Editorial Board

A Refereed Monthly International Journal of Management

Factors Affecting Purchase Intention through Online Shopping Sites During Covid-19 Pandemic: The Moderating Role of Gender

 

Dr. M. Gurupandi

Associate Professor,

Department of Commerce,

Alagappa University, Karaikudi, Tamilnadu, India

 

Dr. D David Winster Praveenraj

Assistant Professor,

School of Business and Management,

Christ University, Bangalore, India

 

Dr. Shrinivas K

Assistant Professor,

School of Business and Management,

Christ University, Bangalore, India

 

Dr. R.S.V. Rama Swathi

Assistant Professor,

Business School, Koneru Lakshmaiah

Education Foundation,

Vaddeswaram, A.P., India

 

Dr. Vinayak Vishwakarma

Assistant Professor,

Department of Operations and Decision Sciences,

Jaipuria Institute of Management,

Indore, Madhya Pradesh, India

 

Abstract

This research seeks to answer the questions of what influences consumers' online shopping behaviour and whether or not demographic variable (Gender) played a role during the Covid-19 pandemic. Seven variables were examined for their potential impact on consumers' propensity to make purchases through e-commerce websites. Using regression analysis, we looked at how seven variables affected consumers' propensity to make purchases through e-commerce websites.Proof of 'Perceived benefits' (PB), 'Perceived ease of use' (PEU), 'Structural Assurance' (SA), 'Social Influence' (SI), and 'Trust' is provided in the study. This suggests that a buyer's preference for a particular purchasing behaviour can be affected by how much they like shopping online. The results of the research confirm that the theoretical model is applicable to online shoppers' intentions to make a purchase. This aids relevant scholars and stakeholders in their pursuit of an understanding of customer behaviour under a given set of circumstances.

Keywords: Online shopping, Gender as Moderator, COVID-19, Purchase Intention

Introduction

Many economies in the globe have embraced the internet as a modern convenience in the 21st century. Consumers' spending habits shifted dramatically during the COVID-19 pandemic, impacting industries as diverse as health care and retail (Dionysiou et al., 2021; Bazi et al., 2022). In 2020 and 2021, the global economy slowed significantly due to trade restrictions enacted by several nations, which promoted the expansion of the e-commerce sector (Shareef et al., 2021; Modgil et al., 2022).E-commerce's meteoric rise has made it one of the most popular methods for acquiring goods and services, even among those who don't often shop this way (Nicewicz & Bilska, 2021; Kawasaki et al., 2022).

The growth of the global e-commerce business has been phenomenal, and the impact of the World Wide Web (www) on people's perspectives and daily lives has been nothing short of amazing. These days, it's easier than ever to do your shopping online because ofwider availability of the internet, fasterconnections, and cheaper smartphones(Bhatt, 2019). Customers can find a better price on the items they need, have access to a wider selection than they would in a physical store, and save time overall (Rahman et al., 2018; Bhatt, 2019). In the two years leading up to the COVID-19 outbreak, the pandemic affected consumer behaviour, resulting in a surge in online purchases. Many buyers have also altered their routines and habits to make more use of internet stores. Market mobility has been replaced by the convenience of staying at home as a result of the lockdown, social distancing, and varied purchase options. Today's shoppers have greater variety and flexibility, and they are no longer restricted by rigid store hours.

The percentage of consumers who shop online is still lower than in many countries around the world (Ministry of Industry and Trade, 2015), despite the fact that it is becoming increasingly common and widespread around the world (Wu et al., 2011). In the context of the expansion of high-speed internet and e-commerce, it is important to understand the elements that affect consumers' decisions to shop online (Lohse et al., 2000). Researchers are now more interested than ever in learning what makes consumers decide to make online purchases than ever before thanks to the explosion of e-commerce.

The aim of the present paper is to investigate the causes and factors that are responsible for influencing consumers' shopping habits.

Literature Review

Consumers' purchase decisions and online shopping habits have been affected by the COVID-19 making use of technology developments (Mason et al., 2020; Sheth 2020a, 2020b). In the context of modern purchasing habits, demographic factors like income, age, and occupation were significant (Valaskova et al., 2021).

Many studies across the globe have proposed various concepts and frameworks that have been theoretically and experimentally verified in an effort to shed light on the motivations of online shoppers. These studies provided explanations for consumer behaviour across multiple dimensions, including: normative and behavioural beliefs about technology adoption (Foucault and Scheufele, 2002).During the course of these studies, a number of elements were uncovered that had the ability to influence the choices and actions of consumers (Sheth, 2020b).

