Smart Green Nudging: Reducing Product Returns Through Digital Footprints and Causal Machine Learning

References

• Abdulla H, Abbey JD, Ketzenberg M (2022) How consumers value retailer’s return policy leniency levers: An empirical investigation. Production Oper. Management 31(4):1719–1733.Crossref, Google Scholar
• Abdulla H, Ketzenberg M, Abbey JD (2019) Taking stock of consumer returns: A review and classification of the literature. J. Oper. Management 65(6):560–605.Crossref, Google Scholar
• Altug MS, Aydinliyim T, Jain A (2021) Managing opportunistic consumer returns in retail operations. Management Sci. 67(9):5660–5678.Link, Google Scholar
• Ambilkar P, Dohale V, Gunasekaran A, Bilolikar V (2021) Product returns management: A comprehensive review and future research agenda. Internat. J. Production Res. 60(12):3920–3944.Crossref, Google Scholar
• Andor MA, Gerster A, Peters J, Schmidt CM (2020) Social norms and energy conservation beyond the US. J. Environ. Econom. Management 103:102351.Crossref, Google Scholar
• Athey S, Imbens GW (2015) Machine learning methods for estimating heterogeneous causal effects. Stat 1050(5):1–26.Google Scholar
• Athey S, Wager S (2021) Policy learning with observational data. Econometrica 89(1):133–161.Crossref, Google Scholar
• Athey S, Keleher N, Spiess J (2023) Machine learning who to nudge: Causal vs predictive targeting in a field experiment on student financial aid renewal. Working paper, Stanford University, Stanford, CA.Google Scholar
• Athey S, Tibshirani J, Wager S (2019) Generalized random forests. Ann. Statist. 47(2):1148–1178.Crossref, Google Scholar
• Bär D, Feuerriegel S, Li T, Weinmann M (2023) Message framing to promote solar panels. Nature Comm. 14(1):7187.Crossref, Google Scholar
• Battocchi K, Dillon E, Hei M, Lewis G, Oka P, Oprescu M, Syrgkanis V (2023) EconML user guide: Forest based estimators. Accessed November 4, 2023, https://econml.azurewebsites.net/spec/estimation/forest.html.Google Scholar
• Bauer K, von Zahn M, Hinz O (2023) Expl(AI)ned: The impact of explainable artificial intelligence on users’ information processing. Inform. Systems Res. 34(4):1582–1602.Link, Google Scholar
• Berg T, Burg V, Gombović A, Puri M (2020) On the rise of fintechs: Credit scoring using digital footprints. Rev. Financial Stud. 33(7):2845–2897.Crossref, Google Scholar
• Borle S, Singh SS, Jain DC (2008) Customer lifetime value measurement. Management Sci. 54(1):100–112.Link, Google Scholar
• Calma J (2023) H&M is the latest brand to charge for returns—and that might be good for the planet. The Verge (September 19), https://www.theverge.com/2023/9/19/23880439/h-m-free-returns-ends-climate-change-pollution-waste.Google Scholar
• Chandy RK, Johar GV, Moorman C, Roberts JH (2021) Better marketing for a better world. J. Marketing 85(3):1–9.Crossref, Google Scholar
• Chernozhukov V, Demirer M, Duflo E, Fernandez-Val I (2018) Generic machine learning inference on heterogeneous treatment effects in randomized experiments, with an application to immunization in India. NBER working paper, National Bureau of Economic Research, Cambridge, MA.Google Scholar
• CivicScience (2015) What your web browser choice says about you. (April 17), https://civicscience.com/what-your-web-browser-choice-says-about-you/.Google Scholar
• Croson R, Treich N (2014) Behavioral environmental economics: Promises and challenges. Environ. Resource Econom. 58:335–351.Crossref, Google Scholar
• Davis S, Gerstner E, Hagerty M (1995) Money back guarantees in retailing: Matching products to consumer tastes. J. Retailing 71(1):7–22.Crossref, Google Scholar
• De Bruyn A, Otter T (2022) Bayesian consumer profiling: How to estimate consumer characteristics from aggregate data. J. Marketing Res. 59(4):755–774.Crossref, Google Scholar
• Demarque C, Charalambides L, Hilton DJ, Waroquier L (2015) Nudging sustainable consumption: The use of descriptive norms to promote a minority behavior in a realistic online shopping environment. J. Environ. Psych. 43:166–174.Crossref, Google Scholar
• Deryugina T, Heutel G, Miller NH, Molitor D, Reif J (2019) The mortality and medical costs of air pollution: Evidence from changes in wind direction. Amer. Econom. Rev. 109(12):4178–4219.Crossref, Google Scholar
