Unveiling the Secrets of Customer Satisfaction  and Loyalty in Online Retail: A Structural Equation Modeling Approach

Abstract

Prior research has been conducted to investigate the impact of digital marketing on customer satisfaction. Many recent studies have shown the future of digital and social media marketing and their impact on businesses. However, scant research reveals the influence of digital marketing dimensions such as website features, information quality on websites and social media, participatory promotional activities to attract or retain customers, cognition behavior of the customers, and their purchase intent on customer satisfaction and loyalty. The investigation demonstrates how crucial website features, information quality, interactivity, participation, cognition, and purchase intent play in boosting customer satisfaction and loyalty. The reliability, validity, and model fit are all checked using principal component analysis, factor analysis, and regression analysis in SPSS, and structure equation modeling is formed in AMOS. The results revealed a positive relationship between these six dimensions and customer satisfaction and loyalty. The results provide practical insights to academics, digital marketing consultants, and online retailers. The study also emphasizes why this association is essential for optimizing benefits in the digital era. By bridging the gap between these variables and their effect on customer satisfaction and loyalty, the research contributed value to unveiling the secrets of customer satisfaction and loyalty in online retail.

Keywords

Digital marketing online retailers customer satisfaction customer loyalty structure equation model

Introduction

Can online retailers achieve customer satisfaction and loyalty by leveraging the power of digital marketing? Without specific tactics, no online retailer can achieve customer satisfaction and loyalty. In the digital age, when technology rapidly advances, online retailers have unparalleled potential to connect with their customers in previously imagined ways. Digital marketing has developed as a potent tool for online businesses to reach their target demographic, boost consumer satisfaction, and promote long-term loyalty (Kingsnorth, 2022). Social media marketing alters customer behavior and corporate operational methods (Dwivedi et al., 2021). Digital marketing is the cornerstone of online retailers’ efforts to meet and surpass growing consumer expectations. In recent years, the arrival of e-commerce platforms and the global presence of internet-enabled devices have altered the shopping experience, yielding consumers extraordinary access to various products and services at their fingertips. Therefore, online retailers build more robust customer bonds by refining a sense of community and belonging, generating trust, loyalty, and advocacy (Bala & Verma, 2018; Smith, 2020). Online retailers can engage with consumers at numerous touchpoints along their purchasing journey using various channels, including social media marketing, email campaigns, search engine optimization (SEO), content marketing, and influencer collaborations. Furthermore, digital marketing empowers constant communication and connection-building between online shops and customers. Social media platforms have evolved as dynamic engagement hubs, allowing brands to communicate with customers in real time, answer inquiries, address problems, and present their products in authentic and appealing ways (Kannan & Li, 2017). This research explains the significance of the association between six dimensions (website features, information quality, interactivity with customers, participatory promotional activities, cognition behavior, and purchase intent) and customer satisfaction.

The research will also deeply examine the association between customer satisfaction and loyalty. Studying consumer behavior in the online retail sector has become crucial in the digital era, as customers are more aware and demanding (Bighiu et al., 2015), behaving as a generation of technology (Indahingwati et al., 2019). As per the report submitted by the Marketing Science Institute (2018), it is increasingly vital to weigh up the roots of loyalty throughout the consumer journey; the engaged customers are the most satisfied with the brand. Following some studies, there is a substantial link between consumer satisfaction and loyalty, as cited by Herhausen et al. (2019), Anderl et al. (2016), Heskett (2002), Bowen (2001), and Pfeifer and Carraway (2000). An increasing number of businesses with happy consumers gain loyal customers, and devoted customers are the company’s greatest asset; they will boost goodwill, which increases sales and ROI. Thus, to keep customers, businesses should prioritize their satisfaction which in turn achieves loyalty (Hueffner, 2023; Millerr, 2021; Zouari & Abdelhedi, 2021).

Research Issues

To supplement the findings, this study crafted the following questions to address the research gaps identified:

Does website features have a significant association with customer satisfaction?

Does information quality on websites and social media have a weighty association with customer satisfaction?

Does interaction have a substantial association with customer satisfaction?

