{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:00:34Z","timestamp":1772906434933,"version":"3.50.1"},"reference-count":87,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T00:00:00Z","timestamp":1708992000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T00:00:00Z","timestamp":1708992000000},"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":["Oper Res Int J"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s12351-023-00813-6","type":"journal-article","created":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T07:02:41Z","timestamp":1709017361000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Customer purchase prediction in electronic markets from clickstream data using the Oracle meta-classifier"],"prefix":"10.1007","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4447-7292","authenticated-orcid":false,"given":"Fatemeh","family":"Ehsani","sequence":"first","affiliation":[]},{"given":"Monireh","family":"Hosseini","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,27]]},"reference":[{"key":"813_CR1","doi-asserted-by":"crossref","unstructured":"Agrawal T, Agrawal T (2021) Hyperparameter optimization using scikit-learn. In: Hyperparameter optimization in machine learning: make your machine learning and deep learning models more efficient, pp 31\u201351","DOI":"10.1007\/978-1-4842-6579-6_2"},{"key":"813_CR2","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.ijinfomgt.2018.06.001","volume":"42","author":"AA Alalwan","year":"2018","unstructured":"Alalwan AA (2018) Investigating the impact of social media advertising features on customer purchase intention. Int J Inf Manag 42:65\u201377","journal-title":"Int J Inf Manag"},{"key":"813_CR3","doi-asserted-by":"crossref","unstructured":"Alghanam OA, Al-Khatib SN, Hiari MO (2022) Data mining model for predicting customer purchase behavior in e-commerce context. Int J Adv Comput Sci Appl 13(2)","DOI":"10.14569\/IJACSA.2022.0130249"},{"issue":"2","key":"813_CR4","doi-asserted-by":"crossref","first-page":"891","DOI":"10.5267\/j.ijdns.2022.12.022","volume":"7","author":"A Alkufahy","year":"2023","unstructured":"Alkufahy A, Al-Alshare F, Qawasmeh F, Aljawarneh N, Almaslmani R (2023) The mediating role of the perceived value on the relationships between customer satisfaction, customer loyalty and e-marketing. Int J Data Netw Sci 7(2):891\u2013900","journal-title":"Int J Data Netw Sci"},{"issue":"5","key":"813_CR5","first-page":"1785","volume":"34","author":"P Anitha","year":"2022","unstructured":"Anitha P, Patil MM (2022) RFM model for customer purchase behavior using K- Means algorithm. J King Saud Univ-Comput Inf Sci 34(5):1785\u20131792","journal-title":"J King Saud Univ-Comput Inf Sci"},{"key":"813_CR6","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2020.106723","volume":"86","author":"BS Arasu","year":"2020","unstructured":"Arasu BS, Seelan BJB, Thamaraiselvan N (2020) A machine learning-based approach to enhancing social media marketing. Comput Electr Eng 86:106723","journal-title":"Comput Electr Eng"},{"key":"813_CR7","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s40547-017-0080-0","volume":"5","author":"E Ascarza","year":"2018","unstructured":"Ascarza E, Neslin SA, Netzer O, Anderson Z, Fader PS, Gupta S, Schrift R (2018) In pursuit of enhanced customer retention management: review, key issues, and future directions. Cust Needs Solut 5:65\u201381","journal-title":"Cust Needs Solut"},{"key":"813_CR8","volume-title":"Landing page optimization: the definitive guide to testing and tuning for conversions","author":"T Ash","year":"2012","unstructured":"Ash T, Ginty M, Page R (2012) Landing page optimization: the definitive guide to testing and tuning for conversions. John"},{"key":"813_CR9","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.eswa.2017.10.046","volume":"94","author":"A Baumann","year":"2018","unstructured":"Baumann A, Haupt J, Gebert F, Lessmann S (2018) Changing perspectives: using graph metrics to predict purchase probabilities. Expert Syst Appl 94:137\u2013148","journal-title":"Expert