{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:27:05Z","timestamp":1742912825457,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031434266"},{"type":"electronic","value":"9783031434273"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43427-3_4","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T21:01:41Z","timestamp":1694898101000},"page":"51-65","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DyCOD - Determining Cash on Delivery Limits for Real-Time E-commerce Transactions via Constrained Optimisation Modelling"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6857-9767","authenticated-orcid":false,"given":"Akash","family":"Deep","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4986-3723","authenticated-orcid":false,"given":"Sri Charan","family":"Kattamuru","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5211-0891","authenticated-orcid":false,"given":"Meghana","family":"Negi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2578-4492","authenticated-orcid":false,"given":"Jose","family":"Mathew","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0083-8502","authenticated-orcid":false,"given":"Jairaj","family":"Sathyanarayana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"key":"4_CR1","first-page":"288","volume":"38","author":"G Aa","year":"2021","unstructured":"Aa, G., Sb, V., Ab, M.K.R.: Deciding bank client\u2019s credit limit and home loan using machine learning. Smart Intell. Comput. Commun. Technol. 38, 288 (2021)","journal-title":"Smart Intell. Comput. Commun. Technol."},{"key":"4_CR2","first-page":"200176","volume":"17","author":"MA Bashar","year":"2023","unstructured":"Bashar, M.A., Nayak, R., Astin-Walmsley, K., Heath, K.: Machine learning for predicting propensity-to-pay energy bills. Intell. Syst. Appl. 17, 200176 (2023)","journal-title":"Intell. Syst. Appl."},{"key":"4_CR3","unstructured":"Economic-Times: Cash on delivery about \\$30 billion of India\u2019s ecommerce market (2022). https:\/\/economictimes.indiatimes.com\/industry\/services\/retail\/cash-on-delivery-about-30-billion-of-indias-ecommerce-market-says-gokwik\/articleshow\/96112033.cms?from=mdr"},{"issue":"5","key":"4_CR4","doi-asserted-by":"publisher","first-page":"5309","DOI":"10.1016\/j.eswa.2011.11.005","volume":"39","author":"YT \u0130\u00e7","year":"2012","unstructured":"\u0130\u00e7, Y.T.: Development of a credit limit allocation model for banks using an integrated fuzzy TOPSIS and linear programming. Expert Syst. Appl. 39(5), 5309\u20135316 (2012)","journal-title":"Expert Syst. Appl."},{"key":"4_CR5","unstructured":"Licari, J., Loiseau-Aslanidi, O., Tolstova, V., Sadat, M.: Determining the optimal dynamic credit card limit (2021)"},{"key":"4_CR6","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.jfds.2022.07.002","volume":"8","author":"A Markov","year":"2022","unstructured":"Markov, A., Seleznyova, Z., Lapshin, V.: Credit scoring methods: latest trends and points to consider. J. Finance Data Sci. 8, 180\u2013201 (2022)","journal-title":"J. Finance Data Sci."},{"key":"4_CR7","unstructured":"Razorpay: Cash on delivery and the challenges that come with it (2022). https:\/\/razorpay.com\/blog\/cash-on-delivery-and-the-challenges\/"},{"key":"4_CR8","unstructured":"Shiprocket: How to minimize cod failures and returns? (2014). https:\/\/www.shiprocket.in\/blog\/minimize-cod-failures-and-returns\/"},{"key":"4_CR9","unstructured":"Shopee: Cash on delivery (COD) at shopee (2022). https:\/\/seller.shopee.com.my\/edu\/article\/11759"},{"key":"4_CR10","unstructured":"Times-Of-India: 90% of rural India\u2019s e-comm orders paid in cash (2021). https:\/\/timesofindia.indiatimes.com\/business\/india-business\/90-of-rural-indias-e-comm-orders-paid-in-cash-study\/articleshow\/88244840.cms"},{"key":"4_CR11","unstructured":"Today, B.: Even as UPI grows rapidly, a majority of customers prefer cash-on-delivery for e-commerce purchases (2022). https:\/\/www.businesstoday.in\/technology\/story\/even-as-upi-grows-rapidly-a-majority-of-customers-prefer-cash-on-delivery-for-e-commerce-purchases-347320-2022-09-15"},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Vardasbi, A., de Rijke, M., Markov, I.: Cascade model-based propensity estimation for counterfactual learning to rank. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2089\u20132092 (2020)","DOI":"10.1145\/3397271.3401299"},{"issue":"1","key":"4_CR13","doi-asserted-by":"publisher","first-page":"2056362","DOI":"10.1080\/23322039.2022.2056362","volume":"10","author":"K Visser","year":"2022","unstructured":"Visser, K., Swart, G., Pretorius, J., Esterhuyzen, L.M., Verster, T., Fourie, E.: Customer comfort limit utilisation: management tool informing credit limit-setting strategy decisions to improve profitability. Cogent Econ. Finance 10(1), 2056362 (2022)","journal-title":"Cogent Econ. Finance"},{"key":"4_CR14","unstructured":"Wong, N., Matthews, C.: Cardholder perceptions of credit card limits. In: 10th AIBF Banking and Finance Conference, Melbourne, Australia (2005)"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Yan, J., Gong, M., Sun, C., Huang, J., Chu, S.M.: Sales pipeline win propensity prediction: a regression approach. In: 2015 IFIP\/IEEE International Symposium on Integrated Network Management (IM), pp. 854\u2013857. IEEE (2015)","DOI":"10.1109\/INM.2015.7140393"},{"key":"4_CR16","doi-asserted-by":"publisher","first-page":"012053","DOI":"10.1088\/1742-6596\/1437\/1\/012053","volume":"1437","author":"H Zhang","year":"2020","unstructured":"Zhang, H., Zeng, R., Chen, L., Zhang, S.: Research on personal credit scoring model based on multi-source data. J. Phys. Conf. Ser. 1437, 012053 (2020)","journal-title":"J. Phys. Conf. Ser."},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Zhang, Y.: Prediction of customer propensity based on machine learning. In: 2021 Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS), pp. 5\u20139. IEEE (2021)","DOI":"10.1109\/ACCTCS52002.2021.00009"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43427-3_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T21:02:12Z","timestamp":1694898132000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43427-3_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434266","9783031434273"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43427-3_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"As we propose a framework to determine the cash-on-delivery limits for e-commerce transactions, we acknowledge the ethical implications of our work. Since our work is closely associated with collecting and processing transaction level data, we assure that any data collected for the research, including any personal information, has been secured and anonymised. We commit to safeguarding and respecting the privacy of individuals\u2019 data. We ensure that our work is agnostic to any personal information including race, gender, religion, or any other personal characteristic. We believe in equality and inclusivity as essential aspects of ethical development. We believe that our work is extensible across industries to many applications within the pay-on-delivery limit and credit limit determination domains. We emphasise that our work should not be used for any harmful purpose.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Statement"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"829","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"196","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.63","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}