The Interface between the Brand of Higher Education and the Influencing Factors

Abstract

The world is changing faster than ever before. Continuous changes are also affecting the higher education sector. The number of programs on offer is growing, attraction strategies are changing, and the branding and positioning of higher education institutions are gaining significance. The growing influence of social networks on personal choice cannot be ignored. Of the world’s population, 57% uses social networks and spends an average of 2.5 h a day on them. The most popular social network, Facebook, has up to 2.9 billion active users every month. Therefore, the questions arise as to which factors influence one or another consumer choice, how social networks contribute to brand awareness, and what impact brand has on the higher education sector. After systematic and comparative analysis of concepts published in the scientific literature, the analysis of brand, brand promotion concepts, and factors that increase brand awareness is performed. This study seeks to determine whether and to what extent individual factors influencing student motivation and social networking influence the distinctiveness of a higher education institution brand and how factors influencing student motivation and social networking affect the distinctiveness of higher education institutions’ brands in general. The results of this study can help higher education institutions to develop their own plans, strategies, and good practices. Research methods: systematic and comparative analysis of concepts and methods published in the scientific literature, mathematical and statistical methods, statistical processing, and expert survey.

1. Introduction

In the higher education sector, universities are competing to attract more students, to have best education programs, to recruit the best staff, and for other reasons. Researchers are in a broad discussion as to whether the university brand is an infrastructure for competitive advantage or not [1,2,3]. Part of the brand image is equated with reputation [4,5]. Brand management helps to separate institutions from the competition. Various strategies are used; brand management can be related to quality or low cost, etc.

Universities’ brand strategy must change as the world changes. This research study aims to find out whether and to what extent individual factors affecting students’ motivation and social networks exert an influence on the uniqueness of the brand of a higher education institution and how the factors affecting students’ motivation and social networks summarily affect the uniqueness of the brand of a higher education institution.

There appears to be a gap in the research regarding the impact of characteristics, such as student motivation and social networks, on the uniqueness of a higher education institution’s brand. As a result, the main objective of this study is to close that gap by performing research and analyzing the relationship between elements impacting student motivation and the usage of social media on the unique qualities of a higher education institution’s brand.

2. Literature Review

2.1. Social Network

In the contemporary world, social networks have become an important marketing instrument, creating a new challenge when user (buyer) attraction is concerned [6]. Social networks are increasingly being used as a platform for conducting marketing and advertising activities, since organizations are increasingly spending their time, money, and other resources on social media ads [7]. The main form of digital marketing is inbound marketing, which requires that the audience is very carefully targeted, and communication is adapted through high-quality content [8]. According to Halligan et al. [9], the main idea of inbound marketing is about using the marketing techniques that arouse buyer interest by sharing creative and useful content on a larger number of network channels, including browsers and social media, all with the aim of turning individuals into permanent buyers. The past decade witnessed the development of sophisticated, diverse, and strengthened interactions between organizations and their buyers through using social networks. On the one hand, organizations use the advantages of different social network platforms to expand geographical reach to buyers [10] and build closer relationships with buyers [11], whereas, on the other hand, buyers are increasingly being empowered through social networks and taking control of the marketing communication process, simultaneously becoming message creators, associates, and commentators [12].

Understanding user motivation and social media interactive characteristics is of decisive significance when designing valuable content, facilitating interactions with the buyers, and stimulating content exchange between them [13]. Designing impressive and valuable content in order to transform social media passive watchers into active participants and associates is becoming an important marketing task [14]. There is extensive literature studying the factors upon which students’ motivation depends [15,16,17,18,19,20,21]. The influence of social media on brand development has been the topic researched by numerous authors in different areas, such as customer purchase intention [22], sports [23], mobile social networks [24], luxury fashion goods [11], fashion brands [25], the airline industry [26], and tourism [27]. In a marketing sense, social networks are considered to be the platforms on which people present and share information and feelings [28]. Social networks are dynamic, interconnected, egalitarian, and interactive organisms [29]. In contrast to traditional media, the specificities of the Internet and social networks and their advantage lie in two-way communication. The role of social networks has gradually evolved from that of a marketing tool to a role as a source of information for the purpose of analyzing and anticipating buyer behavior and achieving a competitive advantage and superior performances as well [30]. Different social media platforms, such as YouTube, Facebook, LinkedIn, and Twitter, are designed for different purposes and can be used in different situations. Web locations for social networks (Facebook), web locations for microblogging (Twitter), and content communities (YouTube) enable building joint interests and values for both organizations and buyers and establishing connections between them in a manner which was impossible in the past [28].

Social networks have also transformed the way in which organizations and buyers communicate with each other, which has an influence on choices and behavior in consumption [31]. Organizations can easily retrieve and beneficially utilize an enormous amount of data from different locations on social networks (blogs, forums, and so on) and in different formats (a text, a video, an image) with the help of contemporary information technologies [32]. In a word, data on social networks can serve as an important source for buyer analysis, market research, and new ideas creation, which may improve marketing results [33]. Marketing on social networks enables organizations to become increasingly more successful in understanding consumers, determining their needs, and conducting market research, communication, and advertising [34].

