The Effect of Service Quality and Customer Satisfaction on Purchasing Intention: A Case Study in Indonesia

Abstract

This research intends to examine and analyze the service quality effect on petrol buying intention through satisfaction when the Maranatha Christian University lecturers spend their money in the Pertamina-affiliated gas stations in Bandung. To attain this goal, we develop four relevant hypotheses to be tested. Furthermore, we use simple random sampling to acquire samples by denoting a sample size set by the Slovin formula. To collect the responses, we use the online survey and fruitfully yield a 53.54% rate with a total of 106 lecturers participating in completing the distributed questionnaire link. This number is near 100; hence, we employ a structural equation model with a variance basis to analyze them. After examining the data and conferring the results, we conclude three things. First, service quality does not affect purchasing intention, but customer satisfaction positively does. Second, service quality has a positive impact on customer satisfaction. Finally, consumer satisfaction successfully mediates the effect of service quality on the intention to buy. Based on these conclusions, this paper finds the superior service to the petrol buyers is the key driver for the gas stations under Pertamina control to effectively compete with their rivals in the marketplace, especially in Bandung.

1. Introduction

Pertamina is an Indonesian state-owned oil and natural gas corporation based in Jakarta that is responsible for producing and distributing petrol and supervising its distribution to society (Kuncoro et al., 2009). By understanding the customer perspective, this corporation can deliver an exceptional experience. Hence, the customer focus can be the priority in every decision made to beat the competition. Nationally, Pertamina has six competitors having gas stations: Vivo Energy (VE), Total Oil Indonesia (TOI), Aneka Retroindo Raya (ART), Exxon Mobil Indonesia (EMI), AKR Corporindo (AKRC), and Shell Indonesia (SI) (Mustika, 2019). In Bandung, Pertamina only has two competitors. They are TOI and SI affiliated with the French and Malaysian companies (Mustika, 2019). By observation of the Google Map, Pertamina has 147 affiliated gas stations providing petrol. Conversely, TOI and SI own one and three stations, individually.

According to Directorate for Human Resource Internal data, Maranatha Christian University (MCU) has 441 lecturers in the nine faculties: medicine, engineering, psychology, letters-and-culture, economics, art-and-design, information technology, law, and dentistry as of March 2020. Based on the information from the head of security and parking in MCU, Buddy Supriadi, 391 of them register their vehicles to get free parking in the working days. This total consists of 73 car owners and 318 motorcycle owners. MCU is located on Jl. Prof. Drg. Suria Soemantri, MPH No.65, Bandung; hence, its lecturers prefer buying petrol in the two gas stations affiliated with Pertamina near their workplace. Therefore, this research is relevant to reveal the determinants of their buying intention in the Pertamina-affiliated gas stations.

As the least possible, two determinants of buying intention exist based on the previous research evidence: service quality and customer satisfaction. However, based on the initial research, their impact is not consistent. For service quality as the first antecedent, Aptaguna and Pitaloka (2016), Banjarnahor (2017), Murwanti and Pratiwi (2017), Widyastuti et al. (2017), Octaviana and Nugrahaningsih (2018), Alharthey (2019), Wiyadi and Ayuningtyas (2019), and Naveed et al. (2019) confirm its positive presence. Unfortunately, other scholars find no impact (Mambu, 2015; Widajanti & Suprayitno, 2017; Wonggotwarin & Kim, 2017).

For customer satisfaction as the second determinant of buying intention, this positive impact is demonstrated by Iskandar et al. (2015), Salem et al. (2015), Banjarnahor (2017), Nodira and Přemysl (2017), Murwanti and Pratiwi (2017), Putri and Astuti (2017), Widajanti and Suprayitno (2017), Alharthey (2019), Hong et al. (2020), Lamai et al. (2020), and Tran and Le (2020). Conversely, another researcher, Octaviana and Nugrahaningsih (2018), shows no effect.

Besides, customer satisfaction can be positively affected by service quality, as shown by Kim (2013), Iskandar et al. (2015), Banjarnahor (2017), Putri and Astuti (2017), Murwanti and Pratiwi (2017), Dawi et al. (2018), Octaviana and Nugrahaningsih (2018), Alharthey (2019), and Tjahjaningsih et al. (2020). On the other hand, the study by Naik et al. (2010) shows no impact.