Syst Appl"},{"key":"813_CR10","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s12599-018-0528-2","volume":"61","author":"A Baumann","year":"2019","unstructured":"Baumann A, Haupt J, Gebert F, Lessmann S (2019) The price of privacy: an evaluation of the economic value of collecting clickstream data. Bus Inf Syst Eng 61:413\u2013431","journal-title":"Bus Inf Syst Eng"},{"key":"813_CR11","unstructured":"Brownlee J (2020) Data preparation for machine learning: data cleaning, feature selection, and data transforms\nin Python. Machine Learning Mastery"},{"issue":"1","key":"813_CR12","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.intmar.2008.10.004","volume":"23","author":"RE Bucklin","year":"2009","unstructured":"Bucklin RE, Sismeiro C (2009) Click here for Internet insight: advances in clickstream data analysis in marketing. J Interact Mark 23(1):35\u201348","journal-title":"J Interact Mark"},{"key":"813_CR13","doi-asserted-by":"crossref","unstructured":"Canbek G, Sagiroglu S, Temizel TT, Baykal N (2017) Binary classification performance measures\/metrics: a comprehensive visualized roadmap to gain new insights. In 2017 international conference on computer science and engineering (UBMK). IEEE, Chicago, pp 821\u2013826","DOI":"10.1109\/UBMK.2017.8093539"},{"issue":"12","key":"813_CR14","doi-asserted-by":"crossref","first-page":"11243","DOI":"10.1016\/j.eswa.2012.03.046","volume":"39","author":"CJ Carmona","year":"2012","unstructured":"Carmona CJ, Ram\u00edrez-Gallego S, Torres F, Bernal E, del Jesus MJ, Garc\u00eda S (2012) Web usage mining to improve the design of an e-commerce website: OrOliveSur.com. Expert Syst Appl 39(12):11243\u201311249","journal-title":"Expert Syst Appl"},{"issue":"4","key":"813_CR15","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1016\/j.ipm.2019.03.007","volume":"56","author":"ECA Carre\u00f3n","year":"2019","unstructured":"Carre\u00f3n ECA, Nonaka H, Hentona A, Yamashiro H (2019) Measuring the influence of mere exposure effect of TV commercial adverts on purchase behavior based on machine learning prediction models. Inf Process Manag 56(4):1339\u20131355","journal-title":"Inf Process Manag"},{"key":"813_CR16","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.neucom.2013.05.059","volume":"135","author":"S Cateni","year":"2014","unstructured":"Cateni S, Colla V, Vannucci M (2014) A method for resampling imbalanced datasets in binary classification tasks for real-world problems. Neurocomputing 135:32\u201341","journal-title":"Neurocomputing"},{"key":"813_CR17","doi-asserted-by":"crossref","first-page":"113622","DOI":"10.1016\/j.dss.2021.113622","volume":"149","author":"N Chaudhuri","year":"2021","unstructured":"Chaudhuri N, Gupta G, Vamsi V, Bose I (2021) On the platform but will they buy? Predicting customers\u2019 purchase behavior using deep learning. Decis Support Syst 149:113622","journal-title":"Decis Support Syst"},{"issue":"5","key":"813_CR18","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1111\/poms.12295","volume":"24","author":"M Chen","year":"2015","unstructured":"Chen M, Chen ZL (2015) Recent developments in dynamic pricing research: multiple products, competition, and limited demand information. Prod Oper Manag 24(5):704\u2013731","journal-title":"Prod Oper Manag"},{"key":"813_CR19","volume":"173","author":"S-X Chen","year":"2021","unstructured":"Chen S-X, Wang X-K, Zhang H-Y, Wang J-Q (2021) Customer purchase prediction from the perspective of imbalanced data: a machine learning framework based on factorization machine. Expert Syst Appl 173:114756","journal-title":"Expert Syst Appl"},{"issue":"1","key":"813_CR20","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1287\/mksc.1110.0678","volume":"31","author":"PK Chintagunta","year":"2012","unstructured":"Chintagunta PK, Chu J, Cebollada J (2012) Quantifying transaction costs in online\/off-line grocery channel choice. Mark Sci 31(1):96\u2013114","journal-title":"Mark Sci"},{"issue":"2","key":"813_CR21","first-page":"299","volume":"3","author":"D Chong","year":"2022","unstructured":"Chong