Adaptively selecting occupations to detect skill shortages from online job ads

Labour demand and skill shortages have historically been difficult to assess given the high costs of conducting representative surveys and the inherent delays of these indicators. This is particularly consequential for fast developing skills and occupations, such as those relating to Data Science and Analytics (DSA). This paper develops a data-driven solution to detecting skill shortages from online job advertisements (ads) data. We first propose a method to generate sets of highly similar skills based on a set of seed skills from job ads. This provides researchers with a novel method to adaptively select occupations based on granular skills data. Next, we apply this adaptive skills similarity technique to a dataset of over 6.7 million Australian job ads in order to identify occupations with the highest proportions of DSA skills. This uncovers 306,577 DSA job ads across 23 occupational classes from 2012-2019. Finally, we propose five variables for detecting skill shortages from online job ads: (1) posting frequency; (2) salary levels; (3) education requirements; (4) experience demands; and (5) job ad posting predictability. This contributes further evidence to the goal of detecting skills shortages in real-time. In conducting this analysis, we also find strong evidence of skills shortages in Australia for highly technical DSA skills and occupations. These results provide insights to Data Science researchers, educators, and policy-makers from other advanced economies about the types of skills that should be cultivated to meet growing DSA labour demands in the future

Adaptively selecting occupations to detect skill shortages from online job ads

Labour demand and skill shortages have historically been difficult to assess given the high costs of conducting representative surveys and the inherent delays of these indicators. This is particularly consequential for fast developing skills and occupations, such as those relating to Data Science and Analytics (DSA). This paper develops a data-driven solution to detecting skill shortages from online job advertisements (ads) data. We first propose a method to generate sets of highly similar skills based on a set of seed skills from job ads. This provides researchers with a novel method to adaptively select occupations based on granular skills data. Next, we apply this adaptive skills similarity technique to a dataset of over 6.7 million Australian job ads in order to identify occupations with the highest proportions of DSA skills. This uncovers 306,577 DSA job ads across 23 occupational classes from 2012-2019. Finally, we propose five variables for detecting skill shortages from online job ads: (1) posting frequency; (2) salary levels; (3) education requirements; (4) experience demands; and (5) job ad posting predictability. This contributes further evidence to the goal of detecting skills shortages in real-time. In conducting this analysis, we also find strong evidence of skills shortages in Australia for highly technical DSA skills and occupations. These results provide insights to Data Science researchers, educators, and policy-makers from other advanced economies about the types of skills that should be cultivated to meet growing DSA labour demands in the future

Layoffs, Inequity and COVID-19: A Longitudinal Study of the Journalism Jobs Crisis in Australia from 2012 to 2020

In Australia and beyond, journalism is reportedly an industry in crisis, a crisis exacerbated by COVID-19. However, the evidence revealing the crisis is often anecdotal or limited in scope. In this unprecedented longitudinal research, we draw on data from the Australian journalism jobs market from January 2012 until March 2020. Using Data Science and Machine Learning techniques, we analyse two distinct data sets: job advertisements (ads) data comprising 3,698 journalist job ads from a corpus of over 8 million Australian job ads; and official employment data from the Australian Bureau of Statistics. Having matched and analysed both sources, we address both the demand for and supply of journalists in Australia over this critical period. The data show that the crisis is real, but there are also surprises. Counter-intuitively, the number of journalism job ads in Australia rose from 2012 until 2016, before falling into decline. Less surprisingly, for the entire period studied the figures reveal extreme volatility, characterised by large and erratic fluctuations. The data also clearly show that COVID-19 has significantly worsened the crisis. We then tease out more granular findings, including: that there are now more women than men journalists in Australia, but that gender inequity is worsening, with women journalists getting younger and worse-paid just as men journalists are, on average, getting older and better-paid; that, despite the crisis besetting the industry, the demand for journalism skills has increased; and that, perhaps concerningly, the skills sought by journalism job ads increasingly include social media and generalist communications

Systematic literature review: an analysis of skill mismatch measurement

The rapid growth of technology in the era of Industry 4.0 has caused the dynamic labor market to grow faster than ever before. This resulted in a mismatch between the jobs offered and the skills required. Thus, it raised the number of unemployability. The objective of this paper is to analyze the measurement of skill mismatch. Shortcomings and flaws in previous measurement methods and a broad definition of skill mismatch hindered the issues to be solved. The introduction of online job analysis has been seen as increasingly more valuable in measuring labor market conditions. Overcoming the issues such as cost, time lag, and biases, this measurement has been seen to be the new trend among scholars to shed the light on skill mismatch measurement. This paper analyzed 402 papers on online job data (vacancy, advertisement, portal) published from 2017 to 2022 from Scopus and Web of Science databases. Preferred Reporting Items for Systematic Review & Meta-Analyses (PRISMA) were used for this study. After the inclusion and exclusion criteria, ten papers from Scopus and five papers from the Web of Science database that matched with the criteria objective have been selected. Therefore, the study found that analyzing online job data is the new trend to be used in improving the labor market with more of the data could be used for the improvement to the previous method of measuring the skill mismatch proble

Using Fuzzy Approach to Model Skill Shortage in Vietnam’s Labor Market in the Context of Industry 4.0

