Hypothesis 2: Perceived price has a positive association with customer satisfaction levels.
Percieved service quality and customer satisfaction
Perception of service quality can occur at multiple levels in an organization
- Corporate service
- Physical environment
- Interaction with the service providers
T. Koivuma¨ ki, A. Ristola and M. Kesti(2006) conceptualized that the service quality depends upon the above mentioned factors. The physical factors involves the physical product and supports, interactive quality refers to the interaction between the customer and the service provider, and the corporate quality is symbolic in nature and involves the perception about the company in the minds of the customers and its brand image. Service quality is defined as a global assessment that is the customer’s overall impression of the relative inferiority/superiority of the service provider and its services. Further, Rusk and Zahorik supported the expectation-perception gap for measuring service quality.
The main antecedents to perceived service quality are the customer expectation and perceived performance (Bitner and Hubert). Further, Richard Spreng, Linda Shi,Thomas Page suggested that net Customer Satisfaction has a marginally higher impact on switching intention than total perceived service quality. Also, A.Taylor,Thomas L.Baker(1994) concluded that Consumer satisfaction moderates the relationship between the quality of service and intention to switch. Zhilin Yang, Robin T. Peterson (2004) found that switching costs moderate the relationship between satisfaction and perceived value. Next, Hsin-Hui (Sunny) Hu, Jay Kandampully and Thanika Devi Juwaheer(2009) examined the association and impact of customer satisfaction and perceived value and found that customer satisfaction is affected by perceived service quality. Das, Bhagaban Mohanty, Sangeeta(2007) carried out a study in the Indian context and found that better perceived service quality leads to increased customer satisfaction.
Customer satisfaction results from the comparison of expected performance with that of the perceived actual performance (Churchill, Suprenant, 1982). Parasuram et al also suggested that service quality is positively related to the customer satisfaction levels and that service quality perceptions are dependent not only on the outcomes but also on the evaluation of the service delivery process by the customers. Venetis and Ghauri (2000) viewed that the service quality is regarded as one of the few means for service differentiation and competitive advantage that attracts new customers and contributes to the market share. Further, Rakshit Negi (2009) concludes that the service quality was found to be significantly linked with the overall cellular phone user satisfaction. Also, Birgit Leisen(2006) suggests, as perceived levels of satisfaction increase, the less likely the customer is going to switch to a more conveniently located competitor and, hence, the more loyal he or she remains. Further, T. Vanniarajan, P. Gurunathan (2009) concluded that the cellular operators need to engage in improving the quality of both the value added services and the main service quality to increase their customer satisfaction which would lead to a greater customer loyalty. Hence differentiation leads to customer satisfaction.
From the above arguments, it is clear that there is a positive relationship between the service quality and customer satisfaction. Hence the hypothesis
Hypothesis 3: Perceived Service quality is related positively to the customer satisfaction
Perceived switching barrier as a moderator
Switching barrier can be seen as a hurdle for the customers to switch from one service provider to another. The Perceived switching barrier is increasingly affecting the intention of the customer to switch from one service provider to another. Anne Wan-Ling Hu and Ing-San Hwang(2009) found that significant correlation exists between various types of switching costs-relational and procedural and customer intention to switch. Further, Albert Caruana(2003) describes three types of costs involved in - learning , contractual and transaction. Moeover, Markus Blut, Heiner Evanschitzky,Verena Vogel,Dieter Ahlert(2007) concluded that switching barriers have an impact on customer loyalty.
Patterson, Paul Gand Smith, Tasman analyzed how switching barriers affect the propensity of the customers to stay with the current service provider. In this study switching barrier is taken in terms of six forms and they are search costs, loss of social bonds, setup costs, functional risk, attractiveness of alternatives and loss of special treatment benefits. The study has proven that switching costs form a major barrier in the customer’s willingness to switch service providers. Stefan Buehler and Justus Haucap suggest that reducing the switching costs is one of the main benefits of Mobile Number Portability. Reducing the switching costs will automatically reduce the switching barrier there by affecting the relationship between the customer satisfaction and their willingness to switch service providers. Shin, DongH. and Kim, Won Y. in their study of Mobile Number Portability in Korean market came to the conclusion that even after the advent of Mobile Number Portability, customers view the switching barrier to be high. This might be due to other hidden costs involved in switching and also on the new customer lock-in strategies developed by the service providers. Dong-Hee Shin and Won-Yong Kim in their study on U.S Market also concluded that the customers view the switching barriers as main hindrance to switch service providers even after the introduction of MNP.MNP is expected to reduce switching costs and there by reduce the switching barriers. But the other costs as mentioned before like termination costs, customer lock-in strategies reduce the effect of MNP in reducing the switching barrier.However,Lukasz Grzybowski studied the relationship between the switching costs and the nature of mobile carriers, which change, basis the customer characteristics .It was found that the time-based service provider choice is because of both the switching related cost and constant tastes. The customer satisfaction is adversely related to the willingness to switch of the customers. If the satisfaction level with the existing service provider is high, then the customers are not expected to switch the service provider. Similarly, if the satisfaction levels are low with the existing service provider, then the customer is expected to switch service provider readily. But the Switching Barrier moderates this relation. Even though the satisfaction level with the current service provider is low, the customers are expected to continue the relationship with the current service provider if the switching barriers are perceived to be high as mentioned by P. Klemperer(1995). So the Perceived Switching Barriers moderates the relationship between the customer satisfaction and the willingness to switch of the customer. This leads us to the hypothesis.
