Online Retail Service Quality: Scale Development and Validation

Abstract

The growth of online retailing has created an opportunity to create a scale specifically for online retail services. At the same time, the increasing rate of internet penetration in India coupled with electronic banking and wallets has formed new market place for many online retailers. In this context, to gain competitive advantage, the online retailers should provide better service quality. Thus, the present research tries to know the various constructs of retail service quality (RSQ) in online format and develop a measurement scale. The study has borrowed the constructs from ‘Retail Service Quality’ (RSQ) and ‘Technology Acceptance Model’ (TAM). For this purpose, we collected data from 600 respondents. The scale has been confirmed and validated by using CFA. The study confirmed that online RSQ (ORSQ) scale consists of four constructs: ‘Ease of Use’, ‘Problem Solving’, ‘Policy’ and ‘Reliability’ with 18 variables. The article has concluded and validated a scale for ORSQ which can help the online retailers to design their service offering. The implications of the ORSQ scale for practitioners, as well as for future research, are discussed in this article.

Keywords

Retail Service Quality Online Retail ORSQ Reliability Confirmatory Factor Analysis

Executive Summary

Service quality (SERVQUAL) has become popular among researchers in recent years (Zeithaml et al., 2000). Customer satisfaction is positively influenced by implementing SERVQUAL measures (Santouridis & Trivellas, 2010). While satisfying customers by offering better SERVQUAL, organizations increase their market share by retaining customers and cross-selling to the existing customers (Finn & Lamb, 1991). The growth of retail has created the opportunity to come with a scale specifically for retail services, as the existing dimensions of SERVQUAL may not be similar in retail services (Dabholkar et al., 1996). Moreover, the increasing rate of internet penetration in India has given birth to many online retailers to reach the customers. And in this context, to gain a competitive advantage the online retailers should provide better SERVQUAL. Thus, the present research makes an attempt to explore the various constructs of retail service quality (RSQ) in online format and develop a measurement scale.

The questionnaire was designed and collected data from 600 customers. Confirmatory factor analysis (CFA) was conducted to validate the measurement model. Analysis of a moment structures (AMOS) has been used as a tool for scale development. The CFA output was analyzed for reliability and validity. The alpha values of the dimensions confirmed the reliability of the scale. The CFA output has also fulfilled the conditions of both convergent and discriminant validity. After fulfilling criteria for reliability and validity, we found the model fit indices to check the overall fitness of the measurement model. The model fit indices were calculated by using AMOS. The model fit indices like goodness of fit index (GFI) = 0.924, comparative fit index (CFI) = 0.960, adjusted GFI (AGFI) = 0.900, root mean square error of approximation (RMSEA) = 0.061 and chi-square = 2.98 are as per the recommended value and thus the scale satisfied a good model fit criteria (Byrne, 2013).

The outcomes of the study provide insight into how online retailers could position themselves with respect to their competitors and target customers’ requirements. It would also contribute for customizing their online retailing offers. The article has concluded and validated a scale consists of four constructs: reliability, ease of use, problem solving and policy for online RSQ (ORSQ) which can help the online retailers to design their service offering.

Introduction

Service quality (SERVQUAL) has become popular among researchers in recent years (Zeithaml et al., 2000). Customer satisfaction is positively influenced by implementing SERVQUAL measures (Santouridis & Trivellas, 2010). While satisfying customers by offering better SERVQUAL, organizations increase their market share by retaining customers and cross-selling to the existing customers (Finn & Lamb, 1991). Past studies on SERVQUAL have used dimensions like ‘reliability’, ‘assurance’, ‘tangibility’, ‘empathy’ and ‘responsiveness’ of SERVQUAL developed by Parasuraman et al. (1988). However, these dimensions may not be appropriate for retail industry in particular, as SERVQUAL may not be similar in retail sector (Dabholkar et al., 1996). It has been observed that, SERVQUAL in retail involved both products and services and services associated with products. In such retail set-up, retailers have to consider both product and service elements to satisfy their customers’ requirements (Dabholkar et al., 1996).

