EMPIRICAL STUDY OF TECHNOLOGY BASED AUTO ......vehicles are unsafe, the auto-rickshaws are polluting, and finally, that they are a major cause of congestion. (Harding et al, 2016).

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International Journal of Management (IJM) Volume 11, Issue 6, June 2020, pp. 01-15, Article ID: IJM_11_06 01 _0Available online at http://iaeme.com/Home/issue/IJM?Volume=11&Issue=6 ISSN Print: 0976-6502 and ISSN Online: 0976-6510 DOI: 10.34218/IJM.11.6.2020.001

© IAEME Publication Indexed Scopus

EMPIRICAL STUDY OF TECHNOLOGY BASED AUTO-RICKSHAW SERVICE QUALITY

PERCEPTION USING SSTQUAL Dr. Vikram K. Joshi

Assistant Professor DMT, Shri Ramdeobaba College of Engineering and Management, –Nagpur, India

Corresponding Author Email: [email protected]

ABSTRACT Technology in transport services is experiencing a paradigm shift, wherein the

customers are empowered to become a part of service process through Self-service technology (SST) and Auto-Rickshaw business is not an exception to it. The study

evaluates the service quality dimensions of Ola and Uber Auto-rickshaw Services offered in Nagpur city of India using SSTQUAL scale and its impact on customer

satisfaction and behavioral intentions. A sample of 60 users of Ola and Uber auto customers is taken for the study. The study used step-wise multiple regression to

identify the significant quality dimensions and its impact on customer satisfaction and behavioral intentions.

Key words: Self-service Technology, SSTQUAL, customer service quality perception, customer satisfaction, customer behavioral intentions Cite this Article: Dr. Vikram K. Joshi, Empirical Study of Technology Based Auto-

Rickshaw Service Quality Perception using SSTQUAL. International Journal of Management, 11 (6), 2020, pp. 01-15. http://iaeme.com/Home/issue/IJM?Volume=11&Issue=6

1. INTRODUCTION The world is experiencing a high degree of urbanization and India is no exception to it.

India‟s thirty percent population lives in cities and the number is on rise due to migration from rural areas for livelihood. The urban transportation is on high demand as the new

residents living far away where housing is more affordable need ways to get to jobs. (Shlaes & Mani, 2013). Auto-rickshaws is the important urban transport in India amongst the other as it provides low cost and flexible mobility in most Indian cities. (Harding et al, 2016). Auto-

rickshaws are a form of intermediate public transport that fill the gap between private transport and formal public transport modes in cities. (Bhat, 2012). Due to its smaller size, it saves space on roads, carries about same number of people on an average, and occupies only one third the parking area and half the space used by a car on road. It produces lower emission as compared to private cars due to smaller engines (around 175cc compared to over 800 cc for

Empirical Study of Technology Based Auto-Rickshaw Service Quality Perception using SSTQUAL


cars). The three-wheeled design makes them easily maneuverable in traffic and reduces the probability of accident and is available at low implicit cost providing mobility option to low

and middle income population. (Garg et al, 2010). But there exists a considerable public debate and criticism about the services offered by auto-rickshaws and rude behavior or

drivers. Some popular criticisms are auto-rickshaw drivers are greedy and „overcharge‟, the vehicles are unsafe, the auto-rickshaws are polluting, and finally, that they are a major cause of congestion. (Harding et al, 2016). Also in many cities the auto-rickshaw drivers refuse to run on meters. Irrespective of this, auto-rickshaw services are considered to be the best means of urban mobility by lower and middle class population in India.

