Stakeholders’ involvement in economics and management programs quality assurance

Abstract

Purpose

This study aims to investigate the stakeholders’ (employers and students) involvement in economics and management programs quality assurance in Italian universities from the external audit perspective.

Design/methodology/approach

The research tests if employers are positively associated with the coherence between program objectives and job prospects, and if student involvement is positively associated with student orientation, tutorship and flexibility for specific types of students (differently abled students and working students). Based on data from the Italian Agency for Quality Assurance (ANVUR) in Italian universities, this study selects a sample of 44 bachelor and master university programs.

Findings

When a program coordinator assures coherence between competencies included in the study plan and job prospect, the employers’ involvement in the plan and management of the program increases and becomes more effective. High-quality services regarding student orientation, tutorship and flexibility for specific types of students increase the students’ involvement in university governance.

Originality/value

Findings contribute to literature extending the stakeholder theory in universities, better specifying how employers and students may play a key role in improving the quality assurance of teaching activities.

Citation

Mazza, T. and Azzali, S. (2024), "Stakeholders’ involvement in economics and management programs quality assurance", Quality Assurance in Education, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/QAE-11-2023-0193

1. Introduction

Universities exist to create value for their stakeholders, and value can be measured with different instruments. Value measured with universities’ financial reporting may show only the financial sustainability at university level. A wider and more complete way to measure the value for stakeholders “produced” by universities is through an external agency that periodically audits their quality assurance (QA) system.

In the field of QA processes and stakeholder theory, most prior research investigates stakeholders mainly with qualitative studies. Very few quantitative studies exist that go into detail about specific stakeholders, e.g. employers and students.

This research focuses on employer and student involvement in the university QA processes. This study, analyzing empirical data from the external audit performed by the Italian Agency for QA, aims to contribute to literature on the stakeholder theory in higher education institutions QA processes. We test two hypotheses on whether the stakeholders’ involvement (employers) is positively associated with the coherence between program objectives and job prospects, and whether the stakeholder involvement (students) is positively associated with student orientation, tutorship and flexibility for specific types of students (differently abled and working students).

Results reveal that both hypotheses are confirmed. Coherence between program study plans and job prospects is strictly connected with employer involvement in programming. Employers select graduates considering competencies and skills coherent with their job expectations and necessity. Programs where employers are involved have more job prospect coherence, and more opportunity to learn competencies and skills in line with job characteristics that employers will ask for in the labor market. In addition, efficient student orientation, tutorship and flexibility for specific types of students (e.g. differently abled and working students) help to improve student involvement in university governance.

To increase value and university quality, an implication of our results shows that the program coordinator should ensure coherence between competencies and skills included in the study plan and job prospects. Therefore, employer involvement in the planning and management of the program will increase and become more effective, with several actions that employers offer to the program manager, such as suggestions about the study plan, as well as opportunities offered directly to students, such as internships, seminars and thesis mentoring.

Another implication is that high-quality services such as student orientation, tutorship and flexibility for specific types of students increase student involvement in university governance; the QA is expected to improve, given that students who are actively involved in university governance have a great chance to cocreate high-level services for all students. Our results contribute to stakeholder theory in universities, better specifying how employers and students may play a key role in improving the QA of teaching activities.

These results could have implications for QA models in other countries. Many nations have developed a national system for QA in higher education (Stensaker and Harvey, 2006). Even if the national contexts may vary dramatically, the methods and assumptions embedded in national QA practices are similar, and the US and Western European models dominate (Altbach et al., 2009). Our quantitative results from the Western Europe model can indicate to policymakers how to develop the external QA model at a national level, giving higher importance to the coherence between program study plans and job prospects, as well as to student orientation services, tutorship and diversity to increase values for stakeholders.

2. Literature and hypothesis development

The literature review considers two main research lines: high education institution (HEI) QA, and stakeholder theory.

