Economic Growth and E-Commerce: Potential for Digitizing MSMEs in East Java
Abstract
The social and economic impacts caused by Covid-19 pandemic have changed the way people live. The existence of social activities and shopping due to spread of Covid-19, resulted in switching from an offline system to an online system, including in the case that number of e-commerce increased during the pandemic. In 2020 number of e-commerce businesses in East Java Province grew to 90.31 percent from 19.92 percent in 2019. According to Liu (2013), number of e-commerce businesses will affect economic growth. Pattern of relationship between e-commerce and economic growth needs to be known for an appropriate policy so that both can continue to develop. Furthermore, it is necessary to segment and optimize aspects that support the development of e-commerce and the digitization of MSMEs. This study aims to analyze effect of e-commerce businesses on economic growth in Java, segmenting and optimizing aspects that support development of e-commerce in the locus of Java Island, especially East Java Province, and complete it with an analysis of big data on public response to digital economy in times of pandemic. Method used are a combination of 5 techniques at once, namely regression analysis, thematic map visualization, clustering, spatial analysis, and text mining. Results show that e-commerce affects economic growth of provinces in Java. E-commerce businesses need to continue to be optimized by improving the quality of HDI and internet networks, especially in priority areas for the development of East Java Province. Programs related to digital economy need to be encouraged to harmonize changes in social order with the ability of MSMEs to adapt to digitalization era.
References
Anselin, Luc. (2018). Local Spatial Autocorrelation (1), Common Univariate Local Statistics. https://geodacenter.github.io/workbook/6a_local_auto/lab6a.html#principle. (7 Juni 2021)
Anvari, R. D., & Norouzi, D. (2016). The impact of e-commerce and R&D on economic development in some selected countries. Procedia-Social and Behavioral Sciences, 229, 354–362.
Bank Indonesia. (2020). Laporan Kebijakan Moneter Triwulan II-2020. Bank Indonesia: Jakarta.
Basu, R., Khatua, A., Jana, A., & Ghosh, S. (2017). Harnessing Twitter Data for Analyzing Public Reactions to Transportation Policies: Evidences from the Odd-Even Policy in Delhi, India. Proceedings of the Eastern Asia Society for Transportation Studies (EASTS).
Badan Pusat Statistik. (2019). Laju Pertumbuhan PDRB atas Dasar Harga Konstan menurut Provinsi. Jakarta: BPS.
Badan Pusat Statistik. (2019a). Statistik e-commerce 2019. Jakarta: BPS.
Badan Pusat Statistik. (2020). Statistik e-commerce 2020. Jakarta: BPS.
Badan Pusat Statistik. (2020). Laju Pertumbuhan PDRB atas Dasar Harga Konstan menurut Provinsi. Jakarta: BPS.
Badan Pusat Statistik Provinsi Jawa Timur. (2021). Provinsi Jawa Timur Dalam Angka 2021. Surabaya: BPS Jatim.
Bustaman, U. dkk. (2013). Penggunaan Metode Geographically Weighted Regression (GWR) untuk Analisis Data Sosial dan Ekonomi. Jakarta: BPS.
Collomb, A., Costea, C., Joyeux, D., Hasan, O., Brunie, L. (2014). A Study and Comparison of Sentiment Analysis Methods for Reputation Evaluation. Rapport de recherche RR-LIRIS-2014-002.
Data Reportal. (2021). “Digital 2021: Indonesia”. https://datareportal.com/reports/digital-2021-indonesia. (27 Mei 2021)
Drapper, N. R. and Smith, H. (1992). Analisis Regresi Terapan (Terjemahan B. Sumantri) dalam Edisi Kedua. Jakarta: PT. Gramedia Pustaka Utama.
Feldman, R. & Dagan, I. (1995). Knowledge discovery in textual databases (KDT). On Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95), Montreal, Canada, August 20-21, AAAI Press, 112-117.
Getis, Arthur. (2009). Handbook of Spatial Analysis. United State of America: Springer Science Business Media.
Go, A., Bhayani, R., Huang, L. (2009). Twitter Sentiment Classification using Distant Supervision. CS224N project report, Stanford, 1(12), 2009.
Hapsoro, B. B., Palupiningdyah, P., & Slamet, A. (2019). Peran digital marketing sebagai upaya peningkatan omset penjualan bagi klaster UMKM di Kota Semarang. Jurnal Abdimas, 23(2), 117-120.
Johnson, Richard. (2007). Applied Multivariate Statistical Analysis. Madison: Pearson Prentice Hall.
Kiplangat, B. J., Shisia, A., & Asienga, I. C. (2015). Effects of human competencies in the adoption of e-commerce strategies among SMEs in Kenya. International Journal of Economics, Commerce and Management, iii, 10.
Liu, S. (2013). An Empirical Study on E-commerce’s effects on Economic Growth. 2013 Conference on Education Technology and Management Science (ICETMS 2013).
Mattjik, A. A., dan Sumertajaya, I. M. (2011). Sidik Peubah Ganda dengan Menggunakan SAS. Bandung: IPB Press.
Office Management and Budget. (2015). Revised Delineations of Metropolitan Statistical Areas, and Combined Statistical Areas, and Guidance on Uses of the Delineations of These Areas. [Bulletin]. OMB Bulletin. No. 15-01.
Pemerintah Kabupaten Pasuruan. (2019). RKPD 2020 dan RPJMD 2018-2023 Kabupaten Pasuruan. Pasuruan: Pemkab Pasuruan.
Pemerintah RI. (2017). Peraturan Pemerintah Nomor 80 Tahun 2019. Jakarta: Pemerintah RI.
Pemerintah RI. (2019). Peraturan Presiden No 74 tahun 2017. Jakarta: Pemerintah RI.
Reimsbach-Kounatze, C. (2015). The Proliferation of “Big Data” and Implications for Official Statistics and Statistical Agencies: A Preliminary Analysis. OECD Digital Economy Papers, No. 245, OECD Publishing, Paris.
Suci, Y. R. (2017). Perkembangan UMKM (Usaha mikro kecil dan menengah) di Indonesia. Cano Ekonomos, 6(1), 51-58.
Shah, M. (2018). SentR. https://rdrr.io/github/mananshah99/sentR/. (3 Juni 2021)
Scrucca, L. (2005). Clustering multivariate spatial data based on local measures of spatial autocorrelation. Quaderni del Dipartimento di Economia, Finanza e Statistica, 20(1), 11.
Supranto. (2004). Analisis Multivariat Arti dan Interpretasi. Jakarta: Rineka Cipta.
Tennekes, M. (2018). tmap: Thematic Maps in R. Journal of Statistical Software, 84(6), pp.1-39.
UN Global Pulse. (2015). Using Twitter Data to Analyze Public Sentiment on Fuel Subsidy Policy Reform in El Salvador. Global Pulse Project Series, (13), 1-2.
Waseem, A., Rashid, Y., Warraich, M. A., Sadiq, I., & Shaukat, Z. (2018). Factors affecting Ecommerce potential of any country using multiple regression analysis. Journal of Internet Banking and Commerce, 24(2), 1–28.
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