Understanding and measuring skill gaps in Industry 4.0 : A review

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Understanding and measuring skill gaps in Industry 4.0 : A review © 2024 The Authors. Published by Elsevier Inc. Published version Rikala, Pauliina; Braun, Greta; Järvinen, Miitta; Stahre, Johan; Hämäläinen, Raija Rikala, P., Braun, G., Järvinen, M., Stahre, J., & Hämäläinen, R. (2024). Understanding and measuring skill gaps in Industry 4.0 : A review. Technological Forecasting and Social Change, 201, Article 123206. https://doi.org/10.1016/j.techfore.2024.123206 2024 Technological Forecasting & Social Change 201 (2024) 123206 Available online 12 January 2024 0040-1625/© 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Understanding and measuring skill gaps in Industry 4.0 — A review Pauliina Rikala a , * , Greta Braun b , Miitta J¨ arvinen a , Johan Stahre b , Raija H¨ am¨ al¨ ainen a a University of Jyv¨ askyl¨ a, Department of Education, PO Box 35, FI-40014 Jyv¨ askyl¨ a, Finland b Chalmers University of Technology, SE-41296 G¨ oteborg, Sweden ARTICLE INFO Keywords: Skill gap Acquired skill Required skill Industry 4.0 Education ABSTRACT This study utilized a systematic-narrative hybrid strategy to overview the concept of skill gap and its measuring approaches. Using the PRISMA guidelines, we conducted a systematic search in January 2023 to retrieve English records from the ProQuest, ScienceDirect, Scopus, and Web of Science databases using the keywords “skill gap,” “skill mismatch,” “skill shortage,” “identifying or measuring,” and “Industry 4.0.” In total, 40 articles met our predefined inclusion criteria, and we analyzed them descriptively and qualitatively using thematic analysis and constant comparisons. We found that skill gaps certainly exist, and that concerns about growing skill gaps have been raised worldwide. Our literature review also revealed the need for a common understanding of skill gaps. Considering this, we provided a skill gap definition. Skill gaps are an extremely nuanced phenomenon, so paying careful attention to their definition and interrelating social, environmental, and technological factors when measuring them is essential. Since we found very few studies that employed different methods and perspectives to research the concept, more research is needed to map actual skill gaps, which can only be done by considering different perspectives and measuring approaches. 1. Introduction Today, industry relies heavily on interconnectivity, automation, artificial intelligence, machine learning, and real-time data. Underpinned by digital transformation, this industrial revolution is also known as Industry 4.0 (Ghobakhloo, 2020)—an umbrella term first coined in 2011 in Germany for production processes that are automated via technology and in which devices communicate with each other along the value chain activities (Karacay, 2018; Sung, 2018). next significant change emerging in the industrial sector is Industry 5.0, which focuses on the synergy between people and autonomous machines (Nahavandi, 2019). Despite the hype surrounding Industry 5.0, skill gaps pose a significant challenge for Industry 4.0 and, evidently, for Industry 5.0. Industries are confronted with the lack of right-skilled workers, which contributes to a slowdown in adopting key technologies and reaching key goals (Bokrantz et al., 2020; Di Battista et al., 2023; Stavropoulos et al., 2023). This mismatch between the skills required by employers and those possessed by employees is often called a skill gap or skills gap (Braun et al., 2022; Enders et al., 2019; McGuinness et al., 2018; Quintini, 2011). There are several causes for skill gaps. In a world gripped by severe environmental, economic, and social challenges and changes (Adepoju, 2022; European Center for the Development of Vocational Training, 2016; Felsberger et al., 2022; L´ opez Pel´ aez et al., 2021; Organisation for Economic Co-operation and Development (OECD), 2017), workers in the industry sector are especially facing higher skill demands (Guo et al., 2022). Due to constant changes, industry sectors must make disruptive changes to their operating environments and workflows. These changes modify employees’ tasks, which demand new skills at all value chain stages of Industry 4.0 (Di Battista et al., 2023; Moldovan, 2019). Hence, owing to the increased complexity of work environments and new operational structures, successfully implementing Industry 4.0 demands a wide range of skills (Karacay, 2018). Concerns about growing skill gaps have thus been raised worldwide (European Centre for the Development of Vocational Training, 2018; Korn Ferry, 2018; OECD, 2017; Quintini, 2011). Consequently, individuals must be prepared to continuously update their skills to meet evolving skill requirements (Clark, 2013). Changes in working life, such as digital transformation, particularly in Industry 4.0, demand specific skills that are not necessarily taught by educational institutes or developed in the labor market. Fostering a work environment in which employees can continuously develop their potential is thus vital (Wallin et al., 2020). Moreover, workforce training/ re-training should be considered a continuous process rather than an onoff activity (Felsberger et al., 2022). Regarding skill gaps, it has been * Corresponding author. E-mail address: [email protected] (P. Rikala). Contents lists available at ScienceDirect Technological Forecasting & Social Change journal homepage: www.elsevier.com/locate/techfore https://doi.org/10.1016/j.techfore.2024.123206 Received 21 June 2023; Received in revised form 23 November 2023; Accepted 1 January 2024 Technological Forecasting & Social Change 201 (2024) 123206 2 argued that radically reforming education and training systems is neither needed nor required and is likely to be both costly and largely ineffective (Rathelot and van Rens, 2017). Instead, skill gaps can be utilized to assess the skills that are lacking and, depending on the organization and the employee, to set different goals and resources for upskilling/reskilling (Braun et al., 2022). Optimal training decisions require accurate information about training needs and, thus, skills and skill gaps (McGuinness and Ortiz, 2016). Hence, industries must understand the scope of the changes, the content of work requirements, and the skills needed from the workforce (Karacay, 2018). Although the need to address skill gaps is evident and more topical than ever, an in-depth understanding of the concept is necessary. The concept has often been studied regionally or by sector, highlighting the skills employers seek (e.g., Chowdhury, 2020; Jayaram and Engmann, 2017). The relationship between the education system’s performance and the labor market’s demands has also been examined (e.g., Baqadir et al., 2011; Dimian, 2014). Furthermore, skill gaps have been considered as either resource-based—i.e., skilled employees as efficient resources—or more market-oriented and competence-based—i.e., skills linked to achieving business objectives (Schwalje, 2012). Thus, various stakeholders have different interests, which leads to disagreements about the exact skills that are needed and how skills issues should be addressed (OECD, 2016). Researchers, in turn, have struggled with the nuances of skill gaps, skill mismatches, and skill shortages, since they are broadly and commonly referred to in policy debates and documentation (McGuinness et al., 2018). Hence, the main challenge of identifying skill gaps is the blurred line between work–life needs, political recommendations, and empirical research results. Since skill gaps can be defined in various ways, it makes it difficult to measure them (Schwalje, 2012). For instance, skills can refer to general cognitive and noncognitive