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International Journal of Accounting & Business Management

www.ftms.edu.my/journals/index.php/journals/ijabm

Vol. 3 (No.1), April, 2015 ISSN: 2289-4519 DOI: 10.24924/ijabm/2015.04/v3.iss1/92.107

This work is licensed under a

Creative Commons Attribution 4.0 International License.

Research Paper

Customer Perceived Values associated with Automobile and Brand Loyalty

Muhammad Yousif Moosa

School of Accounting and Business Management FTMS College, Malaysia

myousif@rocketmail.com

Zubair Hassan

School of Accounting and Business Management FTMS College, Malaysia

Zubai7@gmail.com

Abstract

The purpose of this study is identifying the customer perceived value associated with automobile and examining its impact on customer satisfaction and brand loyalty. A sample size of 198 respondents was chosen from various points in Jeddah, Saudi-Arabia using convenient sampling. A multi-factor CPV questionnaire with a Likert-Scale from 1-5 was used to collected the data to determine customer perceived value associated with automobile and its impact on customer satisfaction and brand loyalty. To ensure reliability and validity of the data set, sample size only includes respondents who have been using/driving an automobile for a year. Descriptive statistics shows that the most significant perceived value associated with automobile is functional value followed by emotional value and epistemic value. Social value was the least reason that respondents purchase an automobile. In terms of correlations, this study found that overall customer perceived value associated with automobile is highly correlated with customer satisfaction and brand loyalty. Bivariate multiple regression analysis shows that there is a significant and positive impact of FV and CV on customer satisfaction. We do not find any significant influence of EV, EPV and SV on customer satisfaction. However we found that there is a significant and positive impact of EV, FV and CV on customer brand loyalty. Again we did not find any significant impact of SV and EPV on brand loyalty. The current study contributes to the body of research by investigating the combined impacts of customer perceived value on automobiles using one instrument on cross-sectional setting. This research shows that customer perceived value associated with automobile is crucial in increasing customer satisfaction and brand loyalty. Future research should be undertaken on different context or by increasing the sample size by widening the research context to ensure validity and reliability of the results.

Key Terms: Customer Perceived Value, Emotional Values, Social Values, Conditional Values, Epistemic Values, Functional Values and Customer loyalty, Automobile

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1. Introduction In past many researches were done on this topic in various parts of the world. Most of the studies were conducted on developed countries compared to developing countries. Among these studies most of the researches were done on U.S.A (Seth, Norman and Newman, 1991; Chi & Kilduff, 2011; Yang & Jolly, 2009). Among these studies most of them were focus on European countries such as UK, Spain, Italy, Portuguese, Norway, and France (Ledden., Kalafatis, & Mathioudakis, 2012; Christine 2011; Desmet 2014). However handfuls of researches were done on developing countries. Among these studies most of the studies were done on developing countries such as Taiwan, Malaysia, Indonesia, china, Saudi Arabia, United Arab Emirates, Korea, and South Korea Pakistan (Rahman, 2014; Yu Wu et al, 2014;Yusuf et al, 2013). In terms of customer perceived value there were very limited researches, particularly in Saudi Arabian context there were shortage of literature on this field (Rahman & Abu Bakar, 2014; Eid, 2011; Abu Bakar, 2013; Alsheikh., & Bojei, 2014). Most of the past studies were taken place on retail, tourism, IT, food, online, airline, bank and automobile industries. (Park & Jung, 2014; Chi & Kilduff, 2011; Change & Tseng, 2013; Vera & Trujillo, 2013; Kwun, 2011). Limited research were done on education, tobacco, furniture, mobile. (Sheth et al, 1991; Ladden et al, 2011; Toivonen, 2012). In terms of studies done on Middle Eastern countries, in particular Saudia Arabia, it seems there is no research done on to identify the impact of CPV on brand loyalty in automobile industry. In past the key themes that were emphasis on measuring CPV includes quality, emotional value, brand image, social value and price (Ladden et al, 2011; Salo et al, 2013; Loureiro et al, 2012). One of the most popular and cited theoretical framework was Sheth et al. (1991)’s proposed five values. As this research intended to conduct on Saudi Arabia, quality, price, design, privacy and satisfaction were identified as most commonly cited variables used to measure customer perceived value. (Rahman & Abu Bakar, 2014; Eid, 2011; Abu Bakar, 2013; Alsheikh, & Bojei, 2014). As this research investigates the key issues such as retention, loyalty, satisfaction and repeat purchase associated with CPV. Most of the research shows CPV has a positive impact on brand loyalty. Also CPV has a positive impact on retention, satisfaction and repeat purchase (Yang & Jolly, 2009 ). There are many factors which impact positively on CPV such as, Price which is considered the most powerful factor for customer retention for an organization. Emotional value also considered one the factor which has positively impact on CPV (Vera & Trujillo, 2013). Customers emotionally satisfied from service or products are enabling to repeat in purchase. Most researches show that quality has positive impact on CPV, whereby the customer become more loyal toward the organization (Yu Wu et al, 2014). If the customer are not satisfied there is chance to lose the market-share. Some of the research shows that purchase intension is influences by CPV (Calabuig et al, 2013; Yusuf et al, 2013). If the requirements of customers are not fulfilled there are many negative impacts for the organization to have risk for having bad word of mouth from their customers, the image of an organization will be affected negatively (Loureiro et al, 2012). As there were no research done on automobile industry on examining the impact of customer perceived value on brand loyalty, particularly in Saudi Arabian automobile industry, this research intends to fill the research gap on CPV impact brand loyalty of Saudi Arabian automobile industry. Therefore the following key objectives are formulated for this research