Social media marketing endeavors to engage buyers in the social locations in which they spend time [35]. Kumar et al. [36] asserted that organizations may take advantage of such an engagement of buyers both at the material level (by generating greater revenue or a bigger profit) and at the nonmaterial level (through feedback or new ideas which help to develop products or services). With the help of social networks, organizations are given the opportunity to easily become familiar with what buyers want and also what they think of a product or a service [37]. Muntinga et al. [38] categorized buyer behavior in connection with social networks into the following three groups: consumption (reading posts), contribution (product rating), and creation (posting brand-related content). Social networks help to create and distribute timely, educational, valuable, and convincing content in different forms with the aim of attracting and retaining buyers [39,40]. By using valuable content to attract goods or service users and increasing their engagement, it is possible to stimulate sales [13]. Social networks are, to a great extent, used as communication tools for branding organizations [41].

2.2. Student Motivation

Students’ motivation plays the most essential role in learning and exerts an influence on different fields of education. Motivation is one of the most powerful determinants of success or failure during a student’s studies [6]. The literature defines motivation as a stimulating force that directs and maintains behavior, while motivation to learn implies a person’s endeavor to acquire different types of knowledge and skills. Students’ motivation is defined as the tendency to find meaningful and useful academic activities and an effort to gain academic benefit from them [7]. Without motivation, an appropriate curriculum and a good teaching process are insufficient to guarantee students’ success [8]. Motivation is important in all aspects of life, no matter whether it refers to one’s professional engagement or studying at university. To be motivated means to be stimulated to undertake some activity. The person who does not feel a stimulus to act is characterized as unmotivated, whereas the person who is inspired to act is considered motivated. The basic two types of motivation are internal and external motivation. Internal motivation refers to the driving force of an individual, when a person is motivated to be active for fun or the challenge that activity brings. External motivation implies external impulses (praises, rewards of money, and suchlike) [9]. Motivational beliefs are very important because they help to determine the extent to which students consider and evaluate the fulfilment of obligations and make an effort and show their interest in the fulfilment of obligations [10].

In the increasingly competitive higher education sector, higher education institutions are faced with significant challenges in attracting new students [42,43]. As is indicated, students’ motivation is the basic ingredient for academic success. It includes the internal and external factors stimulating a person’s wishes, energy, and interestedness in and dedication to the work they do. According to Dornyei [44], motivation is the factor explaining why people decide to do something and how much effort they make and how long they are prepared to perform a particular activity. The manner in which higher education institutions manage their relationships with students and what impression students have of the brand of their institution may have an influence on current and future connectedness with the institution. It is not surprising that the perception of the quality of the offered courses and the reputation of an institution are amongst the most powerful factors upon which students’ choice depends [45]. In order to attract motivated students to a higher education institution, it is necessary not only to analyze the higher education institution itself or external factors but also to closely monitor the actions of the user/student. It is important to analyze their habits and behavior.

When looking at consumer behavior on the Internet, it is noticeable that it is changing. Researchers Hoffman and Novak defined consumer behavior on the Internet as ‘a state characterized by a continuous flow of responses that causes loss of interest and self-awareness and the establishment of one’s ego’ [46]. This means that the user loses his sense of time by immersing himself in the vastness of the Internet, browsing it, and searching for information on it. It should be emphasized that the information is collected by the consumer himself. He chooses only the knowledge that is interesting and relevant to him. Bakanauskas and Liesionis stated that a certain physiological and psychological need brings a customer to the store and a need for information brings a visitor to the Internet [47]. If the consumer does not find the knowledge he needs, he remains dissatisfied, and there is a high chance that he will not return there.

Kotler and Keller presented a model of consumer behavior that distinguishes two groups that influence consumer behavior [48]:

• characteristics of the user (cultural, social, and personal);
• user psychology (motivation, perception, learning, and memory).

When analyzing the pattern of consumer behavior according to Kotler and Keller, external factors are first distinguished. Probably the most important are cultural factors. According to the researchers mentioned in the previous paragraph, culture is a set of socially acquired patterns of behavior, transmitted through language and other means to members of a particular society. Looking at the marketing context, it is perceptible that consumers are influenced by different forms of culture. According to Luna and Gupta, culture primarily influences consumer behavior through cultural values, heroes, rituals, and symbols [49]. It is very important to realize that, to attract as many consumers as possible, it is necessary for marketers to understand the cultural values of their customers and to present the corresponding goods and the ways of their delivery to the market.

The second group of factors is social factors, which include groups, family, roles, and status [50]. Influence groups are groups that do not affect the way a consumer buys a product. Kotler and Keller present the following types of influence group [48]:

• Primary—Groups with which a person interacts on a regular basis. For example, family, friends, co-workers;
• Secondary—These are groups with which communication is more formal. For example, professional, religious, or interest groups.

Another important group of external factors is personal factors. They define human individuality, that is, age, profession, marital status, lifestyle, etc. User behavior on-line depends on personal characteristics. According to Pranulis, each person’s perception of themselves is different, but when properly emphasized and associated with goods and their brands, it can be useful for marketing. Further examination of the pattern of consumer behavior revealed psychological factors. This group includes motivation, perception, learning, and memory. Maslow, examining psychological factors, argued that consumers have the most important needs, which are divided into the most important, the least important, and the unimportant [51]. The consumer will first strive to meet his most important needs. This theory helps marketers understand how different goods find their place in consumer aspirations and plans.

2.3. Measures of Brand Distinctiveness

The creation of a unique brand and differentiating it from the competition implies the creation of key brand elements, such as a visual expression, a brand personality, and brand positioning. A brand personality is built by combining the brand name with its other characteristics, such as symbols, signs, logotypes, music, and images [52]. One of the key marketing functions is making decisions regarding the extent to which a strong brand identity should be invested in, protected, and built, simultaneously containing the unique elements of the identity, such as the logotypes, colors, or characters, that differentiate it from competitors and making its recognition and purchasing easier [53]. Consumers experience a brand as the most visible piece of information [54] that helps to raise one’s awareness of a brand and create a desired image of a brand [55].