2.1 High education institutions quality assurance

QA in higher education institutions aims to systematically evaluate educational services and outcomes to ensure they meet QA standards. The objective is to develop academic excellence, improve teaching and educational services and ensure the accountability of these institutions. The QA mechanisms include internal and external processes designed to ensure that educational services and outcomes meet QA standards of various activities, frameworks and stakeholders, focusing on enhancing the quality of higher education.

QA in universities involves a systematic approach that integrates governance, data collection, continuous improvement, curriculum development, resource allocation, communication and stakeholder engagement. Each component plays a vital role in ensuring that the institution meets QA standards and continuously strives to enhance the educational experience and outcomes for its students. University governance coordinates and legitimizes QA activities at every level of hierarchy. Data collection and analysis allow for the development of the curriculum and various programs with relevance and rigor, and resource allocation supports these efforts. Transparent communication and active stakeholder engagement ensure that QA processes are inclusive and responsive to the needs of all parties involved. This integrated approach ensures that QA is not a standalone activity, but a core part of the university’s strategy.

This line of research reveals that the universities’ QA is analyzed considering different theories, and that the key roles of integration and cooperation among all the components of the QA system are stressed. For example, Becket and Brookes (2006) find that the potential for QA enhancement is determined by the way the evaluation is conducted, and subsequent change implemented. Erittu and Turri (2022) examine the characteristics of the QA internal bodies in terms of composition, activities and factors perceived as critical for the success of QA implementation within HEIs. They based the research on Elken and Stensaker (2018) framework: quality management, quality culture and quality work.

Literature shows that the managerial approach needs to be complemented by the ability to integrate different organizational levels of the university. Thus, the university QA processes need to balance the managerial and cultural dimensions. On one hand, a strong commitment from university top management regarding the QA system is necessary; on the other hand, it is essential that all the academic units are involved in these processes. Jarrell and Kirby (2024) study individuals whose full-time position is to directly oversee program QA processes. Their insights underscore the importance of designing program review processes to cater to the individual needs of a diversity of programs and stakeholders.

This study aims to extend this line of research showing the role of employers and student engagement in the university QA system. We choose to link this research to the stakeholder theory, with the employers and students being the stakeholders.

The literature reveals that stakeholders such as students, administrators and employers have different perspectives on the success of QA (Rahnuma, 2020), and these perspectives are influenced by context, individual experiences and the nature of their participation in QA processes (Garcia and Jamias, 2023). Student perception of the success of QA is chiefly influenced by interactions with faculty and the extent to which their needs are met (Law, 2010). While students tend to emphasize the quality of the learning process, including academic facilities as well as teaching and learning experiences, employers place a greater emphasis on outcomes, such as graduates acquiring the requisite skills needed for the workplace (Choy et al., 2017).

2.2 Stakeholder theory

Friedman’s shareholder view of a firm emphasized financial wealth creation for shareholders as the primary purpose of a company. In contrast, stakeholder theory states that companies do not solely need to satisfy their shareholders but should address the needs and interests of all their key stakeholder groups (Freeman, 1984, 1994, 2010; Laplume et al., 2008). In Freeman’s (1984) view, the management must balance the interests of all stakeholders; they are not always equal, but no single group’s interests should dominate to the detriment of others. His theory includes a powerful ethical view as well as a long-term horizon view of the value creation process that characterizes the entity. Mitroff (1983) integrates the stakeholder theory considering organizations as open systems, where direct and indirect stakeholders are an integral part of this system, and with interdependent interests. Donaldson and Preston (1995) enrich the stakeholder theory by emphasizing its ethical foundations, system perspectives and the need of managing stakeholder relationships comprehensively and responsibly. Hart and Milstein (2003) integrate the stakeholder theory with environmental and sustainability concerns, while Conley (2012) focuses on how companies can bridge the gap between corporate governance theories and their practical implication. Together, they emphasize sustainability, corporate governance and effective communication.

