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Article

Fo-HECE: Future-Oriented Higher Education Degree Employability

by
Herbert Salazar
1,
Yuri Lima
1,
Matheus Argôlo
1,*,
Carlos Eduardo Barbosa
1,2,
Alan Lyra
1 and
Jano Souza
1
1
Programa de Engenharia de Sistemas e Computação, Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa em Engenharia, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-914, Brazil
2
Centro de Análises de Sistemas Navais, Marinha do Brasil, Rio de Janeiro, Brazil
*
Author to whom correspondence should be addressed.
Educ. Sci. 2025, 15(9), 1235; https://doi.org/10.3390/educsci15091235
Submission received: 5 July 2025 / Revised: 1 September 2025 / Accepted: 11 September 2025 / Published: 17 September 2025
(This article belongs to the Section Higher Education)
Browse Figure
Versions Notes

Abstract

Work is historically evolving due to technological advancements, posing challenges for individuals and institutions. The actual Fourth Industrial Revolution, marked by AI, biotechnology, and nanotechnology, has rapidly increased these advancements, while the COVID-19 pandemic has anticipated future expected changes in the labor market. In this context, Higher Education Institutions must match their curricula with this dynamic labor market to equip graduates with relevant skills. However, the slow pace of educational transformation, partly due to a lack of data, hinders this alignment. This research introduces Future-oriented Higher Education Degree Employability (Fo-HECE) as an evaluation tool for the adherence of undergraduate degrees to the demands of the labor market in the next decade. We used a novel approach, combining the Operationalization of a social phenomenon with Multi-Criteria Decision-Making, with the participation of nine experts. As a use case, we applied the new approach to assess the future employability of ten undergraduate programs with the most enrolled students from one of the largest Brazilian universities. As a result, the indicators considered most relevant to measure Fo-HECE are Student-to-Job Ratio, Employment Balance, and Wage Premium. The degrees with the highest Fo-HECE grades were Law and Medicine, while Physical Education had the lowest grade. The Fo-HECE approach, as demonstrated in this case study, shows potential applicability to other HEIs and countries, provided that comparable labor market data are available.

1. Introduction

Work has profoundly changed throughout history and will continue to do so in the future at an unprecedented speed (Barbosa et al., 2017). Two centuries ago, it was common to think that automation and new technologies would eliminate many jobs (Autor, 2015). The Luddite movement, which occurred around 1811, where British weavers destroyed machines that automated work in factories because they threatened their jobs, shows that concerns about the future of work have existed for a long time (Jones, 2006).
More than 200 years after the Luddites, the arrival of the so-called Fourth Industrial Revolution (or Industry 4.0), with the rapid advancement of Artificial Intelligence, biotechnology, and nanotechnology, is changing the world of work and raising fresh questions about the future (Barbosa et al., 2017). These changes are significantly impacting the labor market, and research has shown that many jobs are at risk of being automated in the coming decades (Arntz et al., 2016; Autor, 2015; Eloundou et al., 2023; Frey & Osborne, 2017; Gmyrek et al., 2023; Y. Lima et al., 2021).
The constantly changing nature of work requires a counterpart in Higher Education, as one of its primary responsibilities is to train professionals for employment (Gatbonton & Aguinaldo, 2018). However, it is challenging for Higher Education Institutions (HEIs) to anticipate adjustments to their undergraduate degrees to match future demands on the labor market due to the need for more data to guide public and institutional policies (The Economist, 2020). This lack of data can lead to a disconnect between what Higher Education Institutions teach and what the labor market demands from recent graduates (Rakowska & de Juana-Espinosa, 2021).
The difficulty in adjusting undergraduate degrees to match future labor market demands is an old challenge reflected in current indicators. In the United States, those who graduate see the wage premium—the ratio between the average salary of a graduate and the average salary of someone with only a high school education—decrease, causing enrollments in Higher Education to decrease by 8%, indicating that people fear investing in undergraduate degrees that may not yield a financial return in the future (The Economist, 2020). Meanwhile, the Institute for Fiscal Studies calculated that one in five British students would be more financially successful if they had not gone to university, indicating that undergraduate programs often have adverse financial outcomes for students. (Waltmann et al., 2020).
Despite the increase in concerns regarding the future of Higher Education (Raich et al., 2019), the literature still focuses on current employability (Santos et al., 2023), mainly using the existing context to determine the propensity of graduates to acquire and maintain jobs (Afolabi et al., 2019; Caballero et al., 2020; Campos, 2011; Freire-Seoane et al., 2019; Gatbonton & Aguinaldo, 2018; Harvey, 2001; Hillage & Pollard, 1999; Holmes, 2017; Mishra et al., 2016; Sapaat et al., 2011; Thakar et al., 2017; Van Der Heijde & Van Der Heijden, 2006; Zakaria et al., 2014). Although some studies extend the concept of individual employability to other points of view (Alpek & Tesits, 2020; Laranjeiro et al., 2020; Santos et al., 2023), there is a lack of studies forecasting the future employability of actual graduates (Santos et al., 2023).
Considering this uncertainty of actual undergraduate degrees concerning future employability, this research goes beyond the traditional definition of employability. It expands the Higher Education Degree Employability (HECE) concept, defined as “the potential of undergraduate degrees to have graduates capable of obtaining and keeping a qualified job after graduation” (Santos et al., 2023), to a future-oriented approach (Fo-HECE).
The Fo-HECE approach developed in this research aims to help HEIs assess their actual undergraduate degrees’ adherence to future labor market demands. Applying Operationalization and Multi-Criteria Decision-Making (MCDM) methodologies, the Fo-HECE approach brings a new definition of future employability focused on undergraduate degrees and uses the knowledge of specialists in the areas of Education and Futures Research to create a measurable index to assess the employability of undergraduate degrees over the next decade.
Prior employability research clusters into six streams that differ from our proposal along unit of analysis, time horizon, indicators, and methodology. Classic frameworks, such as Hillage and Pollard (1999) and Harvey (2001), conceptualize current individual employability and inform policy, but they do not evaluate degree-level future fit. Degree-level approaches such as HECE (Santos et al., 2023) operationalize employability at the program level, yet remain present-oriented. Lifetime-employability work (Thijssen et al., 2008) adopts a future perspective but focuses on behavioral tendencies of individuals across careers, not on undergraduate programs over a defined horizon. Tracer and machine-learning studies, such as (Maaliw et al., 2022; Mishra et al., 2016; Sapaat et al., 2011; Thakar et al., 2017), predict short-run graduate outcomes at the individual level. Regional or sectoral indices (Alpek & Tesits, 2020; Firpo et al., 2016; López-Miguens et al., 2021; Michavila et al., 2015) assess systems, not degrees. Finally, occupation-level automation studies (Frey & Osborne, 2017) quantify technological risk but are not integrated into a degree-level readiness index. Fo-HECE is distinct in (i) targeting the degree as the unit of analysis, (ii) adopting a 10-year prospective horizon, (iii) combining labor-market structure, alignment, and technology-risk indicators sourced from administrative datasets (RAIS/CAGED, Census), and (iv) weighting indicators through Operationalization + MCDM with domain experts to yield an actionable index for HEI portfolio decisions.
This research also brings a use case of the Fo-HECE approach, evaluating the future employability of ten undergraduate degrees with the most enrolled students in one of the most prominent universities from Brazil, a continental country with a significant mismatch between graduates and the labor market. By the end of the first semester of 2020, for example, 40% (525,000) of Brazilian young people (22–25 years) with Higher Education did not have a qualified job, working in occupations that did not require graduation (B. Lima & Gerbelli, 2020). On the other hand, half of Brazilian industries said they had problems with a lack of qualified labor (CNI, 2020), and more than 70% of recruiters said that candidates did not have the minimum knowledge for the offered job vacancies (Easy2Recruit & GazzConecta, 2020).
This work is organized as follows. Section 2 describes the methodology used in the research. It describes the Operationalization concept and its five stages, followed by a brief explanation of the MCDM techniques used to create our future employability index. Section 3 details the Fo-HECE approach by showing how Operationalization and MCDM were used to create a measurable index of future employability. Section 4 shows the Fo-HECE application for ten undergraduate degrees at the Federal University of Rio de Janeiro. Section 5 discusses the results of the Fo-HECE use case, providing an overview of the future employability of the most popular undergraduate degrees at one of Brazil’s most prominent universities. Section 6 presents some final remarks, future work, and research limitations.

