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Online Auctions End Time and its Impact on Sales Success – Analysis of the Odds Ratio on a Selected Central European Market


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Allegro.pl (2022). Retrieved from https://www.allegro.pl. Search in Google Scholar

Backus, M., Blake, T., Masterov, D., Tadelis, S. (2015). Is sniping a problem for online auction markets? WWW 2015 – Proceedings of the 24th International Conference on World Wide Web (pp. 88–96). DOI: 10.1145/2736277.2741690.10.1145/2736277.2741690 Search in Google Scholar

Balingit, R., Trevathan, J., Read, W. (2009). Analysing bidding trends in online auctions. In 2009 Sixth International Conference on Information Technology: New Generations (pp. 928–933). DOI: 10.1109/ITNG.2009.315.10.1109/ITNG.2009.315 Search in Google Scholar

Bandyopadhyay, S., Bandyopadhyay, S. (2009). Estimating time required to reach bid levels in online auctions. Journal of Management Information Systems, 26 (3), 275-301. DOI: 10.2753/MIS0742-1222260309.10.2753/MIS0742-1222260309 Search in Google Scholar

Bapna, R., Day R., Rice, S. (2020). Allocative Efficiency in Online Auctions: Improving the Performance of Multiple Online Auctions Via Seek-and-Protect Agents. Production and Operations Management, 29 (8), 1878–1893. DOI: 10.1111/poms.13194.10.1111/poms.13194 Search in Google Scholar

Ben Rhouma, T., Zaccour, G. (2012). An empirical investigation of late bidding in online auctions. Economics Letters, 117 (3), 715–717. DOI: 10.1016/j.econlet.2011.12.022.10.1016/j.econlet.2011.12.022 Search in Google Scholar

Borle, S., Boatwright, P., Kadane, J. (2006). The timing of bid placement and extent of multiple bidding: An empirical investigation using eBay online auctions. Statistical Science, 21 (2), 194–205. DOI: 10.1214/08834230600000012.10.1214/088342306000000123 Search in Google Scholar

Bose, S., Daripa, A. (2017). Shills and snipes. Games and Economic Behavior, 104, 507–516. DOI: 10.1016/j.geb.2017.05.010.10.1016/j.geb.2017.05.010 Search in Google Scholar

Bradlow, E., Park, Y. (2007). Bayesian estimation of bid sequences in Internet auctions using a generalized record-breaking model. Marketing Science, 26 (2), 218–229. DOI: 10.1287/mksc.1060.0225.10.1287/mksc.1060.0225 Search in Google Scholar

Canals-Cerdá, J. (2012). The value of a good reputation online: An application to art auctions. Journal of Cultural Economics, 36 (1), 67–85. DOI: 10.2139/ssrn.1123599.10.2139/ssrn.1123599 Search in Google Scholar

Chan, N., Li, Z., Yau, C. (2014). Forecasting online auctions via self-exciting point processes. Journal of Forecasting, 33 (7), 501–514. DOI: 10.1002/for.2313.10.1002/for.2313 Search in Google Scholar

Chow, V. (2019). Predicting Auction Price of Vehicle License Plate with Deep Residual Learning. Lecture Notes in Computer Science, 11607, 179–188. DOI: 10.48550/arXiv.1910.04879.10.1007/978-3-030-26142-9_16 Search in Google Scholar

Cui, X., Lai, V., Lowry, P., Lei, Y. (2020). The effects of bidder factors on online bidding strategies: A motivation-opportunity-ability (MOA) model. Decision Support Systems, 138, 1–36. DOI: 10.2139/ssrn.3679508.10.2139/ssrn.3679508 Search in Google Scholar

Czerwonka, P. Zakonnik, L., (2015). Analysis of Selected Behavior of Online Auctions Users. Przedsiębiorczość i Zarządzania, XVI (9, II), 67–81. Search in Google Scholar

Czerwonka, P., Zakonnik, L., Podgórski G. (2022). The Issue of Overestimating the Final Price in Online Auctions In The Context of User Experience – Based on Selected EEU Markets. Proceeding 39th International Business Information Management Association Conference (IBIMA). Search in Google Scholar

Du, L., Hua, G. (2010). Bidding strategy and participating threshold in online English auction with buy-it-now option. Proceedings – 2010 IEEE 17th International Conference on Industrial Engineering and Engineering Management. IE and EM2010. DOI: 10.1109/ICIEEM.2010.5646603.10.1109/ICIEEM.2010.5646603 Search in Google Scholar

Elhadary, O. (2010). Is the first bid really important in online auctions? International Conference e-Commerce 2010 (pp. 139–143). MCCSIS. Search in Google Scholar