D, Ali H (2022) Literature review: competitive strategy, competitive advantages, and marketing performance on e-commerce Shopee Indonesia. Dinasti Int J Digit Bus Manag 3(2):299\u2013309","journal-title":"Dinasti Int J Digit Bus Manag"},{"issue":"2","key":"813_CR22","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.ejor.2021.04.021","volume":"296","author":"P Chou","year":"2022","unstructured":"Chou P, Chuang HHC, Chou YC, Liang TP (2022) Predictive analytics for customer repurchase: interdisciplinary integration of buy till you die modeling and machine learning. Eur J Oper Res 296(2):635\u2013651","journal-title":"Eur J Oper Res"},{"key":"813_CR23","unstructured":"Claesen M, Simm J, Popovic D, Moor B (2014) Hyperparameter tuning in Python using optunity. In: paper presented at the proceedings of the international workshop on technical computing for machine learning and mathematical engineering"},{"key":"813_CR24","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.inffus.2017.09.010","volume":"41","author":"RM Cruz","year":"2018","unstructured":"Cruz RM, Sabourin R, Cavalcanti GD (2018) Dynamic classifier selection: recent advances and perspectives. Inf Fusion 41:195\u2013216","journal-title":"Inf Fusion"},{"issue":"1","key":"813_CR25","first-page":"283","volume":"21","author":"RM Cruz","year":"2020","unstructured":"Cruz RM, Hafemann LG, Sabourin R, Cavalcanti GD (2020) DESlib: a dynamic ensemble selection library in Python. J Mach Learn Res 21(1):283\u2013287","journal-title":"J Mach Learn Res"},{"key":"813_CR26","volume":"124","author":"Q Dai","year":"2022","unstructured":"Dai Q, Liu JW, Liu Y (2022) Multi-granularity relabeled under-sampling algorithm for imbalanced data. Appl Soft Comput 124:109083","journal-title":"Appl Soft Comput"},{"issue":"2","key":"813_CR27","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1287\/isre.2015.0568","volume":"26","author":"AW Ding","year":"2015","unstructured":"Ding AW, Li S, Chatterjee P (2015) Learning user real-time intent for optimal dynamic web page transformation. Inf Syst Res 26(2):339\u2013359","journal-title":"Inf Syst Res"},{"issue":"1","key":"813_CR28","doi-asserted-by":"crossref","DOI":"10.1002\/cpe.4882","volume":"31","author":"Y Dong","year":"2019","unstructured":"Dong Y, Jiang W (2019) Brand purchase prediction based on time-evolving user behaviors in e-commerce. Concurr Comput Pract Exp 31(1):e4882","journal-title":"Concurr Comput Pract Exp"},{"issue":"3","key":"813_CR29","doi-asserted-by":"crossref","first-page":"171","DOI":"10.2991\/ijndc.k.200515.006","volume":"8","author":"SA Ehikioya","year":"2020","unstructured":"Ehikioya SA, Lu S (2020) A traffic tracking analysis model for the effective management of e-commerce transactions. Int J Netw Distrib Comput 8(3):171\u2013193","journal-title":"Int J Netw Distrib Comput"},{"issue":"5","key":"813_CR201","first-page":"1","volume":"11","author":"F Ehsani","year":"2023","unstructured":"Ehsani F, Hosseini M (2023a) Consumer segmentation based on location and timing dimensions using big data from business-to-customer retailing marketplaces. Big Data 11(5):1\u201316","journal-title":"Big Data"},{"issue":"3","key":"813_CR202","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1108\/EMJB-08-2021-0121","volume":"18","author":"F Ehsani","year":"2023","unstructured":"Ehsani F, Hosseini M (2023b) Investigation to determine elements influencing customer's satisfaction in the B2C electronic retailing marketplaces. EuroMed J Bus 18(3):321\u2013344","journal-title":"EuroMed J Bus"},{"issue":"5","key":"813_CR31","first-page":"41","volume":"7","author":"E Felix","year":"2015","unstructured":"Felix E (2015) Marketing challenges of satisfying consumers changing expectations and preferences in a competitive market. Int J Mark Stud 7(5):41","journal-title":"Int J Mark Stud"},{"key":"813_CR32","unstructured":"Forslund J, Fahl\u00e9n J (2020) Predicting customer purchase behavior within Telecom: how Artificial Intelligence can be collaborated into marketing efforts. Master of Science