Introduction

Universities play an important role in the dissemination of knowledge, enhancing innovation and enhancing the competitiveness of a country. Universities in Indonesia are very competitive and compete with each other to get quality prospective students. This competition forces us to continuously improve strategic approaches in improving quality (Rafdinal, Mulyawan, et al., 2020). Higher education institutions must make improvements from various aspects, especially for the quality. In terms of education, the quality produced is the education provided - the knowledge, skills and competencies that graduates possess. This is important for every university to improve its quality. However, this simple and rather concise definition of quality requires considerable effort in the real world to obtain the maximum possible quality. Therefore, this study will explain how important the role of university quality is.

The image of a university represents a series of attitudes, ideas, and impressions that prospective students have regarding the college (Rodic-Lukic & Lukic, 2017). Image is an important concept in consumer behavior research because it affects individuals, subjective perceptions, consumer value, satisfaction, and behavioral intentions (Jin, 2015; Rafdinal, Mulyawan et al., 2020). Universities that are known to have a positive image will find it easier to get quality students. This is related to how the role of image on attitudes is formed by students before they enroll in university. It is important to create and manage an image to give a good impression to prospective students (Rafdinal, Mulyawan et al., 2020). A good image will give the impression that the education you will get is of good quality. For this reason, this study will reveal the important role of university image as one of the main factor that will influence the attitudes of prospective students.

In explaining the intention to enroll at universities, this study uses the stimulus-organism-response (S-O-R) model as a process of university intention to enroll. This model is used to analyze user behavior in various fields (Alam & Noor, 2020; Benlian, 2015; Hu et al., 2016; Zhu et al., 2020). The S-O-R framework states that perceived clues (stimuli) from the environment can trigger a person's (organism's) internal state of judgment, which in turn results in positive or negative behavior (response) (Mehrabian & Russell, 1974). The stimulus in this research is university quality and university image which is the university's effort to shape it. Organisms, consisting of attitudes which are formed from a stimulus consisting of cognitive and affective. The response describes the results of the organism, namely the intention to enroll university. Although many studies use the S-O-R model, there have been no previous studies using the S-O-R model in the context of this study. Thus, this research is important to be conducted because it can provide an understanding of how the intention to enroll university processes.

This paper is organized into six parts. This paper begins with an introduction, continues with a literature review and development of hypotheses, research methods, theoretical findings and implications, managerial implications, and finally limitations and ideas for future research.

Literature Review

In this section, we provide a review of the relevant literature as a theoretical basis for the proposed models and hypotheses. Drawing from various papers and studies, we explain and discuss the S-O-R framework, university quality, university image, cognitive attitude, affective attitude, and intention to enroll a university.

Stimulus-Organism-Response (S-O-R) Framework

The Stimulus-organism-response S-O-R framework introduced by Mehrabian & Russell (1974) was used to analyze user behavior in various fields. The framework states that clues (stimulus) that are felt from the environment can trigger a state of internal judgment of a person (organism), which in turn results in positive or negative behavior (response) (Mehrabian & Russell, 1974). In stimuli, where different stimuli from the environment affect the individual's cognitive or emotional experience and then produce a behavioral response to the stimulus after a series of internal psychological activities (Benlian, 2015; Hu et al., 2016). Previous studies used various external factors on stimulus within the S-O-R framework. Zhu et al., (2020) use the quality of information and social presence which affects purchase intentions directly or through satisfaction and trust. Another study by Alam & Noor (2020) analyzes service quality in terms of influence on loyalty. Yasami et al., (2020) uses product images in the stimulus process to influence loyalty intention. Image is defined by not only one aspect but also through cognitive and affective processes (Dichter, 1985). These studies use a variety of external factors in the stimulus process, especially on quality and image. These two factors are important in the context of a university because a good university quality and a positive university image will affect the cognitive and affective attitudes of prospective students to enroll in the university.