The stakeholder theory has been mostly used in strategic management and business ethics literature, with research focusing on large, publicly traded corporations. Studies that employ the stakeholder theory on other organizational types, such as HEIs, are very limited.

The role of stakeholders in the QA of universities is a critical area of study that stresses the relevance of involving key parties in ensuring high-level educational services. Jongbloed et al. (2008) suggest to carefully select stakeholders and identify the “right” degree of differentiation. They also foresee a change towards networked governance and arrangements to ensure accountability along the lines of corporate social responsibility.

Langrafe De Freitas et al. (2020) verify whether the development of improved relationships between HEIs and their stakeholders based on the principles of stakeholder theory creates more value. They find that principles of stakeholder theory e.g. involvement in the decision-making process and alignment of stakeholders’ interests in the strategic planning process, create more value. Bischoff et al. (2018), employing research design with a multiple case study approach, find that stakeholder collaboration and involvement matter in the context of entrepreneur education.

Prior literature analyzes the stakeholder involvement mainly considering them as a whole, while different types of stakeholders need specific investigation. Among the several stakeholders, this work focuses on employers and students. Employers, for economics and management programs, are mainly companies and consultants.

2.3 Employers’ involvement (H1)

Employers and industry partners, as external stakeholders, may positively affect QA in universities by aligning educational outcomes with market needs. The program coordinator, responsible to define the study plan, should fully cooperate with employers so they can contribute to the planning and managing of the program, and affect the competencies and the quality of the student’s skills. The involvement of employers is essential to ensure employment opportunities for graduates that are consistent with the needs of the labor market. We focus on student employability (Römgens et al., 2020) from the perspective of competence development. Employability is the skills and attributes that graduates need to gain to succeed in the workplace (Yorke, 2006).

One of the primary reasons that students invest in higher education is to enhance employment prospects (Saunders and Zuzel, 2010). The importance of university-business cooperation for innovation and education is widely recognized (Rybnicek and Königsgruber, 2019). Arranz et al. (2022) show that fostering active collaboration between the university and the company both in-depth and in breadth facilitates the employment of higher education graduates. Orazbayeva et al. (2019) focus on the question: “How can degree programs be developed in a more flexible manner to better cater for a more diverse community of lifelong learners?” This is considered a priority for university business cooperations in planning for future practice. We aim to extend this strand of research by analyzing the employers’ involvement in the QA processes, considering it a way in which universities create values.

Among the several causes that may affect employer involvement, we focus on the coherence between program objectives that define curricular competencies and job prospects. The coherence with job prospects could explain and justify the employers’ motivation and involvement in planning, managing and controlling. In fact, companies and consultants pay great attention to subjects, curricular competencies and skills that students can study attending the program, and their link to job possibilities. Thus, we aim to test the following hypothesis:

H1.

The employers’ involvement in economics and management course programming is positively associated with the coherence between program objectives and job prospects.

2.4 Student involvement (H2)

ESG (2015) guidelines bear a student-centered approach. Students are at the center of all university teaching activities. Their involvement is essential and can contribute to improving the quality of services. The student as coproducer model is used to characterize the relationship between student and university (McCulloch, 2009). Universities should involve students in their governance because the noninvolvement suggests distance between the student and the educational process and encourages passivity instead of deep student learning. Coproduction requires active engagement with the entire learning process on the part of the student and sees the student as an active participant in the development of knowledge. Coproduction reemphasizes process, and thereby learning, rather than the qualification. To achieve deep learning there should be ongoing engagement between what is being studied and the process of studying.

Lizzio and Wilson (2009) studied the role conceptions and sense of efficacy of student representatives on departmental committees in Australian universities. Student representatives described their role in terms of two broad functions: representing issues initiated by students and responding to proposals initiated by the school or department. Regarding role efficacy, students find some critical issues, the first being role ambiguity; this challenge was most frequently nominated by students in the representative role.

