Vol. 40 (Number 27) Year 2019. Page 8
SUVITSAKDANON, Udom 1 & SORNSARUHT, Puris 2
Received: 24/04/2019 • Approved: 22/07/2019 • Published 05/08/2019
ABSTRACT: Systematic random sampling was used to obtain the study’s 303 small business leasee sample. The CFA and SEM analysis used LISREL 9.10 from which it was concluded that all of the variables affecting a Thai small business leasing company’s customer trust were positive. The causal variables influencing customer trust, from highest to lowest, were service quality (SQ), corporate social responsibility (CSR), customer satisfaction (SAT), and socioeconomic status (SES) with total influences of 0.80, 0.76, 0.64 and -0.14 respectively. |
RESUMEN: Se utilizó un muestreo aleatorio sistemático para obtener la muestra del estudio de 303 pequeñas empresas. El análisis de CFA y SEM utilizó LISREL 9.10, de donde se concluyó que todas las variables que afectaban la confianza de los clientes de una empresa tailandesa de arrendamiento de pequeñas empresas eran positivas. Las variables causales que influyen en la confianza del cliente, de mayor a menor, fueron la calidad del servicio (SQ), la responsabilidad social corporativa (CSR), la satisfacción del cliente (SAT) y el estatus socioeconómico (SES) con influencias totales de 0.80, 0.76, 0.64 y -0.14 respectivamente. |
According to Forbes, vehicle leasing is an important financing method used by company car fleets, which had an estimated market size of 24.8 million vehicles across 37 major countries at the end of 2017 (Figure 1). This accounted for almost 20% of all fleet vehicles in operation (Singh, 2018).
Figure 1
Vehicle fleet leasing portfolio (37 countries)
Source: Frost and Sullivan (2018)
In the United Kingdom, it has been reported that finance contracts such as the Personal Contract Purchase [PCP] and the Personal Contract Hire [PCH] will account for almost 90% of all new car sales (Smith, 2018). In Singapore, leasing is popular due to the island nation’s reputation as being one of the most expensive places in the world to own an automobile with expat leasing having become quite popular. In 2019, thus far bids for a new car’s certificate of entitlement [COE] have ranged from $19,231 to $24,977 USD, significantly contributing to the attractiveness of leasing over ownership. Data supporting this comes from Fitch Solutions which has reported that new vehicle registrations are expected to contract 20.1% in 2019, which is much higher than the 11% contraction reported in 2018 (Singapore Business Review, 2018a; 2018b).
Furthermore, according to a Frost and Sullivan (2018) study, the Thai vehicle leasing market is extremely complex and also faces a variety of restraints and challenges, which includes a very competitive environment, high lease rental, and low residual value. Other factors such as political instability, unstable FDI [foreign direct investment], unclear tax regulations, and rising household debt contribute to the industry’s challenges. Furthermore, within the Thai leasing industry various players contend for market share, including domestic and foreign bank finance companies (Bank of Thailand, 2017), captive finance entities (financing offered by auto manufacturers or distributors), and approximately 300 smaller, non-bank leasing companies (Thanadhidhasuwanna, 2017).
Additionally, corporate entities are the most dominant customer segment and financial leasing the most popular product. At the end of 2016, outstanding debts on just vehicle hire purchases alone had reached $26.8 billion, or 22.7% of all Thai consumer loans issued by the commercial banking sector (Thanadhidhasuwanna, 2017). By comparison, in 2017, total Thai auto loans reached $65.751 billion, which included $50.087 billion for new vehicles and $14.087 billion for used cars and trucks (Pinijparakarn, 2017).
Deloitte (2018) has also indicated that when it comes to the auto financing industry’s trends, vehicle leasing has never been higher. This is supported from U.S. data which shows that the lease/loan mix has grown from 15.7% in 2011 to 24% in 2016, with lease payments averaging 23% lower than finance payments.