2. Materials and Methods

This section is divided into two parts: the first one is about the Operationalization methodology, used to transform an abstract concept, in our case “future-oriented employability,” into a measurable index; and the second one is about MCDM techniques used to leverage the opinion of a group of experts to weight the different variables used to measure Fo-HECE.

2.1. Operationalization

Operationalization is the process of turning a social phenomenon (like future employability) into something you can define and measure in practice (Harvey, 2001; Harvey & MacDonald, 1993), resulting in a type of measurement that contains several indicators used to summarize a general concept into a measurable index (DeCarlo, 2018).
According to Harvey (2001), we need to follow the steps to operationalize a social phenomenon:
  • Define the theoretical concept to be adopted.
  • Break down the theoretical concept into dimensions that cover its meaning.
  • Identify a set of indicators for each dimension.
  • Build information collection instruments for each indicator.
  • Choose the final set of indicators to compose the measurable index: a multidimensional set of indicators, a list of indicators, or a single indicator.
In the first stage of the Operationalization process, we have to choose the definition of the theoretical concept of the phenomenon we are operationalizing. This stage demands a previous analysis of several definitions in the literature, where an existing definition is chosen, or a new definition is created, depending on which point of view we want to measure (Alpek & Tesits, 2020; DeCarlo, 2018; Harvey, 2001; Harvey & MacDonald, 1993; Van Der Heijde & Van Der Heijden, 2006).
The second stage of Operationalization involves breaking the theoretical concept into different dimensions that encompass different aspects of the concept. The measurement of the operationalized social phenomenon results from measuring the various dimensions of the theoretical concept adopted to explain it.
The third stage of Operationalization involves selecting indicators to measure that dimension. Indicators are individual or collective variables describing the dimensions of the theoretical concept (DeCarlo, 2018).
The fourth stage of Operationalization is to determine how to obtain the necessary data for each of the indicators selected in the previous stage and to build a strategy for collecting this information.
The fifth and last stage of Operationalization involves selecting the indicators that will finally be part of the measurement index of the studied social phenomenon. This choice must be made considering the possibility of data collection defined in the previous stage and the relevance of each indicator to create a measurable index (Harvey, 2001; Harvey & MacDonald, 1993; Santos et al., 2023).
Several studies have applied Operationalization to measure employability (Edirisinghe & Randika, 2019; Harvey, 2001; Sanchis, 2017; Santos et al., 2023; Sumanasiri et al., 2015; Van Der Heijde & Van Der Heijden, 2006). However, while some research has attempted to operationalize future employability, their analytical perspective has been centered primarily on the individual (Fajaryati et al., 2020; Gunawan et al., 2019; Helyer & Lee, 2014; Huang et al., 2014). We found studies that examine how individuals perceive their future employability (Gunawan et al., 2019; Huang et al., 2014), and studies that determine which skills are necessary for an individual to remain employable in the future (Fajaryati et al., 2020; Helyer & Lee, 2014). Although the individual-centered analysis of future employability is essential, there remains a gap in studies that examine it from the perspective of undergraduate degrees, operationalizing it in terms of how such degrees prepare students for the evolving demands of the labor market.
Building on the valuable contributions of prior research that has operationalized employability, we incorporated their insights rather than conducting a standard Operationalization process. We used the results from these related works, with the addition of other studies that identify factors that impact employability, to Operationalize the Fo-HECE concept. First, we defined the concept of future employability of Higher Education degrees. Next, instead of creating new dimensions and indicators from scratch in steps 2 and 3, we analyzed the literature and identified possible dimensions and indicators applicable to the concept first defined. In step 4, we analyzed the dimensions and indicators selected in previous stages, modifying existing ones and creating others to fit the new concept defined in step 1. Finally, to create the measurable index, we used a decision-making approach called Multi-Criteria Decision-Making, explained in detail below, to determine each indicator’s weight on the future-based employability index.

