Keywords

E-Satisfaction, E-Loyalty, Online Reviews, Willingness to Pay, Alternative Attractiveness

Introduction

The retail industry worldwide has undergone a rapid and significant transformation as a result of the COVID-19 pandemic (Bhat, Islam, & Lone, 2021) (Statista, 2021¹). The pandemic had far-reaching and difficult implications for many, but it also paved the way for innovative procedures and fresh approaches to commerce (Roggeveen & Sethuraman, 2020). It endowed brand managers and merchants with tremendous opportunities and also opened fresh possibilities for brick-and-mortar stores and online marketplaces (Hallikainen, Luongo, Dhir, & Laukkanen, 2022; Singh & Rosengren, 2020). Lockdowns and other forms of government intervention significantly curtailed consumer freedom and only essential goods were sold in physical stores (Roggeveen & Sethuraman, 2020). Several industries underwent a shift as a result of the drop in sales and insolvencies of individual enterprises. These shifts reverberated into the future of food retail and permanently impacted things like the prevalence of online grocery shopping (OGS) (Brüggemann & Olbrich, 2022). In a developing country like India, online grocery product delivery became evident during the pandemic as the government laid several restrictions to curb the spread of COVID-19. OGS was not only convenient but also a safer option. Increasing urbanization, changing customer preferences, increase in technology usage, rising disposable incomes and busier lifestyles led people in India to increasingly rely on customizable and useful online platforms for grocery shopping (Yelamanchili, Wukadada, Jain, & Pathak, 2021). According to a report by Future Market Insights (FMI)(2022)² the size of the Indian online grocery market is projected to grow to USD 76,761.0 by 2032 from USD 4,540.0 Million in 2022. As Covid 19 pandemic caused a swing in favor of digital adoption, leading both current online grocery app popularity in India and the notion of quick commerce saw a boost (Yelamanchili et al., 2021). Big Basket, Amazon Fresh, Flipkart Grocery, and Jio Mart are some of the most well-known examples of conventional online grocery marketplaces. Quick e-commerce services like Swiggy Instamart, Blinkit, Dunzo, Big Basket Now, and Zepto are among the most recent arrivals.

Need for the Study

Due to the intense competition in the market big as well as small grocery retailers are offering products with significant value to the customers in order to lock them (Lee, Chen, & Ma, 2012). With the low entry barriers small offline retailers are also entering the online space and trying to make their offering more attractive by providing better quality of service, lower prices, quicker delivery and more choices (Bharte & Shah, 2021; Goode & Harris, 2007). Though the packaged products sold on these e-retailer websites (or apps) are similar therefore they try to entice the customers by making their offering attractive by providing better quality of service, lower prices, quicker delivery and more choices (Goode and Harris, 2007). Though the extant literature provides evidence on the linkages between the alternative attractiveness and loyalty but still there is no definite conclusion on the role of Alternative Attractiveness (AA) on the link between satisfaction and loyalty. Specifically, this study adds alternative attractiveness as a moderator between satisfaction-loyalty models in an emerging economy context also for an emerging service theme.

In short this research aims to shed light on the issue of how to foster loyalty in the new emerging service context by enhancing satisfaction. The study further tries to clarify the ambiguous role of loyalty on intention to write online reviews and willingness to pay more. Hence the findings of this study is expected to provide a clearer picture of influence of AA in the satisfaction and loyalty link and also the role of loyalty in the intention to write online reviews and willingness to pay more.

