164 Doctoral Programme in
Information Management
165 Doctoral Programme in
Information Management
On the one hand, this scenery calls attention to the practical implications of this study like the necessity to retain the enrolled students, avoiding evasion and promoting permanence in the HEIs to improve the number of enrolled and undergraduate students. Besides that, the countries' education level has a great impact on its development. Therefore, it would be of great interest and relevance to improve and enlarge the number of undergraduate people.As a result, the HEIs will need to enhance the discussion of forms , models and mechanisms of governance to grant their future maintenance and sustainability.
At the same time, they will need to identify new target groups as potential entrants, as they are faced with the falling birth rate and the increasing of an aging population. Concerning this subject, some potential target entrants, despite the one considered ideal for undergraduate courses, is the so-called “late entry” population (Guimbert et al., 2008); the stranded midway younger students (Hirsch, 1969), and the “academic second-training professional" already undergraduate which would like to embrace a new profession. The HEIs will need to estimate these groups correctly and to evaluate its impact on their offer conditions and proper ways of implementation.
Complementary, price concurrence varies in a high range in the country, depending on knowledge area, country regions, HEIs price, and discount policies and government subsidies. Important to remember that Public Education for undergraduate courses is free of charge. Even though the number of unfilled places continues for the vast majority of undergraduate courses.
On the other hand, focusing on the knowledge discovery among data from the Brazilian Higher Education Census, we conclude that:
HEIs have some similarities and dissimilarities of resources. Based on them, they were grouped into different clusters, independently of their preliminary classification. This new RBV approach promotes a different kind of strategy, based in a collaborative relationship.
Considering that, the HEIs were gathered according to their resources, in order to minimize their weaknesses, and enhance their strengths; or to combine them in different ways. The discovered patterns that generated new knowledge are:
(a) Considering the HEIs dataset (Table 54):
• The HEIs with the highest research expenses.
• The HEIs with the highest and the lowest expenses with teachers and technical-administrative staff remuneration.
• The HEIs with the highest and the lowest incomes and investments.
• The HEIs with no investment at all, and
• The ones with all the above attributes.
(b) Considering the Undergraduate Courses dataset (Table 55):
• The HEIs with the highest number of courses.
• The HEIS with the lowest number of courses.
• The HEIs with the lowest number of enrolled students; and
166 Doctoral Programme in
Information Management
• The HEIs with the highest number of students depending on public financing.
(c) Considering the Professors dataset (Table 56):
• The HEIs with the highest proportion of Ph.D. or master’s degree or Specialist professors.
• The HEIs with the fewest proportion of Ph.D. professors, and
• The HEIs with their respective teacher’s proportion of genre.
(d) Considering the Students dataset (Table 57):
• The HEIS with the greatest number of entrants, enrolled and graduates.
• The HEIS with the lowest number of entrants, enrolled and graduates.
• The HEIs whose students most depend on public funding, and
• The HEIS with their sex prevalence.
(e) Considering all datasets under the Global SOM technique:
• Cluster 1 dedicated to the Education area (OECD 1) and with the highest number of licentiate (teacher training) courses.
• Cluster 2 dedicated to the courses in the Humanities and Arts area (OECD 2).
• Cluster 3 which concentrates its offer in the Science, Math’s and Computing and Engineering, Production and Construction areas.
• Cluster 4 with the highest number of public institutions and most of the universities.
It has the highest number of courses in the Health and Wellness area (OECD 7) and Agriculture and Veterinary (OECD 6). It is the biggest one.
• Cluster 5 with the highest number of private institutions, both for-profit and non-profit and most of the faculties. It concentrates its offer in the Social Sciences, Business, and Law areas (OECD 3).
• Cluster 6 comprises the HEIs with the highest values of incomes, other revenues, the highest investment expenses, the highest values with professor and staff remuneration and the greatest number of students; also, they are the ones with no resources from transference. It groups the HEIs which are eminently private institutions (99%).
These discovered patterns, which consider all the variables of the dimension beforehand analyzed, increment the ones achieved only for the independent annual SOM components and allows a more detailed view of all HEIs, their undergraduate courses, professors and students in the country.
The exploratory studies added to the discovered patterns allowed us to advance in the RBV Theory, as based on the results of the SOM analysis, it was possible to make up the Brazilian Higher Education scenario and to depict the discovered patterns among each group of HEIs, courses, professors, and students, grouping them into different clusters, according to their main features or similar resources, called discovered patterns, which can distinguish them from other groups or clusters, identifying their combination of distinct or complementary resources that others do not have or share, based mainly on the financial
167 Doctoral Programme in
Information Management
(investments, expenses, funding, incomes or revenues), the processes (size, number of courses, courses in different knowledge areas, degree and modality) and the personal (professors, students and administrative staff) resources.Thus, for each SOM dimension (HEIs, Courses, Professors, and Students) it was identified the number of clusters, whose resources are grouped according to the RBV Theory, distinguishing the most relevant variables and the most relevant discovered patterns. If combined, these similar resources can scale up the HEIs position and incr ease their competitive and sustainable advantage. On the other hand, the identification of their distinct or complementary resources, that others do not have, can create synergies among them, through the accumulation of different resources which would be difficult to obtain in isolation.
The knowledge generated plays a significant role in the implementation of competitive responses or decisions to take, affecting in the HEIs’ sustainability and also contributes to advance the Resource-Based View (RBV) theory, which was used to identify the HEIs resources, proposing a new way of combining them.
Signaling the previous hypotheses:
H1– The use of special techniques that promote the visualization and understanding of hidden and unknown patterns through Higher Education public and official data can produce new knowledge.
Yes, the Higher Education official and public data, if analyzed under special techniques, can generate new knowledge as it revealed the patterns discovered through SOM.
H2 – The analysis of Higher Education public and official data and the classification of Brazilian Higher Education Institutions generate strategic information.
No, the Higher Education official (and public) data and the actual classification of Brazilian Higher Education Institutions, in its raw form, do not produce new knowledge nor generate strategic information. Even when the data is available, it is mostly unexplored.
The consecrated taxonomy which groups the Brazilian Higher Education Institutions based on an administrative (public or private) and academic classification (universities, academic centers, faculties, federal technological institutes, and centers) does not create knowledge nor promotes strategic information.
However, on the contrary, as a result of this research, the use of the SOM technique allowed us to group the data into different clusters, according to other common characteristics, thus generating new knowledge.
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