To examine the impact of customer perceived values on customer satisfaction To examine the impact of customer perceived values on brand loyalty To examine the impact of customer satisfaction on brand loyalty

This paper is organised as follows: first part is discussed above. Second part is the theoretical framework and research hypothesis development. Third part discusses the research

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design and methodology, data collection procedures and process. Fourth part presents the results and discussion. Final part is conclusion and recommendation, and implication for further research

2. Literature Review Basic definitions of CPV are overall assessment of the utility of a product based on perception of what is received and what is given (Ulaga, 2000). Some defined CPV as direct determinant of behavioral intensions and consumer loyalty (Yang & Peterson, 2004). While others argue that consumption values explained why consumer chooses to buy or not to buy a specific product (Sheth et al, 1991). Also some defined CPV as overall economic perspective assessment of product effectiveness by the consumers (Ziethmal, 1988). Satisfaction or dissatisfaction has direct effect on consumer’s assessment of service quality and value. (Bolton & Drew, 1991). Monroe & Krishnan (1985) Price Quality Model was widely adopted and discussed model in measuring CPV. This framework focused on the categorization and analysis of price-quality relationship. Monroe & Krishnan (1985) proposed that perceived value is an important factor for customers or consumers purchase decision process and they will buy a product with high perceived value. According to Monroe & Krishnan (1985) that consumers will assess what they give and what they received in their personal perception when they are buying a product/ service. This framework consist only price as its variable to measure the customer perceived value, meanwhile it has shortfall of many variables in order to measure customer perceived value in particular associated with automobile. Another pioneering model is Trade-off model introduced by Zeithmal(1988). This is one of the uni-dimensional models to measure perceived value which applies quality and price as key factors to measure the customer perception toward the products and services (Ziethmal, 1988). Ziethmal (1988), identified four different dimension in terms of customer values such as, ‘value as low price, value as whatever the consumer wants in product, value as quality obtained from the price paid and lastly value as what consumer gets for what he or she paid.’ Further it was explained as trade-off between what is paid and what is received. This suggested that this approach of measuring perceive value is uni-dimensional. This indicated that many shortfalls of this theory in measuring customer perceived value associated with products characteristics, in particular automobiles. Ziethmal (1988) states the perceived quality as benefits and perceived price as sacrifices for a particular product or service. The limitations of this model is much attributed to the service experiences rather than nature of service experiences as multi-dimensional model build with traditional functional dimension such as; quality, benefit and price, perceived risk (Robinson, 2010). Also Typology for Consumer Value introduced by Holbrook (1999) is one of the key theoretical framework use to measure CPV associated with products. This framework contains eight variables for measure the customer perceived value. The variables are efficiency, play, excellence, aesthetics, status, ethics, esteem and spirituality (Holbrook, 1999). The concept of this framework is referring to the value of product which includes the services and goods. This framework is based on three- dimensional paradigms which are those, consumer value can be either extrinsic vs. intrinsic, active vs. reactive and self-oriented or other oriented (Holbrook, 1999). This model is considered to be one of the suitable frameworks to measure CPV associated with automobile such as cars. However this model is less useful in case if customer changes their value preferences overtime. The Value Hierarchy Model developed by Woodruff (1997) was widely adopted to measure CPV associated with services , especially in banking industry. This multi-dimensional framework of Woodruff (1997) to measure customer perceived value is the trade-off between desirable attributes compared with sacrifices attributes. Woodruff (1997) hierarchy model

