Measuring Service Quality Perceptions of Indian E-retailers: An Evaluative Study

Abstract

Indian online retailing landscape is characterized by a plethora of sellers all trying to attract the same set of target customers, making service quality a critical differentiating factor for the players. This study investigates the influence of process quality, outcome quality and service recovery on customer e-satisfaction, and e-loyalty and also the influence of customer e-satisfaction on e-loyalty. Sample size of this quantitative study was 199, sample unit was youth and sampling method was purposive sampling. The study was conducted in April–May 2019 in Bengaluru. Responses were subjected to SEM, factor analysis, hypothesis testing, direct and indirect effects using AMOS20. Process quality, outcome quality and service recovery were seen to significantly influence customer e-satisfaction with process quality having a greater impact. Service recovery positively influences outcome quality. Customer e-satisfaction was found to significantly influence customer e-loyalty. Process quality and service recovery significantly influence customer e-satisfaction while outcome quality did not have any impact on customer e-loyalty. Process quality seem to have more impact than service recovery and outcome quality on customer e-loyalty. The findings are in line with previous study. However, the influence of service recovery on outcome quality is something new this study has revealed.

Keywords

E-services service recovery process quality outcome quality customer e-satisfaction customer e-loyalty

Introduction

With the advancement in internet technology, India is expected to emerge as the largest online market in the world. India has an enormous internet crazy populace that is not just getting to the internet for browsing but also for purchasing on the web.¹ Most of the products such as smartphones, consumer products, and other products are shopped online.² The internet has significantly changed the face of retail, engaging buyers with the huge amount of information to settle on purchasing choices. Internet phenomenon has influenced the behaviour of the firms, especially in terms of products³ and consumer expectations.⁴ Online environment reduces cost (by increasing efficiency), expands revenue (managing relationships and enhancing services), and raises profits.⁵ E-retailers (online marketers) such as Amazon, Flipkart, and others host their virtual store and distribute products through web channels. These web channels disseminate pre-purchase product information, offer an alternative way to buy goods and services, provide an opportunity to expand services, and help in reaching time-starved customers.

E-retailing has dwarfed previous revolutions in retail sectors and customers are shifting from physical store to non-store retailing at a very fast pace. However, the lack of trust amongst customers has reduced the diffusion rate of this online mode. Indian consumers are price-sensitive and generally, they do not trust the internet and perceived security risks. Because of cyber theft issues, they hesitate to make an online payment. That is why 74 per cent of customers prefer cash on delivery mode of payment.⁶ This will be challenging for online retailers. Assessing and building trust in online buyers is essential for web channel players to be efficient and effective. The volume of online sales is ever increasing and online buying is becoming a preferred method for many shoppers all over the world. With numerous e-retailers available with just a click of a button, customers can easily change their e-retailers. So, managing high-quality customer relationship is crucial. Successful e-retailers have understood that customer perception of e-SQ is decisive. Research steered by Fotiadis⁷ has proved that responsible behaviour and information-seeking behaviour of the customers significantly influences perceived e-SQ. By strategically using the ICT and aligning the internal processes effectively, online services can be made successful.⁸

Service quality (SQ) is a subjective assessment of quality when a customer interacts with the service provider. It includes all the specific services that meet the requirements of the customers. This phenomenon of SQ was pioneered by Parasuraman et al.⁹ SQ has been one of the significant constructs within customer services. As the quality of service increases, it brings about recurrent buys and benefits.^10,11 Various studies have shown that the higher the perceived quality, better the yield for the service provider. The vast majority of the current research has been conducted in identifying the contributory factors of quality of e-tailers as perceived by the online shoppers.^12,13 Others have concentrated on customer e-satisfaction and behavioural intentions of online buyers.^7,14 According to Imrie et al.,¹⁵ retail SQ promotes customer retention and customer loyalty that includes online retailers also. Online services exhibit unique characteristics such as troubleshooting internet connection issues, server down issues, and unplanned outages. Hence, it will be appropriate to differentiate SQ dimensions into three subsequent dimensions namely, process quality, outcome quality, and service recovery.¹² As more and more customers flock online, positive online presence helps e-retailers to effectively interact, and engage the customers.¹⁶ This aspect of SQ dimension on THE interaction of the customer with the website is termed as process quality. Prior research by Yoo and Donthu¹⁷ on e-services provided SQ framework for measuring website interactivity, however, e-SQ perspective is much bigger than just how customers interact with the website. As per Holloway and Beatty,¹⁸ the most noted problem in online services is the delivery of desired product or services. This aspect of SQ dimension is termed as outcome quality. While evaluating e-SQ, if e-tailer fails to deliver a product in the desired manner, then previous website interactivity carries little or no meaning. Additionally, service recovery dimension becomes active whenever there is a service failure. Such incidents occur while interacting with self-service technology. As buyers and sellers are tangibly separated, recovery measures are remarkably important. Hence, recovery dimension is included in e-service conceptualization. According to Baker and Crompton,¹⁹ customer satisfaction is directly influenced by SQ and customer satisfaction acts as an antecedent to customer loyalty.²⁰ Further, Liao and Cheung²¹ contended that culture significantly influences online buying behaviour and customer perceptions of e-services are affected by different cultures.²² The online consumption patterns and behaviour of Indian consumers is different when compared with buyers in the West. Theoretical frameworks related to e-SQ and consumer behaviour are developed considering western societies. However, it would be beneficial to understand frameworks on e-quality services, pertaining to Indian e-retailers, to enhance our understanding of e-satisfaction and e-loyalty.