• Dopson E (2024) Ecommerce returns: Expert guide to best practices (2024). Accessed June 18, 2024, https://www.shopify.com/enterprise/ecommerce-returns.Google Scholar
• Dzyabura D, El Kihal S, Hauser JR, Ibragimov M (2023) Leveraging the power of images in managing product return rates. Marketing Sci. 42(6):1125–1142.Link, Google Scholar
• Ebert T, Götz FM, Gladstone JJ, Müller SR, Matz SC (2021) Spending reflects not only who we are but also who we are around: The joint effects of individual and geographic personality on consumption. J. Personality Soc. Psych. 121(2):378–393.Crossref, Google Scholar
• El Kihal S, Shehu E (2022) It’s not only what they buy, it’s also what they keep: Linking marketing instruments to product returns. J. Retailing 98(3):558–571.Crossref, Google Scholar
• El Kihal S, Nurullayev N, Schulze C, Skiera B (2021) A comparison of return rate calculation methods: Evidence from 16 retailers. J. Retailing 97(4):676–696.Crossref, Google Scholar
• European Union (2016) Regulation EU 2016/679 of the European Parliament and of the council of 27 April 2016, article 22. Official J. Eur. Union 119:59.Google Scholar
• European Union (2021) Proposal for a regulation EU of the European Parliament and of the council of April 21, 2021, laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain union legislative acts. Official J. Eur. Union 119.Google Scholar
• Frisch R, Waugh FV (1933) Partial time regressions as compared with individual trends. Econometrica 1(4):387–401.Crossref, Google Scholar
• Ghose A, Lee HA, Nam K, Oh W (2023) The effects of pressure and self-assurance nudges on product purchases and returns in online retailing: Evidence from a randomized field experiment. J. Marketing Res. 61(3):517–535.Crossref, Google Scholar
• Goldstein NJ, Cialdini RB, Griskevicius V (2008) A room with a viewpoint: Using social norms to motivate environmental conservation in hotels. J. Consumer Res. 35(3):472–482.Crossref, Google Scholar
• Goldstein NJ, Griskevicius V, Cialdini RB (2007) Invoking social norms: A social psychology perspective on improving hotels’ linen-reuse programs. Cornell Hotel Restaurant Admin. Quart. 48(2):145–150.Crossref, Google Scholar
• Grahl J, Hinz O, Rothlauf F, Abdel-Karim BM, Mihale-Wilson C (2023) How do likes influence revenue? A randomized controlled field experiment. J. Bus. Res. 167:114133.Crossref, Google Scholar
• Grinstein A, Riefler P (2015) Citizens of the (green) world? Cosmopolitan orientation and sustainability. J. Internat. Bus. Stud. 46(6):694–714.Crossref, Google Scholar
• Guo R, Cheng L, Li J, Hahn PR, Liu H (2020) A survey of learning causality with data: Problems and methods. ACM Comput. Surveys 53(4):1–37.Google Scholar
• Hagmann D, Ho EH, Loewenstein G (2019) Nudging out support for a carbon tax. Nature Climate Change 9(6):484–489.Crossref, Google Scholar
• Harris LC (2008) Fraudulent return proclivity: An empirical analysis. J. Retailing 84(4):461–476.Crossref, Google Scholar
• Hastie T, Tibshirani R, Friedman JH (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, vol. 2 (Springer, Cham, Switzerland).Crossref, Google Scholar
• He G, Pan Y, Park A, Sawada Y, Tan ES (2023) Reducing single-use cutlery with green nudges: Evidence from China’s food-delivery industry. Science 381(6662):eadd9884.Crossref, Google Scholar
• Hitsch GJ, Misra S, Zhang W (2024) Heterogeneous treatment effects and optimal targeting policy evaluation. Quant. Marketing Econom. 22:115–168.Crossref, Google Scholar
• Hu X, Wan Z, Murthy NN (2019) Dynamic pricing of limited inventories with product returns. Manufacturing Service Oper. Management 21(3):501–518.Link, Google Scholar
• Hummel D, Maedche A (2019) How effective is nudging? A quantitative review on the effect sizes and limits of empirical nudging studies. J. Behav. Experiment. Econom. 80:47–58.Crossref, Google Scholar
• Langen H, Huber M (2023) How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign. PLoS One 18(1):e0278937.Crossref, Google Scholar
• Lehner M, Mont O, Heiskanen E (2016) Nudging–a promising tool for sustainable consumption behaviour? J. Cleaner Production 134:166–177.Crossref, Google Scholar
• Liu CW, Agarwal R, Gao G (2016) The dark side of positive social influence. Internat. Conf. Inform. Systems (ICIS) (Association for Information Systems, Atlanta).Google Scholar