In the process of organism, Mehrabian & Russell (1974) only focus on emotional responses, Bitner (1992) combines cognition, emotional, and physiology in S-O-R theory, extending its application to servicescape. In addition, an integrative S-O-R framework is presented with cognitive and affective systems that incorporate all previous involved experiences involving long-term memory (Jacoby, 2002). With regard to the S-O-R model, Kim & Moon (2009) suggest that servicescape lead to positive-negative customer experience behaviors through cognitive and affective processes. The previous studies show that there is a cognitive and affective role in the process of organisms in the S-O-R framework. In the context of this research, cognitive and affective analysis is the impact of the quality and image of the university. Someone who positively perceives the quality and image of the university will influence his cognitive and affective attitudes.

In the early S-O-R model (Mehrabian & Russell, 1974), organisms were described by three emotional states: pleasure; Passion and Dominance (PAD). Due to the narrow scope of the dimensions of PAD, the literature has suggested various other constructs related to the internal state. Based on the categorization of internal states, we combined two components of the organism: cognitive and affective (Jacoby, 2002; Kim et al., 2020; Kim & Moon, 2009). We operationalize attitudes using a two-dimensional approach, both as cognitive and affective. Based on the theoretical arguments mentioned above, this study aims to investigate the effect of university quality and university image on two dimensions of attitude: cognitive and affective, and the intention to enroll in a university.

Cognitive Attitude dan Affective Attitude

Attitude is defined as an evaluation of the likes or dislikes of an object (Ajzen & Fishbein, 2015). Attitude is not a one-dimensional construct - it has several dimensions including: cognition, affect, emotion, value, and consciousness (Fiore & Kim, 2007). We use two attitude dimensions in this study: cognitive attitude and affective attitude. Cognitive attitude refers to the extent to which an individual likes or dislikes an object on the basis of its use and function (Celebi, 2015; Fiore & Kim, 2007; Moon et al., 2017). Affective attitude is related to the sensation and emotional experience of a person originating from an object (Fiore & Kim, 2007). This study analyzes the two types of attitudes as part of the organism process on S-O-R in their effect on the intention to enroll at a university.

Attitude has been used by various kinds of studies and has been used in different contexts (Moon et al., 2017; Rafdinal, Qisthi et al., 2020; Suparno, 2020). A significant and positive relationship exists between cognitive and affective attitudes on their purchase intentions or purchase behavior (Moon et al., 2017; Suparno, 2020). Suparno (2020) analyzes the cognitive and affective attitude towards online purchase intention of halal products. Affective attitude has a greater influence on intention than cognitive attitude. In contrast, Moon, et al., (2017) shows that cognitive attitude has a greater influence on purchase intention than affective attitude. Regardless of which has the greatest influence, cognitive and affective attitude both have an influence on intention. Thus, the proposed hypothesis:

H1 Cognitive attitude has a positive and significant effect on the intention to enroll

H2 Affective attitude has a positive and significant effect on the intention to enroll

University Quality

Quality is defined as "a series of attributes selected on the basis of accuracy and precision of measurement"(Shewfelt, 1999). The product is the result of the process - in terms of education the product is the education provided - the knowledge, skills and competencies that graduates from the study program have. The higher the perception of product quality (namely education) from each interested party (Madzík & Chocholáková, 2016). University quality is an important factor in influencing someone to apply. Students select institutional rankings to help them 'decide where to apply and which schools to attend' (Sauder & Espeland, 2009) because they believe that ratings provide an indication of the 'underlying qualities' of a school(Sauder & Lancaster, 2006). Pizarro Milian & Rizk (2018) found that ranking is important especially for those who enroll university. We assume that many of the conditions above will be different in the selection of universities in Indonesia.

Universities that have good quality will affect the attitude of the applicants. Several Planning 4 1544-1458-20-S2-97 previous studies have shown the effect of quality on cognitive attitude and affective attitude. Research by Ahn & Back (2018) shows that two- way communication, emotional exchange, and brand partner quality have an effect on affective factors such as brand attitude. Other research shows that cognitive attitude and affective attitude are influenced by product utility factors such as product information, monetary savings, convenience, and perceived ease of use (Moon et al., 2017). Other studies show that e-quality factors affect attitude (Alonso-Dos-Santos et al., 2017). In the context of this study, we assessed quality in the university context which is expected to influence cognitive attitude and affective attitude. So, the hypothesis is built as follows:

H3 University quality has a positive and significant effect on cognitive attitude