The fundamental objective of this study is to find out factors that impact e-satisfaction and e-loyalty of Indian customers. Hence, the following objectives have been set for the study

To develop a scale to measure dimensions (process quality, outcome quality, and service recovery) of e-retailing SQ.

To examine the relationship between the dimensions of e-services quality framework, e-satisfaction, and e-loyalty.

Literature Review

E-service Quality Measurement Scale

Experts have concluded that the product quality alone is not sufficient to get the competitive edge. It requires SQ also. Thus, SQ came to prominence in the 1980s. SQ is intricate and troublesome to measure. Many scholars have explored the concept from various perspectives. Conventionally, SERVQUAL developed by Parasuraman et al.^23–25 captures interpersonal interactions. However, Parasuraman and Grewal²⁶ suggest that when customers interact with interface technology rather than with service personnel, to avail e-services, the dimensions of SERVQUAL changes. Gefen²⁷ tried to extend the SERVQUAL dimensions to electronic context. However, dimensions got reduced to three (tangibility, interactivity, and empathy) instead of five. In his research, tangibles significantly influenced loyalty. Here the tangibles of the study were attributed to the appearance of the website. Wolfinbarger and Gilly²⁸ developed a 14-item scale to measure e-tailing quality and named it eTailQ. This scale had 4 constructs, namely: Website design, reliability/fulfilment, privacy/security, and customer services. Research on e-SQ spearheaded by Zeithaml et al.²⁹ developed 11 measurement dimensions. From that point until today, numerous different analysts created and introduced different models for e-SQ.

Most investigations fundamentally focused on the process quality or technical quality, that is, customer’s interaction with e-retailer’s website. Considering this aspect, “process quality” should be used as one of the dimensions to measure e-SQ. Another dimension which goes into e-SQ evaluations is the “outcome quality”, that is, what customer receives or benefits from the service. Hence, this dimension should be considered to measure e-SQ. Besides, if issues emerge, an e-retailer must know about the significance of service recuperation goals. Along these lines, Bhandari et al.³⁰ contended that “service recuperation or service recovery” is an essential dimension in the overall assessment of e-SQ.

Further, the scale suggested by Collier and Bienstock, ¹² and Parasuraman et al.⁹ had three dimensions for e-SQ measurement (Process quality, service recovery, and outcome quality). Therefore, for our study, we have considered these three constructs—process quality, service recovery and outcome quality as measures for e-SQ dimensions.

E-service Quality Framework

In view of e-SQ framework of Collier and Bienstock,¹² and Parasuram et al.,⁹ we hereby propose process quality construct (second dimension), service recovery construct, and outcome quality construct. Here, the process quality refers to the interaction of customer with the website (process), service recovery denotes compensation offered or commitment from the company whenever there is a service failure and outcome quality refers to the way the product or service is delivered. All three constructs represent e-SQ framework for the study.

Process Quality

In a competitive environment, e-service excellence is crucial. E-service should have the ability to provide high quality and timely information to customers. It should assist customers in giving the convenience of price-quality comparison and enable customized search processes. The key determinants of website characteristics are assortment, ease to access, website competitiveness, in-depth product information, financial security, and so on.^31,12 As per Ariely,³² availability and depth of information is central for customers to decide on the product. Privacy and security issues impact customer attitude in availing online financial services.³³ Even colour, size, design of the website, pictures, and animations—have been shown to affect online purchases.³⁴ According to Zeithaml et al.,²⁹ the website quality is not only assessed during interaction with the website but also in post-interaction (fulfilment/returns). Based on past and extant literature, the process quality dimensions of the study involves security (S), functionality (F), convenience (C), and information accuracy (IA). Here, convenience refers to website’s ability to access and transact conveniently. Information accuracy refers to the quality of information being true and correct. It also means that the information must be relevant, complete, and timely. Security refers to the protection of individual identity from hackers, government surveillance, and web setup. It also means that online profiles of the customers should not be shared with third parties without customer approval. Functionality refers to the working condition of the website. It is the interactive part of the website in which the customer commands are executed effectively.

Outcome Quality

Outcome quality is an e-service feature which encompasses order receipt. Assessment of the website intuitiveness will mean little when e-retailer fails to deliver the product/service in the expected manner. At the end of browsing session, the product the customer is left with, is the outcome service quality of the website. It is the ultimate reason why customers turn up to a website. Collier and Bienstock¹² proposed three dimensions for outcome SQ namely, order timeliness, order accuracy and order condition. They condemned uni-dimension conceptualization of outcome quality as they felt it is inappropriate. As per Ahn et al.,³⁵ customer satisfaction increases if there is timely delivery. Customers are knowledgeable and are quick to disparage if the delivery of the service is not reliable. They show displeasure for deficit outcome quality in the form of feedback/ratings. Therefore, in our study, we have considered product completeness, order timeliness, order/billing accuracy, and order condition as outcome quality services. Here, product completeness means that the ordered product quantity is received in full and of the promised quality. Order timeliness means delivery in the stipulated time. Order billing accuracy includes stated price and receipt are accurate. Order condition means a description of the product and actual condition of the product are same.