2.2. Multi-Criteria Decision-Making

Multi-criteria decision-making (MCDM) is a decision-making approach that considers multiple criteria or factors when evaluating and selecting alternatives (Taherdoost & Madanchian, 2023). It aims to provide a systematic and structured framework for decision-makers to make informed choices based on various criteria rather than relying on a single criterion. MCDM has gained significant attention and relevance in various fields due to its ability to handle complex decision problems involving multiple objectives and criteria (Hom et al., 2016).
Four ranking methods are commonly used in MCDM to facilitate decision-making processes: Rank Sum, Rank Exponential, Rank Rec, and Point Allocation. Each method has its advantages and limitations, and the choice of method depends on the specific requirements and preferences of the decision-maker. By understanding and applying these ranking methods, decision-makers can make informed and rational decisions based on multiple criteria.
The Rank Sum method is a multi-criteria decision method that calculates the sum of ranks for each alternative based on their performance on different criteria. The option with the lowest sum of ranks is considered the best choice (Chabuk et al., 2017). This method is straightforward, making it a popular choice in decision-making processes.
The Rank Exponential method is another multi-criteria decision method that assigns exponential weights to the ranks of alternatives. The weights are determined based on the importance of each criterion. The best choice is the alternative with the highest weighted sum of ranks (Mazurek & Strzałka, 2022). This method allows decision-makers to emphasize specific criteria by assigning higher weights to them.
The Rank Rec method is a variation in the Rank Sum method that considers the reciprocal of the ranks. Instead of summing the ranks, the reciprocal of each rank is calculated for each alternative. The alternative with the highest sum of reciprocal ranks is considered the best choice (Mazurek & Strzałka, 2022). This method provides a different perspective on ranking alternatives by considering the inverse of the ranks.
The Point Allocation method is a multi-criteria decision method that assigns points to each alternative based on its performance on different criteria. The points are allocated according to predefined rules or weights assigned to each criterion. The alternative with the highest total points is considered the best choice (Mazurek & Strzałka, 2022). This method allows decision-makers to allocate points based on the relative importance of each criterion.