abilities or the skill characteristic of a particular job, profession, or sector (OECD, 2017). These can thus be technical, cognitive, or soft skills. Previous studies on future skills have, for instance, identified 18 future skills (Enders et al., 2019). Gaps, in turn, have been described and captured in three commonly used terms: skill gaps, mismatches, and shortages. Skill gaps occur when employees do not have adequate skills to perform their tasks (McGuinness et al., 2018). They are also closely related to skill shortage—the mismatch between the demand for and supply of specific skills—which is often used to describe the lack of available and suitably skilled candidates for vacant job positions (McGuinness et al., 2018; Quintini, 2011). Skills mismatch, in turn, reflects the imbalance between an employee’s skill level and the skill level demanded by the work (Brunello and Wruuck, 2021; OECD, 2017). Evidently, no clear definition of skill gaps exists. Essentially, skill gaps can be represented by the mathematical formula A–B =C, where A represents the skills needed for future tasks; B the skills needed for current tasks; and C the skill gap—the skills that should either be hired or developed and trained (Habash, 2019, p. 396). Consequently, skill differences, and thus gaps, are typically measured by comparing the qualifications of employed workers with the required capabilities and, more broadly, the structure of vacancies with the qualifications or training of the working-age population (Brunello and Wruuck, 2021). However, qualifications alone may not effectively reflect the skills demanded by the labor market (Morris et al., 2020); and most significantly, they exclude the skills acquired through informal education and experience. This approach of prioritizing skills over qualifications is called skills-first approach (Di Battista et al., 2023). Moreover, surveys, focus groups, forecasting models, competency assessments, education levels, and labor market analytics are used to demonstrate the existence of skill gaps (e.g., Collins, 2021; International Labor Organization (ILO) et al., 2017). However, can, for example, a Likert scale or self-assessment objectively detect skill gaps? Many workers do not know their own skill levels or which skills are relevant, which makes it harder for them to find proper channels for upskilling/ reskilling (Enders et al., 2019). Thus, there are some factors that weaken the reliability of measurement in surveys, such as subjectivity and peer positivity bias (Kimmell and Martin, 2015; Schwalje, 2012). On the other hand, skills themselves are very multidimensional, but their measurement focuses only on specific selected dimensions, mainly due to data limitations (Brunello and Wruuck, 2021). One can try to measure skills using standardized tests. However, it cannot be guaranteed that employees will be able to apply these skills outside standardized testing (McGunagle and Zizka, 2020). Furthermore, when utilizing only skill databases, the key variables are binary and may not offer detailed information on the quality of skills or upskilling/reskilling goals (Pedota et al., 2023). An ideal dataset would, therefore, explicate a wide range of factors and details about the skill requirements of a job and the skill set of an ideal worker (Rathelot and van Rens, 2017). Moreover, skill gap measurement should target identifiable skills (Clark, 2013). The Program for the International Assessment of Adult Competencies (PIAAC) has taken a step toward measuring and evaluating the proficiency of adults and how they use their skills (OECD, 2021). However, PIAAC does not directly focus on working life, because it measures proficiency in key information-processing skills and examines how adults use various skills at work, at home, and in the wider community (H¨ am¨ al¨ ainen et al., 2019). A more holistic approach to identify skill gaps in working life must be thus adopted. Although adopting a holistic approach to measure skills gap is considered good practice, quantitative, qualitative, and other data sources such as forecasting models, competency assessments, education levels, and labor market analytics are rarely combined (OECD, 2016). To date, the literature has provided mainly descriptive evidence of the impact of skill gaps on company performance (McGuinness and Ortiz, 2016), but concepts related to skill gaps remain ambiguous and difficult to define and measure (Collins, 2021; Schwalje, 2012). The lack of a standard definition, the diversity of data collection methodologies (Centeno et al., 2022), and competing interests of employers, politicians and researchers (OECD, 2016) indicate the limitations of past research, particularly concerning validity and measurement issues. The lack of clear and objective results, in turn, may lead to false arguments based on biased interest or little or no evidence of actual skill gaps (Cappelli, 2015), resulting in misdirected training resources. In our opinion, no studies have provided incontrovertible evidence of skill gaps and how to tackle them, as there is no consensus on what constitutes a skill gap. There thus exists a research gap on the skill gap concept and the related measurement approaches. For digital transformation to sustainably benefit employees and companies, it is essential to understand the skill gap phenomenon and its consequences. Only those employees with the necessary skills, knowledge, and qualifications will be able to adapt to the digital transition, production system changes, and new working methods (Akyazi et al., 2020a). Deeply understanding the skill gap phenomenon could thus help organizations effectively handle digital transformation by recruiting and retaining talent and providing them with necessary training. After reviewing the different definitions, measurements, and analytical approaches used in earlier policy and research papers, we were primarily interested in gaining a more comprehensive understanding of the skill gap phenomenon and the suitable approaches for identifying and measuring skill gaps in the digital era, especially for Industry 4.0, but also more generally for the globalizing, digitalizing, and changing world. We thus utilized a systematic-narrative hybrid approach (e.g., Turnbull et al., 2023) to review the skill gap studies between 2012 and 2022, aiming to shed light on the skill gap concept and the approaches taken to understand and measure it. The following questions anchored our review: a) How has the skill gap concept been understood in the context of today’s globalized, digitalized, and changing world? b) What approaches can be used to measure skill gaps? Based on the literature review, we propose a definition for skill gaps and offer recommendations for future research and practices to understand the importance of different stakeholders’ roles in bridging skill P. Rikala et al. Technological Forecasting & Social Change 201 (2024) 123206 3 gaps. 2. Materials and methods We conducted a literature review of the full-text articles published between 2012 and 2022. A literature review can shed light on the skill gap concept and the related understanding and measurement approaches. Future research and practice on the skill gaps in Industry 4.0 and in other contexts can benefit from a comprehensive conceptualization and synthesis of the existing skill gap literature. Moreover, a literature review can critically evaluate data and offer positive and valuable results (Knopf, 2006). We adhered to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines for systematic reviews. These guidelines help authors report transparently why the literature review was done, what was done, and what was found (Page et al., 2021). Furthermore, we adopted a hybrid approach: our literature review drew on the