PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Mobile app usage and its implications on consumer behavior toward consumer goods

Treść / Zawartość
Warianty tytułu
PL
Wykorzystanie aplikacji mobilnych i ich wpływ na zachowanie klientów przy nabywaniu dóbr konsumpcyjnych
Języki publikacji
EN
Abstrakty
EN
This study attempts to examine the influence of mobile apps on customers’ attitude and finally customers’ actual use behavior toward consumer goods. By applying the theory of mobile marketing and mobile commerce, the authors investigated the antecedents and the consequences of mobile apps to conclude about their implications in marketing. Variables, namely financial value, convenience value and fit of information are proven to have a positive impact on perceived usefulness; app design, ease of understanding and intuitive handling positively affect the perceived ease of use. Perceived variables including usefulness and ease of use have a positive influence on a consumer’s attitude. Finally, purchase intention and sharing intention, followed by actual use behavior are as a consequence in the conceptual model.
PL
W pracy zrelacjonowano próbę zbadania wpływu aplikacji mobilnych na nastawienie klienta i jego rzeczywiste zachowanie w odniesieniu do dóbr konsumpcyjnych. Zastosowano w niej teorię marketingu mobilnego i handlu mobilnego w celu zbadania możliwości i konsekwencji używania aplikacji mobilnych w obrębie grupy docelowej. Udowodniono, że pewne zmienne, a mianowicie wartość finansowa dóbr, wygoda dostępu do informacji i ich dopasowanie do oczekiwań klienta mają pozytywny wpływ na postrzeganą użyteczność aplikacji, a właściwe zaprojektowanie aplikacji, ich przyjazność i intuicyjna obsługa pozytywnie wpływają na łatwość użytkowania. Szczególnie ważne okazały się przydatność i łatwość użytkowania jako czynniki korzystnie oddziałujące na nastawienie konsumenta. Zamiar zakupu i gotowość do udostępniania w sieci informacji na ten temat, a także późniejsze rzeczywiste wykorzystanie aplikacji są zależne od czynników opisanych w koncepcji modelowego zachowania.
Rocznik
Tom
Strony
263--287
Opis fizyczny
Bibliogr. 49 poz., tab.
Twórcy
  • Graduate of Poznan University of Technology, Faculty of Engineering Management
  • Poznan University of Technology, Faculty of Engineering Management
Bibliografia
  • 1. Ajzen, I., (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 197-211.
  • 2. Brown, J., Broderick, A., Lee, N. (2007). Word of mouth communication within online communities: conceptualizing the only social network. Interactive Marketing, 21(3), 2-10.
  • 3. Collier, J.E., Sherrell, D.L. (2010). Examining the Influence of Control and Convenience in a Self-service Setting. Academy of Marketing Science, 38(4), 490-509.
  • 4. Criteo (2016). Mobile Commerce reports, 1.
  • 5. Cyr, D., Head, М., Ivanov, A. (2006). Design Aesthetics Leading to М-Loyalty in Mobile Commerce. Information and Management, 43(8), 950-963.
  • 6. D’Ambra, J., Rice, R.E. (2001). Emerging factors in user evaluation of the World Wide Web. Information & Management, 38(6), 373-384.
  • 7. Davis, F.D. (1986). A technology acceptance model for empirically testing new end user information system: Theory and results. Doctoral Dissertation, Sloan School of Management, Massachusetts Institute of Technology.
  • 8. Davis, F.D., Bagozzi, R.P., Warshaw, P.R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982-1003.
  • 9. Dawei, S., Weiwei, W. (2017). Understanding mobile shopping consumers’ continuance intention. Industrial Management & Data Systems, 117(1), 213-227.
  • 10. Dickinger, A., Kleijnen, M. (2008). Coupons going Wireless: Determinants of Consumer Intentions to Redeem Mobile Coupons. Journal of Interactive Marketing, 22(3), 23-39.
  • 11. Field, A. (2013). Discovering Statistics Using SPSS for Windows: and Sex, Drugs and Rock n ’ Roll. 4th ed. California: SAGE Publication.
  • 12. Flander Investment & Trade (2016). New technology in Poland (IoT, mobile apps, Fintech, Poznan: Vlaabderen.
  • 13. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. (2010). Multivariate data analysis. 7th ed. Upper Saddle River, NJ: Prentice Hall.
  • 14. Heijden, H. v.d. (2003). Factors influencing the usage of websites: the case of a generic portal in The Netherlands. Information & Management, 40(6), 541-549.
  • 15. Hennig-Thurau, Т., Gwinner, K., Walsh, G., Gremler, D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet?. Interactive Marketing, 18(1), 38-52.
  • 16. Hernandez, B., Jimenez, J., Martin, M. (2008). Extending the technology acceptance model to include the IT decision-maker: a study of business management software. Technovation, 28(3), 112-121.
  • 17. Hsua, C.-L., Linb, J.C.-C. (2016). Effect of perceived value and social influences on mobile app stickiness and in-app purchase intention. Technological Forecasting and Social Change, 108, 42-53.
  • 18. Huber, F., Herrmann, A., Morgan, R. (2001). Gaining competitive advantage through customer value oriented management. Consumer Marketing, 18(1), 41-53.
  • 19. Johnson, B., Christensen, L. (2012). Educational research: Quantitative, qualitative, and mixed approaches. 4th ed. California: SAGE, Thousand Oaks.
  • 20. Kapoor, K.K., Dwivedi, Y.K., Williams, M.D. (2014). Innovation adoption attributes: a review and synthesis of research findings. European Journal of Innovation Management, 17(3), 327-348.