Additionally, the previous scholars attempt to prove that customer satisfaction is the mediating factor of the service quality effect on buying intention (Kalia et al., 2016; Murwanti & Partiwi, 2017; Putri & Astuti, 2017; Octaviana & Nugrahaningsih, 2018). However, the findings of these studies are also inconsistent. In their research, Kalia et al. (2016), Murwanti and Pratiwi (2017), and Putri and Astuti (2017) succesfully prove this mediating effect, but Octaviana and Nugrahaningsih (2018) do not.

Based on the above inconsistent findings of different researcher investigations, this study attempts to prove and analyze three things. The first is the effect of service quality (SQ) and consumer satisfaction (CS) on buying intention. The second is the impact of SQ on CS. The third is the CS mediating role on the service quality impact on the purchase intention in Pertamina-affiliated gas stations in Bandung, indeed, based on the perception of the lecturers working at MCU.

2. Literature Review

When studying the service quality effect on store visitor purchase intention, Kim (2013) found a positive relationship. Furthermore, Aptaguna and Pitaloka (2016) showed that service quality positively contributed to the user intention of Go-Ojek. This positive contribution was also supported by Banjarnahor (2017), who analyzed the effects of service quality on purchase intention of internet services in West Jakarta. Likewise, the same result was confirmed by Murwanti and Pratiwi (2017), who investigated the effect of service quality on utilizing the garage of the motorcycle. After studying the impact of service quality on the dealer user intention, Widyastuti et al. (2017) summarized a positive sign. Similarly, Alharthey (2019) proved a positive effect of service quality on grocery buying intention. Also, Naveed et al. (2019) confirmed a similar impact when investigating the canteen products. Likewise, Wiyadi and Ayuningtyas (2019) verified that service quality positively affects purchasing a motorcycle. Hence, the first hypothesis is stated as follows:

H1: Service quality has a positive effect on the intention to buy.

In their study, Iskandar et al. (2015) showed a positive influence of customer satisfaction on durian soup buying intention. Consistently, Salem et al. (2015) found this impact on the college students becoming the users of the Dell-branded laptop, Banjarnahor (2017) and Nodira and Přemysl (2017) on the fruit juice users; Murwanti and Pratiwi (2017), and Putri and Astuti (2017) on the users of pasta and chocolate blends; Widajanti and Suprayitno (2017) on the steak consumers. Also, Alharthey (2019) affirmed this influence when learning about grocery buyers, and so did Hong et al. (2020) and Lamai et al. (2020) when surveying the auto maintenance and repair service users and the restaurant visitors, respectively. Additionally, Tran & Le (2020) found the same tendency when researching convenience store customers. Hence, the second hypothesis is stated as follows:

H2: Customer satisfaction has a positive effect on the intention to buy.

When investigating the service quality effect on store visitor satisfaction, Kim (2013) displayed a positive association. Similarly, Iskandar et al. (2015) found that all the service quality dimensions positively influence customer satisfaction. Likewise, the study of Bajarnahor (2017), Putri and Astuti (2017), Murwanti and Pratiwi (2017), Dawi et al. (2018), Alharthey (2019), and Tjahjaningsih et al. (2020) confirmed that customer satisfaction was positively affected by service quality. Hence, the third hypothesis is stated as follows.

H3: Service quality has a positive effect on customer satisfaction.

When investigating online purchasing behavior in India, Kalia et al. (2016) showed that service quality was a mediating variable in the relationship between electronic service quality and buying intention in the future. Similarly, after using the Sobel test, the study of Murwanti and Pratiwi (2017) and Putri and Astuti (2017) exhibited that customer satisfaction acted as a mediator in the relationship between service quality and buying intention in Indonesia. Hence, the fourth hypothesis is stated as follows.

H4: Customer satisfaction mediates the effect of service quality on the intention to buy.

3. Research Method

3.1. Variables and Operationalization

The employed variables are latent in the two models: the first order and the second one. In the first-order model, the latent variable only has items. Meanwhile, in the second- order model, the latent has dimensions and items. Based on the position, the variable can be divided into two types: exogenous and endogenous. The exogenous has a role in explaining the outcome. However, the endogenous has a function to be described.

The exogenous variable is service quality, where its dimensions and their items are adopted from Tjiptono et al. (2004) (see Table 1).