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includes consumption goals, consequences and attributes. This is measurement framework to monitor the customer perceived value in four types of customers, (1) First-time customer, (2) short-time customer, (3) Long-term customer and (4) defectors (Woodruff, 1997; Parasuraman, 1997). This framework has taken the means-end theory of perceived value and suggested it has wide- range applicability. However this model fails to explain many value based elements (Sanchez-Fernandez and Iniesta-Bonillo, 2007). Also it is difficult to identify what preference attribute that contribute to customer value, and what consequences they want (Griffin and Hauser, 1993) since that the customers may have many preference attributes and consequences value dimensions which is different from each situation or for each customer, whereby the organization cannot work with so many different values at the same time. Also it was argued that this model neglects the most fundamental concept of customer perceived value of trade-off between benefits and sacrifices (Monroe & Krishnan, 1985). Overall, this model fails to address customer’s sacrifices either in pre-purchase stage, in-use stage, or post-use stage (Parasuraman, 1997) In 1990s, a key multi-dimensional theories that contributed toward customer perceived value is Seth et al, 1991) consumption value framework. This framework comprises with five variables to measure customer perceived value. These are functional, social, emotional, epistemic and conditional values (Seth et al, 1991). This framework is very useful in measuring various attributes of a product using customer perspective. As most of the research indicated that customer purchase a product based on benefits (Monroe & Krishnan, 1985). Monroe (Monroe & Krishnan, 1985)’s proposed model of CPV that depicts benefits versus sacrifices failed in detailing the key sources of values. Seth et al (1991) overcome this problem and detail-out various aspects of a product and how these attributes derives function values is very suitable to assess the types of values that a customer derive before and after they purchase a product, in particular a car. In case of measuring CPV associated with automobile, four variables such as functional, social, emotional, and epistemic values seems quite appropriate to measure CPV of a car (Seth et al, 1991). Further Sheth et al. (1991) framework were tested in three different choices such as, whether to buy or not to buy specific product, the choice of one type to another, and lastly choice of brand to another (Gaskill, 2004). Further this model is used to determine the possible reasons for buying or not buying a particular product or service (Candan, 2013) ‘This framework is limited to choice by individual and it only addresses choice, which are systematic and voluntary’ (Gaskill, 2004, p.27). This model is much suitable for voluntary decisions. However values such as conditional factor are less relevant in this framework associated with automobile industry, because conditional value is not easy to understand of customers unless the situation, time and place which might make conditional factor valid (Canadan, 2013).However it is important to retain all the values as proposed in the framework.

