Studies conducted in the fields of management, cognitive psychology, behavioral economics, and neuroscience have confirmed the need for a more in-depth examination of the misalignment between demand and supply in the job market, paving the way for research into practical solutions to address limited rationality in decision-making from various perspectives. The growing importance of open innovation, big data analytics technologies, and the variety and volume of data and information processed through these technologies has driven this paper to study this emerging phenomenon, recognizing it as one of the main drivers of the Industry 4.0 model. This study focuses on the issue of the demand-supply gap in the labor market in relation to big data, analyzing its impact on open innovation, which is leading to the creation of new professional roles. The research goes beyond a theoretical analysis and situates itself concretely in the Italian context, empirically confirming its validity. In fact, the second part of the project conducts a detailed analysis to fully understand the current state of the labor market, which is strongly influenced by new developments in big data and open innovation. The paper presents the findings of the Work Employment Labor Logic Talent Scope (WELLTS) project. This project was implemented to understand employment dynamics to support managerial, strategic, and investment policy decisions by all stakeholders operating in the labor market. The approach of this work is based on the use of semi-automatic scraping and data mining systems. Specifically, the research highlighted the current lack of a single tool capable of providing a comprehensive view of labor market trends on both the demand and supply sides. Thus, the market analysis and the tool proposed in the paper serve a dual function. On one hand, it provides management with a statistically accurate tool, capable of pre-selecting from the total number of requests managed for all job postings offered by the company. This implies a reduction in entrepreneurial risk related both to the actual fit of candidates’ résumés to the offered position/role and to mitigating the risk of information overload inherent in managing large data volumes. On the other hand—considering the perspective of the job seeker—the aim of the text is to help them understand how to accurately analyze the alignment rate of their résumé with a selected job posting or group of postings. Through semantic patterns (e.g., certifications, educational qualifications, years of experience), it highlights areas where skills and knowledge reflect those required by the company in the job posting. A direct implication of this approach is also the possibility of providing a clear identification of potential growth areas and identifying/activating actors involved in training to build those skills. Thanks to the chosen methodology, this work intends to emphasize the importance of developing a continuous labor market analysis to understand trends and opportunities, as well as the skills required within the market itself in the era of big data. Moreover, the work is valuable for supporting public policies, private investment decisions, and guiding professional training and skills updating, both for organizations and public entities as well as for individual candidates.
Open innovation and big data analytics technologies: examining new trends in the labour market
Maria Giovanna Confetto;
2024-01-01
Abstract
Studies conducted in the fields of management, cognitive psychology, behavioral economics, and neuroscience have confirmed the need for a more in-depth examination of the misalignment between demand and supply in the job market, paving the way for research into practical solutions to address limited rationality in decision-making from various perspectives. The growing importance of open innovation, big data analytics technologies, and the variety and volume of data and information processed through these technologies has driven this paper to study this emerging phenomenon, recognizing it as one of the main drivers of the Industry 4.0 model. This study focuses on the issue of the demand-supply gap in the labor market in relation to big data, analyzing its impact on open innovation, which is leading to the creation of new professional roles. The research goes beyond a theoretical analysis and situates itself concretely in the Italian context, empirically confirming its validity. In fact, the second part of the project conducts a detailed analysis to fully understand the current state of the labor market, which is strongly influenced by new developments in big data and open innovation. The paper presents the findings of the Work Employment Labor Logic Talent Scope (WELLTS) project. This project was implemented to understand employment dynamics to support managerial, strategic, and investment policy decisions by all stakeholders operating in the labor market. The approach of this work is based on the use of semi-automatic scraping and data mining systems. Specifically, the research highlighted the current lack of a single tool capable of providing a comprehensive view of labor market trends on both the demand and supply sides. Thus, the market analysis and the tool proposed in the paper serve a dual function. On one hand, it provides management with a statistically accurate tool, capable of pre-selecting from the total number of requests managed for all job postings offered by the company. This implies a reduction in entrepreneurial risk related both to the actual fit of candidates’ résumés to the offered position/role and to mitigating the risk of information overload inherent in managing large data volumes. On the other hand—considering the perspective of the job seeker—the aim of the text is to help them understand how to accurately analyze the alignment rate of their résumé with a selected job posting or group of postings. Through semantic patterns (e.g., certifications, educational qualifications, years of experience), it highlights areas where skills and knowledge reflect those required by the company in the job posting. A direct implication of this approach is also the possibility of providing a clear identification of potential growth areas and identifying/activating actors involved in training to build those skills. Thanks to the chosen methodology, this work intends to emphasize the importance of developing a continuous labor market analysis to understand trends and opportunities, as well as the skills required within the market itself in the era of big data. Moreover, the work is valuable for supporting public policies, private investment decisions, and guiding professional training and skills updating, both for organizations and public entities as well as for individual candidates.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