Self-service technology (SST) is transforming the service economy, from ATM in banking to e-commerce and m-commerce in traveling, resulting into great savings for the

businesses and ultimately lower price and better services to the customers. It is contributing significantly in terms of improvement in quality of life and productivity of businesses (Castro et at 2010). The SST has changed the dynamics of the business in terms of the way customer interacts to achieve higher customer satisfaction, loyalty and behavioral intentions (Shahid et

al 2018). If the customers perceive the technology as useful, enjoyable, easy to use, controllable and less risky, they are more likely to adopt the SST. (Wang et al 2013). Thus the business are aggressive enough to upgrade to better technology platforms to keep pace with

fast changing business environment. This has resulted into increased number of customers interacting with technology for creating service outcome and is growing rapidly resulting into low service employee involvement (Meuter et al 2000). Thus, self-service technology gives the travelers control and convenience to manage the trip by providing them with the technical means.

Online Transportation service is one of the innovative m-commerce where a customer can order a ride (car, motorcycle, etc) through mobile application and the driver respond through

the app. (Wallsten, 2015). Ola, an Indian origin online transportation network company owned by ANI Technologies Pvt. Ltd started in December 2010, in Mumbai. (Ashok Kumar, 2019). In November 2014, Ola diversified to incorporate auto rickshaws on a trial basis in Bangalore. After the trial phase, Ola Auto expanded to other cities like Delhi, Pune, Chennai, Hyderabad and other cities of India and very recently in Nagpur city. (ET, 2014). Similarly,

Uber is a transportation network company headquartered in San Francisco, California. In August, 2013 Uber expanded its business in India. Uber launched its first service in Bangalore and thereafter all over the India. Uber re- launch its Auto services in India, almost two years after shutting down the offering in March 2016, starting with Bangalore and Pune. Now it provides Auto services in many cities of India including Nagpur city. Both are offering app-based self-service technology available through mobile devices which is new and innovative offering for the travelers of auto-rikshaw in Indian market.

Thus, looking at the emergence of service in the form of self-service technology (SST), the present study tries to examine how the technology based Services SSTs impact the customer satisfaction and behavioral intention. In this paper a comparative study is done by measuring the service quality dimensions of app based Ola auto and Uber auto services and its impact on customer satisfaction and behavioral intentions. The rest of the study is organized

as below: Section II comprises of the literature review, Section III discusses the data and methodology, Section IV analyses the data and discusses the findings of the study and Section V concludes.

Dr. Vikram K. Joshi


2. LITERATURE REVIEW Shahid Iqbal et al (2018) study shows that SSTs service quality and behavioural intentions

have positive and significant relationship and customer satisfaction partially mediates the relationship between SSTs service quality and behavioural intentions in service sector of

Pakistan. Also the customers‟ satisfaction is positively and significantly related with SSTs users‟ loyalty.

Pakdil and Kurtulmuşoğlu (2014) used quality function deployment (QFD) in turkey to access the attributes of quality of highway passenger transportation services. The study shows

that the most important expectations of passengers‟ are technical specifications of buses, employees‟ empathetic approach toward customers, competent employees, and error-free

services. The most important technical requirements are technical specifications of buses, employee-oriented technical requirements, and error-free services in highway passenger

transportation. Justitia et al (2019) studied the customer satisfaction factors and satisfaction level in

online taxi services offered using the mobile apps in Indonesia. They showed that showed that the level of customer satisfaction was 76.117% which is considered to be low. The most

significant factors in online taxi mobile apps are route detection, interaction and content quality. The factors that caused dissatisfaction were connection and service quality.

Fauzi (2018) examined the dimensions of electronic service quality on mobile application of online transportation services namely GoJek, Grab, and Uber in Indonesia and the

relationship of the electronic service quality with customer satisfaction and repurchase intention using PLS-SEM. The study concludes that the dimensions of electronic service

quality viz., information quality, application design, payment method and security and privacy positively influence customer satisfaction. Also the customer satisfaction has

significant and direct effect on repurchase intention. Murad et al (2019) examined the dimensions of service quality and its relationship with

customer satisfaction on intelligent transport applications (Uber & Careem) in Jordan using SERVQUAL Model. The various dimensions of study were tangibles, responsiveness,

empathy, assurance and reliability. The study concludes that reliability has a high degree of impact on customer satisfaction. Mudenda & Guga (2017) also examined if there is a relation between quality service and customer satisfaction for Public passenger service transport in Zambia. The study concludes that reliability, assurance and tangibility are the most significant variables leading to customer satisfaction.

