{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T04:17:17Z","timestamp":1746591437414,"version":"3.40.5"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:00:00Z","timestamp":1746489600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:00:00Z","timestamp":1746489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Anhui Province Higher Education Revitalization Plan Project","award":["2013zytz080"],"award-info":[{"award-number":["2013zytz080"]}]},{"name":"2024 Anhui Province Social Science Innovation and Development Research Project","award":["2024CX528"],"award-info":[{"award-number":["2024CX528"]}]},{"name":"Anhui Province Excellent Research and Innovation Team \"Research and Innovation Team for Quality Education of College Students\u201d","award":["2022AH010100"],"award-info":[{"award-number":["2022AH010100"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-025-00285-x","type":"journal-article","created":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T13:08:15Z","timestamp":1746536895000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application research and effectiveness evaluation mechanism of hybrid intelligent algorithm integrating cognitive computing and deep learning for dynamically adjusting employee performance evaluation in multi-scale organizational networks"],"prefix":"10.1007","volume":"5","author":[{"given":"Zhenlin","family":"Luo","sequence":"first","affiliation":[]},{"given":"Kebin","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,6]]},"reference":[{"issue":"1","key":"285_CR1","doi-asserted-by":"publisher","first-page":"2414004","DOI":"10.1080\/22797254.2024.2414004","volume":"57","author":"A Toma","year":"2024","unstructured":"Toma A, Sandric I, Mihai BA. Flooded area detection and mapping from Sentinel-1 imagery. Complementary approaches and comparative performance evaluation. Eur J Remote Sens. 2024;57(1):2414004.","journal-title":"Eur J Remote Sens"},{"issue":"1","key":"285_CR2","doi-asserted-by":"publisher","first-page":"127","DOI":"10.5465\/19416520.2014.873178","volume":"8","author":"A DeNisi","year":"2014","unstructured":"DeNisi A, Smith CE. Performance appraisal, performance management, and firm-level performance: a review, a proposed model, and new directions for future research. Acad Manag Ann. 2014;8(1):127\u201379.","journal-title":"Acad Manag Ann"},{"key":"285_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.srs.2024.100185","volume":"11","author":"C Ardohain","year":"2025","unstructured":"Ardohain C, Fei S. The impacts of training data spatial resolution on deep learning in remote sensing. Sci Remote Sens. 2025;11: 100185.","journal-title":"Sci Remote Sens"},{"issue":"1","key":"285_CR4","first-page":"1","volume":"36","author":"L Cai","year":"2024","unstructured":"Cai L, Wu Z. Intelligent asset allocation portfolio division and recommendation: based on deep learning and knowledge graphs. J Org End User Comput (JOEUC). 2024;36(1):1\u201323.","journal-title":"J Org End User Comput (JOEUC)"},{"issue":"3","key":"285_CR5","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1108\/BPMJ-01-2016-0008","volume":"24","author":"V Uli","year":"2018","unstructured":"Uli V. A co-evolutionary perspective on business processes: evidence from the performance appraisal of a service firm. Bus Process Manag J. 2018;24(3):652\u201370.","journal-title":"Bus Process Manag J"},{"key":"285_CR6","first-page":"30","volume":"07","author":"P Chen","year":"2024","unstructured":"Chen P. The opportunities and risks faced by national governance in the era of artificial intelligence. Dalian Cadre J. 2024;07:30\u20136.","journal-title":"Dalian Cadre J"},{"key":"285_CR7","doi-asserted-by":"crossref","unstructured":"Feng Y. Empowering educational publishing through exploration and practice of digital intelligence.