本研究以 eBay Best Offer 線上議價市場的大規模交易資料為基礎，探討議價歷程中買家報價的出價時機（Offer Timing）如何影響賣家的議價決策與賣家錯失利潤（Seller Regret）。有別於既有文獻多聚焦於單一議價串內買賣雙方每次來回出價的動態與延遲，本研究改採賣家-商品組合的議價歷程（Bargaining Episode）視角，將商品自上架至成交的時間軸正規化為 [0,1] 區間，據以刻畫首筆報價與最高報價的到達時機（Tfirst、Tmax），並納入賣家對該歷程中最高報價的回應時長（SRTmax）等時間特徵。在清理逾兩百萬個議價歷程後，本研究依賣家錯失發生與否及發生後幅度的雙重結構，建立兩階段模型。第一階段以羅吉斯迴歸檢驗賣家錯失是否發生；第二階段則以錯失金額的對數為依變數，採對數線性迴歸檢驗錯失程度。實證結果顯示，在錯失是否發生的第一階段，賣家對最高報價的回應時長為關聯最強的預測變數，當賣家針對最高出價的回應越遲緩，發生錯失的機率顯著較高；此外，首筆報價越早到達，錯失機率亦顯著較高。在錯失程度的第二階段，錯失金額主要由商品初始標價主導，時機變數的整體邊際效果相對有限；但其中最高報價到達時機越早，既發生的錯失金額顯著越大，支持最高報價早現的錯失放大效應。綜合兩階段估計，進一步計算各變數對無條件期望錯失利潤的彈性後，賣家回應時長與首筆報價時機的淨效應，均由發生機率路徑主導。賣家回應越遲緩，期望錯失利潤整體越高，與賣家對最高報價的猶豫效應相符，遲疑不決的賣家往往放任高價過期，以不作為錯過全歷程的最佳成交機會。首筆報價越早到達，期望錯失利潤同樣越高，與早期報價對賣家預期心理的錨定作用相符。上述結論在改採多層次模型控制賣家叢集後依然穩健。本研究顯示報價到達的時間節奏與賣家對商品市場價值的主觀判斷密切相關，部分賣家最終以低於其先前所拒絕價格的金額成交。此發現將事件時間節點影響主觀評價的觀點延伸至線上議價市場，並為電商交易平台與賣家的決策輔助提供初步實證依據。

This study draws on large-scale transaction data from eBay’s “Best Offer” online bargaining market to examine how the timing of buyers’ offers within a bargaining episode affects sellers’ bargaining decisions and seller regret (the seller’s forgone profit). Departing from prior work that centers on the round-by-round dynamics and delays of the offers exchanged within a single bargaining thread, this study adopts a seller–item-pair (bargaining-episode) perspective and normalizes each item’s timeline, from listing to sale, onto the [0, 1] interval, thereby characterizing the timing of the first offer (Tfirst) and of the highest offer (Tmax) and incorporating time-based features such as the seller’s response time to the highest offer in that episode (SRTmax). After cleaning the raw data and consolidating it into more than two million bargaining episodes, this study builds a two-part regression framework that mirrors the dual structure of seller regret, namely whether regret occurs and how large it is once it does. The first stage uses logistic regression to test whether regret occurs, and the second stage uses log-linear (OLS) regression with the logarithm of the regret amount (Regret Price) as the dependent variable to examine the magnitude of regret. In the first stage (whether regret occurs), the seller’s response time to the highest offer (SRTmax) shows the strongest association: regret is significantly more likely the more slowly the seller responds to the highest offer received in the episode, and likewise significantly more likely when the first offer arrives earlier. In the second stage (the magnitude of regret), the regret amount is governed mainly by the item’s list price (the seller’s Buy-It-Now price), and the timing variables exert relatively limited marginal effects overall; nonetheless, regret amounts are significantly larger when the highest offer (Tmax) arrives earlier, supporting the regret-amplification effect of an early highest offer. Synthesizing the two stages’ estimates into the elasticity of unconditional expected regret, this study finds that the net effects of the seller’s response time and of the first offer’s timing are both dominated by the probability-of-occurrence path. Expected regret is higher overall when the seller responds more slowly, consistent with the seller’s hesitation effect regarding the highest offer: indecisive sellers often let the highest offer expire, forgoing the episode’s best deal through inaction. Likewise, expected regret is higher when the first offer arrives earlier, consistent with the anchoring effect of early offers on sellers’ price expectations. These conclusions remain robust in a multilevel specification that accounts for the nesting of episodes within sellers. Overall, this study shows that the temporal rhythm of incoming offers is closely associated with sellers’ subjective judgment of their items’ market value, with some sellers closing deals at prices below those they had previously rejected. By extending to the online bargaining market the view that the temporal placement of events influences subjective evaluation, this study provides preliminary empirical evidence to inform decision support for online marketplaces and their sellers.