The growth of retail has created the opportunity to come with a scale specifically for retail services called as ‘Retail Service Quality (RSQ)’ scale (Dabholkar et al., 1996). This scale consists of ‘physical aspects’, ‘problem solving’, ‘reliability’, ‘personal interactions’ and ‘policy’. However, the increasing rate of internet penetration in India has given birth to many online retailers.¹ These online retailers can also reach the customers those who were not served by the existing store-based retailers.² The retail firms developed their online presence as this reduces the cost due to automation system (Rust & Kannan, 2003). Though retailers. gain higher efficiency by selling products online, unless they understand and meet customers’ requirements, they will fail to provide expected online service output to their customers. Hence, retailers should focus on online services including all aspects of service that arise during, before and after the actual transactions (Zeithaml et al., 2002). Internet can be used properly to enhance the universal service experience and to deliver better online customer service with a wide range of offerings like ease of ordering, enquiring about the availability of product/services, finding and comparing competitive prices and making a rationale purchase decision (Griff & Palmer, 1999). Online retail in India is in its initial stage, the numbers of online shoppers are low and increasing day by day.³ This study makes few significant contributions. First, this is the pioneering research to focus on developing a unique scale for online RSQ (ORSQ). This research is also critical for online retailers in India and will enable them to understand their online shoppers in a better way. Second, while understanding the online shoppers, Indian retailers can devise their strategies to enhance the level of ORSQ. The research proposes ORSQ Scale as an original contribution to the research in the area of retail. As per our knowledge, there are not enough studies on the online retail in developing countries like India; this study will be very much useful to online retailers and researchers with the integrative view of technology acceptance model (TAM) and RSQ.

This article started with an introduction. The next section includes the review of existing literature on SERVQUAL, RSQ and ORSQ. The third section deals with purpose of the study, the following sections deal with research methodology, data collection, analysis and findings including reliability and validity. The subsequent sections include discussion and conclusions containing implications and scope for further study.

Literature Review

Service Quality

SERVQUAL has been viewed as an important element of customer perception with respect to any kind of service. Customers perception in relation to its SERVQUAL is the extent to which they are satisfied with the overall service experience (Zeithaml & Bitner, 2000). SERVQUAL can be explained as perception of customers regarding how well a service meets or exceeds their expectations (Czepiel, 1990). In the retail setting, perception of service encounters is built up over a period of time and the relationship with customers is the outcome of continuous exchanges or interactions between the customer and organization (Czepiel, 1990).

The SERVQUAL measurement has evolved over the years from importance–performance matrix (Martilla & James, 1977) to SERVQUAL model (Parasuraman et al., 1985) and service performance (SERVPERF; Cronin & Taylor, 1992) models. SERVQUAL can be termed as the customers’ overall feeling of the relative superiority/inferiority of a service provided by a service firm (Parasuraman et al., 1988). This kind of notion is often considered as the customers’ overall attitude towards the organization (Bitner, 1990). Parasuraman et al. (1988) have developed a SERVQUAL measurement scale for different sectors including retail services. However, in a later stage to apply SERVQUAL specifically in offline retail environment, Dabholkar et al. (1996) has proposed a new scale for RSQ with five dimensions such as ‘personal interaction’, ‘reliability’, ‘problem solving’, ‘policy’ and ‘physical aspects’.

Retail Service Quality

SERVQUAL is one of the most discussed areas among researchers. One of the ways to conceptualize and measure SERVQUAL would be using SERVQUAL scale (Parasuraman et al., 1988, 1994). The same scale has been applied to retail sector in a number of studies (Wong & Sohal, 2003). However, Dabholkar et al. (1996) argued that the SERVQUAL scale is more suitable for pure services rather than retail sector, which offers combination of both products and services. Thus, on the basis of past studies, they came with the RSQ scale; they have total 28 statements in RSQ scale, of which 17 were taken from SERVPERF and 11 statements were explored with the help of qualitative study. The constructs of RSQ are: (a) Reliability—To the extent to which retailers keep the promises made by them; (b) Personal interaction—To the extent to which retail store employees are helpful, polite and inspire confidence; (c) Physical aspectsRetail store layout and appearance; (d) Policy—Measures taken by the retailers in terms of hours of operations, provisions of credit cards and quality of merchandise and so on (e) Problem solving—managing stock, returns, exchanges and other customer complaints. RSQ has been widely used in academic research for a long time. In a separate study, Hamzah et al. (2017) conducted survey in Malaysia and explored four constructs of RSQ such as ‘tangibles’, ‘empathy’, ‘reliability’ and ‘security’.

Online Retail Service Quality

The SERVQUAL model developed by Parasuraman et al. during 1985 focused on the five dimensions of SERVQUAL scale, known as ‘reliability’, ‘assurance’, ‘tangibility’, ‘empathy’ and ‘responsiveness’. This scale has been applied to different studies. Later, Dabholkar et al., in 1996 argued that SERVQUAL scale is more applicable for pure service than retail services. Hence, based on the literature, Dabholkar et al. (1996) came with a new scale specifically for retail format. However, few dimensions like physical aspects and personal interaction suggested by Dabholkar et al. (1996) in RSQ are not relevant in online environment. So, to understand the shopper behaviour in online context, researchers have used TAM propounded by Davis et al. in 1989. TAM has explained the concept of the behavioural intentions of the consumer in technology domain (Shin, 2010). The dimensions of TAM include constructs such as ‘perceived ease of use’ and ‘perceived usefulness’ to understand the factors leading to technology acceptance such as shopping online or through a website. In an online retail environment, ‘physical aspects’ of the store is not a requirement and ‘personal interactions’ can be replaced by interactions on websites. For online retail environment ‘ease of use’ of the website is relevant to decide whether to adopt technology or not (Davis et al., 1989; Dishaw & Strong, 1999; Long & McMellon, 2004; Panda & Swar, 2014). This is also included as a key construct in the TAM. Therefore, it is imperative to integrate both RSQ and TAM and modify the variables in the current context to meet the objectives of this study.

Raza et al. (2020) collected data from 500 online banking clients in Pakistan and found that SERVQUAL is critical in online retail services. They concluded that there was a positive relationship between customer’s satisfaction and customer’s loyalty. Baljit et al. (2020) approached 545 respondents in online retail to propose a scale to measure e-SERVQUAL. They found that e-SERVQUAL dimensions include reliability, efficiency, system availability, assurance, information quality and usability and security and privacy. They concluded that ‘information quality and usability’ and ‘reliability’ were the two important dimensions of e-SERVQUAL in online retail.

Usman and Kumar (2020) investigated the factors determining consumers’ online shopping behaviour in Nigeria namely; SERVQUAL, perceived price, security and privacy, trust, convenience, accessibility and compatibility. Aref and Okasha (2020) studied the factors affecting Egyptians’ online shoppers buying behaviour. The study confirmed that the online retail shopping behaviour affected by social norms, perceived ease of use, perceived enjoyment and perceived risk.

Al-dweeri et al. (2019) examined the dimensions of online SERVQUAL in Jordanian youth users of online retailing. They identified four constructs to measure the online SERVQUAL such as reliability, privacy, customer service and emotional benefit. The study conducted by Tzavlopoulos et al. (2019) found four dimensions of quality in e-commerce setting such as ease of use of websites, website design, security and responsiveness. They concluded that SERVQUAL leads to satisfaction. Singh and Srivastava (2019) collected data from 344 online shoppers in India and explored the factors influencing online shoppers’ behaviour like perceived self-efficacy, perceived usefulness and perceived risk. Wang and Kim (2019) surveyed 330 consumers in China and found that, there was no difference between male and female consumers with respect to ‘efficiency’ dimension of e-SERVQUAL; whereas, there was a difference between male and female consumers with respect to ‘reliability’ and ‘responsiveness’ dimensions of e-SERVQUAL in online retail.

Dang and Pham (2018) studied 221 consumers in Vietnam and found that perception of consumers towards website design is positively related to ‘customer service’, ‘purchase intention’, ‘privacy’ and ‘reliability’. Pandey and Chawla (2018) investigated the online customer experience (OCE) dimensions and how they influence the satisfaction and loyalty of shoppers in online retail. The study identified two OCE dimensions like psychological factors and functionality factors. They concluded that both the dimensions of OCE impact loyalty through satisfaction.