Students often depend on information provided by their peers in making decisions about programs. This can work to the disadvantage of working-class students and first-generation entrants to higher education, who have fewer sources of peer support on which to draw. They may also act based on inaccurate assumptions (Harvey, 2006). Student involvement in university governance considering students as coproducers helps with the access to good, quality information and the ability to use that information (McCulloch, 2009). We thus hypothesize that student orientation and tutorship that improves the access to good, quality information is positively related to this aspect of coproduction and student involvement in the university governance.

The right to high-quality education remains a challenge for students who are differently abled. Moriña et al. (2015) give recommendations for adequate faculty education and training on both matters of the disability itself, and how to respond to the needs derived from it. Shpigelman et al. (2022) interview students with disabilities and emphasize the need to strive for inclusive change in higher education policies and practices. Following the social model of disability, Matthews (2009) argues that universities should avoid the use of medical labels in identifying the learning needs of differently abled students and should make efforts to institute a diversity of inclusive teaching strategies as part of everyday practice. He also discusses an induction activity which seeks to encourage students to disclose additional learning needs to university staff while opening a discussion around differences and diversity with the student cohort as a whole.

Coproduction places an emphasis on community, rather than on individualism (McCulloch, 2009). The emphasis is not on the individual and his or her performance, but rather is on the “collective” experience of the learning group and the importance of the group in encouraging and enhancing the learning of all students. Thus, the attention to flexibility for specific students is positively related to a system where coproduction is encouraged and developed, and student involvement in university governance is high.

Prior literature has widely investigated the benefits and issues of student involvement; however, research is lacking that studies the association between student involvement in the university governance and student orientation, tutorship and flexibility. Thus, we aim to test the following hypothesis:

H2.

Students’ involvement in the university governance is positively related to student orientation, tutorship and flexibility.

3. Research methodology

3.1 Design

To answer at H1 (employers’ involvement), we use the following equations (1) and (2):

(1) Employers’involvement in programming=+ b Coherence between program objectives and job prospects+ b Control variables

(2) Employers’ involvement in management and control=+ b Coherence between program objectives and job prospects+ b Control variables

See Tables 1 and 2 for variable explanation.

We use two dependent variables that separate the employers’ role in the phase of planning from the phase of managing and monitoring:

Employer involvement in programming R3A1 [equation (1) and Table 1] represents all the activities that the program coordinator implements to develop cooperation with the specific employers, aiming to plan a program that perfectly fits their expectation in the labor market. For example, meeting with the employers to inquire about their expectations and later to show the main characteristics of the program.

Employer involvement in the management and control R3D2 [equation (2) and Table 1] represents all the activities that the program coordinator implements to manage and control the program. For example, company seminars, opportunities for students to collaborate with companies and consultants during their master thesis internships, and involvement in the placement days.

We take these data from the autovaluation – valuation – accreditation (AVA) report issued by the Italian Agency for Quality Assurance (ANVUR) related to the Valuation (V), i.e. “periodic evaluation” in ANVUR (2024) website. This report shows detailed scores out of 10 of several indicators (called “Points of attention”) for the university, departments and programs evaluated. The scores for the points of attention follow a standard that is unique and comparable among universities. ANVUR evaluators assign a score and write a detailed descriptive explanation about their evaluation in this report. ANVUR evaluators perform the evaluation in advance with an audit of documents, and later with a site visit with several interviews with professors, students, administration and stakeholders. AVA model evaluates employer involvement in programming and employer involvement in the management and control with two points of attention codified R3A1 and R3D2 (Table 1), respectively. Our two dependent variables are coded R3 because they are at program level. Both are continuous variables of scores out of 10.

To give these scores, at the document level, the evaluators of ANVUR look at the meeting minutes which indicate whether questionnaires were given to employers during programming, or whether employers held presentations or events during some of the courses of the program. The evaluators look at whether employers are part of the steering committee and whether there is consistency with professional outlets.