The first factor was government policy, which advocated for consumers to use the internet mode during the pandemic as they were subjected to the mandatory restricted movements taken during pandemics. The second important factor was the changing demographics (Sheth and Sisodia, 1999), which showed that while retirees were concerned about their health; young professionals were more likely to shop online. Finally, technology was the third factor. Electronic commerce and the growth of the internet are just two examples of how society has changed as a result of people's willingness to adopt new technologies (Sánchez-Torres and Juarez-Acosta, 2019). Each of these elements played a critical role in inspiring novel patterns of online purchasing.

Although many industries might benefit from the adoption and influence of technology in generating online purchase intent, this is not the case for several reasons (Habib &Hamadneh, 2021). That's why it's so important to learn what customers worry about most when making a decision about whether or not to use e-commerce to satisfy their requirements. Therefore, it is important to zero in on supplementary elements that may define consumers' intentions and actions when shopping online. In response to these calls for research, the current study presents a multi-factor framework for understanding how the pandemic affected people's propensity to shop online. This study's findings would be beneficial for individuals in academia, marketing and advertising, government, and business.

To begin, we examined several factors that are known to influence consumers' likelihood of making purchases through electronic commerce platforms.

Perceived Benefits (PB)

The concept of PBhas been clearly distinguished in numerous studies aimed at boosting consumer spending (Wang et al., 2013). After completing a task, consumers typically gain tangible benefits from the use of a product or service (Kim, 2002). Perceived benefits have been shown to have a significant beneficial effect on online consumer behaviour (Bangkit et al. 2022).

In addition, the most important factor influencing the customer's behaviour intention is the customer's agreement of opinion with the information and attributes offered about the product by a website (Jeong et al. 2003). Moreover, Bai et al. (2008), and Teo (2006) studied the impact of improving website quality, functionality, online usability, and security on customer satisfaction and found positive results.

Jhamb and Gupta, (2016) also highlighted the fact that consumers search for deal evaluation, price comparisons and reviews, the latest fashion trends and brand awareness, variety selection, and product features before going for internet shopping. When purchasing goods online, customers have a number of other expectations, including a dependable supply chain, speedy delivery, simple vendor contact, and hassle-free return transaction policies (Teo, 2006; Min, 2024; Na, 2024). So, we hypothesize

H1: Perceived benefits (PB) havea strong impact on online purchase intention (OPI).

 

Perceived Ease of Use (PEU)

According to Davis (1989, 1993), PEU measures how simple a user thinks a system is to operate. According to Al-Azzam and Fattah (2014), "perceived ease of use" describes a consumer's impression that navigating and making purchases via a website is simple and quick.

Besides its importance in getting people to adopt new technologies, "ease of use" also plays a big factor in whether or not they'll enjoy making purchases on a website (Monsuwe et al., 2004).The likelihood that a consumer will adopt a system while shopping online is higher if doing so requires little to no work on their part (Xia et al., 2008).Users with a lot of online buying expertise benefit greatly from fast connections and easy availability outside of peak shopping times (Cho &Sagynov, 2015). The "PEU" of a website is mostly determined by its qualities (Zeithmal et al., 2002), although there has been just a handful of additional research on the topic.The results of the study also imply that consumers' behavioural intent to shop online is affected by how well they adapt to the system's specific ease of use and how much they reflect on their engagement with the system after several uses.

Consumers value online grocery shopping more if they believe it would save them time and money compared to traditional grocery stores (Yip Hing&NyenVui, 2021). It makes sense to factor in how helpful customers think a service is when determining whether or not it's worth investing in (Manu and Fuad 2022). This leads to our second hypothesis:

H2: Perceived ease of use (PEU) has a strong impact on online purchase intention (OPI).

 

 

Perceived Enjoyment (PE)

Numerous studies, both theoretical and empirical, have demonstrated the importance of intrinsic motivation in e-commerce (Venkatesh and Speier, 1999). They have described the ways in which a consumer finds internet buying entertaining.

The perceived enjoyment was first introduced by Davis et al. (1992) and has been used as a construct of behavioural intention in numerous studies (Koufaris, 2002; Bangkit et al., 2022). It has been observed people are more likely to enjoy themselves while online shopping if they are actively engaged in the process and have a strong sense of personal motivation.

Exploration and curiosity to utilise a system, another latent indicator, is also prevalent when studying the online purchasing setting in addition to reported satisfaction and intrinsic incentives. Teo (2002) reports on empirical research that links online surfing interest to an interest in learning about products and brands that may be purchased online. Half of the people in Teo's study looked around even if they weren't planning to buy anything. So, our third hypothesis would be:

H3: Perceived enjoyment (PE) has a strong impact on online purchase intention (OPI).

