پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 163 |
| تعداد شماره‌ها | 6,762 |
| تعداد مقالات | 72,830 |
| تعداد مشاهده مقاله | 131,677,989 |
| تعداد دریافت فایل اصل مقاله | 103,443,639 |
Presenting an Innovative Model Based on Multi-Mode Network Analysis for Examining Connections and Predicting Future Labor Market Developments | ||
| Industrial Management Journal | ||
| دوره 16، شماره 3، 2024، صفحه 426-456 اصل مقاله (1.38 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/imj.2024.372349.1008124 | ||
| نویسندگان | ||
| Elnaz Nasirzadeh* 1؛ Saeed Rouhani2 | ||
| 1Assistant Prof., Department of Information Technology Management, Faculty of Industrial and Technology Management, College of Management, University of Tehran, Tehran, Iran. | ||
| 2Associate Prof., Department of Information Technology Management, Faculty of Industrial and Technology Management, College of Management, University of Tehran, Tehran, Iran. | ||
| چکیده | ||
| Objective Recently, network analysis has become one of the most popular and practical areas in data science. This technology, by analyzing complex data and identifying communicative patterns among different elements, delves into a deeper understanding of the structure, behavior, and interactions within networks and complex systems. Despite the critical importance of network analysis in data science and social research, particularly in the field of human resources, there remains a lack of comprehensive studies on this topic. In particular, domestic market data have not been fully studied and discussed. This lack of attention misses opportunities for deeper understanding and presenting innovative solutions to future challenges. Therefore, researchers need to employ modern approaches to investigate the challenges in this field and propose new solutions. This study introduces an innovative model that utilizes multimodal network analysis to enhance the understanding of labor market communications and predict its future developments. Methods To design the proposed network, the required primary data were collected from international classifications and the initial network was drawn based on them. Then, to ensure that the network matches the current labor market conditions, data from one of the domestic job search websites were also extracted and the network was updated accordingly. The Jaccard index was employed to quantify the connections between network elements, while the algorithms of preferential attachment, Adamic-Adar, and common neighbors were utilized for network validation. The Dijkstra algorithm was used to calculate the shortest path in the network and the term frequency-inverse document frequency metric was used for ranking. Results This research sought to present a new method for designing a multimodal labor market network and described how it was updated and diversified to match the dynamic changes of the labor market. With the final network obtained, the connections between labor market elements (jobs and skills) were examined, and a method for quantifying these connections was presented. Additionally, a method for calculating the most efficient job transitions within this network was outlined, along with an approach for ranking skills according to various job levels. Finally, the network was validated with three link prediction algorithms. The results indicate that the use of the preferential attachment algorithm will be the best option for predicting the future of this network. Conclusion The innovative model presented in this research offers a powerful tool for network design and analysis in understanding the labor market. It not only provides a comprehensive overview of the current and future state of the domestic labor market but also offers practical solutions for addressing future challenges. In particular, discovering communicative patterns and predicting emerging trends enables better and faster adaptation to labor market changes, which in turn leads to the development of sustainable job opportunities and economic growth. Through a deep analysis of existing data and predicting potential developments, this model can help labor market managers, policymakers, and social analysts design more effective strategies to enhance labor market capacities and optimize human resources. Ultimately, the results demonstrated that innovative models based on network analysis can open new horizons for predicting and managing future developments. | ||
| کلیدواژه‌ها | ||
| Future prediction؛ Innovative model؛ Labor market؛ Multimodal network؛ Network analysis | ||
| مراجع | ||
|
حسینی، سیده مطهره و اقدسی، محمد (1402). مقایسه ساختار رسمی و غیررسمی در سازمان‌ها با استفاده از روش تحلیل شبکه اجتماعی. مدیریت صنعتی، 15 (3)، 365-385.
خلیلی، سید محمد؛ پویا، علیرضا؛ کاظمی، مصطفی و فکور ثقیه، امیرمحمد (1401). طراحی یک شبکه زنجیره تأمین بنزین پایدار و تاب‌آور تحت شرایط عدم قطعیت اختلال (مطالعه موردی: شبکه زنجیره تأمین بنزین استان خراسان رضوی). مدیریت صنعتی، 14 (1)، 27- 79.
