Meta-analytic review of online purchase intention: conceptualising the study variables

Figures & data

Figure 1. Conceptual framework of online purchase intention (OPI).

Table 1. Descriptive statistics and correlational analysis.

Study variable category	Study variables	Hypothesis	Number of studies (k)	Sample size (n)	Combined effect size	Estimate	SE	LCI	UCI	Findings
Perceived utility	Perceived Usefulness	H1a	19	7923	0.110	0.8238	0.0646	0.696	0.949	Significant
	Perceived Ease of Use	H1b	14	5042	0.050	0.767	0.0712	0.628	0.907	Significant
	Congruence	H1c	6	3327	0.311	0.996	0.189	0.525	1.367	Significant
Reliability & reputation	Security	H1d	10	3413	0.030	0.879	0.123	0.638	1.12	Significant
	Fulfilment Reliability	H1e	6	1190	0.392	0.697	0.0649	0.57	0.824	Significant
	Trust	H1f	17	6855	0.266	0.7847	0.0855	0.617	0.952	Significant
	Uncertainty Avoidance	H1g	6	3955	0.155	0.7813	0.156	0.476	1.087	Significant
	Perceived Risk	H1h	15	6333	0.068	0.7702	0.0617	0.6484	0.892	Significant
Contextual factors	e-WOM	H1i	12	2453	0.139	0.7623	0.0834	0.598	0.925	Significant
	Consumer Innovativeness	H1j	7	2854	0.148	0.859	0.111	0.641	1.077	Significant

Table

Study variable category	Study variables	Z	Q	I² (%)	Tau²	Fail-Safe N
Perceived utility	Perceived usefulness	12.7	572.4428	96.86	0.0758	32586.00
	Perceived ease of use	10.8	309.730	95.80	0.0669	12809.00
	Congruence	5.26	401.9784	99.40	0.1756	4343.00
Reliability & reputation	Security	7.14	221.8061	97.29	0.1019	5025.00
	Fulfilment reliability	10.7	14.2501	78.95	0.0132	838.00
	Trust	9.18	760.7542	97.90	0.1207	23786.00
	Uncertainty avoidance	5.01	110.2482	98.08	0.0949	2308.00
	Perceived risk	12.5	322.7041	95.66	0.054	19638.00
Contextual factors	e-WOM	9.13	95.987	93.75	0.0449	3480.00
	Consumer innovativeness	7.73	193.8565	96.90	0.0828	4790.00

Table 2. Publication bias assessment.

S. No	Variables	Egger’s regression	Rank correlation
1	Perceived usefulness	2.074 (p = 0.038) NS	0.141 (p = 0.401) NS
2	Perceived ease of use	2.286 (p = 0.022) S	0.407 (p = 0.047) S
3	Congruence	1.643 (p = 0.100) NS	0.010 (p = 0.100) NS
4	Security	1.384 (p = 0.166) NS	0.238 (p = 0.562) NS
5	Fulfilment reliability	1.468 (p = 0.142) NS	0.333 (p = 0.750) NS
6	Trust	1.178 (p = 0.239) NS	0.147 (p = 0.440) NS
7	Uncertainty avoidance	0.558 (p = 0.557) NS	0.667 (p = 0.333) NS
8	Perceived risk	0.535 (p = 0.592) NS	0.143 (p = 0.495) NS
9	e-WOM	0.513 (p = 0.608) NS	0.429 (p = 0.239) NS
10	Consumer innovativeness	0.072 (p = 0.932) NS	0.143 (p = 0.773) NS

Table 3. Moderation analysis.

Study variable category	Study variables	Hypothesis	Number of studies(k)	Combined effect size	Estimate	SE	LCI	UCI	p-value	Findings
Personal Factors	Demographic Variables	H2	12	−0.031	−0.0124	0.0897	−0.188	0.163	0.89	Non-Significant
	Technology Readiness	H3	6	0.157	0.8473	0.1105	0.6306	1.064	< .0001	Significant
	Social Variables	H4	10	0.380	0.7347	0.1122	0.5148	0.9547	< .0001	Significant
	Attitude	H5	24	0.171	0.8272	0.0765	0.6773	0.9771	< .0001	Significant
Perceived Values	Website Design	H6	15	0.059	0.796	0.078	0.087	0.219	0.016	Significant
	Perceived quality	H7	6	0.002	0.8254	0.1209	0.5884	1.0624	< .0001	Significant
	Price	H8	5	0.264	0.7623	0.1647	0.4395	1.0851	< .0001	Significant
	Hedonic Benefits	H9	8	0.118	0.8127	0.1378	0.5426	1.0828	< .0001	Significant

Figure 2. PRISMA flowchart presenting the process of Literature search.

