M. C. (2001). Shopping online for freedom, control, and fun. California Management Review, 43(2), 34-55.
• Yang, K., 2005. Exploring factors affecting the adoption of mobile commerce in Singapore. Telemat. Inform. 22, 257-277.
• Yang, H., Zhou, L., Liu, H., 2012. Predicting young American and Chinese consumers’ mobile viral attitudes, intents, and behavior. J. Int. Consum. Mark. 24 (1-2), 24-42.
• Yazıcıoğlu, Y. ve Erdoğan, S. (2004). Spss uygulamalı bilimsel araştırma yöntemleri. Ankara: Detay Yayıncılık.
• Yeh, Y.S., Li, Y.M., 2009. Building trust in m-commerce: contributions from quality and satisfaction. Online Inf. Rev. 33 (6), 1066-1086.
• Zanna, M. P., & Rempel, J. K. (1988.). Attitudes: A new look at an old concept.
• Zhao, L., Lu, Y., Zhang, L., Chau, P.Y.K., 2012. Assessing the effects of service quality and justice on customer satisfaction and the continuance intention of mobile value-added services: an empirical test of a multidimensional model. Decis. Support Syst. 52, 645-656.
• Zhao, L., Dai, L., & Zhang, D. (2007). Online shopping acceptance model-A critical survey of consuıner factors in online shopping. Journal of Electronic commerce research, 8(1), 41.