{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T06:03:43Z","timestamp":1762063423366,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T00:00:00Z","timestamp":1661472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Artificial Intelligence National Laboratory Program","award":["RRF-2.3.1-21-2022-00004"],"award-info":[{"award-number":["RRF-2.3.1-21-2022-00004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Given the importance of identifying key performance points in organizations, this research intends to determine the most critical intra- and extra-organizational elements in assessing the performance of firms using the European Company Survey (ECS) 2019 framework. The ECS 2019 survey data were used to train an artificial neural network optimized using an imperialist competitive algorithm (ANN-ICA) to forecast business performance and employee wellbeing. In order to assess the correctness of the model, root mean square error (RMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (r), and determination coefficient (R2) have been employed. The mean values of the performance criteria for the impact of internal and external factors on firm performance were 1.06, 0.002, 0.041, 0.9, and 0.83, and the value of the performance metrics for the impact of internal and external factors on employee wellbeing were 0.84, 0.0019, 0.0319, 0.83, and 0.71 (respectively, for MAPE, MSE, RMSE, r, and R2). The great performance of the ANN-ICA model is indicated by low values of MAPE, MSE, and RMSE, as well as high values of r and R2. The outcomes showed that \u201cskills requirements and skill matching\u201d and \u201cemployee voice\u201d are the two factors that matter most in enhancing firm performance and wellbeing.<\/jats:p>","DOI":"10.3390\/a15090300","type":"journal-article","created":{"date-parts":[[2022,8,28]],"date-time":"2022-08-28T21:22:56Z","timestamp":1661721776000},"page":"300","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Correlation Analysis of Factors Affecting Firm Performance and Employees Wellbeing: Application of Advanced Machine Learning Analysis"],"prefix":"10.3390","volume":"15","author":[{"given":"Jozsef","family":"Pap","sequence":"first","affiliation":[{"name":"Szechenyi University Doctoral School of Management (SzEEDSM), Szechenyi Istvan University, 9026 Gyor, Hungary"}]},{"given":"Csaba","family":"Mako","sequence":"additional","affiliation":[{"name":"Institute of the Information Society, National University of Public Service, 1083 Budapest, Hungary"}]},{"given":"Miklos","family":"Illessy","sequence":"additional","affiliation":[{"name":"Center for Social Sciences, Eotvos Lorand Research Network, 1097 Budapest, Hungary"}]},{"given":"Zef","family":"Dedaj","sequence":"additional","affiliation":[{"name":"Doctoral School of Business Administration, University of Pecs, 7622 Pecs, Hungary"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7744-7906","authenticated-orcid":false,"given":"Sina","family":"Ardabili","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, 81243 Bratislava, Slovakia"}]},{"given":"Bernat","family":"Torok","sequence":"additional","affiliation":[{"name":"Institute of the Information Society, National University of Public Service, 1083 Budapest, Hungary"}]},{"given":"Amir","family":"Mosavi","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, 81243 Bratislava, Slovakia"},{"name":"John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,26]]},"reference":[{"key":"ref_1","first-page":"277","article-title":"Performance evaluation a teaching hospital affiliated to Tehran University of medical sciences based on baldrige excellence model","volume":"3","author":"Farzianpour","year":"2011","journal-title":"Am. J. Econ. Bus. Adm."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1108\/01437720510615125","article-title":"Relationship between strategic human resource management and firm performance","volume":"26","author":"Chang","year":"2005","journal-title":"Int. J. Manpow."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1108\/00251740310496198","article-title":"Business performance measurement\u2013past, present and future","volume":"41","author":"Marr","year":"2003","journal-title":"Manag. Decis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1016\/j.technovation.2005.09.001","article-title":"The development of ICT advisors for SME businesses: An innovative approach","volume":"26","author":"Morgan","year":"2006","journal-title":"Technovation"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.techfore.2005.09.007","article-title":"Development of internal resources and capabilities as sources of differentiation of SME under increased global competition: A field study in Mexico","volume":"74","author":"Rangel","year":"2007","journal-title":"Technol. Forecast. Soc. Change"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1016\/j.ijhcs.2007.04.004","article-title":"Determinants of success for application service provider: An empirical test in small businesses","volume":"65","author":"Lee","year":"2007","journal-title":"Int. J. Hum. Comput. Stud."},{"key":"ref_7","first-page":"238","article-title":"The relationship between commanding leadership style and personality traits of nursing managers of hospitals affiliated to Tehran Medical Sciences Universities in 2014\u20132015","volume":"26","author":"Zare","year":"2016","journal-title":"Med. Sci. J. Islam. Azad Univ.-Tehran Med. Branch"},{"key":"ref_8","first-page":"225","article-title":"SENTiVENT: Enabling supervised information extraction of company-specific events in economic and financial news","volume":"56","author":"Jacobs","year":"2022","journal-title":"Comput. Humanit."