Table A. Summary of studies used in meta-analysis.

S. No	Authors	Sample	Type of consumer behaviour	Method(s) of analysis	Geographical scope	Main findings
1	Goldsmith and Flynn (2004)	805	Online Purchase Intention	Correlation and Multiple Regression	Florida, US	The findings imply that online apparel buying intention is more motivated by the Internet than clothing innovativeness.
2	Kim and Kim (2004)	255 (135 online and 120 non-online customers)	Online Purchase Intention	EFA and Multiple Regression Analysis.	Korea	The research’s findings highlight that prior online purchase experience is the primary governing factor that affects inclinations to make future online purchases.
3	Rosenbaum (2005)	100	Online Purchase Intention	Regression & EFA	USA	Although consumers react to all aspects of the cyberscape, many are "hygiene factors" instead of "satisfiers". Customers cannot approach a site unless those conditions are met, but consumer satisfaction and purchase intention may result when those conditions are exceeded. For instance, product selection emerges as the primary factor in predicting the frequency of online purchases among starting internet users. In contrast, product selection and reliability are crucial for experienced users. Managers must take into account all aspects of the cyberscape. The hygiene variables are necessary for establishing customer satisfaction and loyalty and stimulating approach behaviour, but satisfiers are the boosting dimensions.
4	Ruiz-Mafe and Sanz-Blas (2006)	590	Online Purchase Intention	SEM	Valencia, Spain	According to data analysis, Spanish Internet users exhibit intermediate levels of Internet dependence, indicating that the media dependency scale has to be modified for the Spanish market. Internet dependence impacts willingness to purchase online, with information seeking to make judgements being the most essential element as per this research.
5	Hui and Wan (2007)	140	Online Purchase Intention	EFA & Two-Way ANOVA	Singapore	Even though more people are using the Internet and more things are available online, relatively little research has mainly looked at how gender and educational level affect Internet users’ opinions in the Singaporean context. The results show that Singaporeans generally agree that the Internet is a practical medium for accessing information or purchasing. The study emphasises that individuals with higher levels of education appear to be less worried about security risks. They believe that purchasing online offers better discounts and more significant cost reductions. Women complain bitterly about not being able to enjoy a physically satisfying buying experience online.
6	Ranaweera et al. (2008)	170	Online Purchase Intention	SEM and Regression Analysis	Canada	Personality traits have a considerable moderating impact on the likelihood of making an online purchase. It is interesting to note that risk aversion improves the likelihood of purchase, making the consumers satisfied. Moreover, it has been discovered that dispositional trust does not have a similar impact on technology readiness regarding increasing the likelihood of online purchases. The findings demonstrate that risk aversion, trust orientation, and technology readiness are vital to the literature on online consumer behaviour.
7	Fisher and Chu (2009)	181	Online Purchase Intention	ANCOVA and multiple regression analysis	New Zealand	The results suggest that geographical location appears to influence initial trusting beliefs (perceived trustworthiness) about a vendor’s websites. Overall, the study’s results emphasise trusting beliefs, which strongly influence online purchase intentions.
8	Fogel and Schneider (2010)	527	Online Purchase Intention	Linear and Logistic Regression	USA	This study examines factors impacting three-month online purchases of various types of designer clothing and online intentions to make such purchases. Using logistic and linear regression, cross-sectional and longitudinal studies were carried out. The Theory of Planned Behaviour served as the theoretical foundation. The Consumer Decision-Making Styles Inventory and the Boredom Proneness Scale were additional independent factors. Clothing types were intimate apparel, underwear, swimwear, pants, jeans, skirts/dresses, T-shirts, shoes, sneakers, sweaters, jackets/coats, socks, hosiery, and hats. The finding reveals that increased internet information search was associated with purchases for all clothing types.
9	Jun et al. (2010)	2,470 online surveys	Online Purchase Intention	Logistic Regression	Canada	The study focuses on understanding individual information search strategies when making online travel-related purchases. The findings show that consumers who book flights online use direct information sources and pay attention to transactional factors. Online travellers use a variety of sources, and they pay attention to branding and informative as well as transactional aspects. The study’s findings offer marketing advice for the travel and accommodation sectors and support the theory of constructive consumer decision processes.
