{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T05:58:43Z","timestamp":1775109523860,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T00:00:00Z","timestamp":1663891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JRFM"],"abstract":"<jats:p>The purpose of this study is to investigate and compare the popularity of common grocery apps in Hungary as well as Iran. The data were gathered from Iranian and Hungarian users who had at least one online purchase experience using a grocery app. A Gaussian mixture model (GMM) and multi-layer perceptron (MLP) are used as supervised and unsupervised machine learning algorithms with Python programming to cluster customers and predict consumer behavior. The results revealed that Wolt in Hungary and Snappfood in Iran are the most popular grocery apps. Users in Iran are divided into three groups of users of app services and the type of full covariance has higher accuracy compared to the other three types (96%). Meanwhile, we found that the five apps used in Hungary have provided 95% accuracy from the users\u2019 point of view based on the diagonal covariance. The MSE value (overfitting and cross-validation) is less than 0.1 in the MLP algorithm, which shows an acceptable amount of error. The results of overfitting indicate the proper fit of the MLP model. The findings of this study could be important for managers of online businesses. In the clustering section, the accuracy and value of consumer demographic information have been emphasized. Additionally, in the classification and prediction section, a kind of \u201ccustomization\u201d has been performed with an emphasis on market segmentation. This research used GMM and MLP machine learning algorithms as a creative way to cluster and classify consumers.<\/jats:p>","DOI":"10.3390\/jrfm15100424","type":"journal-article","created":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T01:41:58Z","timestamp":1664156518000},"page":"424","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Grocery Apps and Consumer Purchase Behavior: Application of Gaussian Mixture Model and Multi-Layer Perceptron Algorithm"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6808-1327","authenticated-orcid":false,"given":"Aidin","family":"Salamzadeh","sequence":"first","affiliation":[{"name":"Department of Business Management, Faculty of Management, University of Tehran, Tehran 141556311, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0125-3707","authenticated-orcid":false,"given":"Pejman","family":"Ebrahimi","sequence":"additional","affiliation":[{"name":"Doctoral School of Economic and Regional Sciences, Hungarian University of Agriculture and Life Sciences (MATE), 2100 G\u00f6d\u00f6ll\u0151, Hungary"}]},{"given":"Maryam","family":"Soleimani","sequence":"additional","affiliation":[{"name":"Department of Management, Economics and Accounting, Payame Noor University, Tehran 1599959515, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6058-009X","authenticated-orcid":false,"given":"Maria","family":"Fekete-Farkas","sequence":"additional","affiliation":[{"name":"Institute of Agricultural and Food Economics, Hungarian University of Agriculture and Life Sciences (MATE), 2100 G\u00f6d\u00f6ll\u0151, Hungary"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1080\/08911762.2021.1980640","article-title":"Evaluating the determinants of customers\u2019 mobile grocery shopping application (MGSA) adoption during COVID-19 pandemic","volume":"35","author":"Arefin","year":"2022","journal-title":"Journal of Global Marketing"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Alshurideh, Muhammad, Kurdi, Barween Al, Salloum, Said A, Arpaci, Ibrahim, and Al-Emran, Mostafa (2020). Predicting the actual use of m-learning systems: A comparative approach using PLS-SEM and machine learning algorithms. Interactive Learning Environments, 1\u201315.","DOI":"10.1080\/10494820.2020.1826982"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s40747-021-00358-1","article-title":"An intelligent approach for analyzing the impacts of the COVID-19 pandemic on marketing mix elements (7Ps) of the on-demand grocery delivery service","volume":"8","author":"Altay","year":"2022","journal-title":"Complex & Intelligent Systems"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100017","DOI":"10.1016\/j.stae.2022.100017","article-title":"Determinants of Adoption of Climate Smart Agricultural Technologies among Potato Farmers in Kenya: Does entrepreneurial orientation play a role?","volume":"1","author":"Andati","year":"2022","journal-title":"Sustainable Technology and Entrepreneurship"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s10645-021-09389-y","article-title":"COVID-19 and the Demand for Online Grocery Shopping: Empirical Evidence from the Netherlands","volume":"169","author":"Baarsma","year":"2021","journal-title":"De Economist"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bouzari, Parisa, Salamzadeh, Aidin, Soleimani, Maryam, and