Brand design components are defined as the signs, symbols, and names that identify and differentiate a brand from the competition [56] and are considered the most prominent signs of the brand identity that significantly influence cognitive, affective, and behavioral responses to a brand [57,58,59]. Marketing studies consider them to be the most important factors in building a brand personality [60,61,62]. A brand logotype is a graphical design used by a company with or without its name in order to identify itself or its products and is crucially important in the creation of a brand identity and uniqueness [63]. A logotype design is a critical component in the creation of a brand perception in consumers’ minds [64]. The font, color, and design of a logotype have an influence on the specific perception of a brand, generate strong brand associations [65], increase commitment to a brand [66], and provoke affective responses [67]. It is necessary for brands to achieve positive effects in relation to the logotype, which has an influence on the attitude towards the brand represented by the logotype [68].

Color also plays an important role in visual branding [69,70]. Consumers’ purchasing decisions are, to a great extent, influenced by visual sensory signs, and color is one of the most influential visual elements [71]. Colors enable companies to distinguish themselves and differentiate themselves from their competitors [72], and the perception of a color is sufficient to create an effect, a recognition, and a behavior in accordance with that meaning [73]. Choosing a color is a complex process, because a color should correctly reflect the visual identity of a brand, provoke the wanted image of a brand in consumers’ minds, and contribute to the creation of brand awareness [71,74]. The meaning of colors has been analyzed in detail in the past years [75], e.g., the color blue is associated with trust, the color white with purity, and the color purple implies energy and represents excitement.

A brand in the higher education sector is a multidimensional construct consisting of brand familiarity, brand remarkability, and a brand attitude [76]. The way in which students experience the brand of a higher education institution may have an influence on connectedness with the institution [77]. Students form their perception of a brand image, identity, and meaning before enrolling at a higher education institution and continue to develop it during and after their studies, and, for a brand identity to survive in a changeable environment, it has to be dynamic and flexible so as to meet students’ expectations [78]. The management of the reputation of a higher education institution is extremely challenging since different groups make an assessment of the quality and reputation of the institution based on met expectations [79]. Branding higher education institutions in a time of globalization and technological innovation is considered to be the way to maintain competitiveness in the market and the capability to face challenges [80].

3. Methodological Approach and Methods

In the empirical part of the paper, a survey-conducting method was used, applying the questionnaire technique (the questionnaire was prepared specifically for the purposes of this research study). The research was performed on a sample of 245 respondents (students and employees in academia), who rated a total of 24 statements on a satisfaction scale from 1 to 5 (where 1 stands for “satisfied the least” and 5 stands for “satisfied the most”).

The following hypotheses are defined in this research study:

The main hypothesis:

Hypothesis 0 (H0).

The levels of the factors affecting students’ motivation and social networks influence the level of the uniqueness of the brand of a higher education institution.

Ancillary hypotheses:

Hypothesis 1 (H1).

The level of the factors affecting students’ motivation influences the level of the uniqueness of the brand of a higher education institution.

Hypothesis 2 (H2).

The level of social networking influences the level of the uniqueness of the brand of a higher education institution.

A theoretical research model (SRM—systemic research competitiveness model) was formed for the needs of the research carried out in this paper (Figure 1).

This model was based on the perception of the individual influences of the factors affecting students’ motivation on brand uniqueness and the influence of social networks on brand uniqueness. How the levels of the factors affecting students’ motivation and social media levels summarily influence brand uniqueness were also the subjects of observation.

The following mathematical and statistical methods were used for processing the results obtained through empirical research:

• the Cronbach alpha coefficient, used in order to determine the reliability of the variables of the formed model;
• the Kaiser–Meyer–Olkin measure of sampling adequacy (KMO) and Bartlett’s test of sphericity, used in order to check data suitability for the factor analysis;
• the scree plot, used to determine the breakpoint;
• descriptive statistics, aimed at summing up the collected data in a clear and understandable way;
• correlation analysis, made so as to determine mutual connectedness between the phenomena;
• regression analysis, used with the aim of making an assessment of the connections between the independent and the dependent variables;
• linear regression, which served to model the connection between two variables by forming a linear equation;
• the ANOVA test, performed in order to compare the two groups of variables; and
• multiple linear correlation analysis and regression analysis, which showed the influence of several independent variables.

The following pieces of statistical software were used: SAS JMP v.14. (MedmenhamMarlow, Buckinghamshire, UK), Monte Carlo PCA for Parallel Analysis (version 3) (Phoenix, AZ, USA, and IBM Statistics SPSS, version 22x64 (Armonk, NY, USA).

4. Results

Based on the guidelines for the identification of the factor loadings based on the size of the samples needed for significance, the factor loading significance of 0.40 for the number of the respondents exceeding 200 allowed us to consider the sample of 245 respondents as significant, given the fact that the obtained Cronbach coefficient α = 0.944 was greater than the theoretical. Based on the internal consistency rule, the obtained Cronbach coefficient α = 0.944 was also within the limits α > 0.9 and had an excellent consistency, which shows the excellent reliability and internal consistency of the scale for the set sample (see

Table 1. Statistics reliability.

Cronbach Alpha	Cronbach Alpha Based on Standardized Items	No. of Items
0.944	0.946	26

below).