Djajanto L et al (2014) examined the relationship among self-service technology (SST), service quality, and relationship marketing on customer satisfaction and loyalty in banking sector in Indonesia. The results of study indicate that self-service technology, service quality

and relationship marketing have a significant effect on customer satisfaction. Similarly, relationship marketing has a significant effect on customer loyalty, but self-service

technology and service quality do not significantly effect on customer loyalty. Sindwani and Goel (2014) identified the dimensions of technology based self-service

banking (TBSSB) service quality. TBSSB cover services that customers use independently for banking without any interaction with bank employees. Using confirmatory factor analysis

they identified four dimensions of service quality, namely Convenience, Reliability and – Security, Responsiveness and Personalization all of which are found to be significant. Sindwani and Goel (2015) also investigated the impact of these factors on customer

satisfaction using SEM and found that they convenience and personalization impact customer satisfaction significantly.



Lin and Hsieh (2006) developed the SSTQUAL scale to measure service quality perception towards technology based banking services within self-service technologies. The

study measur the level of customer satisfaction and future behavioral intentions among edcustomers regarding the TBBS (technology based banking services) offered by the selected banks. The study has included fifteen banks that provide a wide range of technology based banking services to their customers across the country. The study help the bank leaders to ed

know the status of customer‟s perceptions and satisfaction towards the services being provided by these banks and gave insights to the banks to improve their services in order to make the customers satisfied.

Radomir and Nistor (2012) used SSTQUAL scale for studying the high-educated consumers‟ perception towards service quality in the Romanian banking industry. Pakurar et

al (2019) also employed STTQUAL scale to evaluate the service quality dimensions that affect customer satisfaction in the Jordanian banking sector. Singh et al (2018) evaluated

passenger‟s service quality towards self-service luggage check-in technologies at airports using SSTQUAL scale.

Thus, the various studies with regard to technology based or traditional transport services used various approaches like QFD, SERVQUAL model (Randheer et al, 2011; Ojo et al,

2014), etc., to analyse the service quality dimensions and its impact on customer satisfaction. Technology based transport service is a new service innovation wherein the service

organization provides self-service platform to the customer to avail the existing service, and customer is technologically empowered to self-service himself. Hence as observed in the study by Lin and Hsieh (2006), the SSTQUAL model which caters to the service quality

dimensions of technology based banking services is employed for technology based transport services (TBTS). Using SSTQUAL scale, the technology based service quality dimensions can be analyzed in a more intense manner and also its impact on customer satisfaction and behavioral intentions.

3. METHODOLOGY AND DATA The study uses SSTQUAL scale developed by Lin and Hsieh (2006) to measure the customer service quality perception of the technology based services. This scale comprises of seven

dimensions namely functionality, enjoyment, security, assurance, design, convenience and customization and two dimension customer satisfaction and customers behavioural intentions.

The study is done by taking the survey from the customers of Ola auto and Uber auto in Nagpur City of India with the help of structured questionnaire. e ultimate objective is to Th

evaluate the service quality dimensions of App based Auto Booking Services. The study included customer perception of technology based service quality CSAT and customer

behavioural intentions CBI as given by Lin and Hiesh (2006) scale. The study used judgement sampling as a part of non- probability sampling to ensure that the qualified participant get selected in the sampling frame. A structured questionnaire was designed on the basis of the various dimensions of SSTQUAL scale developed by Lin and Hsieh (2006) given as below in

table A on a seven point scale of 1 to 7. -Strongly Disagree and 7- Strongly Agree). (1 A sample of 60 respondents in Nagpur city who used both Ola and Uber Auto Sample units

selected for the study were based on judgement sampling technique and were in the age bracket of 15 45 years of age. Respondent having various professions and belonging to –

various demographics were considered to eliminate any bias relating age, sex, occupation, qualification, income, etc.