Service Recovery

As per Bhandari et al.,³⁰ service recovery is crucial in the overall assessment of e-SQ. Bitner et al.³⁶ suggested that customers expect effective recovery services in the event of service failure. Miller et al.³⁷ proposed that customer loyalty enhances when customer satisfaction is clubbed with service recovery. The phenomenon of recovery paradox was suggested by Mc Collough and Bharadwaj³⁸ in which customers exhibit favourable behaviour when he/she has experienced service recovery than those who have not experienced service recovery. According to Bansal et al.,³⁹ customers who have encountered service recovery services take part in positive informal correspondence and show interest in their repurchasing decisions.⁴⁰ As e-services involve remote transactions, by and large, customers perceive less confidence in online shopping. Hence, e-retailers use service guarantee to revoke confidence in the customers and reduce the professed risk associated with online buying. When SQ cannot meet the expected standard, additional compensation may be initiated by the service provider. As per HE and LIU⁴¹ to build confidence in online buying, customers obtain information about e-retailers reputation, service information, and service provider commitment. A service guarantee is an external promise made by a service provider to maintain the quality of the whole service and its attributes. However, the service guarantee also comes with terms and conditions.

Hypotheses

E-customer loyalty is tough and expensive to maintain. It needs a certain grade of service to satisfy the customers.⁴² When modelling the consumer e-buying behaviour, by default, the key variables include SQ, customer satisfaction, and customer loyalty. As per Chow et al.,⁴³ a higher level of service leads to a higher level of customer satisfaction and profits. It is generally said that customer satisfaction is an attitude while customer loyalty is a behaviour. Buyer’s assessment of quality (process) will affect the future assessment of SQ and also develop an impression about online retailers. Hence, we put forward the following hypothesis:

H₁: Process quality significantly influences customer e-satisfaction.

H₂: Process quality significantly influences customer e-loyalty.

Then again, it has additionally been contended that purchasers who are happy with the delivery of e-retailers service (outcome quality) will probably exhibit positive buying behaviour.⁴⁴ This also reflects that customers expect dependable on-time delivery from their e-tailers. Early and late deliveries may sabotage customer overall satisfaction. Therefore, we propose the following hypothesis

H₃: Outcome quality significantly influences customer e-satisfaction.

H₄: Outcome quality significantly influences customer e-loyalty.

Due to unique characteristics of online services, such as connectivity issues, server problems, and an outage of backup information, service failure is inevitable. Hence, service recovery measures considerably influence customer satisfaction and loyalty.⁴⁵ It is also said that service recovery measures strengthen customer relationships. It gives an opportunity to convert a mistake into a profitable situation. Further, Spreng et al.⁴⁶ demonstrated that service recovery affects outcome quality. Therefore, we propose the following hypothesis.

H₅: Service recovery significantly influences customer e-satisfaction.

H₆: Service recovery significantly influences customer e-loyalty.

H₇: Service recovery significantly influences outcome quality.

For any business to be successful, customer satisfaction, and customer loyalty should be incorporated in the long-term goal of the business. The same thing holds true for e-tailers. Customer e-satisfaction measures the current attitudes towards e-services and e-tailers while e-loyalty is associated with repeat purchase from the same e-tailers by ignoring other competitive e-tailers. Numerous studies have concluded that online customer e-satisfaction has a positive and direct impact on customer e-loyalty.⁴⁵ Hence, we propose:

H₈: Customer e-satisfaction significantly influences customer e-loyalty.

Prior studies have indicated that there exists an established link between SQ, customer satisfaction, and customer loyalty based on consumer perception.⁴⁷ These variables have played the role as antecedents or as mediating variable. It has been broadly acknowledged that SQ directly influences customer satisfaction, and customer satisfaction is an antecedent of customer loyalty.⁴⁸ In our study, SQ is conceptualized as outcome quality, process quality, and service recovery, it is hypothesized that SQ constructs have a direct impact on customer e-satisfaction, and customer e-satisfaction impacts customer e-loyalty. Further, service recovery positively influences outcome quality. Based on the past literature review, the study conceptualized the model as shown in Figure 2.

Operational Definitions

In this study, we define e-satisfaction as a customer’s contentment with an online shopping experience and e-loyalty as the propensity of a customer to repeatedly visit and purchase from the same e-tailer.

Research Methodology

As per statista.com report,⁴⁹ the majority (almost 67%) of Indian online customers belong to the “18–35 years” age group. Hence, this survey targeted the population of students in the age group of 21–27 years as participants. Pre-testing of the questionnaire was conducted on 40 student consumers (B-school) and items that did not meet the requirement of the study were dropped. Further, some five e-retailing managers were met to improvise the questionnaire. Based on the feedback, the questionnaire was finalized. There were total 27 question statements, and respondents were asked to rate them on a 5 point Likert scale where 1 stood for “strongly disagree” and 5 stood for “strongly agree”. The scale items were tested for reliability measures and resulted with a Cronbach’s Alpha of 0.898 which is quite satisfactory to use the suggested scales. To ensure the reliability and validity of the questionnaire, the CFA analysis was conducted. All constructs met the required threshold values. All the values had a factor loading > 0.7. The average variance extracted (AVE) varied in the range of 0.865 to 0.976. Composite reliability, discriminant validity, and AVE requirements of measurement scales have been met. The SQ dimensions that were considered and hypothetically conceptualized for this study included process quality, outcome quality, service recovery, customer satisfaction, and customer loyalty. Each construct had a min of 3 to 5 items, and there were total 8 constructs. One of the constructs (process quality) exhibited the second-order factor with 4 sub-constructs. In this research, the purposive sampling method is adopted. Those who have an interest and do online shopping and have an experience of service failure were selected for the study. Questionnaire was framed based on the framework given by Zhang et al.⁵⁰ The scale items have been adapted to suit the requirements of online Indian consumers. This study is analytical and causal in nature. Multiple regression analysis has been conducted to determine the causal effects of SQ on customer e-satisfaction and customer e-loyalty on Indian online buyers. This study was conducted in the month of April–May 2019. The study involved a pencil-and-paper questionnaire and 212 MBA students (respondents) from a business school (affiliated to Bangalore University). Majority of the respondents (95%) had made at least 5–6 online purchases over the last 6 months. Out of 212 questionnaires, 199 (included men and women) were finally considered for the study after removing the outliers and participants who never had experience of service failure. The prescreening criteria on online purchase and service recovery experience helped us in getting a high response rate. The responses were subjected to SEM, factor analysis, hypothesis testing, direct, and indirect effects using AMOS 20.