Prateek Kalia (2017) conducted a literature review with 30 research papers and found that online SERVQUAL dimensions consisted of ‘security or privacy’, ‘website design’, ‘reliability’, ‘responsiveness’ and ‘information’, etc. Panda and Swar (2016) identified the determinants of online shopping such as ‘trust’, ‘convenience’, ‘ease of use’, ‘perceived risk’, ‘price’, ‘effortless shopping’, ‘online purchase experience’, ‘privacy’ and ‘security features’.

Online SERVQUAL and information quality were the two important factors for sustainability and customer satisfaction of e-commerce (Sharma & Lijuan, 2015). Rezaei et al. (2014) surveyed 219 e-shoppers in Malaysia and revealed that perceived usefulness, perceived value, trust and satisfaction have positive impact on online re-patronage intention among Malaysian online shoppers. Brun et al. (2014), investigated 476 consumers to understand relationship quality with respect to both electronic commerce and relationship marketing and concluded that online relationship quality consisted of three major elements like ‘satisfaction’, ‘commitment’ and ‘trust’. Kim and Lennon (2013) obtained data from 219 respondents through an online survey and found that website quality had significant negative impact on perceived risk and significant positive impact on emotion. The future buying intention of the shoppers influenced by perceived risk and emotion in online retail.

The research conducted by Ha and Stoel (2012) among college students of USA in online apparel retailing and identified four factors like ‘privacy/security’, ‘content/functionality of web site’, ‘customer service’ and ‘experiential quality’ has strong influence on online shoppers’ satisfaction. They concluded that e-shopping quality has significant impact on e-satisfaction and on future e-shopping intention in online retail.

Rolland and Freeman (2010) conducted a survey in France and extracted five important elements to measure ORSQ like ease of use, information content, security or privacy, post-purchase customer service and fulfilment reliability. Hossain and Leo (2009) indicated that, there were two elements of retail banking SERVQUAL which affect customers’ perception in Qatar like ‘tangibles’ and ‘competence’. Loonam and O’Loughlin (2008) explored the dimensions for Irish e-banking SERVQUAL such as trust, web usability, service recovery, flexibility, access and information quality service recovery. Francis (2007) identified five factors for internet retailing like ‘customer service’, ‘web site’, ‘delivery’, ‘security’ and ‘transaction’. Kim et al. (2006) conducted a study of 111 USA-based women’s apparel retail websites. And they explored the factors influencing ORSQ such as system availability, efficiency, personalization, privacy, fulfilment, graphic style, responsiveness, contact and information. As per Lee and Lin (2005), SERVQUAL in online shopping gets influenced by ‘reliability’, ‘web site design’, ‘trust’ and ‘responsiveness’.

Purpose of the Study

To understand SERVQUAL, RSQ and ORSQ in Indian online retail format through review of literature.

To identify determinants of ORSQ in Indian online retail environment.

To develop and validate the ORSQ scale by using confirmatory factor analysis (CFA).

Research Methodology

This is a scale development study that needs instrument design, data collection and validation of the scale.

Instrument Design

Considering the extant literature on RSQ and technology adoption, the scale for ORSQ has been built using existing constructs from RSQ scale (Dabholkar et al., 1996) and TAM (Davis et al., 1989). The constructs have been modified and adapted for online retailing. The scale has further been refined by adding new variables through focus group discussions (FGDs) and expert opinion.

The authors adopted the scale from the existing RSQ scale (Dabholkar et al., 1996) and TAM (Davis et al., 1989). The ‘physical aspects’ factor from RSQ was dropped from the scale as there is no physical shopping infrastructure required in online retailing and the factor ‘personal interactions’ from RSQ has been replaced with ‘ease of use’ factor from TAM. We have borrowed 18 variables from RSQ and TAM and were modified in the online retail context. Later, two more variables were added based on FGDs and expert opinion. The variables were generated and modified by the help of review of literature, FGDs and subject expert opinions. To refine the scale, we contacted 10 subject experts and conducted 3 FGDs. Based on their opinions and suggestions, we have added the following two variables (one variable under factor ‘problem solving’ and another under factor ‘policy’) to the list of variables that were identified in the process of generation of variables:

Online store helps in comparing products of different brands (problem solving).