Figure 1 – Conceptual Framework of CPV and its influences on satisfaction and loyalty

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3. Research Design and Methodology Subjects A total of 250 questionnaires were distributed and a total of 213 questionnaires were returned (response rate 85.2%). However, some of these returned questionnaires were excluded from the sample as some respondents do not complete the questionnaire. This means the study only used 198 completed questionnaires. Procedure and data collection The researchers independently contacted the respondents using non-random sampling techniques of convenience sampling techniques. Additionally, permission from the each respondent was obtained by requesting them to sign the Participant Consent Form. Respondents were given a Participant Information Sheet to indicate the purpose and the importance of this research. The completed questionnaires were collected by the researchers and reminded the respondents to inform the researcher if they wish to withdraw from this study before the data were processed (30 days). Questionnaire Questionnaire is designed to gather the data. It consists of three parts 1, 2, and 3. Part – 1: it deals with personal details such as sex, education, age, etc. Part – 2: consists 25 statements to measure the customer perceived value (CPV). Four dimensions such as function value (FV), social value (SV), emotional value (EV), and epistemic value (EPV), conditional values (CV). Part – 3: consists of 10 statements which are divided into two main variables. 5 statements were used to measure customer satisfaction and 5 statements to measure customer brand loyalty. By including variables, questionnaire is prepared with five points Likert scaling system. Then analysis is made with appropriate statistical tools, in order to prove the objectives of the study and to test the causal impact of CPV on customer satisfaction and loyalty. For this study, following baseline models were established. Customer Satisfaction = β0 + β1X1 + β2X2 + β3 X3 + β4X4+ β5X5 --------(1) Brand Loyalty = β0 + β1X1 + β2X2 + β3 X3 + β4X4 + β5X5----------------- (2) Brand Loyalty= β0 + β1 (Customer Satisfaction) ------------------------ (3) Where, X1= Social Values (SV) X2= Emotional Value (EV) X3= Epistemic Value (EPV) X4= Functional Value (FV) X5= Conditional Values (CV)

4. Results and Discussion

Demographic Analysis The respondents are grouped in 7 clusters including age group, gender, automobile brand, occupation, level of education, automobile type, and income level. In terms of respondents 73.2% of respondents are from age 20 to 31 years indicating that most users are young adults.

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Only 8.1% of respondents are above 41 years. In terms of gender, 93.9% of respondents are male and only 6.1% users are female. This significant fewer number of females driving or using an automobile could be attributed to the strict rules against Muslims women in driving a car in Saudi-Arabia. In terms of occupations, most of the car drivers or users are administrative officers (26.8%), supervisors (15.2%) followed by managers (13.1%). Only 7.6% of respondents are business owners. 36.9% of respondents completed certificate level studies, followed by bachelor’s degree with 28.8% of respondents, and 15.7% of respondents obtained master degrees. 56% of respondents earns an average income ranged between US$1000-US$1500 per month indicating that most of the respondents are doing administrative job and also it correlates with the level of education completed (certificate). 80.8% of respondents earned an average income ranged from US$1000 to US$2500. Only 17.2% of respondents earned an average income ranged between US$2600 to US$4500. In terms of automobile type, 83.4% of respondents drives or uses cars followed by truck with 11.7% followed by van with 4%. This indicates the most of the car users are young adults. The users who uses van could have larger families so that they can move around together where Saudi-Arabia is considered as collectivist society. However due to the changing life style, most people , especially young adults aged between 20 years to 30 years may prefer to have their own vehicle such as a car. In terms of brand choice among the users or drivers of automobile, 30.3% respondents drives or uses Toyota brand cars or vehicles followed by Nissan and Honda with each 9.1%. South Korean Hyundai is among the fourth rank with 8.1%. As most of the respondents average income ranged between US$1000-2500, this could influence the car brand choice due to the price and reliability of car. Toyota is considered to be one of the reliable car with most affordable price around the world. The Table below shows the detail demographic aspects of the respondents.