Literature Review and Hypotheses Formulation

E-satisfaction

Anderson & Srinivasan, (2003) defined satisfaction as “the summary psychological state resulting when the emotion surrounding disconfirmed expectations is coupled with a consumer’s prior feelings about the consumer experience” (pp3). Providing a superior service in order to meet the customer expectations is considered important in both online and offline (Bressolles, Durrieu, & Senecal, 2014; Parasuraman, Zeithaml, & Malhotra, 2005). Due to the explosive rise of e-commerce and digital platforms over the past 10 years, the idea of e-satisfaction has received more and more attention. It refers to the level of contentment that consumers experience after interacting with electronic devices, online platforms, and digital services (Chia-Lin Hsu, Chang, & Chen, 2012). Satisfaction is one of the most discussed factors in the literature that leads to e-loyalty (Chen, Rodgers, & He, 2008; Taylor & Strutton, 2010). A high level of e-satisfaction has been linked to various constructs like customer loyalty, repeat purchases, positive word-of-mouth, and enhanced brand reputation (Suh & Han, 2003). Satisfaction on an online medium is not only a critical performance outcome but also an important antecedent towards e-retailors success and durability in the future (Bressolles et al., 2014). Satisfaction is a state that arises after the evaluation of any given transaction occurred where evaluation is subjective in nature and is also a comparative experience (R. E. Anderson & Srinivasan, 2003). E-Satisfaction is a crucial construct that can influence customers' loyalty and purchase behavior in the digital marketplace. In the present study we have taken e-satisfaction as a judgment of consumers' online retail experience in comparison to their experience with any other offline or online grocery store. Studies have found that e-satisfaction is a critical factor in developing e-loyalty among online shoppers (Chin-Lung Hsu & Lin, 2023; Soyoung Kim & Stoel, 2004; Lu & Wang, 2008). Kim & Stoel, (2004) investigated the factors that influence e-loyalty among online shoppers. They found that e-satisfaction had a significant positive effect on e-loyalty. The authors suggested that online retailers need to ensure that their websites are user-friendly, informative, and offer high-quality products and services to enhance customer satisfaction furthermore enhancing e-loyalty.

H₁: E-Satisfaction has a Significant Positive Influence on E-Loyalty.

E-loyalty

In previous literature the term "loyalty" has been used to describe a customer's willingness to continue making repeat purchases from a preferred vendor (Oliver, 2010). With the advancement made in terms of technology, loyalty paved the way to e-loyalty that exists in the online contexts. Extant literature has described E-loyalty as “a commitment to consistently revisit a website because of a preference for shopping on that website without switching to other websites” (Chou, Chen, & Lin, 2015, pp 544). E-loyalty is a situation when customers show a propensity to shop many times from the same online store, and even go as far as to persuade their friends to do the same (Kaya, Behravesh, Abubakar, Kaya, & Orús, 2019). In the present study authors have considered e-loyalty as a degree to which customers prefer to buy from a particular online retailer over its competitors due to the satisfaction they derive from their online shopping experience. E-loyalty has become increasingly important for online retailers as it has been found to be one of the strong predictors of customer retention, repeat purchases, and positive word-of-mouth recommendations. Certain factors, such as positive word-of-mouth, can stand in for loyalty and are best viewed as proxies for the notion as a whole (Zeithaml, Berry, & Parasuraman, 1996). Loyal customers are more likely to repurchase, refer friends and family, and be less price-sensitive (Reichheld & Schefter, 2000). This shows that electronic loyalty is vital for building a customer base that generates long-term revenue for e-commerce businesses. E-loyalty is crucial for building a loyal customer base and generating long-term revenue for e-commerce businesses.

Loyal customers are more likely to write positive reviews because they feel a sense of commitment to the website and want to support it (Berezina, Bilgihan, Cobanoglu, & Okumus, 2016; Tran & Strutton, 2020) Hence we propose:

H₂: E-Loyalty has a Positive Influence on Intention to Write Online Reviews.

Attractiveness of Alternatives

Attractiveness of alternatives can be defined as the “customers’ perceptions of the extent to which viable competing alternatives are available in the marketplace” (Singh & Rosengren, 2020; pp. 3). Studies conducted earlier have established that users are more likely to find alternatives appealing if they are of the opinion that the fundamental characteristics of the competing services are superior (L. Wang, Luo, & Yang, 2022). The growth of the online grocery market has been remarkable in recent years, with the pandemic causing a significant shift in consumer behavior towards online grocery shopping. Consumers can be motivated to switch online grocery retailers if the offerings provided by alternative retailers are deemed to have superior performance (Dick & Basu, 1994). As consumers seek for alternative shops, their intention to move to a competitive store depends on their "confidence in the firm's capability" to meet their demands (Schlosser, White, & Lloyd, 2006). As stated previously, online grocery shopping is becoming increasingly popular among consumers, and a rising number of grocery merchants (both online and offline stores) now provide an online option; online grocery buyers now have access to a variety of alternatives with just a click (Singh & Rosengren, 2020). Customer’s satisfaction with the online grocery store is one of the key determinants for their patronage with the store but with the number of options available online that too with almost no switching costs and attractive offers by other e-retailers, it becomes urgent to understand the role of availability of alternatives in the satisfaction-loyalty relationship. Therefore to get more insights on the topic we propose:

H₃: Relationship between E-Satisfaction and E-Loyalty is Moderated by Alternative Attractiveness.

