Introduction

The establishment of private HEIs propels the advancement of education system in the country and promotes healthy competition in advancing the quality of the education system towards developing Malaysia as a regional centre of excellence. The private HEIs were selected for this study because these HEIs have demonstrated a steady and solid industry growth over the years (Prashalini & Nor Emmy Shuhada, 2016). In an increasingly competitive industry, the private HEIs experience significant challenges when it comes to the recruitment of new students (Dennis, Papagiannidis, Alamanos & Bourlakis, 2016). According to Buckner (2017), there are various factors that influence the students’ decision-making to enrol at a private HEI and one of them was HEI’s ranking that would attracts student to enrol. Added to the latter, Hazliza, Nur Azlin, Jasmalina & Ku Nazirah (2017) highlighted that financial aid plays a significant role on students’ decision-making to enrol at a particular private HEI. Hence, it is pivotal for private HEIs to identify significant factors that influences students’ decision-making to enrol in formulating an effective strategy to attracts potential students to enrol at their institutions.

Literature Review

Study in regards HEIs had increased tremendously in the past decades, however, literature about the factors influencing students’ choice to further at private HEI is rather limited that incorporates theory of planned behavior (Ajzen, 2015; Shah, Nair & Bennett, 2013; Allam et al., 2018). Ranking of institution is seen as an important factor that influences students’ decision-making to enrol at private Higher Education Institution (HEI). As most of students demonstrated their concern on the ranking of an institution prior to deciding to enrol, this study suggests the significance of this factor, especially on the first impression of towards HEIs. In general, ranking of institution depicts elements such as the number of students, student’s feedback about programmes offered, the availability of the facilities, and the quality of academic and non-academic staff. Given the expansion of existing and new private HEIs across the country, it is inevitable that students become more selective when it comes to deciding the place to further their study.

Conversely, choosing the right HEIs relative to reputations and ranking may is highly considered by students, parents and stakeholders as it may lead to employability of graduates. As elaborated by Agrey & Lampadan (2014), ranking of an institution is an important factor that sought after by prospective students, along with the employability upon graduation. Similarly, more recent study by Nuseir & El Refae (2021) reported that students who graduate from the HEI with high ranking have more employment opportunities; and the ranking of institution in terms of its reputation can have a positive impact on the students’ employment opportunities and future career development (Cao, Zhu & Meng, 2016). Although HEI’s ranking is built over time, it remains imperative for HEI to initiate a concerted effort in order to establish positive reputation within and beyond the immediate time frame (Agrey & Lampadan, 2014).

Students’ Decision Making (DM)

According to Muhammad Tahir & Ammari (2016), the students’ decision-making process governs almost every phase of their choice of HEI. Factors that influences student decisions usually come from a variety of factors (Shamsudin et al., 2019). Student’s decisionmaking can be viewed as a process where it involves several phases during decision making whether to enrol or not at selected HEI (Hossler & Gallagher, 1987). Hence, it can be considered as an individual behavioural control in selecting the HEI of their choice. Hossler & Gallagher (1987) emphasised the complexity of the decision-making processes and the interrelationship of different factors that influences decision-making (which may also change over time). Adding to that, the selection of HEI can also be observed as (1) predisposition phase (e.g., the students first decide whether to enrol at HEI and subsequently confirm the decision of enrolling at HEI with career aspirations), (2) search phase (e.g., the students search for general information on HEIs, form a choice set, consider several potential HEIs, visit the potential HEIs, and seek guidance), and (3) choice phase (e.g., the students narrow down their search into single HEI of their choice), resulting in the final decsion (Paulsen, 1990).

Ranking of Institution (RI)

Higher Education Institutions (HEIs) have become increasingly concerned of their ranking (Williams et al., 2018) given its significance in influencing the students’ decisionmaking to enrol at the HEI. Accordingly, there are two studies conducted within the Malaysian context had revealed HEIs ranking was the first aspect that the students consider when they began to search for potential HEIs (Azmi et al., 2015). The other study also reaffirmed the significance of HEI ranking on students’ decision-making to enrol at the HEI (Migin et al., 2015).

