Keywords

Commercial Sphere, Legislative, Socio-Psychographic, Characteristic

Introduction

Conjoint analysis is currently widely used mainly in the commercial sphere in marketing research, where it is used to estimate preferences and especially the importance of individual tested attributes of products or services across different categories (Kuzmanovic et al., 2019). Thanks to this technique, researchers are able to determine which tested variables most influence the consumer's choice of the product, or which combinations of the attributes are preferred by the user (Tahal et al., 2017). One of the advantages of conjoint analysis is that it can be used in various areas of issues (Kozel et al., 2011). The current situation in education system of the universities is characterized by a number of problems and challenges that are related not only to demographic, but also to legislative and socio-psychographic changes. One of the significant consequences of this situation is that education can be seen as a product requiring marketing activities (Gottwald et al., 2015). Especially in the context of higher education, within which it gradually accelerates the "Struggle for the student" causing a number of consequences that universities are confronted not only at the level of strategic, but also tactical and operational management.

The aim of the paper is to put forward the proposals for the managing the value of higher education as a product in the context of the specific conditions of the Slovak Republic. Conjoint analysis was used to obtain relative data for the suggestions because it allows generalizing the behaviour of the individual to the whole sample. Conjoint analysis simulates the customer's decision-making, which is model, but the essential attributes are included, so the results quantify what is a real benefit for customers. Because many times the consumer does not make his decision on the basis of one characteristic, but chooses from products that have different combinations of characteristics (Garbarova et al., 2017). The assessment of the properties themselves is performed in summary. The use of conjoint analysis is beneficial, because it is closer to reality and allows to more faithfully create a real problem, i.e., a decision-making situation (Hebák et al., 2013).

Literature Review

One of the first university studies was that of Krampf & Heinlen (1981), which focused on potential future students of a large university in the middle west of the United States. By analysing the factors, they found that the students had chosen their preferred school according to: university awards, campus attractiveness, informative-education lectures, positive opinions on the university by family, good programs in their chosen field, informative university catalogues, proximity to home, and friendly atmosphere on the campus. Another university preference study was that of Hooley & Lynch (1981) in the UK. They approached exploring the process of choosing priorities of potential UK university students. This qualitative research has defined the attributes that influence decision-making of potential students as follows: satisfactory facultative focus, university location, academic reputation, distance from home, type of university (old, modern), opinion of family and professors on the university Thanks to this conjoint analysis, the most important of the attributes in determining the decision-making of potential students was defined - the satisfactory focus of the faculty to which they applied. It turned out that students are ready to accept almost any of the other attributes that are not according to their ideas, as long as they get to the faculty they prefer. However, a serious shortcoming of this study was the narrow sample of students studied. Oosterbeek (1992) dealt with university selection, with a consequent impact on the possible earnings of graduates in the Netherlands. Their intention was to determine whether different universities were associated with specific earnings prospects. And to what extent this has influenced the choice of a particular university. Although this research has shown significant differences, earnings prospects were not a significant factor in the choice of a particular university.

Mazzarol’s study examined the factors affecting international students in choosing a destination for a university in Australia. The students evaluated the importance of 17 factors that influenced their decision-making on the choice of study at the university. The study showed that the most important factor for them was the impact of the highest available qualifications that could subsequently be applied in future jobs. This was followed by: the school's reputation and quality, the ability to recognize qualifications and the quality of teaching staff (Soutar & Turner, 2002). Lin (1997) tried to find out the reasons for choosing universities of Dutch students. The most significant reason for the selection was the quality of the offered study followed by: employment opportunities, school reputation, internship offer, faculty qualifications, academic standards, availability of modern approaches, and emphasis on school curricula, student life and international collaboration among students. Turner's research focused on college students from economic schools. Students identified the most important factors for choosing a particular school as follows: prospects for future potential jobs, acquired qualifications that will later be appreciate by employers, the possibility of using modern technologies, standards and quality of learning, international awareness of university programs (Soutar & Turner, 2002). Soutar & Turner (2002) also formulated a generalized list of potential factors affecting upcoming students. The result of the study is the definition of the four most important determinants that have the greatest impact in the choice of universities. These are: course suitability, academic reputation, job prospects and quality of teaching (Tvaronaviciene & Jurgelevicius, 2020).