Intention to Write Positive Online Reviews

Word of mouth (WOM) is a source of communication between a non-commercial communicator and a receiver about a brand, a product, or a service (E. W. Anderson, 1998; Dichter, 1966; Westbrook, 1987). With the easy internet accessibility, the electronic commerce world has embosomed WOM communication which has enabled shoppers to share their experiences, feelings and views to the online community worldwide (Dellarocas, 2003). People refer to online reviews before making any purchase (online or offline) (Arning et al., 2019; Kashyap, Kesharwani, & Ponnam, 2022; Kashyap & Ponnam, 2019). Extant literature contains various studies in order to explore the attitude of consumers towards online reviews and their intention towards adopting these reviews (Tata, Prashar, & Parsad, 2019; Vijay, Prashar, & Parsad, 2017).

Dick & Basu (1994), first suggested the link between loyalty and word of mouth in the conceptual framework which was later investigated by few other studies (Carpenter & Fairhurst, 2005; Choi & Choi, 2014; Gounaris & Stathakopoulos, 2004; Reynolds & Arnold, 2000; Sichtmann, 2007). De Matos & Rossi, (2008), carried out a meta-analysis to explore the connection between loyalty and word-of-mouth. Their findings revealed that customers who exhibit loyalty to a particular service provider tend to speak positively of the company to others in their reference group. Conversely, customers who switch to other providers due to their disloyalty are more inclined to spread negative word-of-mouth about the company. While earlier studies have backed the notion that loyalty has an impact on the intention to spread positive word-of-mouth recommendations (Choi & Choi, 2014; Rehman, Woyo, Akahome, & Sohail, 2022) it is yet to be determined how the relationship between these two concepts applies in the specific context of online grocery shopping. Therefore, to get the better understanding, we hypothesize:

H₄: E-loyalty has a positive influence on intention to write online reviews.

Willingness to Pay More

E-loyalty refers to the extent to which customers are committed to a particular online brand or website. It involves a combination of trust, satisfaction, and loyalty towards a particular e-commerce platform. Research has shown that customers who are loyal to an e-commerce platform are willing to pay more for the same products or services compared to non-loyal customers. For example, a study conducted by Schepers & Wetzels (2007), found that e-loyalty positively influences customers' willingness to pay a higher price for the same product.

In addition, e-loyalty creates a perception of added value for the customers, which results in a higher willingness to pay more for the products and services offered by the e-commerce platform. As noted by Sanghyun Kim & Park (2013), the trust and satisfaction that come with e-loyalty make customers more willing to pay more for the products or services provided. Therefore, there is a positive relationship between e-loyalty and customers' willingness to pay more for products or services provided by an e-commerce platform. Businesses could strive to build e-loyalty through various strategies such as personalized marketing, excellent customer service, and user-friendly interfaces to maximize the potential for higher revenues. Therefore we hypothesize in Figure 1:

H₄: E-loyalty has a positive influence on willingness to pay more.

Research Methodology

Questionnaire Designing

In order to accomplish the objective of the study, all the constructs were adopted from the relevant studies. In the first part of the questionnaire, the main purpose of the study was explained and the second part of the study was devoted to demographic questions. While the last part was adopted to measure the main scale items. Seven-point likert scales were adopted to measure the main scale items. W. Wang, Wang, Mo, & Tseng (2017)’s scale was adopted to measure e-satisfaction. E-loyalty was validated using scale items extracted from Hur, Ko, & Valacich (2011), study. Alternative Attractiveness items for were adapted from Sharma & Patterson (2000). Willingness to pay more and intention to write online reviews were tested from items suggested by Srinivasan, Anderson, & Ponnavolu (2002) and Thakur (2018).

