{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T10:38:45Z","timestamp":1776681525400,"version":"3.51.2"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T00:00:00Z","timestamp":1667174400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T00:00:00Z","timestamp":1667174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"PNG Science and Technology Secretariat","award":["1-3962 PNGSTS"],"award-info":[{"award-number":["1-3962 PNGSTS"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The purpose of this study was to evaluate the effectiveness of using natural language processing (NLP) artificial intelligence (AI) in enterprise resources planning (ERP) to identify specialized job candidates in real-time big data\u2014globally across the internet. The central problem was that companies using traditional methods for recruiting remote specialists were missing good candidates because the skilled employees may not be looking for a job yet they may be receptive to an offer. The auxiliary problem was too much data on the internet for human resources management (HRM) staff to make sense of to find the best-fitting candidate. Thus, the research question was: could NLP AI identify good candidates for ERP remote specialist jobs using internet real-time big data? Job criteria were developed using machine learning to identify key skills from existing staff in a case study company. The skills were transformed into ERP remote specialists hiring criteria. The NLP AI software was activated to find the best candidate. The HRM staff at the case study company evaluated the effectiveness of the candidate selected by the NLP AI. The case study company set 70% as the acceptable mean evaluation score. ANOVA was used to determine if HRM staff agreed about their evaluation scores. A Z-test was used to determine if the NLP AI was faster than the mean time needed for HRM to select ERP candidates. The results were that the NLP AI outperformed the humans by a factor of almost 8\u00a0h. All HRM staff agreed that the NLP AI was effective in selecting a candidate to match the hiring criteria. The proposed approach might facilitate the research and development of big data, data analytics, NLP AI, and HRM process improvement.<\/jats:p>","DOI":"10.1007\/s44163-022-00037-1","type":"journal-article","created":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T19:02:52Z","timestamp":1667242972000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["ERP Staff versus AI recruitment with employment real-time big data"],"prefix":"10.1007","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4333-4399","authenticated-orcid":false,"given":"Kenneth David","family":"Strang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0780-3271","authenticated-orcid":false,"given":"Zhaohao","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,31]]},"reference":[{"key":"37_CR1","volume-title":"Management information systems: managing the digital firm","author":"KG Laudon","year":"2020","unstructured":"Laudon KG, Laudon KC. Management information systems: managing the digital firm. 16th ed. Harlow: Pearson; 2020.","edition":"16"},{"key":"37_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IRMJ.291691","volume":"35","author":"S Bhardwaj","year":"2022","unstructured":"Bhardwaj S. Data analytics in small and medium enterprises (SME): a systematic review and future research directions. Inf Resour Manag J. 2022;35:1\u201318. https:\/\/doi.org\/10.4018\/IRMJ.291691.","journal-title":"Inf Resour Manag J"},{"key":"37_CR3","first-page":"63","volume":"9","author":"R Geetha","year":"2018","unstructured":"Geetha R, Bhanu SR. Recruitment through artificial intelligence: a conceptual study. Int J Mech Eng Technol. 2018;9:63\u201370.","journal-title":"Int J Mech Eng Technol"},{"key":"37_CR4","volume-title":"Artificial intelligence: a modern approach","author":"S Russell","year":"2020","unstructured":"Russell S, Norvig P. Artificial intelligence: a modern approach. 