The online store has loyalty programmes (policy).

So, the review of literature and qualitative study provided us the variables responsible for determining ORSQ. Thus, we designed a questionnaire with 20 variables on a five-point scale for collecting data (Table 1). The proposed scale to be tested consisted of these 20 variables. The final proposed scale consisted of these constructs: reliability, problem solving, policy and ease of use.

Table 1.

List of Factors and Variables

Factors	Variables	Variable Details
Reliability	V1	If the online store promises to do something, it will do so
–	V2	Online store provides services as per the time promised
–	V3	Online store performs the service right the first time
–	V4	Online store has the merchandise as per customer’s requirement
–	V5	Online store provides error-free transactions and records.
Ease of use	V6	I am comfortable with the website of the retailer
–	V7	Navigation within the website is easy
–	V8	Online store is flexible to interact with
–	V9	Retailer’s website has features to interact with the customer care or other customer service.
–	V10	Merchandise delivery and payment process is easy to do in the website
Problem solving	V11	Online stores happily handle returns and exchange of merchandise
–	V12	Online store eagerly solves customer’s problems
–	V13	Online store is capable to handle customer complaints effectively
–	V14	Online store helps in comparing products of different brands
Policy	V15	Merchandise available in the online store are of high quality
–	V16	Online store provides cash on delivery options to their customers
–	V17	Online store delivery charges are reasonable
–	V18	Online store accepts most of the major credits cards/debit cards or wallet payments
–	V19	The refund or exchange policy of the online store is efficient
	V20	The online store has loyalty programmes

Source: Compiled by the authors (adopted and modified from RSQ scale, TAM model and qualitative study).

Data Collection

For data collection, we designed questionnaire with 20 variables in a five-point Likert scale where 1 denotes strongly disagree and 5 denotes strongly agree. The questionnaires were distributed to 600 customers and 530 questionnaires were received (which can be used for analysis) with a response rate of 88.33%. To develop the measurement scale, this study has used the existing procedures involving comprehensive research (Dana & Dumez, 2015) and followed by purification and validation of the data (Churchill, 1979; Peter 1981). CFA was conducted to validate the model. AMOS has been used as a tool for scale development.

Data Analysis

The CFA output suggested that the scale consists of 18 variables. The following two variables were dropped as their factor loadings were less than 0.5 (Hair et al., 1998):

Online store helps in comparing products of different brands (factor loading = 0.32).

The online store has loyalty programmes (factor loading = 0.41).

After deletion of these two variables, we found there was an improvement of the alpha value of the respective constructs. The improved alpha values showed good internal consistency among variables within each construct. Therefore, the 18-variables scale was considered for the final study with 530 respondents. The factor loadings and Cronbach’s alpha values (results of CFA) with 530 respondents are given in Table 2.

Table 2.

Summary of Results from Purification of Scale

Dimensions	No. of Variables	Variables	Factor Loadings	Reliability (Alpha Values)
–	–	R1: If the online store promises to do something, it will do so.	0.958	0.945
–	–	R2: Online store provides services as per the time promised.	0.871	–
–	–	R3: Online store performs the service right the first time.	0.805	–
–	–	R4: Online store has the merchandise as per customer’s requirement.	0.895	–
–	–	R5: Online store provides error-free transactions and records.	0.860	–
Ease of Use	5	EU1: I am comfortable with the website of the retailer	0.603	0.869
–	–	EU2: Navigation with the website is easy	0.842	–
–	–	EU3: Online store is flexible to interact with	0.745	–
–	–	EU4: Retailer’s website has features to interact with the customer care or other customer service.	0.803	–
–	–	EU5: Merchandise delivery and payment process is easy to do in the website	0.822	–
Problem Solving	3	PS1: Online stores happily handles returns and exchange of merchandise	0.793	0.784
–	–	PS2: Online store eagerly solves customer’s problems	0.650	–
–	–	PS3: Online store is capable to handle customer complaints effectively	0.789	–
Policy	5	P1: Merchandise available in the online store are of high quality	0.903	0.930
–	–	P2: Online store provides cash on delivery options to their customers	0.717	–
–	–	P3: Online store delivery charges are reasonable	0.925	–
–	–	P4: Online store accepts most of the major credits cards/debit cards or wallet payments	0.911	–
–	–	P5: The refund or exchange policy of the online store is efficient	0.819	–

Source: Primary data.