10	Lee and Huddleston (2010)	261	Online Purchase Intention	SEM	USA	This study first breaks down total perceived risk into four dimensions: privacy, time, performance, and financial risk, before looking at the links between domain-specific innovativeness, overall perceived risk, and online buying behaviour. This study shows that online buying behaviour is inversely connected to privacy, time, performance, and financial risk in the overall perceived risk. The study’s findings confirm that privacy is the most significant perceived risk when purchasing online. It has been discovered that domain-specific inventiveness directly affects online purchase behaviour. Additionally, the association between domain-specific innovativeness and online buying behaviour is partially mediated by overall perceived risk. Finally, this study offers empirical support for the beneficial influence of internet use on behaviour related to online purchases.
11	Keisidou et al. (2011)	232	Online Purchase Intention	EFA and Regression Analysis	Greece	Personal Innovativeness of Information Technology (PIIT), perceived security, and product engagement have been found to impact attitudes about online buying; however, the findings may vary depending on the product type.
12	Oly Ndubisi et al. (2011)	102	Online Purchase Intention	Regression	Malaysia	This paper examines the factors that may affect consumers’ intentions to repurchase products and services online. The emerging results provide significant evidence supporting the proposed hypotheses and validate that the repurchase intention online is impacted by perceived value, ease of use, usefulness, trust, reliability, functionality, privacy and the firm’s reputation.
13	Wen (2011)	540	Online Purchase Intention	SEM	US	This study empirically tests a structural model of factors affecting consumers’ online purchase intentions for travel products. The findings indicate that information, service, and system quality are valid measurements to evaluate the quality of travel-oriented website designs. Quality of travel website design, travellers’ attitudes, and customers’ satisfaction significantly influenced travellers’ purchase intentions. Travellers’ attitudes and customer satisfaction also strongly mediate the links between the quality of travel website design and purchase intentions.
14	Bianchi and Andrews (2012)	176	Online Purchase Intention	SEM	Chile	The study’s findings reveal that attitude has a favourable impact on intentions to continue buying. Alternatively, that attitude also has an inverse association with perceived threats online. Here, trust has emerged as a crucial determinant of the intention to continue purchasing online.
15	Sabiote et al. (2012)	150 British & 150 Spanish - a Total of 300	Online Purchase Intention	SEM	Spanish and British Customers	The findings show that uncertainty avoidance moderates the formation of overall perceived value, impacting both online purchases and enjoyment.
16	Amoroso and Mukahi (2013)	940	Online Purchase Intention	SEM	Online Survey	According to the authors’ findings, trust, over the long run, impacts the innate drive for online purchase habits.
17	Gong et al. (2013)	503	Online Purchase Intention	Regression	China	Chinese consumers’ age, income, education, marital status, and perceived usefulness are significant predictors of online shopping intention.
18	Bezes (2013)	1478	Online Purchase Intention	SEM & ANOVA	French Customers	This study assesses whether a retailer’s reputation is enhanced by perceived channel congruence beyond the unique contributions of its website and physical stores. The analysis results indicate that congruent channels enhance retailer image even when these channels have a negative reputation. Channel congruence, however, cannot be made into a general rule because it solely affects multichannel and internet customers and has no adverse effects on a retailer’s reputation, as highlighted in the study.
19	Bijmolt et al. (2014)	2978	Online Purchase Intention	Logistic Regression Analysis	Pan-European survey - Denmark, Ireland, Sweden, Austria, Finland, Germany, Luxembourg, Netherlands, Belgium, France, Greece, Italy, Portugal, Spain	The study’s findings highlight that the service recovery paradox in an online situation is supported by the fact that the customers who complained and were happy with how their complaints were handled had the highest repurchase intentions.
20	Ahmed and Akhlaq (2015)	286	Online Purchase Intention	EFA and Regression Analysis	Pakistan	The findings of this study suggest that online shopping intentions vary depending on perceived usefulness (PU) and ease of use (PEOU), as well as other factors related to digital engagement, such as distrust, perceived risk (PR), perceived enjoyment (PE), and legal framework (LF)).
21	Urueña and Hidalgo (2016)	303	Online Purchase Intention	SEM	Spain	The study explores the connections between trust, service recovery satisfaction, happy and negative emotions, and distributive, interactional, and procedural fairness. The results highlight the importance of procedural and interactional justice, joyful feelings, and satisfaction with service recovery.
22	Liu et al. (2016)	16 product categories, 4270 data	Online Purchase Intention	Regression	China	Online reviews had little influence on users’ selections to buy products with high ranks, but they did have a strong positive influence on users’ choices of low-ranking products. This study reveals this in its findings, thus supporting the online purchase.
23	Law et al. (2016)	300	Online Purchase Intention	SEM	Online Survey	This article examines the determinants and mediators of the purchase intention of non-online purchasers between ages 31 and 60 who mostly have strong purchasing power. It proposes a new online purchase intention model by integrating the technology acceptance model with additional determinants and adding habitual online usage as a new mediator. This revised model illustrates habitual online usage’s role in purchase intentions. The findings suggest that marketers assist them in understanding the target group’s characteristics and formulating better online strategies.