H4 University quality has a positive and significant effect on affective attitude

University Image

Image plays an important role in the process of buying or choosing a service and accelerating or facilitating customer choice (Khodadad Hosseini & Behboudi, 2017). Customers, when making decisions for their purchases, are not only affected by the tangible benefits it provides (quality and price) but also intangible assets, such as image, brand association, or business reputation (Cretu & Brodie, 2007). A good image makes consumers believe in product quality and helps consumers to make choices and feel comfortable when buying their products (Chih-Chung et al., 2012). Company image is an asset that requires distinctiveness and familiarity because it has an impact on customer perceptions of the company's operations (Kang & James, 2004). Image in the context of this research focuses on a person's perception of a particular university image. Students who see a positive image of a university will shape their attitudes and interests to enroll the university in the future.

A positive image is associated with good quality. The influence between quality and image has been widely analyzed in previous studies. In the context of universities, empirical results show that there is a relationship between image, quality and innovation in university governance (Salvioni et al., 2017). Research by Hassan & Shamsudin (2019) analyzes the influence of service quality at institutions that affects the image of the institution. Salvioni, et al., (2017) analyzes American universities which are highly oriented towards a competitive approach based on image to attract consensus and resources. Every eligible asset becomes a competitive mechanism: from students to professors, from researchers to sports stars (Salvioni et al., 2017). This shows a relationship between quality and the image created. Thus, the hypothesis is built as follows:

H5 University quality has a positive and significant effect on university image.

University Image and Attitude

Many previous studies have analyzed the effect of image on attitudes in various contexts such as tourism (Huang & van der Veen, 2019), CSR activities (Ramesh et al., 2019), Presidential election (Sihombing, 2019), etc. Liu, et al., (2020) shows that brand image has a direct effect on two brand attitudes, namely cognitive and affective. Image consists of two main elements, namely functional quality and emotional quality (Saleh et al., 2017), where functional quality refers to tangible aspects that can be recognized and assessed and emotional quality related to the psychological components expressed by customers. This functional quality is related to the individual cognitive elements that assess the benefits of the image, while the emotional quality is related to the affective element which is a person's feelings towards the image. University image as the main antecedent in influencing one's attitude in choosing a university (Rafdinal, Mulyawan et al., 2020). Thus, we assume that university image has a significant

H6 University image has a positive and significant effect on cognitive attitude

H7 University image has a positive and significant effect on affective attitude

Methods

Operationalization of the Constructs

The measurement of each construct in this study was adapted from previous research. Intention to enroll is measured by four items, namely intending to enroll at a university that you are interested in, wanting to get more information about the university you are interested in, it is possible to apply to a university you are interested in, very sure to apply to a university you are interested in, and have a good chance of applying. to universities of interest (Mathieson, 2005; Simiyu et al., 2020). The quality of a university is measured by four items, namely being happy with a quality university, happy with a well-prepared university, happy with a university that has good quality standards, and being happy with a university that has poor performance (Sweeney & Soutar, 2001) Then image is measured from universities that are widely recognized by people, have a good image, and have a good reputation (Chen, 2016; Schlesinger et al., 2017). Cognitive attitude is measured by four items, namely like universities with easy graduates. looking for a job, having good prospects for the future, offering reasonable tuition fees, and value for money (Moon et al., 2017; Suparno, 2020). Then, effective commitment is measured by four items, namely it is important to be able to enjoy the university to be entered. Can feel comfortable at the university that will be entered, feel worthy of the university that will be entered, and can feel comfortable at the university that will be entered (Moon et al., 2017; Suparno, 2020).

Data Collection and Sampling

The questionnaire is used as a primary data collection tool. Researchers collected data from various high schools in Bandung at different times and days. Respondents were selected through judgment sampling which in this case involved the status of schools, namely public schools and private schools to provide the information needed for research. The sample qualification criteria are twofold. First, respondents are active high school students and intend to continue to university. A self-administered questionnaire was used to obtain data for September 2020. The questionnaire took about 10 minutes to complete. During the survey, the researcher informed respondents that their participation in the survey was voluntary and also assured them of its anonymity and confidentiality. Within a certain period of time, the questionnaire was filled in and returned to the researcher. A total of 411 questionnaires can be processed for analysis.

