Shopping on the internet has gained widespread popularity in the last few decades. The rapid growth of internet penetration since 1991, enabled e-commerce on the internet and led to many businesses cashing on the opportunity. The rise of internet has led to the creation of a global marketplace where locational barriers do not exist anymore.

E-commerce, a subcategory of the overall e-business universe, is focused on transactions. To drive consumer and customer transactions, businesses use different marketing and e-marketing tools. At the core of these marketing efforts is the need to attract new customers via creating and communicating an enhanced value proposition. This means e-commerce businesses have to invest time, and effort on understanding their target group’s attitudes and usage behaviors while and also factoring in their ethical and legal considerations. To create a unique point of differentiation versus their competitors, these companies must focus not just on operations but more importantly, compete for customers treating them as the most important asset and building long term customer relationships. Therefore, companies need to invest and drive relationship marketing which has been called a process, strategy , an art that cannot be ignored in today’s time.

The main types of electronic commerce are: business-to-business (B2B); business to- consumer (B2C); business-to-government (B2G); consumer-to-consumer (C2C); and mobile commerce (m-commerce).

E-commerce in India

E-commerce in India is still at a nascent stage, but even the most conservative projections predict the industry to rapidly increase in the future. Many domestic and international players have already set up shop in India and new E-commerce players continue to enter this sector. A lot of traditional offline retail companies have also forayed into setting up an E-commerce portal in addition to their offline business. According to Abhijit (2013), there are many sites selling diverse items from cinema tickets to household items, Durable and electronics, and services like flower delivery etc.

According to a IBEF report, the Indian E-commerce market is expected to grow from US$ 46 bn to US$ 111 bn at a CAGR of 19%.

Some of the key facilitators for the E-commerce growth in India are (1) Massive internet penetration with over 300 mn users (2) Growing Smartphone penetration consisting of 35% of the mobile phone market in India (3) Transformation from cash to digital economy and the growth of payment gateways (4) Strides in analytics solutions for actionable insights on the consumer (5) Rise of social media as a platform for brand building, advertisements, developing a community of trusted users, spreading word of mouth, communicating offers etc. (6) Government of India initiatives, namely Digital India, Make in India, Start-up India, Skill India.

Impact of Covid-19 on E-commerce

Many sectors have benefitted as a result of lockdowns and distancing norms due to Covid’19. This has led to increased adoption of E-commerce both for B2C as well as B2B players. Within B2C sales, Household categories have seen the highest spike.

According to a Shopify report, Consumers are reluctant to go back to shopping in physical stores.

• 84% of consumers surveyed have purchased products online post Covid-19. Only 38% claim to have bought products from physical stores.
• 85% claim that they will continue to purchase from E-commerce over the next few months.
• The shift towards E-commerce post Covid-19 is more evident in younger (18-34 yrs) and middle aged (35-54 yrs). 85% of younger and 90% of middle aged consumers claim to have shifted to E-commerce shopping post Covid-19.

Going virtual will continue to enhance the shopping experience online. With the advancement in technology and platforms, engaging with consumers will evolve from a “nice to have” to a “must have” in the post-pandemic era. Even though 2021 will likely see the return of consumers to physical locations, consumers will continue to embrace the virtual engagements that add the most value, putting pressure on businesses to ramp up investments.

Relationship Marketing (RM) and e-RM

Various authors have described the importance of Relationship marketing. Magnum (2008) states that “Relationship marketing includes all the ways you use relationships to promote your business”. The advantage of building long term relationships is that it earns a high level of trust with customers that results in high levels of information exchange and cooperation, which in turn drives long term profits for companies. Palmatier (2008) characterised RM as “the process of identifying, developing, maintaining, and terminating relational exchanges with the purpose of enhancing performance”.

Businesses around the world have seen a tectonic movement towards a consumer pull (consumer focused) strategy. This shift has been further aided by technological advancements and one of the most visible manifestation is the investments companies have made in customer relationship management (CRM) systems. According to Winer (2001), CRM integrates information technology and business processes, which allows executing of relationship marketing at a company-wide level. As customers become increasingly demanding and seek more information and attention, companies are increasingly leveraging technology to address their customer’s needs.