24	Voorveld et al. (2016)	1071 consumers through 17 online and offline channels	Online Purchase Intention	Regression	Dutch respondents	This study examined how cross-channel usage varies between different product kinds and between online and offline purchasers of a particular product. Results showed that consumers used more online channels when making an online purchase than when making an offline purchase but used offline channels to the same extent when buying a product online as when buying a product offline.
25	Cihan et al. (2017)	114	Online Purchase Intention	Regression and ANOVA	Eastern Europe and Turkey	The most crucial factors in online ordering are delivery options and privacy policies, but there is no real relationship between income level and customer satisfaction. The findings of this study imply that any issues with the delivery procedure may negatively impact the purchase and the consumers.
26	SivaKumar and Gunasekaran (2017)	150	Online Purchase Intention	Regression, ANOVA & EFA	Chennai, India	The primary goal of this study is to examine the factors influencing millennial consumers’ online shopping habits. The elements affecting online purchases are analysed, and suggestions for online businesses are made.
27	Adityan et al. (2017)	210	Online Purchase Intention	SEM	India	In this study, a hypothesised and empirically tested consumer behaviorally insightful model with the psychological traits information acquisition (IA), consumer innovativeness (CIN), consumers’ need for uniqueness-creative choice counter-conformity - (CNFU-CCC) dimension, and online shopping tendency (OST) was developed. The results of structural equation modelling (SEM) showed that the hypothesised model was compatible with the data and that the psychological constructs CIN, CNFU-CCC, and OST were positively correlated with IA. The IA's facilitating role has also been established.
28	Geraldo and Mainardes (2017)	704	Online Purchase Intention	Regression	Brazil	The results of this research showed that the purchase intention of online consumers could be positively affected by the factors of virtual stores and promotions. Therefore, from the results presented, it is suggested that understanding factors that may affect online consumers’ purchase intention in e-commerce can help online retailers and researchers understand and explain consumption behaviour in virtual environments.
29	Mohseni et al. (2018)	409	Online Purchase Intention	PLS-SEM	Malaysia	The research emphasises the significance of personal value as a characteristic that can significantly influence one’s intention to purchase online. In addition, this study offers a clear, multidimensional picture of the causal relationship between the online purchasing environment by merging user attributes and website characteristics, additionally driving the purchase intention.
30	Rahman and Mannan (2018)	300	Online Purchase Intention	PLS-SEM	Dhaka, Bangladesh	The adoption of consumer information positively impacted consumer online purchase behaviour. It was discovered that e-WOM partially mediated the association between information adoption and consumer purchase behaviour. The findings highlighted that online brand experience influences consumer online purchase behaviour favourably. Also, online brand familiarity partially mediates the association between online brand experience and consumer buying behaviour.
31	Han et al. (2018)	309	Online Purchase Intention	PLS-SEM	Korea	Data indicates that attitudes regarding purchasing products online from foreign websites are favourably influenced by views about E-S-QUAL and Consumer Need for Uniqueness (CNFU), positively affecting purchasing intention.
32	Jordan et al. (2018)	190	Online Purchase Intention	SEM	Slovenia	According to this study’s findings, there is a positive correlation between perceived risk and intention to make an online purchase and a negative correlation between perceived risk and fear of financial losses, reputational damage, and perceived risk.
33	Bringula et al. (2018)	230	Online Purchase Intention	EFA & Hierarchial Regresssion Analysis	Manila	The study’s findings justified the contradictory findings of earlier studies regarding cost and quality. The study identified the variables that might affect smartphone purchases made online.
34	Lopez et al. (2018)	305	Online Purchase Intention	PLS-SEM	European and Non-European Nations	The results showed that website functions also increase users’ loyalty, and they emphasised the significance of assessing how all website features together affect online purchase intention. This study significantly adds to the body of consumer literature on local food websites, an understudied area, and its potential effects on customer purchase behaviour and e-loyalty.
35	Ha et al. (2019)	423	Online Purchase Intention	EFA and Regression Analysis	Vietnam	The results show that perceived usefulness, ease of use, attitude, subjective norm and trust positively affected consumers’ online shopping intention.