Table 1: Socio-demographic Profile of the Participants

Variable Categories Frequency Percent Cumulative

Percent Age Group

20 29 14.6 14.6

21-30 116 58.6 73.2

31-40 37 18.7 91.9

>41 16 8.1 100 Gender

Female 12 6.1 6.1

Male 186 93.9 100 Automobile Brand

Toyota 60 30.3 30.3

Nissan 18 9.1 39.4

Hyundai 16 8.1 47.5

Honda 18 9.1 56.6

Others 86 43.4 100 Occupation

Manager 26 13.1 13.1

Own Business 15 7.6 20.7

Supervisor 30 15.2 35.9

Admin Officer 53 26.8 62.7

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Office/factory

secretory/clerk 20 10.1 72.8

Others 54 27.2 100 Level of Education

PHD 4 2 2

Masters 31 15.7 17.7

Bachelors 57 28.8 46.5

Diploma 27 13.6 60.1

Certificate 73 36.9 97

Professional

Accounting 6 3 100

Automobile Type

Car 167 84.3 84.3

Truck 23 11.7 96

Van 8 4 100 Income Level

US$1000-1500 111 56 56

US$1600-2000 33 16.7 72.7

US$2100-2500 16 8.1 80.8

US$2600-3500 21 10.6 91.4

US$3600-4500 13 6.6 98

US$4600-5500 2 1 99

>US$5600 2 1 100

Reliability and Validity

Before applying statistical tools, testing of the reliability of the scale is very much important as its shows the extent to which a scale produces consistent result if measurements are made repeatedly. This is done by determining the association in between scores obtained from different administrations of the scales. If the association is high, the scale yields consistent result, thus is reliable. Cronbach’s alpha is most widely used method. It may be mentioned that its value varies from 0 to 1 but, satisfactory value is required to be more than 0.6 for the scale to be reliable (Malhotra, 2002; Cronbach, 1951). If we compare our reliability value with the standard value alpha of 0.6 advocated by Cronbach (1951), our scale is highly reliable in most cases. Nunnally & Bernstein (1994) or with the standard value of 0.6 as recommended by Bagozzi & Yi’s (1988) we find that the scales used by us are highly reliable for factor analysis, except emotional values as it is 0.555 , which is lower than 0.6. However we decided to retain the variable of emotional value as overall reliability for CPV is 0.869, which exceeds 0.6. The Cronbach alpha results listed in Table 2 , were based on all the retained items and offered strong support for reliability in four customer perceived value dimensions.

Table 2- Reliability Analysis Variable Driver Number of items Cronbach Alpha (α)

Customer Perceived Value

Social Value 5 0.695

Emotional Value 5 0.555

Epistemic Value 5 0.743

Functional Value 5 0.674

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Conditional Value 5 0.791 Overall CPV 25 0.869

Satisfaction Satisfaction 5 0.747

Brand Loyalty Loyalty 5 0.794

Factor Analysis To assess the dimensionality of customer perceived value (social, emotional, epistemic , functional and conditional value scale) associated with automobiles among Saudi Arabians, factor analysis (principal component, varimax rotation) was conducted on the items listed in Tables 3. The indicators related to customer perceived value were function perceived functional values, emotional values, social values, and epistemic values. The 25 retained items from 5 variables of CPV.

Table 3-Rotated Component Matrixa

Component

CV EPV EV&SV EPV&SV FV FV&EV EV

CV4 .827

CV3 .802

CV2 .770

CV5 .674

EPV1

.749

EPV2

.643

EPV4

.634

EPV3

SV1

CV1

SV4

.717

EV1

.637

SV2

.633

EV2

SV3

.745

EPV5

.648

SV5

.633

FV2

FV4

.752

FV5

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EV3

.795

FV3

.629

FV1

EV5

.747

EV4

.613

Eigen

Value

6.188 2.93 1.577 1.272 1.247 1.045 1.023

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 10 iterations.

The factor analysis further grouped the functional and emotional values as one factor. An analysis of the Eigen values and the scree plot for customer perceived value suggested that seven (7) factors exist related to the Customer perceived value (Table 3). The total variances extracted by the 7 factors were 61.1%. To retain the dimensions, the Eigen value must be 1.0. or must exceed 1.0. All the components in used in the construct , including customer perceived value associated with automobile is more than 1.0 and cumulative variance for all the items included in the construct exceeded 60%, we decided to retain all the items falls under each variable. Sample Adequacy After checking the reliability and validity of scale, we tested whether the data so collected is appropriate for factor analysis or not. The appropriateness of factor analysis is dependent upon the sample size. A study conducted by MacCallum, Windaman, Zhang & Hong (1999) have shown that the minimum sample size depends upon other aspects of the design of the study. According to them, as communalities become lower, the importance of sample size increases. They have argued that if all communalities are above 0.5, relatively small samples (less than 300) may be perfectly adequate. It is clear that a sample size of 198 as is used in this current research is good for a suitable factor solution because all commonalities are 0.5 and above except for FV2, FV5 and CV1. This suggested to examine the sample adequacy using Kaiser-Mayer-Olkin (KMO) method.