Does participation have a significant association with customer satisfaction?

Does cognition have a significant association with customer satisfaction?

Does purchase intent have a significant association with customer satisfaction?

Does customer satisfaction and loyalty are correlated in online retailing?

Research Objectives

The following are the two primary objectives of this research:

Study the association between six dimensions of digital marketing (website features, information quality, interactivity, participation, cognition, and purchase intent) on customer satisfaction.

Study the association between customer satisfaction and loyalty in the online retail sector.

Literature Review and Hypothesis Development

Online Retail

Research on online commerce from business to consumer began after the internet was introduced worldwide (Chen et al., 2002). E-commerce platforms and the growth of social media upsurge the scope of e-retailing in India (Hyun et al., 2022). The internet’s interactive nature offers buyers many opportunities for online shopping (Alba et al., 1997) and enhances the eWOM or electronic word-of-mouth. Digital media provide tremendous opportunities for retailers to exploit internet offerings for success in the virtual market (Ajzen et al., 1985). The proliferation of Web 2.0 shoots up eWOM to influence customers’ purchasing decisions and affects customer satisfaction and loyalty (Lee et al., 2011). Customers can readily gather information about goods and services from a vast, geographically distributed population that has purchased and utilized potential goods or services. Online retailers need help retaining and developing a loyal consumer base in the digital age. Some research is required to outline the strategies for ensuring consumer satisfaction and loyalty in online retail. This suggests the necessity for additional research investigations to examine.

Website Features

A website is where a company puts all kinds of information and offers their products/services, and customers interact with them on the World Wide Web. Website features form an interaction between humans and computers. (Ahn et al., 2007) asserts that the quality of the website features crater user experiences. According to Bilgihan and Bujisic (2015), website features are essential for online relationships between customers and marketers. A unique feature of web designs helps customers to make purchase decisions. It should be easily navigated and convenient for online customers (Aladwani & Palvia, 2002; Huizingh, 2000). Grange and Benbasat (2010) stated that technical features are responsible for user experiences and are a competitive advantage for companies. Tzeng (2021) stated that websites that deliver quality information about products or services positively lead to consumer satisfaction. To enhance the user experience, a website’s design should be aesthetically pleasing and intuitive. Easy-to-use interfaces and clear navigation encourage positive website interactions, increasing customer satisfaction, as cited by Cha (2009), Liu and Arnett (2000), and Teo and Tan (1998). Online retailers can create a seamless and engaging shopping environment that fosters trust and loyalty by optimizing website features to cater to the needs and preferences of customers. Hence, the following hypothesis was adapted based on the literature:

H₁: Website features have a positive association with customer satisfaction.

Information Quality

Customers’ preferences are always based on the information they understand about the product or service (Han, 2020). Shodiq et al. (2018) emphasized the importance of information quality in satisfying customers in online retail. He clearly stated that the web design and the quality of information on the website affect customer satisfaction. According to Jun and Kang (2013), all apparatuses of information quality, except pricing information quality, were exposed to be strongly connected to consumer satisfaction. Customers today do not shop with just one click; they also read and evaluate all the information available on the company’s websites, whether it is price or the rankings from former buyers. Completely relevant, timely, and consistently formatted information displayed on a website or mobile application of goods or services is known as “information quality”, as cited by Noori-daryan (2023) and Delone (2003). Customer reviews are the main component of purchase decisions (Tan et al., 2018). According to Han et al. (2007), low-quality content on a company’s website or app can lead to client dissatisfaction and decreased sales. Information is a decisive factor in the customer buying decision process, as cited by Chen and Schwartz (2006). This will add importance to information quality dimensions for assessing customer satisfaction in online retail.

H₂: Information quality on websites and social media has a weighty association with customer satisfaction.