Further research to determine the decisive factors affecting the choice of university and fields of study was conducted by Maringe (2006). Important factors in deciding on a university based on this study are: interest in the subject, prestige, job opportunities, employment profile. There are many other studies within education systems, but their focus is not on observing important attributes influencing the university selection process but on examining students’ preferences for different aspects of education (Zwarts et al., 2015; Lizbetinova, 2017a, Lizbetinova 2017b, Walsh & Cullinan, 2017; Weberova et al., 2017, Carey et al., 2018, Domanska, 2018, Malarkodi et al., 2018). Within the Slovak Republic, a survey of testing general study prerequisites at secondary schools was carried out. The results showed which fields of study students are interested in, but also what criteria they are interested in when choosing a university. The main criteria in their choice were: the quality of teaching, application in practice, good earnings in the profession and the location of the university (The News Agency of the Slovak Republic, 2016). The research was evaluated only in the absolute and percentage quantification of the answers, which represents a certain disadvantage of this type of survey (Valaskova & Krizanova, 2008).

Methodology and Data

The basis of conjoint analysis is a suitable determination of the investigated attributes. These are product features that are relevant to the research goal. A larger number of examined attributes increases the difficulty for respondents and this is the reason why it is recommended to limit the number of attributes to a maximum of seven (Cermáková, 2003), as a higher number may degrade the quality of research results (Hartman, Sattler, 2002).

The next decision that needs to be made concerns the levels of the individual attributes (Bachanova et al., 2016). It must be taken into account that more levels for individual attributes ultimately represent a significantly greater burden on respondents. The number of levels in the range of 2 to 5 is most often used, and their number may vary from attribute to attribute. It is recommended to choose a similar number of levels for all examined attributes, because researches have shown that due to the artificially created high number of levels of individual attributes increases their relative importance, i.e., it also distorts the results (Hair et al., 2010). The problem of a high number of possibilities (profiles) even in the selection of low values of attributes and levels can be eliminated by using only a selected part of these profiles instead of all profiles (full factorial design) (Raghavarao, et al., 2010; Gustafsson et al., 2013). This is a fractional factorial design, where an orthogonal plan is created and used, it is a special subset of all possible combinations of attribute levels, each level of one attribute occurring in combination with each level of another attribute with the same or at least proportional count (Adelowo & Surujlal, 2020). This ensures the independence of the main effects and no loss of information (Cernákova, 2003; Rao, 2014; Grover and Vriens, 2006). The total number of profiles to be evaluated by respondents should not exceed 30 (Hair et al., 2010). Because the growing number of profiles causes a decrease in the concentration of respondents and also the time they spend assessing individual profiles and thus a decrease in the quality of the data obtained (Leigh et al., 1984).

In this context, Marketing Engineering for Excel software was used to create the orthogonal plan. The whole conjoint analysis was also performed through it. Based on secondary data from the studies in the literature review, we decided to compile the number of attributes to six and the number of levels varies from two to three, what is shown in Table 1.

Table 1 Examined Attributes of University Selection Preference With Levels
Attributes	Level 1	Level 2	Level 3
Studying reqirements	Studying is simple	Studying is appropriately challenging	Studying is challenging
Employability of graduates	I am interested in applying graduates in practice	I am not interested in applying graduates in practice	-
Academic reputation	I am influenced by independent university evaluations	I am influenced by the opinion of friends	-
Social background	I want to study where my friends are and to be close to my famid12qly	I want to study where my friends are	I want to study close to my family
Campus atmosphere	The campus atmosphere is more important to me than the quality of the education	The campus atmosphere and the quality of the education are equally important to me	The campus atmosphere is less important to me than the quality of the education
Field of study	I want to study a particular field	I want to study in a particular area	-