Hypothesis 4: Perceived Switching Barriers by the customers act as moderator in the relationship between customer satisfaction and their willingness to switch service provider.
The Proposed Model
Research Methodology
Measures
The constructs Perceived Price ,Perceived Service Quality, Customer Satisfaction,Perceived Switching Barrier and the Willingness to switch are measured using Multi Item Scale. We use a five point likert scale for this purpose with responses ranging from 1 to 5,where 1 corresponds to “Strongly Disagree” and 5 corresponds to “Strongly Agree” Perceived Service Quality, Perceived Price and Customer Satisfaction are measured using multi-item scales adopted from Chadha, S. K., and Deepa Kapoor
Perceived Switching Barrier and Willingness to switch are measured using the multi-item scale borrowed from Dong-Hee Shin and Won-Yong Kim
The scales borrowed for Perceived Service Quality has been modified to fit the Indian Market and also demographic profile of the target sample.
Data Collection
We made use of an online intranet survey ,by creating a questionnaire and circulating it to our target set of respondents. Thus the sampling technique used was convenience sampling.
Sample Characteristics
The sampling was done on a target population belonging to the age group of 20-35 years of age. There were 45 females and 110 males, comprising a sum of 155 respondents. These respondents were mainly resident students of Indian B-schools.
Sampling Technique
Non-random Convenient Sampling is used where the sample is taken from convenient set of B-School students.
Response Rate
the Survey Questionnaire was floated to around 300 students of which 155 respondents responded to the survey.
Factor Analysis
We use factor analysis to ascertain unidimensionality of the constructs .This will help in verifying the operationalization of the constructs.
All the constructs with the exception of Perceived Service Quality passed the unidimensionality test.
The construct ‘Perceived Quality of Service’ showed two dimensions upon Factor Analysis.
First Dimension had items 1,2,3,4,5,15 of perceived service quality(psq)
Second Dimension had items items 7,8,9,10,11 of perceived service Quality(psq)
Based on the items inside each dimension, we have named the first dimension as Relational quality of the service provider and second dimension as Network Quality of Service provider.
Upon doing the reliability for each of the two dimensions of Perceived service quality, we found that provider and Network Quality of Service provider has a higher reliability (Cronbach alpha 0.933) as compared to Network Quality of Service provider (0.872). Further, based on our literary review, we found that the three major areas of concern for providers of service are as follows:
- Intangibility: Inability of service to be counted, stored and measureable. This changes customer perception of quality.
- Heterogeneity – The performance of service provider employees varies across customers. It need not be consistent across the board.
- Inseparability – The producer and consumer of a service need to be present simultaneously for the fulfilment of a service.
All these parameters can be satisfied by means of relational quality of Service Provider. Further the importance of relational quality over call quality/transmission quality in predicting customer satisfaction which in turn affects customer loyalty was proved in the research by Eshghi, Roy and Ganguli (2008).
Based on this we go ahead with Relational quality of Service Provider as the only dimension in the construct ‘Perceived Customer Quality’.
Factor Analysis Results
Reliability Analysis
The reliability of the scale is measured using Cronbach alpha.Cronbach alpha is manily used to measure the internal consistency of the items of the scale and how is the reliability of the group identified.
As can be seen, the Cronbach alpha co-efficient of all the constructs except Perceived Switching Barrier lies between 0.772 and .933.This implies high consistency of the items of the scale with the other items of the scale.
For Perceived Switching Barrier, the reliability of the scale is coming out to be 0.655.Perceived Switching Barrier is considered to be a moderator for the relationship between customer satisfaction and willingness to switch. Also, few researchers like Chadha, S. K., and Deepa Kapoor suggests that Cronbach alpha value of above 0.60 can be considered as reliable. So we are considering the scale for perceived switching barrier to be reliable and we are keeping the construct in our model.
Content Validity
This type of validity refers to the subjective agreement among professionals that a scale logically appears to accurately measure what it is intended to measure.Based on our extensive literary review on MNP related studies across various countries and sample population demographics,we have identified that the scale items are validated by relevant telecom sector SMEs(Subject Matter Experts)
Mediator Analysis
According to our proposed model, Customer Satisfaction is to act as mediator between Perceived Price and Willingness to Switch and also between Perceived Service Quality and Willingness to Switch. To test the significance and effect of mediation, Baron & Kenny method is used.
•A regression analysis was conducted with Perceived Price and Perceived Service Quality predicting Willingness to switch.