In general students had realised HEI ranking plays a vital role in linked with financial aid, because HEI's that have an excellent institution ranking able to create a competitive advantage in an increasingly competitive market from another (Shamsudin et al., 2019). A study by Cokgezen (2014) postulated that students of private HEIs appeared more concerned of the ranking of institution. This is true even a small change in the ranking of institution may affect the number of students to enrol at a particular HEI since the ranking of HEI reflects its quality (Goodman et al., 2015). From the students’ perception, the consideration of the ranking of institution as an essential characteristic of HEI as an important criterion to considered in making decision about HEI (Arar, Abramovitz & Bar-yishai, 2015). Analogously, study by Yaacob, Darus, Mokhtar & Omar (2020) stated that ranking of an institution was a significant factor that influences the students’ decision-making to enrol at HEI. Given the increasing number of HEIs, students have become more critical on the characteristics of HEIs in their decision-making to enrol at the HEI (Anis & Islam, 2019; Aida Suraya, Ibrahim & Chang, 2015). Therefore, ranking of institution would be a significant factor to majority of potential students in their decision to choose HEI to further studies (Aida Suraya et al., 2015).

In view of the above, the following hypothesis is developed:

H₁: There is a significant relationship between the ranking of institution and the students’ decision-making to enrol at private HEI.

Financial Aid (FA)

Prior study by Richards, Awokoya, Bridges & Clark (2018) stated that ranking of institution influences the HEIs’ strategic positioning and planning, staffing and organisation, quality assurance, resource allocation, and fundraising, admissions, and financial aid. From student’s viewpoints, ranking of institution is imperative as well as the availability of financial aid in HEIs. Henceforth, HEIs with high ranking supported by availability of financial aid enables HEIs to gain competitive advantage in an increasingly competitive market. According to Ivy (2001), agencies that award grants are guided by the ranking of these various institutions (for quality), and for certain prospective students who have a strong desire and to further study (Maringe, 2006). Therefore, the ranking of institution influences stakeholders such as sponsors and community at large (Moogan, 2011). The latter also echoed by Lin (2016) in regard to student’s HEIs of choice also due to affordability and value that are always compared.

In view of the above, the following hypotheses was developed:

H_1a: There is a significant relationship between financial aid and the students’ decision-making to enrol at private HEI.

H_1b: Financial aid significantly moderates the relationship between the ranking of institution and the students’ decision-making to enrol at private HEI.

Theory of Planned Behaviour

The Theory of Planned Behavior (TPB) was applied in this study to examine the direct effects of perceived behavioural control on students’ decision-making in terms of the fixed characteristics of HEI, namely as ranking of institution. Secondly, the adoption of this theory in this study was also to examine the moderating role of financial aid in the relationship between the ranking of institution and the students’ decision-making to enrol at a private HEI.

The perceived behavioural control was typically found in most studies to be mutually correlated with the students’ decision-making and subsequently, form behavioural intention. As highlighted by Ajzen (2002, 2015), the higher the perceived behavioural control, the stronger the intention to perform the behaviour in question. In short, this study adapted the TPB to determine the influence of the identified factor that influence the students’ decision-making to enrol at a private HEI and the moderating role of financial aid. These theories assisted numerous prior studies to gain an in-depth understanding, anticipation, and simulation of the human behaviour across different contexts (Ajzen, 2012).

Proposed Research Model

Figure 1 below show the theoretical framework adapted from Chapman (1981); Hossler & Gallagher (1987).

Research Methodology

This study adapted the questionnaire survey approach to address the objectives of the study and the corresponding research questions. The questionnaire for this study was developed based on previously validated measures. It is important to note that all the items in the questionnaire were modified to fit with Malaysia context. In addition, this study also adopted the Likert scale to measure the responses. The Likert scale has remained as one of the most fundamental and repeatedly used as a psychometric tool in the educational and social sciences studies (Joshi, Kale, Chandel & Pal, 2015); and five-point Likert scale would reduce the risk of misunderstanding among the respondents (Sekaran & Bougie, 2016). The adopted five-point Likert scale in this study was applied to measure the level of influence of each item according to the endpoints of “not at all influential” to “extremely influential”.

The determination of sample size is essential to ensure that the study obtains a reliable and valid sample for the generalisation of findings (Cavana, Delahaye & Sekaran, 2001). According to Krejcie & Morgan (1970), the recommended sample size for a population of between 10,000 and 15,000 was 370 to 375. The January intake for 23 selected private HEIs in Kuala Lumpur and Selangor recorded 12,139 students. According to Krejcie & Morgan (1970), based on the population, this study targeted sample size of 375. With that, this study distributed 500 questionnaires to meet the minimum sample size of 375.