 

Structural Assurance (SA)

Given the increased risk of information interceptions and hacking with mobile banking and online banking, the term "structural assurance" is used to describe the presence of legal and technological frameworks that are meant to guarantee security during online payment.

Users are understandably concerned about the security of their accounts and the ability to make financial transactions when using mobile networks, as stated by Zhou (2011). Yousuf (2018) claims that e-payment makes up a small percentage of e-commerce transactions, whereas cash on delivery (COD) accounts for the vast majority. Consumers' spending habits in e-markets can be affected by their faith in payment systems, the unpredictability of security and privacy concerns, and the prevalence of cybercrimes (Pang et al., 2016). However, in light of the recent COVID-19 pandemic, Pollak et al. (2022) claimed that markets have quickly adapted to substandard conditions, suggesting that distinct shifts in consumer behaviour and preferences have been observed during times of disaster. Our forth hypothesis is:

H4:Structural Assurance (SA) has strong impact on online purchase intention (OPI).

 

Social Influence (SI)

The term "social influence" was coined by sociologist B. F. Friedkin (2011) to describe the process through which an individual's attitudes and actions are modified to accommodate the expectations of their peers. Intentional or unintended, one person can alter the actions of another by the use of social influence, social variables, subjective norms, or social norms.

Social pressure causes individuals to adjust their thoughts and deeds in order to conform to the norms of a society (Mei & Aun, 2019), and this has been cited by numerous studies as a major element in consumer behaviour and intentions. Wei et al. (2009), citing Rogers' (1995) theory of social influence, also noted that this theory has two components: media and personal relationships along with Mass media or external influence and interpersonal influence (Bhattacherjee, 2000; Pietro et al., 2012).

Additional insight into the impact of social norms and networks on consumers is provided by Kuswanto et al. (2019), respectively, given that an individual's reference group, in this case "friends," can have a significant impact on their actions in particular contexts. According to Qiu and Benbasat (2005) and Herrando et al. (2021), many websites now feature social presence technologies that allow users to communicate with one another, such as a chat window where trained professionals can answer questions and shape users' perceptions. Customers' participation and socialisation in online purchasing been shown to improve their experience and their perception of its social benefits (Cui et al., 2013; Hassanein and Head, 2007). Our fifth hypothesis is:

H5: Social Influence (SI) hasstrong impact on online purchase intention (OPI).

 

Trust

Trust in the brand and online supplier influences consumer's purchase intentions and product consumption, according to studies on consumer online shopping behaviour (Chinomona&Sandada, 2013; Dabholkar & Sheng, 2012). One of the most important criteria that determines whether or not a customer would make a purchase online is trust. One of the primary reasons why people don't shop online is because they don't trust the websites they see (Lee, & Turban, 2001; Yadav, & Mahara, 2017). Online business won't take place until customers have faith in the site (Winch & Joyce, 2006). Due to the fact that consumers cannot investigate the legitimacy of sellers or the quality of products before making purchases online, so, when it comes to making purchases online, trust is crucial. (Pavlou, 2003). So our sixth hypothesis would be:

H6:Trust hasstrong impact on online purchase intention (OPI).

 

Perceived Risk (PR)

According to research by Chang & Chen (2008), consumers' perceptions of security threats can discourage them from making purchases online. When consumers shop online and make payments electronically, they may be exposed to such dangers. Yenisey, O zok, and Salvendy (2005) argue that this aspect is irrelevant to the likelihood that a consumer will make an online purchase because of the associated financial and product risks (Bauer, 1960).

Since there are several dangers associated with online transactions, knowing how customers feel about the risks is crucial in making these kinds of purchases (Bhattacherjee, 2000; Ha & Nguyen, 2016). Financial, security, privacy and seller risk are the four broad categories used by Pavlou (2003) to describe potential negative outcomes. A number of research (Du & Mao, 2018) has discovered an inverse association between perceived risk & desire to buy online. Our seventh hypothesis would be:

H7: Perceived risk (PR) hasstrong impact on online purchase intention (OPI).

 

Gender as Moderating Variable

Gender is a demographic variable that has been accounted for in a variety of marketing and consumer behaviour research. Yousafzai and Yani-de-Soriano (2012) claim that men have a more robust and noticeable impact on PB and behavioural intention to use, while women have a more robust and noticeable impact on PEU and OPI, demonstrating the importance of gender in shaping attitudes towards and adoption of technological innovations. Venkatesh et al. (2003) corroborated the notion that men are more likely than women to be comfortable using computers and to embrace new technologies in order to take advantage of the conveniences of online shopping.Several studies (Zhang, 2005) support this theory.