  • 21. Karjaluoto, H., Lehto, H., Leppaniemi, М., Jayawardhena, C. (2008). Exploring gender influence on customer’s intention to engage permission-based mobile marketing. Electronic Marketing, 18(3), 242-259.
  • 22. Kim, E., Lin, J.S., Sung, Y. (2013). To App or Not to App: Engaging Consumers via Branded Mobile Apps. Journal of Interactive Advertising, 13(1), 53-65.
  • 23. Kleijnen, М., Ruyter, K.,Wetzels, M. (2007). An assessment of value creation in mobile service delivery and the moderating role of time consciousness. Retailing, 83(1), 33-46.
  • 24. Liu, F., Zhao, X., Chau, P.Y., Tang, Q. (2015). Roles of Perceived Value and Individual Differences in the Acceptance of Mobile Coupon Applications. Internet Research, 25(3), 471-495.
  • 25. Loiacono, E.T., Watson, R.T., Goodhue, D.L. (2007). WebQual: An Instrument for Consumer Evaluation of Web Sites. International Journal of Electronic Commerce, 11(3), 51-87.
  • 26. Mort, G., Drennan, J. (2007). Mobile communications: a study of factors influencing consumer use of m-services. Advertising Research, 47(3), 302-312.
  • 27. Nasco, S.A., Kulviwat, S., Kumar, A., Bruner, G.C. (2008). The CAT model: Extensions and moderators of dominance in technology acceptance. Psychology & Marketing, 25(10), 987-1005.
  • 28. Noh, М., Lee, K. (2015). An analysis of the relationship between quality and user acceptance in smartphone apps. Systems and e-Business Management, 14(2), 273-291.
  • 29. Nugroho, Y.A. (2009). The effect of Perceived usefulness, perceived ease of use, perceived risk, and trust toward behaviour intention ro transaction by internet. Business and Entrepreneurial Review, 9(1), 79-90.
  • 30. Pallant, J. (2013). SPSS survival manual: A step-by-step guide to data analysis using IBM SPSS. 5th ed. London: Open University Press.
  • 31. Pavlou, P. (2003). Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model. International Journal Electronic Commerce, 7(3), 69-103.
  • 32. Peter, J. Olson, J. (2010). Consumer behavior and marketing strategy. New York: McGraw-Hill.
  • 33. Polak, K. (2015). Mobile apps bring benefits and problems for enterprises in Poland. http://www.computerweekly.com/featare/Mobile-apps-bear-benefits-and-problems-for-enterprises-in-Poland (2.01.2018).
  • 34. Poushter, J. (2016). Smartphone Ownership and internet usage continuoues to climb in Emerging Economies: But advanced economies still have higher rates of technology use, 1-44. PewResearchCentre.
  • 35. Richard, M.-O., Chandra, R. (2005). A model of consumer web navigational behavior: conceptual development and application. Business Research, 58(8), 1019-1029.
  • 36. Saifi, R. (2017). The 2017 Mobile App Market: Statistics, Trends, and Analysis, https://www.business2community.com/mobile-apps/2017-mobile-app-market-statistics-trends-analysis-01750346 (10.01.2018)].
  • 37. Schmitz, C., Bartsch, S., Meyer, A. (2016). Mobile App Usage and Its Implications for Service Management - Empirical Findings from German Public Transport. Procedia - Social and Behavioral Sciences, 224, 230-237.
  • 38. Shankar, V. et al. (2016). Mobile Shopper Marketing: Key Issues, Current Insights, and Future Research Avenues. Interactive Marketing, 34, 37-48.
  • 39. Sheng, L., McGregor, K.K. (2010). Lexical-semantic organization in children with specific language impairment. Speech, Language, and Hearing Research: JSLHR, 53(1), 146-159.
  • 40. Shukairy, A. (2017). Mobile Commerce Statistics and Trends, https://www.invespcro.com/blog/mobile-commerce/ (01.2018).
  • 41. Statista (2017). Mobile App Usage - Statistics & Facts, https://www.statista.com/topics/1002/mobile-app-usage/ (10.01.2018).
  • 42. Tak, P., Panwar, S. (2017). Using UTAUT 2 model to predict mobile app based shopping: evidences from India. Journal of Indian Business Research, 9(3), 248-264.
  • 43. Tavakol, М., Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53-55.
  • 44. Taylor, D., Levin, M. (2014). Predicting mobile app usage for purchasing and information-sharing. International Journal of Retail & Distribution Management, 42(8), 759-774.
  • 45. Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D. (2003). User acceptance of IT: Toward a unified view. MIS Quarterly, 27(3), 425-478.
  • 46. Versaw, R. (2017). The Future Of Mobile Apps Is Now. https://www.forbes.com/ sites/forbestechcouncil/2017/08/29/the-future-of-mobile-apps-is-now/# 161 ceb7a 1152 (10.01.2018).
  • 47. Wang, R.J.-H., Malthouse, E.C., Krishnamurthi, L. (2015). On the Go: How Mobile Shopping Affects Customer Purchase Behavior. Retailing, 91, 217-234.
  • 48. Yang, Z., Cai, S., Zhou, Z., Zhou, N. (2005). Development and validation of an instrument to measure user perceived service quality of information presenting web portals. Information & Management, 42(4), 575-589.
  • 49. Yoon, C., Kim, S. (2007). Convenience and TAM in a ubiquitous computing environment: the case of wireless LAN. Electronic Commerce Research and Applications, 6(1), 102-112.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-53e4c0e6-535e-448b-95d9-cabff0ee87a5
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.