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			<depositor_name>Institute for Management, Business, and Accounting Studies</depositor_name>
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				<full_title>Journal of Digital Business and Management Studies</full_title>
				<abbrev_title>J. Digit. Bus. Manag. Stud.</abbrev_title>
				<issn>3149-9597</issn>
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					<year>2026</year>
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					<volume>6</volume>
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				<issue>1</issue>
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					<title>Digital Subscription Business Models and Customer Retention Management: A Critical Review of Recurring Revenue, Churn Control, and Value Renewal</title>
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          					<person_name sequence="first" contributor_role="author">
            <given_name>Kwame</given_name>
            <surname>Mensah</surname>
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            <given_name>Kojo</given_name>
            <surname>Asante</surname>
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          					<person_name sequence="additional" contributor_role="author">
            <given_name>Linda</given_name>
            <surname>Owusu</surname>
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								<publication_date>
					<year>2026</year>
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					  <unstructured_citation>Bischof SF, Boettger TM, Rudolph T. Curated subscription commerce: A theoretical conceptualization. J Retail Consum Serv. 2020;54:101822.</unstructured_citation>
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          					<citation key="rk-10.68159/j481686906-f37c2a53-563e-4109-949d-069ac75b552a">
					  <unstructured_citation>Andonova Y, Anaza NA, Bennett DH. Riding the subscription box wave: Understanding the landscape, challenges, and critical success factors of the subscription box industry. Bus Horiz. 2021;64(5):631-46.</unstructured_citation>
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          					<citation key="rk-10.68159/j481686906-98cdfd49-e5c5-47c4-b20b-32e37b6a6902">
					  <unstructured_citation>McCarthy DM, Fader PS, Hardie BG. Valuing subscription-based businesses using publicly disclosed customer data. J Mark. 2017;81(1):17-35.</unstructured_citation>
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					  <unstructured_citation>Sanches HE, Possebom AT, Aylon LB. Churn prediction for SaaS company with machine learning. Innov Manag Rev. 2025;22(2):130-42.</unstructured_citation>
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					  <unstructured_citation>Kotan M, Faruk Seymen Ö, Çallı L, Kasım S, Çarklı Yavuz B, Över Özçelik T. A novel methodological approach to SaaS churn prediction using whale optimization algorithm. PloS one. 2025;20(5):e0319998.</unstructured_citation>
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          					<citation key="rk-10.68159/j481686906-61faa547-bb8b-44bf-8c7e-dc54b3855cbe">
					  <unstructured_citation>Kumar V, Rajan B, Gupta S, Pozza ID. Customer engagement in service. J Acad Mark Sci. 2019;47(1):138-60.</unstructured_citation>
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          					<citation key="rk-10.68159/j481686906-cf255296-3f82-4352-ab96-1cfba76f4a3c">
					  <unstructured_citation>Hollebeek LD, Sprott DE, Andreassen TW, Costley C, Klaus P, Kuppelwieser V, et al. Customer engagement in evolving technological environments: synopsis and guiding propositions. Eur J Mark. 2019;53(9):2018-23.</unstructured_citation>
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          					<citation key="rk-10.68159/j481686906-92dfdd83-5c66-40b4-9ba6-b1cb3d455360">
					  <unstructured_citation>Ascarza E. Retention futility: Targeting high-risk customers might be ineffective. J Mark Res. 2018;55(1):80-98.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j481686906-cb714055-d69c-4e65-898a-a0a7b71c6d6c">
					  <unstructured_citation>Ascarza E, Neslin SA, Netzer O, Anderson Z, Fader PS, Gupta S, et al. In pursuit of enhanced customer retention management: Review, key issues, and future directions. Cust Needs Solut. 2018;5(1):65-81.</unstructured_citation>
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          					<citation key="rk-10.68159/j481686906-c47065a0-5a82-4a3e-9afc-62a613a7b72b">
					  <unstructured_citation>Wu B, Guo G, Luo P. The effect of subscriptions on customer engagement. J Bus Res. 2024;178:114638.</unstructured_citation>
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          					<citation key="rk-10.68159/j481686906-a1480886-3532-45b2-ad47-4e3d62aa6efd">
					  <unstructured_citation>Muratcehajic D, Loureiro SM. Subscriber retention management: SRM framework and future research agenda. J Serv Mark. 2024;38(8):1030-57.</unstructured_citation>
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          					<citation key="rk-10.68159/j481686906-525607f7-b808-482a-8b63-8f5ae636fe1c">
					  <unstructured_citation>Verhoef PC, Broekhuizen T, Bart Y, Bhattacharya A, Dong JQ, Fabian N, et al. Digital transformation: A multidisciplinary reflection and research agenda. J Bus Res. 2021;122:889-901.</unstructured_citation>
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          					<citation key="rk-10.68159/j481686906-ec7519b3-078e-43e9-9ba0-76616d1a0ebc">
