Analysis of Consumers’ Intention to Use Smartphone-Based Application in Purchasing Organic Agricultural Products
Recently, there have been many emerging smartphone-based applications that are used in marketing activities including agricultural products. However there has not much published about what drives people to engage in online purchasing of agriculture products. This study aims to reveal the factors that influence the intention to use smartphone-based application in purchasing organic agriculture products. The smartphone-based application used in this study namely Kecipir. Samples were taken by non-probabilistic method of consumers who intend to use Kecipir application to buy organic agriculture products, with the sample of 150 respondents in Jabodetabek area of Indonesia. Data analysis using Structural Equation Modeling (SEM), while technique research conducted by survey with the online questionnaire as a tool of data collection. The result showed that all variables used in this study, namely compatibility, altruism, perceived risk, perceived ease of use, and perceived usefulness were significant determinant factors of behavioral intention to use Kecipir, whether it directly influenced intention or indirectly influence intention through attitude toward using the application. The limitations of this study will be discussed further.
KeywordsIntention Organic agriculture product Smartphone application usage Structural equation modeling technology Acceptance model (TAM)
Association of Internet Network Providers Indonesia
attitude toward using
Average Variance Extracted
behavioral intention to use
Confirmatory Factor Analysis
comparative fit index
goodness of fit index
Innovation Diffusion Theory
normed fit index
perceived ease of use
root mean square residual
root mean square error of approximation
Structural Equation Modeling
Technology Acceptance Model
Theory of Reasoned Action
The authors gratefully acknowledge the valuable suggestions and comments of the reviewer of International Conference on Tropical Agriculture 2017 on earlier drafts of this paper as well as the Editor for Springer-ICTA Proceeding 2017.
- 1.Asosiasi Penyelenggara Jasa Internet Indonesia. Statistik pengguna dan perilaku pengguna internet Indonesia [Statistic and behavior of Indonesian internet user]. 2016. www.apjii.or.id. Accessed 17 Mar 2017. [in Bahasa Indonesia].
- 2.Hadi F. Transaksi e-Commerce di Indonesia pada 2016 mencapai 4, 89 miliar dolar AS [E-commerce transactions in Indonesia in 2016 reached 4.89 billion US Dollars]. 2017. http://www.tribunnews.com/bisnis/2017/02/20/transaksi-e-commrece-di-indonesia-pada-2016-mencapai-489-miliar-dolar-as. Accessed 15 Mar 2017. [in Bahasa Indonesia].
- 3.Anggraini L. Kecipir.com Ingin Bantu Distribusi Produk Petani Lebih Baik [Kecipir.com wants to help distribution of farmers’ products better]. 2016. http://teknologi.metrotvnews.com/read/2016/04/25/519039/Kecipir-com-ingin-bantu-distribusi-produk-petani-lebih-baik. Accessed 17 Mar 2017. [in Bahasa Indonesia].
- 5.Fishbein M, Ajzen I. Belief, attitude, intention, and behavior: an introduction to theory and research. Reading: Addison-Wesley; 1975.Google Scholar
- 8.Choo H, Chung JE, Pysarchik DT. Antecedents to new food product purchasing behavior among innovator groups in India. EJM. 2004;38(5/6):608–25.Google Scholar
- 10.Eagly AH, Chaiken S. The psychology of attitudes. Orlando: Harcourt Brace Jovanovich College Publishers; 1993.Google Scholar
- 12.Wei WC. A technology acceptance model: mediate and moderate effect. APMR. 2009;14(4):461–76.Google Scholar
- 13.El-Kasim M. A test of technology acceptance model in the use of social media among pr practitioners in Nigeria. Search. 2016;8(2):19–33.Google Scholar
- 16.Pavlou PA. Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model. IJEC. 2003;7(3):101–34.Google Scholar
- 18.Juniwati. Influence of perceived usefulness, ease of use, risk on attitude and intention to shop online. EJBM. 2014;6(7):21–226.Google Scholar
- 20.Akram MS. How perceived risk affects online purchase intention: consumer’s perspective. Clos Guiot Puyricard: Institut d’Administration des Entreprises; 2008.Google Scholar
- 21.Rogers EM. The diffusion of innovation. 5th ed. New York: Free Press; 2003.Google Scholar
- 24.Jayasingh S, Eze UC. An empirical analysis of consumer behavioral intention toward mobile coupons in Malaysia. IJOBM. 2009;4(2):221–42.Google Scholar
- 26.Sugiyono. Metode penelitian kuantitatif kualitatif dan R&D [Quantitative, qualitative, and R&D research methods]. Bandung: Alfabeta; 2010. [in Bahasa Indonesia].Google Scholar
- 27.Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis. 7th ed. Upper Saddle River: Prentice Hall; 2010.Google Scholar
- 28.Chin WW. The partial least squares approach for structural equation modeling. In: Marcoulides GA, editor. Modern method for business research. Mahwah: Lawrence Erlbaum Associates, Inc; 1998. p. 295–336.Google Scholar
- 29.Hooper D, Coughlan J, Mullen MR. Structural equation modelling: guidelines for determining model fit. EJBRM. 2008;6(1):53–60.Google Scholar
- 30.Ghozali I. Model persamaan struktural konsep dan aplikasi dengan program AMOS 22.0 [Model of structural equations, concepts and applications with the AMOS 22.0 program]. Semarang: Badan Penerbit Universitas Diponegoro; 2014. [in Bahasa Indonesia].Google Scholar