After the Cronbach coefficient was determined, a check of data suitability for the factor analysis was performed to judge whether the factor analysis was justified for the set assertions. The sampling adequacy was measured according to the Kaiser–Meyer–Olkin measure of sampling adequacy (KMO), as well as the value of the Bartlett’s test of sphericity indicator (

Table 2. KMO and Bartlett’s test.

Kaiser–Meyer–Olkin Measure of Sampling Adequacy		0.922
	Approx. chi-square	4016.811
Bartlett’s test of sphericity	df	325
	Sig.	0.000

). The calculated value of the sampling adequacy indicator according to the KMO should be either equal to or greater than 0.6—in the research study presented in this paper, that value was 0.922. The calculated value of Bartlett’s test of sphericity is statistically significant if p < 0.05—in the research study presented in this paper, that value was p = 0.000. The calculations showed that both stated parameters were within the prescribed limits, so the data were suitable for further research activities.

Further, in the research study, the values of the correlation coefficients were based on which check was performed to make sure that they were good for factorization, where the condition was that the obtained values had to have a greater part of correlation coefficients r ≥ 0.30. It was concluded that the factor analysis of the data performed in this research study was suitable.

The determination of how many factors should be singled out for the research was performed according to the Kaiser criterion, where the components, i.e., factors, whose value is 1 or greater than 1 are included. According to

Table 3. The explained total variance.

Total Variance Explained
Component	Initial Eigenvalues			Extraction Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	11.311	43.503	43.503	11.311	43.503	43.503
2	1.837	7.065	50.568	1.837	7.065	50.568
3	1.579	6.073	56.641	1.579	6.073	56.641
4	1.277	4.911	61.552	1.277	4.911	61.552
5	0.986	3.792	65.344
6	0.903	3.472	68.816
7	0.838	3.223	72.039
8	0.772	2.971	75.010
9	0.722	2.776	77.785
10	0.633	2.434	80.219
11	0.593	2.281	82.500
12	0.548	2.107	84.607
13	0.447	1.720	86.327
14	0.420	1.615	87.942
15	0.401	1.544	89.486
16	0.382	1.471	90.957
17	0.340	1.307	92.264
18	0.324	1.247	93.511
19	0.276	1.062	94.573
20	0.260	0.999	95.572
21	0.237	0.912	96.484
22	0.236	0.907	97.391
23	0.202	0.777	98.168
24	0.177	0.682	98.850
25	0.159	0.610	99.460
26	0.140	0.540	100.000
Extraction method: principal component analysis.

below (the total variance explained), it is evident that the first four components had characteristic values exceeding 1 (11.311, 1.837, 1.579, and 1.277). These four components represent the factors that explain as much as 61.552% of the variance in total.

Additional checks were performed with the aim of determining the number of factors that should be singled out for the research. A scree plot was created, and the breakpoint, the so-called “break elbow”, was sought in the plot, with only the components above that point being retained. In the research study presented in this paper, the diagram broke at the point where the first and the second factors met. As only the factors above that point were kept, this diagram (Figure 2) recommended that only one factor be kept; therefore, no factor analysis was needed.

The checking procedure continued with additional criteria. A parallel analysis was carried out based on the list of the characteristic values from

Table 4. The comparison of the characteristic values, the obtained PCA, and the threshold values obtained through the parallel analysis.

No. of Components	Characteristic Value Obtained from PCA	Threshold Values Obtained through the Parallel Analysis	Decision
1	11.311	1.6532	accepted
2	1.837	1.5508	accepted
3	1.579	1.4675	accepted
4	1.277	1.4020	rejected

, and statistical software was used which could generate random numbers using a simulation method. In this research study, based on the 26 analyzed variables, the total of 245 participants in the sample, and as many as 100 replicas, Monte Carlo PCA for Parallel Analysis, version 3, and IBM Statistics SPSS, version 22x64, where, by default, the Kaiser criterion is used and all the components whose characteristic values exceed the value 1 are kept, were used. In this case, the values of the threshold obtained through the parallel analysis were greater on the third factor, so the three factors shown in

Table 5. The explained total variance.

Total Variance Explained
Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total
1	11.311	43.503	43.503	11.311	43.503	43.503	8.701
2	1.837	7.065	50.568	1.837	7.065	50.568	8.434
3	1.579	6.073	56.641	1.579	6.073	56.641	6.130
4	1.277	4.911	61.552
5	0.986	3.792	65.344
6	0.903	3.472	68.816
7	0.838	3.223	72.039
8	0.772	2.971	75.010
9	0.722	2.776	77.785
10	0.633	2.434	80.219
11	0.593	2.281	82.500
12	0.548	2.107	84.607
13	0.447	1.720	86.327
14	0.420	1.615	87.942
15	0.401	1.544	89.486
16	0.382	1.471	90.957
17	0.340	1.307	92.264
18	0.324	1.247	93.511
19	0.276	1.062	94.573
20	0.260	0.999	95.572
21	0.237	0.912	96.484
22	0.236	0.907	97.391
23	0.202	0.777	98.168
24	0.177	0.682	98.850
25	0.159	0.610	99.460
26	0.140	0.540	100.000
Extraction method: principal component analysis.

below were kept for further research activities.

The final decision on the number of the factors was made based on the component matrix consisting of the unrotated factor loadings of each of the items (the variables) for the four components (the factors) and the pattern matrix showing the factor loadings for the initial four factors (greater than 0.3). Based on all the performed criteria of the factor analysis, a solution was imposed with a total of three factors. Based on the three-factor solution, the total percentage share of the variance explained by that solution (as is given in Table 5 below) was checked. The three-factor solution explained 56.641% of the variance, whereas the four-factor solution explained 61.552%; the difference was only 4.911%, so the solution with a smaller number of the factors was adopted.