36	Ofori and Appiah-Nimo (2019)	580 undergraduate students	Online Purchase Intention	PLS-SEM & SEM	Ghana	The findings demonstrated a strong correlation between usability and simplicity of usage. Although perceived cost (PC) was discovered to be the most crucial variable influencing students’ actual use (AU) of online shopping, the perceived cost (PC) had no discernible impact on purchase intention (PI). The study’s findings suggest that online retailers reduce costs and increase the efficiency of online purchasing while ensuring the security of both the product and the transactions.
37	Anic et al. (2019)	1990 Internet Users	Online Purchase Intention	SEM	Croatia	This study provides empirical support for the notion that Online Privacy Concerns (OPC) negatively impact information-sharing intentions and positively correlate with the fabrication of personal data. The findings also indicate no connection between OPC and online purchasing. Nonetheless, OPC influences views regarding internet buying, which influences online purchases.
38	Dewi et al. (2019)	668	Online Purchase Intention	PLS-SEM	Indonesia	This research attempts to identify the variables influencing customers’ online purchase intentions and investigate whether there are any appreciable differences between males and females. According to the findings, the three influential characteristics of performance expectancy, effort expectancy, and personal innovativeness have more significant path coefficients in males and females. Anxiety has a negative and significant association with online purchase intention for female customers as opposed to male consumers. Additionally, the analysis’s findings show no appreciable differences between males and females in most factor coefficients determining whether a person will make an online purchase.
39	Kaur and Thakur (2019)	600	Online Purchase Intention	SEM	India	The paper’s findings reveal that technology readiness, consumer innovativeness, fondness for branded products and perceived brand unavailability act as determinants of online shopping attitude, and there is a positive relationship between online shopping attitude and online purchase intention among Tier 2 consumers in India.
40	Çera et al. (2020)	690	Online Purchase Intention	Logistic Regression	Albania	Online purchases are more likely to be made by people with high levels of digital banking usage, financial guidance, past bank experience, technology usage, and low payment risk and risk tolerance attitudes.
41	Qiu et al. (2020)	331	Online Purchase Intention	PLS-SEM	China	The results show that air pollution makes consumers more anxious and irritated, which promotes their online shopping behaviour by making them less likely to engage in outdoor consumption. Customers’ assessment of the intensity of their discomfort and annoyance induced by air pollution is moderated by their regulatory focus (promotion focus vs. prevention focus).
42	Silitonga et al. (2020)	300	Online Purchase Intention	PLS-SEM	Indonesia	The research aim is to examine the driving factors for shaping customer retention by understanding the pattern of transaction characteristics and trust in online purchases in e-commerce. Factors such as customisation, contact interactivity, care, and character factors positively and significantly affected trust and buyer retention. Trust has proven to be essential in shaping buyer retention on e-commerce sites.
43	Ventre and Kolbe (2020)	380	Online Purchase Intention	PLS-SEM	Mexico	The findings imply that businesses should work to encourage customers to post favourable online reviews to increase trust and promote online sales. This study adds a deeper understanding of the factors influencing online purchasing intent in developing economies.
44	Buhalis et al. (2020)	584	Online Purchase Intention	PLS-SEM	Spain	In a generational context, this study analyses the influence of young consumers’ external and internal variables on their e-loyalty to tourism sites. The study includes two external variables (site design and eWOM) and two internal variables (trust and satisfaction), to which the intention to purchase online is tested. The results show that the impact of consumers’ internal variables is more significant than that of external ones. Moreover, the proposed causal model is practical and can be easily applied by tourism companies to improve site e-loyalty in market orientation.
45	Rosillo-Díaz et al. (2020)	346	Online Purchase Intention	Regression & CFA	Online Shoppers	The findings show that uncertainty avoidance and collectivism are cultural factors that considerably impact perceived product quality, perceived risk, and buying intention in e-commerce platforms.
46	Cheong et al. (2020)	215	Online Purchase Intention	PLS- SEM	Kuala Lumpur Malaysia	The finding suggests that the most crucial factor influencing Malaysian millennials’ intention to buy electronic devices online is the review’s timeliness.
47	San-Martin et al. (2020)	382 tourists	Online Purchase Intention	PLS- SEM	Spain	The findings supported the moderation effect by demonstrating a positive relationship between the values associated with travel anxiety, risk, and experience, as well as online purchase intention and e-WOM.
48	Shekhar and Jaidev (2020)	280	Online Purchase Intention	Regression	India	The study findings highlighted that trust mediates the following relationships: 1) the social commerce construct, 2) perceived usefulness and purchase intention, and 3) perceived usability and purchase intention. The relationship between the constructs was empirically explored in this study.