In Thailand, Thanadhidhasuwanna (2017) also reported that auto hire-purchase agreements provide credit to buyers of automobiles and motorcycles, which for motorcycles is done by the smaller, non-bank leasing companies. Gao (2018) has additionally stated that in these agreements, lenders retain the ownership rights to vehicles bought on hire purchase until all payments have been made in full and on time. At the end of this period, the buyer becomes the owner. This is one way in which hire purchase differs from leasing, because in leasing agreements, the leasee may either extend the period of the lease or return the vehicles when the contract expires. Leases are thus much more popular with corporate buyers, who need to acquire the use of items in large numbers, and want to get the use of vehicles, plant and equipment without paying the full cost all at once (Mott, 2012).
In spite of these challenges, certain factors such as a growing economy, growth in popularity of company cars, expected rise in number of SMEs [small-medium enterprises], rising currency, and changing mindset regarding vehicle ownership, are some of the key drivers that are anticipated to sustain a growing market in the short to medium term (Frost & Sullivan, 2018).
However, a growing body of evidence also suggests that socioeconomic conditions may render some consumers more vulnerable than others (Clifton, Díaz-Fuentes, & Fernández-Gutiérrez, 2014; George, Graham, & Lennard, 2011). Data supports this as in the UK, 12% of vehicle leasees will eventually hand back their car because they cannot afford the payments, which usually results in the individual having to pay a penalty (Smith, 2018).
As such, these issues led to the study’s inclusion of the leasee’s socioeconomic status (SES), which included both the individual’s highest education level (X4) and their monthly income (X5). This is consistent with Winkleby, Jatulis, Frank, and Fortmann (1992), which indicated that SES is usually measured by determining an individuals’ education, income, occupation, or a composite of these dimensions. Furthermore, Brandt, Wetherell, and Henry (2014) demonstrated increases in socioeconomic status as measured by income, predicts increases in social trust.
Although the Thai leasing sector is booming, there are often significant stresses to the multiple parties involved in the contract’s long-term financial commitment. According to Poonsuwan (2018), guarantors have long sustained great disadvantages in hire-purchase company contract obligations. Recently, however, a series of new Thai laws have shifted the focus when the principal debtor defaults in payment. Now, creditors have no right, as enjoyed in the past, to enforce these contracts immediately and directly on the guarantor. Now, if the contract involves an individual, the leasing company must first sue the primary obligor and exhaust their remedy through Thai courts, which could take years, before they have a right of action against the guarantor. This inevitably diminishes the asset’s value.
Some might view these new laws as an outcome of a growing movement concerning a corporation’s social responsibility (CSR) to the community it serves. Scholtens (2006) has suggested that finance can promote socially desirable activities, while also discouraging detrimental activities. This is consistent with policies set forth by the World Business Council for Sustainable Development [WBCSD] in their Vision 2050 plan in which it is suggested that new rules for financing and innovative financial products stimulate widespread entrepreneurship and participation in an inclusive and innovative global economy (WBSCD, 2010). This economy thus creates a significant number of new jobs, while also improving labor productivity. Therefore, after a review of the literature, the authors chose social responsibility (X1), human rights respect (X2), and operating regulations (X3) as CSR’s key observed variables.
Numerous other studies have also determined that service quality (SQ) within the automotive finance and service sectors are key elements in an organization’s success (Chaichinarat, Ratanaolarn, Kiddee, & Pimdee, 2018; Spina & Kleiner, 1997). International automotive marketing campaigns have also thrust quality into the forefront with slogans such as ‘Quality is Job 1’ (Meredith, 1998) and ‘The best built cars in the world’ (Pope, 2016). Quality is, therefore, concerned with product longevity and strength. Service quality is also integral to consumer satisfaction in the after-sales service process (Chaichinarat et al., 2018). Therefore, after a review of the literature, the authors chose service provider performance (Y1), responding to service recipients (Y2), and customer’s trust in company (Y3) as SQ’s key observed variables.
Auh and Johnson (1997) also indicated that customer satisfaction (SAT) is a key element within a highly competitive automotive industry. This is consistent with Berger, Peter, and Herrmann (1997), which additionally indicated that customer satisfaction is a key to customer loyalty within the automotive industry. Therefore, after a review of the literature, the authors chose total satisfaction (Y7), service needs (Y8), and good attitude and feeling (Y9) as SAT’S key observed variables.