On the other hand, a consumer's age can have a significant effect on their actions related to the adoption of a technology (Molinillo et al., 2021; Kusuma, 2022). In this regard, some research has found a positive correlation between a consumer's age and the likelihood that they will make an online purchase. Individuals with higher levels of education will be exposed to technologies and will acquire a greater inclination towards them (S'anchez-Torres et al., 2017), and this has been the subject of research on consumer behaviour in the context of technology (Ou, 2007).

Current research goals anticipate gender to play a moderating role in the study. Venkatesh, Thong, and Xu (2012) and Kanwal et al. (2021) have conducted detailed reviews of the literature on the moderating effect of gender on technology adoption and consumer purchasing intentions.It was found by Venkatesh and Zhang (2010) that younger males greatly moderated the notion of performance expectancy in the behavioural intention of information technology, while older female customers strongly moderated the idea of effort expectancy. Liébana et. Al. (2014), on the other hand, showed that customers older than 35 were more influenced by social influence, whereas consumers under 35 were more influenced by trust. Females regulated personalization and security of trust more than males, according to Shao et al. (2019), who also stated that males moderated mobility and reputation in the trust-formation process of mobile payments. In addition, Pascualet.al. (2015) discovered that women's effort expectancy and social influence moderated online purchasing intent more so than men's.

H8: Perceived benefits (PB) by male customers have astrong impact on OPI.

H9: Perceived benefits (PB) by female customers have astrong impact on OPI.

H10: Perceived Ease of Use (PEU) by male customers has a strong impact on OPI.

H11: Perceived Ease of Use (PEU) by female customers has a strong impact on OPI.

H12: Perceived Enjoyment (PE) by male customers has a strong impact on OPI.

H13: Perceived Enjoyment (PE) by female customers has a strong impact on OPI.

H14: Structural Assurance (SA) as Perceived by male customers has a strong impact on OPI.

H15: Structural Assurance (SA) as Perceived by female customers has a strong impact on OPI.

H16:Social Influence (SI) as Perceived by male customers has a strong impact on OPI.

H17: Social Influence (SI) as Perceived by female customers has a strong impact on OPI.

H18: Trust as Perceived by male customers has a have a strong impact on OPI.

H19: Trust as Perceived by female customers has a strong impact on OPI.

H20: Perceived Risk (PR) as Perceived by male customers has a strong impact on OPI.

H21: Perceived Risk (PR) as Perceived by female customers has a strong impact on OPI.

 

Research Objectives

The present study proposes to scrutinize the correlation of the effect of gender, in the context of online purchase intentions, via online shopping sites, during the COVID-19 pandemic.

The foremost objectives of the study are:

• To recognize the factors influencing online purchase intentions.
• To recommend an abstract model assessing the association among all the factors taken into consideration and online purchase intention, with the moderating effect of gender.
• To undertake an empirical analysis of the projected model of assessing the relationship among online purchases and factors considered.

 

 

 

Conceptual Model

The proposed model includes seven influencing factors which are as follows: Perceived Benefits (PB), Perceived Ease of Use (PEU), Perceived Enjoyment (PE), Structural Assurance (SA), Social Influence (SI), Trust and Perceived Risk (PR).

The study also examines the association between consumer’s demographic characteristic (Gender)as moderating variable between seven influencing factors and online shopping behaviours.

Figure 1: Frameworkshowingthe relationship between all the factors and online purchase intention, with the overall effect of gender and age.

 

Research Methodology

Study Design:A pre-structured questionnaire was used to conduct a survey. Data was collected through snowball or convenience sampling from those who made online purchases.

The questionnaire used in the study consists of two sections: The first section includes a breakdown of the respondents' demographics, while the second contains statements about the factors that influence customers' propensity to make purchases online. The statements were evaluated using a five-point Likert scale, from "strongly agree" (1) to "strongly disagree" (5).

Sample: A total of 400 questionnaires were distributed of which 358 were returned with no blanks or obvious mistakes. A response rate of 89.5% was obtained after careful examination, which is excellent.

 

Data Analysis:

For the analysis of data, SPSS software version 20 was used. The data was analysed using a regression analysis to statistically verify the model assumption and establish the hypothesised connections between the variables. The demographics of the respondents were analysed using frequency and percentages derived from descriptive statistics. Cronbach's Alpha was used to establish the trustworthiness of the survey. Factor analysis is used in the process of checking for validity.

Demographic Profile of the Respondents

The descriptive statistics provide an in-depth look at the 358 respondents (male and female) that made up the final sample.