3. Fo-HECE Approach

The Fo-HECE approach is an operationalized future employability phenomenon from the point of view of the undergraduate degree. This approach is applicable in different contexts, allowing the analysis of the future employability of various HEIs’ degrees in other locations worldwide. We conducted the operationalization process by analyzing existing literature about employability definitions and previous operationalization applications. The aim was to ensure the robustness and reliability of the Fo-HECE approach.
The initial step involved an analysis of definitions in existing literature, where we examined diverse perspectives on employability, considering the varied viewpoints. This critical analysis enabled us to pinpoint the core theoretical concepts essential for the first step of Operationalization. The definition of employability can differ depending on the perspective being analyzed. Still, the literature primarily focuses on the analysis of individual employability, although some studies focus on other perspectives (Santos et al., 2023).
We can encounter many definitions of individual employability in the literature, like “the personal adequacy to perform a job” (Thijssen et al., 2008) or “the ability to get a satisfying job” (Harvey, 2001). Nonetheless, one of the most used definitions of individual employability is the capability to acquire, maintain a job, or acquire a new one if needed (Hillage & Pollard, 1999). Expanding this concept, Santos et al. (2023) operationalized the employability concept to an undergraduate degree point of view, defining Higher Education Degree Employability as “the potential of the degree to have graduates capable of obtaining a first qualified job, keeping this job, or obtaining a new qualified job, if necessary.”
However, more than these definitions are needed to explore the changing nature of the labor market. Thijssen et al. (2008) created a novel definition to fill this gap of future employability from an individual point of view: lifetime employability. They defined lifetime employability as the behavioral tendency to acquire, maintain, and use qualifications to cope with a changing labor market during all career stages (Thijssen et al., 2008).
However, there still needs to be definitions of future employability from the point of view of an undergraduate degree. Higher education will influence the employability of recent graduates with limited professional experience, reflecting on the first few years after graduation (Smith et al., 2000). Therefore, when assessing the future employability of undergraduate programs, it makes sense to focus on a limited timeframe rather than lifetime employability. Hence, we expand the HECE concept and define Fo-HECE as the degree’s potential for graduates to obtain and retain qualified jobs in the next decade, leveraging the skills, abilities, and knowledge acquired during their undergraduate studies without the need for requalification.
Subsequently, we analyzed previous literature to break the theoretical concept into dimensions and indicators, adapting and creating new dimensions to embody the different facets of Fo-HECE. The dimensions and indicators of Fo-HECE are shown in Table 1.
Afterward, we outlined strategies for data collection, adapting the tool created by Santos et al. (2023) to analyze HECE to extract information from the diverse databases based on the new requirements presented by Fo-HECE. The databases used for data extraction will be discussed in the next section, where we will detail the Fo-HECE application in some use cases.
Finally, we used the MCDM approach to gain an insight into how much impact each indicator can have on occupations that graduates may pursue in the next decade with the knowledge acquired during their undergraduate studies. These indicators were presented to a group of specialists previously selected according to their recognized academic and professional expertise in higher education management, employability research, and university administration. The panel included experts with backgrounds in STEM as well as in education and social sciences, in order to combine technical and institutional perspectives. Nevertheless, we acknowledge that the panel was somewhat skewed toward STEM fields; this constitutes a limitation of the study, and future applications of the Fo-HECE approach should seek greater disciplinary balance to mitigate potential biases in expert judgment. We conducted structured interviews with these specialists to rank the indicators. With the indicators ranked, we could use Rank Sum, Rank Exp, and Rank Rec methods to define the weight of each indicator.
The interviews were all conducted remotely via videoconferencing. The first question asked was used to define the interviewees’ background, presented in Table 2. Then, a brief presentation of the research was performed to provide context and explain what would be expected from the interviewee at the end of the presentation: to fill out a questionnaire ranking the 14 factors related to future employability. Here, we underline the importance of giving the interviewee a detailed explanation of employability concepts and, most importantly, how it differs from the Fo-HECE concept. The questionnaire can be found in Appendix A.

4. Fo-HECE Application

We selected the Federal University of Rio de Janeiro for our application of the Fo-HECE approach. This step consisted of a case study designed to illustrate the applicability of the model in a real-world setting, rather than a validation or empirical test of the approach. The university was founded in 1792 with the Polytechnical School, the seventh school of Engineering in the world and the oldest in the Americas. As of 2018, it ranks as the third-best university in Brazil and 7th in Latin America according to the QS Rankings. The institution offers 172 undergraduate programs, has 4242 faculty members, and approximately 53,500 undergraduates.
To test the Fo-HECE approach, we selected the ten degrees with the most first-year students, which also figured in the list of top degrees with the most last-year students (Table 3). Together, considering first and last-year students, these degrees have a total of 3772 students.