Feng, C., Fay, S., Sivakumar, K. (2016). Overbidding in electronic auctions: factors influencing the propensity to overbid and the magnitude of overbidding. Journal of the Academy of Marketing Science, 44 (2), 241–260. DOI: 10.1007/s11747-015-0450-9.10.1007/s11747-015-0450-9 Search in Google Scholar

Haruvy, E., Popkowski-Leszczyc, P. (2010). Search and choice in online consumer auctions. Marketing Science, 29 (6), 1152–1164. DOI: 10.1287/mksc.1100.0601.10.1287/mksc.1100.0601 Search in Google Scholar

Hayne, S., Wang, H., Mendonca, S. (2012). eBay as the ‘Terminator’: Determining User Suspension From Feedback Ratings. Journal of Organizational Computing and Electronic Commerce, 22 (2), 160–183. DOI: 10.1080/10919392.2012.667714.10.1080/10919392.2012.667714 Search in Google Scholar

IBM Support, Cox-Snell and Nagelkerke R^2 (R-squared) statistics – formula and references (2022). Retrieved from https://www.ibm.com/support/pages/cox-snell-and-nagelkerke-r2-r-squared-statistics-formula-and-references. Search in Google Scholar

Jank, W., Shmueli, G. (2010). Forecasting online auctions using dynamic models. Frontiers in Artificial Intelligence and Applications, 218, 137–148. DOI: 10.3233/978-1-60750-633-1-137.10.1002/9780470642603 Search in Google Scholar

Kamins, M., Noy, A., Steinhart, Y., Mazursky, D. (2011). The Effect of Social Cues on Sniping Behavior in Internet Auctions: Field Evidence and a Lab Experiment. Journal of Interactive Marketing, 25 (4), 241–250. DOI: 10.1016/j.intmar.2011.03.002.10.1016/j.intmar.2011.03.002 Search in Google Scholar

Kaur, P., Goyal, M., Lu, J. (2017). A Comparison of Bidding Strategies for Online Auctions Using Fuzzy Reasoning and Negotiation Decision Functions. IEEE Transactions on Fuzzy Systems, 25 (2), 425–438. DOI: 10.1109/tfuzz.2016.2598297.10.1109/TFUZZ.2016.2598297 Search in Google Scholar

Khadge, M., Kulkarni, M. (2016). Machine learning approach for predicting end price of online auction. Proceedings of the International Conference on Inventive Computation Technologies. ICICT. DOI: 10.1109/INVENTIVE.2016.7830232.10.1109/INVENTIVE.2016.7830232 Search in Google Scholar

Klemperer, P. (2004). Auctions: Theory and Practice. Princeton: Princeton University Press.10.1515/9780691186290 Search in Google Scholar

Kuruzovich, J. (2012). Time and online auctions. Journal of Electronic Commerce Research, 13 (1), 23–32. Search in Google Scholar

Li, C. (2012). Characteristics of bid processes in online auctions. Advanced Materials Research, 403–408, 5199–5203. DOI: 10.4028/www.scientific.net/AMR.403-408.5199.10.4028/www.scientific.net/AMR.403-408.5199 Search in Google Scholar

Li, X., Dong, H., Han, S. (2020). Multiple Linear Regression with Kalman Filter for Predicting End Prices of Online Auctions. Proceedings – IEEE 18th International Conference on Dependable, Autonomic and Secure Computing. DASC/PiCom/CBDCom/CyberSciTech. DOI: 10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00042.10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00042 Search in Google Scholar

Liu, Y., Feng, Y., Shao, Z. (2009). Online auction final price forecasting model based on Bagging and decision tree. Systems Engineering – Theory & Practice, 29 (12), 134–140.10.1016/S1874-8651(10)60093-2 Search in Google Scholar

Majadi, N., Trevathan, J., Bergmann, N. (2018). Real-time collusive shill bidding detection in online auctions. Lecture Notes in Computer Science, 34, 184–192. DOI: 10.1016/j. elerap.2019.100831.10.1016/j.elerap.2019.100831 Search in Google Scholar

Majadi, N., Trevathan, J., Gray, H. (2018). A run-time algorithm for detecting shill bidding in online auctions. Journal of Theoretical and Applied Electronic Commerce Research, 13 (3), 17–49. DOI: 10.4067/S0718-18762018000300103.10.4067/S0718-18762018000300103 Search in Google Scholar