Data Collection

The data were collected with the help of questionnaire survey approach using convenience sampling method. Before data collection a pilot study was conducted in order to check the understandability and the validity of the questionnaire. Considering the suggestions from the pilot survey, few sentences were corrected and refined in the questionnaire in order to make it more understandable from the consumer’s perspective. The target population of the study were the people residing in urban area as the services of online grocery ordering and delivery is more prevalent in the urban area. Using group administration approach, link of the questionnaire was sent to 2500 people. Group administration approach was used as it allows fast data collection with high response rate (Adler & Clark, 2014). A total of 988 were returned and after excluding incomplete responses and extreme outliers only 955 (38.2%) valid responses were considered for the study. Table 1 contains some basic information regarding the sample.

Table 1 Descriptive Statistics Result
Gender	Male (%)	Female (%)
Age	18-25 (years)	25-35 (years)	35-45 (years)	45 years and above
Educational qualification	Intermediate	Graduate (number, percent)	Post graduate	Doctoral
Family Income	below 25,000	25,000-50,000	50,000-75,000	75,000 and above
Frequency of visit to e-retailer  website	Once a week (9.8%)	Twice a week (12.16%)	Thrice a week (68%)	More than 3 times a week (21%
80 Rs (Indian Rupee) is Equivalent to 1 USD

Statistical Analysis

Data collected was analyzed using SPSS 23 and AMOS 26. A two-step model approach was used: Measurement Model and Structural model (J. C. Anderson & Gerbing, 1988). Measurement model is used to perform confirmatory factor analysis and check the reliability and validity among the constructs and items. Structural models are used for testing the hypothesis and evaluating the fit of the model.

Data Screening

Before applying the measurement model, the data were screened for the normality assumption and presence of outliers in the data set in order to fulfill the general linear model assumptions. To identify the outliers, cooks distance value is calculated and if it exceeds 1 for any response then it has to be deleted (Stevens, 2012). A total of outliers were deleted from the sample obtained. While testing for the normality, data was found to be normal as the data didn’t deviated much. The kurtosis and skewness were found to be in the range of +3 and -3 and +10 and -10 respectively (Kline, 2023). Fulfillment of the assumptions of general linear model paves way to the measurement model.

Reliability and Validity Analysis

As the measures were adapted from previous studies therefore confirmatory factor analysis (CFA) was performed. Studies have suggested that CFA is an important tool to authenticate that the observed variables belong to their respective constructs (Hair et al, 2020). Overall fit of the model was examined using χ2 statistics, the suggested value of χ2 /df is < 3:1(Hooper, Coughlan, & Mullen, 2008). Though it should be noted that the sample size affect χ2 statistics and a large sample size indicates a poor fit. The values of other goodness of fit measures (GFI, NFI, CFI) ranges from 0.9 to 1.0 and the badness of fit indicator (RMSEA) falls below o.8 then the results indicate a good model fit (Hooper et al., 2008). At first, CFA results were satisfactory (χ = 614.532, χ /df = 4.328, GFI = 0.935, TLI = 0.952, CFI = 0.961, IFI = 0.961, RMSEA = 0.059). However one item (AA5) was deleted due to low standardized factor loading (<0.6). Deletion of such item decreases the measurement error and increases the reliability among the construct items (Ford et al., 1986) and in the present study also the model fit improved (χ = 517.693, χ /df =2.523, GFI = 0.967, TLI = 0.98, CFI = 0.985, IFI = 0.985, RMSEA = 0.04). All the model fit indices were found to be within acceptable range. In order to measure the reliability among the items of each construct, Cronbach's α values were assessed. Cronbach's α values ranged from 0.857 to 0.917, which meets the cutoff value of 0.7 (Nunnally, 1967).

Convergent Validity and Discriminant Validity

Convergent validity was assessed using: factor loading (standardized estimates), Average Variance Extracted (AVE) and Composite Reliability (C.R). The standardized factor loading of all the constructs were found to be between (0.719 to 0.896) which are above the recommended level of 0.6 (Chin et al., 1997). The Composite reliability (C.R) values were found to be between 0.858 to 0.784 and meets the suggested criterion of 0.6 and higher (Bagozzi & Yi, 2012). The AVE of each construct ranged from 0.602 to 0.900 and meets the suggested criterion of 0.5 (Fornell & Larcker, 1981). Table 2 provides the detail of convergent validity.