These numbers of attributes and levels determined a total of 216 possible profiles. Using an orthogonal plan, we reduced their number to 16, which also corresponds to the minimum number of profiles according to Raghavarao, et al., (2010). Based on the decision on the researched product, our customers were potential students, where the largest group consists of high school graduation students. To obtain information, we created a questionnaire, which consisted of two segmentation questions and 16 possible profiles. The questions concerned gender and type of secondary school and were chosen to identify possible differences in the preferences of individual segments. This survey was carried out using the CAWI (Computer Assisted Web Interview) method. The questionnaire was distributed electronically, mainly using Facebook, as more than 90% of young Slovaks have an account on this social network (Slovak News Agency, 2016). The size of the representative sample was determined by the following equation 1:

Where,

n – The minimum number of respondents;

tα – The critical value of the normal distribution at significance level α=0.05;

p – The likely sample proportion, expressed as a decimal;

d – The confidence interval, expressed as a decimal (Fila et al., 2020).

The confidence level was set at 95%. The critical value of the normal distribution at confidence level α=0.05 was 1.96. This is based on the fact, that % of the area of the normal distribution is within 1.96 standard deviations of the mean (Vartiak, 2017). For those cases where the likely sample proportion was not known, p was set at 50%. The confidence interval was set at 5% (Palus et al., 2014). The required minimum sample size is therefore 384 respondents. The result of the conjoint analysis represents data on the utility scores, called part worth, of the attributes’ single performance levels. Utility scores are measures of how important each characteristic is to the respondent’s overall preference for a product (Louviere et al., 2005). They are captured by the procedure through a set of regression of the rankings on the profiles (Jabbar & Fakhrul Islam, 2010). As for the utility score, it does not matter in itself, because it is a dimensionless measure, but it can be used to find the global utility (relative importance compared to other attributes) of every attribute according equation 2:

Where:

O_p – the relative importance of the product attribute

max u_p – utility of the attribute’s most preferred level

min u_p – utility of least preferred performance level of the attribute (Kotri, 2006).

Results and Discussion

The most important part of the output of conjoint analysis is relative importance. This value indicates the share of a certain attribute in the overall evaluation of concepts. These concepts consist of a set of attributes. The resulting values obtained by calculation based on Equation 2 are shown in Table 2.

Table 2 Utility Scores and Relative Importance of Attributes
Attribute	Level	Part Worth Utility	Relative importance	Relative importance (%)
Studying requirements	Studying is simple	3804	1536	10,52
	Studying is appropriately challenging	6007
	Studying is challenging	4471
Employability of graduates	I am interested in applying graduates in practice	8161	7783	53,29
	I am not interested in applying graduates in practice	378
Academic reputation	I am influenced by independent university evaluations	2505	1630	11,16
	I am influenced by the opinion of friends	875
Social background	I want to study where my friends are and to be close to my family	4726	2350	16,09
	I want to study where my friends are	2376
	I want to study close to my family	2992
Campus atmosphere	The campus atmosphere is more important to me than the quality of the education	2396	353	2,42
	The campus atmosphere and the quality of the education are equally important to me	3854
	The campus atmosphere is less important to me than the quality of the education	4207
Field of study	I want to study a particular field	1993	952	6,52
	I want to study in a particular area	10410

By calculating percentages of attribute’s relative importance, it is possible to determine the proportion of attribute importance within the overall assessment of reason for the university selection by respondents (Figure 1). Because the set of attribute’s relative importance values constitute a total of 100 percent, we can say from the results that students make decisions about the preferences of university selection mainly on the basis of Employability of graduates. The relative importance of this attribute far exceeds the other ones examined. On the other hand, when choosing a university in general, students do not take into account, or only very little, the attributes of Campus atmosphere and Field of study. Both of these attributes reached a relative importance of less than 10%.

To identify possible differences in the preferences of the individual segment by gender, the results were also evaluated by the percentage of attribute’s relative importance assigned by male and female respondents (Figure 2).

The results showed gender equality in assigning attributes relative importance. Both segments consider the Employability of graduates attribute to be clearly the most important almost in the same proportion. For the other attributes (Studying requirements, Academic reputation, Campus atmosphere), the results also differ only slightly by gender. However, the greatest variation in the responses of these two segments was demonstrated in the Field of study attribute. Also, the Social background attribute was four-fifths more important for women than for men. The difference in the answers according to the type of secondary school attended is presented in Figure 3.