Ebrahimi, Pejman (2021). Online Social Networks and Women\u2019s Entrepreneurship: A Comparative Study between Iran and Hungary. Journal of Women\u2019s Entrepreneurship and Education, 61\u201375.","DOI":"10.28934\/jwee21.34.pp61-75"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1108\/BFJ-04-2021-0430","article-title":"Smart shopping: The adoption of grocery shopping apps","volume":"124","author":"Bruwer","year":"2021","journal-title":"British Food Journal"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1177\/0972622519885502","article-title":"Indian shoppers\u2019 attitude towards grocery shopping apps: A survey conducted on smartphone users","volume":"18","author":"Chakraborty","year":"2019","journal-title":"Metamorphosis"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chakraborty, Debarun, Bhatnagar, Shakti Bodh, Biswas, Wendrila, and Khatua, Ajay Kumar (2022, March 09). What Drives People to Adopt Grocery Apps? The Moderating Role of Household Size. Business Perspectives and Research, Available online: https:\/\/journals.sagepub.com\/doi\/abs\/10.1177\/22785337221091640.","DOI":"10.1177\/22785337221091640"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"101088","DOI":"10.1016\/j.retrec.2021.101088","article-title":"Analysis of critical factors affecting the final decision-making for online grocery shopping","volume":"87","year":"2021","journal-title":"Research in Transportation Economics"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ceh.2020.11.002","article-title":"An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN)","volume":"4","author":"Desai","year":"2021","journal-title":"Clinical eHealth"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1108\/YC-12-2018-0922","article-title":"Understanding online shopping behaviours and purchase intentions amongst millennials","volume":"22","author":"Dharmesti","year":"2019","journal-title":"Young Consumers"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.jretconser.2019.02.005","article-title":"Online grocery shopping in Thailand: Consumer acceptance and usage behavior","volume":"48","author":"Driediger","year":"2019","journal-title":"Journal of Retailing and Consumer Services"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ebrahimi, Pejman, Salamzadeh, Aidin, Soleimani, Maryam, Khansari, Seyed Mohammad, Zarea, Hadi, and Fekete-Farkas, Maria (2022a). Startups and Consumer Purchase Behavior: Application of Support Vector Machine Algorithm. Big Data and Cognitive Computing, 6.","DOI":"10.3390\/bdcc6020034"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ebrahimi, Pejman, Khajeheian, Datis, and Fekete-Farkas, Maria (2021a). A SEM-NCA Approach towards Social Networks Marketing: Evaluating Consumers\u2019 Sustainable Purchase Behavior with the Moderating Role of Eco-Friendly Attitude. International Journal of Environmental Research and Public Health, 18.","DOI":"10.3390\/ijerph182413276"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ebrahimi, Pejman, Khajeheian, Datis, Soleimani, Maryam, Gholampour, Abbas, and Fekete-Farkas, Maria (2022b). User engagement in social network platforms: What key strategic factors determine online consumer purchase behaviour?. Economic Research-Ekonomska Istra\u017eivanja, 1\u201332. Available online: https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/1331677X.2022.2106264.","DOI":"10.1080\/1331677X.2022.2106264"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ebrahimi, Pejman, Hamza, Khadija Aya, Gorgenyi-Hegyes, Eva, Zarea, Hadi, and Fekete-Farkas, Maria (2021b). Consumer knowledge sharing behavior and consumer purchase behavior: Evidence from E-commerce and online retail in Hungary. Sustainability, 13.","DOI":"10.3390\/su131810375"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ebrahimi, Pejman, Basirat, Marjan, Yousefi, Ali, Nekmahmud, Md, Gholampour, Abbas, and Fekete-Farkas, Maria (2022c). Social Networks Marketing and Consumer Purchase Behavior: The Combination of SEM and Unsupervised Machine Learning Approaches. Big Data and Cognitive Computing, 6.","DOI":"10.3390\/bdcc6020035"},{"key":"ref_19","unstructured":"Ebrahimi, Pejman, Hasani, Mohammad Naim, Salamzadeh, Aidin, Khansari, Seyed Mohammad, and Fekete-Farkas, Maria (2022d). A machine learning decision tree model to predict consumer purchase behaviour: A microeconomics view from online social platforms in Iran. International Journal of Business and globalization, in press."