\u00a0Front Dig Educ 2024, 1\u20138.","DOI":"10.1007\/s44366-024-0027-6"},{"key":"285_CR8","doi-asserted-by":"crossref","unstructured":"Liang L, Liu B, Su Z, Cai X. Forecasting corporate financial performance with deep learning and interpretable ALE method: evidence from China.\u00a0J Forecasting. 2024.","DOI":"10.1002\/for.3138"},{"key":"285_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113194","volume":"147","author":"M Bilal","year":"2020","unstructured":"Bilal M, Oyedele LO. Big Data with deep learning for benchmarking profitability performance in project tendering. Expert Syst Appl. 2020;147: 113194.","journal-title":"Expert Syst Appl"},{"issue":"3","key":"285_CR10","first-page":"311","volume":"8","author":"EW Rogers","year":"1998","unstructured":"Rogers EW, Wright PM. Measuring organizational performance in strategic human resource management: problems, prospects and performance information markets. Hum Resour Manag Rev. 1998;8(3):311\u201331.","journal-title":"Hum Resour Manag Rev"},{"issue":"3","key":"285_CR11","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1504\/IJCAT.2024.141940","volume":"74","author":"Q Nie","year":"2024","unstructured":"Nie Q. Application of artificial intelligence in enterprise human resource management and employee performance evaluation. Int J Comput Appl Technol. 2024;74(3):186\u201396.","journal-title":"Int J Comput Appl Technol"},{"issue":"8","key":"285_CR12","doi-asserted-by":"publisher","first-page":"22909","DOI":"10.1007\/s11042-023-16382-x","volume":"83","author":"Z Amiri","year":"2024","unstructured":"Amiri Z, Heidari A, Navimipour NJ, Unal M, Mousavi A. Adventures in data analysis: a systematic review of Deep Learning techniques for pattern recognition in cyber-physical-social systems. Multimedia Tools Appl. 2024;83(8):22909\u201373.","journal-title":"Multimedia Tools Appl"},{"key":"285_CR13","first-page":"353","volume":"29","author":"MU Scherer","year":"2015","unstructured":"Scherer MU. Regulating artificial intelligence systems: risks, challenges, competencies, and strategies. Harv JL Tech. 2015;29:353.","journal-title":"Harv JL Tech"},{"issue":"3","key":"285_CR14","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1108\/JQME-05-2013-0029","volume":"19","author":"U Kumar","year":"2013","unstructured":"Kumar U, Galar D, Parida A, Stenstr\u00f6m C, Berges L. Maintenance performance metrics: a state-of-the-art review. J Qual Maint Eng. 2013;19(3):233\u201377.","journal-title":"J Qual Maint Eng"},{"issue":"1","key":"285_CR15","first-page":"8277426","volume":"2022","author":"B Luo","year":"2022","unstructured":"Luo B. A method for enterprise network innovation performance management based on deep learning and Internet of Things. Math Probl Eng. 2022;2022(1):8277426.","journal-title":"Math Probl Eng"},{"issue":"1","key":"285_CR16","doi-asserted-by":"publisher","first-page":"22329","DOI":"10.1038\/s41598-024-73643-x","volume":"14","author":"N Bhatt","year":"2024","unstructured":"Bhatt N, Bhatt N, Prajapati P, Sorathiya V, Alshathri S, El-Shafai W. A Data-Centric Approach to improve performance of deep learning models. Sci Rep. 2024;14(1):22329.","journal-title":"Sci Rep"},{"issue":"1","key":"285_CR17","first-page":"1418020","volume":"2022","author":"Q Ni","year":"2022","unstructured":"Ni Q. Deep neural network model construction for digital human resource management with human-job matching. Comput Intell Neurosci. 2022;2022(1):1418020.","journal-title":"Comput Intell Neurosci"},{"issue":"1","key":"285_CR18","doi-asserted-by":"publisher","first-page":"1642293","DOI":"10.1080\/23311975.2019.1642293","volume":"6","author":"J Sahlin","year":"2019","unstructured":"Sahlin J, Angelis J. Performance management systems: reviewing the rise of dynamics and digitalization. Cogent Bus Manage. 2019;6(1):1642293.","journal-title":"Cogent Bus Manage"},{"issue":"6","key":"285_CR19","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1093\/bib\/bbae476","volume":"25","author":"MY Fatemi","year":"2024","unstructured":"Fatemi MY, Lu Y, Diallo AB, Srinivasan G, Azher ZL, Christensen BC, Levy JJ. An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology. Brief Bioinform. 