Dr. Vikram K. Joshi


Table A Service Quality Dimension based on SSTQUAL Scale

4. DATA ANALYSIS & INTERPRETATIONS The primary data for the study was collected by surveying the customers who are using apps with the help of structured questionnaire on the seven-point Likert scale (from 1 = strongly disagree to 7- strongly agree) containing 26 items based SSTQUAL scale as suggested by Lin

and Hsieh (2006). The data is collected from survey of 60 customers on the basis of convenience sampling method. The data was collected using a survey instrument developed by Lin and Hsieh (2006) on seven point Likert scale.

Item Dimension CSQP* Functionality FUNCTN1 I can get my service done with the app in a short time. FUNCTN2 The process of booking the auto is very easy. FUNCTN3 Using the app requires little efforts. FUNCTN4 I can get my service within the stipulated period of time. FUNCTN5 Each service item/ function of this app is error free. Enjoyment ENJOY1 The booking process of auto is very interesting with the help

of this app. ENJOY2 I feel good being able to use this app. ENJOY3 The app has interesting additional functions. ENJOY4 The app provides me with all relevant information. Security SECUR1 I feel safe in my transactions with the app. SECUR2 A clear privacy policy is stated when I use the app. Assurance ASSUR1 The app providing the services is well known. ASSUR2 The app company which provide this services has good

reputation. Design DESIGN1 The layout of the app is very user friendly. DESIGN2 I enjoy the aesthetics when using the app. Convenience CONVEN1 This app has operating hours convenient to the customer. CONVEN2 It is easy and convenient. Customization CUSTOM1 The app understands my specific needs. CUSTOM2 They have my best interest at heart. CUSTOM3 The app has features that are personalized for me. CSAT Customer Satisfaction CSAT1 Overall, I am satisfied with the services offered. CSAT2 The service offered exceeds my expectations. CSAT3 The service offered by the app is close to my ideal services. CBI Customer‟s Behavioural Intentions CBI1 The probability that I will use the service again is higher. CBI2 The likelihood that I would recommend my friend to avail

this service. CBI3 If I had to do over again, I would like to travel again. Source: Adopted from SSTQUAL scale developed by Lin and Hsieh (2006) * CSQP Customer Service Quality Perception. –



4.1. Demographic Considerations The survey asked the respondents about their demographic such as age, gender and about the app which they use. Table 1 display the sample distribution age- wise.

Table 1 Age of the Customers

Findings Over 70 per cent of the sample is between the age of 20-30 years, and 23.3 per cent of the sample is between the age of 30-45, and only 6.7 per cent of the sample is between the ages of 15-20 years.

Table 2 below shows the sample distribution gender wise:

Table 2 Gender

Frequency Percentage Cumulative Percentage Female 39 65 65 Male 21 35 100 Total 60 100

Findings It is observed that there are more female users than the male users. 65% women and 35 % men are the users of technology based auto services.

The table 3 below shows the average score of customers‟ responses based on various

service quality dimensions of Ola and Uber Auto Services in Nagpur region:

Table 3 Dimension wise Average Score of Customer‟s Responses

No. Service Quality Dimension Ola Uber 1 Functionality 5.36 5.84 2 Enjoyment 5.08 5.62 3 Security 5.05 5.78 4 Assurance 5.65 5.98 5 Design 5.55 5.52 6 Convenience 5.42 5.69 7 Customization 5.11 5.49 8 Customer's Satisfaction 5.24 5.65 9 Customer's Behavioural Intentions 5.77 5.96

Findings As can be observed in Table 3, the average score of an individual service quality dimension is

ranging from 5.05 to 5.98 (on seven point likert scale where 1= strongly disagree and 7= strongly agree) for all dimensions, CSAT and CBI. This indicates that the technology based service application (Mobile App) is performing well while providing the auto services to their customers. The analysis indicates that Uber Auto has score higher against its competitor on the basis of average score of customers‟ satisfaction, enjoyment, customization, functionality,

Age Group Count Percentage Cumulative Percentage 15-20 4 6.7 6.7 20-30 42 70.0 76.7 30-45 14 23.3 100 Total 60 100

Dr. Vikram K. Joshi


security, assurance. The Uber Auto customer found its mobile application most enjoyable and customized. The customer found Ola App aesthetically appealing with updated technology as far as design dimension is concern.