Table 2 Convergent Validity Measurement
Construct	Items	Standardized Loading	Cronbach's a	AVE	C.R
e-Satisfaction	sat1	0.88	0.916	0.784	0.916
	sat2	0.896
	sat3	0.88
Alternative Attractiveness	AA1	0.803	0.857	0.602	0.858
	AA2	0.811
	AA3	0.766
	AA4	0.719
e-Loyalty	L1	0.877	0.914	0.782	0.915
	L2	0.909
	L3	0.866
Willingness to pay more	WPM1	0.87	0.909	0.715	0.909
	WPM2	0.867
	WPM3	0.811
	WPM4	0.782
Intention to write online reviews	R1	0.887	0.917	0.734	0.917
	R2	0.861
	R3	0.844
	R4	0.835

Discriminant validity was assessed with the help of the square root of average variable explained (AVE) of each construct and comparing it with correlation value of each construction. If the square root of AVE for each construct is higher than its correlation's value, then it ensures the discriminant validity of the constructs (Chin, Gopal, & Salisbury, 1997). Table 3 provides the details about discriminant validity statistics.

Table 3 Divergent Validity Measurement
	1	2	3	4	5
1. E-satisfaction	0.885
2. E-Loyalty	0.151	0.884
3.Intention to write reviews	0.198	0.199	0.857
4. Willingness to pay more	0.193	0.270	0.369	0.845
5.Alternative attractiveness	0.095	0.240	0.473	0.498	0.776
Mean (SD)
The bold diagonal values in italics represent the square root of AVE

Model fit and Hypothesis Testing

Measurement model justification paves the way for analyzing structural model. The goodness of fit indices of theoretical framework as assessed using the structural model. SEM was conducted in order to verify goodness of fit of the full framework and the output obtained suggested a good data fit (χ² = 1308.493, χ²/df = 4.926, GFI = 0.927, TLI = 0.945, CFI = 0.955, IFI = 0.955, RMSEA = 0.079). In Table 4, the column of fit indices before modification, showed that the χ²/df ratio was 4.526, which was lower than the recommended level of 5 (Marsh & Hocevar, 1985). Due to the sensitivity of χ² to sample size, more indices were therefore used for assessing the model fit. The observed value of Root Mean Square Error Approximation (RMSEA) was 0.079 which justify the criterion of < 0.08 (Rigdon, 1996). The other fit indices (such as GFI, TLI, CFI, IFI) were above the recommended criteria of close to 0.9 and higher value (Bagozzi & Yi, 1994). Table 4 contains the model fit indices.

Table 4 Model Fit
Fit Index	Criterion	Model Values
Χ2/df	<5	4.926
CFI	> 0.9	0.955
RMSEA	< 0.08	0.079
GFI	> 0.9	0.927
AGFI	> 0.80	0.896
NFI	>0.90	0.948
TLI	> 0.90	0.945
IFI	< 0.08	0.955
Note: N= 955

Hypotheses Testing

The structural model results along with the path coefficients were depicted in table 5. Hypothesis H1 is supported (β = 0.163, p =0.001) which speak for a strong and positive association between e-satisfaction and e-loyalty. Whereas H2 is also supported (β = 0.029, p = .01) with a significant but reduced beta value than H1, which indicates a partial moderation effect of “alternative attractiveness” on the e-satisfaction and e-loyalty relationship. H3 is supported (β= 0.205, p= .001) which shows a positive and significant influence of e-loyalty on “intention to write online reviews”. H4 is also supported (β = 0.198, p = .01), this indicates a strong positive significant influence of e-loyalty on “willingness to pay more”. Table 5 provides β values of the proposed theoretical framework.