Students of economic high schools rely most on the attribute of the Employability of graduates. Which does not change the fact that all segments have chosen this attribute as the most important? However, students from economic schools from other segments expressed lower interest in the attributes of Studying requirements and Academic reputation. After taking into account the individual applied approaches, it is possible to summarize the identified preferences in relation to maximizing the subjectively perceived value of the product in Table 3, which contains preferences in general and also according to segments based on observed differences in preferences.

Table 3 Summary of Preferences
The nature of the approach		Preferences
		1	2	3
General		Employability of graduates	Social background	Academic reputation
Gender	Male	Employability of graduates	Campus atmosphere	Studying requirements
	Female	Employability of graduates	Social background	Studying requirements
Type of high school	Grammar school	Employability of graduates	Studying requirements	Social background
	Economic high school	Employability of graduates	Social background	Campus atmosphere
	Vocation high school	Employability of graduates	Social background	Studying requirements

As the results of the survey show, the importance of sources of subjectively perceived value of the product varies, which in practice raises the need to apply this knowledge in the marketing practice of universities. With regard to whether the target segment of higher education within the individual study programs can be internally differentiated according to gender, or the type of secondary school attended.

The levels that should be achieved within the individual attributes in order to be able to define the product as valuable are the following:

• Employability of graduates - I am interested in applying graduates in practice.

• Social background - I want to study where my friends are and be close to my family.

• Academic reputation - I am influenced by the opinion of friends in my area about universities.

• Studying requirements - Studying is appropriately challenging.

• Campus atmosphere - The campus atmosphere and the quality of education are equally important to me.

It follows from the above that in terms of the subjectively perceived value of the product, the attribute Field of the study is irrelevant. This knowledge indicates the intensification of the struggle for the student, because for the student the Field of study is not the primary priority when choosing a university. And the competitive struggle for the student will take place primarily at the level of other attributes. According to the results of the implemented conjoint analysis, it is possible to develop recommendations for building and managing the subjectively perceived value of the product in the context of marketing communication activities of universities with its potential students.

Conclusion

The aim of the paper was to put forward the proposals for the managing the value of higher education as a product in the context of the specific conditions of the Slovak Republic. The basic research method used was conjoint analysis, which is characterized by eliminating the shortcomings of traditional methods of conducting surveys in the surveyed field. We consider the stated goal to be fulfilled on the platform of information obtained by implementing the selected analysis. We found that the profile of the optimal value of the product is characterized by these characteristics of the examined attributes: studying is appropriately challenging; employability of graduates in practice is high; the reference group evaluates the university positively; acquaintances are studying at the university and at the same time the university is currently close to where students live; the atmosphere of the campus is as high as the quality of education. The importance of individual attributes varies across the criteria of the respondent's gender and the type of high school attended, which implies the need to apply a diverse approach to building and managing the value of higher education as a product in terms of marketing.

However, the individual conclusions formulated are not universally applicable. Their implementation in marketing (especially communication) activities of universities is not recommended, for example, in the case of double degree studies, as the target group of the survey were high school graduates in the Slovak Republic and it is also necessary to take into account the socio-cultural national profile of customers. It also opens up space for further elaboration of the issue, in the context of the formulation of recommendations increasing the value of the product within its individual partial sources.

Acknowledgement

This research was funded by project VEGA 1/0064/20: Behaviorism in a socially responsible communication strategy of enterprises.

References

Adelowo, C.M., & Surujlal, J. (2020). Academic entrepreneurship and traditional academic performance at universities: Evidence from a developing country. Polish Journal of Management Studies, 22(1), 9-25.

Crossref, Google scholar

Bachanova, P.H., Garbarova, M., & Vartiak, L. (2016). Economic and Social Factors Influencing the Localization of Advertising Agencies in the Regional Context. Proceedings of the International Scientific Conference: Region V Rozvoji Spolecnosti 2016, 267–275.

