Keywords

Service Quality, Satisfaction, Behavioural Intention, Mediation, Moderation.

Introduction

With the advent of technology and growing customer’s inclination toward e-services has generated opportunity for business to interact with consumers though electronic commerce. The online service users have been rising exponentially and have spread in varying backgrounds. The development of online transactions has additionally encouraged different online services including online food delivering services. Online food marketing system has immensely worked on the quality of food services being provided in the form of mobility, offers, deals, quick and easy order cancellation plans (in case of change in plans or better deals). Online services also enable to compare the restaurants in regard to various parameters that would provide the customer with maximum satisfaction.

The economic, social and cultural changes are converting the eating habits of consumers. As a result of which there is increase in online food aggregators. Due to increased product knowledge, fierce competition and changes in local eating habits forced online ordering companies to improve their connectivity and level of services. Hence, it is of significant importance to understand this industry in terms of Service Quality (SQ), Customer Satisfaction (CS) and Behavioural Intention (BI).

Customer Satisfaction and Behavioural Intention has considered being an important aspect for online service industry. One way of enhancing customer satisfaction is through superior service quality. Perceived service quality act as a strategic tool for positioning as well as for achieving high business performance (Mehta et al., 2000). Perceived service quality has a close association with customer satisfaction which further enhances customer’s intention to revisit and recommend others as well (Cronin & Taylor, 1992). Service quality is an abstract and not easily quantifiable concept. There is a wide variety of perceived service quality measurement tools & amongst them SERVQUAL is the most widely used tool. Earlier studies have identified that service quality in online environments is an important factor for the effectiveness of online shopping (Yang, 2001; Janda et al., 2002). There is need to emphasize more on customer service quality constructs evaluation because service quality assessment differ between e-commerce and physical marketplace services (Parasuraman & Grewal, 2000). Additionally, Van Riel et al. (2001) proposed that the SERVQUAL scale items need to be reformulated before they could be used meaningfully in the online shopping context. Present study examines the influence of performed e- services on customer response in terms of customer satisfaction and behavioural intentions (Parasuraman & Grewal, 2000; Jeong et al., 2003). Service quality is perceived differently by male & female as a result of which moderating role of gender is also used for studying the relationship between service quality and satisfaction. This study aims to:

1. Develop e-service quality instrument based on SERVQUAL and to modify it in research context of online food ordering services.

2. Examine the influence of service quality on satisfaction & behavioral intention; also identify the mediating effect of satisfaction on the relationship between service quality and behavioral intention.

3. To identify the impact of gender based difference in perceived service quality dimension on satisfaction.

Literature Review

Service Quality implemented in several industries has constantly been an area of research in dynamic world. What’s satisfactory today won't be even relevant the next day, therefore it becomes very critical for researchers to hold on studies regularly to better apprehend the dynamics and spotlight the gaps for the betterment of the enterprise. Over the years, several researches were carried out to spotlight the distance between Service Quality and Customer Satisfaction and advised methods to understand and bridge them collectively. Online food ordering is basically a self-provider technique, for this reason analyzing a number of the studies which have been performed on purchaser’s behavior of self-service procedures become obligatory (Hair et al., 2010).

Service Quality (SQ), Customer Satisfaction (CS) and Behavioural Intention (BI)

SQ is defined as the overall evaluation of a service by the customer (Eshghi, 2008). In words of Kotler & Keller (2006) CS can be built through the quality of service. Quality is one of the element that consumers look for in an offer where as service happens to be the other (Negi, 2009). Bowen & David (2005) explained that SQ in the management and marketing literature is the extent to which customer perception of service meet or exceed their expectation. Thus, SQ can intend to be the way in which customers are served in an organization which could be good or poor (Guo et al., 2012).