Analysis

Scale Assessment

As per Nunnally and Bernstein,⁵¹ the recommended value for composite reliability measures should be greater than 0.7 for all the constructs. Furthermore, the factor loading values should be greater than 0.5 for the factor analysis to be valid. In a well-structured model, the cumulative variance of all the factors put together must reach 50 per cent. The validity measures such as composite reliability, discriminant validity, and AVE were used to assess the validity concerns. This study implements CFA to judge the uni-dimensionality of each item. Each construct had at least 3–5 items. Our measurement analysis revealed that all factor loadings are above 0.5 and the cumulative variance was 85.033. The composite reliability values and discriminant validity measures were above 0.7. The average variance extracted ranged from 0.650 to 0.879. All composite reliability values were above the threshold value of 0.7. As the average variance extracted values are less than maximum shared value, discriminant validity condition is met (refer to Table 1). All standardized factor loadings of each construct were found to be greater than 0.7 (refer to Figure 1). The fit indices of the measurement model are Chi-square (χ²) = 369.953, CMIN/df = 1.250, p = 0.002, root mean square of approximation (RMSEA) = 0.036, comparative fit index (CFI) = 0.984, normalized fit index (NFI) = 0.925, and adjusted goodness-of-index (AGFI) = 0.848. This indicates that the proposed scale fits for measurement.

Figure 1.

Source: The authors.

Table 1.

Composite Reliability, Convergent Validity, and Discriminant Validity

	CR	AVE	MSV	ASV	CL	C	IA	F	OQ	SR	CS	S
CL	0.936	0.830	0.205	0.090	0.911
C	0.851	0.656	0.138	0.062	0.241	0.810
IA	0.956	0.879	0.113	0.076	0.325	0.223	0.937
F	0.910	0.772	0.141	0.069	0.271	0.207	0.199	0.879
OQ	0.953	0.801	0.108	0.050	0.107	0.266	0.328	0.180	0.895
SR	0.949	0.824	0.162	0.105	0.403	0.372	0.328	0.376	0.168	0.908
CS	0.921	0.797	0.205	0.094	0.453	0.212	0.336	0.270	0.290	0.331	0.893
S	0.848	0.650	0.078	0.031	0.108	0.165	0.105	0.279	0.116	0.223	0.167	0.806

Source: The authors.

Notes: CR > 0.7 (composite reliability is met), AVE > 0.5 (convergent validity is met), MSV < AVE (discriminant validity is met).

CR: Composite reliability, MSV: Maximum shared variance, ASV: Average shared variance, CL: Customer Loyalty, OQ: Outcome Quality, SR: Service recovery, CS: Customer satisfaction, S: security, F: Functionality, C: Convenience and IA: Information Accuracy.

Table 2.

Fit Statistics in the Structural Model

Sl. No.	Goodness-of-fit Model Index	Recommended Value*	Observed Value	Conclusion
1.	Chi-square/degree of freedom**	< = 2.00	1.472	Model is fit
2.	Goodness-of-index	> = 0.80	0.860
3.	Adjusted goodness-of-index	> = 0.80	0.831
4.	Tucker–Lewis index	> = 0.90	0.964
5.	Comparative fit index	> = 0.90	0.968
6.	Normalized fit index	> = 0.80	0.907
7.	Root mean square of approximation	< = 0.08	0.049

Source: The authors.

Note: These criteria are conferring to Arbuckle and Worthke,⁵² and Hair et al.⁵³ recommended values. Segars and Grower⁵⁴ recommended chi-square/degree of freedom of < = 3.00.

Path Analysis

After establishing a valid and reliable measurement model, SEM analysis is performed to test the hypothesis and predictive relationship between the constructs of the proposed model. In this analysis, maximum likelihood procedure available in AMOS 20 is used to test the hypothesis. The fit indices of the final SEM model are χ² = 460.688, CMIN/df = 1.472, p = 0.000, RMSEA = 0.049, CFI = 0.968, NFI = 0.907, and AGFI = 0.831. The results indicate that structure model is fit for prediction and interpretation. All the parameter estimate values of the SEM have critical ratio values greater than 1.96, which gives enough evidence to reject the null hypothesis (refer to Table 2).

From Table 3, it can be seen that process quality, service recovery, and outcome quality significantly influence customer e-satisfaction. Hence, H₁, H₃, and H₅ have been supported. Among the three constructs of e-SQ, process quality (0.388) has more impact on customer e-satisfaction than outcome quality (0.181) and service recovery (0.178). In addition, service recovery positively influences outcome quality. Thus, H₇ has been supported.