2024;25(6):476.","journal-title":"Brief Bioinform"},{"issue":"7","key":"285_CR20","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1007\/s42979-024-03216-2","volume":"5","author":"P Shetty","year":"2024","unstructured":"Shetty P, Kini S, Fernandes R. A comprehensive analysis of \u2018machine learning and deep learning\u2019methods for sentiment analysis in twitter. SN Comput Sci. 2024;5(7):915.","journal-title":"SN Comput Sci"},{"issue":"4","key":"285_CR21","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MITP.2017.3051321","volume":"19","author":"M Tarafdar","year":"2017","unstructured":"Tarafdar M, Beath CM, Ross JW. Enterprise cognitive computing applications: opportunities and challenges. IT Prof. 2017;19(4):21\u20137.","journal-title":"IT Prof"},{"key":"285_CR22","doi-asserted-by":"crossref","unstructured":"Schlott CK. Design Thinking and teamwork\u2014measuring impact: a systematic literature review.\u00a0J Org Design, 2024, 1\u201334.","DOI":"10.1007\/s41469-024-00177-x"},{"issue":"10","key":"285_CR23","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0306629","volume":"19","author":"H Jin","year":"2024","unstructured":"Jin H, Peng Y. The impact of team psychological safety on employee innovative performance a study with communication behavior as a mediator variable. PLoS ONE. 2024;19(10): e0306629.","journal-title":"PLoS ONE"},{"key":"285_CR24","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.techfore.2019.04.027","volume":"145","author":"S Kaur","year":"2019","unstructured":"Kaur S, Gupta S, Singh SK, Perano M. Organizational ambidexterity through global strategic partnerships: a cognitive computing perspective. Technol Forecast Soc Chang. 2019;145:43\u201354.","journal-title":"Technol Forecast Soc Chang"},{"key":"285_CR25","doi-asserted-by":"crossref","unstructured":"Aghav-Palwe S, Gunjal A. Introduction to cognitive computing and its various applications. In: Cognitive computing for human-robot interaction\u00a0(pp. 1\u201318). Academic Press. 2021.","DOI":"10.1016\/B978-0-323-85769-7.00009-4"},{"key":"285_CR26","doi-asserted-by":"crossref","unstructured":"Manoharan G, Sawant PD, Vanitha J, Lourens M, Anusuya R, Bhati I. Cognitive Computing for HR Decision-Making. In: 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES)\u00a0(pp. 1\u20135). IEEE. 2024.","DOI":"10.1109\/IC3TES62412.2024.10877480"},{"issue":"3","key":"285_CR27","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1007\/s11257-023-09383-w","volume":"34","author":"D Paulino","year":"2024","unstructured":"Paulino D, Correia A, Barroso J, Paredes H. Cognitive personalization for online microtask labor platforms: a systematic literature review. User Model User-Adap Inter. 2024;34(3):617\u201358.","journal-title":"User Model User-Adap Inter"},{"key":"285_CR28","doi-asserted-by":"crossref","unstructured":"Li C, Li X, Chen M, Sun X. Deep learning and image recognition. In: 2023 IEEE 6th international conference on electronic information and communication technology (ICEICT)\u00a0(pp. 557\u2013562). IEEE. 2023.","DOI":"10.1109\/ICEICT57916.2023.10245041"},{"key":"285_CR29","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1016\/j.neucom.2021.05.103","volume":"470","author":"I Lauriola","year":"2022","unstructured":"Lauriola I, Lavelli A, Aiolli F. An introduction to deep learning in natural language processing: models, techniques, and tools. Neurocomputing. 