The independent variable “Coherence between program objectives and job prospects R3A3” [equations (1) and (2) is also evaluated using the continuous variable of scores out of 10 given by ANVUR with the AVA model. The evaluators, at the document level, look at the study plan with the list of all courses, at their individual syllabi and read the program objectives. Then, the evaluators look at the job prospects defined in the SUA-CdS (Annual Single Form of a Course Program]. Finally, they evaluate if students who learn the program objectives are prepared for the jobs defined by the program directors. If evaluators think that there are critical issues that need to be addressed, they ask questions during the site visit such as: “Why are program objectives lacking?” and “How many students have you hired?”

Regarding H2 (students’ involvement), we use the following equation (3):

(3) Student involvement in the University Governance == b Student orientation and tutorship+b Program Flexibility+ b Control variables

The student role in university governance [dependent variable in equation (3)] represents the role that the university assigns to students. It is coded R1A4 because this type of student role is evaluated by AVA model at university level (R1). It is a continuous variable of scores out of 10.

The student role may be an active or passive role:

Universities should ensure that the programs are delivered in a way that encourages students to play an active role in creating the learning process, and that the assessment of students reflects this approach (ESG, 2015).

All universities usually decide to give an active role to students, guaranteeing them representation in the governance: the board of directors, departments, programs and QA. However, this is not always the case, and there are times when universities do not ensure representation, or when student representation is inactive. Evaluators assign a score to this issue.

Among the factors that could be associated with the dependent variable we choose the following independent variables [in equation (3)] student orientation and tutorship R3B1; and program flexibility R3B3, both scores out of 10 in the AVA report at program level (R3).

A high score in student orientation and tutorship means that the universities implement the following activities:

1. Student orientation activities help students choose the right course, specifically, the course that better meets their interests and objectives. These activities may be online or in person, in universities or other higher education institutions and involve teachers or students that already attended university in order for them to explain their experiences.

2. Tutorship activities involve students working to help other students learn specific material and pass their exams. Students selected for these activities usually benefit in terms of money or European Credit Transfer.

3. Program flexibility means, for example:

   • Respect for the diversity of students and their needs, enabling flexible learning paths.

   • Consideration and use of different modes of delivery, where appropriate.

   • Use of a variety of pedagogical methods in a flexible manner (ESG, 2015).

Examples of questions asked during the site visit by the evaluators to students for this point are: “What did the council require you to do?” “Have they prepared you to be student representatives?”

At the document level, the evaluators looked at student council minutes at university level; program council minutes (even if the students do not vote); Internal Evaluation Commission Students-Professors annual report.

3.2 Sample

We investigate a sample of universities with economics and management programs evaluated by ANVUR. The sample period is between 2018 and 2022. Data are from the publicly available ANVUR evaluation reports (ANVUR, 2024), and they include scores and assessments from E to A level (Table 3). A, B and C level means that the university is fully accredited. For D level (conditional judgment), the validity of accreditation is shorter than the ordinary one. E-level means that the university is not accredited and must stop the activity.

The dependent variable of H1 and the independent variable of H2 are measured at program level, thus we restrict the analysis to the university that received an evaluation for programs in the field of economics and management. The selected economics and management programs, including tourism, statistics and public administration, are the following: L-18 economics and management, L-15 tourism management, L-16 management and organization, L-41 statistics, LM-49 tourism management, LM-56 economics, LM-63 public administration, LM-77 economics and management. These programs are both at bachelors (L) and at masters (LM) level. Some universities have been evaluated by a program in economics and management, but they have not been evaluated by the Department of Economics and Management. We restrict the analysis by dropping the evaluation of the department level.

Four universities (Carlo Cattaneo, Libera Università Internazionale degli Studi Sociali Guido Carli, Libera Università Mediterranea and Università degli Studi Internazionali di Roma) have been evaluated by two different Economics and Management programs, given the small university size. We kept both in the sample. Thus, this study includes a sample of 44 programs, both bachelor’s and master’s, of 40 universities, evaluated with the AVA model of ANVUR.