Table1.DescriptiveStatisticsofDemographicProfile

		Frequency	Valid%
Gender profile	Male	234	65.4
	Female	124	34.6
Age profile	21-29 years	48	13.4
	30-39 years	105	29.3
	40-49 years	67	18.7
	49-55 years	86	24.0
	60 years and older	52	14.5
Highest education level	Bachelor degree	44	12.3
	Master degree	96	26.8
	Professional Education	152	42.5
	Other	66	18.4
Working Experience	1-2 years	80	22.3
	3-5 years	125	35
	6-10 years	131	36.6
	11+ years	22	6.1

Table 1 displays the percentage of gender, age, education, designation and Length of affiliation in this field study. The respondents' gender distribution reveals that there are somewhat more men (65.4%) than women (34.6) among them. The age of the sample ranges from 21years to 60 years. Maximum respondents were of the age range of 21 to 39 years (42.7%) followed by the age group of 40-49 years as 18.7% and 49-55 years of age group as 24.0%. The highest education level of about 69.3 % of respondents was a Master's degree or Professional education while 12.3% of the respondents had a bachelor's degree as their highest education level.  About 93.8% of them had an experience of less than or equal to 10 years.

ExploratoryFactorAnalysis

Exploratory factor analysis (EFA) was carried out utilising the principal components analysis (PCA) technique for consistent constructs.Factor loadings above 0.30 are considered to be adequate, loadings above 0.40 are noteworthy, and loadings above 0.50 are considered to be quite significant, as stated by Hair et al. (1998). The cut-off for significance in this study was set at a factor loading of 0.50.

KMO values between 0.5 and 1.0 often represent an optimal range for factor analysis of the data. Items in the variable are correlated, as indicated by Bartlett's test of spherical city. The test result is displayed as a significance level. The presence of significant connections between the variables is indicated by very small values (lessthan0.05). If the value is greater than 0.10, it may indicate that the data are unfit for factor analysis. These two analyses show that factor analysis can be successfully applied to the data at hand. After eliminating three items with loadings below 0.5, the total number of items included in the study was thirty.

Table2:ResultsofExploratoryFactorAnalysis

Statement	Factor loadings	KMO Measure of Sample Adequacy (>0.5)	Bartlett's Test ofSphericity		Items confirmed	Items dropped	Cum % of loading
			Chi Square	Sig. (<.10)
Perceived benefits (PB) -1	0.895	0.735	631.651	0.000	4	1	54.833
Perceived benefits (PB) -2	0.748
Perceived benefits (PB) -3	0.670
Perceived benefits (PB) -4	0.491
Perceived benefits (PB) -5	0.831
Perceived ease of use (PEU)-1	0.766	0.725	2.83.254	0.000	4	1	43.863
Perceived ease of use (PEU)-2	0.795
Perceived ease of use (PEU)-3	0.084
Perceived ease of use (PEU)-4	0.735
Perceived ease of use (PEU)-5	0.654
Perceived enjoyment (PE)-1	0.195	0.849	1.404E3	0.000	4	1	69.339
Perceived enjoyment (PE)-2	0.913
Perceived enjoyment (PE)-3	0.934
Perceived enjoyment (PE)-4	0.944
Perceived enjoyment (PE)-5	0.913
Structural assurance (SA)-1	0.838	0.742	349.298	0.000	4	0	57.423
Structural assurance (SA)-2	0.794
Structural assurance (SA)-3	0.586
Structural assurance (SA)-4	0.789
Social influence (SI)-1	0.940	0.707	4.054E3	0.000	5	0	88.381
Social influence (SI)-2	0.936
Social influence (SI)-3	0.940
Social influence (SI)-4	0.948
Social influence (SI)-5	0.936
Trust-1	0.872	0.817	1.047E3	0.000	5	0	68.179
Trust-2	0.894
Trust-3	0.866
Trust-4	0.797
Trust-5	0.682
Perceived risk-1	0.619	0.656	784.719	0.000	4	0	65.945
Perceived risk-2	0.844
Perceived risk-3	0.932
Perceived risk-4	0.820

 

 

ReliabilityAnalysis

The internal consistency of the questionnaire has been determined by the use of Chronbach Alpha. For new scales, Nun a and Bernstein (1994) advise a lower minimum alpha value of 0.60; for established scales, an alpha value of 0.70 is typically considered the requirement for internal consistency.

Cronbach's alpha was determined to be beyond the cut off value of 0.7, but still within the allowed range, hence this value was used as the cut off for the study. Cronbach's alpha for the entire questionnaire is quite high, at 0.981 (see Table 3), indicating that it is a credible research tool.