Muthitacharoen, A., Tams, S. (2017). The Role of Auction Duration in Bidder Strategies and Auction Prices. International Journal of Electronic Commerce, 21 (1), 71–102. DOI: 10.1080/10864415.2016.1204190.10.1080/10864415.2016.1204190 Search in Google Scholar

Niedzwiedziński, M., Zakonnik, L. (2018). Participants of Online Auctions and their Behavior in the Process of Shaping Prices – Consumer Surveys. Przedsiebiorczosc i Zarzadzanie, XIX (5, I), 237–250. Search in Google Scholar

Ødegaard, F., Puterman, M. (2012). Estimating intermediate price transitions in online auctions. Applied Stochastic Models in Business and Industry, 28 (6), 529–541. DOI: 10.1002/asmb.928.10.1002/asmb.928 Search in Google Scholar

Onur, I., Velamuri, M. (2014). Competition, endogeneity and the winning bid: An empirical analysis of eBay auctions. Information Economics and Policy, 26 (1), 68–74. DOI: 10.1016/j.infoecopol.2013.11.003.10.1016/j.infoecopol.2013.11.003 Search in Google Scholar

Park, Y., Bradlow, E. (2005). An integrated model for bidding behavior in Internet auctions: Whether, who, when, and how much. Journal of Marketing Research, 42 (4), 470–482. DOI: 10.1509/jmkr.2005.42.4.470.10.1509/jmkr.2005.42.4.470 Search in Google Scholar

Porebski, K., Zakonnik, L. (2019). Analysis of the Impact of Specific Factors on Purchase Decisions. Przedsiebiorczosc i Zarzadzanie, XX (12, 1), 213–225. Search in Google Scholar

Rószkiewicz, M. (2011). Analiza Klienta. Kraków: SPPS Polska. Search in Google Scholar

Srinivasan, K., Wang, X. (2010). Bidders’ experience and learning in online auctions: Issues and implications. Marketing Science, 29 (6), 988–993. DOI: 10.1287/mksc.1100.0581.10.1287/mksc.1100.0581 Search in Google Scholar

Trevathan, J., Read, W. (2006). Undesirable and Fraudulent Behaviour in Online Auctions. Proceedings of the International Conference on Security and Cryptography (pp. 1–9). Setúbal. DOI: 10.5220/0002100704500458.10.5220/0002100704500458 Search in Google Scholar

Trevathan, J., Read, W., Lee, Y., Atkinson, I. (2011). Targeting the strategies of a bid sniper. Proceedings of the Annual Hawaii International Conference on System Sciences. DOI: 10.1109/HICSS.2011.396.10.1109/HICSS.2011.396 Search in Google Scholar

Tsai, M., Huang, T. (2011). Using genetic algorithm to help the seller strategies in online auction. Key Engineering Materials, 474–476, 1760–1763. DOI: 10.4028/www.scientific.net/KEM.474-476.1760.10.4028/www.scientific.net/KEM.474-476.1760 Search in Google Scholar

Yokotani, T., Huang, H., Kawagoe, K. (2012). Predicting online auction final prices using time series splitting and clustering. Lecture Notes in Computer Science, 7235, 207–218. DOI: 10.1007/978-3-642-29253-8_18.10.1007/978-3-642-29253-8_18 Search in Google Scholar

Zafari, B., Soyer, R. (2020). Modeling first bid in retail secondary market online auctions: A Bayesian approach. Applied Stochastic Models in Business and Industry, 36 (3), 452–464. DOI: 10.1002/asmb.2498.10.1002/asmb.2498 Search in Google Scholar

Zafari, B., Soyer, R. (2021). Assessment of uncertainty in bid arrival times: A Bayesian mixture model. Journal of the Operational Research Society, 72 (11), 2517–2528. DOI: 10.1080/01605682.2020.1796539.10.1080/01605682.2020.1796539 Search in Google Scholar

Zakonnik, L. (2019). Consumer behavior and the price formation of used goods on the electronic market. Zachowania konsumenta a kształtowanie się ceny dóbr używanych na rynku elektronicznym. Łódź: Uniwersytet Łódzki.10.18778/8088-934-7 Search in Google Scholar

Zakonnik L. (2021). Editorial models of 19th-century polish bibles – An attempt at identification, Biblical Annals, 11 (2), 327–374, DOI: 10.31743/biban.12185.10.31743/biban.12185 Search in Google Scholar

Zakonnik, L., Czerwonka, P., Podgórski, G., Zajdel, K., Zajdel, R. (2022). Art Market Investment Bubble during COVID-19 – Case Study of the Rare Books Market in Poland. Sustainability, 14 (18), 11648, DOI: 10.3390/su141811648.10.3390/su141811648 Search in Google Scholar

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