characteristics of both the narrative and systematic review traditions (Turnbull et al., 2023). The search protocols and inclusion/ exclusion criteria were drawn from the elements of the PRISMA practice. However, we applied a more narrative approach to analyze and synthesize data (see Section 2.5), since we included qualitative, quantitative, and mixed-methods studies in our review. The systematic review approach requires the application of data analysis processes such as the meta-analysis of quantitative data (Magarey, 2001). Conducting a systematic-narrative hybrid literature review thus enabled us to employ qualitative and quantitative elements. Moreover, using thematic analysis (Braun and Clarke, 2006) and conducting constant comparisons (Whittemore and Knafl, 2005), we descriptively (Loeb et al., 2017) and qualitatively analyzed the data. Generally, the procedure followed three phases—planning, doing, and writing—and included formulating the research questions, choosing and justifying the methodology, selecting the literature sources, planning the search parameters, cleaning and synthesizing the data, and writing (Turnbull et al., 2023). All the authors contributed to the study’s conception and design. The first two authors planned the inclusion/ exclusion criteria and search strategy. The first author conducted database searches and removed duplicates. The first three authors performed the selection process, including critical appraisals, data collection, and analysis. The first three authors wrote the first draft of the manuscript. All the authors read, reviewed, commented on, edited, and approved the manuscript. The literature review procedure is described in more detail in the following subsections following PRISMA guidelines. 2.1. Eligibility criteria Table 1 presents the inclusion/exclusion criteria. The timeframe for the articles (2012–2022) was based on the concept of Industry 4.0—a term first coined in 2011 to refer to a phenomenon that began in Germany (Sung, 2018). The non-reputable journals were identified based on Elsevier’s CiteScore rating, the impact factor, and/or the journal citation indicator calculated by Clarivate and the Finnish scientific community’s Publication Forum (https://jfp.csc.fi/en/). We did not limit the studies to specific methodological approaches, as diverse sources of information can offer a holistic understanding of a topic (Whittemore and Knafl, 2005). However, we limited our review to relevant peer-reviewed, English-language, full-text articles published between 2012 and 2022. We are from two European universities, and the availability of academic journals, made possible by the university subscriptions of academic publication channels—book and journal publishers, conferences, and print and digital publication series specialized in publishing results of scientific research—is typically quite comprehensive with some exceptions. These channels have an editorial board of experts and follow a peer-review practice. We thus excluded full-text articles that were unavailable through the university subscriptions or other free channels and databases, such as Google Scholar, which also offer access to the content of academic publishing channels. 2.2. Information sources and search strategy We conducted the systematic search in January 2023, applying the search strings shown in Table 2 to the ProQuest, ScienceDirect, Scopus, and Web of Science databases. These four databases, commonly used to conduct literature reviews (see Behl et al., 2022), were selected by the authors in the planning phase to achieve sufficient coverage. The search included three sets of keywords: 1) skill gap, skill mismatch, and skill shortage; 2) identifying or measuring: measure, scale, assessment, questionnaire, instrument, tool, evaluation, or analysis; and 3) context: Industry 4.0. 2.3. Selection process After searching the four databases, we imported the search results, including titles and abstracts, to the Zotero reference management software. The primary search yielded 53 studies: ProQuest Central (n = 6), Scopus (n =18), Web of Science (n =31), and ScienceDirect (n =5). Fig. 1 presents a PRISMA flow diagram of the study selection process. The duplicates (n =2) and the full texts that were unavailable through the university subscriptions or other channels, such as Google Scholar (n =5), were removed. Skimming the abstracts of these five articles, we found that their themes were beyond the focus of our review (aircraft incidents and skill mismatch issues, institutional changes, employability, and the effectiveness of teaching or programs). However, because we could not access the full text of these articles and thus could not determine whether their focus was ultimately relevant, we acknowledge that we might have excluded some relevant studies. The first three authors screened the titles and abstracts independently using the abovementioned criteria (see Table 1). The authors read the title and abstract of each article and decided on inclusion using a “no,” a “maybe,” or a “yes.” “No” meant that a source did not meet the inclusion criteria and that it should not be included in the systematic review; “maybe” signaled that there is not much information to be gleaned from the title and abstract and that the full-text is worth screening; and “yes” meant that the source met the inclusion criteria and should be moved to the full-text screening stage. After screening the titles and abstracts, 41 studies remained. That an article was published in a non-reputable journal—journals published in channels other than academic publication channels and, Table 1 Inclusion/exclusion criteria. Inclusion Exclusion Peer-reviewed articles with their full texts written in English and published between 2012 and 2022. Studies that covered skill gaps, skills shortages, skill mismatches, skills gaps, or Industry 4.0-related training, skill upgrading, or development and Industry 4.0, or digitalization- and globalization-driven changes in skills in the labor market or labor force. Studies that focused on identifying, understanding, measuring, or tackling skill gaps. Studies covering irrelevant topics and foci. Dissertations, theses, conference abstracts, books, editorial letters, policy reports, and book reviews. Non-English articles. Articles published in non-reputable journals. Duplicate results. Articles whose full texts are unavailable through the available subscriptions or free channels. P. Rikala et al. Technological Forecasting & Social Change 201 (2024) 123206 4 thus, do not follow the peer-review practice and or do not have an editorial board—was the most frequent reason for exclusion during the title and abstract screening phase. The exclusion was based on ratings and indicators (i.e., the CiteScore metric, impact factor, journal citation indicator, and Publication Forum rating). After the ratings and metrics were considered, the final decision was made according to the Publication Forum classification (level 1, 2, or 3). The Publication Forum, a rating and classification system based on the quality assessment of research outputs, aims to recognize non-reputable journals by defining their classifications. Only academic publication channels are eligible for classification. A panel of 23 discipline-specific experts and 300 distinguished researchers evaluate publication channels using several impact indicators and by indexing data (Publication Forum, 2021). The Publication Forum’s procedure for ensuring scientific quality is thus trustworthy. We then assessed the remaining 41 studies for which full texts were available to determine their eligibility. The full texts of all the eligible articles (i.e., “yes” and “maybe”) were retrieved, and the first three authors independently performed full-text assessments to decide