Another factor important to the automotive leasing/finance sector is a customer’s trust (TRUST) of the party’s involved in the lease. According to the Open Government Partnership (2017), trust is crucial for business, which is the glue that binds companies to their customers and to the communities where they operate. The Edelman trust surveys support this, as it is reported that 91% of 25-to-64-year-olds globally indicate that they bought a product or service from a company they trusted, while 77% refused to buy a product or service from a company they did not trust (Edelman, 2009). Also, within the financial services sector, 41% of the global respondents used products/services of trusted financial services companies in the last year, with 31% recommending those same services to others (Edelman, 2018). Therefore, after a review of the literature, the authors chose customer’s confidence in company (Y4), the company’s reliability (Y5), and the company’s credibility (Y6) as TRUST’S key observed variables.
From the introduction’s overview concerning a Thai leasing company’s customer trust (TRUST), several relationships were determined between socioeconomic status (SES), corporate social responsibility (CSR), service quality (SQ), customer satisfaction (SAT), and customer trust (TRUST). From this, a questionnaire was developed, whose validity and reliability were pre-tested and confirmed. A confirmatory factor analysis (CFA) was also conducted prior to the structural equation modeling (SEM) of the following nine hypotheses (Figure 2).
H1: SES positively and directly influences SQ.
H2: SES positively and directly influences TRUST.
H3: SES positively and directly influences SAT.
H4: CSR positively and directly influences SQ.
H5: CSR positively and directly influences Trust.
H6: CSR positively and directly influences SAT.
H7: SQ positively and directly influences SAT.
H8: SQ positively and directly influences TRUST.
H9: SAT positively and directly influences TRUST.
Figure 2
Conceptual model
The study’s population consisted of customers who participated in small business lease agreements from one of 34 leasing companies that were members in 2018 of the Thai Leasing Business Association (2018). The link to the association members can be found here: http://www.thpa.or.th/directory. Concerning the study’s sample size requirement, research has shown there are no ‘hard rules’ on sample size. However, one method that has been suggested in SEM, is to use a ratio of 20 surveys per observed variable (Hair, Tatham, Anderson, & Black, 1998). Furthermore, Norusis (2010) has suggested that a sample should contain at least 300 cases.
Therefore, systematic random sampling was used from August to October 2018 to obtain the study’s sample from every fifth individual who was taking out a small business lease (Tee, Preko, & Tee, 2018). For this study the authors used a multiple of 20 for the 14 observed variables, which established a preliminary target sample size of 280. However, to increase and to adhere to a sample size of at least 300 (Norusis, 2010), 340 were targeted and obtained. From this process, 303 audited surveys were used, representing 89% of the 340 obtained.
For this study, the primary measurement instruments for the constructs was a 7-level Likert scale questionnaire which had six parts. Part 1 contained six items related to the leasee’s personal information including gender, age, education level, marriage status, occupation, and monthly income (Table 1). Items three (highest education level) and item six (monthly income) from Part 1 were used for the study’s SES construct. Part 2 contained 8 items focused on the leasee’s opinions concerning the leasing company’s corporate social responsibility (CSR), Part 3 contained 7 items about the leasing company’s service quality (SQ), Part 4 contained 9 items about the individual’s customer satisfaction (SAT) with the leasing company, Part 5 was concerned with 8 issues concerning the leasee’s customer trust (Trust). There were a total of 32 items in Parts 2-6.
First, in order to investigate whether the factor structure can be replicated in the new dataset from 303 participants, confirmatory factor analysis (CFA) was conducted. Several model fit indices and their criteria were used to examine the goodness-of-fit of the model with the given dataset. These included the goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), normed fit index (NFI), comparative fit index (CFI), and root mean square error of approximation (RMSEA) (Table 7). After evaluating the model fit, construct reliability (CR) was calculated for the convergent validity, while the average variance extracted (AVE) was used for discriminant validity. After performing the CFA, a more suitable structure was determined for the new dataset, after which SEM was done. Finally, the reliability of items in each factor was examined by Cronbach’s α, and LISREL 9.10 software was used for both the CFA and SEM analysis.
Initial questionnaire validity advice and recommendations were obtained from the primary author’s advisory committee. Afterwards, the questionnaire was checked by five experts including two leasing company managers and three academic experts in customer loyalty. The Index of Item-Objective Congruence (IOC) was used so as to find the content validity (Tavakol & Dennick, 2011), with survey items having scores ≥ 0.5 retained.