Human resources development is one of the main issues in the socio-economic development strategy and the transform of any region in the context of Industry 4.0. However, Vietnamese human resources have been poorly evaluated in the areas of quality, lack of dynamism, and creativity. Therefore, this paper presents a fuzzy logic approach to ranking seven skills shortage in Vietnam’s Labor Market, namely lifelong learning, adaptive capacity, information technology capacity, creativity and innovation capacity, problem-solving capacity, foreign language competency, and organizing and managing competency. The results showed that the problem-solving skill has the largest gap between an enterprise’s requirements and the actual response of employees

Framing Professional Learning Analytics as Reframing Oneself

Central to imagining the future of technology-enhanced professional learning is the question of how data are gathered, analyzed, and fed back to stakeholders. The field of learning analytics (LA) has emerged over the last decade at the intersection of data science, learning sciences, human-centered and instructional design, and organizational change, and so could in principle inform how data can be gathered and analyzed in ways that support professional learning. However, in contrast to formal education where most research in LA has been conducted, much work-integrated learning is experiential, social, situated, and practice-bound. Supporting such learning exposes a significant weakness in LA research, and to make sense of this gap, this article proposes an adaptation of the Knowledge-Agency Window framework. It draws attention to how different forms of professional learning locate on the dimensions of learner agency and knowledge creation. Specifically, we argue that the concept of “reframing oneself” holds particular relevance for informal, work-integrated learning. To illustrate how this insight translates into LA design for professionals, three examples are provided: first, analyzing personal and team skills profiles (skills analytics); second, making sense of challenging workplace experiences (reflective writing analytics); and third, reflecting on orientation to learning (dispositional analytics). We foreground professional agency as a key requirement for such techniques to be used effectively and ethically

Human capital investment for front-line non managerial employees in the hospitality sector in Dubai (U.A.E.)

The topic of this research is “Human Capital Investment for front-line non managerial employees in the hospitality sector in Dubai (U.A.E)”. The purpose of this research is to explore the commitment towards human capital investment offered by five star hotels in Dubai to lower level employees within the Rooms and Food and Beverage departments from a training perspective. Dubai is known for its worldwide reputation of delivering high quality service and luxury products to its international clientele. Dubai is attracting millions of guests into the hotels every year, and forecasting a tremendous growth of the travel, tourism, and hospitality sectors in future. Despite its oil production, Dubai has realised the potential and growth of these sectors as well as retail, medical and finance. Hence, this laid emphasis on the responsibility of hotel companies to offer quality training to its workforce in order to continue with growth, success, expansion plans, and increased worldwide reputation. Furthermore, the country is a creator of employment since it is heavily dependent on labour migrants and expatriates, originating from all over the world, attracting primarily people from the Subcontinent, Asian, European, and other Middle Eastern countries. Consequently, a much-diversified workforce with different levels of education, skills, and background is employed in the hospitality industry. Therefore, there is a need to conduct this study focusing on the following aims. Firstly, to examine the current human capital needs for employees in the hospitality sector in Dubai (U.A.E.). Secondly, to consider and evaluate current efforts by hotel training departments to devise and deliver training to front-line employees. Thirdly, to assess employees’ views regarding the current investment in human capital and to identify areas for improvement. Lastly, to develop a training model underlining the importance of HCI and its constructs within five star hotels in Dubai (U.A.E.). This was achieved through both primary and secondary research. Based on the literature review a suggested training model was developed which kept changing according to the results of the primary research. The methodological approach of this study is twofold. A mixed methods approach is been adopted and hence the study starts with the collection of data through the qualitative phase including interviews with Learning and Development Directors/managers of five star hotels, followed by observations of training programmes. The second phase relates to the quantitative data collection with the use of a questionnaire self-administered to front-line employees of five star hotels in Dubai. The key findings of this study demonstrate that the company regards front-line employees as key assets and hence human capital investment in five star hotels in Dubai is high. Furthermore, there is organisational and management support towards training, which supports the function of the Learning and Development department despite being just a support department. The effectiveness of the Learning and Development department is reflected upon the adequacy of the training department, and high levels of satisfactions are expressed by front-line employees as well as by Learning and Development managers. Because of these training interventions, positive outcomes are associated with employees, customers and organisations. Furthermore, front-line employees agree that because of training, benefits relate not only to their knowledge, skills, and abilities but also to their attitude and behaviour. Learning and Development managers expressed concerns with challenges closely link to the Human Resource practices as well as lack of support by line-managers, whereas front-line employees shared high levels of satisfaction with line-managers support towards their training and developmental activities. Furthermore, despite the effectiveness of the Learning and Development, a major weakness relates to the lack of evaluation and follow up which in turn results to lack of reporting return-on-investment to executive board as well as shareholders. The study hence developed two additional models based on each set of results. Besides, by using data triangulation the study recommends a training model underlining the importance of HCI and its constructs within five star hotels in Dubai based on the new constructs emerged from the findings. Conclusions are drawn based on model 4 which explains human capital investment from a training perspective in the luxury hotel sector within Dubai reflecting on the contribution to academic knowledge as well as limitations and suggestions for further research are proposed