The growth of internet-based businesses has given a platform for delivering Relationship marketing features over the web resulting in a new concept: e-Relationship marketing (E-RM or ERM). E-RM focusses on the internet or web-based interaction between businesses and customers. According to Rosenbaum & Huang (2002), a key reason for companies adopting E-RM is the rationale that it enhances the customer loyalty through improved customer satisfaction. There is widespread agreement within the research and industry communities on the benefits of e-RM on customer satisfaction.

The present research study is an endeavor to assess if there are any differences of Relationship Marketing and Purchase Intention from a demographic standpoint in the domain of E-commerce in India by using quantitative research method.

Literature Review

RM has originated from its genes and is based on the Relationship Marketing (RM) doctrines, which are well thought of as one of the essential aspects of contemporary marketing growth and have generated excessive research interest for a number of years (Sheth and Parvatiyar, 2000). Relationship marketing was introduced in the early 1990s as a way for marketing teams to learn more about their clients, consider their interests, and thereby improve the likelihood of retaining them (Dyche, 2002). In their work, Peppers and Rogers (1993) highlighted relationship marketing as a one-to-one marketing strategy that Dyche (2002) later explored in his book.

Brodie et al. (1997) referred to marketing of relationships as a new concept and, according to the authors, marketing of relationships originated from a sequence of research flows. The first flow analyses marketing taking a service perspective (Berry, 1983, 1995, Gronroos, 1990); while the ensuing next flow centers on organizational or corporate exchange connections (Berry, 1983, 1995, Gronroos, 1990) (Dwyer, et al., 1987, Ford, 1990, Hakansson, 1982, Wilson, 1995). Similarly, the third stream is focused on channel literature, for example, improvement in relationships between operative and competent networks (Buzzell and Ortmeyer, 1995).The subsequent part of the studies, which is the fourth part, dwell on the stream of dynamic relationships studies (eg, Axelsson and Easton, 1992, Johanson and Mattsson, 1985, 1988).

The fifth research stream underlies the modern relationship marketing model emanate from strategic management discourse and is based on latest conceptualizations on the existence of ties in value pattern or chains (Normann and Ramirez, 1993). Finally, the last section is focused on literature on information and computerised technology, and explores the strategic effect of such on intra and inter-organisational relationships (Scott Morton, 1991).The authors have clarified that the synthesis of these research flows provides the basis for the current pragmatic statement that considers marketing an integration operation involving employees across the company, with specific attention all the time to facilitation, creation and preservation of relationships.

There is widespread agreement within the research and industry communities on the benefits of e-RM on customer satisfaction.

Purchase Cycle

It is possible to split the buying decision process into pre, during and post-purchase phases (Solomon, 2006). In the various phases of the transaction cycle, the expectations of consumers and related activities vary given the thoughts and requirements of consumer assessment in the advance purchasing process vary from post-purchase.

Sterne. (1996). proposes a framework consisting of three phases: pre-sales, sales and post-sales experiences to distinguish the online consumer experience. Lu (2003) uses this context in further studies to assess the e-commerce outcome of functionality or efficacy satisfaction, highlighting that E-attributes RM’s leads distinctively to the overall satisfaction related with individual modes of the transaction process.

In a related research, Feinberg et al. (2002) split E-RM functionality of websites into three distinct phases, that is, the pre-sales, sales, and after-sales stages to examine the association connecting E-RM and customer gratification. The review of literature indicates that the online consumer purchase journey can be divided into three stages: Pre purchase, During purchase and Post purchase. Given the different consumer needs and expectations at each stage of this journey, from an RM standpoint, these can be categorised into: Communicational RM, Transactional RM and Relational RM.

Pre-purchase or communicational RM features

Researchers who are studying the success of E-RM have suggested that an E-RM program should include several elements in the pre-purchase phase that will lead to pre-purchase satisfaction. Anderson and Kerr (2001) state that the first phase of E-RM is to provide information to customers, and at this stage companies try to get information from potential customers and learn more about them.