Interactivity

Falkner et al. (2011) stated that interaction is a must-spice for engaging customers since traditional marketing. Internet skyrocketed eWOM techniques in the retailing sector. Social media and the internet allow customers to publicize their opinions about products or services (Chen et al., 2011). Comments, reviews, ratings, and scoring stars all come under “interactivity” (Kang et al., 2021). Interaction spreads positive feelings in the hearts of customers, which leads to customer satisfaction (Bickart & Schindler, 2001), whereas fake reviews or news spread negativity about products and brands, which will hinder the customer loyalty journey (Baccarella et al., 2018; Di Domenico et al., 2021). Mende et al. (2013) asserted that attachment to customers is always positively significant in customer–firm relationships. According to Bonaretti et al. (2020), IT-enabled technology such as chatbots and social media bots helps companies interact with visitors and make them engage customers. Literature indicates the impact of interactivity on customer satisfaction. Therefore, this study will use the following hypothesis statement for further analysis.

H₃: Interactivity has a significant association with customer satisfaction.

Participation

Jiang et al. (2019) asserted that participating customers are more satisfied as they become familiar with the brand, products, or services. Customer participation is a crucial aspect of promotional activities and online retail. It involves the time, commitment, and decision-making processes that customers invest in to enhance their experience and create a sense of ownership. This participation also contributes significantly to improving customer satisfaction levels (Chiu et al., 2005). Increasing client participation in the value-creating phase allows them to devote their efforts to co-creating the services (Bagherzadeh et al., 2020). Social customer involvement, adopted by most firms, has been regarded as an effective customer engagement technique (Asokan Ajitha et al., 2019). Literature represents the evidence that customer participation in promotional activities has much impact on customer engagement, cited by Sadighha et al. (2024), Hong et al. (2023), and Cui and Meng (2021). The following hypothesis was developed:

H₄: Participation has a substantial association with customer satisfaction.

Cognition

Many Research studies recognize the importance of cognition factors in customer buying behavior research (Blumenthal, 1977; Herzog, 1984). According to Dabholkar (1996), cognitive styles are essential for understanding the critical aspects of how consumers receive information. Simon and Usunier (2007) suggested that cognitive, demographic, and environmental factors influence customer service preferences in the digital arena. Cognition is a very well-known factor that impacts customer satisfaction, as cited by Le et al. (2022), Tifferet and Herstein (2012), and Luo et al. (2012). Cognition and consumer happiness are inextricably linked; hence, the following hypothesis:

H₅: Cognition has a significant association with customer satisfaction.

Purchase Intent

Arya et al. (2019) emphasize remarketing for brand experiences. Customers who want to buy frequently from the brand it means that they are satisfied and loyal customers. According to Cretu and Brodie (2007), company reputation plays a vital role in making intention to buy from the company, whereas Gensler et al. (2013) stated that social media influencers play a significant role in making intention within customers. Maximum empirical studies have presented customer satisfaction as having an optimistic association with purchase intent (Baumann et al., 2018; Kim et al., 2024; Rather & Hollebeek, 2021). The following hypothesis was adopted:

H₆: Purchase intent has a significant association with customer satisfaction.

Customer Satisfaction and Loyalty

The main instruments for success in the digital arena are higher conversion rates (Gopalakrishna et al., 2022) and higher customer satisfaction rates (Kim et al., 2024). Online retailer’s strategic planners, marketing consultants, and practitioners increasingly focus on “customer satisfaction” (Fornell, 1992; Kotler, 2000) in creating customer loyalty and value (Brodie et al., 2011; Chen et. al, 2002). Bowden (2009) suggested that engaged customers are loyal customers. Various research studies have successfully stated that there is a significant association between customer satisfaction and customer loyalty (Ball & Tasaki, 1992; Bansal et al., 2004; Biyalogorsky et al., 2001; Van Doorn et al., 2010). Thus, the following hypothesis was developed:

H₇: Customer satisfaction and loyalty are significantly correlated in online retail.