Data Analysis and Findings

Measurement Model

The two-step process begins by focusing on the assessment of the measurement model. A systematic PLS evaluation involves the variables’ reliability and validity estimation using criteria associated to reflective and formative measurement model. The estimation of the inner path model makes sense when the validity and reliability of the observed latent variables is deemed sufficient for further analysis. Figure 2 displays the measurement model.

An initial test is conducted to evaluate the internal consistency of the data before conducting Confirmatory Factor Analysis (CFA). In order to get a significant result, the lower loading factors will be removed for the constructs. Therefore, some items had been removed from the original measurement model as a technique of model improvement. The modified model shown below in Figure 3.

The Composite Reliability (CR) and Cronbach’s Alpha (CA) are used to assess the internal consistency reliability of the measurement model. CR measures how well a construct is reflected to its assigned items, while CA is used to check the reliability of construct (Cronbach, 1951). A measurement model has satisfactory internal consistency reliability when the CR and CA of each construct exceeds the threshold value 0.7 (Hair et al., 2014).

According to Table 1 shows that the loadings of the indicators range from 0.813 to 0.939. Thus, the results indicate that the items used to represent the constructs have satisfactory internal consistency reliability. Convergent Validity refers to a set of indicators that are assumed to measure the same construct (Kline, 2005). Convergent Validity indicates the strength of the relationships among items which are predicted to express the same latent construct and is usually tested by using Average Extracted Variance (AVE) (Jin et al., 2013). Convergent validity is adequate when constructs have an AVE value of at least 0.5 or more. In this study, all constructs have AVE values ranging from 0.687 to 0.878.

Table 1 Outer Loadings
Construct	Items	Loadings	AVE	CR
Raking of Institution	IR1	0.933	0.878	0.956
	IR2	0.939
	IR3	0.939
Financial Aid	FA1	0.854	0.795	0.921
	FA3	0.922
	FA4	0.898
Students Decision Making	DM4	0.835	0.687	0.868
	DM5	0.839
	DM6	0.813
Moderator	FA1 * IR1		0.876	0.985
	FA1 * IR2
	FA1 * IR3
	FA3 * IR1
	FA3 * IR2
	FA3 * IR3
	FA4 * IR1
	FA4 * IR2
	FA4 * IR3

Structural Model

The following sections discuss the tests used to assess the validity of structural models for this study. The validity of the structural model is measured by using the coefficient of determination () and path coefficients. The Coefficient of Determination (R²) measures the model’s predictive accuracy. The R² value indicates the amount of variance in dependent variables that is explained by the independent variables. R² values are described by Richter et al. (2016) as substantial, weak and moderate, for the following values i.e., 0.67, 0.19 and 0.33, respectively.

Thus, R-squared is said to be moderate if only a few exogenous latent variables explain the endogenous latent variable of the inner path model structures. As display in Table 2, the R² changes of 0.447 indicates that with the addition of one interaction term, the R² has changes about 44.7%.

Table 2 Coefficient of Determination R2
	R Square	R Square Adjusted
Std Decision Making	0.447	0.444

The effect size (f²) is a measure of the impact of a specific construct on an endogenous construct known as independent variable. In measuring f², values of 0.02, 0.15 and 0.35 are defined to have small, medium and large effect respectively (Cohen, 1988). In Table 3, the results of the effect size denote that all relationships have effect size between small to large except for Std Decision Making and Raking Institution*Financial Aid which has negative influence.

Table 3 Assessment of Effect Size F2
	Financial Aid	Raking Institution	Raking Institution*Financial Aid	Std Decision Making
Financial Aid				0.024
Ranking Institution				0.042
Ranking Institution*Financial Aid				0.004
Std Decision Making				0

The Fornell-Larcker is able to show the construct which shares more variance with its indicators compared to the indicators in other constructs. In order to achieve this, Fornell and Larker (1981) suggested that the value of the average variance extracted (AVE) for each construct needs to be higher than the value of the correlation with the other constructs. Table 4 shows Fornell-Larcker values for each construct. AVE values are bold and placed diagonal so that comparisons with correlation values in other constructs can be done through columns and rows. The results of the analysis show that all the value of the AVEs are greater than the other construct correlation values, indicating acceptable discriminant validity. This situation means that the constructs used in this study are unique and there is no overlap with the other constructs.