2022;470:443\u201356.","journal-title":"Neurocomputing"},{"issue":"1","key":"285_CR30","first-page":"101","volume":"3","author":"Q Liu","year":"2023","unstructured":"Liu Q, Wan H, Yu H. The application of deep learning in human resource management: a new perspective on employee recruitment and performance evaluation. Acad J Manage Soc Sci. 2023;3(1):101\u20134.","journal-title":"Acad J Manage Soc Sci"},{"key":"285_CR31","doi-asserted-by":"crossref","unstructured":"Alnasyan B, Basheri M. The power of deep learning techniques for predicting student performance in virtual learning environments: a systematic literature review. Comput Educ AI. 2024","DOI":"10.21203\/rs.3.rs-3888441\/v1"},{"key":"285_CR32","doi-asserted-by":"crossref","unstructured":"Sathya A, Naveen KT, Narendran M, Basker SP, Nirudeeshwar B, Yuvaraj, S. K. VIBE: A Data-Driven Approach to Career Guidance and Skill Development. In: 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)\u00a0(pp. 1\u20136). IEEE. 2024.","DOI":"10.1109\/ADICS58448.2024.10533596"},{"key":"285_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.asieco.2024.101791","volume":"94","author":"C Cao","year":"2024","unstructured":"Cao C, Zeng D, Wan Q, Li Y. Employee satisfaction and digital transformation: evidence from China\u2019s Top 100 Best Employers list. J Asian Econ. 2024;94: 101791.","journal-title":"J Asian Econ"},{"issue":"5","key":"285_CR34","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1108\/JMD-09-2015-0134","volume":"35","author":"RE Masa'deh","year":"2016","unstructured":"Masa\u2019deh RE, Obeidat BY, Tarhini A. A Jordanian empirical study of the associations among transformational leadership, transactional leadership, knowledge sharing, job performance, and firm performance: a structural equation modelling approach. J Manage Dev. 2016;35(5):681\u2013705.","journal-title":"J Manage Dev"},{"key":"285_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2024.108468","volume":"164","author":"D Shang","year":"2025","unstructured":"Shang D, Zhang C, Li JIN. Exploring the impact of integrated design on employee learning engagement in the ubiquitous learning context: a deep learning-based hybrid multistage approach. Comput Hum Behav. 2025;164: 108468.","journal-title":"Comput Hum Behav"},{"key":"285_CR36","doi-asserted-by":"crossref","unstructured":"Dimitrios P, Spandonidis C. Hybrid fault diagnostic method via a novel IoT solution. 2023.","DOI":"10.1049\/icp.2024.0953"},{"issue":"6","key":"285_CR37","first-page":"8069","volume":"39","author":"R Dhanalakshmi","year":"2020","unstructured":"Dhanalakshmi R, Sri Devi T. Adaptive cognitive intelligence in analyzing employee feedback using LSTM. J Intell Fuzzy Syst. 2020;39(6):8069\u201378.","journal-title":"J Intell Fuzzy Syst"},{"key":"285_CR38","doi-asserted-by":"crossref","unstructured":"Sharma R, Jain A, Manwal M. Enhancing human resource management through deep learning: a predictive analytics approach to employee retention success. In: 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS)\u00a0(pp. 1\u20134). IEEE. 2024.","DOI":"10.1109\/ICITEICS61368.2024.10625175"},{"issue":"8","key":"285_CR39","doi-asserted-by":"publisher","first-page":"3254","DOI":"10.3390\/app14083254","volume":"14","author":"LG Tanasescu","year":"2024","unstructured":"Tanasescu LG, Vines A, Bologa AR, V\u00eergolici O. Data analytics for optimizing and predicting employee performance. Appl Sci. 2024;14(8):3254.","journal-title":"Appl Sci"},{"key":"285_CR40","doi-asserted-by":"crossref","unstructured":"Zheng Z, Sun Y, Song X, Zhu H, Xiong H. Generative learning plan recommendation for employees: a performance-aware reinforcement learning approach. In: Proceedings of the 17th ACM Conference on Recommender Systems\u00a0(pp. 443\u2013454). 2023.","DOI":"10.1145\/3604915.3608795"},{"key":"285_CR41","doi-asserted-by":"publisher","unstructured":"Sequeira P, Melo F, Paiva A. Emotion-based intrinsic motivation for reinforcement learning agents 2011, 326\u2013336. https:\/\/doi.org\/10.1007\/978-3-642-24600-5_36.","DOI":"10.1007\/978-3-642-24600-5_36"},{"issue":"18","key":"285_CR42","doi-asserted-by":"publisher","first-page":"3916","DOI":"10.3390\/math11183916","volume":"11","author":"EV Orlova","year":"2023","unstructured":"Orlova EV. Dynamic regimes for corporate human capital development used reinforcement learning methods. Mathematics. 