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					  <unstructured_citation>García MR, Fernández AN, Romero IG, Prado-Prado JC. Examining the business model of replenishment subscriptions: a longitudinal case study. Dir Organ. 2022:40-53.</unstructured_citation>
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          					<citation key="rk-10.68159/j481686906-2509d344-9a12-40a4-9979-260132b35b3d">
					  <unstructured_citation>Yang J, Kwon Y. Are digital content subscription services still thriving? Analyzing the conflict between innovation adoption and resistance. J Innov Knowl. 2024;9(4):100581.</unstructured_citation>
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          					<citation key="rk-10.68159/j481686906-cf3e85ee-3338-4d26-a73c-c5de44a9a3e5">
					  <unstructured_citation>Kerschbaumer RH, Kreimer D, Foscht T, Eisingerich AB. Subscription commerce: an attachment theory perspective. Int Rev Retail Distrib Consum Res. 2023;33(1):92-115.</unstructured_citation>
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          					<citation key="rk-10.68159/j481686906-3d2e907c-ad32-4db1-9f26-2e73d43d5bd5">
					  <unstructured_citation>Lee JG, Sadachar A, Manchiraju S. What&#039;s in the box? Investigation of beauty subscription box retail services. Fam Consum Sci Res J. 2019;48(1):85-102.</unstructured_citation>
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					  <unstructured_citation>Van Droogenbroeck E, Willems K. Unpacking preferences for surprise subscription boxes-A Kano-based study of present, former, and non-users. J Retail Consum Serv. 2025;87:104326.</unstructured_citation>
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					  <unstructured_citation>Lima D, Ramos RF, Oliveira PM. Customer satisfaction in the pet food subscription-based online services. Electron Commer Res. 2024;24(2):745-69.</unstructured_citation>
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					  <unstructured_citation>Fader PS, Hardie BG, Liu Y, Davin J, Steenburgh T. “How to project customer retention” revisited: The role of duration dependence. J Interact Mark. 2018;43(1):1-6.</unstructured_citation>
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          					<citation key="rk-10.68159/j481686906-73e4e0f4-0fe2-4031-8a98-d06a516c151c">
					  <unstructured_citation>Matuszelański K, Kopczewska K. Customer churn in retail e-commerce business: Spatial and machine learning approach. J Theor Appl Electron Commer Res. 2022;17(1):165-98.</unstructured_citation>
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          					<citation key="rk-10.68159/j481686906-b2a86143-255f-4118-8b34-60b8858012ea">
					  <unstructured_citation>Xiahou X, Harada Y. B2C E-commerce customer churn prediction based on K-means and SVM. J Theor Appl Electron Commer Res. 2022;17(2):458-75.</unstructured_citation>
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					  <unstructured_citation>Al-Najjar D, Al-Rousan N, Al-Najjar H. Machine learning to develop credit card customer churn prediction. J Theor Appl Electron Commer Res. 2022;17(4):1529-42.</unstructured_citation>
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					  <unstructured_citation>Shahabikargar M, Beheshti A, Zhang X, Foo J, Jolfaei A. A comprehensive survey on customer churn analysis studies. J Inf Telecommun. 2026;10(1):24-70.</unstructured_citation>
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          					<citation key="rk-10.68159/j481686906-85df60b8-4df8-4143-8dea-15e7be356115">
					  <unstructured_citation>Lalwani P, Mishra MK, Chadha JS, Sethi P. Customer churn prediction system: a machine learning approach. Computing. 2022;104(2):271-94.</unstructured_citation>
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					  <unstructured_citation>Haddadi SJ, Farshidvard A, dos Santos Silva F, dos Reis JC, da Silva Reis M. Customer churn prediction in imbalanced datasets with resampling methods: A comparative study. Expert Syst Appl. 2024;246:123086.</unstructured_citation>
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          					<citation key="rk-10.68159/j481686906-76076f00-13a7-4fa8-9a12-1eed683ca197">
					  <unstructured_citation>Gattermann-Itschert T, Thonemann UW. How training on multiple time slices improves performance in churn prediction. Eur J Oper Res. 2021;295(2):664-74.</unstructured_citation>
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          					<citation key="rk-10.68159/j481686906-f11f8682-1f94-4729-b34c-07abbf14bdb8">
					  <unstructured_citation>De Caigny A, Coussement K, De Bock KW. A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. Eur J Oper Res. 2018;269(2):760-72.</unstructured_citation>
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          					<citation key="rk-10.68159/j481686906-2db43791-25d1-43fe-baae-98d8fc3861ec">
					  <unstructured_citation>Gosain A, Sardana S. Handling class imbalance problem using oversampling techniques: A review. In2017 international conference on advances in computing, communications and informatics (ICACCI). IEEE;2017. p. 79-85.</unstructured_citation>
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					  <unstructured_citation>Coussement K, Benoit DF, Van den Poel D. Improved marketing decision making in a customer churn prediction context using generalized additive models. Expert syst Appl. 2010;37(3):2132-43.</unstructured_citation>
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