In last three decades research on SQ has grown a lot and has emerged as a crucial element in marketing. Parasuraman et al. (1988) developed a model of SERVQUAL. In which there are five influential elements: Tangiblility, Reliability, Responsiveness, Assurance and Empathy (Parasuraman et al., 1988; Zeithaml, et.al, 1996). Study in different service categories proves that the service quality components Reliability, Responsiveness, Assurance, Empathy and Tangibility have found to be the important predictors of CS (Parasuraman et.al., 1988; Zeithaml, et.al, 1990). For online business the challenge is in measuring web-based SQ as the traditional SERVQUAL may not fit. Parasuraman & Grewal (2000) suggested that revision of classical SERVQUAL is necessary for measuring web-based SQ. In online services customers interact with technology rather than the traditional service personnel. Parasuraman et al. (2002) developed an e-SERVQUAL scale to study how customers judge e-SQ. The modified identified scale includes Website Design (WD) which describes the application of user interface design that it presents to customers (Kim & Lee, 2002).The quality of website design is significantly important for online store (Ranganathan & Grandon, 2002). It has been observed that WD is strong predictor of quality and satisfaction (Wolfinbarger & Gilly, 2003). Reliability (RE) describes the ability of the web site to fulfill orders on time, keep personal information confident & has a direct positive effect on perceived SQ (Parasuraman et al., 1988; Janda et al., 2002; Kim & Lee, 2002; Zhu et al., 2002). Trust (TR) is a substantial originator of contribution in business especially in online settings because of the increased simplicity with which online stores can behave resourcefully (Reichheld & Schefter, 2000). A well-designed and user friendly ordering system give customers substantial control over the pace of their transaction and allow them to limit the amount of personal interaction they experience. This increased level of control over the system has led to higher CS and greater intent to use or recommend the service to others. Collier & Sherrell (2010) reported perceived convenience of a self-service system also leads to an increase in both adoption and overall CS.

CS and BI are directly associated with SQ (Qu, 1997; Pettijohn et al., 1997; Oh, 2000; Ladhari et al., 2008; Kim et al., 2009). It is not necessary that a satisfied customer will always be a loyal but it is certain that a dissatisfied one will never turn for repeat visit (Soriano, 2002). It can be clearly said that CS is necessary for service oriented organizations because it positively affects the attitudes and intentions of customers (Taylor & Baker, 1994; Mattila, 2000). BI and CS are not similar but interrelated because a satisfied customer has positive reinforcement for using a particular brand or service for particular instances (Oliver, 1980; Cronin & Taylor, 1992). According to Zeithaml et al. (1996), Word-of-mouth, repurchase intentions, complaining behavior and loyalty help in predicting BI. Studies revealed that CS is peculiar in online food industry as it signifies loyalty of customers, making new customers through positive word of mouth and repeat purchases (Oh, 2000; Yüksel & Yüksel, 2002). According to Gupta et al. (2007), the online food industry earns profit if the customer is satisfied and repeatedly indulges in buying from the same service provider. Therefore studies examining the link between CS and repeat purchase have been plentiful and the literature reveals that there is a strong relationship between CS with repeat-purchase intentions (Stevens et al., 1995; Pettijohn et al., 1997; Kivela et al., 1999; Sulek & Hensley, 2004; Söderlund & Öhman, 2005; Cheng, 2005). Various studies have recognized the relationships between SQ, CS and BI: namely intention to revisit and recommend. Hence, the following hypotheses are proposed:

H₁a: Website Design (WD) positively influences customer satisfaction (CS).

H₁b: Website Design (WD) positively influences behavioral intention (BI).

H₂a: Reliability (RE) positively influences customer satisfaction (CS).

H₂b: Reliability (RE) positively influences behavioral intention (BI).

H₃a: Trust (TR) positively influences customer satisfaction (CS).

H₃b: Trust (TR) positively influences behavioral intention (BI).

H₄: Customer Satisfaction (CS) mediates the relationship between Service Quality components (SQ) and Behavioral Intention (BI).