whether the articles should be included or excluded. Any disagreement regarding eligibility based on the title, abstract, and full-text screening was resolved by consensus. Any discrepancies were resolved according to the authors’ notes or discussions. There were no significant conflicts of opinion, and the authors’ estimates were quite similar. Sixteen research papers raised concerns, but only four contradicted each other. The first author made the final decision by carefully reviewing the authors’ notes and the article titles, abstracts, and/or full texts against the inclusion and exclusion criteria. Of these 41 papers, one was not eligible Fig. 1. Flow diagram showing the PRISMA review process. (Adapted from Page et al. (2021).) Table 2 Search strategy. Data sources Keywords ProQuest Central Title (“skill* gap*” OR “skill* mismatch*” OR “skill* shortage*”) AND (measure OR scale OR assessment OR questionnaire OR instrument OR tool OR evaluation OR analysis) AND “Industry 4.0” AND PEER (yes). Date: from January 2012 to December 2022; full text Scopus TITLE-ABS-KEY (“skill* gap*” OR “skill* mismatch*” OR “skill* shortage*” AND measure OR scale OR assessment OR questionnaire OR instrument OR tool OR evaluation OR analysis AND “industry 4.0”) PUBYEAR >2012 AND (LIMIT-TO (DOCTYPE, “ar”)) Web of Science Results for “skill* gap*” OR “skill* mismatch*” OR “skill* shortage*” (Title) AND measure OR scale OR assessment OR questionnaire OR instrument OR tool OR evaluation OR analysis (all fields) AND “Industry” (all fields) Timespan: 2012-01-01 to 2022-12-31 (publication date) ScienceDirect Title, abstract, or author-specified keywords (“skills gaps” OR “skills mismatches” OR “skills shortages”) AND (measure OR scale OR assessment OR evaluation OR analysis) AND (“Industry 4.0”) Year(s) 2012–2022 P. Rikala et al. Technological Forecasting & Social Change 201 (2024) 123206 5 for inclusion. Finally, 40 full-text articles were included in the synthesis (listed in Table 4). The electronic database from which the articles were extracted (ProQuest =P, ScienceDirect =SD, Scopus =S, and Web of Science =W) and the coding number (issued by alphabetical order) of the articles are provided and used to identify specific papers in the result tables (Tables 4, 5, and 6). 2.4. Risk of bias assessment The first three authors performed critical appraisals using a modified version of the Critical Appraisal Skills Program (CASP, 2022) systematic review, qualitative research, and cohort study checklists. The assessment scale was “yes,” “no,” or “cannot say.” The questions were as follows: Was the purpose of the study clear? Was the methodology/design described and suitable for the study? Can the target group of the study be identified? Are the analysis and findings sufficiently described? Are the results of the study clear and plausible? These CASP assessment scales were chosen because the included studies were diverse. The quality assessment turned out to be slightly challenging, as we did not filter or limit the methodological approach of the studies or exclude conference papers. For example, compared to conference papers, journal papers often contain more essential information, as they do not have page limits, whereas content-centered literature reviews could describe researchers’ choices such as selection criteria more accurately. The included articles were of good or fair quality, probably because the articles published in non-reputable journals were excluded during the screening phase. The quality of one article was poor, but we decided to include it because of its highly relevant content: the article defined skill gaps and presented a competency model. The challenges regarding fair and poor articles were mostly due to the lack of coherence and clarity in the articles. The quality assessment results are detailed in the supplementary material (Appendix A, Table A.1). 2.5. Data collection, processing, and analysis We collected data regarding the purpose of each study, how the skill gap concept was understood, how skill gaps were identified and/or measured, and other interesting observations and highlights (e.g., ways of tackling skill gaps) for further analysis. Although we collected diverse information, our review focused on the skill gap concept and approaches to measuring skill gaps. The additional extracted variables included the authors’ names, year of publication, methodology, study region, and title of the paper, which we used to obtain an overview of the included studies. We analyzed the data descriptively and qualitatively using thematic analysis, and we made constant comparisons to identify the themes and subthemes related to the research questions and summarize the particulars. We chose descriptive analysis because it helps answer questions concerning who, when, where, and to what extent (Loeb et al., 2017). We chose thematic analysis because it is a flexible and relatively quick way to highlight the similarities and differences in texts and summarize the most important features of a dataset (Braun and Clarke, 2006). In addition, we used a constant comparison approach to iteratively identify and compare important and accurate patterns and themes (Whittemore and Knafl, 2005). The constant comparative method, typically associated with grounded theory, can be extended to other qualitative research to produce higher levels of abstraction (Pawluch, 2005). Thus, our study applied a more narrative approach to analyze and synthesize data, which can be considered a systematic-narrative hybrid approach (see Turnbull et al., 2023). Thematic analysis emphasizes identifying, organizing, and interpreting the themes discovered from the extracted data (Braun and Clarke, 2006). Creativity and criticality, in turn, are key elements in constant comparisons for visualizing and comparing data and clarify the empirical and/or theoretical support that emerges from interpretations (Whittemore and Knafl, 2005). We combined these two approaches, beginning with thematic analysis and then conducting constant comparisons to draw final interpretations and create visualizations. The first three authors conducted the thematic analysis by following the steps of Braun and Clarke (2006): 1) becoming familiar with the data, 2) generating codes, 3) generating themes, 4) reviewing themes, 5) defining and naming themes, and 6) summarizing data (i.e., writing up). The third author coded and analyzed the skill gap concept and understanding, and the second author coded and analyzed the measurement approaches. The first author coded and analyzed both the definitions of skill gap and its measurement approaches. The generated themes were then compared and organized, and the final themes were decided through consultation. This iterative consensus process ensured interrater consistency and reliability (Hemmler et al., 2022). Table 3 shows how authors applied specific codes and themes to one paper and how they combined themes through consensus to determine the final themes. Disagreements concerning the generated themes or subthemes were discussed to reach a consensus. We interpreted the generated themes through constant comparisons, iteratively examining the patterns we began to discern. We considered the similarities and differences among the themes, made comparisons, and started to identify common patterns with the aim of organizing particulars into a general concept. We considered the different factors and relationships between the various factors and tried to build logical links between them. Finally, we created data visualizations with different programs (see Figs. 2, 3, and 4). 3. Results This section presents insights into the literature on the skill gap. We begin with an overview of the basic descriptive statistics typical of systematic reviews, followed by subsections where we answer our research questions more narratively. 3.1. Overview of the included studies The selected literature (Table 4) revealed that concerns about skill gaps have increased across the world, especially in the past few years, as reflected in the research conducted. Most of the articles were published in 2020 (n =8), 2021 (n =10), and 2022 (n =10). Most of the studies were from Europe (n =15), followed by North America (n =6), West Africa (n =4), Southeast Asia (n =4), and Oceania (n =3). Regarding industry sectors, most research focused on manufacturing (n =6), followed by information technology (IT; n =4), Industry 4.0 (n =3), finance (n =3), construction (n =3), and e-business (n =3). However, gaps in the labor market have also been examined more generally. Overall, the studies examined skill gaps from the perspectives of working life and education, current and future knowledge and skill demands, requirements, profiles, and statuses, with the aim of identifying strategies, tools, models, and databases to narrow skill gaps. Moreover, different methods, such as reviews (n =3), case studies (n =2), pilot studies (n =2), mixed-methods studies (n =6), and qualitative approaches (n =6), were used. However, the methods were largely quantitative, such as surveys (n =14). 3.2. Skill gap The first research question was: How has the skill gap concept been understood in the context of today’s globalized, digitalized, and changing world? Skill gap, the studies showed, is a very nuanced phenomenon that lacks a clear definition. Generally, it can be understood as the difficulty of providing the right skills to the right people at the right time (Anshari and Hamdan, 2022) to enhance employee productivity and innovation, improve and advance organizational performance, create value (Horbach and Rammer, 2022; Ayodele et al., 2021), support digital transformation (Akyazi et al., 2020a; Li et al., 2021), and narrow reduce gaps P. Rikala et al. Technological Forecasting & Social Change 201 (2024) 123206 6 and shortages in the constantly changing business environment and labor market (Sharma et al., 2016; Li et al., 2021). Skill discrepancies make it increasingly difficult to align skilled people and processes with an organization’s goals (Arthur-Mensah, 2020; Francalanza et al., 2021). Though employees and employers play a major role in these rapidly changing business realities (Abbasi et al., 2018; Ayodele et al., 2020; Arthur-Mensah, 2020; Ho, 2016), training providers are equally important as well (Adepoju and Aigbavboa, 2021; Akyazi et al., 2020b), as they can instill the necessary skills (see Fig. 2). The figure below illustrates our main findings, including our proposed skill gap definition. Hence, a skill gap can be understood as a gap between the demand for and supply of skills (all the articles shared this view at some level; see Table 5). In other words, there is a gap, lack, shortage, or mismatch between the knowledge, skills, and abilities employees possess and those that employers expect, require, and demand. It is a gulf between the current and future industry requirements and the ability of the workforce to satisfactorily meet the industry needs (Arcelay et al., 2021). Employers’ expectations, opinions, and ways of thinking can deepen a company’s skill gap (e.g., through discrimination, preferences for who is hired, or unrealistic expectations; Abbasi et al., 2018; Ayodele et al., 2020; Cohen and Eyal, 2021; Cukier, 2019; Mori, 2021). However, a skill gap can also widen because of employee factors (e.g., lack of Table 3 An example of a coding application, how the skill gap was understood, and the themes derived from the coding application. [P2] Coder 1 codes Coder 2 codes Coder 1 themes Coder 2 themes Finalized themes Lack of talent Lack of talent Skill mismatch between employers and graduates/ higher education Gaps between the employees’ competencies and those demanded by employers Gaps between the employees’ competencies and those demanded by employers/ industry Skilled workforce shortage Skilled workforce shortage Mismatches/skill gaps between employer/ industry demands and employee skills Labor market challenge Skill gaps as a labor market challenge/gap (e.g., workforce shortages, unfilled jobs, lack of talent) No ability to work in the digital future Employees do not have the right skills (i.e., to work in the digital future) Skilled workforce shortage/lack of talent Mismatch between unfilled jobs and candidates, and employers’ challenges in identifying qualified employees with the right skills Unfilled jobs Undergraduates’ lack of important skills The gap between knowledge, skills, and abilities and what is crucial for companies Fig. 2. Skill gap with a complex combination of causes and consequences. P. Rikala et al. Technological Forecasting & Social Change 201 (2024) 123206 7 interest, resistance to change, occupational preferences, personal preferences, and familial, professional, and identity considerations; Arthur- Mensah, 2020; Ho, 2016). Moreover, it should be noted that rapidly changing businesses and working environments and skill requirements lead to formerly useful skills becoming outdated or the quality of skills gradually deteriorating (Anshari and Hamdan, 2022; Cohen and Eyal, 2021; Novakova, 2020; Viganego et al., 2022). Employees are expected to evolve; hence, the challenge is not only to attract and retain talented people with the required skills but also to develop the skills of the existing workforce (Butt, 2020; Adepoju and Aigbavboa, 2021). Without skill development, there exists the risk of a skill obsolescence gap emerging; therefore, one could argue that a skill gap results from an education/training gap (Adepoju and Aigbavboa, 2021; Novakova, 2020; Viganego et al., 2022)—a misalignment between the skills needed by the industry and those developed by the education/training providers (Akdur, 2021). In other words, education and training providers struggle to adapt to the changing industry demands and business realities. The reviewed studies highlighted the need to address/tackle such skill gaps by developing curricula, education, and training that can better prepare the workforce for the future (Akyazi et al., 2020b; Oladokun and Olaleye, 2018). Thus, a skill gap is a gap between education and training outcomes and industry-specific skill needs and/or a gap between the skills employees possess and those the industry identifies as important (Carlisle et al., 2021). There are various reasons for skills becoming obsolete and for a demand for new skills emerging. A skill gap can also thus be seen as a combination of complex causes and consequences (see Table 5). Megatrends such as digitalization, globalization, green transformation, and demographic changes, as well as constantly changing operating environments—in terms of economic, financial, sociopolitical, physical, and time factors—may cause an imbalance between skill supply and demand (Arthur-Mensah, 2020; Carlisle et al., 2021; Chang-Richards et al., 2017; Fig. 3. Approaches to measuring skill gaps clustered by data sources and methods. Fig. 4. The steps of the most-often-used approach for measuring skill gaps. P. Rikala et al. Technological Forecasting & Social Change 201 (2024) 123206 8 Horbach and Rammer, 2022; Moore and Morton, 2017; Novakova, 2020). These different skill problems, in turn, are reflected in business realities. Skill gaps may hinder the development of a company, sector, industry, and, more broadly, a country, thus generating business gaps. For instance, in some countries, manufacturing sectors rely mainly on low-skilled foreign workers, preventing them from investing in automation and technology upgrades (Husin et al., 2022), meaning that industries and sectors struggle to support their new business models. Because of skill gaps, industries struggle to ensure the value creation necessary to remain competitive and productive and to take advantage of new technologies and practices in their rapidly evolving operations (Akyazi et al., 2020a; Francalanza et al., 2021; Maheso et al., 2019). For example, green skills are vital for maintaining a competitive edge (Akyazi et al., 2020b). Hence, a suitable set of such skills may ensure clients’ satisfaction, optimize returns, increase firms’ competitiveness, and maintain employees’ professional relevance (Ayodele et al., 2020). Furthermore, skill gaps and outdated skills can make it challenging to find and recruit adequately trained workers to fill vacancies and properly conduct work—in other words, skill gaps cause labor market challenges and skill shortages (Chang-Richards et al., 2017; Horbach Table 4 Publications selected for the systematic literature review. Paper code Author(s) Year Study region Industry/sector Methodology Research perspective W1 Abbasi et al. 2018 Pakistan Financial services Quantitative The gap between the demand for and supply of skills W2 Adepoju and Aigbavboa 2021 Nigeria Construction Quantitative The gap between the demand for and supply of skills W3 Akdur 2021 Turkey IT sector Quantitative The potential education/training gap S1 Akyazi, Goti, Oyarbide et al. 2020 Europe Food Desk research The gap between the demand for and supply of skills S2 Akyazi, Goti, Oyarbide- Zubillaga, et al. 2020 Europe Manufacturing Desk research The current and future skill needs S3 Albizu et al. 2022 Basque Country Business Mixed methods Mismatch approaches S4 Anshari and Hamdan 2022 – Industry 4.0 Qualitative The current and future skill needs S5 Arcelay et al. 2021 Europe/Spain Energy Desk research The current and future skill needs W4 Arthur-Mensah 2020 US Manufacturing Qualitative An education/training approach W5 Ayodele et al. 2021 Nigeria Real estate Quantitative The gap between the demand for and supply of skills. W6 Ayodele et al. 2020 Nigeria Real estate Quantitative The gap between the demand for and supply of skills SD1 Babic et al. 2022 US Manufacturing Content analysis The potential education/training gap S6 Butt 2020 – Manufacturing Literature review A process management approach W7 Carlisle et al. 2021 United Kingdom (UK) Tourism Quantitative The gap between the demand for and supply of skills W8 Chang-Richards et al. 2017 New Zealand Construction Quantitative A human resources management approach W9 Cohen and Eyal 2021 Israel Life sciences Qualitative Mismatch approaches P1 Cukier 2019 Canada Labor markets in general Literature review The current and future skill needs W10 Do et al. 2023 (online 2022) Taiwan Robotics Mixed methods The gap between the demand for and supply of skills S7 Francalanza et al. 2021 Europe Industry 4.0 Mixed methods The gap between the demand for and supply of skills W11 Ho 2016 Hong Kong Construction Mixed methods Labor and skill shortages W12 Horbach and Rammer 2022 Germany Labor markets in general Quantitative Skill shortages S8 Husin et al. 2022 Malaysia IT-sector Mixed methods The current and future skill needs SD2 Li et al. 2021 US Manufacturing Desk research The current and future skill needs SD3 Maheso et al. 2019 South Africa Manufacturing Concept design An education/training approach SD4 Moldovan 2019 Europe Industry 4.0 Quantitative The gap between the demand for and supply of skills W13 Moore and Morton 2017 Australia Labor markets in general Qualitative The gap between the demand for and supply of skills W14 Mori 2021 Vietnam Manufacturing Mixed methods The employers’ perceptions W15 Morris et al. 2020 UK Labor markets in general Desk research Skill shortages SD5 Novakova 2020 Slovakia Industry 4.0 Literature review The potential threats of automation W16 Oladokun and Olaleye 2018 Nigeria Real estate Quantitative The employers’ perceptions W17 Oldford et al. 2022 Canada Financial services Content analysis and Case study The potential education/training gap P2 Qiu et al. 2020 US E-business Case study The gap between the demand for and supply of skills S9 Romero-G´ azquez et al. 2022 Europe Industry 4.0 Quantitative The gap between the demand for and supply of skills S10 Romero G´ azquez et al. 2021 Europe Industry 4.0 Pilot case study The status of Industry 4.0 adoption W18 Royle and Laing 2014 UK E-business Qualitative The gap between the demand for and supply of skills W19 Sharma et al. 2016 Australia Agriculture, forestry, and fisheries Quantitative Skill shortages W20 Singh Dubey et al. 2022 India IT-sector Quantitative The gap between the demand for and supply of skills W21 van Romburgh and van der Merwe 2015 South Africa Financial services Pilot case study The gap between the demand for and supply of skills P3 Viganego et al. 2022 Spain Labor markets in general Qualitative The gap between the demand for and supply of skills W22 Zheng and Shi 2021 China E-business Quantitative An education/training approach P. Rikala et al. Technological Forecasting & Social Change 201 (2024) 123206 15 clarified what constitutes them (Royle and Laing, 2014)—a significant research gap we aimed to fill through this study. We thus propose the following definition: a skill gap can be understood as a difficulty in providing the right skills to the right people at the right time to enhance employee productivity, improve and advance organizational performance, create value, support digital transformation, and narrow gaps in business realities and labor markets (Fig. 5). The reviewed literature reinforced the view that a skill gap is a gap between employers’ demands for specific skills and the skills that employees possess (McGuinness et al., 2018; Quintini, 2011). Furthermore, a skill gap can occur between education and training outcomes and industry-based skill needs (Carlisle et al., 2021). Our findings also highlighted that, in today’s constantly changing world, it is becoming increasingly tricky to align skilled people and processes with organizational goals due to skill discrepancies. It was even emphasized that skill gaps result from poor collaboration between trainers, employers, employees, and graduates (Abbasi et al., 2018). Thus, according to the reviewed articles, skill development is a specific area in which companies and education providers should allocate the necessary resources. Especially in industry, employees must become familiar with new working methods involving continuous learning to ensure that they remain valuable to their organizations and society (Braun et al., 2022). It is necessary to start training employees so that they can gain the skills needed to adapt to the constantly changing work environment (Albizu et al., 2022). However, it takes time and effort to plan and implement effective training (Qiu et al., 2020). Therefore, optimal training decisions and tools require accurate information about skill gaps (McGuinness and Ortiz, 2016). Hence, industries must understand the scope of the changes, and the work and skills needed from the workforce to deal with the changes (Karacay, 2018). 