The data to evaluate the model in the Brazilian scenario comes from three primary sources: RAIS, CAGED, and the Higher Education Census. The Annual Report of Social Information (RAIS, in Portuguese) is a yearly labor market data-gathering tool that the Brazilian government uses. Companies must provide details about their formal workforce through this mechanism, including data about workers’ age, occupation, wage, race, and work city. The General Register of Employees and Unemployed (CAGED, in Portuguese) is a significant monthly national formal labor market information source. It was established as a tool for monitoring and overseeing workers’ hiring and dismissal processes governed by the Consolidated Labor Laws, aiming to assist the unemployed and support measures against unemployment. CAGED is also mandatory for companies and has a similar structure to the RAIS database, albeit smaller due to its monthly frequency. Both databases, RAIS and CAGED, are under the responsibility of the Ministry of Labor.
Finally, the Higher Education Census, conducted annually by the Brazilian Ministry of Education, is the country’s most comprehensive research tool on Higher Education institutions offering undergraduate and specific training degrees and their students and faculty. The census collects information about the institutions’ infrastructure, available seats, candidates, enrollments, graduates, and teachers. Its purpose is to provide reliable statistics to understand and monitor Brazil’s Higher Education system, aid the Ministry of Education in evaluation and expansion efforts, offer data for public policy formulation, and support institutional managers, researchers, experts, and international bodies.
The three primary databases were not used in isolation but combined according to the nature of each indicator. For example, the Student-to-Job Ratio required data from the Higher Education Census (number of students enrolled) and CAGED (admissions and dismissals) to calculate the balance between student supply and job creation. Similarly, Labor Market Alignment integrates Census data on graduates with CAGED data on employment flows (admissions and dismissals), allowing us to assess whether the labor market absorbs both new graduates and experienced workers. Indicators such as Average Workforce Age, Average Age of New Hires, Employment Duration, Wage, and Education Level of Occupations were derived from RAIS, which provides detailed individual-level records of the formal workforce. Other indicators, such as Degree Ranking and Teaching Staff Level, came exclusively from the Census, while Employment Balance and Average Hiring Wage were calculated directly from CAGED. Finally, to complement these sources, we included the Automation Probability indicator, obtained from an external study that measured the automation probability of Brazilian occupations (Y. Lima et al., 2021).
After conducting structured interviews with the selected experts, the indicators of Table 1 were ranked according to their perceived impact on future employability. As described earlier, the interviews followed the MCDM approach, in which specialists compared and ordered the indicators. These rankings were then processed using four different methods (Rank Sum, Rank Exponential, Rank Rec, and Point Allocation) to calculate the weight of each indicator. The MCDM methods use these weights to generate a single composite index, scaled from 0 to 1, where higher values indicate that a degree has stronger future employability, since the most relevant indicators are performing well. The results, illustrated in Figure 1, show that the indicators considered more relevant are the Student-to-Job Ratio (1), Employment Balance (11), and Wage Premium (13).
The final Fo-HECE index is presented in Table 4, where we see the ten degrees selected for the case study with their enrollments in 2021. Since the Fo-HECE index ranges from 0 to 1, its interpretation is straightforward: higher values indicate stronger future employability, as they reflect that the most relevant indicators are well evaluated. To obtain these results, all indicators were first presented to the specialists, who were asked to rank them from the most to the least important. After that, they were asked to freely distribute a total of 100 points among the indicators, assigning more points to those considered more relevant and fewer points to the less relevant ones. The information collected through these two tasks was then processed using the Rank Sum, Rank Exponential, Rank Reciprocal, and Point Allocation methods. Since each of these methods produces values on different scales, the results were normalized so that the weights always summed to one, allowing comparisons across the different methods. Once the weights were established, the raw values of each indicator were discretized into categories, ensuring that they could be compared on the same scale. After all indicators were discretized in this way, the weights from each MCDM method were applied to produce the composite Fo-HECE index. The final results show that the index ranges from 0 to 1, where higher values indicate stronger future employability, as they reflect that the most relevant indicators are well evaluated.
To clarify this process, let us consider the example of the indicator Automation Probability. This indicator was discretized into four levels: values higher than 75% were assigned 0 (very unfavorable for future employability), values between 30% and 75% were assigned 0.5 (moderate impact), and values below 30% were assigned 1 (favorable, since the risk of automation is low). For example, in the case of the Law degree, the probability of automation for related occupations was estimated at 5%, which corresponds to the best discretized value, equal to 1. To incorporate this indicator into the final Fo-HECE index, this value of 1 was multiplied by the specific weight previously calculated for “Automation Probability” using each of the MCDM methods. For example, under the Rank Sum method, the weight of Automation Probability was 0.07, so the contribution of this indicator to the Law degree’s index was 1 × 0.07 = 0.07. The same procedure was repeated with the weights from the Rank Exponential (0.079), Rank Reciprocal (0.103), and Point Allocation (0.069) methods, resulting in contributions of 0.079, 0.103, and 0.069, respectively. These weighted contributions were then added with the values of all other indicators to produce the overall Fo-HECE score for the Law degree.
The ranking for all degrees tends to show an increase between ranking methodologies. The degree’s highest value is 0.75 for Law and Medicine, both in the Point Allocation method. Conversely, using the Rank Sum method, the lowest value is 0.42 for the Physical Education degree. Considering that the values can range from 0 to 1, the values for the study case show that the degrees are in a regular to good position regarding future employability. It must be taken into account that the data comes from 2021, when the Brazilian and world economies were still recovering from the COVID-19 pandemic.