Uber Auto customer showed the strongest behavioural intentions to repeat the usage of their services and will recommend them to their friends. Ola customer reports the lowest

average score of security and enjoyment dimension.

4.2. Company- Customer Service Quality Perception (CSQP) wise The table 4 below shows the company-wise customer service quality perception scores:

Table 4 Company-wise Customer Service Quality Perception (CSQP)

No Name of App Average CSQP 1 Ola 5.32 2 Uber 5.70

Findings

The CSQP score is the average score of all the 7 service quality dimensions viz., functionality, enjoyment, security, assurance, design, convenience and customization. It can be seen that the average CSQP score of Uber Auto customers is higher than the Ola Auto customers. This shows that Uber Auto customers have better service quality perception than the Ola Auto customers.

4.3. Company-wise Customer Satisfaction (CSAT) The respondents were asked three questions pertaining to the CSAT on the seven point Likert scale from 1 to 7 where 1= strongly disagree to 7= strongly agree (Table A The results are ).summarized in table 5 below:

Table 5 Customer Satisfaction towards Apps

Rank Name of App Average Customer Satisfaction (CSAT) 1 Ola 5.24 2 Uber 5.65

Findings It is observed that the CSAT score of Uber Auto customers is more than Ola Auto customers. This shows that Uber customers are more satisfied with the technology based service than the Ola customers.

4.4. Assessment of Customer Satisfaction (CSAT) using CSQP Dimensions The step-wise multiple regression analysis is used to assess the relationship between the

customer satisfaction (CSAT) and the CSQP dimensions. The CSQP dimensions (functionality, enjoyment, security, assurance, design, convenience and customization) of Ola and Uber customers are taken as the independent variables in the model and CSAT is taken as

the dependent variable. The analysis of Ola auto and Uber auto is done separately and is presented in table 6 and table 7 below:



4.4.1. Multiple Regression Model Summary for Ola Auto

Table 6 Stepwise Regression Model Summary (N= 33)

(Assessment of CSAT in terms of CSQP dimensions)

Findings

The step-wise multiple regression analysis shows that the service quality dimensions functionality and customization are significant at 5% and 1% level of significance

respectively. The r-square value is 0.592 imply that the above two dimensions explain 59.2% influence on CSAT. The value of F is significant at 1% level of significant shows the

goodness of fit of the model. The service assurance, design, enjoyment, convenience and security are not significantly impacting the CSAT, so eliminated through step-wise regression analysis. 4.4.2. Multiple Regression Model Summary for Uber Auto

Table 7 Stepwise Regression Model Summary (N= 27)

(Assessment of CSAT in terms of CSQP dimensions)

Model 1 R R Square Adjusted R Square Standard Error of Estimate

0.769 0.592 0.564 0.654 ANOVA

Df Sum of Squares Mean Square F Significance F Regression 2 18.570 9.285 21.721 0.00* Residual 30 12.824 0.427 Total 32 31.394

Regression Output - Stepwise Coefficients Standard Error t Stat P-value Intercept 0.693 0.741 0.936 0.357 Functionality 0.369 0.161 2.286 0.029** Customization 0.503 0.135 3.728 0.001* * Significant at 1 % level of significance. ** Significant at 5 % level of significance.