Khalifa and Shen (2005; 2009), deconstructed E-RM pre-purchase functionality into six constructs (site customisation, customer education, alternative channels, loyalty programs, search capabilities and alerts).

At-purchase or Transactional RM features

In this step, the different E-RM functions will influence the decision of the client to complete the transaction online. The value of dynamic pricing at this point was emphasised by Khalifa and Shen (2005; 2009).

Further, information on purchase conditions was emphasised by Khalifa and Shen (2005; 2009) and Feinberg et al. (2002). They clarify that the guidance given on the procedures on how to purchase the product, what requirements to consider are an important aspect.

In addition, Liu et al. (2008) highlighted the value of the safety / privacy aspect affecting the decision of a customer to make a purchase via the website of the company. Therefore, at this point, websites should provide some E-RM features to reduce any perceived risk and provide adequate protection to customers. It is crucial that web designers make consumers feel that the Internet is an easy, secure and efficient way of making transactions for these reasons.

Post-purchase or Relational RM features

The last step to complete the electronic transaction is after the purchase. At this stage, Ross (2005) pointed out that customer service plays an important role because a company will perform a customer assessment so that the company knows the level of customer satisfaction with the delivered product and electronic services. Using this approach, the company can improvise its products and services to satisfy its customers. Customer service also acts as a communication platform between customers and management to answer all questions and interact with customers, especially when customers face problems and difficulties with electronic transactions (Khalifa & Shen, 2005).

Feinberg et al., (2002) support the reporting functionality of those Web sites that provide a specific area for customers where they can leave their complaints. While Feinberg et al., (2002); and Khalifa and Shen (2005) support the availability of the problem solving function in which visitors can solve their problems with products or services with the help of the online self-help feature.

E-RM, E-satisfaction and Purchase Intention

For many researchers, electronic satisfaction has become an object of interest. “Contentment and gratification have a major influence on the acquisition and ability to retain customers and overall firm profitability”, Anderson and Srinivasan (2003). Sterne (1996) proposed a structure to relate the experiences of online consumers in the sense of e-commerce, which comprises of three phases: before-sales, during sales and after-sales inter-activity. Lu (2003) demonstrated impact showing RM’s add diversely to the gratification associated with each stage of the transaction. In the pre-sales, sales and after-sales phases of the investigation into E-RM and satisfaction relationships, Feinberg et al. (2002) maps the electronic characteristics of retail web-platforms along the same axis.

Purchase motive is regarded to be a critical precipitating factor of the attitude-behavior relationship, which is ideal for determining customer behaviour. The purpose of purchasing is characterised as the individual’s judgement on the service and the decision to commit in the future. [Hellier P. K. U.a. et.al. Zeithaml V. A. (2003); et., et. Al’s (1996)]. Scholars have concentrated on various aspects of the purpose of purchasing. A. Bhattacherjee (2001), postulates that the primary determinants of purchase intent are assurance and satisfaction. Oh, Jones M. A. (1998) considered that the intention to purchase is directly influenced by the adjustment of obstacles.

Zeithaml and Bitner (2000) suggested that “customer satisfaction relates to the consumer satisfaction response and is an emotional response to the difference between what consumers expect and what they get”. For happy clients, the beneficial behavioural intentions towards the service provider increase (Chen, Liu, Sheu and Yang, 2012). Future buying intentions are related with client’s gratification (Patterson et al 1997, Durvasula et al., 2004). The majority of empirical evidence substantiates that gratification and contentment is a critical aspect connected to the motivation to purchase (Day, Denton & Hickner, 1988, Kotler, 1994, Cronin & Taylor, 1992, Patterson, 1997, Johnson & Spreng, 2001, Mittal & Kamakura).

Analysis of existing research-related literature; E-RM, E-Satisfaction, and the plan to purchase, have highlighted a number of gaps to be discussed in this study. Firstly, as seen in the previous sections, there are no studies that concentrate on E-RM in general with regard to all the purchase cycle stages (before-purchase, during purchase and after-purchase). While a specific study focusing on the features of E-RM and online satisfaction was proposed by Khalifa and Shen (2005; 2009), there is no full conceptual approach that centers on all the purchase cycle stages. Secondly, although there are some studies that include a systematic scrutinization of these components, there is minimal evidence that link the purchase cycle stages to both E-Satisfaction/E-Gratification and E-Purchase Intention. Finally, there have been no empirical studies in the Indian perspective that focus on the E-RM factors that influence the consumer’s decision to purchase consumer goods online and how they differ from a demographic standpoint.

Research Objectives

1. To explore the dimensions of communicational, transactional, and relational E-Relationship marketing on E-commerce platforms.
2. To analyze the role of demographic variables on the dimensions of communicational, transactional, and relational E-Relationship marketing.

Research Methodology

The present research is an attempt to examine the differences in relationship marketing practices, e-satisfaction and purchase intention across demographic characteristics of the respondents. After defining the concept of relationship marketing and proposing a framework to assess the effect of RM dimensions on purchase intention, hypotheses were developed and tested using a quantitative design. The study was done using administered questionnaires with consumers shopping online. The respondents were contacted using stratified random sampling. Following a pilot study (50 respondents), further, a total of 516 questionnaires were used for the main empirical analysis of study. The study uses a quantitative empirical approach with structured data collection through a questionnaire. The present study employs t-test and ANOVA to analyze the differences across demographic characteristics of the respondents.

Analysis

The coding scheme for the different variables/items is as below:

Demographic Characteristics of the respondents

Demographic profile of the respondents is demonstrated in Table 1. It classifies the respondents on the basis of gender, age, educational qualification and profession. The results show that male respondents have participated more than female respondent. Most of the participation comes from the youngsters whose age is 16 to 24 years. From educational qualification, respondents having college degree have participated more in the study. Self employed individuals have higher level of participation in the study than individuals from any other profession.

Table 1. Demographic profile of the respondents

Validity and reliability analysis of the scale used in the study

According to Straub et al., (2004) reliability and construct validity are obligatory validities for measurement model fit assessment. While reliability is concerned of measurement within a construct and construct validity has to do with measurement between constructs. To achieve the validity of assessment instruments, results should be reliable and valid for study. Consequently, reliability and validity should be examined for each measures of assessment model and the measurement model should indicate good quality of reliability and validity including convergent validity and discriminant validity.

To assess Reliability of the instrument, Cronbach Alpha values were deduced as summarized in Table 2. Basis this, no item needs to be deleted from the scale and the measures of the study were sufficiently found reliable to conduct further analysis. To assess Validity, standards include: (a) the standardized regression or factor loadings of the indicators should be greater than 0.5; (b) The composite reliability of various dimensions is higher than 0.70; (c) Average Variance Extracted (AVE) is higher than 0.50 . Referring to Table 3, we can observe that all factor loadings are above 0.50, with majority of the values significantly higher than 0.70. Convergent validity criteria for standardized regression weights and critical ratio indicate reasonable score achieved as the obtained values are above the range. Hence, above mentioned both the conditions for convergent validity are satisfied. Next item average variance extracted (AVE) can be determined as the sum of squared multiple correlations divided by the number of factors. Principally obtained value of AVE should be greater than 0.5. AVE results are as follows.

Table 2. Summary of Cronbach’s Alpha Values for the Survey Questionnaire

Table 3.  Obtained convergent validity

 T-test and ANOVA

Mean differences related to communicational transactional and relational ERM feature between male and female

The above table shows the results of T-test applied to determine the differences in antecedents of Website Design (WEBD), Website usability (WEBU), Search and compare capabilities (SRCC), Information Quality (INFQ), Loyalty and Rewards program (LYRP), Responsiveness (RSPNS), Security and Privacy (SCPY), Order Tracking (ORDRT), On Time delivery (OTDL), Customer Service (CSER), Lower Rates (LRATES), Use of Social Media (USMEDIA), E-Satisfaction (ESTFN) and Purchase Intention (PINTN) across Genders.

The Sig. value is greater than 0.05 for maximum of the dependent variables across the table. This supports the hypotheses H01.1, H01.2, H01.3, H01.5, H01.6, H01.8, H01.9, H01.10, H01.11, H01.12 and H01.13 while the Sig. value is less than 0.05 for INFQ, SCPY, PINTN. Therefore, the null hypotheses H01.4, H01.7, and H01.14 is rejected between the Genders.