5. Discussion

Unlike previous employability frameworks (Harvey, 2001; Santos et al., 2023), Fo-HECE uniquely incorporates automation probability and student-to-job ratios, allowing a novel employability analysis of Higher Education degrees. By analyzing the literature, Fo-HECE uses related studies to increase its robustness and reliability, using previously validated dimensions and indicators adapted for a future-oriented approach. Fo-HECE focuses on measuring future employability, considering the technological and social changes happening today that will shape the job market in the next decade. In contrast, existing literature primarily examines the current state of graduates’ employability (Santos et al., 2023).
However, the main contribution of the Fo-HECE approach lies in its attempt to provide a structured index of future employability for undergraduate degrees. By combining Operationalization and MCDM, the study incorporates expert judgment to weigh multiple indicators that are theoretically linked to future labor market performance. This integration offers a systematic framework to explore how different dimensions of employability may interact and provides an initial pathway for higher education institutions to reflect on the long-term alignment of their programs with labor market demands.
Most studies on employability provide insights into this phenomenon and the factors that impact it (Di Fabio, 2017; Finch et al., 2013; Firpo et al., 2016; Fugate & Kinicki, 2008; Harry et al., 2018; Jiang et al., 2023; López-Miguens et al., 2021; Maaliw et al., 2022; Michavila et al., 2015; Mpia et al., 2022; Neroorkar, 2022; Panityakul et al., 2022; Ye & Jiang, 2014). Some studies introduce methods to measure employability, focusing on individual employability (Harvey, 2001; Mishra et al., 2016; Sapaat et al., 2011; Thakar et al., 2017; Van Der Heijde & Van Der Heijden, 2006). Few studies extend this measurement to other perspectives (Alpek & Tesits, 2020; Santos et al., 2023). However, none have explicitly addressed the challenge of systematically assessing future employability at the degree level. In this sense, Fo-HECE should be viewed as a promising conceptual and exploratory step. At this stage, the model does not yet include predictive or longitudinal validation. For example, testing whether past Fo-HECE scores would have anticipated actual graduate outcomes. Incorporating such analyses in future research would be essential to strengthen the theoretical and methodological grounding of the approach and to support its potential as a predictive tool.
By focusing on the future and employing robust measurement methods, this study provides valuable insights that can shape how HEIs prepare their students for the challenges of the ever-changing professional world. However, it is crucial to transition from theoretical concepts and leverage real-world data to effectively grasp the dynamics of the present and future labor markets to help HEIs prepare students. Unfortunately, data concerning HEIs and the job market may be challenging to obtain in some countries. Even if this data exists, its availability, accuracy, completeness, currency, and many other quality factors may need to be included. Therefore, such limitations restrict the direct replication of Fo-HECE without prior adaptations.
In Brazil, data about the formal labor market and HEIs are available from many public sources. The data gathered for this study has good quality and is up to date, as the government agencies responsible for data acquisition have been carrying out this job for many years, usually under regulations and legal constraints, and have developed detailed and robust methodologies concerning the way data is collected, processed, stored and made available, and strongly rely on the data they gather to make decisions and guide public policies. Here, we can highlight the Brazilian Ministry of Labor and Employment and the Brazilian Ministry of Education and Culture.
Based on these official data sources, Fo-HECE brings the discussion about employability to a more centralized position in the management of HEIs’ degree offerings. Since finding a good job in terms of salary is a preoccupation for most students, Fo-HECE can help both the Government and HEIs in different ways.
The Ministry of Education and Culture oversees the Higher Education degrees offered in Brazil. Measures such as Degree Ranking and Teaching Staff Level that we used to create the Fo-HECE approach are made available by the Ministry during the degrees’ evaluations. Still, it would be helpful to the Brazilian HEI system if measures regarding the labor market help identify which degrees have better employability prospects for students.
Intelligent use of data that is already available would allow more innovative monitoring of HEIs portfolio of degrees in terms of the capability that they have to deliver education that provides students with a better prospect in terms of career development while justifying the investments that students, in the case of private HEIs, or the public sector, in the case of public HEIs, make in the institutions.
Besides serving as an essential monitoring instrument for the government, Fo-HECE also has the potential to improve HEIs’ management by allowing the usage of data about employability to support decision-making in processes such as portfolio management, developing new degrees, or even stopping the opening of new classes for specific low-performing degrees.
If institutions take this critical step, they can benefit from this approach by bringing employability discussion to the table while attracting new students or motivating existing ones to stay in the degree; preoccupations that every degree manager, even more in private HEIs, has on a day-to-day basis.

6. Conclusions

Concerns about the future of employment are not new. Two centuries ago, the idea of new technologies replacing the human workforce led a group of textile workers to destroy textile machinery—a movement known as the Luddites (Jones, 2006). Nowadays, the Fourth Industrial Revolution—also known as Industry 4.0—brings new questions about the future with the advance of Artificial Intelligence, biotechnology, and nanotechnology, accelerating changes and bringing new concerns about technological unemployment, since some professions are prone to automation and losing job vacancies day by day. The COVID-19 pandemic made unemployment concerns more critical. Unemployment rates during COVID-19 were comparable to the Great Recession, accelerating the changes in the labor market (Coibion et al., 2020).
In this context of long-term career disappearance, workers tend to qualify themselves to increase their employability. However, people only spend time and money on education in a recession if the new education converts into an increase in income (The Economist, 2020).
Therefore, synergy with the labor market demands is crucial for higher education institutions’ relevance in a world with new, cheaper, and faster certifications that propose qualifications that are aligned with labor market demands. As such, HEIs need to develop and implement strategies to maintain their relevance over time, including actions to increase their students’ employability.
However, assessing the actual contribution of HEIs to employability, especially considering the future of work, is an even more significant challenge. Several contextual factors that compose the context where HEIs operate and their alums are expected to work can impact future employability, including the current and future state of the economy, localization, institutions, workers, organizations, and others.
This study aimed to broaden the traditional definition of employability by introducing the concept of Fo-HECE. Fo-HECE allows HEIs to evaluate the relevance of their undergraduate programs to the future by using a comprehensive framework that considers both historical data and future projections. The Fo-HECE approach can help HEIs worldwide to implement changes that will enhance their future employability.
The relevance of this research becomes evident when we analyze the literature. Although there is much research on employability, only some studies examine this social phenomenon from a perspective other than individual, leaving HEIs with little or no resources to take action that could impact the future employability of their graduates.
With the development of the Fo-HECE approach and its methodology, HEI managers can assess the likelihood of their degrees remaining relevant in the next decade, enabling them to make data-driven, future-changing decisions.
To demonstrate the applicability of our index, we applied it to assess the future employability of the top ten undergraduate degrees with the most enrolled students at one of Brazil’s most prominent universities. This empirical analysis demonstrated Fo-HECE’s applicability in real-world scenarios, emphasizing its potential to guide strategic decisions in HEIs. Nonetheless, future research should include formal statistical validation procedures—such as reliability tests, construct validity, or longitudinal comparisons between Fo-HECE scores and actual graduate labor market outcomes—to further strengthen the robustness and generalizability of the index.
The Fo-HECE concept could be adapted in many countries, provided that adequate labor market and higher education data are available. Though the measurement of Higher Education degree employability depends on good quality data about the local job markets, the country’s Higher Education institutions, and the degrees themselves, it would be possible to adapt or even use alternative indicators if the country cannot match the exact indicators we used.
A valuable next step would be to validate the Fo-HECE approach empirically. Future empirical testing of the Fo-HECE approach could involve longitudinal validation, comparing Fo-HECE scores with the actual labor market trajectories of graduates over the next decade to assess whether higher-scoring degrees indeed lead to stronger employability outcomes. The model could also be replicated across other Higher Education Institutions and national contexts to test its robustness and generalizability. Moreover, combining the current use of administrative datasets with primary data collected from graduates and employers could further strengthen the robustness of the index.
Some limitations of this work are yet to be explored. Employability measurements depend on data that may not be available or have poor quality. If the indicator used should be changed or adapted in a country, some may need help to match the country’s particularities. The analysis and measurements are restricted to a single country, as there are no internationally standardized indicators for us to compare employability measurements between countries, even when dealing with the same degree.
Another limitation of this study is that we rely solely on secondary data. The Fo-HECE approach could benefit from using primary data collected by specific HEIs that provide insights into their reality. In this case, we envision future work on taking the proposed approach to a more granular level that could make it an even more reliable decision-making tool for HEIs.

Author Contributions

Conceptualization, H.S., Y.L. and M.A.; methodology, Y.L.; validation, H.S., Y.L. and M.A.; formal analysis, H.S., Y.L. and M.A.; investigation, H.S. and M.A.; resources, J.S.; writing—original draft preparation, H.S., Y.L. and M.A.; writing—review and editing, C.E.B. and A.L.; supervision, J.S.; project administration, J.S.; funding acquisition, Y.L. and J.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Prince Mohammad Bin Fahd Center for Futuristic Studies at Prince Mohammad Bin Fahd University, in association with the World Futures Studies Federation, through the Third Futures Research Grant.

Institutional Review Board Statement

As previously informed, our study did not require prior ethical approval due to its nature. Therefore, no protocol number, ethics committee approval, or related documentation is applicable. The relevant guideline from our institution’s ethics committee, which clarifies that this type of study does not require submission for review, has already been provided. Ethical review and approval were waived for this study due to its nature as an anonymous opinion research. According to the CEP/CONEP system, such studies do not require registration or evaluation by an ethics committee, as explicitly stated in its guidelines, which exclude public opinion research with unidentified participants from mandatory review. In line with these institutional norms, the interviews conducted in this study fall under this category, since participants provided their responses anonymously and with consent.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study. Verbal informed consent was secured at the beginning of each interview, following a standardized script available for submission upon request. In addition, a blank written-consent form (without names or signatures) has been prepared to meet MDPI’s requirement for unlimited permission to publish under open-access licensing.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
Fo-HECEFuture-oriented Higher Education Degree Employability
CAGEDGeneral Register of Employees and Unemployed
RAISAnnual Report of Social Information
HEIHigher Education Institution
HECEHigher Education Degree Employability
MCDMMulti-Criteria Decision-Making

Appendix A

This appendix presents the structured interview form used to collect participants’ evaluations of various factors affecting future employability. To improve clarity and facilitate participants’ responses, the form was organized into two distinct parts, each focusing on a different type of judgment task. Table A1 shows a ranking task, where participants ordered 14 factors from most (1) to least (14) important, with no ties allowed. Meanwhile, Table A2 presents a point distribution task, where participants allocated 100 points among the same 14 factors, giving more points to those considered more relevant.
Table A1. Interview Form—Ranking.
Table A1. Interview Form—Ranking.
Ranking Form
Determine the relative positions of the 14 factors based on their importance regarding future employability. The most important factor will occupy position number 1 (first position), and the least important factor will occupy position 14 (last position). More important factors will occupy higher positions than less important factors, and two factors should always occupy distinct positions.
FactorRanking
DEGREE RANKING—Official degree ranking.
EMPLOYMENT MARKET ADHERENCE—Comparison between the quantity of qualified unemployed workers and the rate of new worker admissions.
AVERAGE EMPLOYMENT DURATION—Mean length of employment for workers in occupations related to the degree.
FACULTY LEVEL—Level of the institution’s teaching staff.
AVERAGE HIRING SALARY—Average salary of workers who are being admitted.
AVERAGE AGE OF WORKERS—Mean age of individuals employed in occupations related to the degree.
EDUCATIONAL LEVEL—Educational level of occupations related to the degree.
AVERAGE LAYOFF DURATION—Mean length of employment for workers who are being laid off.
AVERAGE AGE OF HIRES—Mean age of individuals who are being hired in occupations related to the degree.
NUMBER OF STUDENTS VERSUS NUMBER OF JOBS—Quantity of students in a region compared to the number of jobs being generated.
PROBABILITY OF AUTOMATION—Likelihood of automation for professions related to the degree.
AVERAGE SALARY—Mean salary of employed workers.
EMPLOYMENT BALANCE—Employment balance of graduates in the region.
WAGE PREMIUM—Difference in average salary between graduates and workers with a high school education.
Table A2. Interview Form—Points Distribution.
Table A2. Interview Form—Points Distribution.
Points Distribution Form
Distribute a total of 100 points among the 14 factors that influence future employability. Factors considered more important should receive more points than those considered less important. Any factor can receive a quantity of points between 0 and the remaining total of undistributed points.
FactorPoints
DEGREE RANKING—Official degree ranking.
EMPLOYMENT MARKET ADHERENCE—Comparison between the quantity of qualified unemployed workers and the rate of new worker admissions.
AVERAGE EMPLOYMENT DURATION—Mean length of employment for workers in occupations related to the degree.
FACULTY LEVEL—Level of the institution’s teaching staff.
AVERAGE HIRING SALARY—Average salary of workers who are being admitted.
AVERAGE AGE OF WORKERS—Mean age of individuals employed in occupations related to the degree.
EDUCATIONAL LEVEL—Educational level of occupations related to the degree.
AVERAGE LAYOFF DURATION—Mean length of employment for workers who are being laid off.
AVERAGE AGE OF HIRES—Mean age of individuals who are being hired in occupations related to the degree.
NUMBER OF STUDENTS VERSUS NUMBER OF JOBS—Quantity of students in a region compared to the number of jobs being generated.
PROBABILITY OF AUTOMATION—Likelihood of automation for professions related to the degree.
AVERAGE SALARY—Mean salary of employed workers.
EMPLOYMENT BALANCE—Employment balance of graduates in the region.
WAGE PREMIUM—Difference in average salary between graduates and workers with a high school education.