Table 3.

Path Coefficients and Determination Coefficients of the Structural Model

Outcome Variables		Causal Variables	CR	p	Path Coefficient	Determination Coefficient (R²; %)	Hypotheses
Customer satisfaction	←	Process quality	2.673	0.008*	0.388	22.6	H₁ supported
Customer satisfaction	←	Outcome quality	2.545	0.011*	0.181		H₃ supported
Customer satisfaction	←	Service recovery	2.508	0.012*	0.178		H₅ supported
Customer loyalty	←	Process quality	2.022	0.043*	0.268	27.3	H₂ supported
Customer loyalty	←	Outcome quality	–1.176	0.240	–0.081		H₄ not supported
Customer loyalty	←	Service recovery	3.386	**	0.236		H₆ supported
Customer loyalty	←	Customer satisfaction	2.978	0.003*	0.257		H₈ supported
Outcome quality	←	Service recovery	2.271	0.023*	0.167		H₇ supported

Source: The authors.

Note: *p < 0.05, **p < 0.001.

Further, it is also found that customer e-satisfaction significantly influences customer e-loyalty. Therefore, H₈ has been supported.

Meanwhile, the coefficient of determination (R²) values for customer e-satisfaction is 22.6 per cent and customer e-loyalty is 27.3 per cent. As per Falk and Miller,⁵⁵ the minimum threshold value of R² is 10 per cent. As we have met the criteria of R², our SEM is valid.

In addition, process quality, service recovery, and outcome quality constructs were tested to find the impact on customer e-loyalty. Process quality and service recovery significantly influenced customer e-satisfaction, while outcome quality did not have any impact on customer e-loyalty. Hence, H₂ and H₆ are supported and H₄ was not supported. From the data analysis, it can be inferred that customer e-satisfaction plays a mediating role between outcome quality and customer e-loyalty. This also tells us that customer satisfaction is extremely important for a customer to remain loyal to an e-tailer. The reason for H₄ not being supported could be that fulfilling an order is not just enough to get loyal customers. E-tailers need to have full details of the customers as to how, when and where the package needs to be delivered. These aspects have to be looked upon very closely. Managers should be aware that customers judge quality of e-tailer by outcome quality of the service.

Total, Direct, and Indirect Effects

Table 4 shows the direct, indirect, and total effects of the constructs on the dependent variables. The sum of the direct and indirect effect gives the total effect. In our study, the dependent variables are customer e-satisfaction and customer e-loyalty. The total effect of process quality on customer e-satisfaction is 0.388, consisting of the direct effect of 0.388 and no indirect effect. The total effect of outcome quality on customer e-satisfaction is 0.181, consisting of the direct effect of 0.181 and no indirect effect. The total effect of service recovery on customer e-satisfaction is 0.209, consisting of the direct effect of 0.178 and an indirect effect of 0.03. The result analysis reveals that process quality attribute is highly influential on customer e-satisfaction.

Table 4.

Direct, Indirect Effects, and Total Effects of Customer E-satisfaction and Customer E-loyalty

Path	Effect	Estimates	t-values
Process quality → Customer e-satisfaction	Direct	0.388
Process quality → Customer e-satisfaction	Indirect	0.000
	Total	0.388	2.673*
Process quality → Customer e-loyalty	Direct	0.268
Process quality → Customer e-loyalty	Indirect	0.100
	Total	0.368	2.022*
Outcome quality → Customer e-satisfaction	Direct	0.181
Outcome quality → Customer e-satisfaction	Indirect	0.000
	Total	0.181	2.545*
Outcome quality → Customer e-loyalty	Direct	0.000
Outcome quality → Customer e-loyalty	Indirect	0.046
	Total	0.046	–1.176 (NS)
Service recovery → Customer e-satisfaction	Direct	0.178
Service recovery → Customer e-satisfaction	Indirect	0.031
	Total	0.209	2.508*
Service recovery → Customer e-loyalty	Direct	0.236
Service recovery → Customer e-loyalty	Indirect	0.054
	Total	0.290	3.386**
Customer e-satisfaction → Customer e-loyalty	Direct	0.257
Customer e-satisfaction → Customer e-loyalty	Indirect	0.000
	Total	0.257	2.978*

Source: The authors.

Notes: *p < 0.05, **p < 0.001; NS: Not significant.

The total effect of customer e-satisfaction on customer e-loyalty is 0.257. The total effect of process quality on customer e-loyalty is 0.368, consisting of the direct effect 0.236 and an indirect effect of 0.054. The total effect of outcome quality on customer e-loyalty is 0.046, with no direct effect and an indirect effect of 0.046. The total effect of service recovery on customer e-loyalty is 0.290, consisting of direct effect of 0.236 and an indirect effect of 0.054. The result analysis reveals that process quality attribute is highly influential on customer e-loyalty. Further analysis has revealed that process quality and service recovery exhibit indirect effects on customer e-loyalty, with customer e-satisfaction construct playing a partial mediating role.

To aggregate up, given the setting of online buying by Indian customers, the outcomes by and large affirm that e-SQ–customer e-satisfaction–customer e-loyalty relationship model is in line with the previous studies. E-service is differentiated into process quality, outcome quality, and service recovery in our study. Further, the influence of process quality and service recovery on customer e-loyalty is being mediated by customer e-satisfaction. It can be seen from the causal relationship that e-service dimensions are antecedents of customer e-satisfaction and all the three e-service dimensions have a positive and direct effect on customer e-satisfaction. It is also observed from the study that service recovery is an antecedent of outcome quality. The findings of this study can be theorized into subscales based on the specification of e-SQ dimensions.