3.3 Descriptive statistics

Table 4 shows descriptive statistics. Scores for the variables R can go from 1 to 10. In our sample, we can see a minimum score of 4 and a maximum score of 9 given by AVA reports to the degree programs. Mean and median show the score of 6–7 is the average in the sample. Higher variability in the scores is for R1A4 and R1A1, where it is possible to see larger differences among universities.

We have very different university sizes in the sample, from five programs (LIUC) to 342 programs (Sapienza, Roma, the largest Italian university). Given this high variability, we use the natural logarithm to measure university size in the regression analysis.

The 41% of professors is tenured, while women are well represented both at professor level (40%) and at student level (58%). In the economics and management programs, it is possible to see that the level of international students and outgoing mobility is quite low, with an average of 5% and 4%, respectively, with programs that also have 0 internationalization, considering these scores.

Our sample reflects the Italian context where the public system prevails (70%). Universities are similarly located in the areas of the north, center and south. These are three dummy variables; we consider the south as a control group in the regressions. We compute the correlation matrix, and we see that there is quite a large correlation (above 45%) between outgoing mobility and some control variables, such as university size, public universities and tenured professors. Thus, to avoid multicollinearity problems, we dropped outgoing mobility from the regression analysis.

However, regressions including outgoing mobility show the same results for our hypothesis. The largest correlation is among public universities and control variables that show how public universities are the largest, as well as those with more tenured professors among the type of professors. This correlation is above 80%. Thus, to avoid multicollinearity problems, we dropped it from the regression analysis. We can also see that the number of programs proxy for university size is highly correlated with R1A1 and the number of tenured professors. However, given the importance of university size, we dropped this variable only in the robustness analysis. Results are confirmed and not driven by this possible multicollinearity.

4. Findings

Tables 5 and 6 show regression results for our hypotheses. Looking at H1, we can see that the employers’ involvement in economics and management course programming, both with the variables used as proxy R3A1 and R3D2, is positively related to coherence between program objectives and job prospects R3A3. There is a positive and significant regression coefficient of 0.139 (p-value = 0.043) with stakeholder involvement in programming and of 0.391 (p-value = 0.065), and with stakeholder involvement in management and control. Employers in the start-up phase of the program are involved in meetings where they evaluate the main characteristics of the course, such as the job prospects and the study plan. They are asked to make suggestions that need to be considered to improve the course.

In the management and control phase of the program, the employers are involved in several different kinds of services, such as:

• seminars that complement the theoretical lessons;

• student internships in the company; and

• meetings with those e responsible for the program to improve the QA system.

This result confirms the importance of the university-business cooperation for innovation and education (Rybnicek and Königsgruber, 2019). The student employability (Römgens et al., 2020; Yorke, 2006; Saunders and Zuzel, 2010) evaluated by employers through the coherence between program objectives and job prospects is a key element that justifies the involvement of this kind of stakeholder in the QA processes at program level.

This is important overall in a context where jobs are rapidly changing and updating the competencies and skills that are required to graduate. Only the continuous comparison between employer expectations and competencies included in the study plan of the program can assure a high level of satisfaction for all parties: students, universities and employers.

Looking at H2, we can see that high-quality student involvement in the university governance is positively related to student orientation and tutorship (coefficient of 0.508, p-value = 0.026) and flexibility for specific students (coefficient of 0.368, p-value = 0.074). Results confirm the model of students as coproducer (McCulloch, 2009), showing that efficient services of student orientation, tutorship and flexibility increase student involvement in university governance.

Specifically, efficient student orientation services help to avoid the issue of student abandonment, which creates great damages and costs for both universities and students; efficient student tutorship services that directly involve students in programs for other students and allow them to earn money and/or credit for their study plans; flexible services for specific categories of students (differently abled and working students) that assure equal access to disadvantaged students or students that need to simultaneously work and study.