Customer’s Satisfaction (CS) and Behavioural Intentions (BI)

According to Zeithaml et al. (1996) the BI is an indication whether customers remain with or defect from the company. Positive word of mouth, more spending with the service provider, paying a price premium and remaining loyal are the sign of favourable BI whereas leaving the service provider, less spending with the company and/or taking legal action, negative word of mouth are included in unfavourable BI (Ali & Amin, 2013; Ladhari, 2009). Similarly, Oliver (1997) described BI as a stated likelihood to engage in behaviour. In this context, BI is considered to include revisit and word-of-mouth intentions (Jani & Han, 2011). Eventually, the experience of existing customers’ with a product or service builds the attitude of the customer toward the provider which is significantly connected with consumer intentions to repurchase and recommend (Han & Kim, 2009).

The intensive research has shown empirical evidence of a positive relationship between CS and BI (Fornell et al., 1996; Han & Ryu, 2009). Fornell et al. (1996) indicated that if satisfaction level is high it helps in building positive BI which increases the repurchase likelihood and price tolerance for products and services. In addition, Han & Ryu (2009) found that that the direct affect of CS on BI was statistically significant. By repatronizing the service/product, by spending more and by spreading positive word-of-mouth (WOM), satisfied customers will have positive intentions towards the service providers. As there is intense competition, it is important that food ordering platform should understand their weaknesses and design policies and strategies to improve customer satisfaction (Hsiao et al., 2016). Repeat purchase behavior is predominantly affected by customer satisfaction (He & Song, 2009). Loyal customer and new customers are predictors of customer satisfaction (Barber et al., 2011; Tuu & Olsen, 2009) and they bring huge paybacks to a company (Brunner et al., 2008).

There have been numerous studies on the relationship of service quality and satisfaction (Parasuraman et al., 1988; Cronin & Taylor, 1992) and further the satisfaction and intentions of customers (McDougall & Levesque, 2000).The attitude and buying intention relationship are controlled by customer satisfaction (Taylor & Baker, 1994; Mattila, 2000), but at the same time satisfaction and perceived service quality are two different entities. Hypothesis based on literature are as follows:

H₅: Customer Satisfaction (CS) significantly influences behavioral intention (BI).

Moderating Role of Gender

Role of gender has always been given importance to various management and marketing based studies (Babin & Boles, 1998; Eagly et al., 1995; Ergeneli & Arıkan, 2002; Karatepe & Tekinkus, 2006; Yavas et al., 2008). Gender is a demographic construct that differentiate men and women with peculiar characteristics like values, attitudes and behavior (Palan, 2001). Male and Female have appeared to vary in their dispositions toward both the Internet and shopping, it appears to be astonishing that there is little research that expressly tends to express gender orientation contrasts in on-line purchasing (Dittmar et al., 2004). The differences among men and women exist due to biological, behavioural and social causes (Sun et al., 2010).

Male and female have distinctive inclinations for website composition. Males are generally in search of products, information and aspire for achievement via use of technology (Smith & Whitlark, 2001). Females reflect low computer aptitude and high anxiety (Venkatesh & Morris, 2000). Positive states of mind to websites for male than female drives Simon and Peppas to explore attributes required to make website female oriented (Simon & Peppas, 2005). The differentiation between male and female has connotation about how both assess the environment and information. Female process information in more detail whereas male use simple heuristics and use information based on less knowledge (Karatepe & Tekinkus, 2006). Female puts more emphasis on quality as they examine in detail every single aspects of service they procure whereas male evaluate the service on overall basis. The expectation of services for females are likely to be higher than male customers, together with lower perceptions score report than male customers (Juwaheer, 2011), which in turn, affects the level of the satisfaction. Females emphasise less on trust as compared to male while going for online shopping (Awad & Ragowsky, 2008). Garbarino & Strahilevitz (2004) opined that as compared to male, female perceive high level of risk in online purchase. Females were unwilling for giving up there common traditional shopping method & represents low reliability on online shopping in compression to males (Cho & Jialin, 2008). In addition to that it was also found that males are having more trust on online purchase in compression to Females (Riquelme & Román, 2014). Hence, the hypotheses proposed are follows:

H₆: Gender significantly moderates the influence of service quality (Website Design (WD), Reliability (RE) & Trust (TR)) on Customer Satisfaction (CS).