Discussion and Managerial Implications

From the analysis, it can be found that process quality, outcome quality, and service recovery significantly influence customer e-satisfaction. This is in line with several other research works.^31,56,12 However, in our study, we found that service recovery is not only influencing customer e-satisfaction but it also significantly influences outcome quality. This observation is unique and was not witnessed by Collier and Bienstock.¹² The reason could be that our service recovery definition had content of compensation, reflecting service guarantee to the products bought online. Another reason could be that service guarantee enables better understanding of SQ and service price. If the services are timely and accurate, it has a direct impact on the outcome quality. As majority of the Indian customers are uncertainty avoiders, perhaps service guarantee can raise expectations of the customers and this may in turn influence outcome quality. It is observed that customer service guarantee has become a part of customer service strategy and the same is shared on the e-tailers websites. Timely and accurate delivery of the products and handling numerous orders have become basic needs to customers. If these commitments are assured by online marketers, customer-perceived risk reduces and motivates more customers to accept the online mode of buying.

Figure 2.

Source: The authors.

From our research study, it can be found that process quality has the greatest influence on customer e-satisfaction followed by service recovery and outcome quality. Our finding is in line with Collier and Bienstock¹² but as per Line et al.,⁵⁷ outcome quality (delivery factor) was found to be the most important factor. Our study indicates that websites of Indian e-retailers are convenient to access, safe to use, highly interactive, and carry the visual appeal of web design. Web visibility can be further impoverished to enrol more number of online customers. Further, the website should also facilitate product comparison and product selection as most of the shoppers compare on the web. Not only they compare different vendors but also products from the same vendors. An effectively designed website with good ergonomics is an important criterion for an Indian online consumer. Online retailers can create trustworthiness by having transparent privacy policies and secured payment option equipped with SSL technology (standard security technology).

Service recovery has a positive significant effect on customer e-satisfaction and customer e-loyalty, these results are consistent with empirical studies of Chang and Chang,⁵⁸ and Kandulapati and Bellamkonda.⁵⁹ Previous research has shown that service failures and resulting complaints negatively affect company performance.⁶⁰ After undergoing negative experience, almost 91 per cent of unhappy customers never do business with the same company again.⁶¹ Therefore, e-retailers should design the service recovery well, for example, providing superior recovery services when problems occur. Being prepared when a failure occurs is essential to build and maintain customer relationships. Measures such as the opportunity to talk to a person, fairness of policies and procedures, compensation and apologies, hassle-free easy return policies will help e-retailers to improve their service recovery. Effective service recovery practices will help in regaining customer e-satisfaction and customer e-loyalty. It also helps in maintaining long-term relationships.⁴⁵

E-retailers should pay attention to outcome quality by ensuring the quality and quantity of the order placed. Since outcome quality has an impact on customer satisfaction and consequently loyalty, e-retailers should have suitable mechanism to ensure that their vendors deliver as promised. Descriptions and pictures of the product displayed on the website should match with the actual product. Correct information should be given in the bills and invoices. Timeliness of delivery and efficiency in handling multiple orders are also important in ensuring outcome quality.

The empirical findings suggest that SQ dimensions (of Indian e-retailers) significantly influence customer e-satisfaction and customer e-loyalty (except outcome quality). So, all efforts should be done to ensure that high-quality service is delivered.

In conclusion, this research reveals that online customers are positive and satisfied with the e-retail services of Indian players. There is a direct effect of e-services on customer e-satisfaction. However, there is always scope for improvement to enhance the customer e-satisfaction level and customer e-loyalty levels. It is also recommended that service recovery levels can be improved by anticipating service failure and implementing superior recovery levels. As e-tailers improve their customer experiences, the profits soar. This research has laid down the groundwork for e-tailers to implement their service framework and try to make processes efficient and customer friendly.

Limitations and Future Research

Since the samples are student population, the research cannot be generalized to a larger set of population. This study can be extended to general public and results of the study can be cross-validated with the current research work. As research has considered Indian e-retailers in totality, it is not focusing on specific e-retailers, that is, giant e-retailer like Amazon, they themselves are high-tech players. Separate studies can be conducted to understand e-retailing quality practices. Companies may currently be clustering their websites with services that add little value to their target customers, while at the same time, they neglect services (niche) that could commit the customers more strongly to the site. This aspect can be explored. It is not just enough to have a website: it should be of good quality. Since websites vary in their sophistication, an interesting issue is whether customer dissatisfaction with the website also has a negative effect on commitment to the corresponding physical service outlets. This aspect can be explored as Amazon has recently moved into brick-and-mortar retail. Studies can be undertaken to explore the contents of service guarantee as it leads to improved SQ. Furthermore, future studies on e-quality dimensions should cover all types of services: online retail services, after-sales and free of charge services, professional, and consumer services, and so on. Additionally, research study to explore how in-store experience push customers to shop online can be undertaken.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

E-service Quality Questionnaire

The following statements ask your thoughts about the service provided to you by e-retailers. Please think about the e-retailer that you purchase from frequently. Please circle the number that best matches how much you agree or disagree with each statement. There are no right or wrong answers. (Values range from 1, totally disagree, to 5, totally agree.)