Table 4-KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .815

Bartlett's Test of Sphericity

Approx. Chi-Square 1644.115

df 300

Sig. .000

Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is still another useful method to show the appropriateness of data for factor analysis. The KMO statistics varies between 0 and 1. Kasier (1974) recommends that values greater than 0.5 are acceptable. Between 0.5 and 0.7 are mediocre, between 0.7 and 0.8 are good, between 0.8 and 0.9 are superb (Field, 2000). In this study, the value of KMO for customer perceived value or the whole construct is 0.815 suggesting that the factor analysis is good and statistically significant (Kaiser-Meyer- Olkin = 0.815, Bartlett’s test of Sphericity was significant at p = 0.000 level). Therefore this suggests to retain all the variables proposed initially in the scale construct. Descriptive Statistics

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Table 5-Descriptive Statistics

N Minimu

m

Maximu

m

Mean Std.

Deviation

Skewness Kurtosis

Statistic Statistic Statistic Statistic Statistic Statistic Std.

Error

Statistic Std.

Error

SV 198 1.60 5.00 3.4798 .85656 -.088 .173 -.995 .344

EV 198 1.20 5.00 3.7747 .69015 -.551 .173 .349 .344

EPV 198 1.60 5.00 3.7677 .80944 -.374 .173 -.796 .344

FV 198 2.20 5.00 4.0828 .67441 -.648 .173 -.320 .344

CV 198 1.40 5.00 3.6313 .97545 -.308 .173 -.846 .344

SATIS 198 2.20 5.00 4.0788 .71351 -.458 .173 -.685 .344

LOYALTY 198 1.20 5.00 3.8879 .79117 -.824 .173 .565 .344

Valid N

(listwise)

198

Looking into Table 5, it shows that distribution of data is normal as it satisfies the skewness and kurtosis rules. The general rule is that the skewness should of the curve should not exceed more than 1 or less than -1. Similar rules apply to kurtosis. This means the peak value of the curve should not exceed 1 and the flatness of the curve should not go below -1. In terms of the CPV, functional value is mostly associated with automobile compared to all other CPV (M=4.08, SD=0.674). Emotional value is the second most important CPV associated with automobiles with a mean value of 3.77 (SD=0.690). The least CPV associated with automobile is social value (M=3.4798, SD=0.857). Most of the respondents seems very satisfied with their automobile with the highest mean score (M=4.078, SD=0.714) compared to brand loyalty. Correlation

Table 6- Correlation between customers perceived value and brand loyalty Factor Correlation

Satisfaction P <0.05 Correlation

with Brand Loyalty

P <0.05

Social values 0.202** Significant 0.308** Significant

Emotional values 0.308** Significant 0.397** Significant

Epistemic values 0.341** Significant 0.197** Significant

Functional values 0.576** Significant 0.319** Significant

Conditional Values

0.250** Significant 0.383** Significant

Customer Satisfaction 1 0.484** Significant

**. Correlation is significant at the 0.01 level (2-tailed).

With reference to the above Table 6 the result shows that all the customer perceived value has a positive and significant relation with the customer satisfaction and brand loyalty. Also this study found that customer satisfaction is significant and positively correlated with brand loyalty.

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Multi-Co Linearity Two major methods were utilized in order to determine the presence of multi co linearity among independent variables in this study. These methodologies involved calculation of both a Tolerance test and Variance Inflation Factor –VIF (Kleinbaum et al, 1988). The results of this analysis are presented in Table 7.