Methodology

A quantitative method is utilized to examine the association among the different factors. A planned questionnaire was sent out to the people of Rohtak to gather first-hand input. The questionnaire was divided into two sections: The first section enquired about the demographic features, and the second section queried several claims, each using a Likert scale of 1–7 (“1” denotes Strongly Agree and “7” denotes Strongly Disagree). As per the sample size formula given by Krejcie (1970), 550 questionnaires were distributed in Rohtak city, and 370 of them were submitted entirely, which implies the response rate is 67%; 80 of them were rejected as either incomplete or incorrectly completed. Therefore, the sample size is 370, far higher than the required threshold of 200 for developing the structural equation model (Hoogland, 1999). Primary sources provide the data used in this investigation. Items for measuring customer satisfaction (Bassi & Guido, 2022; Fornell, 1992; ‌Kotler, 2000; Lin et al., 2011; Wirtz & ‌Lee, 2016), customer loyalty (Herhausen et al., 2019; Söderlund, 2006), website features (Abbaspour & HazarinaHashim, 2015), information quality (Park & Kim, 2003), interactivity (Blasco-Arcas, 2016; Jan et al., 2020), participation (Bazrkar et al., 2021; Chae & Ko, 2016; Khajeheian & Ebrahimi, 2021), cognition (Da Silva & Alwi, 2006), and purchase intent (Banu et al., 2014; Muzakir, 2018) were adapted from previous studies.

Data Analysis and Findings

Demographic Analysis

The demographic analysis is essential to know about the respondents. It can be determined by the analysis what type of respondents have opinions on the research hypothesis statement.

Table 1 represents that female respondents (237) participated more than male respondents (133). Regarding age, most responders (133) were 29–39 years old, with the remaining (90) beyond the age of 50. In this study, unmarried respondents (227) outnumber married respondents (143). Most respondents (190) have a postgraduate degree and (72) are either professionals or business owners. Overall, 110 respondents’ monthly income is between ₹20,000 and ₹30,000.

Table 1.

Demographic Profile of the Respondents.

Variable	Categories	Frequency	Percentage (%)
Age	18–28	104	28.1
	29–39	133	35.9
	40–50	43	11.6
	>50	90	24.3
	Total	370	100
Marital status	Married	143	38.6
	Unmarried	227	61.4
	Total	370	100
Gender	Male	133	35.9
	Female	237	64.1
	Total	370	100
Education	UG	108	29.2
	PG	190	51.4
	Doctorate/Professional	72	19.5
	Total	370	100
Occupation	Salaried	83	22.4
	Professional	131	35.4
	Businessperson	105	28.4
	Other	51	13.8
	Total	370	100
Monthly Income (INR)	<20,000 PM	101	27.3
	20,001–30,000	110	29.7
	30,001–40,000	80	21.6
	40,001–50,000	52	14.1
	>50,000	27	7.3
	Total	370	100

Figure 1 represents the hypothesized model of this study. Respondents have a favorable attitude toward online shopping, as most respondents prefer to shop 4–5 times a month (see Figure 2).

Figure 1.

Hypothesized Model.

Figure 2.

Source: Primary data.

Reliability Test

The reliability statistics show the internal consistency of the variables in the study. Cronbach alpha values greater than 0.70 are recommended; this value always indicates data acceptance and leads to further analysis (DeVellis & Thorpe, 2021; Kline, 2005). Table 2 shows the reliability test results for six items to measure the stated constructs accurately, and the aggregate data is likewise dependable, with an alpha value of 0.848, which is greater than the threshold limit of 0.7.

Table 2.

Cronbach’s Alpha Results.

	Cronbach’s Alpha	Remarks
Overall reliability	0.848	Extremely reliable
Website feature	0.782	Reliable
Information quality	0.782	Reliable
Interactivity	0.885	Extremely reliable
Cognition	0.795	Reliable
Participation	0.879	Extremely reliable
Purchase intent	0.885	Extremely reliable
Customer satisfaction	0.782	Reliable
Customer loyalty	0.782	Reliable

Model Fit Analysis

Before forming the model, it becomes necessary to calculate the pre-requisite statistics to obtain results that are close to the recommended results given by the statisticians. Table 3 discloses the model fit index detected value; all six items are within the threshold limits to develop a structure equation model.

Table 3.

Measurement Model Fit.