Table 4 Fornell-Larcker Criterion
	Financial Aid	Raking Institution	Raking Institution*Financial Aid	Std Decision Making
Financial Aid	0.892
Raking Institution	0.392	0.937
Raking Institution*Financial Aid	0.828	0.824	0.936
Std Decision Making	0.503	0.601	0.648	0.829

The Discriminant Validity explains on the degree to which two conceptually similar concepts are distinct from each other. Table 5 clearly shows that loading of each indicator are bigger than its other cross loadings. This shows that the items are grouped to the best fit and at the same time distinct with the rest.

Table 5 Cross Loadings
	Financial Aid	Raking Institution	Raking Institution*Financial Aid	Std Decision Making
DM4	0.355	0.500	0.506	0.835
DM5	0.424	0.478	0.518	0.839
DM6	0.465	0.513	0.582	0.813
FA1	0.854	0.352	0.717	0.426
FA1 * IR1	0.746	0.786	0.924	0.589
FA1 * IR2	0.737	0.791	0.927	0.579
FA1 * IR3	0.739	0.780	0.920	0.600
FA3	0.922	0.336	0.745	0.456
FA3 * IR1	0.794	0.763	0.938	0.614
FA3 * IR2	0.795	0.775	0.950	0.609
FA3 * IR3	0.794	0.763	0.941	0.626
FA4	0.898	0.360	0.751	0.462
FA4 * IR1	0.800	0.767	0.949	0.622
FA4 * IR2	0.778	0.761	0.935	0.597
FA4 * IR3	0.785	0.762	0.939	0.622
IR1	0.385	0.933	0.776	0.562
IR2	0.345	0.939	0.763	0.545
IR3	0.370	0.939	0.777	0.580

Heterotrait-Monotrait (HTMT) criterion is another method used to assess the discrimination validity. If HTMT value is below 0.90, discriminant validity has been established between two reflective constructs (Hair et al., 2014). The value range shown in Table 6 is between 0.435 to 0.895, which is less than 0.90; therefore, the constructs are sufficient for discrimination validity

Overall, Fornell-Larcker, Cross loading and Heterotrait-Monotrait (HTMT) provides evidence for the construct validity. As shown in Table 2, Table 3, Table 4, Table 5 and Table 6, all model evaluation criteria have been supported for reliability and validity measures. In conclusion, all indicators used in this study are confirmed fit and adequate to be used in the structural model.

Table 6 Heterotrait -Monatrait Ratio (HTMT)
	Financial Aid	Raking Institution	Raking Institution*Financial Aid	Std Decision Making
Financial Aid
Raking Institution	0.435
Raking Institution*Financial Aid	0.895	0.862
Std Decision Making	0.610	0.707	0.741

According to the result as illustrated in Table 7, the hypothesized model which has been empirically tested with three hypotheses. It shows that two out of three hypotheses are met and significant. The result of the path coefficient reveals that financial aid (β=2.827, p<0.5) has significant effects over decision making; and raking of institution (β=3.281, p<0.5) has significant effects over decision making. However, moderator financial aid has not significant effect between ranking of institution and students’ decision-making (β=0.321, p>0.5).

Table 7 Path Coefficient
	Original Sample (O)	Sample Mean (M)	Standard Deviation (STDEV)	T Statistics (|O/STDEV|)	P Values	Significance Level
Financial Aid -> Std Decision Making	0.509	0.504	0.180	2.827	0.005	Significant
Raking Institution -> Std Decision Making	0.668	0.665	0.204	3.281	0.001	Significant
Raking Institution*Financial Aid -> Std Decision Making	-0.059	-0.058	0.060	0.994	0.321	Not Significant

Conclusion And Contributions

As a conclusion, the analysis outcome of the focal study had expounded for direct relationship significant between financial aid and ranking of institutions towards decision making to enrol at private HEIs. While the result of moderation assessment had confirmed that the financial aid has not significant moderation role on relationship path between ranking of institution and decision making. Thus, analysis on structural model had further verified that hypotheses h1 and hypotheses h1a of focal study were accepted, h1b rejected.

This study theoretically contributed to the existing literature and theory development in two major ways by (1) revalidating the relationships of ranking of institution and the students’ decision-making to enrol at private HEI; and (2) establishing the moderating role of financial aid in the relationships of ranking of institution with the students’ decision-making to enrol at private HEI. Besides that, this study was expected to benefit HEIs in their efforts to re-strategise their marketing strategies to increase the student enrolment at their institutions.

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