Table 7- Test of Co Linearity Satisfaction Brand Loyalty Variable Tolerance VIF Tolerance VIF

Social value 0.555 1.803 0.555 1.803

Emotional value 0.606 1.649 0.606 1.649

Epistemic value 0.587 1.704 0.587 1.704

Functional value 0.671 1.491 0.671 1.491

Conditional value 0.747 1.340 0.747 1.340

As can be seen from this data, none of the Tolerance level is < or equal to 1; and all VIF values are well below 10. Thus, the measures selected for assessing independent variables in this study do not reach level indicated of multi co linearity as shown in Table 8.

Table 8-Test of Durbin-Watson Variable Durbin-Watson

Satisfaction 1.4848

Brand Loyalty 2.196

The acceptable Durbin – Watson range is between 1.5 and 2.5. In this analysis Durbin – Watson values for supervisor is 2.190 which is highest score. There was no auto correlation problems in the data used in the research. Thus, the measures selected for assessing dependent variables in this study do not reach level indicate of multi co linearity. Regression Analysis For this study, regression analysis was performed to predict the level of student satisfaction based on five independent factors. The five independent factors/dimensions of TL are idealized attributes, idealized behavior, intellectual stimulations, inspirational motivation and individual consideration.

Table 9-Model Summary in predicting Customer Satisfaction

Model R R Square Adjusted R Square Std. Error of the

Estimate

1 0.606a 0.367 0.350 0.57512

The Table 9 shows that R is 0.606, R square is 0.367 and adjusted R square is 0.350. This indicates that 35% of the variance in customer satisfaction can be explained by the changes in independent variables of CPV. However as a general rule, this model is considered as a ‘poor fit’ as this multiple regression model fails to explain 60% of variance in dependent variable (customer satisfaction).

Table 10-Regression model for customer satisfaction

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Model Unstandardized Coefficients Standardized Coefficients

t Sig.

B Std. Error Beta

1

(Constant) 1.202 .296 4.062 .000

SV -.118 .064 -.141 -1.830 .069

EV .127 .076 .122 1.659 .099

EPV .004 .066 .004 .054 .957

FV .582 .074 .550 7.844 .000

CV .115 .049 .158 2.372 .019

Dependent variable: Customer Satisfaction

The result of regression analysis shows that out of the five indicators of CPV in influencing customer satisfactions, only two are significant as shown in Table 9. The two significant factors are functional value (FV) with P value =0.000 (P<0.05), conditional value(CV) with a P value=0.019 (P<0.05). The other three variables are not significant in influencing customer satisfaction. However the constant is significant. Therefore the model can be written as:

Customer Satisfaction = 0.550 (FV) + 0.158 (CV) + 1.202 This model suggest that when the most significant two factors of CPV is not associated with automobile of the preferred brand, customer satisfaction is still positive and by associating any of the two CPV with the preferred brand, the empirical model can increase the level of satisfaction when other things remain constant. The model above suggested that the changes in functional value(FV) can have the biggest influence on level of customer satisfaction as its Beta coefficient is the most significant and highest.

Table 11-Model Summary in predicting Brand Loyalty

Model R R Square Adjusted R Square Std. Error of the

Estimate

2 0.509a 0.259 0.240 0.68994

The second model is about brand loyalty. The Table 11 shows that R is 0.509, R square is 0.259, and adjusted R square is 0.240. This shows that only 24% of the variance in brand loyalty can be explained by the changes in CPV. Also as a general rule, this model is considered as a ‘poor fit’. This is because the adjusted R square is less than 60%. Based on the Table 12 below, it shows that three CPV are significant and positive in influencing brand loyalty. These are emotional value (EV) with Beta Coefficient of 0.235 (P=0.04), where P<0.05 is significant, functional value (FV) with Beta Coefficient of 0.250 (P=0.001) where P<0.05, and conditional values with Beta Coefficient of 0.278 (P=0.000) where P<0.05. However we found that social and epistemic values are not significant in influencing customer brand loyalty. Therefore the empirical model is written as follows

Brand Loyalty = 0.235(EV)+0.0.250 (FV) + 0.278 (CV) + 1.305 This model suggest that when the most significant three factors of CPV is not associated with automobile, customer brand loyalty is still positive and if any of the three CPV is associated with the automobile it can increase the level of brand loyalty among the care users or drivers when other things remain constant. The model above suggested that the changes in conditional value(CV) can have the biggest influence on level of customer satisfaction as its Beta coefficient is the most significant and highest.