Methodology

Quantitative research approach has been used in this research. Cross-sectional survey design was used for data collection from a sample that truly represents the population to which generalization is made (Cooper & Schindler, 2011). This approach is generally used for establishing the nature and degree of relationship between the study variables (Kerlinger, 1986) (Figure 1 and Table 1).

Table 1 Construct Definition
Constructs	Definition	References
Website Design (WD)	Customer Perception about user friendliness while making online ordering.	Parasuraman et al. (1988) and Kim & Lee (2002)
Reliability (RE)	Customer Perception for trustworthiness and safety in online transaction.	Parasuraman et al. (1988) and Kim & Lee (2002)
Trust (TR)	Perception of customers for trust mechanism provided during online purchase.	Kimery & McCord (2002)
Customer Satisfaction (CS)	Right decision, Repetitive choice and Satisfying Experience.	Oliver (1980), Taylor & Baker (1994)
Behavioural Intention (BI)	Customer behavior in terms of Intention to revisit & recommend.	Zeithaml et al.(1996).

Adapted constructs were measured by using five-point Likert scale (ranging from 1 “strongly disagree” to 5 “strongly agree”). The study was conducted in Delhi NCR region, India. The respondent area includes East, West, North, South Delhi & NCR. Respondents were identified on the basis of concentration of online food aggregators (Restaurants online portal & other app. based services). Data was collected in the year 2018. Samples were selected on the basis of convenience sampling. A total of 540 questionnaires were distributed and only 370 questionnaires complete in all aspects were considered for the study with a response rate of 68.51% (Table 2).

Table 2 Demographic Characteristics
Characteristics	Percentage
Gender
Male	51.3
Female	48.7
Education Profile
Graduate	36.4
Postgraduate	52.6
Others	11.0
Occupation
Student	35.7
Working Professionals	56.5
Retired	5.7
Others	2.1
Online Food Ordering
Once or more in a week	20.1
Once or more in a fifteen days	48.8
Once or more in a month	31.1

The measured constructs were subjected to Confirmatory Factor Analysis to overcome the issue of Convergent Validity and Discriminant Validity (Anderson & Gerbing 1988; Joreskog & Sorbom, 1996). A series of Regression Analysis was used to test hypothesis. To test likely Mediating & Moderating effect step by step guide by Baron & Kenny (1986) was used. Hierarchical multiple regression with Standardized Beta coefficient at statistical significance 0.05 was reported in the study.

Results

Measurement Result

Confirmatory Factor Analysis was used to access the Measurement Model. The observed value of χ² was 2.34 well below the acceptable threshold limit of 3 as recommended in previous research studies by Bagozzi & Yi (1988). Other measurement indices also exhibit good fit for the model. Goodness of Fit Index (GFI) is 0.92 higher than the standard 0.9 as suggested by Joreskong. AGFI (Adjusted Goodness of Fit Index) is 0.86 lower than standard but acceptable, CFI (Comparative Fit Index) is 0.92 well above the acceptable threshold of 0.9 & Root Mean Square of Approximation (RMSEA) is 0.048 as recommended by Bagozzi & Yi (1988).

Composite reliability values of each construct were well above the acceptable threshold of 0.70 suggested by (Nunnally & Bernstein, 1994). The standardized factor loadings were ranging from 0.696 to 0.890 with convergence of items on the identified factors (Anderson & Gerbing, 1988). All loading exceeds 0.7 except one i.e. 0.696, support convergent validity (Bagozzi & Yi, 1988). The Average variance for each identified factors were well above the defined range of 0.5 (Fornell & Larcker, 1981) (Table 3).