Table 5 Hypothesis Testing Result
Hypothesis	β Value	t Value	ρ value	Relationship
E-satisfaction has a positive influence on e-loyalty (H1)	0.163	5.132	0.001	Supported
Relationship between e-satisfaction and e-loyalty is moderated by alternative attractiveness (H2)	0.029	4.05	0.01	Supported
E-loyalty has a positive influence on intention to write online reviews (H3)	0.205	9.47	0.001	Supported
E-loyalty has a positive influence on willingness to pay more (H4)	0.198	6.832	0.01	Supported

Findings, Limitations and Future Research

The paper primarily aims to discover the relationship between e satisfaction and e loyalty in the changing service marketing conditions. By using 955 respondents from urban areas of India, this study finds that, e-satisfaction and e-loyalty are positively correlated which contradicts Juwaini et al., (2022) finding but supports earlier research findings (Al-dweeri, Obeidat, Al-dwiry, Alshurideh, & Alhorani, 2017; Aladwani, 2006; Haq & Awan, 2020; Prougestaporn, Visansakon, & Saowapakpongchai, 2015; Rodríguez, Villarreal, Valiño, & Blozis, 2020; Zhang & Von Dran, 2000). Also it supports work of Shankar, Smith, & Rangaswamy, (2003) where they advocate satisfaction-loyalty relationship is stronger in virtual environment than offline environment. This means the probability is high for the urban customers who derive satisfaction from the online grocery shopping activity for a given online app will consider the same app for their next online grocery shopping activity, in turn will became brand loyal for the specific online grocery app. According to the results, e-satisfaction and e-loyalty relationship is moderated by alternative attractiveness which supports Chuah, Marimuthu, Kandampully, & Bilgihan, (2017) which says an urban customer’s satisfaction level strongly determined their loyalty quotient with the given online grocery brand if the customer has high perception about alternative attractiveness. These results also contribute to the existing literatures (Al-dweeri et al., 2017) which says deriving loyalty is more difficult and complex in the virtual marketing environment than traditional marketing environment, for which the relationship require the intervention of several other variables like alternative attractiveness (Chuah et al., 2017). These results will provide better understanding to the practitioners to formulate strategies which will include land marking role of alternative attractiveness in converting the satisfied customer into loyal customer for higher market share in online retail marketing specifically in grocery segment.

Furthermore, the study findings indicate a positive and significant relationship between e-loyalty and willingness to pay which is in contrast to the result of Wieseke, Alavi, & Habel, (2014) where they stated that loyal customers are always eager to get some added benefits such as price off, discounts, rewards etc. The current study findings might be due to the result of pandemic situation in the market, as it restricted the outside movement of customers and made them rely mostly on online services, also the customers became more concerned about availing quality and hygienic offerings. Hence customers were prone more to depend only on the trusted online brands for which they are loyal and didn’t mind to pay more for superior and quality grocery items (Casaló, Flavián, & Guinalíu, 2008; Kumar Roy, M. Lassar, & T. Butaney, 2014). This study finding will be very much beneficial for the managers to concentrate more on providing quality and superior offerings to their customers in an unexpected virtual marketing environment. Extant literature in the field suggests that loyalty reflects customer’s favorable attitude towards the brand or an organization (Dick & Basu, 1994; Evanschitzky, Iyer, Plassmann, Niessing, & Meffert, 2006) which motivates consumers to spread positive word of mouth and recommend their preferred brands to their social communities (Hallowell, 1996).

The study findings limit itself to the geographical location (India) and pandemic situation. The study can be extended to other cultural setting and a comparative work can be done between developed and developing countries. The study findings also limit itself to the product category that is only grocery segment, future study can be done by taking other retailing categories such as fashion, banking etc. Future studies also can include other behavioral variables such as e- customer engagement, quality, trust, switching cost, technology savviness and extend this model.

Endnotes

¹https://www.statista.com/topics/3792/e-commerce-in-europe/ (2021), Accessed 14^th Jan 2022
²https://www.futuremarketinsights.com/reports/india-online-grocery-market

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Received: 24-May-2023, Manuscript No. AMSJ-23-13628; Editor assigned: 25-May-2023, PreQC No. AMSJ-23-13628 (PQ); Reviewed: 29-Jun-2023, QC No. AMSJ-22-13628; Revised: 20-Aug-2023, Manuscript No. AMSJ-23-13628 (R); Published: 09-Sep-2023