Findings and Discussion

Sample Characteristics

After the data collection process through a questionnaire obtained respondent data. The number of respondents was 411 high school students. They consisted of 126 female (31%) and 285 male (69%) respondents. Furthermore, 372 or 91% of the respondents were from year 12. The majority of respondents came from public secondary schools as many as 225 respondents (55%) followed by private schools as many as 186 respondents (45%). Parents' jobs, especially as employees of private companies were 174 respondents (42%), entrepreneur’s as many as 126 respondents (31%), and other 111 respondents (27%). These results can be seen in the following table:

Table 1 Respondent Characteristics
Variable	Description	Frequency	(%)
Gender	Female	126	31
	Male	285	69
Class	Class 12	372	91
	Class 10 and 11	39	9
School Status	Public	225	55
	Private	186	45
Parent Occupancy	Private Employee	174	42
	Entrepreneur	126	31
	Others	111	27

Measurement Model

First, convergent validity is tested. Convergent validity is referred to as 'the degree to which multiple items to measure the same concept are in agreement' (Hair et al., 2017). Specific construct indicators must be highly convergent with the construction. This can be confirmed by testing factor loading with a minimum value of 0.7, average variance extracted (AVE) with a minimum threshold value of 0.5 and a composite reliability cut-off value of at least 0.7 (Hair et al., 2017). Table 2 illustrates that all loading factors are at values above 0.7. For the AVE value, each construct shows that the AVE value ranges from the highest 0.740 to the lowest 0.602. Thus, the AVE for each latent construct exceeds the cut-off value of 0.5 recommended by (Chin, 1998). Then, the CR value exceeds the threshold value of 0.7 which ranges from 0.905 to 0.857 (Kock, 2011). Therefore, convergent validity is considered adequate in this measurement model.

Table 2 The Result of Measurement Model
Constructs and items	Loading	Cronbach’ alpha	CR	AVE
University Quality		0.859	0.905	0.704
I am happy with the university quality	0.826
I am happy with a university that is well-prepared	0.908
I like universities that have good quality standards	0.82
I like universities that have good staff and lecturers	0.8
University image		0.826	0.895	0.74
I like universities that many people recognize	0.908
I like universities that have a good image	0.827
I like universities that have a good reputation	0.843
Cognitive attitude		0.781	0.857	0.602
I like universities where graduates are easy to find work	0.814
I like universities that have good prospects for the future	0.861
I like universities that offer reasonable tuition fees	0.709
I like universities that value-for-money	0.707
Affective attitude		0.854	0.901	0.696
It is important for me to be able to enjoy the university that I will enter	0.854
It is important for me to be able to feel comfortable in the university I will be attending	0.826
It is important for me to be able to feel worthy of the university I will enter	0.87
It is important for me to be able to feel good at the university I will enter	0.785
Intention to enroll		0.843	0.886	0.61
I intend to enroll in a university that I am  interested in	0.812
I would like to get more information about the university I am interested in	0.774
I am very likely to apply to a university that I am interested in	0.756
I firmly believe in applying to the university that I am interested in	0.84
I have a good chance of applying to the university I am interested in	0.716

Furthermore, discriminant validity is assessed to ensure each latent construct is different from other constructs in the same measurement model (Chin, 1998; Hair et al., 2017; Kock, 2011). To measure discriminant validity, the square root of AVE was used to compare it with correlation (Fornell & Larcker, 1981). As long as the square root of AVE for each latent construct shows a value greater than or greater than the correlation value on the same row and column, this confirms the formation of convergent validity. Table 3 shows that the square root of AVE diagonal is greater than the other correlation values shown in the row and column respectively. Therefore, it can be claimed that each latent construct is different from one another. Thus, discriminant validity can be accepted in this measurement model.