The research study further showed (in

Table 6. The component correlation matrix.

Component Correlation Matrix
Component	1	2	3
1	1.000	0.509	0.425
2	0.509	1.000	0.427
3	0.425	0.427	1.000
Extraction method: principal component analysis. Rotation method: Oblimin with Kaiser normalization.

below) the strength of the correlation between the three factors that were singled out, which were as follows: Factor 1 and Factor 2 = 0.509, Factor 1 and Factor 3 = 0.425, and Factor 2 and Factor 3 = 0.427, which shows the presence of a positive but relatively weak mutual dependence between those three factors.

Table 7. The pattern matrix.

Pattern Matrix
	Component
	1	2	3
Q19	0.879
Q18	0.828
Q20	0.737
Q21	0.678
Q17	0.667
Q16	0.662
Q22	0.658	0.331
Q14	0.582
Q15	0.532	0.333
Q23	0.395		0.375
Q4		0.821
Q2		0.786
Q1		0.763
Q6		0.761
Q5		0.720
Q3		0.669
Q7		0.605
Q9		0.403	0.303
Q8		0.379	0.350
Q10		0.353	0.311
Q12			0.708
Q25		0.322	0.699
Q13			0.648
Q26			0.604
Q24			0.443
Q11			0.325
Extraction method: principal component analysis. Rotation method: Oblimin with Kaiser normalization.

below accounts for the factor loadings of all the variables for the three-factor solution. In this research study, the three-factor solution was founded on the minimum threshold of the factor loading significance (0.30). The main factor loadings of component 1 were given by the assertions Q19, Q18, Q20, Q21, Q17, Q16, Q22, Q14, Q15, and Q23 and are referred to as the Factors Affecting Students’ Motivation; the factor loadings of component 2 were given by Q4, Q2, Q1, Q6, Q5, Q3, Q7, Q9, Q8, and Q10 and are referred to as Brand Uniqueness; the factor loadings of component 3 were given by Q12, Q25, Q13, Q26, Q24, and Q11 and are referred to as The Social Network.

Table 8. The structure matrix.

Structure Matrix
	Component
	1	2	3
Q19	0.888	0.481	0.357
Q16	0.828	0.596	0.525
Q20	0.826	0.441	0.560
Q18	0.797	0.409
Q22	0.774	0.614
Q14	0.743	0.538	0.500
Q15	0.737	0.639	0.452
Q21	0.735	0.417	0.399
Q17	0.725	0.407	0.406
Q23	0.526	0.306	0.519
Q4	0.395	0.822	0.389
Q6	0.507	0.809	0.327
Q2	0.472	0.799
Q1	0.483	0.775
Q7	0.559	0.736	0.377
Q5	0.351	0.732	0.378
Q3	0.366	0.718	0.439
Q8	0.598	0.659	0.621
Q9	0.369	0.550	0.490
Q10	0.369	0.515	0.486
Q26	0.430	0.501	0.721
Q12	0.364		0.718
Q25		0.472	0.713
Q13	0.448	0.306	0.705
Q24	0.333		0.505
Q11	0.380	0.313	0.442
Extraction method: principal component analysis. Rotation method: Oblimin with Kaiser normalization.

below accounts for the structure matrix, i.e., the matrix of the correlation coefficients between the variables and the factors of the structure matrix within the Oblimin rotation.

The factor loading matrix (the pattern matrix) and the structure matrix, as well as one part of the variance explained by the mutual factors (communalities) for the factor analysis applying the principal components analysis technique with the Oblimin rotation of the three-factor solution for all the assertions, are given in

Table 9. The matrix of the factor loadings, the correlation between the variables, and the part of the variance explained by the mutual factors for the three-factor solution.

Pattern Matrix				Structure Matrix			Communalities
	Component			Component
	1	2	3	1	2	3
Q19	0.879	0.050	−0.038	0.888	0.481	0.357	0.792
Q18	0.828	0.038	−0.118	0.828	0.596	0.525	0.646
Q20	0.737	-0.048	0.267	0.826	0.441	0.560	0.738
Q21	0.678	0.029	0.098	0.797	0.409	0.250	0.550
Q17	0.667	0.018	0.115	0.774	0.614	0.298	0.538
Q16	0.662	0.190	0.163	0.743	0.538	0.500	0.746
Q22	0.658	0.331	−0.123	0.737	0.639	0.452	0.676
Q14	0.582	0.164	0.183	0.735	0.417	0.399	0.612
Q15	0.532	0.333	0.084	0.725	0.407	0.406	0.643
Q23	0.395	−0.055	0.375	0.526	0.306	0.519	0.386
Q4	−0.047	0.821	0.058	0.395	0.822	0.389	0.678
Q2	0.119	0.786	−0.113	0.507	0.809	0.327	0.653
Q1	0.167	0.763	−0.171	0.472	0.799	0.273	0.634
Q6	0.145	0.761	−0.059	0.483	0.775	0.226	0.670
Q5	−0.056	0.720	0.094	0.559	0.736	0.377	0.544
Q3	−0.048	0.669	0.173	0.351	0.732	0.378	0.539
Q7	0.245	0.605	0.015	0.366	0.718	0.439	0.588
Q9	0.035	0.403	0.303	0.598	0.659	0.621	0.383
Q8	0.256	0.379	0.350	0.369	0.550	0.490	0.620
Q10	0.057	0.353	0.311	0.369	0.515	0.486	0.354
Q12	0.105	-0.082	0.708	0.430	0.501	0.721	0.524
Q25	−0.292	0.322	0.699	0.364	0.274	0.718	0.601
Q13	0.212	-0.079	0.648	0.170	0.472	0.713	0.528
Q26	0.067	0.209	0.604	0.448	0.306	0.705	0.569
Q24	0.144	0.002	0.443	0.333	0.265	0.505	0.272
Q11	0.206	0.069	0.325	0.380	0.313	0.442	0.244

below.