Table 3: Results of Reliability Test

VARIABLE	Cronbach alpha
Perceived benefits  (PB)	0.806
Perceived ease of use (PEU)	0.723
Perceived enjoyment (PE)	0.945
Structural assurance (SA)	0.738
Social influence (SI)	0.967
Trust	0.882
Perceived risk (PR)	0.826
Online purchase intention (OPI)	0.875
Over all Reliability of the Questionnaire	0.981

 

CorrelationAnalysis

Independent variable correlation analysis results are consistent with a strong relationship between all of the variables. There is a substantial correlation between the eight variables we looked at and the complete set.Amongst allthefactorstaken under consideration, all the sevenindependentvariables and one dependent variable (OPI)havea significant relationship witheachotheras shown in Table4.The highest level of correlation (0.939) was found between Trust and Social Influence (SI)variables and the lowest significant relationship (0.736) was between PEU and PB.

Table 4: Correlations

	PB	PEU	PE	SA	SI	TRUST	PR	OPI
PB	1
PEU	.736**	1
PE	.904**	.823**	1
SA	.862**	.791**	.895**	1
SI	.860**	.796**	.901**	.816**	1
TRUST	.856**	.802**	.920**	.831**	.939**	1
PR	.877**	.744**	.908**	.854**	.870**	.877**	1
OPI	.857**	.809**	.894**	.856**	.904**	.928**	.861**	1

**Correlationissignificantatthe0.01level(2-tailed).

 

Regression Analysis

The predictor-criterion link between the dependent and in dependent variables was determined by a multi-step regression analysis. This study examined the impact of gender on the relationships between elements that influence people's intentions to shop online during the CoVD-19 pandemic.

 

Online purchase intention (OPI)asDependentVariable: Table 5a and 5b show the results of a stepwise regression analysis, which shows that the seven components PB, PEU, PE, SA, SI, Trust, and PR all have a significant impact on OPI. Table 5a shows that these seven variables account for 89.3 % of the variance in OPI (R square = 0.893). Table 5c presents the summary coefficients for the seven components that influence OPI, and their respective beta values are. These values are.100,102,-.102,.193,.148,.518 and.041.

Table 5:Regression analysis: OPI as Dependent Variable

Table 5a:  Model Summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.945a	.893	.891	.26378
a. Predictors: (Constant), PR, PEU, PB, SI, SA, PE, Trust

Table 5b:  ANOVA

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	203.739	7	29.106	418.310	.000a
	Residual	24.353	350	.070
	Total	228.092	357
a. Predictors: (Constant), PR, PEU, PB, SI, SA, PE, Trust
b. Dependent Variable: OPI

Table 5c:  Coefficients^’

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	.012	.068		.181	.857
	PB	.098	.044	.100	2.213	.028
	PEU	.113	.037	.102	3.107	.002
	PE	-.086	.054	-.102	-1.580	.115
	SA	.197	.044	.193	4.524	.000
	SI	.122	.046	.148	2.678	.008
	TRUST	.490	.056	.518	8.750	.000
	PR	.040	.044	.041	.900	.369
a. Dependent Variable: OPI

 

Moderating Role of Gender:To observe the moderating role of gender (male / female) on the online purchase intention (OPI), first the dummy variables of male and female values were created by recoding the values as 0 and 1 for both the genders.

Then new variables are being computed by interacting the all 7 independent variables with recoded values of both the genders, as PR_MALE, PEU_MALE, SI_MALE, SA_MALE, PB_MALE, PE_MALE, TRUST_MALE and PR_FEMALE, PEU_FEMALE, PB_FEMALE, TRUST_FEMALE, SA_FEMALE, PE_FEMALE, SI_FEMALE respectively.

 

Moderating Role of gender: Male

The regression analysis has been performed using the 7 new interacting independent variables and dependent variable (OPI). Results from a step-wise regression analysis are shown in tables 6a and 6b,that 7 factors (PR_MALE, PEU_MALE, SI_MALE, SA_MALE, PB_MALE, PE_MALE, TRUST_MALE) are noteworthyconjecturers of OPI.

Table 6a shows that these seven factors account for 49% of the variance in OPI across all online shoppers. The corresponding R square value is 0.497.Table 6c of the summary of coefficients provides reasonably typical beta values of 1.391, 0.986, and 0.185 for the three Factors PE_MALE, SI_MALE, and TRUST_MALE, respectively, in terms of their impact on OPI.ThesmallerBeta valuesof rest four factors (PR_MALE, PEU_MALE, SA_MALE, PB_MALE) indicatesthat it is not being well controlled toleverage OPI. Thus,thefactorPE_MALE, SI_MALE and TRUST_MALEareemergingasakeyinfluencingvariablefor online purchase intention (OPI).