References

Sahney

, Ghosh

, Shrivastava

. “Buyer’s motivation” for online buying: An empirical case of railway e-ticketing in Indian context. J Asia Bus Stud. 2013 Dec 20; 8(1):43–64.

Tandon

. Indian online shoppers are getting more discerning. 2016 Apr 2. Available from https://www.livemint.com/Consumer/XBd7RwcDBDjsZAqmLFrjFP/Indian-online-shoppers-are-getting-more-discerning.html

Sharma

. Impact of internet on business. 2019 Jan 16. Available from http://www.klientsolutech.com/impact-of-internet-on-business/

McGaughey

, Mason

. The internet as a marketing tool. J Mark Theory Pract. 1998 Jul 1; 6(3):1–1.

Rust

, Kannan

. E-service: A new paradigm for business in the electronic environment. Commun ACM. 2003 Jun 1; 46(6):36–42.

ASSOCHAM India Over 120 million Indian consumers expected to shop online in 2018-Resurgent study. 24 June 2018. Available from http://www.assocham.org/newsdetail.php?id=6866

Fotiadis

. Customer participation, e-service quality, satisfaction:(e) Service dominant logic trinity. J Promot Manage. 2019 Apr 16; 25(3):394–418.

Lovelock

, Yip

. Developing global strategies for service businesses. Calif Manage Rev. 1996 Jan; 38(2): 64–86.

Parasuraman

, Zeithaml

, Malhotra

. ES-QUAL: A multiple-item scale for assessing electronic service quality. J Serv Res. 2005 Feb; 7(3):213–33.

10.

Parasuraman

. Superior customer service and marketing excellence: Two sides of the same success coin. Vikalpa. 2000 Jul; 25(3):3–14.

11.

Rao

, Kelkar

. Relative impact of performance and importance ratings on measurement of service quality. J Prof Serv Mark. 1997 May 30; 15(2):69–86.

12.

Collier

, Bienstock

. Measuring service quality in e-retailing. J Serv Res. 2006 Feb; 8(3):260–75.

13.

Kim

, Kim

, Kandampully

. Buying environment characteristics in the context of e-service. Eur J Mark. 2009 Sep 18; 43(9/10):1188–204.

14.

Rajic

, Dado

, Taborecka-Petrovicova

. Linking retail service quality, satisfaction and perceived value to customer behavioral intentions: Evidence from Serbia. 2013. Available from http://www.ekonomie-management.cz/download/1404723520_b0ce/2013_2+Linking+Retail+Service+Quality%2C+Satisfaction+and+Perceived+Value+to+Customer+Behavioral+Intentions+Evidence+from+Serbia.pdf

15.

Imrie

, Durden

, Cadogan

. Towards a conceptualization of service quality in the global market arena. Adv Int Mark. 2000; 10(1):143–62.

16.

Slywotzky

, Morrison

. The rise of the active customer. Mark Manag. 2001 Jul 1; 10(2):22.

17.

Yoo

, Donthu

. Developing a scale to measure the perceived quality of an internet shopping site (SITEQUAL). Q J Electron Commer. 2001; 2(1):31–45.

18.

Holloway

, Beatty

. Service failure in online retailing: A recovery opportunity. J Serv Res. 2003 Aug; 6(1):92–105.

19.

Baker

, Crompton

. Quality, satisfaction and behavioral intentions. Ann Tour Res. 2000 Jul 1; 27(3):785–804.

20.

Petrick

, Backman

. An examination of golf travelers’ satisfaction, perceived value, loyalty, and intentions to revisit. Tour Anal. 2001; 6(3/4):223–37.

21.

Liao

, Cheung

. Internet-based e-shopping and consumer attitudes: An empirical study. Inf Manage. 2001 Apr 1; 38(5):299–306.

22.

Kim

, Kim

. E-service quality perceptions: A cross-cultural comparison of American and Korean consumers. J Res Interact Mark. 2010 Aug 20; 4(3):257–75.

23.

Parasuraman

, Zeithaml

, Berry

. SERVQUAL: A multiple-item scale for measuring customer perceptions of service quality, Report No. 86–108. Cambridge, MA: Marketing Science Institute;1986

24.

Parasuraman

, Zeithaml

, Berry

. SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. J Retail. 1988; 64:12–40.

25.

Parasuraman

, Zeithaml

, Berry

. Refinement and reassessment of the SERVQUAL scale. J Retail. 1991; 67(4):420–5.

26.

Parasuraman

, Grewal

. The impact of technology on the quality-value-loyalty chain: A research agenda. J Acad Mark Sci. 2000 Jan; 28(1):168–74.

27.

Gefen

. Customer loyalty in e-commerce. J Assoc Inf Syst. 2002 Mar 1; 3(1):2.

28.

Wolfinbarger

, Gilly

. eTailQ: Dimensionalizing, measuring and predicting etail quality. J Retail. 2003 Jan 1; 79(3):183–98.

29.

Zeithaml

, Parasuraman

, Malhotra

. A conceptual framework for understanding e-service quality: Implications for future research and managerial practice. Cambridge, MA: Marketing Science Institute;2000.

30.

Bhandari

, Tsarenko

, Polonsky

. A proposed multi-dimensional approach to evaluating service recovery. J Serv Mark. 2007 May 29; 21(3):174–85.

31.

Endo

, Yang

, Park

. The investigation on dimensions of e-satisfaction for online shoes retailing. J Retail Consum Serv. 2012 Jul 1; 19(4):398–405.

32.