Table 1: The Dimensions of Service Quality and Their Indicators

The endogenous variable is customer satisfaction and intention to buy, where the items are adopted from Olsen et al. (2013) and Wang and Hazen (2016) (see Table 2).

Table 2: Customer Satisfaction, Intention to Buy, and their Indicators

3.2. Population, Samples, and Method to Accumulate Data

This study population is 391 lecturers working at MCU having registered vehicles with free parking at the MCU. Moreover, to estimate the sampling size, the Slovin formula is utilized. According to Suliyanto (2009), this formula involves the margin of error and the population size, as seen in the first equation.

\(n=\frac{N}{1+N e^{2}}\)       (1)

In the first equation, n and N show the sample number and population size, respectively. Furthermore, e is the error margin decided by the researchers. By utilizing a 5% margin of error, we get the total samples \((n)=\frac{391}{1+391^{*}(5 \%)^{2}}=197.72 \approx 198\).

Then, we distributed questionnaires to 198 lecturers through the online mode. Likewise, to measure their response, we employed a 5-point Likert scale, where “1” means strongly disagree, and “5’’ means strongly agree (Sugiyono, 2019).

3.3. Validity and Reliability Test

Validity and reliability test are utilized to measure the instrument quality, taking the form of the questionnaire.

• The validity measures the respondent response accuracy. Furthermore, we use confirmatory factor analysis (CFA) to execute it by loading factor (LF) and average variance extracted (AVE). According to Sholihin and Ratmono (2013), if the LF and AVE exceed 0.5, the respondent answer on the question items is convergently and discriminantly valid, respectively.

• The reliability measures the response consistency. Moreover, we employ the composite reliability coefficient to inspect the reliable condition, as explained by Sholihin and Ratmono (2013). If this coefficient exceeds 0.7 as its cut-off point, the valid answer on the question items is consistent.

3.4. Method for Analyzing Data

This study employs the structural equation model based on variance. It is because the total number of lecturers who responded to the questionnaires was 106. This number is close to Ghozali (2008), who stated 100 as the maximum. The research model is shown in Figure 1.

Figure 1: Research Model

Contrasting the probability of t-statistic for the path coefficient of γ₁, γ₂, and β₁ from the WarpPLS7 output, a 5% significant level (α) is essential to test the first, second, and third null hypotheses. If the probability is lower than α, the null hypothesis becomes accepted, and vice versa.

Additionally, this study uses the Sobel test. This test utilizes the path coefficients and their standard error based on the model estimation output to prove the mediating effect of consumer satisfaction. The formula is in the second equation.

\(Z \text { -statistic for Sobel }=\frac{\gamma_{2} * \beta_{1}}{S E}\)    (2)

where \(\mathrm{SE}=\operatorname{SQRT}\left(\left(\gamma_{2}^{2}\right)^{*}\left(\mathrm{SE}_{3}^{2}\right)+\left(\beta_{1}^{2}\right)^{*}\left(\mathrm{SE}_{2}^{2}\right)+\left(\mathrm{SE}_{2}^{2 *} \mathrm{SE}_{3}^{2}\right)\right)\)

Moreover, the probability (2-tailed) of the Z-statistic for Sobel is calculated by the Microsoft Excel formula as shown in equation 3.

\(\text { NORM.DIST (Z-statistic; } 0 ; 1 ; \text { FALSE) }\)       (3)

To prove the mediating effect of CS, we follow the following rule:

a. If the probability of the Z-statistic is lower than 5%, the mediating effect exists.

b. If the Z-statistic probability is similar to or higher than 5%, the mediating effect is not present.

4. Results and Discussion

4.1. Descriptive Statistics

This survey was held in April 2020. Of 198 lecturers, only 106 participated. Thus, the response rate was \(\frac{106}{198} \times 100 \%=53.54 \%\). Fortunately, this rate is still acceptable because of above 20%, as Sugiyanto et al. (2018) suggest. Furthermore, 106 lecturers are from nine faculties, with the total detailed in Panel A of Table 3. Their other features: gender and tenure, are also presented in Panels B and C.

Table 3: The Total Lecturers Based on Faculty Origin, Gender, and Tenure

4.2. Validity and Reliability Test Result

Table 4 presents the relevant loading factor for consumer satisfaction, intention to buy, and the dimension of tangibility, empathy, reliability, responsiveness, assurance items (see the grey-highlighted value). All the highlighted loading factor values exceed 0.5; hence, the respondent answer achieves convergent validity. Also, AVE goes beyond 0.5 so that their response fulfills the discriminant validity. The composite reliability coefficient is over 0.7. Therefore, their response is consistent or accomplishes the reliability test.