Model 1 R R Square Adjusted R Square Standard Error of

Estimate 0.886 0.784 0.745 0.695

ANOVA

Df Sum of Square Mean of Square F Significance F

Regression 4 38.605 9.651 20.007 0.00* Residual 22 10.613 0.482 Total 26 49.218

Regression Output Coefficients Standard Error t Stat P-value Intercept -2.559 0.960 -2.667 0.014** Security 0.392 0.157 2.497 0.021** Assurance 0.331 0.166 1.996 0.058*** Design 0.253 0.128 1.984 0.060*** Customization 0.468 0.145 3.222 0.004* * Significant at 1 % level of significance. ** Significant at 5 % level of significance. *** Significant at 10 % level of significance.

Dr. Vikram K. Joshi


Findings The step-wise multiple regression analysis shows that the service quality dimensions security,

assurance, design and customization are significant at 5%, 10%, 10% and 1% level of significance respectively. The r-square value is 0.784 indicating that the above four

dimensions explain 78.4.2% influence on CSAT of Uber Auto. The value of F is significant at 1% level of significant shows the goodness of fit of the model. The functionality, enjoyment, and convenience are not significantly impacting the CSAT, so eliminated through step-wise regression analysis.

4.5. Assessment of Customer Behavioural Intentions (CBI) Using CSQP Dimensions Customer behavioural intentions (CBI) refer to customer feelings towards technology based service for repeat purchase and recommend the chnology (service app) to their friends and te

relatives for usage. The step-wise multiple regression analysis was conducted to assess customer behavioural intentions (CBI) towards technology based service in terms of CSQP

dimension to know which CSQP dimension influences the CBI. The CSQP dimensions (functionality, enjoyment, security, assurance, design, convenience and customization) of Ola and Uber auto are taken as independent variables and the score of CBI of Ola and Uber auto customers is taken as the dependent variable. The analysis of Ola auto and Uber auto is done separately and is presented in table 8 and table 9 below: 4.5.1. Multiple Regression Model Summary of Ola Auto

Table 8 Stepwise Regression Model Summary (N=33)

(Assessment of CBI in terms of CSQP Dimensions)

Finding

The step-wise multiple regression analysis shows that the service quality dimensions assurance and customization are significant at 5% and 1% level of significance respectively. The r-square value is 0.570 imply that the above two dimensions explain 57% influence on CBI. The value of F is significant at 1% level of significant shows the goodness of fit of the

model. The service functionality, design, enjoyment, convenience and security are not significantly impacting the CBI, so eliminated through step-wise regression analysis.

Model 1 R R Square Adjusted R Square Standard Error of

Estimate 0.755 0.570 0.541 0.585

ANOVA

Df Sum of Square Mean of Square F Significance F Regression 2 13.614 6.807 19.880 0.000* Residual 30 10.272 0.342 Total 32 23.886

Regression Output Coefficients Standard Error t Stat P-value Intercept 1.6904 0.6961 2.4284 0.0214** Assurance 0.3397 0.1386 2.4518 0.0203** Customization 0.4221 0.1161 3.6358 0.0010* * Significant at 1 % level of significance. ** Significant at 5 % level of significance.



4.5.2. Multiple Regression Model Summary of Uber Auto


(Assessment of CBI in terms of CSQP Dimensions)

Findings The step-wise multiple regression analysis shows that the service quality dimensions

assurance, convenience and customization are significant at 5%, 5%, and 1% level of significance respectively. The r-square value is 0.685 indicating that the above three

dimensions explain 68.5.2% influence on CBI of Uber Auto. The value of F is significant at 1% level of significant shows the goodness of fit of the model. The functionality, enjoyment, security and design are not significantly impacting the CBI, so eliminated through step-wise regression analysis.

4.6. Assessment of Customer Behavioural Intentions (CBI) Using CSAT and CSQP Dimensions There exists a strong and positive relationship between customer satisfaction and behavioural intentions (Wahyuningsih & Nurdin, 2010; Ismail et al, 2017). Hence, the multiple regression model was conducted to assess CBI (dependent variable) as a function of CSAT and CSQP dimensions (independent variable).