Model Fit  Index	Threshold  Limit	Detected  Value	Remarks	Advocated by
Chi-square statistics	<5	3.883	Accepted because the detected value is greater than the threshold limit	Tabachinck (2013)
GFI	>0.80	0.908	Accepted because the detected value is greater than the threshold limit	Hair (2010)
AGFI	>0.80	0.88	Accepted because the detected value is greater than the threshold limit	Hair (2010)
CFI	>0.90	0.93	Accepted because the detected value is greater than the threshold limit	Carmines and McIver (1981)

To achieve the first objective of this study, Pearson correlation techniques were employed to analyze the association between the six dimensions of customer satisfaction. Table 4 shows that customer satisfaction is approvingly correlated with “website features” (0.699), “information quality” (0.658), and “cognition” (0.642). This suggests that three dimensions (website features, information quality, and cognition behavior) affect customer satisfaction in online retail.

Table 4.

Association Between Six Dimensions of Digital Marketing on Customer Satisfaction.

		Customer Satisfaction
N		370
Website feature	Correlation value	0.699
Information quality	Correlation value	0.658
Interactivity	Correlation value	0.538
Cognition	Correlation value	0.642
Participation	Correlation value	0.512
Purchase intent	Correlation value	0.231

Note: The Pearson correlation technique was significant at 0.01 level (2-tailed).

Table 5 indicates that customer satisfaction is highly correlated with customer loyalty by employed Pearson correlation is significant at a 0.01 level (2-tailed).

Table 5.

Association Between Customer Satisfaction and Customer Loyalty.

		Customer Loyalty
Customer satisfaction	Pearson correlation	0.698
Customer satisfaction	N	370

The hypothesis was evaluated by putting regression weights. Table 6 reveals that an additional one unit of “website features” increases customer satisfaction by 0.64 units, followed by information quality, which increases customer satisfaction by 0.60. Whereas cognition increases customer satisfaction by 0.59, all are statistically proven at a p value of .001. It was also found that the r-value of customer satisfaction is 0.66, which is also significant at 0.001, which means customer satisfaction is highly correlated with customer loyalty (refer Figure 3).

Table 6.

Regression Analysis.

Dependent Variable	Independent Variable	R	R²	Adjusted r²	f	p Value
Customer satisfaction	Website features	0.64	0.38	0.35	93.5	<.001
Customer satisfaction	Information quality	0.60	0.33	0.31	92.2	<.001
Customer satisfaction	Interactivity	0.58	0.26	0.23	78.6	<.001
Customer satisfaction	Participation	0.47	0.24	0.21	77.3	<.001
Customer satisfaction	Cognition	0.59	0.30	0.29	87.2	<.001
Customer satisfaction	Purchase intent	0.46	0.25	0.20	56.4	<.001
Customer loyalty	Customer satisfaction	0.66	0.40	0.48	91.1	<.001

Figure 3.

Association Molding of Six Dimensions of Digital Marketing with Customer Satisfaction and Loyalty.

Conclusion

The study strongly supports the hypothesized relationship mode among six digital marketing dimensions, customer satisfaction, and loyalty (Figure 3). This study tries to add a new paradigm, as there has been negligible research in this area, by examining the impact of digital marketing dimensions adopted by online retailers on customer satisfaction and loyalty. The study established seven hypotheses in this regard, and the discoveries show that all the hypotheses are supported by output from data analysis.

Outcomes indicated that website features, information quality, and cognition are the weightiest variables, followed by interactivity, participation, and purchase intent in achieving customer satisfaction, and customer satisfaction substantially impacts customer loyalty. Happiness among customers is critical to building consumer loyalty. This aligns with research indicating that satisfied customers are more likely to continue patronizing a business, recommend it to others, and become loyal brand advocates (refer Table 7).

Table 7.

Hypothesis Results.