• Lokhorst AM, Werner C, Staats H, van Dijk E, Gale JL (2013) Commitment and behavior change: A meta-analysis and critical review of commitment-making strategies in environmental research. Environ. Behav. 45(1):3–34.Crossref, Google Scholar
• Loock CM, Staake T, Thiesse F (2013) Motivating energy-efficient behavior with green is: An investigation of goal setting and the role of defaults. MIS Quart. 37(4):1313–1332.Crossref, Google Scholar
• Loschelder DD, Siepelmeyer H, Fischer D, Rubel JA (2019) Dynamic norms drive sustainable consumption: Norm-based nudging helps café customers to avoid disposable to-go-cups. J. Econom. Psych. 75:102146.Crossref, Google Scholar
• Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Neural Inform. Processing Systems (NeurIPS), vol. 30 (Curran Associates, Inc., Red Hook, NY).Google Scholar
• Luo X, Bhattacharya CB (2006) Corporate social responsibility, customer satisfaction, and market value. J. Marketing 70(4):1–18.Crossref, Google Scholar
• Luo X, Bhattacharya CB (2009) The debate over doing good: Corporate social performance, strategic marketing levers, and firm-idiosyncratic risk. J. Marketing 73(6):198–213.Crossref, Google Scholar
• Luo X, Lu X, Li J (2019) When and how to leverage e-commerce cart targeting: The relative and moderated effects of scarcity and price incentives with a two-stage field experiment and causal forest optimization. Inform. Systems Res. 30(4):1203–1227.Link, Google Scholar
• Minnema A, Bijmolt TH, Gensler S, Wiesel T (2016) To keep or not to keep: Effects of online customer reviews on product returns. J. Retailing 92(3):253–267.Crossref, Google Scholar
• Mirsch T, Lehrer C, Jung R (2017) Digital nudging: Altering user behavior in digital environments. Leimeister JM, Brenner W, eds. Proc. 39th Internat. Conf. Inform. Systems (ICIS) (Association for Information Systems, Atlanta), 634–648.Google Scholar
• Momsen K, Stoerk T (2014) From intention to action: Can nudges help consumers to choose renewable energy? Energy Policy 74:376–382.Crossref, Google Scholar
• Moorthy S, Srinivasan K (1995) Signaling quality with a money-back guarantee: The role of transaction costs. Marketing Sci. 14(4):442–466.Link, Google Scholar
• Murakami K, Shimada H, Ushifusa Y, Ida T (2022) Heterogeneous treatment effects of nudge and rebate: Causal machine learning in a field experiment on electricity conservation. Internat. Econom. Rev. 63(4):1779–1803.Google Scholar
• Nageswaran L, Cho SH, Scheller-Wolf A (2020) Consumer return policies in omnichannel operations. Management Sci. 66(12):5558–5575.Link, Google Scholar
• Narayanan A, Mathur A, Chetty M, Kshirsagar M (2020) Dark patterns: Past, present, and future: The evolution of tricky user interfaces. Queue 18(2):67–92.Crossref, Google Scholar
• Ofek E, Katona Z, Sarvary M (2011) “Bricks and clicks”: The impact of product returns on the strategies of multichannel retailers. Marketing Sci. 30(1):42–60.Link, Google Scholar
• Oprescu M, Syrgkanis V, Wu ZS (2019) Orthogonal random forest for causal inference. Kamalika C, Ruslan S, eds. Internat. Conf. Machine Learn. (ICML) (PMLR, New York), 4932–4941.Google Scholar
• Osman M (2020) Backfiring, reactance, boomerang, spillovers, and rebound effects: Can we learn anything from examples where nudges do the opposite of what they intended? Accessed June 18, 2024, https://osf.io/preprints/psyarxiv/ae756.Google Scholar
• Pålsson H, Pettersson F, Hiselius LW (2017) Energy consumption in e-commerce vs. conventional trade channels-insights into packaging, the last mile, unsold products and product returns. J. Cleaner Production 164:765–778.Crossref, Google Scholar
• Peer E, Egelman S, Harbach M, Malkin N, Mathur A, Frik A (2020) Nudge me right: Personalizing online security nudges to people’s decision-making styles. Comput. Human Behav. 109:106347.Crossref, Google Scholar
• Pei Z, Paswan A (2018) Consumers’ legitimate and opportunistic product return behaviors in online shopping. J. Electronic Commerce Res. 19(4):301–319.Google Scholar
• Pfrang T, Spreer P (2022) Behavioral return interventions: How behavioral science helps prevent returns in e-commerce. Marketing Rev. St. Gallen 39(4):24–33.Google Scholar
• Posner N, Simonov A, Mrkva K, Johnson EJ (2023) Dark defaults: How choice architecture steers political campaign donations. Proc. Natl. Acad. Sci. USA 120(40):e2218385120.Crossref, Google Scholar