صفاری، حمید؛ عباسی، مرتضی و قیدر خلجانی، جعفر (1402). طراحی شبکه در اتحاد استراتژیک تحت عدم قطعیت با رویکرد موازنه بین ریسک و عملکرد شبکه. مدیریت صنعتی، 15 (1)، 112-149.
فریدوند، محمدمهدی؛ الیاسی، مهدی و رادفر، رضا (1402). انتخاب مدل مناسب ضمانت اعتبار شرکت‌های فناور نوپا در ایران با استفاده از فرایند تحلیل شبکه. مدیریت صنعتی، 15 (4)، 595-620.
References Abu-Rasheed, H., Dornhöfer, M., Weber, C., Kismihók, G., Buchmann, U. & Fathi, M. (2023, July). Building contextual knowledge graphs for personalized learning recommendations using text mining and semantic graph completion. In 2023 IEEE International Conference on Advanced Learning Technologies (ICALT) (pp. 36-40). IEEE. Addison, J. T., Portugal, P. & Raposo, P. S. (2023). Retrieving the Returns to Experience, Tenure, and Job Mobility from Work Histories. Aufiero, S., De Marzo, G., Sbardella, A. & Zaccaria, A. (2023). Mapping job complexity and skills into wages. arXiv preprint arXiv:2304.05251. Chala, S. & Fathi, M. (2017, March). Job seeker to vacancy matching using social network analysis. In 2017 IEEE international conference on industrial technology (ICIT) (pp. 1250-1255). IEEE. Clochard, A. (2022). Using Network Analysis of Job Transitions to Inform Career Advice (Doctoral dissertation, Massachusetts Institute of Technology). Dave, V. S., Zhang, B., Al Hasan, M., AlJadda, K. & Korayem, M. (2018, October). A combined representation learning approach for better job and skill recommendation. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 1997-2005). de Groot, M., Schutte, J. & Graus, D. (2021). Job posting-enriched knowledge graph for skills-based matching. arXiv preprint arXiv:2109.02554. Dijkstra, E. W. (2022). A note on two problems in connexion with graphs. In Edsger Wybe Dijkstra: His Life, Work, and Legacy (pp. 287-290). Faridvand, M.M., Elyasi, M. & Radfar, R. (2023). Selecting the Appropriate Credit Guarantee Model for New Technology-based Firms (NTBFs) in Iran using the Analytical Network Process (ANP). Industrial Management Journal, 15(4), 595-620. (in Persian) Fettach, Y., Bahaj, A. & Ghogho, M. (2024). JobEdKG: An uncertain knowledge graph-based approach for recommending online courses and predicting in-demand skills based on career choices. Engineering Applications of Artificial Intelligence, 131, 107779. Giabelli, A., Malandri, L., Mercorio, F., Mezzanzanica, M. & Seveso, A. (2021). Skills2Job: A recommender system that encodes job offer embeddings on graph databases. Applied Soft Computing, 101, 107049. Goyal, N., Kalra, J., Sharma, C., Mutharaju, R., Sachdeva, N. & Kumaraguru, P. (2023, May). JobXMLC: EXtreme Multi-Label Classification of Job Skills with Graph Neural Networks. In Findings of the Association for Computational Linguistics: EACL 2023 (pp. 2136-2146). Graham, C. M. & Lu, Y. (2023). Skills expectations in cybersecurity: semantic network analysis of job advertisements. Journal of Computer Information Systems, 63(4), 937-949. Hevey, D. (2018). Network analysis: a brief overview and tutorial. Health Psychology and Behavioral Medicine, 6(1), 301-328. Hosseini, S.M. & Aghdasi, M. (2023). Comparison of Formal and Informal Structures in Organizations Using the Social Network Analysis. Industrial Management Journal, 15(3), 365-385. (in Persian) Huang, A. Y., Fisher, T., Ding, H. & Guo, Z. (2021). A network analysis of cross-occupational skill transferability for the hospitality industry. International Journal of Contemporary Hospitality Management, 33(12), 4215-4236. Jia, S., Liu, X., Zhao, P., Liu, C., Sun, L. & Peng, T. (2018, December). Representation of job-skill in artificial intelligence with knowledge graph analysis. In 2018 IEEE symposium on product compliance engineering-asia (ISPCE-CN) (pp. 1-6). IEEE. Khalili, S.M., Pooya, A., Kazemi, M. & Fakoor Saghih, A.M. (2022). Designing a sustainable and resilient gasoline supply chain network under uncertainty (Case study: gasoline supply chain network of Khorasan Razavi province). Industrial Management Journal, 14(1), 27- 79. (in Persian) Khan, B. S. & Niazi, M. A. (2017). Network community detection: A review and visual survey. arXiv preprint arXiv:1708.00977. Khaouja, I., Mezzour, G., Carley, K. M. & Kassou, I. (2019). Building a soft skill taxonomy from job openings. Social Network Analysis and Mining, 9, 1-19. Khousa, E. A. & Atif, Y. (2018). Social network analysis to influence career development. Journal of Ambient Intelligence and Humanized Computing, 9, 601-616. Lee, D. M. Y., Ang, D. W. X., Pua, G. M. C., Ng, L. N., Purbowo, S., Choy, E. W. J. & Shim, K. J. (2020, December). A Social Network Analysis of Jobs and Skills. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 5747-5749). IEEE. Liben-Nowell, D. & Kleinberg, J. (2003, November). The link prediction problem for social networks. In Proceedings of the twelfth international conference on Information and knowledge management (pp. 556-559). Liu, L., Zhang, W., Liu, J., Shi, W. & Huang, Y. (2021, July). Learning multi-graph neural network for data-driven job skill prediction. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. Maghsoudi, M. (2023). Uncovering the skillsets required in computer science jobs using social network analysis. Education and Information Technologies, 1-22. Maurya, A. (2018, September). Understanding job-skill relationships using big data and neural networks. In Bloomberg Data for Good Exchange Conference. Rahhal, I., Carley, K. M. & Kassou, I. (2022, November). Employment Stakeholder Analysis using Social Network Analysis in Morocco. In 2022 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD) (pp. 1-7). IEEE. Saffari, H., Abbasi, M. & Gheidar Kheljanie, J. (2023). Network Design in Strategic Alliance under Uncertainty with a Trade-off between Risk and Performance. Industrial Management Journal, 15(1), 112-149. (in Persian) Sibarani, E. M. & Scerri, S. (2019). Generating an evolving skills network from job adverts for high-demand skillset discovery. In Web Information Systems Engineering–WISE 2019: 20th International Conference, Hong Kong, China, January 19–22, 2020, Proceedings 20 (pp. 441-457). Springer International Publishing. Simonet, D. V. & Castille, C. M. (2020). The search for meaningful work: A network analysis of personality and the job characteristics model. Personality and Individual Differences, 152, 109569. Sosa, S. (2022). Social network analysis. In Encyclopedia of animal cognition and behavior (pp. 6527-6544). Cham: Springer International Publishing. Sun, Y., Zhuang, F., Zhu, H., Zhang, Q., He, Q. & Xiong, H. (2021). Market-oriented job skill valuation with cooperative composition neural network. Nature communications, 12(1), 1992. Van Hooft, E. A., Kammeyer-Mueller, J. D., Wanberg, C. R., Kanfer, R. & Basbug, G. (2021). Job search and employment success: A quantitative review and future research agenda. Journal of Applied Psychology, 106(5), 674. Xu, T., Zhu, H., Zhu, C., Li, P. & Xiong, H. (2018, April). Measuring the popularity of job skills in recruitment market: A multi-criteria approach. In Proceedings of the AAAI conference on artificial intelligence (Vol. 32, No. 1). Yao, J., Xu, Y. & Gao, J. (2023). A Study of Reciprocal Job Recommendation for College Graduates Integrating Semantic Keyword Matching and Social Networking. Applied Sciences, 13(22), 12305. | ||
|
آمار تعداد مشاهده مقاله: 262 تعداد دریافت فایل اصل مقاله: 207 |
||
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب