{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T14:35:48Z","timestamp":1777127748507,"version":"3.51.4"},"reference-count":53,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006595","name":"Ministry of Research, Innovation and Digitization, CNCS\/CCCDI-UEFISCDI","doi-asserted-by":"publisher","award":["ERANET-ERAMIN-3-ValorWaste-1"],"award-info":[{"award-number":["ERANET-ERAMIN-3-ValorWaste-1"]}],"id":[{"id":"10.13039\/501100006595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JTAER"],"abstract":"<jats:p>Pricing strategy is a critical challenge in e-commerce, where businesses must balance competitive pricing with profitability. Traditional pricing models rely on historical data and statistical methods but often lack interpretability and adaptability. In this study, we propose a novel approach that leverages Diverse Counterfactual Explanations (DiCE) to enhance pricing strategies for mobile phones. Unlike previous research that applied counterfactual analysis in customer segmentation, energy forecasting, and retail pricing, our method directly integrates explainability into product-level pricing decisions. Our approach identifies actionable product features, such as improved hardware specifications, that can be modified to increase the predicted price. By generating counterfactual explanations, we provide insights into how businesses can optimize product attributes to maximize revenue while maintaining transparency in pricing decisions. This framework bridges explainable AI with pricing strategies, allowing companies to justify price points and improve market positioning dynamically. Furthermore, we identify other features that could lead to the same price goal. The linear regression model achieved an R2 score of 96.15% on the test set, along with a mean absolute error (MAE) of 108.31 and mean absolute percentage error (MAPE) of 5.43%, indicating strong predictive performance. Through DiCE, the model identified actionable modifications (e.g., increasing front camera resolution and battery capacity) that effectively raise predicted prices by 15\u201320%. This insight is particularly valuable for product design and pricing optimization. The model provided a ranking of features based on their impact on price increases, revealing that front camera and battery capacity are more influential than RAM in driving pricing changes.<\/jats:p>","DOI":"10.3390\/jtaer20020096","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T11:37:02Z","timestamp":1746704222000},"page":"96","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Diverse Counterfactual Explanations (DiCE) Role in Improving Sales and e-Commerce Strategies"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9005-5181","authenticated-orcid":false,"given":"Simona-Vasilica","family":"Oprea","sequence":"first","affiliation":[{"name":"Economic Informatics and Cybernetics Department, Bucharest University of Economic Studies, 010374 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0961-352X","authenticated-orcid":false,"given":"Adela","family":"B\u00e2ra","sequence":"additional","affiliation":[{"name":"Economic Informatics and Cybernetics Department, Bucharest University of Economic Studies, 010374 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Oprea, S.-V., and B\u00e2ra, A. (2025). Is Artificial Intelligence a Game-Changer in Steering E-Business into the Future? Uncovering Latent Topics with Probabilistic Generative Models. J. Theor. Appl. Electron. Commer. Res., 20.","DOI":"10.3390\/jtaer20010016"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"119898","DOI":"10.1016\/j.ins.2023.119898","article-title":"On generating trustworthy counterfactual explanations","volume":"655","author":"Herrera","year":"2024","journal-title":"Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2770","DOI":"10.1007\/s10618-022-00831-6","article-title":"Counterfactual explanations and how to find them: Literature review and benchmarking","volume":"38","author":"Guidotti","year":"2024","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3111","DOI":"10.1007\/s10994-023-06319-8","article-title":"PreCoF: Counterfactual explanations for fairness","volume":"113","author":"Goethals","year":"2024","journal-title":"Mach. Learn."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Labaien Soto, J., Zugasti Uriguen, E., and De Carlos Garcia, X. (2023). Real-Time, Model-Agnostic and User-Driven Counterfactual Explanations Using Autoencoders. Appl. Sci., 13.","DOI":"10.3390\/app13052912"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103995","DOI":"10.1016\/j.artint.2023.103995","article-title":"Counterfactual explanations for misclassified images: How human and machine explanations differ","volume":"324","author":"Delaney","year":"2023","journal-title":"Artif. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"72363","DOI":"10.1109\/ACCESS.2022.3189432","article-title":"FCE: Feedback Based Counterfactual Explanations for Explainable AI","volume":"10","author":"Suffian","year":"2022","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Scharowski, N., Perrig, S.A.C., Svab, M., Opwis, K., and Br\u00fchlmann, F. (2023). Exploring the effects of human-centered AI explanations on trust and reliance. Front. Comput. Sci., 5.","DOI":"10.3389\/fcomp.2023.1151150"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"82736","DOI":"10.1109\/ACCESS.2022.3196917","article-title":"The Robustness of Counterfactual Explanations Over Time","volume":"10","author":"Ferrario","year":"2022","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103840","DOI":"10.1016\/j.artint.2022.103840","article-title":"On the robustness of sparse counterfactual explanations to adverse perturbations","volume":"316","author":"Virgolin","year":"2023","journal-title":"Artif. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1660","DOI":"10.3390\/jtaer19030081","article-title":"Analyzing the Dynamics of Customer Behavior: A New Perspective on Personalized Marketing through Counterfactual Analysis","volume":"19","author":"Elmaghraby","year":"2024","journal-title":"J. Theor. Appl. Electron. Commer. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1108\/IJOA-03-2013-0645","article-title":"Fairness judgments and counterfactual thinking: Pricing goods versus services","volume":"23","author":"Naquin","year":"2015","journal-title":"Int. J. Organ. Anal."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1438","DOI":"10.1109\/TVCG.2020.3030342","article-title":"DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models","volume":"27","author":"Cheng","year":"2021","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1007\/s11129-023-09269-6","article-title":"Counter-cyclical price promotion: Capturing seasonal changes in stockpiling and endogenous consumption","volume":"21","author":"Kwon","year":"2023","journal-title":"Quant. Mark. Econ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mertes, S., Huber, T., Weitz, K., Heimerl, A., and Andr\u00e9, E. (2022). GANterfactual\u2014Counterfactual Explanations for Medical Non-experts Using Generative Adversarial Learning. Front. Artif. Intell., 5.","DOI":"10.3389\/frai.2022.825565"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Matsui, T., Taki, M., Pham, T.Q., Chikazoe, J., and Jimura, K. (2022). Counterfactual Explanation of Brain Activity Classifiers Using Image-To-Image Transfer by Generative Adversarial Network. Front. Neuroinform., 15.","DOI":"10.3389\/fninf.2021.802938"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Terra, A., Inam, R., Batista, P., and Fersman, E. (2022, January 25\u201328). Using Counterfactuals to Proactively Solve Service Level Agreement Violations in 5G Networks. Proceedings of the IEEE International Conference on Industrial Informatics (INDIN), Perth, WA, Australia.","DOI":"10.1109\/INDIN51773.2022.9976075"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ma, H., Mcareavey, K., Mcconville, R., and Liu, W. (2022, January 1\u20133). Explainable AI for Non-Experts: Energy Tariff Forecasting. Proceedings of the 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022, Bristol, UK.","DOI":"10.1109\/ICAC55051.2022.9911105"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.renene.2020.11.067","article-title":"Integration of non-conventional renewable energy and spot price of electricity: A counterfactual analysis for Colombia","volume":"167","author":"Perez","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"105874","DOI":"10.1016\/j.eneco.2022.105874","article-title":"Market premia for renewables in Germany: The effect on electricity prices","volume":"109","author":"Frondel","year":"2022","journal-title":"Energy Econ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1002\/jeab.897","article-title":"A neural autopilot theory of habit: Evidence from consumer purchases and social media use","volume":"121","author":"Camerer","year":"2024","journal-title":"J. Exp. Anal. Behav."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1287\/mksc.2018.1108","article-title":"Pennies for your thoughts: Costly product consideration and purchase quantity thresholds","volume":"37","author":"Huang","year":"2018","journal-title":"Mark. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, Y., Song, X., Wei, T., and Zhu, B. (2023, January 22\u201324). Counterfactual learning in customer churn prediction under class imbalance. Proceedings of the 2023 6th International Conference on Big Data Technologies, Qingdao, China. ACM International Conference Proceeding Series.","DOI":"10.1145\/3627377.3627392"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"118761","DOI":"10.1016\/j.eswa.2022.118761","article-title":"An inverse classification framework with limited budget and maximum number of perturbed samples","volume":"212","author":"Koo","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"101869","DOI":"10.1016\/j.ribaf.2022.101869","article-title":"A sparsity algorithm for finding optimal counterfactual explanations: Application to corporate credit rating","volume":"64","author":"Wang","year":"2023","journal-title":"Res. Int. Bus. Financ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"120616","DOI":"10.1016\/j.ins.2024.120616","article-title":"COCOA: Cost-Optimized COunterfactuAl explanation method","volume":"670","year":"2024","journal-title":"Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s41060-022-00365-6","article-title":"CARE: Coherent actionable recourse based on sound counterfactual explanations","volume":"17","author":"Rasouli","year":"2024","journal-title":"Int. J. Data Sci. Anal."},{"key":"ref_28","unstructured":"Cozgarea, A.N., Cozgarea, G., Boldeanu, D.M., Pugna, I., and Gheorghe, M. (2023). Predicting economic and financial performance through machine learning. Econ. Comput. Econ. Cybern. Stud. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e54","DOI":"10.1002\/ail2.54","article-title":"Explaining autonomous drones: An XAI journey","volume":"2","author":"Stefik","year":"2021","journal-title":"Appl. AI Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"117133","DOI":"10.1016\/j.eswa.2022.117133","article-title":"Customer price sensitivities in competitive insurance markets","volume":"202","author":"Verschuren","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"11974","DOI":"10.1109\/ACCESS.2021.3051315","article-title":"A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence","volume":"9","author":"Stepin","year":"2021","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"69543","DOI":"10.1109\/ACCESS.2022.3177783","article-title":"Model-Agnostic Counterfactual Explanations in Credit Scoring","volume":"10","author":"Dastile","year":"2022","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1287\/mksc.2023.1449","article-title":"Automating the B2B Salesperson Pricing Decisions: A Human-Machine Hybrid Approach","volume":"43","author":"Netzer","year":"2024","journal-title":"Mark. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hua, J., Yan, L., Xu, H., and Yang, C. (2021, January 14\u201318). Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and Multi-Period Optimization Approach. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Singapore.","DOI":"10.1145\/3447548.3467083"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1080\/01621459.2021.2004895","article-title":"Do We Exploit all Information for Counterfactual Analysis? Benefits of Factor Models and Idiosyncratic Correction","volume":"117","author":"Fan","year":"2022","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sakkas, N., Yfanti, S., Shah, P., Sakkas, N., Chaniotakis, C., Daskalakis, C., Barbu, E., and Domnich, M. (2023). Explainable Approaches for Forecasting Building Electricity Consumption. Energies, 16.","DOI":"10.20944\/preprints202308.1230.v1"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bhatnagar, P., Lokesh, G.H., Shreyas, J., Flammini, F., Panwar, D., and Shree, S. (2024). Prediction of Mobile Phone Prices using Machine Learning. ICMLT 2024, Proceedings of the 2024 9th International Conference on Machine Learning Technologies, Oslo, Norway, 24\u201326 May 2024, Association for Computing Machinery.","DOI":"10.1145\/3674029.3674031"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Rana, D.S., Dhondiyal, S.A., Singh, S., Kukreti, S., and Dhyani, A. (2024, January 23\u201324). Predicting Mobile Prices with Machine Learning Techniques. Proceedings of the 2024 International Conference on Computational Intelligence and Computing Applications (ICCICA), Samalkha, India.","DOI":"10.1109\/ICCICA60014.2024.10585222"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Priyaa, N.V., and TamilSelvan, S. (2025). Comparison of improved XGBoost algorithm with random forest regression to determine the prediction of the mobile price. Applications of Mathematics in Science and Technology, Taylor & Francis. [1st ed.].","DOI":"10.1201\/9781003606659-120"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Mothilal, R.K., Sharma, A., and Tan, C. (2020, January 27\u201330). Explaining machine learning classifiers through diverse counterfactual explanations. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, Barcelona, Spain.","DOI":"10.1145\/3351095.3372850"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.ins.2023.01.012","article-title":"Counterfactual explanation generation with minimal feature boundary","volume":"625","author":"You","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1186\/s40537-023-00719-2","article-title":"Early prediction of MODS interventions in the intensive care unit using machine learning","volume":"10","author":"Liu","year":"2023","journal-title":"J. Big Data"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Afrin, F., Hamilton, M., and Thevathyan, C. (2023). Exploring Counterfactual Explanations for Predicting Student Success. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer.","DOI":"10.1007\/978-3-031-36021-3_44"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tang, Z., King, Z., Segovia, A.C., Yu, H., Braddock, G., Ito, A., Sakamoto, R., Shimaoka, M., and Sano, A. (2023, January 15\u201318). Burnout Prediction and Analysis in Shift Workers: Counterfactual Explanation Approach. Proceedings of the BHI 2023-IEEE-EMBS International Conference on Biomedical and Health Informatics, Pittsburgh, PA, USA.","DOI":"10.1109\/BHI58575.2023.10313392"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Artelt, A., and Gregoriades, A. (2023, January 24\u201326). \u201cHow to Make Them Stay?\u201d: Diverse Counterfactual Explanations of Employee Attrition. Proceedings of the International Conference on Enterprise Information Systems, ICEIS, Prague, Czech Republic.","DOI":"10.5220\/0011961300003467"},{"key":"ref_46","first-page":"151","article-title":"Factors Affecting Mobile Phone Purchase in the Greater Accra Region of Ghana: A Binary Logit Model Approach","volume":"5","author":"Dziwornu","year":"2013","journal-title":"Int. J. Mark. Stud."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.inffus.2021.11.003","article-title":"Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications","volume":"81","author":"Chou","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"982","DOI":"10.2307\/2327568","article-title":"Principles of Corporate Finance","volume":"36","author":"Stapleton","year":"1981","journal-title":"J. Financ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bui, T.D., Ali, M.H., Tsai, F.M., Iranmanesh, M., Tseng, M.L., and Lim, M.K. (2020). Challenges and Trends in Sustainable Corporate Finance: A Bibliometric Systematic Review. J. Risk Financ. Manag., 13.","DOI":"10.3390\/jrfm13110264"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1111\/joms.12715","article-title":"The Endurance of Shareholder Value Maximization as the Preferred Corporate Objective","volume":"59","author":"Inkpen","year":"2022","journal-title":"J. Manag. Stud."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.jbusres.2022.07.042","article-title":"Value-based pricing in digital platforms: A machine learning approach to signaling beyond core product attributes in cross-platform settings","volume":"152","author":"Christen","year":"2022","journal-title":"J. Bus. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s00163-020-00346-5","article-title":"Optimal resource allocation for dynamic product development process via convex optimization","volume":"32","author":"Zhao","year":"2021","journal-title":"Res. Eng. Des."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"110273","DOI":"10.1016\/j.knosys.2023.110273","article-title":"Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities","volume":"263","author":"Saeed","year":"2023","journal-title":"Knowl. Based Syst."}],"container-title":["Journal of Theoretical and Applied Electronic Commerce Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/0718-1876\/20\/2\/96\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:29:45Z","timestamp":1760030985000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/0718-1876\/20\/2\/96"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,8]]},"references-count":53,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["jtaer20020096"],"URL":"https:\/\/doi.org\/10.3390\/jtaer20020096","relation":{},"ISSN":["0718-1876"],"issn-type":[{"value":"0718-1876","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,8]]}}}