Demographic Analysis

The final analysis has been done with the data collected from 530 respondents. Out of 530 respondents, 64% (339) were male and the remaining 36% (191) were female. When it comes to age group, 56% of the respondents were in the age range of 16–34 years; 20% were 35–44 years; 12% were 45–54 years; 9% were 55–64 years and 3% were more than 65 years. With regards to educational qualifications, 33% were graduates, 48% were under graduates and 19% were post-graduations. With respect to the average frequency of usage of Internet, 38% of the respondents purchase ‘once in three months’, 27% ‘once in a month’, 20% ‘two-three times in a month’, 11% ‘once a week’ and four per cent shop ‘more than once a week’ (Table 3).

Table 3.

Demographic Analysis

Demographic Profile	Measures	Frequency	Percentage
Gender (n = 530)	Male	339	64
	Female	191	36
	Total	530	100
Age in years (n = 530)	16–24	127	24
	25–34	170	32
	35–44	106	20
	45–54	64	12
	55–64	47	9
	More than 65	16	3
	Total	530	100
Education (n = 530)	High School or lower	106	20
	College	148	28
	Graduate	175	33
	PG	101	19
	Total	530	100
On an average, how often do you use retailers’ site for online shopping, or other	Once in 3 months	202	38
	Once a month	143	27
	2–3 times in a month	106	20
	Once a week	58	11
Purposes? (n = 530)	More than once a week	21	4
	Total	530	100

Source: Primary data.

Reliability and Validation of the Scale

The CFA output was analyzed for reliability and validity.

Reliability

The summary of the CFA output using AMOS and reliability is shown in Table 2. The reliability of the scale was calculated by using Cronbach’s alpha (Nunnally et al., 1967). The alpha values of the scale dimensions reliability (0.945), ease of use (0.869), problem solving (0.784) and policy (0.930) met the recommended criteria of 0.70 (Hair et al., 2008) and hence confirmed the reliability of the scale.

Convergent, Discriminant and Nomological Validity

Convergent Validity

To verify convergent validity, we extracted construct reliability and average variance explained (AVE). The output was compared with the conditions for convergent validity: the construct reliability should be more than 0.70, the AVE should be more than 0.50 and the construct reliability should be more than AVE (Hair et al., 2011). The output fulfilled the conditions of convergent validity (Table 4).

Table 4.

Scale Validation (Convergent Validity)

Constructs	Constructs Reliability	Average Variance Explained (AVE)	Scale Reliability	Convergent validity
Reliability	0.945	0.773	Yes	Yes
Ease of use	0.869	0.589	Yes	Yes
Problem solving	0.784	0.557	Yes	Yes
Policy	0.930	0.737	Yes	Yes

Source: Primary data.

Discriminant Validity

The scale was tested for discriminant validity. The guidelines for the discriminant validity are the square root of the AVE from the constructs should be more than the correlation between the construct and other constructs in the model. In our case these conditions for discriminant validity are satisfied (Table 5).

Table 5.

Scale Validation (Discriminate Validity)

Constructs	Average Variance Explained (AVE)	Reliability	Ease of Use	Problem Solving	Policy
Reliability	0.773	0.879	–	–	–
Ease of use	0.589	0.348	0.767	–	–
Problem solving	0.557	0.329	0.293	0.746	–
Policy	0.737	0.408	0.281	0.298	0.858

Source: Primary data.

Nomological Validity

Nomological validity is tested by examining whether the correlations between the constructs in the measurement scale make sense or not. For this, the covariance matrix of construct correlations is useful, and the condition is that the inter-construct correlations must be positive and definite. The correlations matrix extracted for the four constructs was shown in Table 6. All the values of correlations fall between 0.281 and 0.408 indicating positive and definite values. So, the criteria for nomological validity were satisfied.

Table 6.