49	Janavi et al. (2021)	410	Online Purchase Intention	PLS- SEM	Iran	This study has provided a research framework for analysing how users’ online purchase behaviours are affected by their media demands and social media adoption. The influence of tension on social media was the strongest and has significantly impacted adoption. Examining the conflicting issues shows that in a culture with a youthful population, elements like media attachment, keeping social ties, engaging in exchanges with friends or strangers, and avoiding problems will lead to online adoption.
50	Kautish et al. (2021)	442	Online Purchase Intention	SEM, Hierarchical Regression analytics (HRA), and bootstrap procedure	India	The research reveals a considerable impact on customers’ online purchase intentions for fashion apparel purchasing from the comparative efficacy of two e-tail servicescape elements, such as product assortment and order fulfilment. The moderating impact of fulfilment reliability is likewise validated, as is the mediating effect of shopping assistance and efficiency.
51	Uzir et al. (2021)	259	Online Purchase Intention	PLS-SEM	Dhaka, Bangladesh	The findings highlighted that customer satisfaction was influenced by service quality, customer perceived value, and trust. Trust partially mediates the relationships between service quality and customer satisfaction and between perceived value and contentment. By extending the SERVQUAL model to include perceived value in the presence of trust and adhering to expectation disconfirmation theory, the findings help to construct and validate a trust-based satisfaction model.
52	Melović et al. (2021)	813 millennial consumers	Online Purchase Intention	SEM & ANOVA	Montenegro	The findings revealed that millennial consumers’ behaviour while making an online purchase is greatly influenced by their demographic traits. Male consumers spend more money on internet shopping than female consumers, despite both sexes doing it equally frequently. Also, more often than women, males shop online on well-known websites. Consumers of any age can shop online, but younger Millennials tend to make this purchase more frequently than older Millennials.
53	Cuevas et al. (2021)	200	Online Purchase Intention	SEM	USA	Consumers’ flow experiences in social search on Instagram were enhanced by visual appeal, text information, clarity, and interaction. Further analyses showed that mental simulation and perceived task ease mediate the flow experience, ultimately increasing purchase intention.
54	Jebarajakirthy et al. (2021)	160	Online Purchase Intention	Logistic Regression & ANOVA	India	The research results demonstrated that customers prefer to buy fashion items in person rather than online when highly effectively committed to the purchase. Moreover, hedonic advantages mediate the link between affective commitment and channel preference.
55	Alvarez-Risco et al. (2022)	371	Online Purchase Intention	PLS-SEM	Peru	The findings suggested that customer satisfaction and trust positively impact online repurchase intention.
56	Chopdar et al. (2022)	275	Online Purchase Intention	PLS-SEM	India	This study identifies and empirically analyses the importance of numerous technology-related consumer attributes and situational variables (Stimuli) in establishing impulsive behaviours among mobile shoppers, building on the stimulus-organism-response (S-O-R) theory. Results show that hedonic incentives, personalisation, product variety, and mobility substantially impact impulsivity. The research informs managers of impulsivity’s role in promoting split loyalties among mobile shoppers and suggests new tactics for the long-term use of online marketplaces.
57	Salem and Alanadoly (2022)	346	Online Purchase Intention	PLS-SEM	Malaysia	This study explores how customers’ online purchasing behaviour may be enhanced by the effects of fashion participation, opinion seeking, and online experience on perceived value in terms of quality and price.
58	Truong and Truong (2022)	24,998 useable cases from the Census Bureau’s Household Pulse Survey Phase 3.1	Online Purchase Intention	Logistic Regression	US	The findings demonstrate that shopping behaviour changes are influenced by anxieties regarding one’s health and fears regarding one’s financial situation. Also, demographic factors, including age, gender, colour, income, and marital status, greatly influence their purchasing choices.
59	Hyun et al. (2022)	342	Online Purchase Intention	SEM	US	Experience makes social commerce successful, according to a verified model of customers’ actual SNS buying usage. The findings give businesses who use SNS commerce up-to-date knowledge and critical details, enabling them to modify their marketing plans to serve customers better.
60	Soares et al. (2023)	1052 Online Consumers	Online Purchase Intention	PLS-SEM	Brazil	The risks associated with leaving home to make purchases can lead customers to look online to accomplish such tasks during the pandemic and socially isolating times. Concerning that, this study aims to examine how COVID-19 affects consumers’ online shopping habits. The study’s results highlight that the perceived risk of contracting COVID-19 when making a transaction in person came out as a major finding, which also had a favourable impact on the perceived usefulness and simplicity of purchase. Although perceived usefulness and simplicity of investment have a substantial positive correlation with perceived usefulness and perceived buying intent, they had no statistically significant impact on online purchasing intent. Online shopping is showing a positive impact on online purchase intent.