Thesis TRITA-ITM-EX 2020:356, KTH Industrial Engineering and Management, Stockholm"},{"key":"813_CR33","doi-asserted-by":"crossref","unstructured":"Ghosh S, Banerjee C (2020) A predictive analysis model of customer purchase behavior using modified random forest algorithm in cloud environment. In: 2020 IEEE 1st international conference for convergence in engineering (ICCE). IEEE, pp 239\u2013244","DOI":"10.1109\/ICCE50343.2020.9290700"},{"key":"813_CR34","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.indmarman.2019.11.002","volume":"86","author":"CP Holland","year":"2020","unstructured":"Holland CP, Thornton SC, Naud\u00e9 P (2020) B2B analytics in the airline market: harnessing the power of consumer big data. Ind Mark Manage 86:52\u201364","journal-title":"Ind Mark Manage"},{"issue":"5","key":"813_CR35","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1587\/transinf.2017EDL8210","volume":"101","author":"C Hou","year":"2018","unstructured":"Hou C, Chen C, Wang J (2018) Tree-based feature transformation for purchase behavior prediction. IEICE Trans Inf Syst 101(5):1441\u20131444","journal-title":"IEICE Trans Inf Syst"},{"issue":"1","key":"813_CR36","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.eswa.2009.05.065","volume":"37","author":"C-C Huang","year":"2010","unstructured":"Huang C-C, Liang W-Y, Lai Y-H, Lin Y-C (2010) The agent-based negotiation process for B2C e-commerce. Expert Syst Appl 37(1):348\u2013359","journal-title":"Expert Syst Appl"},{"issue":"13","key":"813_CR37","doi-asserted-by":"crossref","first-page":"6711","DOI":"10.3390\/app12136711","volume":"12","author":"A Huidobro","year":"2022","unstructured":"Huidobro A, Monroy R, Cervantes B (2022) A High-level representation of the navigation behavior of website visitors. Appl Sci 12(13):6711","journal-title":"Appl Sci"},{"key":"813_CR38","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/978-3-030-05318-5","volume-title":"Automated machine learning: methods, systems, challenges","author":"F Hutter","year":"2019","unstructured":"Hutter F, Kotthoff L, Vanschoren J (2019) Automated machine learning: methods, systems, challenges. Springer Nature, p 219"},{"issue":"03","key":"813_CR39","doi-asserted-by":"crossref","first-page":"448","DOI":"10.4236\/tel.2018.83032","volume":"8","author":"R Joshi","year":"2018","unstructured":"Joshi R, Gupte R, Saravanan P (2018) A random forest approach for predicting online buying behavior of Indian customers. Theor Econ Lett 8(03):448","journal-title":"Theor Econ Lett"},{"key":"813_CR40","doi-asserted-by":"crossref","unstructured":"Kabir MR, Ashraf FB, Ajwad R (2019). Analysis of different predicting model for online shoppers\u2019 purchase intention from empirical data. In: 2019 22nd international conference on computer and information technology (ICCIT). IEEE, pp 1\u20136","DOI":"10.1109\/ICCIT48885.2019.9038521"},{"key":"813_CR41","first-page":"2020","volume":"421","author":"H Khachatryan","year":"2020","unstructured":"Khachatryan H, Hodges AW, Hall C, Palma M (2020) Production and marketing practices and trade flows in the United States green industry, 2018. South Coop Ser Bull 421:2020\u20132021","journal-title":"South Coop Ser Bull"},{"key":"813_CR42","unstructured":"Kircova I, SaglamMH, Kose SG (2021) Artificial intelligence in retailing. University of South Florida (USF) M3 Publishing, 5, p 73"},{"key":"813_CR43","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2020.113342","volume":"150","author":"D Koehn","year":"2020","unstructured":"Koehn D, Lessmann S, Schaal M (2020) Predicting online shopping behaviour from clickstream data using deep learning. Expert Syst Appl 150:113342","journal-title":"Expert Syst Appl"},{"issue":"2","key":"813_CR44","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1007\/s00521-017-3047-z","volume":"31","author":"A Kumar","year":"2019","unstructured":"Kumar A, Kabra G, Mussada EK, Dash MK, Rana PS (2019) Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention. Neural Comput Appl 31(2):877\u2013890","journal-title":"Neural Comput