Table 1 shows the results from the study’s 303 questionnaires. From the respondents’ responses concerning their personal and professional environments, 49.17% indicated they were men, while 50.83% were women. There was also a large concentration of individuals between 31-40 years of age (40.59%). It was also interesting to note that 53.14% of the leasing company’s small business customers surveyed earned less than $626 per month.
Table 1
Respondents’ general characteristics (n=303)
|
Frequency |
% |
Male |
149 |
49.17 |
Female |
154 |
50.83 |
Total |
303 |
100.00 |
|
|
|
Between 21-30 years of age. |
46 |
15.18 |
Between 31-40 years of age. |
123 |
40.59 |
Between 41-50 years of age. |
76 |
25.08 |
Between 51-60 years of age. |
44 |
14.52 |
Over 60 years of age. |
14 |
4.62 |
Total |
303 |
100.00 |
|
|
|
Lower than primary school |
10 |
3.30 |
primary school |
50 |
16.50 |
Lower secondary school |
28 |
9.24 |
High school graduate |
53 |
17.49 |
Vocational certificate/ High vocational certificate/ Diploma |
76 |
25.08 |
B.A./B.S. degree |
84 |
27.72 |
Higher than bachelor's degree |
2 |
0.66 |
Total |
303 |
100.00 |
|
|
|
Single |
73 |
24.09 |
Married |
198 |
65.35 |
Divorced / widowed |
30 |
9.90 |
Other |
2 |
0.66 |
Total |
303 |
100.00 |
|
|
|
Government employee |
15 |
4.95 |
State enterprise employee |
14 |
4.62 |
Private company employee |
102 |
33.66 |
General employee |
97 |
32.01 |
Entrepreneur (small business owner) |
45 |
14.85 |
Other |
30 |
9.90 |
Total |
303 |
100.00 |
|
|
|
Less than 10,000 baht (10,000 Thai baht = $313 USD.) |
69 |
22.77 |
Between 10,001-20,000 baht. |
161 |
53.14 |
Between 20,001-30,000 baht. |
50 |
16.50 |
Between 30,001-40,000 baht. |
16 |
5.28 |
Between 40,001-50,000 baht. |
7 |
2.31 |
Total |
303 |
100.00 |
1 Note. Item 3 and Item 5 were used for the SES construct analysis.
A 2-step analysis was conducted in which analysis of the measurement model and both sets of internal and external variables were conducted separately (Tables 3 and 4), In the second step, the analysis of the SEM for a Thai small business leasing company’s customer trust (TRUST) was measured. Furthermore, Table 2 shows the results from the questionnaire’s analysis, with the 7-level Likert type agreement scale using the following values: strongly agree = 6.11-7.00, agree = 5.26-6.10, agree slightly = 4.41-5.25, no comments = 3.56-4.40, disagree slightly = 2.71-3.55, disagree = 1.86-2.70, and disagree strongly = 1.00-1.85.
Table 2
The questionnaire’s latent variables results
Latent Variable |
Items |
Mean |
S.D. |
Level |
Skewness |
Kurtosis |
Corporate Social Responsibility (CSR) |
8 |
5.45 |
.98 |
agree |
-.65 |
.70 |
Service Quality (SQ) |
7 |
5.72 |
.97 |
agree |
-.68 |
.10 |
Customer Satisfaction (SAT) |
9 |
5.65 |
1.00 |
agree |
-.79 |
.47 |
Customer Trust (TRUST) |
8 |
5.73 |
.95 |
agree |
-.67 |
.10 |
Total and Averages |
32 |
5.64 |
.98 |
agree |
- |
- |
Note. S.D. = standard deviation
-----
Table 3
CFA of the external latent variables
Latent variables |
a |
AVE |
CR |
Observed variables |
Loading |
R2 |
Corporate Social Responsibility (CSR) |
0.92 |
0.77 |
0.91 |
social responsibility (X1) |
0.75 |
0.56 |
human rights respect (X2) |
1.08 |
1.00 |
||||
operating regulations (X3) |
0.76 |
0.57 |
||||
Socioeconomic Status (SES) |
- |
0.34 |
0.49 |
highest education level (X4) |
0.71 |
0.51 |
monthly income (X5) |
0.41 |
0.17 |
Note. a = significance level, Chi-Square=0.02,
df=1, p-value=0.898, RMSEA=0.000.