Ariely

. Controlling the information flow: Effects on consumers’ decision making and preferences. J Consum Res. 2000 Sep 1; 27(2):233–48.

33.

Montoya-Weiss

, Voss

, Grewal

. Determinants of online channel use and overall satisfaction with a relational, multichannel service provider. J Acad Mark Sci. 2003 Oct; 31(4):448–58.

34.

Hoque

, Lohse

. An information search cost perspective for designing interfaces for electronic commerce. J Mark Res. 1999 Aug; 36(3):387–94.

35.

Ahn

, Ryu

, Han

. The impact of the online and offline features on the user acceptance of Internet shopping malls. Electron Comer Res Appl. 2004 Dec 1; 3(4):405–20.

36.

Bitner

, Booms

, Tetreault

. The service encounter: Diagnosing favorable and unfavorable incidents. J Mark. 1990 Jan; 54(1):71–84.

37.

Miller

, Craighead

, Karwan

. Service recovery: A framework and empirical investigation. J Oper Manage. 2000 Jun 1; 18(4):387–400.

38.

McCollough

, Bharadwaj

. The recovery paradox: An examination of consumer satisfaction in relation to disconfirmation, service quality, and attribution based theories. Mark Theory Appl. 1992 Mar; 119.

39.

Bansal

, Irving

, Taylor

. A three-component model of customer to service providers. J Acad Mark Sci. 2004 Jul; 32(3):234–50.

40.

Lin

. The determinants of consumers’ switching intentions after service failure. Total Qual Manag Bus Excell. 2012 Aug 1; 23(7–8):837–54.

41.

, LIU

. An Empirical Study of Service Guarantees on the Impact of Consumer Confidence under the Network Environment [J]. Soft Sci. 2008; 7.

42.

Cristobal

, Flavian

, Guinaliu

. Perceived e-service quality (PeSQ) Measurement validation and effects on consumer satisfaction and web site loyalty. Manag Serv Qual, Int J. 2007 May 22; 17(3):317–40.

43.

Chow

, Lau

, Lo

, Sha

, Yun

. Service quality in restaurant operations in China: Decision-and experiential-oriented perspectives. Int J Hosp Manage. 2007 Sep 1; 26(3):698–710.

44.

Carlson

, O’Cass

. Developing a framework for understanding e-service quality, its antecedents, consequences, and mediators. Manag Serv Qual, Int J. 2011 May 17; 21(3):264–86.

45.

Kuo

, Wu

. Satisfaction and post-purchase intentions with service recovery of online shopping websites: Perspectives on perceived justice and emotions. Int J Inf Manage. 2012 Apr 1; 32(2):127–38.

46.

Spreng

, Harrell

, Mackoy

. Service recovery: Impact on satisfaction and intentions. J Serv Mark. 1995 Mar 1; 9(1):15–23.

47.

Chen

. Investigating structural relationships between service quality, perceived value, satisfaction, and behavioral intentions for air passengers: Evidence from Taiwan. Transp Res A, Policy Pract. 2008 May 1; 42(4):709–17.

48.

Cronin Jr

, Brady

, Hult

. Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. J Retail. 2000 Jun 1; 76(2):193–218.

49.

Statista.com E-commerce in India—Statistics & facts. Report by Statista Research Department. 4 Sep 2019. Available from https://www.statista.com/topics/2454/e-commerce-in-india/

50.

Zhang

, Huang

, He

, Wang

. E-service quality perceptions: An empirical analysis of the Chinese e-retailing industry. Total Qual Manag Bus Excell. 2015 Dec 2; 26(11–12):1357–72.

51.

Nunnally

, Bernstein

. Psychometric theory 3E. New York, NY: Tata McGraw-Hill Education;1994.

52.

Arbuckle

, Wothke W. Amos 4.0 user’s guide. Chicago, IL: SmallWaters Corporation;1999.

53.

Hair

, Anderson

, Tatham

, Black

. Multivariate data analysis. Upper Saddle River, NJ: Prentice Hall; 1998. p;730.

54.

Sengars

, Grover

. Re-examining perceived ease of use and usefulness: A confirmatory factor analysis. Manage Inf Syst Q. 1993; 17(4):7.

55.

Falk

, Miller

. A primer for soft modeling. Akron, OH: University of Akron Press;1992.

56.

Van Vaerenbergh

, De Keyser

, Larivière

. Customer intentions to invoke service guarantees: Do excellence in service recovery, type of guarantee and cultural orientation matter? Manag Serv Qual, Int J. 2014 Jan 7; 24(1):45–62.

57.

Lin

, Wu

, Chang

. The critical factors impact on online customer satisfaction. Procedia Comput Sci. 2011 Jan 1; 3:276–81.

58.

Chang

, Chang

. Does service recovery affect satisfaction and customer loyalty? An empirical study of airline services. J Air Transp Manage. 2010 Nov 1; 16(6):340–2.

59.

Kandulapati

, Bellamkonda

. Examining the structural relationship s of service recovery, customer satisfaction and image in online retailing. Oper Supply Chain Manag. 2014 May 1; 7(2):70–8.

60.

Surachartkumtonkun

, Patterson

, McColl-Kennedy

. Customer rage back-story: Linking needs-based cognitive appraisal to service failure type. J Retail. 2013 Mar 1; 89(1):72–87.

61.

Maritz

. When your customer is unhappy, what will you do? Available from https://www.maritzcx.com/servicerecoveryonbusiness/.