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Elsharkawy, M., Sharafeldeen, A., Soliman, A., Khalifa, F., Ghazal, M., El-Daydamony, E., Atwan, A., Sandhu, H.S., and El-Baz, A.J.D. (2022). A novel computer-aided diagnostic system for early detection of diabetic retinopathy using 3D-OCT higher-order spatial appearance model. Diagnostics, 12.","DOI":"10.3390\/diagnostics12020461"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"109473","DOI":"10.1016\/j.ymssp.2022.109473","article-title":"Physics-informed ensemble learning for online joint strength prediction in ultrasonic metal welding","volume":"181","author":"Meng","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9912","DOI":"10.1038\/s41598-022-13925-4","article-title":"A machine learning based model accurately predicts cellular response to electric fields in multiple cell types","volume":"12","author":"Sargent","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s13673-018-0152-7","article-title":"Classifying 3D objects in LiDAR point clouds with a back-propagation neural network","volume":"8","author":"Song","year":"2018","journal-title":"Human-Centric Comput. Inf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"121716","DOI":"10.1016\/j.techfore.2022.121716","article-title":"What is the Market Value of Artificial Intelligence and Machine Learning? The Role of Innovativeness and Collaboration for Performance","volume":"180","author":"Parida","year":"2022","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_14","first-page":"1736","article-title":"Knowledge discovery in manufacturing datasets using data mining techniques to improve business performance","volume":"26","author":"Shaaban","year":"2022","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1007\/s13198-021-01594-x","article-title":"Business boosting through sentiment analysis using Artificial Intelligence approach","volume":"13","author":"Ahmed","year":"2022","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_16","unstructured":"Van Houten, G., and Russo, G. (2020). European Company Survey 2019: Workplace Practices Unlocking Employee Potential, Eurofound."},{"key":"ref_17","unstructured":"Eurofound, and Cedefop (2020). European Company Survey 2019 Series, Publications Office of the European Union. Available online: https:\/\/www.cedefop.europa.eu\/en\/publications\/2228."},{"key":"ref_18","first-page":"36","article-title":"Munkaszervezeti modellek Eur\u00f3p\u00e1ban \u00e9s az emberier\u0151forr\u00e1s-gazd\u00e1lkod\u00e1s n\u00e9h\u00e1ny jellemz\u0151je K\u00eds\u00e9rlet a munkaszervezetek nemzetk\u00f6zi paradigmat\u00e9rk\u00e9p\u00e9nek elk\u00e9sz\u00edt\u00e9s\u00e9re (II. r\u00e9sz)","volume":"40","author":"Valeyre","year":"2009","journal-title":"Vez. Bp. Manag. Rev."},{"key":"ref_19","first-page":"S3","article-title":"Work organization and technology: Introduction to the theme of the special issue","volume":"8","author":"Haapakorpi","year":"2018","journal-title":"Nord. J. Work. Life Stud."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"124216","DOI":"10.1016\/j.jclepro.2020.124216","article-title":"Modeling of geothermal power system equipped with absorption refrigeration and solar energy using multilayer perceptron neural network optimized with imperialist competitive algorithm","volume":"276","author":"Khosravi","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/S0890-6955(00)00073-0","article-title":"Training multilayered perceptrons for pattern recognition: A comparative study of four training algorithms","volume":"41","author":"Pham","year":"2001","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/0010-0277(91)90022-V","article-title":"U-shaped learning and frequency effects in a multilayered perceptron: Implications for child language acquisition","volume":"38","author":"Plunkett","year":"1991","journal-title":"Cognition"},{"key":"ref_23","unstructured":"Shepherd, A.J. (2012). Second-Order Methods for Neural Networks: Fast and Reliable Training Methods for Multi-Layer Perceptrons, Springer Science & Business Media."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Taud, H., and Mas, J. (2018). Multilayer perceptron (MLP). Geomatic Approaches for Modeling Land Change Scenarios, Springer.","DOI":"10.1007\/978-3-319-60801-3_27"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Atashpaz-Gargari, E., and Lucas, C. (2007, January 25\u201328). Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. Proceedings of the 2007 IEEE Congress on Evolutionary Computation, Singapore.","DOI":"10.1109\/CEC.2007.4425083"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3455","DOI":"10.1007\/s00521-016-2251-6","article-title":"A hybrid intelligent model combining ANN and imperialist competitive algorithm for prediction of corrosion rate in 3C steel under seawater environment","volume":"28","author":"Mohammadi","year":"2017","journal-title":"Neural Comput. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/JPHOTOV.2019.2898521","article-title":"Prediction model for PV performance with correlation analysis of environmental variables","volume":"9","author":"Kim","year":"2019","journal-title":"IEEE J. Photovolt."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.enbuild.2018.01.066","article-title":"Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches","volume":"166","author":"Ahmad","year":"2018","journal-title":"Energy Build."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1007\/s12652-019-01317-y","article-title":"Forecasting heating and cooling loads of buildings: A comparative performance analysis","volume":"11","author":"Roy","year":"2020","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.apenergy.2014.01.044","article-title":"Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network","volume":"119","author":"Roy","year":"2014","journal-title":"Appl. Energy"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"102963","DOI":"10.1016\/j.advengsoft.2020.102963","article-title":"Comparative analysis of image projection-based descriptors in Siamese neural networks","volume":"154","year":"2021","journal-title":"Adv. Eng. Softw."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/9\/300\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:15:53Z","timestamp":1760141753000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/9\/300"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,26]]},"references-count":31,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["a15090300"],"URL":"https:\/\/doi.org\/10.3390\/a15090300","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2022,8,26]]}}}