According to Table 9, the main factor loadings for the components were as follows:

• for Component 1: Factors Affecting Students’ Motivation: Q19, Q18, Q20, Q21, Q17, Q16, Q22, Q14, Q15, and Q23;
• for Component 2: Brand Uniqueness: Q4, Q2, Q1, Q6, Q5, Q3, Q7, Q9, Q8, and Q10; and
• for Component 3: The Social Network: Q12, Q25, Q13, Q26, Q24, and Q11.

Further, in the research, the descriptive statistics of the answers given by the respondents to the set assertions were analyzed.

Table 10. Statistics.

No.	Assertion	Response
		1	2	3	4	5	Total	Mean	Std Dev
Q1	The brand of a private faculty creates a clear image in your mind of that faculty, which makes it different from the competition.	0 0.0%	2 0.8%	47 19.2%	64 26.1%	132 53.9%	245	4.3306	0.8103
Q2	The brand of a private faculty identifies a brand as a unique value.	2 0.8%	1 0.4%	35 14.3%	90 36.7%	117 47.8%	245	4.3020	0.7883
Q3	The brand of a private faculty enables growth.	1 0.4%	3 1.2%	44 18.0%	65 26.5%	132 53.9%	245	4.3224	0.8384
Q4	The brand of a private faculty is unique (original) and differs from the brands of other faculties.	5 2.0%	0 0.0%	15 6.1%	58 23.7%	167 68.2%	245	4.5592	0.7851
Q5	The brand of a private faculty is motivational and easy to remember.	2 0.8%	8 3.3%	24 9.8%	69 28.2%	142 58.0%	245	4.3918	0.8550
Q6	The brand of a private faculty is easy to understand.	2 0.8%	9 3.7%	35 14.3%	81 33.1%	118 48.2%	245	4.2408	0.8889
Q7	The brand of a private faculty is well positioned for achieving a long-term success.	0 0.0%	8 3.3%	38 15.5%	76 31.0%	123 50.2%	245	4.2816	0.8434
Q8	The slogan of a private faculty is convincing.	0 0.0%	10 4.1%	29 11.8%	62 25.3%	144 58.8%	245	4.3878	0.8497
Q9	The brand of a private faculty is capable of dealing with the competition.	5 2.0%	6 2.4%	30 12.2%	71 29.0%	133 54.3%	245	4.3102	0.9242
Q10	It is important for the brand of a private faculty that its professors have good contact with the students.	1 0.4%	1 0.4%	13 5.3%	38 15.5%	192 78.4%	245	4.7102	0.6221
Q11	The brand of a private faculty has good promotion (marketing, communication).	9 3.7%	30 12.2%	84 34.3%	81 33.1%	41 16.7%	245	3.4694	1.0263
Q12	The YouTube channel has an interesting video material.	2 0.8%	1 0.4%	10 4.1%	63 25.7%	169 69.0%	245	4.6163	0.66510
Q13	Students’ shared experiences on the faculty’s website are credible.	0 0.0%	3 1.2%	12 4.9%	43 17.6%	187 76.3%	245	4.6898	0.6221
Q14	The brand of a private faculty has good study programs.	4 1.6%	5 2.0%	39 15.9%	81 33.1%	116 47.3%	245	4.2245	0.9023
Q15	The brand of a private faculty stimulates scientific research.	4 1.6%	6 2.4%	51 20.8%	55 22.4%	129 52.7%	245	4.2204	0.9669
Q16	The brand of a private faculty encourages ambition and interests.	4 1.6%	9 3.7%	33 13.5%	93 38.0%	106 43.3%	245	4.1755	0.9131
Q17	The brand of a private faculty has a favorable price for the tuition fee.	2 0.8%	2 0.8%	21 8.6%	64 26.1%	156 63.7%	245	4.5102	0.7554
Q18	Satisfaction in learning is the strength of the brand of a private faculty.	10 4.1%	18 7.3%	47 19.2%	71 29.0%	99 40.4%	245	3.9429	1.1221
Q19	The brand of a private faculty offers the satisfactory knowledge and skills necessary for students’ future work.	0 0.0%	5 2.0%	34 13.9%	81 33.1%	125 51.0%	245	4.3306	0.7898
Q20	The brand of a private faculty offers good prospects for a career.	0 0.0%	9 3.7%	34 13.9%	77 31.4%	125 51.0%	245	4.2980	0.8426
Q21	The brand of a private faculty offers students the opportunity to engage themselves in students’ organizations.	0 0.0%	3 1.2%	20 8.2%	79 32.2%	143 58.4%	245	4.4776	0.6987
Q22	The brand of a private faculty encourages creativity.	5 2.0%	5 2.0%	45 18.4%	68 27.8%	122 49.8%	245	4.2122	0.9516
Q23	The brand of a private faculty has a good location.	6 2.4%	5 2.0%	30 12.2%	38 15.5%	166 67.8%	245	4.4408	0.9547
Q24	The Facebook page of a private faculty is of a high quality.	9 3.7%	16 6.5%	44 18.0%	53 21.6%	123 50.2%	245	4.0816	1.1278
Q25	The website of a private faculty is customized for mobile phones.	1 0.4%	5 2.0%	18 7.3%	58 23.7%	163 66.5%	245	4.5388	0.7544
Q26	The website of a private faculty is of a high quality.	4 1.6%	10 4.1%	31 12.7%	62 25.3%	138 56.3%	245	4.3061	0.9539