Scale Validation (Nomological Validity)

Correlations Between Constructs	Estimates
Ease of use ↔ Problem solving	0.293
Problem solving ↔ Reliability	0.329
Reliability ↔ Policy	0.408
Ease of use ↔ Reliability	0.348
Ease of use ↔ Policy	0.281
Problem solving ↔ Policy	0.298

Source: Primary data.

After fulfilling criteria for reliability and validity, we found the model fit indices to check the overall fitness of the measurement model. The model fit indices were calculated by using AMOS. The model fit indices like GFI = 0.924, CFI = 0.960, AGFI = 0.900, RMSEA = 0.061 and chi-square = 2.98 are as per the recommended value (Table 7) and thus the scale satisfied a good model fit criterion (Byrne, 2013).

Table 7.

Model Fit Indices

Index	Recommended Value	Model Fit Indices	Remarks
GFI	>0.90	0.924	Appropriately fit
CFI	>0.95	0.967	Appropriately fit
CMIN/df	<3	2.98	Appropriately fit
AGFI	>0.80	0.900	Appropriately fit
RMSEA	<0.08	0.061	Appropriately fit

Source: Primary data.

Based on these reliability and validity analysis, it can be concluded that ORSQ scale consists of four identified constructs comprising 18 variables.

Discussion

The study identified four broad factors which determine the ORSQ. These are ‘reliability’, ‘ease of use’, ‘problem solving’ and ‘policy’. These factors consist of 18 variables as per the analysis. Online retailers should know that customers evaluate their service on the basis of those variables. This implies that retailers should be able to prioritize these variables while providing online service to meet customer expectations. It has been concluded that to ensure ORSQ, the retailers should give importance to ‘reliability’ by ‘having a website where the customer feel comfortable’, ‘performing the service right the first time’, ‘ensuring availability of products as and when customers required’ and ‘providing error-free transactional details’. To ensure ‘ease of use’ the online retailers should take care about the ‘easy operation of the web site’, ‘flexibility in interaction’, ‘have some features in their website to interact with customer care’, make sure that ‘merchandise delivery and payment process is easy on the website’. To create a ‘problem solving’ environment, the retailers should ‘happily handle the returns and exchange of the merchandise’, ‘eagerly solves customer’s problems’ and ‘develop ability to handle customer complaints effectively’. To strengthen the ‘policy’, the retailers must have ‘high quality merchandise’, ‘options for cash on delivery’, ‘reasonable delivery charges’, ‘acceptance of major credits/debits cards or e-wallet payments’ and ‘efficient refund and exchange policy’. The study concluded by highlighting that, how online shoppers are more concerned about their ‘error free transactions’, ‘clarity in interactions’ and ‘effective solutions of their problems’. So, the online retailers should take care of all these elements while designing their service for an online environment to ensure better SERVQUAL and enhance customer satisfaction.

Conclusions

Customer satisfaction is critical for any retailer. Past researches have proved that SERVQUAL is an antecedent to customer satisfaction. Due to the spread of technology, today’s customers are shopping online and are looking for SERVQUAL in the online platform as well. Hence, understanding the key determinants for ORSQ is critical for e-retailers. In addition, the competition among online retailers compels them to provide enhanced SERVQUAL. So, the proposed scale will be useful to today’s online retailers to provide SERVQUAL and to enhance customer satisfaction. The current research has many important implications which may add value to the existing literature. A few significant implications are as follows:

The outcomes of the study provide insight into how retailers could position themselves with respect to their competitors and target customers’ requirements accordingly.

The research will help the online retailers to understand the online buying behaviour and help them to revisit strategies accordingly.

This will add value to the knowledge base of the Indian online retail industry. It would also contribute for customizing their online retailing offers.

This will be useful for retailers in the online environment to devise a competitive strategy, to strengthen customers’ satisfaction and trust.

The study is limited to one of the cosmopolitan cities like Bangalore, hence, to generalize the findings further study may be done in other geographical regions. Further, researchers may conduct this study to compare various online retail formats by using ‘Online Retail Service Quality’ (ORSQ) scale. Researchers may also use ORSQ scale to understand its association with customer satisfaction and in evaluating the importance of each of the dimensions of ORSQ in enhancing customer satisfaction. The ORSQ scale can also be tested across various demographic variables.

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.

Notes

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