4th ed. New Jersey: Prentice Hall; 2020.","edition":"4"},{"key":"37_CR5","doi-asserted-by":"publisher","first-page":"108","DOI":"10.21791\/IJEMS.2021.1.10","volume":"6","author":"J Fraij","year":"2021","unstructured":"Fraij J, Laszlo V. A literature review: artificial intelligence impact on the recruitment process. Int J Eng Manag Sci. 2021;6:108\u201321. https:\/\/doi.org\/10.21791\/IJEMS.2021.1.10.","journal-title":"Int J Eng Manag Sci"},{"key":"37_CR6","first-page":"41","volume":"3","author":"S Zang","year":"2015","unstructured":"Zang S, Ye M. Human resource management in the era of big data. J Hum Resour Sustain Stud. 2015;3:41\u20135.","journal-title":"J Hum Resour Sustain Stud"},{"key":"37_CR7","first-page":"23","volume":"8","author":"N Nawaz","year":"2019","unstructured":"Nawaz N. How far have we come with the study of artificial intelligence for recruitment process. Int J Sci Technol Res. 2019;8:23\u201341.","journal-title":"Int J Sci Technol Res"},{"key":"37_CR8","first-page":"42","volume":"7","author":"H Rodney","year":"2019","unstructured":"Rodney H, Val\u00e1\u0161kova K, Durana P. The artificial intelligence recruitment process: how technological advancements have reshaped job application and selection practices. Psychosociol Issues Hum Resour Manag. 2019;7:42\u20137.","journal-title":"Psychosociol Issues Hum Resour Manag"},{"key":"37_CR9","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1080\/23276665.2020.1816188","volume":"42","author":"P Henman","year":"2020","unstructured":"Henman P. Improving public services using artificial intelligence: possibilities, pitfalls, governance. Asia Pac J Pub Adm. 2020;42:209\u201321. https:\/\/doi.org\/10.1080\/23276665.2020.1816188.","journal-title":"Asia Pac J Pub Adm"},{"key":"37_CR10","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1080\/13678868.2020.1818513","volume":"24","author":"Z Su","year":"2021","unstructured":"Su Z, Togay G, Cote A-M. Artificial intelligence: a destructive and yet creative force in the skilled labour market. Hum Resour Dev Int. 2021;24:341\u201352. https:\/\/doi.org\/10.1080\/13678868.2020.1818513.","journal-title":"Hum Resour Dev Int"},{"key":"37_CR11","volume-title":"Transparent data mining for big and small. Data studies in big data","author":"G Comand\u00e8","year":"2017","unstructured":"Comand\u00e8 G. Regulating algorithms\u2019 regulation? First ethico-legal principles, problems, and opportunities of algorithms. In: Cerquitelli T, Quercia D, Pasquale F, editors. Transparent data mining for big and small. Data studies in big data, vol. 32. New York: Springer; 2017. (10.1007\/978-3-319-54024-5_8)."},{"key":"37_CR12","unstructured":"Murad A. The computers rejecting your job application. BBC News [Business Section] World ed. London; 2021. p. 1\u20139."},{"key":"37_CR13","first-page":"59","volume":"60","author":"PR Daugherty","year":"2019","unstructured":"Daugherty PR, Wilson HJ, Chowdhury R. Using artificial intelligence to promote diversity. MIT Sloan Manag Rev. 2019;60:59\u201363.","journal-title":"MIT Sloan Manag Rev"},{"key":"37_CR14","doi-asserted-by":"crossref","unstructured":"Bertrand M, Mullainathan S. Are emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. National Bureau of Economic Research Working Paper Series. vol. 9873. 2003. http:\/\/www.nber.org\/papers\/w9873.pdf.","DOI":"10.3386\/w9873"},{"key":"37_CR15","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1177\/1094670517752459","volume":"21","author":"H Ming-Hui","year":"2018","unstructured":"Ming-Hui H, Rust RT. Artificial intelligence in service. J Serv Res. 2018;21:155\u201372.","journal-title":"J Serv Res"},{"key":"37_CR16","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1108\/SHR-07-2018-0051","volume":"17","author":"AK Upadhyay","year":"2018","unstructured":"Upadhyay AK, Khandelwal K. Applying artificial intelligence: implications for recruitment. Strateg HR Rev. 2018;17:255\u20138.","journal-title":"Strateg HR Rev"},{"key":"37_CR17","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1145\/3015456","volume":"60","author":"L Cao","year":"2017","unstructured":"Cao L. Data science: challenges and directions. CACM. 2017;60:59\u201368.","journal-title":"CACM"},{"key":"37_CR18","first-page":"1523","volume":"66","author":"H Ekbia","year":"2015","unstructured":"Ekbia H, Mattioli M, Kouper I, Arave G, Ghazinejad A, Bowman T, Suri VR, Tsou A, Weingart S, Sugimoto CR. Big data, bigger dilemmas: a critical review. J Am Soc Inf Sci. 2015;66:1523\u201345.","journal-title":"J Am Soc Inf Sci"},{"key":"37_CR19","first-page":"337","volume":"10","author":"U Jovanovi","year":"2015","unstructured":"Jovanovi U, Stimec A, Vladusi D. Big-data analytics: a critical review and some future directions. Int J Bus Intell Data Min. 2015;10:337\u201355.","journal-title":"Int J Bus Intell Data Min"},{"key":"37_CR20","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1093\/nsr\/nwt032","volume":"1","author":"J Fan","year":"2014","unstructured":"Fan J, Han F, Liu H. Challenges of big data analysis. Natl Sci Rev J. 2014;1:293\u2013314.","journal-title":"Natl Sci Rev J"},{"key":"37_CR21","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1111\/jlme.12214","volume":"43","author":"N Terry","year":"2015","unstructured":"Terry N. Navigating the incoherence of big data reform proposals. J Law Med Ethics. 