2023;11(18):3916.","journal-title":"Mathematics"},{"issue":"5","key":"285_CR43","first-page":"3916","volume":"46","author":"R Obiedat","year":"2022","unstructured":"Obiedat R, Toubasi SA. A combined approach for predicting employees\u2019 productivity based on ensemble machine learning methods. Informatica. 2022;46(5):3916.","journal-title":"Informatica"},{"key":"285_CR44","unstructured":"Qi M, Yang Y, Ma H. Learning group interactions and semantic intentions for multi-object trajectory prediction. 2024. arXiv preprint arXiv:2412.15673."},{"issue":"16","key":"285_CR45","doi-asserted-by":"publisher","first-page":"2570","DOI":"10.3390\/math12162570","volume":"12","author":"W Deng","year":"2024","unstructured":"Deng W, Ma X, Qiao W. A hybrid intelligent optimization algorithm based on a learning strategy. Mathematics. 2024;12(16):2570.","journal-title":"Mathematics"},{"key":"285_CR46","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.jmsy.2018.01.003","volume":"48","author":"J Wang","year":"2018","unstructured":"Wang J, Ma Y, Zhang L, Gao RX, Wu D. Deep learning for smart manufacturing: methods and applications. J Manuf Syst. 2018;48:144\u201356.","journal-title":"J Manuf Syst"},{"key":"285_CR47","doi-asserted-by":"crossref","unstructured":"Jaya R, Soms N, Isaac LD, Priya SS. Hybrid Intelligent System for Improved Decision Support in Customer Churn Prediction for a Telecommunication Company. In\u00a0International Conference on Microelectronics, Electromagnetics and Telecommunication\u00a0(pp. 109\u2013118). Singapore: Springer Nature Singapore. 2023.","DOI":"10.1007\/978-981-97-8422-6_9"},{"issue":"1","key":"285_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.giq.2024.102006","volume":"42","author":"HK Liu","year":"2025","unstructured":"Liu HK, Tang M, Collard ASJ. Hybrid intelligence for the public sector: a bibliometric analysis of artificial intelligence and crowd intelligence. Gov Inf Q. 2025;42(1): 102006.","journal-title":"Gov Inf Q"},{"issue":"6","key":"285_CR49","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1108\/02683940910974116","volume":"24","author":"SC Payne","year":"2009","unstructured":"Payne SC, Horner MT, Boswell WR, Schroeder AN, Stine-Cheyne KJ. Comparison of online and traditional performance appraisal systems. J Manag Psychol. 2009;24(6):526\u201344.","journal-title":"J Manag Psychol"},{"key":"285_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102705","volume":"114","author":"H Chen","year":"2025","unstructured":"Chen H, Shen QG, Skibniewski MJ, Cao Y, Liu Y. Dynamic prediction and optimization of tunneling parameters with high reliability based on a hybrid intelligent algorithm. Information Fusion. 2025;114: 102705.","journal-title":"Information Fusion"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00285-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00285-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00285-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T13:08:28Z","timestamp":1746536908000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00285-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,6]]},"references-count":50,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["285"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00285-x","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,6]]},"assertion":[{"value":"21 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Ethics Committee of Anhui Xinhua University (Project Number: AXHUEC-2024-001). The research was carried out in accordance with the guidelines provided by this committee. Informed consent was obtained from all participants in accordance with ethical standards. For participants under the age of 16, consent was obtained from their parents or legal guardians. Participants were fully informed of their rights, and a sample consent form was made available upon request.","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":"45"}}