Crossref, Google scholar

Carey, J.M., Carman, K.R., Clayton, K.P., Horiuchi, Y., Htun, M., & Ortiz, B. (2018). Who wants to hire a more diverse faculty? A conjoint analysis of faculty and student preferences for gender and racial/ethnic Diversity, Politics, Groups, and Identities, 1, 1-49.

Crossref, Google scholar

Cernáková, A. (2003). Special possibilities of conjoint analysis, in Teaching mathematics at non-university universities (32-39), Turnov: Unipress

Google scholar

Domanska, A. (2018). Cooperation between knowledge-based institutions and business: Empirical studies and network theories. Forum Scientiae Oeconomia, 6(4), 81-94.

Google scholar

Fila, M., Levicky, M., Mura, L., Maros, M., & Korenkova, M. (2020). Innovations for business management: motivation and barriers. Marketing and Management of Innovations, 4, 266-278.

Google scholar

Garbarova, M., Bachanova, P.H., Vartiak, L. (2017). Purchasing Behaviour of E-Commerce Customers. V A. Sujova (Ed.), Management and Economics in Manufacturing (s. 160–165). Technicka Univerzita Zvolene.

Crossref, Google scholar

Gottwald, D., Lejskova, P., Svadlenka, L., & Rychnovska. V. (2015). Evaluation and management of intellectual capital at pardubice airport: Case study. Procedia Economics and Finance, 34, 121-128.

Crossref, Google scholar

Grover, R., Vriens, M. (2006). The handbook of marketing research: Uses, misuses, and future advances. SAGE publications.

Google scholar

Gustafsson, A., Herrmann, A., & Huber, F. (2013). Conjoint measurement: Methods and applications. Heidelberg: Springer.

Crossref, Google scholar

Hair, J.F., Anderson, R.E., Babin, B.J, & Black, W.C. (2010). Multivariate data analysis. New York: Pearson.

Google scholar

Hartman, A., & Sattler, H. (2002). Commercial use of conjoint analysis in Germany, Austria and Switzerland. Research Pepers on Marketing and Relailing. Hamburg: University of Hamburg.

Crossref, Google scholar

Hooley, G.J., & Lynchl, J.E. (1981). Modelling the student university choice process through the use of conjoint measurement techniques. European Research, 9(4), 158-170.

Crossref, Google scholar

Jabbar, M.A., & Admassu, S.A., (2010). Assesing consumer preferences for quality and safety attributes of food in the absence of official standards: The case of beef, raw milk and local butter in Ethiopia, in M.A. Jabbar, D. Baker, M.L. Fadiga (Eds.), Demand for livestock products in developing countries with a focus on quality and safety attributes: Evidence from Asia and Africa (pp. 38-58), Addis Ababa: International Livestock Research Institute.

Crossref, Google scholar

Jabbar, M.A., & Fakhrul, S.M.I. (2010). Urban consumer preferences for quality and safety attributes of meat and milk in Bangladesh. In M.A. Jabbar, D. Baker, M.L. Fadiga (Eds.), Demand for livestock products in developing countries with a focus on quality and safety attributes: Evidence from Asia and Africa (pp. 17-37), Addis Ababa: International Livestock Research Institute.

Kotri, A. (2006). Analyzing customer value using conjoint analysis: The example of a packing company, available at: https://ssrn.com/abstract=950497

Crossref, Google scholar

Kozel, R., Mynárová, L., & Svobodová, H. (2011). Modern methods and techniques of marketing research. Praha: Grada Publishing.

Google scholar

Krampf, R.F., & Heinlein, A.C. (1981). Developing marketing strategies and tactics in higher education through tagret market research. Decision Sciences, 12(2), 175-192.

Crossref, Google scholar

Kuzmanovic, M., Andjelkovic Labrovic, J., & Nikodijevic, A. (2019). Designing e-learning environment based on student preferences: Conjoint analysis approach. International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 7(3), 37-47.

Crossref, Google scholar

Lin, L. (1997). What are the student education and educational related needs? Marketing and Research Today, 3(3), 199-212.