below accounts for the magnitudes of the frequencies and the percentage share of the answers to the set assertions. It is possible to see that the highest mark was given for the attitude “I completely disagree” (the maximum disagreement with the set assertion) for the set assertion Q18, and that mark was 10 (i.e., 4.1% of the total of 245 respondents), and that the highest mark (the maximum agreement with the set assertion) for the attitude “I completely agree” was given for the assertion Q10, and that mark was 192 (i.e., 78.4% of the total of 245 respondents). It can also be seen that the lowest mark was given for the attitude “I completely disagree” (the maximum disagreement with the set assertion), which was given in the case of a larger number of the set assertions Q1, Q7, Q8, Q13, Q19, Q20, and Q21, and that mark was 0 (i.e., 0.00% of the total of 245 respondents), whereas the lowest mark (the maximum agreement with the set assertion) was given for the attitude “I completely agree” for the assertion Q11, and that mark was 41 (i.e., 16.7% of the total of 245 respondents). The highest mean value of the marks was that of the assertion Q10, which was 4.7102040816, whereas the lowest mean value of the marks is that of the assertion Q11, which was 3.4693877551. The highest standard deviation was that of the assertion Q24, and was 1.1278285878, whereas the lowest standard deviation value was equally assigned to the assertions Q10 and Q13, which was 0.6220816779.

In

Table 11. The factors.

Factors Affecting Students’ Motivation		The Social Network	Brand Uniqueness
Mean	4.2832653	4.4095238	4.3836735
Std Dev	0.6899198	0.5732739	0.6001868
Std Err Mean	0.0440774	0.0366251	0.0383445
Upper 95% Mean	4.370086	4.4816656	4.459202
Lower 95% Mean	4.1964446	4.3373821	4.308145
N	245

below, the magnitudes of mean and standard deviation are given for the following components: Factors Affecting Students’ Motivation, The Social Network, and Brand Uniqueness. The highest mean mark was that of the The Social Network component (4.4095238), but the same component was assigned the lowest standard deviation (0.5732739); then followed the Brand Uniqueness component, with the mean mark 4.3836735 and the standard deviation 0.6001868; finally, the Factors Affecting Students’ Motivation component was assigned the lowest mean mark 4.2832653 but the highest standard deviation 0.6899198.

Further, in the research study, both the correlation analysis and the regression analysis of the model were carried out. Figure 3 below shows the Pearson correlation values for all the components of the set theoretical model. It can be seen that all the directions of the connections between the components are positive, which means that there was a positive correlation. The highest correlation coefficient was that between the independent component Factors Affecting Students’ Motivation and the dependent component Brand Uniqueness (0.7230), which was considered to be medium strong, whereas the determination coefficient was 0.522729, i.e., the dependent component Brand Uniqueness could be 52.27% explained by the independent component Factors Affecting Students’ Motivation. The correlation coefficient between the independent component The Social Network and the dependent component Brand Uniqueness was 0.6729, which made it medium strong, whereas the determination coefficient was 0.45279441, i.e., the dependent component Brand Uniqueness could be 45.27% explained by the independent component The Social Network. The correlation coefficient between the independent components Factors Affecting Students’ Motivation and The Social Network was 0.7155, and that correlation was medium strong, whereas the determination coefficient was 0.51194025, i.e., the component The Social Network could be 51.19% explained by the component Factors Affecting Students’ Motivation.

Based on the theoretical systematic model from Figure 1, for the single linear dependences,

Table 12. The regression analyses data for the dependent variable Brand Uniqueness.

Independent Component	ANOVA	Std Beta	RSquare (%)	Connectedness	Hypothesis	Regression Equation
Factors Affecting Students’ Motivation	[F(1243) = 266.2097, p < 0.0001]	0.72304	52.27	Medium strong	H1—accepts the level Factors Affecting Students’ Motivation influences the level of Brand Uniqueness.	$y=1.6894949+0.6290011·x_1$ $Brand Uniqueness=1.6894949+0.6290011·Factors Affecting Student{s}^{\prime }Motivation$
The Social Network	[F(1243) = 201.0202, p < 0.0001]	0.67285	45.27	Medium strong	H2—accepts the level of The Social Network influences the level of Brand Uniqueness.	$y=1.2774376+0.7044379·x_2$ $Brand Uniqueness=1.2774376+0.7044379·The Social Network$

below shows the marks for the statistical significances of the influence of the independent component Factors Affecting Students’ Motivation on the dependent component Brand Uniqueness and the independent component The Social Network on the dependent component Brand Uniqueness (ANOVA, as well as the interpretation: std beta, RSquare (%), the connectedness between the components, the hypotheses, and the regression equation).

In

Table 13. The multiple regression analysis data for the dependent variable Brand Uniqueness.