4.2. Measuring skill gaps What approaches can be used to measure skill gaps? Though multiple approaches have been used, a holistic and validated approach is still lacking. Although earlier research has highlighted that adopting a holistic approach for skill gap measurement is considered good practice (OECD, 2016), quantitative, qualitative, and other data sources such as forecasting models, competency assessments, education levels, and labor market analytics are rarely combined. Despite fact that the concept of a skill gap is multifaceted, there seem to be some generally accepted ways of measuring it (e.g., by creating a skill framework and using it to design surveys). Nonetheless, there are some weaknesses regarding the reliability of measurement in surveys, such as subjectivity and peer positivity bias (Kimmell and Martin, 2015; Schwalje, 2012). Many workers do not know their own level of skills or even do not know which skills are relevant, making it harder to find proper channels for upskilling/reskilling (Enders et al., 2019). Can a Likert scale or selfassessment objectively describe a skill gap? This is a relevant question since these were the most utilized ways to measure skill gaps. Furthermore, as the reviewed literature showed, these approaches have not been validated or involve the “time-space gap”—researchers examined and measured skills only at specific times and thus failed to monitor changes in skill gaps over time. We thus believe that converging data from multimodal and multiple approaches, systems, and sources with Table 6. cont. W7 Yes No No Minor Skills framework, survey (Likert le), employees, skill gap, sustainably skill gap W8 No No Yes Minor Survey, field visits (observations and interviews) as a follow up, focus groups and secondary data to gain further insights, sector, and skill challenges W9 N/A N/A N/A N/A Links between skills mismatch and international mobility W10 Yes No No Minor Skills framework, interviews (semi-structured), experts, importance-performance analysis, and technical skill gap W11 N/A N/A N/A N/A Conceptual labor supply framework W12 Yes Yes No Fair Data analytics, Community Innovation Survey (CIS), job openings and qualifications, and skill shortages and innovation W13 No Yes No Minor Interviews, managers, supervisors, perceptions, and attitudes about graduates’ writing skills W14 No No No Minor Interviews, employers, and perceptions of skill demand and mismatch W15 Yes Yes No Fair Data analytics, ESS, and ABS data (UK region-industry data), and regional skill gaps W16 N/A N/A N/A N/A Mid-school internship as a skillgap-bridging scheme W17 No No No Minor Content analysis, undergraduate course textbooks and instructor materials, and the current state of environmental, social, and governance pedagogy W18 Yes No No Minor Skills framework, focus group, industry professionals, key competencies, and skills mode W19 No No No Minor Surveys, firms, and skill shortage issues W20 Yes Yes No Fair Skills framework, surveys, IT- professionals and students, and gaps in soft skills traits and factors W21 Yes No No Minor Skills framework, surveys (Likerttype), firm representatives, and skills shortage W22 Yes Yes No Fair Skills framework, surveys, students (self-evaluation) and employers, and skill gap P. Rikala et al. Technological Forecasting & Social Change 201 (2024) 123206 16 more traditional measures, such as surveys, could offer a more comprehensive understanding of the skill gap phenomenon. Eye tracking, for example, is a versatile, accurate, and reliable methodology (Antonenko, 2019). We agree with the previous literature that an ideal dataset should explicate a wide range of factors and details about the skill requirements of a job and the skill set of an ideal worker (Rathelot and van Rens, 2017). Hence, a more holistic approach is needed to identify and measure skill gaps. First, it is important to know what companies want and need (Maheso et al., 2019). Second, exploring the skill gaps among employees is important (Adepoju and Aigbavboa, 2021). Understanding the coverage of these necessary skill sets in education and training is critical (Akdur, 2021). One reviewed paper, for instance, suggested that technology-enabled talent-matching platforms could be one way to bridge the skill gap (Cukier, 2019). Thus, at best, the idea of a skill gap can be utilized to assess the skills someone lacks and, depending on the organization and the employee, to set different goals and resources for upskilling/reskilling (Braun et al., 2022). After our database searches (i. e., January 2023), some interesting and more holistic skill gap approaches emerged, such as Fareri et al. (2023), whose data-driven evaluation tool is user-friendly and whose methodology is easily adaptable to different contexts and goals. 5. Study limitations and strengths Our review has some clear strengths. First, it was not limited to a specific research area or method. Diverse sources of information can offer a holistic understanding of a topic (Whittemore and Knafl, 2005). We were interested in gaining a clearer understanding of skill gaps and its measurement approaches in the digital era, especially for Industry 4.0, but also for the increasingly globalizing, digitalizing, and changing world. Therefore, the skill gap may manifest similarly in other work environments as well, although skill requirements may differ. However, a multi-approach perspective also limited the systematic evaluation of the evidence related to measurement approaches, which might have been possible if the study had focused only on, for example, quantitative studies. Because of data heterogeneity and the different research approaches, we did not conduct a meta-analysis but aimed to provide a narrative overview of the existing empirical evidence on skill gap measurement. The second strength of the review is the effort we made to reduce bias in selecting relevant publications by having three researchers participate in the selection and analysis processes. We employed thematic analysis and constant comparison approaches to organize the particulars into a general concept. These analysis methods clarified the interpretations that emerged flexibly and creatively from the data (Braun and Clarke, 2006; Whittemore and Knafl, 2005). Any disagreements during the review and analysis processes were discussed to reach a consensus. The review was limited to the ProQuest Central, Scopus, Web of Science, and ScienceDirect databases. Researchers who want to examine skill gaps more thoroughly, especially in different contexts, should expand the search to include, for example, the PsycINFO, ERIC, and Google Scholar databases. In addition, clarity could be increased if the search terms focused only on skill gaps, although that might involve a risk of excluding relevant studies with different search terms. In addition, this review may have excluded other relevant information contained in books or theses. We recognize that we did exclude some relevant studies when we excluded the full texts that were unavailable through university subscriptions or other channels, such as Google Scholar. We thus missed five papers that should have been screened, though their abstracts indicated that their focus might not have been relevant for our review. 6. Conclusions, implications of the study and directions for future research and practice Our literature review revealed the need for a common understanding of skill gaps and provided a definition for this multifaceted phenomenon (see Section 4.1). Understanding skill gaps is important because, in recent years, industrial changes have often overtaken workers’ learning speed, leading to delays and failures in technology adoption and sustainable development. However, providing the right skills to the right people at the right time in a constantly changing world is difficult. Skill gaps stem from the preferences, expectations, and supply of three actors—employees, employers, and education providers—that are broadly influenced by megatrends, operating environments, and business realities. Therefore, an actual skill gap may be hard to grasp using the existing measurement methods and good-practice approaches. Throughout our review, we identified the existing gaps in skill gap measurement (e.g., focus, effects, and/or objectivity). Moreover, we found that previous research did not define (Table 5) or map the actual skill gaps (Table 6). This calls for more studies to evaluate the long-term effects of skill gaps, consider social, environmental, and technological factors from different perspectives, and combine different approaches—i.e., a holistic validated perspective. 