•A regression analysis was conducted with Perceived Price and Perceived Service Quality predicting Customer Satisfaction.
•A regression analysis was conducted with Perceived Price, Perceived Service Quality, and Customer Satisfaction predicting Willingness to switch.
The results of the regression done are:
As can be seen, perceived price become insignificant after the addition of customer satisfaction as independent variable to predict willingness to switch. Customer Satisfaction fully mediates the relationship between Perceived Price and Willingness to switch.
The Co-efficient of Service quality reduces after adding customer satisfaction as an independent variable. This indicates that Customer Satisfaction partially mediates the relationship between Perceived Price and Willingness to switch.
To measure the effect of mediation, SOBEL Test is performed. Sobel Equation is given by:
z-value = a*b/SQRT(b2*sa2 + a2*sb2)
For Perceived Price, the result came out to be
For Perceived Service Quality, the results were
As can be seen, the Z-value of both Perceived Price and Perceived Service Quality are greater than the critical value of 1.96(95% of unit distribution)
This proves that customer satisfaction acts as significant mediator between perceived price and willingness to switch and also between perceived service quality and willingness to switch.
Moderator Analysis
Our model suggests that perceived Switching Barrier acts as moderator in relationship between customer satisfaction and Willingness to switch. To test whether the moderator effect is there, we did moderator analysis.
This moderator analysis can be done by means of regression approach where the first step is to test the interaction between customer satisfaction and switching barrier. This can be done by creating an interaction item (customer satisfaction * perceived switching barrier). We can say if the addition of the interaction term causes significant change in the R2 value, then the relation ship between the customer satisfaction and perceived switching barrier is confirmed. This approach has two problems
Multicollinearity Problem: Either Customer Satisfaction or Perceived Switching barrier might be highly correlated with the interaction term (Customer satisfaction * Perceived Switching Barrier), which will impact the regression coefficients
We are measuring the impacts on extreme scenarios only. The impact of customer satisfaction on willingness to switch is measured when there is no perceived switching barrier and the impact of Perceived Switching barrier on willingness to switch is measured when there is no customer satisfaction.Karin Boonlertvanich(2009) suggests that existence of alternatives also influences customer decison to switch when satisfaction is low.
To avoid these two issues, we have centered and standardized the variables, customer satisfaction and perceived switching barrier. This procedure converts the variables into their Z scores.
Centering: Here our intention is to have same spread with mean as zero. To achieve this mean is subtracted from each value.
Standardising: The intention is to get a standard deviation of one. This is done by dividing the centered variable by standard deviation.
These standardized values of customer satisfaction and perceived switching barriers are then multiplied to get the interaction term. Then the effective change in R2 value due to the interaction term is measured and then the moderating effect is gauged.
As can be seen, though there is a change in R2 value, the significance is greater than 0.05 .So it can be concluded that Perceived Switching Barrier produces no significant moderator effect.
Perceived Switching Barrier is found to have a moderating effect on relationship between customer satisfaction and Willingness to switch in the study conducted by Dong-Hee Shin and Won-Yong Kim in US market. In US Market, in spite of the advent of the MNP, perceived switching barrier has significant moderating effect.
In the case of Indian market, it is found that perceived switching barrier does not moderate the relationship between customer satisfaction and willingness to switch. This can be due to the advent of MNP technology. It can also be due to the sample characteristics.
Discussion and Conclusion
In our study, which is based on Indian Market there is no moderating effect of Perceived Switching Barrier with the advent of MNP. Customer Satisfaction acts as full mediator in case of relationship between perceived price and willingness to switch. This proves that price affects the willingness to switch only through customer satisfaction
Limitations
One of the major limitations of this study is the sample size, which is restricted to the age group of 20-30. A sample size involving people of varied ages would have enables us to analyze the willingness to switch in greater depth. Further, the sample size of 155 is small compared to the number of mobile users in Jharkhand. This study did not incorporate the effect of differences in willingness to switch for people of different Economic levels, their income levels, education and awareness levels, etc which would have the study more robust and comprehensive. Further, the test was administered among respondents whose education level was high (post graduates); hence their awareness level about MNP is high. If the research is conducted on a different sample set where the customers are unaware of the MNP, the results might be different and they might still perceive Switching barriers as significant.
Scope for future research
The study can be conducted across different countries and regions. There are many other factors like the Income levels of the customers, the economic levels, their personalities, and their educational backgrounds etc whose effects on the willingness to switch do not contact excessive research support. Data on these factors can throw more light to how these factors influence the willingness to switch and hence gives more reliable results. Our study was restricted only to the people belonging to similar demographic profile within India. The scope of the study can be widened by involving people from various different sates in India and also by moving our focus across borders to other countries as well to obtain a holistic view of the customers’ intentions to switch. Effects of Perceived Switching Costs, Corporate Brand Image can be analyzed on the model
Appendix A: Multi-Scale Items used to measure the constructs
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