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Table 12- Regression model for customer brand loyalty

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

2

(Constant) 1.305 .355 3.676 .000

SV .024 .077 .026 .316 .753

EV .269 .091 .235 2.939 .004

EPV -.141 .079 -.144 -1.776 .077

FV .293 .089 .250 3.291 .001

CV .225 .058 .278 3.863 .000

Dependent variable: Brand Loyalty

Table 13-Model Summary of brand loyalty

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

1 .484a .235 .231 .69391

a. Predictors: (Constant), SATIS

The second model is about brand loyalty. The Table 13 shows that R is 0.484, R square is

0.235, and adjusted R square is 0.231. This shows that only 23.1% of the variance in brand loyalty can be explained by the changes in customer satisfaction. Also as a general rule, this model is considered as a ‘poor fit’. This is because the adjusted R square is less than 60%.

Table 14-Regression model for customer brand loyalty

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 1.697 .287

5.915 .000

SATIS .537 .069 .484 7.752 .000

a. Dependent Variable: LOYALTY

Based on the Table 14 above, it shows that customer satisfaction is significant and

positive in influencing brand loyalty (adjusted R square =0.484, p=0.000). Therefore the empirical model is written as follows

Brand Loyalty = 0.484(SATIS) +1.697

This model suggest that when customer satisfaction is improved while other things remain constant, customer brand loyalty will improve.

Discussions and Conclusion In recent years numerous theories on customer perceived values (CPV) have emerged in various countries (Vera & Trujillo, 2013; Toivonen, 2012; Abu-Bakar, 2014; Alsheikh & Bojei, 2014). Also CPV in have been examined in many sectors (Yu Wu, 2014; Vera, 2013; Yusuf, 2013). However, there appears to be little research available on CPV in relation to customer satisfaction and loyalty in developing countries, particularly in Saudi-Arabia. Therefore, in this particular research, we attempted to shed light on the dimensions of CPV and its impact on

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customer satisfaction and brand loyalty in automobile industry in Saudi Arabi. However, because of the small sample size due to the limited coverage (only Jeddah), therefore it will be appropriate to repeat this research with a large sample size covering the whole Saudi Arabia, especially all the key sectors. By conducting research on these areas, we can re-examine the impact of CPV on brand loyalty to ensure the validity and reliability of the results. Moreover, future research should continue to address specific business sectors (national cars and foreign cars). As this study attempted to cover some of the demographic factors, a future study could examine how each of these factors could affect the way customer perceived value in relation to services and products. Overall, we found that this research fulfilled its purposes by identifying the degree of CPV associated with automobile and its impact on customer satisfaction and loyalty. In short the conclusions are

Functional value(FV) positively influences customer satisfaction and loyalty. Also it is important to note that FV is considered as one of the most important values that perceived by customer when they purchase a car, van or a truck.

Conditional value (CV) is the second most important value that are perceived by customer when they made a purchasing decision of an automobile. Similarly, CV is significant and positively influence customer satisfaction and loyalty.

Emotional value can increase brand loyalty and also it is considered as one of the key CPV associated with automobile.

It is important to note that improved customer satisfaction due to CPV has a significant and positive influence on customer brand loyalty

Implications for practice: Based on the findings, it is evident that improving functional, emotional and conditional values would improve customer satisfaction and brand loyalty. Therefore it is important to emphasis on improving functional aspects of automobile. Perhaps engine reliability, fuel consumption and design along with recognized brand of the automobile could increase customer satisfaction and loyalty. This could result increase in sales.

Further Research Direction: Since this study was conducted only on Jeddah, Saudi Arabia , it may not be able to generalize the finding. However by conducting this research on wider context of a particular country could confirm the findings and maybe a significant contribution to this field of study.

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