Google scholar

Lizbetinova, L. (2017a). Competences of university graduates in field of transport and logistics. In K.S. Soliman (Ed.), 29th International-Business-Information-Management-Association, Sustainable economic growth, education excellence, and innovation management (pp. 3662-3669), Norristown: INT Business Information Management Assoc-IBIMA.

Google scholar

Lizbetinova, L. (2017b). Attitude college students in south bohemian region to opportunities of increasing employability in the labour market. In V. Klimova, V. Zitek (Eds.), Conference: 20th International Colloquium on Regional Sciences (pp. 296-302), Brno: Masarykova univerzita.

Crossref, Google scholar

Malarkodi, M., Indumathi, V.M., & Praveena, S. (2018). Preference towards online mode of distance education courses–conjoint analysis. International Journal of Bioresource and Stress Management, 9(1), 178-182.

Crossref, Google scholar

Maringe, F. (2006). University and course choice. International Journal of Educational Management, 20(6), 466-479.

Crossref, Google scholar

Oosterbeek, H., Groot, W., & Hartog, J. (1992). An empirical analysis of university choice and earnings. De Economist, 140(3), 293-309.

Crossref, Google scholar

Palus, M., Matova, H., Krizanova, A., & Parobek, J. (2014). A survey of awareness of forest certification schemes labels on wood and paper products. Acta Facultatis Xylologiae Zvolen, 56(1), 129-138.

Crossref, Google scholar

Raghavarao, D., Wiley, J.B., & Chitturi, P. (2010). Choice-based conjoint analysis: Models and designs. Chapman and Hall/CRC.

Crossref, Google scholar

Rao, V.R. (2014). Applied conjoint analysis. Heidelberg: Springer.

Crossref, Google scholar

Slovak News Agency (2016). Almost all young Slovaks use Facebook, they sleep less.

Soutar, G.N., Turner, J.P. (2002). Students’ preferences for university: A conjoint analysis. International Journal of Educational Management, 16(1), 40-45.

Crossref, Google scholar

Tahal, R. (2017). Marketing research: Procedures, methods, trends. Prague: Grada Publishing.

Google scholar, Indexed at

The News Agency of the Slovak Republic (2016). SURVEY: Almost half of high school students want to apply for a degree in the Czech Republic.

Crossref, Google scholar

Tvaronaviciene, M., & Jurgelevicius, A. (2020). The new concept of human capital and its impact on economy in European countries. Polish Journal of Management Studies, 22(2), 561-575.

Crossref, Google scholar

Valaskova, M., & Krizanova, A. (2008). The passenger satisfaction survey in the regional integrated public transport system. Promet-Traffic & Transportation, 20(6), 401-404.

Crossref, Google scholar

Vartiak, L. (2017). Global sustainable and responsible investment activities and strategies of companies. V C. Bektas (Ed.), Selected Papers of 5th World Conference on Business, Economics and Management (bem-2016) (s. 77–87). Sciencepark Sci, Organization & Counseling Ltd.

Crossref, Google scholar

Walsh, S., & Cullinan, J. (2017). Factors influencing higher education institution choice, In J. Cullinan, D. Flannery (Eds.), Economic Insights on Higher Education Policy in Ireland (81-108), Palgrave Macmillan, Cham.

Crossref, Google scholar

Weberova, D., Starchon, P., & Lizbetinova, L. (2017). Comparison of motivational preferences of university students and employees. In K.S. Soliman (Ed.), 30th International Business-Information-Management-Association, Sustainable economic development, innovation management, and global growth (pp. 4184-4193), ), Norristown: INT Business Information Management Assoc-IBIMA.

Google scholar

Zwarts, T.M., Vanthournout, G., Gijbels, D., & Van den Bossche, P. (2015). Exploring conjoint analysis as a methodology to study implicit preferences within the context of educational and training sciences. Methodological Challenges in Research on Student Learning, 1, 77-91.

Google scholar

Received: 22-Nov-2021, Manuscript No. asmj-21-9149; Editor assigned: 24-Nov-2021; PreQC No. asmj-21-9149(PQ); Reviewed: 09-Dec-2021, QC No. asmj-21-9149; Revised: 16-Dec-2021, Manuscript No. asmj-21-9149 (R); Published: 22-Dec-2021