Significant control variables also show that the larger presence of female professors improves employer and student involvement. Females have greater awareness of the different stakeholders in firm governance compared to males (Adams et al., 2011), and a better understanding of differences in culture (Davidson and Burke, 2000). This intrinsic female characteristic could facilitate the involvement of different stakeholders in university management.

Comparing locations in Italy, university programs located in the south of Italy have higher employer involvement. In addition, research and teaching quality in university strategy prove to be an important motivator of student involvement.

Robustness analysis, repeating the regression without considering the size of the university to monitor possible multicollinearity issues, shows the same results for our hypotheses.

5. Discussion and conclusion

The university system has positively perceived the inherent potential in the application of an efficient system of centralized and systematic evaluation of universities and study programs, associating a high value in terms of prestige and reputation. Nonetheless, in the first cycle of accreditation, critical aspects of a different order emerged. See the following examples:

• Competencies, independence and experiences of evaluators organized in the Commission of Experts for the Valuation (CEV) are not homogeneous. They work as a group with specific, internal responsibilities and experts with higher competencies and experiences also have the aim to develop assurance quality skills in the other components of the CEV.

• The quality of documents that universities offer to the CEV for the evaluation is different. Universities use different methods in the preparation of documents that are useful for the CEV evaluation. The wide variety of documentation, ranging from an excessive amount of paperwork to completely inaccessible documentation, does not help the CEV in their work and additionally decreases the quality of the audit activities.

• Cooperation of universities differs. Most universities are aware of the great importance of external audits but are also afraid of the accreditation and its level (A, B, C, D, E). This results in university cooperation with the CEV fluctuating. This is another element that may negatively affect the quality of the audit evaluation.

• The AVA model used by ANVUR is constantly changing. AVA has been modified over time and every time the model has improved; however, a perfect model does not exist. As a result, each model has strengths and weaknesses.

Universities produce public value with three main activities:

1. teaching;

2. research; and

3. a third mission.

This research mainly focuses on teaching activities, and the awareness that all university actions are complementary and interdependent. Despite prior critical aspects, results of this research show several benefits connected to stakeholder involvement. Results show that the university stakeholders, the employers and students in this study can create value. Measuring value with the score given by ANVUR for university QA, the study shows that both employers and students receive added value with respect to investment in teaching services offered by universities, in both bachelor’s and master’s programs.

The added value for employers is the availability of graduates who are ready to work efficiently in companies and as consultants. The added value for students is the opportunity to be hired by companies soon after graduation. Finally, the added value for the university is the efficient production of teaching services that satisfy the main stakeholders, the employers and students. Moreover, a way to improve active student involvement in university governance is through orientation services, tutorship and flexibility for specific types of students. These services can create value because they strengthen student involvement in the university governance.

Table 1.

Data from ANVUR

Panel A	Code	Description
Dependent variables
	Program
Y – H1	R3.A.1	Employers’ involvement in programming
Y – H1	R3.D.2	Employers’ involvement in the management and control
Independent variables
	Program
X – H1	R3.A.3	Coherence between program objectives and job prospects
Panel B	Code	Description
Dependent variables
	University
Y – H2	R1.A.4	Students' role in the university governance
Independent variables
	Program
X – H2	R3.B.1	Student orientation and tutorship
X – H2	R3.B.3	Flexibility for specific students (workers and disables)

Source: Authors’ own work

Table 2.

Control variables

Variable	Description
R1.A.1	Research and teaching quality in university’s strategies
University size	Natural logarithm of number of programs
Professors	% tenured professors % female professors
Student diversity	% international students % outgoing mobility % female students
Public	Public or private university
Location	North, center or south

Source: Authors’ own work

Table 3.

Score and assessments at university level

Final score (0–10)	Descriptive assessment
More or equal to 7, 5	A: very positive
From 6, 5 to 7, 5	B: fully satisfactory
From 5, 5 to 6, 5	C: satisfactory
From 4 to 5, 5	D: conditional judgment
Less than 4	E: not satisfactory

Source: Authors’ own work

Table 4.