2015;43:44\u20137.","journal-title":"J Law Med Ethics"},{"key":"37_CR22","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.ijinfomgt.2014.10.007","volume":"35","author":"A Gandomi","year":"2015","unstructured":"Gandomi A, Haider M. Beyond the hype: Big data concepts, methods, and analytics. Int J Inf Manag. 2015;35:137\u201344.","journal-title":"Int J Inf Manag"},{"key":"37_CR23","volume-title":"Business intelligence and analytics: systems for decision support","author":"R Sharda","year":"2018","unstructured":"Sharda R, Delen D, Turba E. Business intelligence and analytics: systems for decision support. 10th ed. Boston: Pearson; 2018.","edition":"10"},{"key":"37_CR24","volume-title":"Understanding big data: analytics for enterprise class hadoop and streaming data","author":"P Zikopoulos","year":"2011","unstructured":"Zikopoulos P, Eaton C, DeRoos D, Deutsch T, Lapis G. Understanding big data: analytics for enterprise class hadoop and streaming data. New York: McGraw-Hill Osborne Media; 2011."},{"key":"37_CR25","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.ijpe.2015.02.014","volume":"165","author":"RY Zhong","year":"2015","unstructured":"Zhong RY, Huang GQ, Lan S, Dai QY, Chen X, Zhang T. A big data approach for logistics trajectory discovery from RFID-enabled production data. Int J Prod Econ. 2015;165:260\u201372.","journal-title":"Int J Prod Econ"},{"key":"37_CR26","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1111\/jlme.12258","volume":"43","author":"MA Rothstein","year":"2015","unstructured":"Rothstein MA. Ethical issues in big data health research: currents in contemporary bioethics. J Law Med Ethics. 2015;43:425\u20139.","journal-title":"J Law Med Ethics"},{"key":"37_CR27","volume-title":"Engineering the American dream: a study of bias and perceptions of merit in the high-tech labor market","author":"C Barabas","year":"2015","unstructured":"Barabas C. Engineering the American dream: a study of bias and perceptions of merit in the high-tech labor market, vol. 2022. Boston: MIT; 2015."},{"issue":"2","key":"37_CR28","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1080\/08874417.2019.1571456","volume":"61","author":"Z Sun","year":"2021","unstructured":"Sun Z, Huo Y. The spectrum of big data analytics. J Comput Inf Syst. 2021;61(2):154\u201362. https:\/\/doi.org\/10.1080\/08874417.2019.1571456.","journal-title":"J Comput Inf Syst"},{"key":"37_CR29","volume-title":"Frontiers in massive data analysis","author":"National Research Council","year":"2013","unstructured":"National Research Council. Frontiers in massive data analysis. Washington DC: The National Research Press; 2013."},{"issue":"236","key":"37_CR30","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1093\/mind\/LIX.236.433","volume":"59","author":"AM Turing","year":"1950","unstructured":"Turing AM. Computing machinery and intelligence. Mind. 1950;59(236):433\u201360.","journal-title":"Mind"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-022-00037-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-022-00037-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-022-00037-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T19:13:20Z","timestamp":1667243600000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-022-00037-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,31]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["37"],"URL":"https:\/\/doi.org\/10.1007\/s44163-022-00037-1","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,31]]},"assertion":[{"value":"21 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 October 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The research project was approved by Strang\u2019s employer and all participants gave ethical consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"21"}}