Appl"},{"key":"813_CR45","volume":"113","author":"Z Li","year":"2021","unstructured":"Li Z, Xie H, Xu G, Li Q, Leng M, Zhou C (2021) Towards purchase prediction: a transaction-based setting and a graph-based method leveraging price information. Pattern Recogn 113:107824","journal-title":"Pattern Recogn"},{"issue":"2","key":"813_CR46","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1111\/deci.12445","volume":"52","author":"BD Liengaard","year":"2021","unstructured":"Liengaard BD, Sharma PN, Hult GTM, Jensen MB, Sarstedt M, Hair JF, Ringle CM (2021) Prediction: coveted, yet forsaken? Introducing a cross-validated predictive ability test in partial least squares path modeling. Decis Sci 52(2):362\u2013392","journal-title":"Decis Sci"},{"key":"813_CR47","doi-asserted-by":"crossref","unstructured":"Lin W, Milic-Frayling N, Zhou K, Ch'ng E (2019) Predicting outcomes of active sessions using multi-action motifs. In: IEEE\/WIC\/ACM International Conference on Web Intelligence, pp 9\u201317","DOI":"10.1145\/3350546.3352495"},{"issue":"4","key":"813_CR48","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1509\/jmkg.71.4.019","volume":"71","author":"Y Liu","year":"2007","unstructured":"Liu Y (2007) The long-term impact of loyalty programs on consumer purchase behavior and loyalty. J Mark 71(4):19\u201335","journal-title":"J Mark"},{"issue":"1","key":"813_CR49","first-page":"1","volume":"16","author":"B Liu","year":"2021","unstructured":"Liu B, Zhang H, Kong L, Niu D (2021) Factorizing historical user actions for next-day purchase prediction. ACM Trans Web (TWEB) 16(1):1\u201326","journal-title":"ACM Trans Web (TWEB)"},{"issue":"1","key":"813_CR50","doi-asserted-by":"crossref","first-page":"2016039","DOI":"10.1080\/23311975.2021.2016039","volume":"9","author":"AN Lubis","year":"2022","unstructured":"Lubis AN, Lumbanraja P, Hasibuan BK (2022) Evaluation on e-marketing exposure practice to minimize the customers\u2019 online shopping purchase regret. Cogent Bus Manag 9(1):2016039","journal-title":"Cogent Bus Manag"},{"issue":"13","key":"813_CR51","doi-asserted-by":"crossref","first-page":"2163","DOI":"10.1080\/02642069.2010.503885","volume":"31","author":"MM Luo","year":"2011","unstructured":"Luo MM, Chen JS, Ching RK, Liu CC (2011) An examination of the effects of virtual experiential marketing on online customer intentions and loyalty. Serv Ind J 31(13):2163\u20132191","journal-title":"Serv Ind J"},{"key":"813_CR52","doi-asserted-by":"crossref","unstructured":"Malmasi S, Tetreault J, Dras M (2015) Oracle and human baselines for native language identification. In: proceedings of the tenth workshop on innovative use of NLP for building educational applications, pp 172\u2013178","DOI":"10.3115\/v1\/W15-0620"},{"issue":"7","key":"813_CR53","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1057\/jors.2012.120","volume":"64","author":"AI Marqu\u00e9s","year":"2013","unstructured":"Marqu\u00e9s AI, Garc\u00eda V, S\u00e1nchez JS (2013) On the suitability of resampling techniques for the class imbalance problem in credit scoring. J Oper Res Soc 64(7):1060\u20131070","journal-title":"J Oper Res Soc"},{"issue":"4","key":"813_CR54","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1287\/mksc.1040.0073","volume":"23","author":"AL Montgomery","year":"2004","unstructured":"Montgomery AL, Li S, Srinivasan K, Liechty JC (2004) Modeling online browsing and path analysis using clickstream data. Mark Sci 23(4):579\u2013595","journal-title":"Mark Sci"},{"key":"813_CR55","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.4018\/978-1-5225-1793-1.ch054","volume-title":"Advertising and branding: concepts, methodologies, tools, and applications","author":"S Nasir","year":"2017","unstructured":"Nasir S (2017) Customer retention strategies and customer loyalty. Advertising and branding: concepts, methodologies, tools, and applications. IGI Global, pp 1178\u20131201"},{"key":"813_CR56","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.jretconser.2017.07.010","volume":"39","author":"TM Nisar","year":"2017","unstructured":"Nisar