The CSQP dimensions (functionality, enjoyment, security, assurance, design, convenience and customization) of Ola and Uber auto and CSAT are taken as independent variables. The CBI of Ola and Uber auto customers is taken as the dependent variable. The analysis of Ola auto and Uber auto is done separately and is presented in table 10 and table 11 below:


0.827 0.685 0.643 0.739 ANOVA

Df Sum of Square Mean of Square F Significance F Regression 3 27.283 9.094 16.642 0.00* Residual 23 12.569 0.546 Total 26 39.852

Regression Output Coefficients Standard Error t Stat P-value Intercept -1.646 1.106 -1.488 0.150 Assurance 0.448 0.161 2.784 0.011** Convenience 0.409 0.151 2.701 0.013** Customization 0.474 0.138 3.436 0.002* * Significant at 1 % level of significance. ** Significant at 5 % level of significance.

Dr. Vikram K. Joshi


4.6.1. Multiple Regression Model Summary of Ola Auto


(Assessment of CBI in terms of CSAT and CSQP Dimension)

Findings

The step-wise multiple regression analysis shows that the service quality dimensions assurance and customization are significant at 5% and 1% level of significance respectively. The r-square value is 0.570 imply that the above two dimensions explain 57% influence on CBI. The value of F is significant at 1% level of significant shows the goodness of fit of the

model. The service quality dimensions functionality, design, enjoyment, convenience and security and CSAT are not significantly impacting the CBI, so eliminated through step-wise regression analysis. 4.6.2. Multiple Regression Model Summary of Uber Auto


(Assessment of CBI in terms of CSAT and CSQP Dimension)


0.755 0.570 0.541 0.585 ANOVA

Df Sum of Square Mean of Square F Significance F

Regression 2 13.614 6.807 19.880 0.00* Residual 30 10.272 0.342 Total 32 23.886

Regression Output

Coefficients Standard Error t Stat P-value

Intercept 1.690 0.696 2.428 0.021** Assurance 0.340 0.139 2.452 0.020** Customization 0.422 0.116 3.636 0.001* * Significant at 1 % level of significance. ** Significant at 5 % level of significance.


0.858 0.736 0.714 0.662 ANOVA

Df Sum of Square Mean of Square F Significance F Regression 2 29.333 14.667 33.464 0.00* Residual 24 10.519 0.438 Total 26 39.852 Coefficients Standard Error t Stat P-value Intercept 0.673 0.781 0.862 0.397 Convenience 0.266 0.142 1.872 0.073*** CSAT 0.668 0.104 6.409 0.0000013* * Significant at 1 % level of significance. ** Significant at 5 % level of significance. *** Significant at 10 % level of significance.



Findings The step-wise multiple regression analysis shows that the service quality dimensions

convenience and CSAT are significant at 10% and 1% level of significance respectively. The r-square value is 0.736 indicating that the above two dimensions explain 73.6% influence on CBI of Uber Auto. The value of F is significant at 1% level of significant shows the goodness

of fit of the model. The service quality dimensions functionality, enjoyment, security, assurance, design and customization are not significantly impacting the CBI, so eliminated through step-wise regression analysis.

5. RESULTS AND DISCUSSIONS 5.1. Assessment of Customer Satisfaction (CSAT) using CSQP Dimensions

The regression analysis models showing the relationship between service quality and customer satisfaction for Ola and Uber Auto is presented below for comparison: Ola CSAT = 0.369*Functionality + 0.503*Customization+ Error Uber CSAT = 0.392* Security+ 0.331* Assurance+ 0.253* Design+0.468* Customization+ Error

It can be seen that customization dimension is common in both the models significantly impacting customer satisfaction. Thus, it can be inferred that functionality and customization are positively associated with CSAT in case of Ola auto services and in case of Uber Auto services, the quality dimensions security, assurance, design and customization are positively associated with CSAT and are highly significant. This is the reason why Uber auto scored

higher than Ola auto in terms of CSAT. Also the overall average score of Uber Auto on average CSQP is higher than Ola Auto. This indicates than Uber Auto is the most preferred in comparison with Ola Auto.