-----
Table 4
CFA of the internal latent variables
|
a |
AVE |
CR |
Observed variables |
Loading |
R2 |
Service Quality (SQ) |
0.96 |
0.85 |
0.94 |
Service provider performance (Y1) |
0.90 |
0.81 |
|
|
|
Responding to service recipients (Y2) |
0.93 |
0.87 |
|
|
|
|
Customer’s trust in company (Y3) |
0.93 |
0.87 |
|
Customer Trust (TRUST) |
0.97 |
0.85 |
0.94 |
Customer’s confidence in company (Y4) |
0.94 |
0.88 |
|
|
|
Company’s reliability (Y5) |
0.93 |
0.87 |
|
|
|
|
Company’s credibility (Y6) |
0.89 |
0.79 |
|
Customer Satisfaction (SAT) |
0.98 |
0.89 |
0.96 |
Total satisfaction (Y7) |
0.93 |
0.86 |
|
|
|
Service needs (Y8) |
0.95 |
0.90 |
|
|
|
|
Good attitude and feeling (Y9) |
0.95 |
0.90 |
Note. a = significance level, Chi-Square=4.57,
df=14, p-value=0.991, RMSEA=0.000
Table 5 shows the model’s discriminant validity with the diagonal values in bold indicating the square root of the AVE for the construct, while the other values are the correlation between the respective constructs. The discriminant validity is achieved when the diagonal value in bold is higher than the values in its row and column (Ahmad, Zulkurnain, & Khairushalimi, 2016).
Table 5
Discriminant validity
Latent variables |
CSR |
SES |
SQ |
SAT |
TRUST |
Corporate Social Responsibility (CSR) |
1 |
|
|
|
|
Socioeconomic Status (SES) |
-.11 |
1 |
|
|
|
Service Quality (SQ) |
.79** |
-.15** |
1 |
|
|
Satisfaction (SAT) |
.75** |
-.17** |
.87** |
1 |
|
Trust |
.74** |
-.16** |
.85** |
.89** |
1 |
rV (AVE) |
0.85 |
0.34 |
0.86 |
0.88 |
0.84 |
rC (Construct Reliability) |
0.94 |
0.49 |
0.95 |
0.96 |
0.94 |
|
0.92 |
0.58 |
0.93 |
0.94 |
0.92 |
Note. **Sig. < .01
Table 6 presents the DE, IE, and TE of each construct.
Table 6
Standard coefficients of influence in the SEM for small business leasing customer trust
Dependent variables |
R2 |
Effect |
CSR |
SES |
SQ |
SAT |
Service Quality (SQ) |
.72 |
DE |
0.83** |
-0.09* |
|
|
IE |
- |
- |
|
|
||
TE |
0.83** |
-0.09* |
|
|
||
Satisfaction (SAT) |
.65 |
DE |
0.17* |
-.0.09* |
0.72** |
|
IE |
0.60** |
-0.07* |
- |
|
||
TE |
0.77** |
-0.16* |
0.72** |
|
||
Customer Trust (TRUST) |
.63 |
DE |
-0.01 |
-0.01 |
0.34** |
0.64** |
IE |
0.77** |
-0.13* |
0.46** |
- |
||
TE |
0.76** |
-0.14* |
0.80** |
0.64** |
Note. *Sig. < 0.05, **Sig. < 0.01, CSR = corporate social responsibility,
SES = socioeconomic status, direct effect = DE, indirect effect = IE, and total effect = TE
Figure 3’s SEM results show the analysis of the variables’ effects on a Thai small business leasing customer’s trust (TRUST), as the model met the required criteria as the chi-square index was not statistically significant at 34.13, the p-value was = 0.731, and the RMSEA = 0.000. Furthermore, from Table 7 it can be seen that the GFI = 0.99, the AGFI = 0.97, and the standardized root mean square residual [SRMR] = 0.09.