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101927","DOI":"10.1016\/j.jretconser.2019.101927","article-title":"Investigating the impact of Internet of Things services from a smartphone app on grocery shopping","volume":"52","author":"Eriksson","year":"2020","journal-title":"Journal of Retailing and Consumer Services"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Fekete-Farkas, Maria, Gholampour, Abbas, Bouzari, Parisa, Jarghooiyan, Hadi, and Ebrahimi, Pejman (2021). How gender and age can affect consumer purchase behavior? Evidence from A microeconomic perspective from Hungary. AD-Minister, 25\u201346. Available online: http:\/\/www.scielo.org.co\/scielo.php?script=sci_arttext&pid=S1692-02792021000200025.","DOI":"10.17230\/Ad-minister.39.2"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1108\/IJRDM-10-2021-0484","article-title":"The relationship between retailer app use, perceived shopping value and loyalty: The moderating role of deal proneness","volume":"50","author":"Flacandji","year":"2022","journal-title":"International Journal of Retail & Distribution Management"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1108\/REGE-02-2018-0037","article-title":"Factors affecting satisfaction and loyalty to online group buying","volume":"27","author":"Garcia","year":"2020","journal-title":"Revista de Gest\u00e3o"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103088","DOI":"10.1016\/j.jretconser.2022.103088","article-title":"Consequences of personalized product recommendations and price promotions in online grocery shopping","volume":"69","author":"Hallikainen","year":"2022","journal-title":"Journal of Retailing and Consumer Services"},{"key":"ref_25","first-page":"109","article-title":"Co-Creation in Provider Side for Developing Innovative Services: How New Technology-Based Firms Benefit from Social Media Platforms","volume":"2","author":"Horst","year":"2021","journal-title":"Nordic Journal of Media Management"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.jik.2019.01.006","article-title":"Development of hierarchical structure and analytical model of key factors for mobile app stickiness","volume":"5","author":"Hsu","year":"2020","journal-title":"Journal of Innovation & Knowledge"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.jretconser.2017.11.003","article-title":"Like throwing a piece of me away: How online and in-store grocery purchase channels affect consumers\u2019 food waste","volume":"41","author":"Ilyuk","year":"2018","journal-title":"Journal of Retailing and Consumer Services"},{"key":"ref_28","first-page":"793","article-title":"Towards application of various machine learning techniques in agriculture","volume":"51","author":"Jagtap","year":"2022","journal-title":"Materials Today: Proceedings"},{"key":"ref_29","first-page":"499","article-title":"Media branding and value co-creation: Effect of user participation in social media of newsmedia on attitudinal and behavioural loyalty","volume":"16","author":"Khajeheian","year":"2021","journal-title":"European Journal of International Management"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2672","DOI":"10.3390\/jtaer16070147","article-title":"Use of Mobile Grocery Shopping Application: Motivation and Decision-Making Process among South Korean Consumers","volume":"16","author":"Kim","year":"2021","journal-title":"Journal of Theoretical and Applied Electronic Commerce Research"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1108\/IJRDM-05-2018-0087","article-title":"Online grocery retailing\u2013exploring local grocers beliefs","volume":"47","author":"Kureshi","year":"2019","journal-title":"International Journal of Retail & Distribution Management"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1057\/s41270-021-00117-8","article-title":"A study on the downloading intention of fashion retailers\u2019 apps","volume":"9","author":"Llorens","year":"2021","journal-title":"Journal of Marketing Analytics"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1108\/REGE-03-2018-031","article-title":"Factors and characteristics that influence consumers\u2019 participation in social commerce","volume":"25","author":"Maia","year":"2018","journal-title":"Revista de Gest\u00e3o"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105948","DOI":"10.1016\/j.appet.2022.105948","article-title":"Transformation in culinary behaviour during the COVID-19 pandemic: In-depth interviews with food gatekeepers in urban India","volume":"172","author":"Menon","year":"2022","journal-title":"Appetite"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"102695","DOI":"10.1016\/j.jretconser.2021.102695","article-title":"The abandonment behaviour of the branded app consumer: A study using interpretive structural modelling approach","volume":"63","author":"Mondal","year":"2021","journal-title":"Journal of Retailing and Consumer Services"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"124901","DOI":"10.1016\/j.jhydrol.2020.124901","article-title":"Streamflow forecasting using extreme gradient boosting model coupled with Gaussian mixture model","volume":"586","author":"Ni","year":"2020","journal-title":"Journal of Hydrology"},{"key":"ref_37","unstructured":"Pallathadka, Harikumar, Mustafa, Malik, Sanchez, Domenic T., Sajja, Guna Sekhar, Gour, Sanjeev, and Naved, Mohd (2021). Impact of machine learning on management, healthcare and agriculture. Materials Today: Proceedings, in press."