TM, Prabhakar G (2017) What factors determine e-satisfaction and consumer spending in e-commerce retailing? J Retail Consum Serv 39:135\u2013144","journal-title":"J Retail Consum Serv"},{"issue":"2","key":"813_CR57","first-page":"130","volume":"1","author":"T Noviantoro","year":"2021","unstructured":"Noviantoro T, Huang JP (2021) Applying data mining techniques to investigate online shopper purchase intention based on clickstream data. Rev Bus Account Financ 1(2):130\u2013159","journal-title":"Rev Bus Account Financ"},{"key":"813_CR58","doi-asserted-by":"crossref","unstructured":"P\u0142o\u0144ski P, Zaremba K (2014) Visualizing random forest with self-organising map. In: artificial intelligence and soft computing: 13th international conference, ICAISC 2014, Zakopane, Poland, June 1\u20135, 2014, proceedings, Part II 13. Springer International Publishing, pp 63\u201371","DOI":"10.1007\/978-3-319-07176-3_6"},{"issue":"3","key":"813_CR59","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.intmar.2011.04.004","volume":"25","author":"G Punj","year":"2011","unstructured":"Punj G (2011) Effect of consumer beliefs on online purchase behavior: the influence of demographic characteristics and consumption values. J Interact Mark 25(3):134\u2013144","journal-title":"J Interact Mark"},{"key":"813_CR60","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1007\/s10660-015-9191-6","volume":"15","author":"J Qiu","year":"2015","unstructured":"Qiu J, Lin Z, Li Y (2015) Predicting customer purchase behavior in the e-commerce context. Electron Commer Res 15:427\u2013452","journal-title":"Electron Commer Res"},{"key":"813_CR61","doi-asserted-by":"crossref","DOI":"10.1016\/j.jretconser.2021.102566","volume":"61","author":"MA Rahim","year":"2021","unstructured":"Rahim MA, Mushafiq M, Khan S, Arain ZA (2021) RFM-based repurchase behavior for customer classification and segmentation. J Retail Consum Serv 61:102566","journal-title":"J Retail Consum Serv"},{"issue":"3","key":"813_CR62","doi-asserted-by":"crossref","first-page":"2949","DOI":"10.48084\/etasr.1917","volume":"8","author":"A Rahman","year":"2018","unstructured":"Rahman A, Khan MNA (2018) A classification based model to assess customer behavior in banking sector. Eng Technol Appl Sci Res 8(3):2949","journal-title":"Eng Technol Appl Sci Res"},{"key":"813_CR63","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1016\/j.compag.2018.12.013","volume":"156","author":"S Rajeswari","year":"2019","unstructured":"Rajeswari S, Suthendran K (2019) C5. 0: advanced decision tree (ADT) classification model for agricultural data analysis on cloud. Comput Electron Agric 156:530\u2013539","journal-title":"Comput Electron Agric"},{"key":"813_CR64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.elerap.2017.09.003","volume":"26","author":"O Raphaeli","year":"2017","unstructured":"Raphaeli O, Goldstein A, Fink L (2017) Analyzing online consumer behavior in mobile and PC devices: a novel web usage mining approach. Electron Commer Res Appl 26:1\u201312","journal-title":"Electron Commer Res Appl"},{"issue":"1","key":"813_CR65","doi-asserted-by":"crossref","first-page":"19","DOI":"10.24917\/20833296.171.2","volume":"17","author":"J Rudewicz","year":"2021","unstructured":"Rudewicz J, Sala K (2021) New professional competencies in the era of WEB 2.0 and 3.0 and the dissemination of ICT. Przedsi\u0119biorczo\u015b\u0107-Edukacja 17(1):19\u201334","journal-title":"Przedsi\u0119biorczo\u015b\u0107-Edukacja"},{"key":"813_CR66","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/j.econmod.2013.08.011","volume":"35","author":"NS Safa","year":"2013","unstructured":"Safa NS, Ismail MA (2013) A customer loyalty formation model in electronic commerce. Econ Model 35:559\u2013564","journal-title":"Econ Model"},{"issue":"10","key":"813_CR67","doi-asserted-by":"crossref","first-page":"6893","DOI":"10.1007\/s00521-018-3523-0","volume":"31","author":"CO Sakar","year":"2019","unstructured":"Sakar CO, Polat SO, Katircioglu M, Kastro Y (2019) Real-time prediction of