Hypothesis	Results	Reason
H₁: Website features have a positive association with customer satisfaction.	Supported	Regression and correlation coefficients are strong and positive.
H₂: Information quality on websites and social media has a weighty association with customer satisfaction.	Supported	Regression and correlation coefficients are strong and positive.
H₃: Interactivity has a significant association with customer satisfaction.	Supported	The correlation and regression coefficients are positive but not very strong.
H₄: Participation has a substantial association with customer satisfaction.	Supported	The correlation and regression coefficients are positive but not very strong.
H₅: Cognition has a significant association with customer satisfaction.	Supported	Regression and correlation coefficients are strong and positive.
H₆: Purchase intent has a significant association with customer satisfaction.	Supported	The correlation and regression coefficients are positive but not very strong.
H₇: Customer satisfaction and loyalty are significantly correlated in online retail.	Supported	Both regression and correlation analysis show positive results.

The study’s empirical findings indicate crucial insights that the design and functionality of online retail websites serve as the digital storefront’s gateway, establishing visitors’ first impressions and experiences. Providing accurate and trustworthy information to online shoppers is crucial for building trust and confidence in a company. This information can range from product descriptions and specifications to customer reviews and ratings. Customers with access to high-quality information can make informed purchasing decisions, increasing satisfaction and loyalty. This work has presented a novel approach to use concepts from cognitive psychology and behavioral economics to create convincing messages and incentives that appeal to customers’ underlying motivations and preferences, ultimately increasing buy intent and loyalty. By optimizing various variables and matching them with customer wants and expectations, online retailers may boost conversion rates and maximize the return on their digital marketing spending.

Practical Implication

This study covered many grounds in digital marketing and its impact on customer satisfaction, which will help marketers better understand this relationship model. Marketers may benefit from this study by noting that pleased customers are likelier to share their great experiences with friends and family, resulting in new customers and increased revenue. It increases trust and credibility. Customers develop confidence in a brand and its products or services after having a positive experience. This increases their likelihood of remaining loyal over time.

The study’s findings highlight the importance of focusing on customer satisfaction to build strong relationships with consumers and foster long-term loyalty to a business. Strategic planners, marketers, and online retailers must also understand that website features, information quality, and cognition can be directly applied to improve various aspects of the customer experience. Based on the findings of this study, if online retailers enhance their website design functionality and convenience by reducing fake reviews, improving interactivity, and drawing cognition needs, they can achieve customer satisfaction. Features on websites are essential for increasing client loyalty and happiness. Businesses should prioritize user flows, conduct user testing, optimize for various devices, and pay close attention to content quality to enhance the customer experience. Generating buyer profiles, integrating customer feedback systems, and tailoring product suggestions, marketing, and CRM can be improved. Businesses may develop user-centric websites with high-quality information by tracking essential metrics, doing A/B tests, and iteratively improving. This will ultimately increase customer satisfaction, loyalty, and business success. Businesses can use research findings to create user-centric websites with high-quality information by implementing these ideas. Additionally, businesses should prioritize customer satisfaction, which is crucial for building strong relationships that lead to long-term commitment to a brand. Future research should consider the potential effects of more dimensions of digital marketing on customer satisfaction and loyalty.

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.

Supplemental material

ORCID iD

Monika Sehgal

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Supplementary Material

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Unveiling the Secrets of Customer Satisfaction and Loyalty in Online Retail: A Structural Equation Modeling Approach

Abstract

Keywords

Introduction

Research Issues

Research Objectives

Literature Review and Hypothesis Development

Online Retail

Website Features

Information Quality

Interactivity

Participation

Cognition

Purchase Intent

Customer Satisfaction and Loyalty

Methodology

Data Analysis and Findings

Demographic Analysis

Demographic Profile of the Respondents.

Hypothesized Model.

Reliability Test

Cronbach’s Alpha Results.

Model Fit Analysis

Measurement Model Fit.

Association Between Six Dimensions of Digital Marketing on Customer Satisfaction.

Association Between Customer Satisfaction and Customer Loyalty.

Regression Analysis.

Association Molding of Six Dimensions of Digital Marketing with Customer Satisfaction and Loyalty.

Conclusion

Hypothesis Results.

Practical Implication

Footnotes

Declaration of Conflicting Interests

Funding

Supplemental material

ORCID iD

References

Supplementary Material

Unveiling the Secrets of Customer Satisfaction  and Loyalty in Online Retail: A Structural Equation Modeling Approach