It is also observed that female employees have slightly higher Mean score in all the cases. However, it is statistically confirmed through t-test that the above antecedents of Website design (WEBD) and website usability (WEBU), responsiveness (RSPNS), Loyalty and Rewards program (LYRP), Search and compare capabilities (SRCC), On Time delivery (OTDL), Customer Service (CSER), Lower Rates (LRATES), Use of media (USMEDIA) E-satisfaction (ESTFN) and does not have any significant difference across genders as the significance value is higher than 0.05.

Table 4. Mean differences between Males and Females

Mean differences related to communicational transactional and relational E-RM features across Age Groups

Table 5. Mean differences among age groups

The above table shows the results of T-test applied to determine the differences in antecedents of Website Design (WEBD), Website usability (WEBU), Search and compare capabilities (SRCC), Information Quality (INFQ), Loyalty and Rewards program (LYRP), Responsiveness (RSPNS), Security and Privacy (SCPY), Order Tracking (ORDRT), On Time delivery (OTDL), Customer Service (CSER), Lower Rates (LRATES), Use of Social Media (USMEDIA), E-Satisfaction (ESTFN) and Purchase Intention (PINTN) across Age groups.

The Sig. value is greater than 0.05 for maximum of the dependent variables across the different age groups. This supports the hypothesis H_02.1, H_02.2, H_02.3, H_02.4, H_02.5, H_02.7, H_02.8, H_02.9, H_02.10, H_02.11, H_02.12, H_02.13, and H_02.14 while the significance value is less than 0.05 for RSPNS. Therefore, null Hypothesis H02.6 is rejected across Age groups.

It is also observed that the age group of 25-34 years have slightly higher Mean score in almost all the cases. However, it is statistically confirmed through T test that the above antecedents of Website Design (WEBD), Website usability (WEBU), Search and compare capabilities (SRCC), Information Quality (INFQ), Loyalty and Rewards program (LYRP), Security and Privacy (SCPY), Order Tracking (ORDRT), On Time delivery (OTDL), Customer Service (CSER), Lower Rates (LRATES), Use of Social Media (USMEDIA), E-Satisfaction (ESTFN) and Purchase Intention (PINTN) across Age Groups does not have any significant difference as the significance value is higher than 0.05.

Mean differences related to communicational transactional and relational E-RM features across Education Levels

Table 6. Mean differences among levels of education

 The above table shows the results of T-test applied to determine the differences in antecedents of Website Design (WEBD), Website usability (WEBU), Search and compare capabilities (SRCC), Information Quality (INFQ), Loyalty and Rewards program (LYRP), Responsiveness (RSPNS), Security and Privacy (SCPY), Order Tracking (ORDRT), On Time delivery (OTDL), Customer Service (CSER), Lower Rates (LRATES), Use of Social Media (USMEDIA), E-Satisfaction (ESTFN) and Purchase Intention (PINTN) across Education levels.

The significance value is greater than 0.05 for maximum of the dependent variables across the table. This supports the hypothesis H_03.1, H_03.3, H_03.4, H_03.5, H_03.7, H_03.8, H_03.9, H_03.10, H_03.11, H_03.12, H_03.13 and H_03.14 while the significance value is less than 0.05 for responsiveness. Therefore, null Hypothesis H_03.6 is rejected across Education levels.

It is statistically confirmed through T test that the above antecedents of Website Design (WEBD), Search and compare capabilities (SRCC), Information Quality (INFQ), Loyalty and Rewards program (LYRP), Security and Privacy (SCPY), Order Tracking (ORDRT), On Time delivery (OTDL), Customer Service (CSER), Lower Rates (LRATES), Use of Social Media (USMEDIA), E-Satisfaction (ESTFN) and Purchase Intention (PINTN) across Education levels does not have any significant difference as the significance value is greater than 0.05.