online shoppers\u2019 purchasing intention using multilayer perceptron and LSTM recurrent neural networks. Neural Comput Appl 31(10):6893\u20136908","journal-title":"Neural Comput Appl"},{"issue":"2","key":"813_CR68","doi-asserted-by":"crossref","first-page":"339","DOI":"10.5937\/jaes0-32019","volume":"20","author":"L Samboteng","year":"2022","unstructured":"Samboteng L, Rulinawaty R, Kasmad MR, Basit M, Rahim R (2022) Market basket analysis of administrative patterns data of consumer purchases using data mining technology. J Appl Eng Sci 20(2):339\u2013345","journal-title":"J Appl Eng Sci"},{"key":"813_CR69","doi-asserted-by":"crossref","unstructured":"Shamsudin H, Yusof UK, Jayalakshmi A, Khalid MNA (2020) Combining oversampling and undersampling techniques for imbalanced classification: a comparative study using credit card fraudulent transaction dataset. In: 2020 IEEE 16th international conference on control & automation (ICCA). IEEE, pp 803\u2013808","DOI":"10.1109\/ICCA51439.2020.9264517"},{"key":"813_CR70","doi-asserted-by":"crossref","unstructured":"Shuai Y, Zheng Y, Huang H (2018) Hybrid software obsolescence evaluation model based on PCA-SVM-GridSearchCV. In: 2018 IEEE 9th international conference on software engineering and service science (ICSESS). IEEE, pp 449\u2013453","DOI":"10.1109\/ICSESS.2018.8663753"},{"key":"813_CR71","volume":"95","author":"H Song","year":"2021","unstructured":"Song H, Ruan WJ, Jeon YJJ (2021) An integrated approach to the purchase decision making process of food-delivery apps: focusing on the TAM and AIDA models. Int J Hosp Manag 95:102943","journal-title":"Int J Hosp Manag"},{"issue":"2","key":"813_CR72","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1509\/jm.15.0398","volume":"81","author":"S Tillmanns","year":"2017","unstructured":"Tillmanns S, Ter Hofstede F, Krafft M, Goetz O (2017) How to separate the wheat from the chaff: improved variable selection for new customer acquisition. J Mark 81(2):99\u2013113","journal-title":"J Mark"},{"issue":"4","key":"813_CR73","doi-asserted-by":"crossref","first-page":"269","DOI":"10.11611\/yead.542249","volume":"17","author":"I Topal","year":"2019","unstructured":"Topal I (2019) Estimation of online purchasing intention using decision tree. Y\u00f6netim Ve Ekonomi Ara\u015ft\u0131rmalar\u0131 Dergisi 17(4):269\u2013280","journal-title":"Y\u00f6netim Ve Ekonomi Ara\u015ft\u0131rmalar\u0131 Dergisi"},{"key":"813_CR74","doi-asserted-by":"crossref","unstructured":"Valecha H, Varma A, Khare I, Sachdeva A, Goyal M (2018) Prediction of consumer behaviour using random forest algorithm. In: 2018 5th IEEE Uttar Pradesh section international conference on electrical, electronics and computer engineering (UPCON). IEEE, pp 1\u20136","DOI":"10.1109\/UPCON.2018.8597070"},{"issue":"3","key":"813_CR75","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.ejor.2019.08.015","volume":"281","author":"T Van Nguyen","year":"2020","unstructured":"Van Nguyen T, Zhou L, Chong AYL, Li B, Pu X (2020) Predicting customer demand for remanufactured products: a data-mining approach. Eur J Oper Res 281(3):543\u2013558","journal-title":"Eur J Oper Res"},{"issue":"2","key":"813_CR76","doi-asserted-by":"crossref","first-page":"70","DOI":"10.4067\/S0718-18762019000200107","volume":"14","author":"N Vasi\u0107","year":"2019","unstructured":"Vasi\u0107 N, Kilibarda M, Kaurin T (2019) The influence of online shopping determinants on customer satisfaction in the Serbian market. J Theor Appl Electron Commer Res 14(2):70\u201389","journal-title":"J Theor Appl Electron Commer Res"},{"issue":"4","key":"813_CR77","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1016\/j.jretai.2020.12.001","volume":"97","author":"XS Wang","year":"2021","unstructured":"Wang XS, Ryoo JHJ, Bendle N, Kopalle PK (2021a) The role of machine learning analytics and metrics in retailing research. J Retail 97(4):658\u2013675","journal-title":"J Retail"},{"key":"813_CR78","doi-asserted-by":"crossref","first-page":"108061","DOI":"10.1016\/j.patcog.2021.108061","volume":"119","author":"Z