Table 3 Reliability and Validity Of Constructs
Constructs	Factor Loading	AVE	CR
Online Interface (OI)
OI1: The online interface is visually appealing.	0.836	0.614	0.865
OI2: The information displayed is clear & distinct.	0.796
OI3: It is easy to pick & place order online.	0.718
OI4: It is quick to perform transactions online.	0.780
Reliability (RE)
RE1: The online orders are performed within the stipulated period of time.	0.861	0.653	0.819
RE2: Online transactions are safe & secure.	0.795
RE3: Service providers show sincere interest in solving problem.	0.767
Responsiveness (RES)
RES1: Online service provider provides prompt service	0.871	0.644	0.859
RES2: Online Service provider provides are always willing to help customers	0.796
RES3: Service providers are never too busy to respond	0.757
RES4: Service providers always provide correct information about when service will be performed	0.784
Trust (TR)
TR1: Service providers are trustworthy	0.890	0.637	0.817
TR2: Feel safe while sharing personal information	0.797
TR3: Online service providers inculcate confidence in customers	0.696
Personalization (PER)
PER1: Online service provider give individualized attention	0.879	0.644	0.819
PER2: Company always have customers best interest at heart	0.786
PER3: Services are designed as per customers need	0.737
Customer Satisfaction (CS)
CS1: It was a right decision to order from the website	0.872	0.653	0.818
CS2: I will again consider the same website for ordering	0.787
CS3: Overall it was a satisfying experience	0.762
Behavioural Intention (BI)
BI1: I intend to revisit the website again	0.796	0.583	0.836
BI2: I will recommend this website to others also	0.762
BI3: I consider this as my first choice for ordering online	0.732

Result in Table 4 represents that the mean of the variables ranged from 3.437 to 3.065 and standard deviations ranged from 0.947 to 1.002. Out of the three perceived service quality Components customer perceives Reliability in online platform is most significant contributor for judging the service quality of online food ordering service providers followed by trust and Website Design. All of the bivariate correlations among variables were less than 0.90 and statistically significant (p<0.01), signifying that the data was not affected by major collinearity problem and giving support for discriminant validity for measure. Moreover, the correlations among the study variables provided initial support for our hypotheses.

Table 4 Descriptive and Correlation
	Mean	SD	1	2	3	4	5
Website Design	3.065	0.954	0.783
Reliability	3.437	0.970	0.434**	0.808
Trust	3.386	0.989	0.362**	0.388**	0.798
Satisfaction	3.125	1.002	0.253**	0.155**	0.173**	0.808
Behavioural Intention	3.166	0.947	0.588**	0.370**	0.414**	0.404**	0.763

As reported by Sureshchandar et al. (2002), there is a significant correlation between service quality and customer satisfaction, the two variables are not same from the customer’s point of view and cannot be construed to mean an absolute causal relationship (Howel, 2007). The regression analysis was used to calculate the direction and level of relationships among the variables (perceived service quality constructs, customer satisfaction and behavioural intention) in the successive analyses.

Test of Research Hypotheses

The extent of effects of Service Quality components on Behavioural Intention and the mediating effect of Customer Satisfaction was analyzed by using Hierarchical Regression. For testing mediating effect of Customer Satisfaction four stages strategy propounded by Baron & Kenny’s (1986) was used to confirm the presence of mediation. First and foremost the predictor variable (Service Quality components) must have a significant effect on the mediator variable (Customer Satisfaction). Second one, the mediator variable (Customer Satisfaction) must have a major effect on the dependent variable (Behavioural Intention). Third is the predictor variable (Service Quality components) must have a significant effect on the dependent variable (Behavioural Intention). Finally, the result of the interpreter (Service Quality components) should not be important (in case of full mediation) or should be concentrated in strength (in case of partial mediation) after it was measured for the mediator variable (Customer Satisfaction).