Table 7
Goodness-of-fit appraisal values
Criteria Index |
Criteria |
Values |
Results |
Supporting theory |
Chi-square: χ2 |
p ≥ 0.05 |
0.99 |
passed |
(Jöreskog, Olsson, & Fan, 2016) |
Relative Chi-square: χ2/df |
≤ 2.00 |
0.53 |
passed |
(Byrne, 2010) |
RMSEA |
≤ 0.05 |
0.00 |
passed |
(Hu & Bentler, 1999) |
GFI |
≥ 0.90 |
0.99 |
passed |
(Jöreskog, Olsson, & Fan, 2016) |
AGFI |
≥ 0.90 |
0.97 |
passed |
(Hooper, Coughlan, & Mullen, 2007) |
RMR |
≤ 0.05 |
0.00 |
passed |
(Hu & Bentler, 1999) |
SRMR |
≤ 0.05 |
0.00 |
passed |
(Hu & Bentler, 1999) |
NFI |
≥ 0.90 |
0.99 |
passed |
(Schumacker & Lomax, 2010) |
CFI |
≥ 0.90 |
1.00 |
passed |
(Schumacker & Lomax, 2010) |
Cronbach’s Alpha |
≥ 0.70 |
0.92-0.98 |
passed |
(George & Mallery, 2010) |
-----
Figure 3
SEM modeling results of Thai small
business leasing customer trust
Note. Chi-Square = 34.13, df = 40, p-value = 0.731, RMSEA=0.000,
SES & CSR = external latent variables, SQ, TRUST, & SAT = internal latent variables.
-----
Table 8
Hypotheses testing results
Hypotheses |
Coef. |
t-value |
Results |
H1: SES positively and directly influences SQ. |
-0.09 |
-2.03* |
supported |
H2: SES positively and directly influences TRUST. |
0.00 |
-0.10 |
unsupported |
H3: SES positively and directly influences SAT. |
-0.09 |
-2.09* |
supported |
H4: CSR positively and directly influences SQ. |
0.83 |
15.52** |
supported |
H5: CSR positively and directly influences Trust. |
-0.01 |
-0.12 |
unsupported |
H6: CSR positively and directly influences SAT. |
0.17 |
2.92* |
supported |
H7: SQ positively and directly influences SAT. |
0.72 |
10.73** |
supported |
H8: SQ positively and directly influences TRUST. |
0.34 |
4.62** |
supported |
H9: SAT positively and directly influences TRUST. |
0.64 |
9.29** |
supported |
Note. *Sig. < .05, **Sig. < .01
The study’s SEM analysis concluded that all of the variables affecting a Thai small business leasing customer’s trust were positive, which can be explained by 63% of the variance in a Thai leasing company’s customer loyalty (R2). The causal variables influencing customer trust, from highest to lowest, were service quality (SQ), corporate social responsibility (CSR), customer satisfaction (SAT), and socioeconomic status (SES), with total influences of 0.80, 0.76, 0.64 and -0.14 respectively.
The results from H1 testing verified that the positive role that SES plays on SQ, with the level of education speculated to play a key role. In the current study there was a wide spread of each leasee’s education level, with only 27.7% having a university degree. While in a similar Bahraini commercial bank study concerning the hypothesized relationship between SES and SW, education showed no significant difference as 67% had university degrees (Ramez, 2011). This seems to suggest that the higher the customer’s education level, the less impact there is on service quality. This is consistent with a study from the U.S.A in which satisfaction with quality of life was determined to be influenced by an individual’s satisfaction with their financial status and future plans (Mugenda, 1988).
However, H2’s testing results determined that there was no positive correlation between SES and TRUST. This is supported by other studies including Keijzer and Corten (2017) in which it was stated that there is a fundamental debate about the link between socioeconomic status and attained trust, with the positive effect of SES on trust and trustworthiness not undisputed. Furthermore, research showed that individuals with low SES will be happier following the norm of trustworthiness than individuals with higher SES, because being trustworthy is more important to them (Keijzer & Corten, 2017). The rational for this is that lower status individuals lack material resources which increases their need to rely on others’ resources. However, higher status individuals utilize their economic capital to face life’s challenges and problems, while lower status individuals have to compensate by placing trust and behaving trustworthy.