Wang","year":"2021","unstructured":"Wang Z, Zhao S, Li Z, Chen H, Li C, Shen Y (2021b) Ensemble selection with joint spectral clustering and structural sparsity. Pattern Recognit 119:108061","journal-title":"Pattern Recognit"},{"issue":"18","key":"813_CR79","doi-asserted-by":"crossref","first-page":"7513","DOI":"10.1016\/j.eswa.2013.07.053","volume":"40","author":"JT Wei","year":"2013","unstructured":"Wei JT, Lee MC, Chen HK, Wu HH (2013) Customer relationship management in the hairdressing industry: an application of data mining techniques. Expert Syst Appl 40(18):7513\u20137518","journal-title":"Expert Syst Appl"},{"issue":"4","key":"813_CR80","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1080\/10580530.2020.1814459","volume":"38","author":"J Weingarten","year":"2021","unstructured":"Weingarten J, Spinler S (2021) Shortening delivery times by predicting customers\u2019 online purchases: a case study in the fashion industry. Inf Syst Manag 38(4):287\u2013308","journal-title":"Inf Syst Manag"},{"key":"813_CR81","doi-asserted-by":"crossref","unstructured":"Wen YT, Yeh PW, Tsai TH, Peng WC, Shuai HH (2018) Customer purchase behavior prediction from payment datasets. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp 628\u2013636","DOI":"10.1145\/3159652.3159707"},{"issue":"6","key":"813_CR82","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.21629\/JSEE.2019.06.12","volume":"30","author":"XU Xiaolong","year":"2019","unstructured":"Xiaolong XU, Wen CHEN, Yanfei SUN (2019) Over-sampling algorithm for imbalanced data classification. J Syst Eng Electron 30(6):1182\u20131191","journal-title":"J Syst Eng Electron"},{"key":"813_CR83","unstructured":"Zavali M, Lacka E, De Smedt J (2021) Shopping hard or hardly shopping: revealing consumer segments using clickstream data. IEEE Trans Eng Manag"},{"key":"813_CR84","doi-asserted-by":"crossref","unstructured":"Zeng H, Pan D (2010) A knowledge discovery and data mining process model in E-marketing. In: 2010 8th World Congress on Intelligent Control and Automation. IEEE, pp 3960\u20133964","DOI":"10.1109\/WCICA.2010.5553834"},{"key":"813_CR85","doi-asserted-by":"crossref","unstructured":"Zheng B, Liu B (2018) A scalable purchase intention prediction system using extreme gradient boosting machines with browsing content entropy. In: 2018 IEEE International Conference on Consumer Electronics (ICCE). IEEE, pp 1\u20134","DOI":"10.1109\/ICCE.2018.8326351"},{"issue":"1","key":"813_CR86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s41512-020-00090-3","volume":"5","author":"QM Zhou","year":"2021","unstructured":"Zhou QM, Zhe L, Brooke RJ, Hudson MM, Yuan Y (2021) A relationship between the incremental values of area under the ROC curve and of area under the precision-recall curve. Diagn Progn Res 5(1):1\u201315","journal-title":"Diagn Progn Res"}],"container-title":["Operational Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12351-023-00813-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12351-023-00813-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12351-023-00813-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T19:54:37Z","timestamp":1710273277000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12351-023-00813-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,27]]},"references-count":87,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["813"],"URL":"https:\/\/doi.org\/10.1007\/s12351-023-00813-6","relation":{},"ISSN":["1109-2858","1866-1505"],"issn-type":[{"value":"1109-2858","type":"print"},{"value":"1866-1505","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,27]]},"assertion":[{"value":"21 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 December 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article. They did not receive support from any organization for the submitted work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict interest"}},{"value":"The authors declare that this study does not involve human or animal participants.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"11"}}