Table 6: Regression results for gender: Male

Table 6a: Model Summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.497a	.247	.231	.70074
a. Predictors: (Constant), PR_MALE, PEU_MALE, SI_MALE, SA_MALE, PB_MALE, PE_MALE, TRUST_MALE

Table 6b:  ANOVA^s

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	56.230	7	8.033	16.359	.000a
	Residual	171.861	350	.491
	Total	228.092	357
a. Predictors: (Constant), PR_MALE, PEU_MALE, SI_MALE, SA_MALE, PB_MALE, PE_MALE, TRUST_MALE
b. Dependent Variable: OPI

Table 6c:  Coefficients^’

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	2.577	.061		42.576	.000
	PB_MALE	-.414	.139	-.748	-2.977	.003
	PEU_MALE	-.605	.099	-1.124	-6.136	.000
	PE_MALE	.750	.162	1.391	4.621	.000
	SA_MALE	-.068	.138	-.119	-.490	.624
	SI_MALE	.536	.135	.986	3.963	.000
	TRUST_MALE	.106	.179	.185	.596	.552
	PR_MALE	-.217	.136	-.378	-1.596	.111
a. Dependent Variable: OPI

Moderating Role of gender: Female

In the same manner, in table 7a, R square at 0.458 indicates thatthesesevenvariables (PR_FEMALE, PEU_FEMALE, PB_FEMALE, TRUST_FEMALE, SA_FEMALE, PE_FEMALE, SI_FEMALE) areabletoexplainonline purchase intention (OPI)totheextentof 45.8percent

Table 7a:  Model Summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.458a	.210	.194	.71755

1. Predictors: (Constant), PR_FEMALE, PEU_FEMALE, PB_FEMALE, TRUST_FEMALE, SA_FEMALE, PE_FEMALE, SI_FEMALE

Table 7b:  ANOVA^s

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	47.886	7	6.841	13.287	.000a
	Residual	180.205	350	.515
	Total	228.092	357

1. Predictors: (Constant), PR_FEMALE, PEU_FEMALE, PB_FEMALE, TRUST_FEMALE, SA_FEMALE, PE_FEMALE, SI_FEMALE
2. Dependent Variable: OPI

Table 7c: Coefficients^’

Model		Un standardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	2.521	.046		54.289	.000
	PB_FEMALE	-.340	.201	-.620	-1.695	.091
	PEU_FEMALE	-.650	.166	-1.172	-3.906	.000
	PE_FEMALE	.881	.240	1.629	3.676	.000
	SA_FEMALE	.051	.222	.091	.228	.819
	SI_FEMALE	.628	.271	1.171	2.318	.021
	TRUST_FEMALE	-.049	.293	-.088	-.166	.868
	PR_FEMALE	-.397	.257	-.708	-1.543	.124

1. Dependent Variable: OPI

Table 7c presents a summary of the coefficients, which includes the beta values for three factors PE_FEMALE, SI_FEMALE and SA_FEMALE as 1.629, 1.171 and 0.091respectively, which are fairly representative of their impact on online purchase intention (OPI). The smaller Beta values of rest four factors (PR_FEMALE, PEU_FEMALE, TRUST_FEMALE, PB_FEMALE) indicates that they are not adequately managed to leverage online purchase intention (OPI). Thus, the factor PE_FEMALE, SI_FEMALE and SA_FEMALE are emerging as a key influencing variable for online purchase intention (OPI).

 

ResultsofHypothesesTesting

Table 8 shows that of the initial 21 hypotheses proposed in the conceptual study framework, 3 have been rejected and the remaining 18 have all been accepted.