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1108\/BFJ-01-2020-0074","article-title":"Food delivery apps (FDAs) in Asia: An exploratory study across India and the Philippines","volume":"124","author":"Pandey","year":"2021","journal-title":"British Food Journal"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1037\/0021-9010.88.5.879","article-title":"Common method biases in behavioral research: A critical review of the literature and recommended remedies","volume":"88","author":"Podsakoff","year":"2003","journal-title":"Journal of Applied Psychology"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Rana, Arti, Rawat, Arvind Singh, Bijalwan, Anchit, and Bahuguna, Himanshu (, January August). Application of multi layer (perceptron) artificial neural network in the diagnosis system: A systematic review. Paper presented at 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE), San Salvador, El Salvador.","DOI":"10.1109\/RICE.2018.8509069"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Rinaldi, Chiara, D\u2019Aguilar, Marlene, and Egan, Matt (2022). Understanding the Online Environment for the Delivery of Food, Alcohol and Tobacco: An Exploratory Analysis of \u2018Dark Kitchens\u2019 and Rapid Grocery Delivery Services. International Journal of Environmental Research and Public Health, 19.","DOI":"10.3390\/ijerph19095523"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine learning: Algorithms, real-world applications and research directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Computer Science"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Segovia, Michelle, Grashuis, Jasper, and Skevas, Theodoros (2021). Consumer preferences for grocery purchasing during the COVID-19 pandemic: A quantile regression approach. British Food Journal, ahead-of-print.","DOI":"10.1108\/BFJ-05-2021-0474"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.1080\/10447318.2021.1912471","article-title":"E-Grocery retailing mobile application: Discerning determinants of repatronage intentions in an emerging economy","volume":"37","author":"Singh","year":"2021","journal-title":"International Journal of Human\u2013Computer Interaction"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1108\/JIBR-01-2016-0001","article-title":"Factors affecting satisfaction and loyalty in online grocery shopping: An integrated model","volume":"9","author":"Sreeram","year":"2017","journal-title":"Journal of Indian Business Research"},{"key":"ref_46","unstructured":"Statista (2021, March 01). Number of Facebook Users in Hungary from September 2018 to January 2022. Available online: https:\/\/www.statista.com\/statistics\/1029770\/facebook-users-hungary\/."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"102667","DOI":"10.1016\/j.jretconser.2021.102667","article-title":"Why do people purchase from food delivery apps? A consumer value perspective","volume":"63","author":"Tandon","year":"2021","journal-title":"Journal of Retailing and Consumer Services"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Tax, Niek, de Vries, Kees Jan, de Jong, Mathijs, Dosoula, Nikoleta, van den Akker, Bram, Smith, Jon, Thuong, Olivier, and Bernardi, Lucas (2021). Machine learning for fraud detection in e-Commerce: A research agenda. International Workshop on Deployable Machine Learning for Security Defense, Springer.","DOI":"10.1007\/978-3-030-87839-9_2"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"122692","DOI":"10.1016\/j.energy.2021.122692","article-title":"Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms","volume":"240","author":"Xu","year":"2022","journal-title":"Energy"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Yi, Hong, Meng, Xue, Linghu, Yuting, and Zhang, Ziyu (2022). Can financial capability improve entrepreneurial performance? Evidence from rural China. Economic Research-Ekonomska Istra\u017eivanja, 1\u201320. Available online: https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/1331677X.2022.2091631.","DOI":"10.1080\/1331677X.2022.2091631"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s10660-020-09411-6","article-title":"Product advertising recommendation in e-commerce based on deep learning and distributed expression","volume":"20","author":"Zhou","year":"2020","journal-title":"Electronic Commerce Research"}],"container-title":["Journal of Risk and Financial Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1911-8074\/15\/10\/424\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:38:02Z","timestamp":1760143082000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1911-8074\/15\/10\/424"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,23]]},"references-count":51,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["jrfm15100424"],"URL":"https:\/\/doi.org\/10.3390\/jrfm15100424","relation":{},"ISSN":["1911-8074"],"issn-type":[{"value":"1911-8074","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,23]]}}}