Table 5 represents that the service quality dimension Reliability is the most significant predictor of customer satisfaction followed with Trust and Website Design. Results also validate the significant positive influence of Customer Satisfaction on Behavioural Intention. The first two conditions of testing mediation were satisfied and support the hypothesis H₁a, H₂a, H₃a & H₄. Moreover, all service quality dimensions significantly & positively influences Behavioural Intention. Introduction of Satisfaction in the model as mediator reduces the impact of service quality and change in R² value (ΔR²) is 4.1% {i.e. 77% - 72.9%, = 4.1%} shows decrease in strength of relationship between perceived service quality and behavioral intention. Customer Satisfaction partially mediates the influence of Website Design, Reliability & Trust on Behavioural Intention, while it fully mediates the effect of Trust on Behavioural Intention. Although the ΔR² value is small, the finding of this study support hypothesis H₁a, H₂a, H₃a & H₅.

Table 5 Hierarchical Multiple Regression Direct & Mediating Effect
Dependent Variable & Standardized Regression Weights
	Customer Satisfaction	Behavioural Intention
Independent Variable	Step I	Step II	Step III	Step IV
1. Service Quality Dimensions
Website Design	0.103**		0.267***	0.265***
Reliability	0.257***		0.298***	0.272***
Trust	0.167***		0.282***	0.261***
(II) Customer Satisfaction		0.404***		0.149***
F	17.894***	71.920***	105.394***	94.389***
R2	0.443	0.404	0.729	0.770
ΔR2	0.236	0.163	0.591	0.040

Result in Table 6 delineates the moderating effect of gender. The interaction (Gender * Website Design) has a significant positive influence on Customer Satisfaction makes significant contribution R² in the model (ΔR² = 0.63, p < 0.001). Furthermore, the Gender & Reliability interaction also has significant positive influence makes significant contribution R² in the model (ΔR² = 0.008, p < 0.01). Furthermore, the interaction of Gender with Trust was found to be insignificant. Hence, the findings support the hypothesis H₆ that gender moderates the relationship between perceived service quality (Website Design & Reliability) and satisfaction.

Table 6 Hierarchical Multiple Regression & Moderating Effect
Dependent Variable & Standardized Regression Weights
Customer Satisfaction
Independent Variable	Step I	Step II	Step III
Website Design	0.153**	0.153**	0.152**
Gender		-0.001	0.005
Gender * Online Interface			0.251***
F	8.816**	4.396*	11.555***
R2	0.023	0.023	0.086
ΔR2	0.023	0.000	0.063
Independent Variable	Step I	Step II	Step III
Reliability	0.165***	0.164**	0.177**
Gender		0.004	0.002
Gender * Reliability			0.088**
F	10.306**	5.143**	4.398**
R2	0.027	0.027	0.035
ΔR2	0.027	0.000	0.008
Independent Variable	Step I	Step II	Step III
Trust	0.173**	0.174**	0.163**
Gender		-0.003	0.000
Gender * Trust			-0.073
F	11.420**	5.696**	4.460**
R2	0.030	0.030	0.035
ΔR2	0.030	0.000	0.005