Descriptive statistics

Variables		Mean	SD	25%	Median	75%	Min	Max
Y – H1	R3.A.1	6.02	0.73	5.50	6.00	7.00	5.00	7.00
Y – H1	R3.D.2	6.39	0.89	6.00	6.00	7.00	4.00	9.00
X – H1	R3.A.3	6.25	0.87	6.00	6.00	7.00	4.00	8.00
Y – H2	R1.A.4	6.27	1.13	5.50	6.00	7.00	4.00	9.00
X – H2	R3.B.1	6.66	0.86	6.00	7.00	7.00	5.00	8.00
X – H2	R3.B.3	6.68	0.91	6.00	7.00	7.00	5.00	9.00
Control	R1.A.1	6.68	1.20	6.00	7.00	8.00	4.00	9.00
Control	N programs	76.75	74.11	20.00	66.00	97.50	5.00	342
Control	Ln (N programs)	3.78	1.20	3.00	4.19	4.58	1.61	5.83
Control	% tenured professors	0.41	0.20	0.25	0.45	0.57	0.05	0.74
Control	% female professors	0.40	0.07	0.37	0.41	0.43	0.23	0.54
Control	% international students	0.05	0.04	0.02	0.04	0.07	0.00	0.19
Control	% outgoing mobility	0.04	0.04	0.01	0.02	0.05	0.00	0.14
Control	% female students	0.58	0.12	0.53	0.58	0.63	0.35	0.85
Control	Public	0.70	0.46	0.00	1.00	1.00	0.00	1.00
Control	Private	0.30	0.46	0.00	0.00	1.00	0.00	1.00
Control	North	0.34	0.48	0.00	0.00	1.00	0.00	1.00
Control	Center	0.27	0.45	0.00	0.00	1.00	0.00	1.00
Control	South	0.39	0.49	0.00	0.00	1.00	0.00	1.00

Source: Authors’ own work

Table 5.

Regressions H1

	Employers involvement in start-up phase R3.A.1		Employers involvement in management and control phase R3.D.2
	Coefficient	p-value	Coefficient	p-value
Coherence between program objectives and job prospects R3.A.3	0.319	0.043	0.391	0.065
Research and teaching quality in university’s strategies R1.A.1	0.151	0.262	0.094	0.600
University size	0.196	0.235	0.192	0.387
% tenured professors	−1.106	0.251	−1.344	0.301
% female professors	4.423	0.094	1.383	0.693
% international students	1.314	0.704	−3.529	0.452
% female students	−2.193	0.169	−1.080	0.611
North	−0.567	0.105	0.052	0.910
Center	−0.464	0.101	−0.248	0.509
South	Control group		Control group
Intercept	2.505	0.013	3.424	0.012
N	44		44
Adj R2	0.310		0.160

Source: Authors’ own work

Table 6.

Regressions H2

	Students' role in the university governance R1.A.4		Students' role in the university governance R1.A.4
	Coefficient	p-value	Coefficient	p-value
Student orientation and tutorship R3.B.1	0.508	0.026
Flexibility for specific students (workers and disables) R3.B.3			0.368	0.074
Research and teaching quality in university’s strategies R1.A.1	0.313	0.141	0.391	0.065
University size	−0.098	0.711	−0.243	0.374
% tenured professors	0.170	0.910	−0.065	0.966
% female professors	7.340	0.090	7.652	0.086
% international students	−0.995	0.856	−4.446	0.412
% female students	−3.078	0.209	−2.611	0.296
North	−0.413	0.465	0.100	0.864
Center	−0.569	0.209	−0.438	0.348
South	Control group		Control group
Intercept	0.322	0.860	0.912	0.619
N	44		44
Adj R2	0.219		0.177

Source: Authors’ own work