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Education 15 01235 g001
Figure 1. Weights of the Fo-HECE indicators calculated with the four selected MCDM methods.
Figure 1. Weights of the Fo-HECE indicators calculated with the four selected MCDM methods.
Education 15 01235 g001
Table 1. List of selected dimensions and indicators.
Table 1. List of selected dimensions and indicators.
Dimension#IndicatorDescription
Socio-Demographic1Student-to-Job RatioThe ratio between the number of students in an undergraduate degree in a region and the number of created jobs.
2Average Workforce AgeThe average age of individuals employed in occupations related to the degree.
3Average Age of New HiresThe average age of individuals being hired in occupations related to the degree.
Work Experience4Average Employment DurationThe average duration of employment for workers in occupations related to the degree.
5Employment Duration of Dismissed WorkersThe average duration of employment for dismissed workers.
Education-Job Alignment6Labor Market AlignmentThe comparative analysis between the number of unemployed qualified workers and the rate of new worker admissions.
7Education Level of
Occupations		The average education level of workers in occupations related to a degree.
HEI’s Quality/Reputation8Degree RankingRelevant authorities provide the official ranking of the degree.
9Teaching Staff LevelThe qualification level of teaching staff at the HEI.
Labor Market Context10Average WageThe average wage of employed workers.
11Employment BalanceThe balance of created and destroyed jobs in occupations related to the degree.
12Average Hiring WageThe average wage of hired workers.
13Wage PremiumThe difference between the mean wage of graduate workers and those of workers with high school education.
14Automation ProbabilityThe probability of automation for occupations related to the degree.
Table 2. Description of the interview participants.
Table 2. Description of the interview participants.
NumberDescriptionAcademic Background
1Professor, works with the university administration researcher.Chemistry, Biotechnology
2Professor, works with the university administration.Pharmacy, Biophysics
3Professor, undergraduate program coordinator, works with the university administration.Pharmacy, Biology, Medicine,
Biotechnology
4University dean.Economy
5Professor.Computer Science
6Works with university administration.Chemistry, Pharmacy
7Works with the university administration, and researcher.Economy
8Researcher.Education, Sociology
9Professor, undergraduate program coordinator, and researcher.Education, Sociology, Anthropology
Table 3. List of selected degrees with the number of first/last-year students in 2021 (Source: Higher Education Census).
Table 3. List of selected degrees with the number of first/last-year students in 2021 (Source: Higher Education Census).
DegreeFirst-Year StudentsLast-Year Students
Law526440
Pharmacy322115
Architecture and Urban Planning243130
Economics20799
Medicine202167
Physical Education (Teacher Training)20175
Accounting18956
Psychology185118
Portuguese (Teacher Training)18355
Social Work18178
Total24391333
Table 4. Fo-HECE index grades for each degree, calculated using different methods.
Table 4. Fo-HECE index grades for each degree, calculated using different methods.
DegreeEnrollmentsFo-HECE Grade
Rank SumRank ExpRank RecPoint
Law5260.700.710.720.75
Pharmacy3220.540.540.530.59
Architecture and Urbanism2430.630.640.660.67
Economics2070.650.650.650.70
Medicine2020.690.710.720.75
Physical Education2010.420.420.450.44
Accounting1890.680.690.680.73
Psychology1850.640.640.650.68
Portuguese Letters1830.520.520.560.56
Social Services1810.660.670.680.70
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Salazar, H.; Lima, Y.; Argôlo, M.; Barbosa, C.E.; Lyra, A.; Souza, J. Fo-HECE: Future-Oriented Higher Education Degree Employability. Educ. Sci. 2025, 15, 1235. https://doi.org/10.3390/educsci15091235

AMA Style

Salazar H, Lima Y, Argôlo M, Barbosa CE, Lyra A, Souza J. Fo-HECE: Future-Oriented Higher Education Degree Employability. Education Sciences. 2025; 15(9):1235. https://doi.org/10.3390/educsci15091235

Chicago/Turabian Style

Salazar, Herbert, Yuri Lima, Matheus Argôlo, Carlos Eduardo Barbosa, Alan Lyra, and Jano Souza. 2025. "Fo-HECE: Future-Oriented Higher Education Degree Employability" Education Sciences 15, no. 9: 1235. https://doi.org/10.3390/educsci15091235

APA Style

Salazar, H., Lima, Y., Argôlo, M., Barbosa, C. E., Lyra, A., & Souza, J. (2025). Fo-HECE: Future-Oriented Higher Education Degree Employability. Education Sciences, 15(9), 1235. https://doi.org/10.3390/educsci15091235

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