Table 8: SummaryofHypothesesTesting

Hy. No.	Independent Variables	Dependent Variables	R- Square	BetaCoefficient	t-value	P value	Status   of Hypotheses
H1	PB	OPI	0.893	0.100	2.213	0.028	Accepted
H2	PEU	OPI		0.102	3.107	0.002	Accepted
H3	PE	OPI		-0.102	-1.580	0.115	Rejected
H4	SA	OPI		0.193	4.524	0.000	Accepted
H5	SI	OPI		0.148	2.678	0.008	Accepted
H6	Trust	OPI		0.518	8.750	0.000	Accepted
H7	PR	OPI		0.041	0.900	0.369	Rejected
H8	PB_MALE	OPI	0.247	-0.748	-2.977	0.003	Accepted
H9	PB_FEMALE	OPI	0.210	-0.620	-1.695	0.091	Rejected
H10	PEU_MALE	OPI	0.247	-1.124	-6.136	0.000	Accepted
H11	PEU_FEMALE	OPI	0.210	-1.172	-3.906	0.000	Accepted
H12	PE_MALE	OPI	0.247	1.391	4.621	0.000	Accepted
H13	PE_FEMALE	OPI	0.210	1.629	3.676	0.000	Accepted
H14	SA_MALE	OPI	0.247	-0.119	-0.490	0.624	Rejected
H15	SA_FEMALE	OPI	0.210	0.091	0.228	0.819	Rejected
H16	SI_MALE	OPI	0.247	0.986	3.963	0.000	Accepted
H17	SI_FEMALE	OPI	0.210	1.171	2.318	0.021	Accepted
H18	TRUST_MALE	OPI	0.247	0.185	0.596	0.552	Rejected
H19	TRUST_FEMALE	OPI	0.210	-0.088	-0.166	0.868	Rejected
H20	PR_MALE	OPI	0.247	-0.378	-1.596	0.111	Rejected
H21	PR_FEMALE	OPI	0.210	-0.708	-1.543	0.124	Rejected

 

Discussion

Perceived benefits (PB), PEU, SA, SI, and Trust are all factors that consumers look for while making purchases online and this study backs them up. This suggests that a buyer's preference for a particular purchasing behaviour can be affected by how much they like shopping online. Consumers like the ease and convenience of shopping online, and they will have a better time if websites are both easy to use and visually appealing.  In contrast, consumers' need for "convenience" is undermined by a cumbersome website.Researchers have looked into the effects of gender as a moderator in several contexts, including the management and use of technological systems (Yousafzai and Yani-de-Soriano, 2012), with mixed results.

The results of the current study show that gender plays a major role in the connection between perceived enjoyment (PE) and Social Influence (SI) and behavioural intention, but a minor role in the connection between structural assurance (SA) and trust (T) and behavioural intention. The coefficient for the moderating effect of gender on the connection between perceived benefits and behavioural intentions is negative, indicating that the relationship is significant but weak.

This research confirms that there are substantial moderating effects of gender on some of the variables studied. In particular, both men and women were affected by Perceived Enjoyment (PE) and Social Influence (SI). Structural Assurance (SA), Trust, and Perceived Risk (PR) are not significantly moderated by gender. The outcomes of the tests validate the theoretical model for e-commerce site purchase intent. This aids relevant researchers and stakeholders by allowing them to better comprehend customers' actions under a given scenario.

 

There is no evidence that a consumer's gender has any bearing on the links explored between structural assurance (SA), trust (TR), and perceived risk (PR), and online purchase intention (OPI). Online intent may be formed by a person's Structural Assurance (SA), Trust, and Perceived Risk (PR) regardless of their gender. When designing online portals, websites, and e-commerce platforms, businesses should take into account customers of all gender identities and expressions in terms of usability and satisfaction.

Conclusion

In this study, seven factors affecting the intention to purchase via e-commerce site were studied and gender was studied as amoderator. The factors taken into consideration were Perceived Benefits (PB), Perceived Ease of Use (PEU), Perceived Enjoyment (PE), Structural Assurance (SA), Social Influence (SI), Trust and Perceived Risk (PR).

Based on this understanding, researchers created a theoretical model and pilot tested it on people from different income classes. The authors found a significant and unmistakable link between all the variables in their model.

Recommendations

Consumers are influenced by their surroundings to shop online, and this trend accelerates in times of crisis such as pandemics. Tech-savvy internet users should be surveyed for their insights. However user education and training methods for new technology are always evolving. Larger samples, irrespective of academic level, may be used in future studies in this field. Longitudinal studies may be used in the future to determine if customer behaviour is consistent regarding payment mode or if it shifts at trying periods like the COVID-19 epidemic. It will be important to look into the various external factors that govern the connection between intentions and actions at specific periods and how these factors evolve over time.

In addition, it is recommended that future researchers devote more time and energy to comparing and contrasting findings from studies conducted in diverse countries and cultural contexts. Second, the study only looked at one of the most fundamental participant demographics—gender—as a potential moderator. Moderators can have a variety of effects on the model's many constructions. As a result, it is suggested that researchers in the future look into user behaviour while controlling for factors like experience, age, education, culture, etc.

Limitations

Although the study's aims were met, there are many areas for improvement that might be explored in follow-up studies. First, more data should be gathered as part of the on-going study. Limitations in generalizability arise from the study's reliance on a single data collection method (a self-assessment survey). It is suggested that other approaches be used to confirm the findings of the current study. Online retailers' ethical and environmental responsibilities should be evaluated alongside their operational criteria.

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