Table 3 Discriminant Validity (Fornell–Larcker Criterion)
	1	2	3	4	5
01 University Quality	0.839
02 University Image	0.554	0.86
03 Cognitive Attitude	0.559	0.693	0.776
04 Affetive Attitude	0.695	0.686	0.726	0.834
05 Intention to enroll	0.511	0.543	0.551	0.65	0.781

Structure Model

The main evaluation criterion for assessing the goodness of structural models is to test R2 which represents the effect of exogenous variables combination on endogenous variables (Hair et al., 2017). The value of R2, namely the three endogenous variables of cognitive value, affective value, and intention to enroll, are 0.523, 0.611, and 0.435. This means that the quality of the university and university image is able to explain 52.3% of the variance of cognitive attitude and to explain 61.1% of the variance of affective attitude. Meanwhile, the cognitive attitude and affective attitude explain 43.5% of the variation in intention to enroll. Based on Hair, et al., (2017); Chin (1998) rule of thumb, cognitive attitude, affective attitude, and intention to enroll show a moderate level of predictive power because the R2 value exceeds the threshold value of 0.33-0.67.

The effect sizes can be categorized into three levels: large (0.35), medium (0.15) and small (0.02) (Cohen, 1988). Based on the results of the f2 effect size test, there are three relationships that have a threshold value of more than 0.35 (large effect size): university quality towards university image (f2=0.444), university quality towards affective attitude (f2=0.368), and university image towards cognitive attitude (f2=0.442). Then, there are two relationships that have a value of 0.15-0.35 (medium size): university image towards effective attitude (f2=0.333) and affective attitude towards intention to enroll (f2=0.233). Meanwhile, the effect size on the criteria small 0.02-0.15 shows the effect of university quality on cognitive attitude (f2=0.093) and cognitive attitude towards intention to enroll (f2=0.233). For predictive relevance to measure how well the conservation value is generated by the model and also the parameter estimate of intention to enroll (Q2=0.801). Q-square value>0 indicates that the model has predictive relevance.

Table 4 shows the results of hypothesis testing. The results of hypothesis testing showed that cognitive attitude had no significant effect on intention to enroll (β=0.168, ρ>0.05), so that H1 was rejected. Meanwhile, affective attitude has a significant effect on intention to enroll (0.528, ρ<0.05), so that H2 is accepted. At university quality, university quality has a significant effect on cognitive attitude (β=0.253, ρ<0.05), on affective attitude (β=0.454, ρ<0.05), and on university image (β=0.554, ρ<0.05), so that H3, H4 and H5 accepted. In university image, university image has a significant effect on cognitive attitude (β=0.552, ρ<0.05) and affective attitude (β=0.432, ρ<0.05), so that H6 and H7 are accepted. These results indicate that the greatest influence is the influence of university quality on the university image (β=0.554) and the university image on cognitive attitude (β=0.552). However, the calculation of the total effect in table 5 shows that the largest total effect is the effect of university quality on affective attitude (β=0.694). This shows that university quality, university image, cognitive attitude, and affective attitude are important variables in the intention to enroll in a university.

Table 4 The Summary of Relationships Assessment
Relationships (Hypothesis)		β			T value
Cognitive attitude -> Intention to enroll		0.168			1.182ns
Affective attitude -> Intention to enroll		0.528			4.230**
University image -> Cognitive attitude		0.552			5.775**
University image -> Affective attitude		0.432			4.978**
University quality -> University image		0.554			6.613**
University quality -> Cognitive attitude		0.253			2.186*
University quality -> Affective attitude		0.454			4.795**

Table 5 Direct, Indirect, and Total Effect
			Direct effect			Indirect effect			Total effect
	Variables		β	T-value		β		T-value	β	T-value
	Cognitive attitude -> Intention to enroll		0.168	1.182ns		-		-	0.168	1.182ns
	Affective attitude -> Intention to enroll		0.528	4.230**		-		-	0.528	4.230**
	University image -> Cognitive attitude		0.552	5.775**		-		-	0.552	5.775**
	University image -> Affective attitude		0.432	4.978**		-		-	0.432	4.978**
	University quality -> University image		0.554	6.613**		-		-	0.554	6.613**
	University quality -> Cognitive attitude		0.253	2.186*		0.306		4.731**	0.558	5.417**
	University quality -> Affective attitude		0.454	4.795**		0.24		4.590**	0.694	8.578**