Table 4: Cross-loading Factors, AVE, the Composite Reliability Coefficient

4.3. Estimation Result of Structural Equation Model

Table 5 exhibits the estimation result of the structural equation model. In this table, the path coefficient, standard error, t-statistic, and probability value to test hypotheses one, two, and three are available (see Panel A) and parameters related to the Sobel test (see Panel B).

Table 5: The Estimation Result of the Structural Equation Model Based on Variance

To prove the first, second, and third null hypotheses, we liken the probability of t-statistic with a 5% significance level. In Table 5 of Panel A, this value is 0.448 for SQ → ITB. Because this value is higher than 5%, we accept the null hypothesis declaring that service quality does not influence the intention to buy.

In the same panel, the probability is < 0.0001 for SQ → CS and CS → ITB. Because this probability is lower than 5%, we accept the alternative hypothesis declaring that service quality positively affects customer satisfaction, and customer satisfaction positively impacts the intention to buy.

To verify consumer satisfaction as the mediating effect, we employ the Sobel test, where the result is shown in Panel B of Table 5. In this panel, the probability of Z-statistic is 0.0000, which is lower than 5%. Consequently, consumer satisfaction mediates the effect of service quality on the intention to buy.

4.4. Discussion

From the prior section, it can be revealed that service quality does not directly affect the buying intention, but it indirectly affects this intention through customer satisfaction. Hence, customer satisfaction can mediate the service quality influence on purchase/buying intention. The absence of the service quality effect on this intention supports the study of Mambu (2015), Widajanti and Supriyatno (2017), and Wonggotwarin and Kim (2017). Furthermore, a positive influence of customer service on the purchase/buying intention is in line with the studies of Iskandar et al. (2015), Salem et al. (2015), Banjarnahor (2017), Nodira and Přemysl (2017), Murwanti and Pratiwi (2017), Putri and Astuti (2017), Widajanti and Suprayitno (2017), Alharthey (2019), Hong et al. (2020), Lamai et al. (2020), and Tran and Lee (2020). Meanwhile, a positive impact of service quality on customer satisfaction confirms the earlier research of Kim (2013), Iskandar et al. (2015), Banjarnahor (2017), Putri and Astuti (2017), Murwanti and Pratiwi (2017), Dawi et al. (2018), Octaviana and Nugrahaningsih (2018), Alharthey (2019). Additionally, the mediating effect of customer satisfaction confirms the work of Kalia et al. (2016), Murwanti and Pratiwi (2017), and Putri and Astuti (2017).

Hence, from the above results, we can conclude that MCU lecturers prefer gas stations affiliated with Pertamina. To be attractive, the owners of a gas station, through their staff members on duty giving the service to customers, must conform to the Pertamina regulations regarding safety, quality, and reliability. The members have to remind the drivers or passengers to turn off their engine and cellphones during refueling. This rule is useful to avoid static electricity leading to a spark of flame. As the worst consequence, it can explode the petrol station. Besides, ease of payment is also a positive point; for example, customers can pay online via an electronic data capturing machine. Additionally, the staff members must be polite, dress neatly, wear the proper uniform, answer customer questions patiently and correctly, and be accurate and honest when refueling their vehicles.

5. Conclusion

As the answer to this research goal, we infer three points. First, the purchase intention is not determined by service quality but positively affected by customer satisfaction. Second, customer satisfaction is positively influenced by service quality. Last, the service quality impact on the intention to buy is successfully mediated by consumer satisfaction.

By concerning the limitations of this study, such as utilizing the respondents in the narrow population scope: the lecturers working at Maranatha Christian University and using one determinant factor of customer satisfaction and intention to purchase, the future scholars are expected to overcome these two things based on the following suggestions.

• They can utilize the lecturers from the higher education from the private and public institutions in West Java to represent their perspective and the sub-group analysis testing to prove the institution types as the moderating variable.

• They can use the other determinants of customer satisfaction and intention to buy. For customer satisfaction antecedents, the customer experience and product quality become the proposed alternative. Meanwhile, they can apply the perceived value, customer trust, brand image, and price for purchasing intention determining factors.