6.1. Recommendations for future practice Solving skill gaps, which are very nuanced phenomena, is a challenge—no single and clear solution exists. We thus suggest that educators, employers, employees, students, and political decision-makers should understand the importance of specific skills and recognize their roles in bridging skill gaps in today’s digitalizing, globalizing, and changing world. Collaboration must be done at many different levels, and long-term and effective cooperation with various stakeholders is needed. Collaboration helps understand the different needs and expectations to avoid disagreements and lays the groundwork for approaches to bridge skill gaps. In addition, individuals must have the desire and readiness to develop their own skills, as skill gaps largely depend on employees and their skills and preferences. At the same time, employers’ support is essential as well. The employers, educators, and policymakers should clearly define responsibilities—i.e., whose responsibility it is to provide, finance, and determine upskilling/reskilling so that employees can develop their potential. The future is difficult to predict. However, the goal should be to utilize research and evidence-based data from different perspectives to make effective recruiting, re-training, and training investments. Stronger university–industry cooperation may help establish different ways to Fig. 5. A skill gap is a difficulty in providing the right skills to the right people at the right time in changing business realities. P. Rikala et al. Technological Forecasting & Social Change 201 (2024) 123206 17 identify and bridge the skill gap (e.g., skill frameworks, tools to adapt and create learning modules and curricula, and customized learning opportunities to meet individuals’ needs). Therefore, deep, researchbased knowledge and evidence-based understanding could help organizations thrive in digital transformation and constantly changing operating environments. Knowledge mining, learning analytics, and artificial intelligence may open new opportunities for utilizing information from human resources management systems, educational databases, job posting data, and other databases and create skill frameworks to identify and visualize actual skill gaps. Furthermore, recommender systems could be developed and utilized to offer personal and recruiting recommendations and bridge the skill gap. Only with such humancentered means can robots and intelligent machines work successfully alongside people, which can increase resilience and help achieve the goals of sustainable development. 6.2. Suggestions for future research Future skill gaps researchers must have a coherent understanding of the phenomenon. They must clearly define the concept, the measurement dimensions, and the associated scales—only then it can be guaranteed that a study would offer clear evidence of skill gaps and how to address them. Since the literature does not offer enough data to clearly map the skill gap concept and develop a validated approach for measuring it, we suggest empirical data collection to understand how skill gaps are perceived by different stakeholders. In addition, skill gaps are often assessed and judged using multiple-choice questionnaires rather than proficiency and performance tests or observations. Tested and validated approaches are needed to explore the ideal state of skills and measure skill gaps. Furthermore, combining several approaches—such as observations, analysis of evidence of performance, interviews, multifaceted feedback, case studies, and simulations—might lead to a better understanding of actual skill gaps. It is also essential to research the links between the effectiveness of training approaches and skill gaps from the point of view of different actors, such as education providers, employees, and employers. Furthermore, based on the reviewed literature, skill gaps have mainly been viewed at the macro (i.e., organizational) and meso (i.e., general labor market) levels, with only few studies considering the micro level (i.e., individual employees). Future researchers should measure individuals’ skill gaps to help them set goals and prepare for new working environments. Thus, the development of individual skills should be incorporated into future research. In addition, cross-sectional longitudinal studies should be conducted to identify the short- and long-term effects of training and skill gaps. Future research may also consider the team dimension—the ways in which a team’s skills complement each other, and the skills that contribute to its success. Supplementary data to this article can be found online at https://doi. org/10.1016/j.techfore.2024.123206. CRediT authorship contribution statement Pauliina Rikala: Conceptualization, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. Greta Braun: Conceptualization, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. Miitta J¨ arvinen: Conceptualization, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. Johan Stahre: Conceptualization, Supervision, Writing – original draft, Writing – review & editing. Raija H¨ am¨ al¨ ainen: Conceptualization, Supervision, Writing – original draft, Writing – review & editing. Declaration of competing interest None. Data availability Data will be made available on request. Acknowledgments We are grateful for the valuable feedback we received from five anonymous reviewers. 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(JGIM) 30 (7), 1–32. https://doi.org/10.4018/JGIM.292522. Pauliina Rikala is a postdoctoral researcher at the University of Jyv¨ askyl¨ a, Department of Education. Her multidisciplinary background combines Information Technology, Pedagogy, Social Gerontology, Psychology, and Marketing. Her research interests include digital transformation, learning, and well-being and promoting them in a meaningful and sustainable manner in society and organizations. Greta Braun is a Ph.D. candidate at Chalmers University of Technology, Division of Production Systems. She has a background in Electrical Engineering and Learning and Leadership. Her research focuses on the competencies and skills needed in future industrial work, toward a digitalized, inclusive, and sustainable work environment. She also looks into upskilling and skills matching in human-centered manufacturing projects. Miitta J¨ arvinen works as a Ph.D. student in the Department of Education at the University of Jyv¨ askyl¨ a, Finland. She works in a research program focusing on higher engineering education. Her research deals with the learning experiences and environments of engineering students, engagement in studies and teacher-student interaction in technologyenhanced learning environments. Johan Stahre works as a professor and is head of the Division Production Systems at Chalmers University of Technology. His research interests are human-centric production, the future of work in industry, and upskilling. He is a well-appreciated keynote speaker on national as well as international stages. He is Vice-Chair of the EIT Manufacturing Supervisory Board, and Co-Director of the Swedish production program Produktion2030. Raija H¨ am¨ al¨ ainen works as a professor in the field of technology-enhanced learning at the University of Jyv¨ askyl¨ a, Department of Education. H¨ am¨ al¨ ainen’s research interests include collaboration, interaction and creativity at the professional learning settings. She is a well-recognized keynote speaker on novel methods, an associate editor of Educational Research Review -journal, a profiling area leader in JYU, and WP leader in a Centre of Excellence (InterLearn). P. Rikala et al.