• Richter I, Thøgersen J, Klöckner CA (2018) A social norms intervention going wrong: Boomerang effects from descriptive norms information. Sustainability 10(8):2848.Crossref, Google Scholar
• Schär A, Stanoevska-Slabeva K (2019) Application of digital nudging in customer journeys—A systematic literature review. Americas Conf. Inform. Systems (AMCIS), 13.Google Scholar
• Schölkopf B, Locatello F, Bauer S, Ke NR, Kalchbrenner N, Goyal A, Bengio Y (2021) Toward causal representation learning. Proc. IEEE 109(5):612–634.Crossref, Google Scholar
• Schubert C (2017) Green nudges: Do they work? Are they ethical? Ecological Econom. 132:329–342.Crossref, Google Scholar
• Schuler A, Baiocchi M, Tibshirani R, Shah N (2018) A comparison of methods for model selection when estimating individual treatment effects. Preprint, submitted April 14, https://arxiv.org/abs/1804.05146.Google Scholar
• Senoner J, Netland T, Feuerriegel S (2022) Using explainable artificial intelligence to improve process quality: Evidence from semiconductor manufacturing. Management Sci. 68(8):5704–5723.Link, Google Scholar
• Shead S (2021) Amazon plans to cut waste following backlash over the destruction of unused products. CNBC (August 4), https://www.cnbc.com/2021/08/04/amazon-plans-to-cut-waste-following-backlash.html.Google Scholar
• Shehu E, Papies D, Neslin SA (2020) Free shipping promotions and product returns. J. Marketing Res. 57(4):640–658.Crossref, Google Scholar
• Su X (2009) Consumer returns policies and supply chain performance. Manufacturing Service Oper. Management 11(4):595–612.Link, Google Scholar
• Syrgkanis V, Lewis G, Oprescu M, Hei M, Battocchi K, Dillon E, Pan J, et al. (2021) Causal inference and machine learning in practice with EconML and CausalML: Industrial use cases at Microsoft, Tripadvisor, Uber. Proc. 27th ACM SIGKDD Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 4072–4073.Google Scholar
• Thaler RH, Sunstein CR (2003) Libertarian paternalism. Amer. Econom. Rev. 93(2):175–179.Crossref, Google Scholar
• Thaler RH, Sunstein CR (2009) Nudge. Improving Decisions About Health, Wealth and Happiness (Penguin Books, London).Google Scholar
• Tian X, Sarkis J (2022) Emission burden concerns for online shopping returns. Nature Climate Change 12(1):2–3.Crossref, Google Scholar
• Wager S, Athey S (2018) Estimation and inference of heterogeneous treatment effects using random forests. J. Amer. Statist. Assoc. 113(523):1228–1242.Crossref, Google Scholar
• Walsh G, Möhring M (2017) Effectiveness of product return-prevention instruments: Empirical evidence. Electronic Marketing 27(4):341–350.Crossref, Google Scholar
• Weinmann M, Schneider C, vom Brocke J (2016) Digital nudging. Bus. Inform. Systems Engrg. 58(6):433–436.Crossref, Google Scholar
• Wensing J, Caputo V, Carraresi L, Bröring S (2020) The effects of green nudges on consumer valuation of bio-based plastic packaging. Ecological Econom. 178:106783.Crossref, Google Scholar
• White K, Habib R, Hardisty DJ (2019) How to shift consumer behaviors to be more sustainable: A literature review and guiding framework. J. Marketing 83(3):22–49.Crossref, Google Scholar
• Xu Y, Hua G, Cheng T, Choi TM, Li Y, Liu S (2023) Retailing and ordering strategies for online apparel retailers facing bracketing purchase behaviour. Internat. J. Production Res. 61(9):2841–2853.Crossref, Google Scholar
• Yoganarasimhan H, Barzegary E, Pani A (2023) Design and evaluation of optimal free trials. Management Sci. 69(6):3220–3240.Link, Google Scholar
• Zhang M, Luo L (2023) Can consumer-posted photos serve as a leading indicator of restaurant survival? Evidence from Yelp. Management Sci. 69(1):25–50.Link, Google Scholar
• Zheng S (2023) The true cost of apparel returns: Alarming return rates require loss-minimization solutions. Coresight Research (April 4), https://coresight.com/research/the-true-cost-of-apparel-returns-alarming-return-rates-require-loss-minimization-solutions/.Google Scholar
• Zhou W, Hinz O (2016) Determining profit-optimizing return policies—A two-step approach on data from taobao.com. Electronic Marketing 26(2):103–114.Crossref, Google Scholar
• Zhou W, Hinz O, Benlian A (2018) The impact of the package opening process on product returns. Bus. Res. 11(2):279–308.Crossref, Google Scholar