Admati, A. R., & Perry, M. (1987). Strategic delay in bargaining. The Review of Economic Studies, 54(3), 345–364. https://doi.org/10.2307/2297563
Backus, M., Blake, T., Larsen, B., & Tadelis, S. (2020). Sequential bargaining in the field: Evidence from millions of online bargaining interactions. The Quarterly Journal of Economics, 135(3), 1319–1361. https://doi.org/10.1093/qje/qjaa003
Bray, R. L. (2023). Operational transparency: Showing when work gets done. Manufacturing & Service Operations Management, 25(3), 812–826. https://doi.org/10.1287/msom.2020.0899
Cotet, M., Zhao, W. J., & Krajbich, I. (2025). Deliberation during online bargaining reveals strategic information. Proceedings of the National Academy of Sciences, 122(7), e2410956122. https://doi.org/10.1073/pnas.2410956122
Cramton, P. C. (1992). Strategic delay in bargaining with two-sided uncertainty. The Review of Economic Studies, 59(1), 205–225. https://doi.org/10.2307/2297934
Desai, P. S., & Jindal, P. (2024). Getting a break in bargaining: An upside of time delays. Marketing Science, 43(6), 1260–1278. https://doi.org/10.1287/mksc.2021.0296
Fong, J., & Waisman, C. (2025). The effects of delay in bargaining: Evidence from eBay. Management Science, 71(12), 9976–9997. https://doi.org/10.1287/mnsc.2023.01562
Galinsky, A. D., & Mussweiler, T. (2001). First offers as anchors: The role of perspective-taking and negotiator focus. Journal of Personality and Social Psychology, 81(4), 657–669. https://doi.org/10.1037/0022-3514.81.4.657
Kahneman, D., Fredrickson, B. L., Schreiber, C. A., & Redelmeier, D. A. (1993). When more pain is preferred to less: Adding a better end. Psychological Science, 4(6), 401–405. https://doi.org/10.1111/j.1467-9280.1993.tb00589.x
Kim, J., Lim, W., & Schweighofer-Kodritsch, S. (2024). Patience is power: Bargaining and payoff delay [Discussion Paper No. 0015, Berlin School of Economics. Revise & Resubmit at Journal of the European Economic Association]. https://doi.org/10.48462/opus4-4946
Lai, M. H. C., & Kwok, O.-m. (2015). Examining the rule of thumb of not using multilevel modeling: The “design effect smaller than two” rule. The Journal of Experimental Education, 83(3), 423–438. https://doi.org/10.1080/00220973.2014.907229
Lipp, W. E., Smolinski, R., & Kesting, P. (2023). Beyond the first offer: Decoding negotiation openings and their impact on economic and subjective outcomes. Group Decision and Negotiation, 32(2), 395–433. https://doi.org/10.1007/s10726-023-09813-5
Loschelder, D. D., Trötschel, R., Swaab, R. I., Friese, M., & Galinsky, A. D. (2016). The information-anchoring model of first offers: When moving first helps versus hurts negotiators. Journal of Applied Psychology, 101(7), 995–1012. https://doi.org/10.1037/apl0000096
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/002224378001700405
Perry, M. (1986). An example of price formation in bilateral situations: A bargaining model with incomplete information. Econometrica, 54(2), 313–321. https://doi.org/10.2307/1913153
Rubinstein, A. (1982). Perfect equilibrium in a bargaining model. Econometrica, 50(1), 97–109. https://doi.org/10.2307/1912531
Schweinsberg, M., Petrowsky, H. M., Funk, B., & Loschelder, D. D. (2023). Understanding the first-offer conundrum: How buyer offers impact sale price and impasse risk in 26 million eBay negotiations. Proceedings of the National Academy of Sciences, 120(32), e2218582120. https://doi.org/10.1073/pnas.2218582120
Sinaceur, M., Maddux, W. W., Vasiljevic, D., Perez Nückel, R., & Galinsky, A. D. (2013). Good things come to those who wait: Late first offers facilitate creative agreements in negotiation. Personality and Social Psychology Bulletin, 39(6), 814–825. https://doi.org/10.1177/0146167213483319
Sommet, N., & Morselli, D. (2017). Keep calm and learn multilevel logistic modeling: A simplified three-step procedure using stata, r, mplus, and spss. International Review of Social Psychology, 30(1), 203–218. https://doi.org/10.5334/irsp.90
Sommet, N., & Morselli, D. (2021). Keep calm and learn multilevel linear modeling: A three-step procedure using spss, stata, r, and mplus. International Review of Social Psychology, 34(1), 24. https://doi.org/10.5334/irsp.555
Teichmann, L., Petrowsky, H. M., Escher, Y. A., Lee, A. J., & Loschelder, D. D. (2025). Optimizing counteroffers: How timing and magnitude shape sale prices and impasses in 26 million asynchronous online negotiations. Group Decision and Negotiation, 34(4), 823–848. https://doi.org/10.1007/s10726-025-09932-1