Discussion

The analytical findings of the study states that Service quality dimensions are significant contributors to customer satisfaction. Although, Website design had only a minor effect on customer satisfaction but it should not be underestimated. Companies should design the interface in such a way that it should be readable, visually appealing and allowing customers to use the web site easily. The results are consistent with (Lee & Lin, 2005) which shows that Online firms are enabled to know the customer’s shopping propensities, inclinations and requirements. Customer Satisfaction significantly and positively influences revisit & recommendation for the online ordering platform. The findings are consistent with the results of Ranaweera & Prabhu (2003). The finding of the study confirms that almost all service quality components are true predictor of Behavioural Intention. The customer satisfaction partially mediates the relationship between service quality and behavioural intention. If these dimensions (Website Design & Reliability) are emphasized more strongly, customer satisfaction will exhibit more tendencies to mediate the relationship between service quality and intention to revisit & recommend. It means that there are many other factors like value, restaurant image, hedonic and utilitarian benefits except customer satisfaction which are the probable measures of behavioural intention (Ryu & Jang, 2007; Ryu & Jang, 2008). The study finds a positive relationship between service quality, customer satisfaction and behavioural intentions in an online store which are consistent with the previous researches (Baker & Crompton, 2000; Sivadas & Prewitt, 2000; Zhu et al., 2002). The moderating effect of gender reported in the study determines that the impact of Reliability on satisfaction is higher for female as compared to male. Significant influence of reliability on female consumers is a challenge for online food service industry. Such findings attributed to the fact that Women emphasize more on pleasant service experience along with effective problem solution. Male as compared to female are concerned more about website design and trust in online transactions.

Conclusion

Increasing competition among online ordering services and the growing importance of customer compels companies for better service. In order to enhance the service quality Online food companies must focus on appeal, appearance of website, timely order, error free transactions, interest in customer problem, Confidence & dependability in customers for their online platform. Service providers should take customer feedback about the services received and could mark out the customers with evaluation and complaint about the service quality. The results have showed that customer satisfaction is positively and significantly related to likelihood of repeat patronage and positive recommendations. There was a significant positive impact of service quality on behavioural intention. This means that increase in service quality components increases consumer’s intention to revisit and recommend. The result of the study is in continuation to the result reported by Hamza (2013) that service quality positively affects the behavioural intentions. With the introduction of customer satisfaction as a mediator the strength of service quality as a predictor for behavioural intention got reduced this confirms mediation. The outcome of the study relationship between service quality and behavioural intention is mediated by customer satisfaction was supported by (Clemes et al., 2011; Kitapcia et al., 2014). Employees should be trained in such a manner that they always keep customer best interest at their heart in order to have long term relationship. The female customers’ satisfaction with online services is positively influenced by reliability construct of service quality. Online service providers should perform promised service on time, their eagerness to solve problem on time encourages female customers more as compared to male customers. The analytical findings confirm that men and women provide relative importance to different dimension of service quality while judging their satisfaction level. High level of satisfaction enables online food aggregators to provide efficient operation, increase in sales, enhance customer patronage, increase positive word of mouth and reduces transaction cost (Beatty et al., 1996; Buttle, 1996). By understanding customer expectations the online food providers become capable to offer superior quality thus facilitating business growth and survival in this competitive environment. The online service providers have capability to deliver relatively error-free service that contents customers by enhancing the quality of services. It also helps in matching and understanding the expectation of customers. It can also be clearly evaluated that by meeting the expectations of customers, the company has the tendency to broaden market share and retain customers’ patronage which ultimately enrich business profitability (Iacobucci, & Ostrom, 1993). The consistently expanding populace swarmed metro urban communities and longer travel times are drivers for the helpful, prepared to eat and less expensive choices of having sustenance and food supplies conveyed at the doorstep. Businesses who keep their value proposition and their brand active in consumer’s minds, will take the biggest share of the Indian online food service pie (Noble et al., 2006).

Limitation and Future Research

To start with, future research can utilize different methodologies such as longitudinal research design, interviews and focused group discussions to analyze the connection between service quality and behavioural intention. Second, the development of the web and web based shopping will proceed, and future research can repeat comparable examinations exclusively including on the web customers, estimating real buy practices rather than aims. This method is intended to comprehend if there are any huge distinction in the impression of e-benefit nature of web clients and web buyers. Third, in spite of the fact that the scales utilized for estimating measurements of service quality are like existing scales, additionally researcher should think about growing more intricate measures to take into consideration other scope of service quality scales. Consequently, the investigation can be replicated in various societies to give culturally diverse correlations (cross cultural comparisons).

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