5.2. Assessment of Customer Behavioural Intentions (CBI) Using CSQP Dimensions

The regression analysis models showing the relationship between service quality and customer behavioural intentions for Ola and Uber Auto is presented below for comparison: Ola CBI = 1.69 + 0.34* Assurance + 0.42* Customization + Error

Uber CBI = -1.646 + 0.448*Assurance + 0.409* Convenience + 0.474* Customization + Error

It can be seen that assurance dimension is common in both the models significantly impacting the CBI. Thus, it can be inferred that assurance and customization are positively associated with CBI in case of Ola auto services and in case of Uber Auto services, the quality

dimensions assurance, convenience and customization are positively associated with CBI. Thus, it can be inferred that the customers of Uber auto are likely to recommend the service to other and will repeat the service purchase than Ola auto.

5.3. Assessment of Customer Behavioural Intentions (CBI) Using CSAT and CSQP Dimensions The regression analysis models showing the relationship between the CBI, CSAT and CSQP dimensions for Ola and Uber Auto is presented below for comparison:

Ola CBI = 1.69 + 0.340* Assurance 0.422* Customization + Error + Uber CBI = 0.673 0.266* Convenience + 0.668* CSAT + Error +

Dr. Vikram K. Joshi


It can be seen that assurance and customization are positively associated with CBI in case of Ola auto services but CSAT is not significant. In case of Uber Auto services, the quality dimension convenience and CSAT are positively associated with CBI. Thus, it can be inferred

that customer satisfaction plays significant role in influencing the behavioral intentions in Uber auto, but since the customer satisfaction is not significant in case of Ola auto, the

customers may not go for repeat purchase.

6. CONCLUSION The study indicates that both the service providers are performing well while providing the technology based auto booking services to their customers. The analysis indicates that Uber has scored on higher side against its competitor on the basis of average score of customers‟ satisfaction, enjoyment, customization, functionality, security, assurance. The Uber customer found its apps most enjoyable and customized, whereas the customers of Ola App found it

aesthetically appealing with having up to date technology as far as design dimension is concern . Uber App customer showed the strongest behavioural intentions to repeat the ed

usage of their services and recommend them to their friends. The analysis shows that th Uber auto customers are more satisfied with the services e

offered by the app. The research indicates that the service quality dimensions of functionality and customization combined together appear to explain customer satisfaction in Ola Auto and the dimension of security, assurance, design and customization in Uber auto. Customization

dimension is common in both the services. The study also indicates that service quality dimensions of assurance and customization in case of Ola auto and assurance, convenience and customization in case of Uber auto combine together to explain customers behavioural

intention. Because a customer has a positive attitude towards a service, the customer‟s intention would be positive. For this reason a regression model was conducted to address CBI as a function of CSAT and CSQP quality dimensions.

These findings seemed to validate that service quality is an antecedent of CSAT and CBI. The model indicates that customer satisfaction and service quality dimensions are able to

explain 74 per cent of variability of customer behavioural intentions. That is why customer satisfaction (CSAT) shapes a customer‟s attitude, which determines the behavioural intention

in the Uber auto. In case of Ola auto it seem that customer satisfaction is not significant towards customer behavioural intention. Other dimensions like assurance and customization are positively associated with the CBI.

A positive attitude leads to repeat purchases, a key to success in today‟s competitive

environment. The service provider should make their customers satisfied by providing them quality services. As per the findings, a satisfied customer has a positive behavioural intention to reuse the app. In order to invite customers to reuse and recommend the services to their social networks, the service providers should focus on other dimensions as well in general and on convenience dimension of the services in particular.

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