Independent Component	ANOVA	Std Beta	RSquare (%)	Connectedness	Hypothesis	Regression Equation
Factors Affecting Students’ Motivation and The Social Network	[F(2242) = 161.9446, p < 0.0001]	0.75654	57.23	Medium strong	H0—accepts the levels of Factors Affecting Students’ Motivation and The Social Network influence the level of Brand Uniqueness.	$y=1.0679202+0.4306664·x_1+0.3336176·x_2$$Brand Uniqueness=1.0679202+0.4306664·Factors Affecting Student{s}^{\prime } Motivation+0.3336176·The Social Network$

below, the multiple linear dependence is rated by the mark for the statistical significance of the influence(s) of the independent components Factors Affecting Students’ Motivation and The Social Network on the dependent component Brand Uniqueness (ANOVA, as well as the interpretation: std beta, RSquare (%), the connectedness between the components, the hypothesis, and the multiple regression equation).

Based on Figure 4, the graphs of the two linear single regression equations and one multiple regression equation in function of the dependent component Brand Uniqueness (Figure 4) were projected.

5. Discussion

There is extensive literature studying the factors upon which students’ motivation depends [2,15,16,17,18,19,20]. The influence of social media on brand development has been a topic researched by numerous authors in different areas, such as customer purchase intention [21], sports [22], mobile social networks [23], luxury fashion goods [11], fashion brands [24], the airline industry [25], and tourism [26]. There, however, seems to be a gap in re-searching the influence of the factors affecting students’ motivation and social networks on the uniqueness of the brand of a higher education institution. The results of this research may help higher education institutions in preparing their plans, strategies, and good practices.

There is a lot of research that proves that a brand image helps a company to stand out and create a competitive advantage [81,82,83,84,85].

Future research directions could be the influence of social networks and motivational factors on brand competitiveness in different regions and cities.

6. Conclusions

In the contemporary world, omnipresent changes influence the way in which many activity branches work, and the higher education sector is not exempt. The significance of the creation of a unique brand for higher education institutions has been gaining importance with the increase in the number of private higher education institutions and competition, as well as, at the same time, the change in the attitude towards education and change in the types and scopes of courses which are offered. It is strategically significant that the internal and external factors on which students’ motivation depend, which inspire the wishes, energy, interestedness and dedication of students, are studied, and meaningful and useful academic activities should be discovered, and an effort should be made to gain an academic advantage from them. Motivational beliefs are becoming very important because they help to determine the extent to which students consider and evaluate their obligations and make an effort to perform them and show an interest in performing them.

In parallel with the above, social networks are becoming an important marketing instrument, and are increasingly being used as a platform for performing marketing and advertising activities, which organizations are increasingly spending their time, money, and other resources on. The role of social networks has gradually evolved from a marketing tool to a role as the source of information for the purpose of analyzing and anticipating buyer behavior and achieving a competitive advantage and superior performances. In the past decades, complex, diverse, and strengthened interactions have developed between organizations and their buyers through social networks. The advantages of different social network platforms are used to expand the geographical reach to buyers and build closer connections with buyers. Therefore, understanding user motivation and social networks’ interactive characteristics is crucially important for designing valuable content, facilitating interactions with buyers, and exchanging content between them.

Designing impressive and valuable content to transform passive watchers on social media into active participants and associates has become an important marketing task. Different social network platforms, such as YouTube, Facebook, LinkedIn, and Twitter, have different purposes and can be used in different situations. Those platforms enable building common interests and values for both organizations and buyers and connecting in the ways that used to be impossible. With the help of contemporary information technologies, it is possible to easily utilize the enormous amount of data from different locations on social networks (blogs, forums, etc.) and in different formats (a text, a video, an image). Thanks to marketing on social networks, organizations are increasingly becoming more and more successful in understanding consumers, determining their needs, and conducting market research, communication, and advertising, because they have the opportunity to easily become knowledgeable of what buyers want and what they think of a product or a service as well. With the growth of competition, the creation of a unique brand differentiating an institution or product from the competition, which implies the creation of the key brand elements (such as a visual expression, a brand personality, and brand positioning), has increasingly gained significance.

Brand uniqueness is built by combining the brand name with other characteristics, such as symbols, signs, logotypes, music, and images. The way in which students experience the elements of the brand of a higher education institution, such as the signs, symbols, and names, that both identify and differentiate a brand from its competition, may have an influence on connectedness with the institution. In a time of globalization and technological innovation, branding higher education institutions is a way to maintain competitiveness in the market and the ability to face numerous challenges.

The research study presented in this paper was motivated by the wish to identify the connections between different factors affecting students’ motivation, social network instruments, and the user satisfaction expressed through brand uniqueness, which may be useful for developing strategies and good practices for achieving loyalty to the brand of a higher education institution. The research study showed that the dependent component Brand Uniqueness could be 52.27% explained by the independent component Factors Affecting Students’ Motivation, which confirmed hypothesis H1 that the level of the Factors Affecting Students’ Motivation influences the level of Brand Uniqueness. It was also established that the dependent component Brand Uniqueness could be 45.27% explained by the independent component The Social Network, which confirmed hypothesis H2 that the level of The Social Network influences the level of Brand Uniqueness. It was also established that the dependent component Brand Uniqueness could be 57.23% explained by the independent components the levels of Factors Affecting Students’ Motivation and The Social Network, which confirmed hypothesis H0 that the levels of Factors Affecting Students’ Motivation and The Social Network influence the level of Brand Uniqueness.

This study successfully determined whether and to what extent individual factors affecting students’ motivation and social networks have an impact on the uniqueness of a higher education institution’s brand, as well as how the factors affecting students’ motivation and social networks collectively affect the brand’s uniqueness. The findings of this study could aid higher education institutions in developing future goals, strategies, and best practices. The impact of social networks and motivating variables on brand competitiveness in different locations and cities could be future research topics.