Discussion

As hypothesized, the attitude construct, namely affective attitude, has a significant effect on intention to enroll. This result is in line with previous research that shows a significant and positive relationship exists between cognitive attitude and affective attitude on their purchase intentions or purchase behavior (Moon et al., 2017; Suparno, 2020). However, the two constructs cannot be separated because they provide predictive power to intention as seen from the R2 value of 43.5%. This provides an understanding that the cognitive attitude and affective attitude which are the organisms of the S-O-R model are important factors in the intention to enroll in university. Based on the SOR model in this study, a person's internal assessment - Cognitive attitude shows the extent to which an individual likes or dislikes an object on the basis of its use and function (Celebi, 2015; Moon et al., 2017) and affective attitude is related to emotional sensations and experiences. Someone who comes from an object (Fiore & Kim, 2007). The results of this study prove that both types of attitude are part of the organism process in S-O-R as an important factor in influencing the intention to enroll at a university.

University image is found to have a significant effect on cognitive attitude and affective attitude. These results are in line with previous research in the context of tourism (Huang & van der Veen, 2019), CSR activities (Ramesh et al., 2019), Presidential election (Sihombing, 2019), etc. University image as the main antecedent in influencing one's attitude in choosing a university. A good image makes consumers believe in product quality and helps consumers to make choices and feel comfortable when buying their products (Chih-Chung et al., 2012). This shows that a good university image will affect the cognitive attitude (benefits to be obtained) and affective attitude (emotional feelings that will be obtained) by students in the process of choosing a university. The results of this study were successful in proving the important role of image in influencing attitudes, namely cognitive attitude and affective attitude.

University quality, which is part of the stimuli in the S-O-R model, shows a significant effect on university image. This is in line with previous research. The empirical results show that there is a relationship between image, quality and innovation in university governance (Salvioni et al., 2017). This is in line with Hassan & Shamsudin (2019) which shows the effect of service quality at institutions on institutional image. University quality which is part of the stimulus is a factor that must be established by the university. Universities that have good quality will be better known by the public, this will create an image that the university is worthy of being an option in continuing education.

Similar results are also shown by the effect of university quality on cognitive attitude and affective attitude. These results are in line with previous studies. Cognitive attitude and affective attitude are influenced by quality, namely product utility such as product information, monetary savings, convenience, and perceived ease of use (Moon et al., 2017). Another study by Alonso-Dos-Santos, et al., (2017) shows that the e-quality factor affects attitude. In the context of this study, students assessed the quality of the university in deciding where to apply because they believed that the quality of the university gave an indication of the compatibility of expectations with university performance that they would get later in terms of benefits (cognitive) and emotional experience (affective). These results show the importance of university quality in influencing university image, cognitive attitude, and affective attitude.

Managerial Implication

The results of this study have shown how the intention to enroll in the process is influenced by the stimulus (university quality and university image) and organisms (cognitive attitude and affective attitude) in the S-O-R model. The results of the study can contribute to increasing intention to enroll. First, improve the quality of the university by setting good educational standards, having good quality staff and teaching staff. In addition, it is important to improve university rankings to assist students in deciding which university to choose as university rankings provide an indication of the quality of the university. Second, creating a good university image by making the university's name known, reputable, and a good image in society. This is because a positive image is related to good quality which will then affect the attitude and intention to enroll at the university. Third, creating a positive attitude from a cognitive perspective, namely by creating graduates who are quick to get jobs, provide good prospects for graduates, offer acceptable tuition fees, and value for money. Then, in terms of affective attitude, namely by making the university a comfortable and attractive place. Overall, these activities can increase the intention to enroll at a university.

Limitations and Future Research

There are several limitations that must be recognized in this study. First, the researcher collected data on students in a particular city, namely Bandung, which is one of the major cities in Indonesia. Research results may differ in other cities or countries. For future research, a similar survey can be carried out in different cities and other countries. Second, there is no sampling frame where the sampling selection is based on non-probability sampling techniques. Samples were collected in several schools which did not necessarily prove its generalization. Therefore, samples should be taken in a wider scope. Finally, the potential for general method bias that most social science research faces. This can be caused by applying the self-report method: self-administered questionnaire to collect samples. In the future, this can be minimized by conducting several method bias tests such as the Harman Single factor and the correlation between constructs.

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