Mean differences related to communicational transactional and relational E-RM features across the respondents’ profession

Table 7. Mean differences among various professions

The above table shows the results of T-test applied to determine the differences in antecedents of Website Design (WEBD), Website usability (WEBU), Search and compare capabilities (SRCC), Information Quality (INFQ), Loyalty and Rewards program (LYRP), Responsiveness (RSPNS), Security and Privacy (SCPY), Order Tracking (ORDRT), On Time delivery (OTDL), Customer Service (CSER), Lower Rates (LRATES), Use of Social Media (USMEDIA), E-Satisfaction (ESTFN) and Purchase Intention (PINTN) across professions.

The significance value is greater than 0.05 for only a few of the dependent variables across the table supporting only the hypothesis H_04.1 H_04.2 H_04.4 H_04.5 H_04.6 H_04.8 H_04.9 H_04.11 H_04.12 while the significance value is less than 0.05 for SRCC, SCPY, OTDL, CSER, ESTFN and PINTN Therefore, Hypothesis H_04.3, H_04.7, H_04.10, H_04.13, H_04.14 is rejected across Education levels. This indicates that consumers from different professions respond differently. It is statistically confirmed through T test that the above antecedents of Website Design (WEBD), Responsiveness (RSPNS), Order Tracking (ORDRT) across professions does not have any significant difference as the Sig. value is higher than 0.05.

Conclusions and Discussion

The objective of the present study is to examine the differences in relationship marketing practices, e-satisfaction and purchase intention across demographic characteristics of the respondents. Present study uses t-test and ANOVA to test the hypotheses.

The results show that Male and female consumers differ in their attitudes with respect to factors like website quality, trust, e-satisfaction and e-loyalty which help in determining the online consumer behavior (Ladhari and Leclerc, 2013). Similar findings have been recorded for the present study which demonstrates the gender differences lies in information quality, security and privacy, and purchase intention. However, gender differences are insignificant for website quality, website usability, search/compare capabilities, lower rates, responsiveness, order tracking, on time delivery, and customer service. Hence the results suggest that the gender differences are partial predictor of relationship marketing and purchase intention.

Previous researches have suggested that the differences in the age of consumers strongly influence the attitude and behavior of the same consumers (Hervé and Mullet, 2009; Hervé et al., 2004; Nussbaum et al., 2000; Adelman et al., 1992). The present study does not support the hypothesis. The study fails to record significant differences in communicational, transactional and relational marketing.

Behavioral attitude of the consumers is influenced by the level of educational qualification of consumers (Sahney et al., 2013; Punj, 2011). This is based on the assumption that as the level of education increases, information, knowledge, exposure also increases which help in shaping the behavioral attitude of the consumers. However, the present study finds insignificant differences across different levels of educational qualification.

Consumer behaviour can be predicted if his/her occupation is known. Several previous studies have documented the variation in consumer behaviour across different types of occupations (Kumar and Kumar, 2019; Jones et al., 2000). The findings of the present study confirm the occupational differences in search/compare capabilities, security/privacy, order delivery, customer service, e-satisfaction, and purchase intention. Insignificant differences have been found across different types of occupations in website design, website usability, information quality, responsiveness, order tracking, order tracking, and loyalty and reward programs. Therefore, it can be summarized that the difference across occupations partially predicts the relationship marketing, e-satisfaction and purchase intention.

Every study possesses few limitations, so does the present study : (a). The study has contextual limitations as its conducted only in Indian set up limiting the socio-economic-demographic background. (b). The study was done with a focus on E-commerce and does not consider the dynamics  of any other sector in India. (c). The study focusses on consumers from Delhi-NCR region and the same may not hold true for other regions in India.

Based on the findings and limitations of the present study, some of the directions for the future research have been ascertained. The findings of the present study can be validated by the future researchers in order to ascertain the effectiveness of the present findings. Future researchers may attempt to understand the online consumer behaviour by applying other methodologies e.g. experimental research methods, mixed methods, longitudinal research method. Researchers may also examine the effect of relationship marketing on actual purchase behaviour.

Since the present study included respondents from a metro city (Delhi), future researchers may seek the online consumer behaviour of the individuals living in suburbs/smaller towns. The future researchers can also use the present model of the research in other instances by the making the study specific to other sectors.