Concerning SES’s positive role on SAT, this was also determined to be positive in H3. This is consistent with a study across 12 European country’s utility services in which strong evidence was found to support the idea that consumers’ socio-economic characteristics matter. From this, it was determined that consumers with lower levels of education, the elderly and those not employed exhibit particular expenditure patterns on, and lower satisfaction levels with, some utility services (Clifton et al., 2014).
Hypothesis H4 was also determined to be supported, with CSR positively influencing SQ. This is consistent with Kim and Kim (2016) which determined that within the hotel industry, the influence of satisfaction on customer loyalty is mediated by trust.
Concerning H5’s hypothesis that CSR has a positive effect on customer trust (Trust), the study found the hypothesis to be non-supported. Reasons for this are speculated to be the lack of the leasing industry’s focus on CSR and the customer’s lack of understanding concerning the nature of CSR. This is consistent with Choi and La (2013) in which they determined it was the perception of CSR that creates a significant impact on customer trust. Given the low level of education (thus perception) of the study’s participants and the fact that Thai leasing companies have limited exposure to CSR benefits and issues, it is easier to understand why this hypothesis is rejected. Future studies might want to explore how much, if any, CSR activity is undertaken by Thai leasing companies.
Hypothesis H6, however, was supported and CSR was shown to positively affect SAT. Related studies have confirmed that CSR can positively impact consumer assessments by improving the company’s image and trustworthiness and increasing customer satisfaction (Wan, Poon, & Yu, 2016). This is consistent with Luo and Bhattacharya (2006) which found a direct relationship between CSR and customer satisfaction. CSR activities should also involve a frank long-term effort to build customer trust (Choi & La, 2013), with the organization adjusting the guidelines to suit the nature of the organization by considering "what the organization Is going to do" and what the organization ‘wants to be’. This means that the direction of CSR must be transmitted from the top level to the bottom level of the organization clearly y defining the vision, mission, policies, strategies, and guidelines. There is an evaluation process that consists of periods, indicators and goals, as well as a support system to promote the success of the organization's CSR (Hitchcock & Willard, 2008)
The study also determined that SQ had a positive effect on SAT (H4) and Trust (H5). These findings are consistent with other studies including Berry, Bennet, and Brown (1989), which determined that SQ leads to customer loyalty and attracts new customers, while also contributing to the company through a customer’s a positive word-of-mouth influences.
Also, from the study’s survey, the respondent’s indicated that a leasing company’s reliability was very important ( = 5.71) which is consistent with other studies in which reliabilitywas viewed as the most critical factor amongst the SERVQUAL dimensions (Chaichinarat et al., 2018). Angelova and Zekiri (2011) also stated that in a highly competitive environment, high quality service delivery is the key for a sustainable competitive advantage. Berndt (2009) also indicated that a customer’s confidence and trust in an automotive dealership was translated as how knowledgeable the service advisor was and how polite and courteous they were with the customer.
The study also verified the positive relationships between customer satisfaction and customer trust (H9). This is consistent with Fornell, Johnson, Anderson, Cha, and Bryant (1996) which also indicated that overall SAT is obtained from the customer’s quality perception and their value perception. Also, SAT is the result of a customer’s perception of the value received, where value equals the perception of service quality relative to price (Cronan, Brady, & Hult, 2000; Hallowell, 1996). Additional support for customer satisfaction’s importance in reinforcing customers' trust came from research conducted in Taiwan by Liang and Wang (2007). The authors also suggested that managers should segment their customers into groups and target different marketing programs to the characteristics of the each group’s consumers.
The study concluded that Thai small business leasing consumers are overall happy and trusting with the services they are being provided by their leasing companies. Additionally, service quality ranked highest, along with reliability and financial confidentiality. It also appeared that online services were not that important as Thai leasing customers preferred a brick and mortar presence. As such, smiling, helpful, and courteous staff were a must. Word-of-mouth also played a key role in providing positive feedback to other potential customers. Customer level of education and their perception (or lack thereof) of leasing company CSE activities also plays key roles.
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1. Doctoral candidate with the Faculty of Administration and Management. King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand. E-mail: u.suvitsakdanon@gmail.com
2. Assistant Professor and Lecturer with the Faculty of Administration and Management. King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand. E-mail: drpuris.s@gmail.com