Diah Oktavia
Department of Management Technology, Institut Teknologi Sepuluh Nopember

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Analysis of the Relationship of Late Payment Customers' Characteristics Based on Payment Bills Diah Oktavia; Joko Lianto; Sutikno Sutikno
IPTEK Journal of Proceedings Series No 5 (2019): The 1st International Conference on Business and Management of Technology (IConBMT)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (816.946 KB) | DOI: 10.12962/j23546026.y2019i5.6285

Abstract

An increase number of internet usage has an influence on service operators to be able to provide a variety of services (multi services) and improve internet network technology for their customers. One of them is an Internet Service Provider (ISP) company. The customer's obligation is to pay bills on time in accordance with company's terms and conditions agreed upon at the beginning of the internet installation. However, late payments still occur at least once a year. This affects the company's business processes and finances. While on the customer side, this causes internet isolation. The purpose of this study is to analyze any variables that have a relationship with the late payment based on customer bills. The analytical method used was the Chi-Square test. The number of samples used was 400 customers with purposive sampling technique. In addition, an analysis of the pattern of customer characteristics in bill payments was done to determine whether there was a dependency based on regional mapping in Surabaya using the Morans'I and LISA Cluster Map indexes. The result showed that there was a significant relationship between address, income level, employment status, and payment period with payment status. There was no significant relationship between customer type, subscription package, and educational level with payment status. In addition, it is known that customers tend to make payments above the agreed time period. The mapping results by region showed that there were customer patterns that were clustered and having similar characteristics in adjacent locations. Autocorrelation showed positive dependencies of total income and employment status variables