Table B. Theories used in the study.

S. No	Theories used	Studies
1	TAM	Ahmed and Akhlaq (2015); Amoroso and Mukahi (2013); Çera et al. (2020); Ha et al. (2019); Keisidou et al. (2011); Ofori and Appiah-Nimo (2019); Law et al. (2016); Shekhar and Jaidev (2020); Geraldo and Mainardes (2017); Kaur and Thakur (2019); Soares et al. (2023), Oly Ndubisi et al. (2011)
2	Theory of Reasoned Action (TRA)	Ahmed and Akhlaq (2015); Bonera (2011); Ranaweera et al. (2008); Buhalis et al. (2020); Hyun et al. (2022); Han et al. (2018); Shekhar and Jaidev (2017); Kaur and Thakur (2019)
3	Unified Theory of Acceptance and Use of Technology (UTAUT)	Amoroso and Mukahi (2013); Dewi et al. (2020)
4	Diffusion of Innovation Theory	Bijmolt et al. (2014); Ranaweera et al. (2008); Lee and Huddleston (2010)
5	Theory of Planned Behaviour (TPB)	Fogel and Schneider (2010); Ha et al. (2019); Wen (2011); Buhalis et al. (2020); Han et al. (2018); Kaur and Thakur (2019), Wen (2011)
6	Stimulus-Organism-Response Theory	Chopdar et al. (2022); Hyun et al. (2022)
7	Perceived Risk Model	Mohseni et al. (2018); Lee and Huddleston (2010)
8	Theory of Consumer Trust	Jun et al. (2010), Wen (2011), Truong and Truong (2022)
9	Mass Media Dependency Theory	Ruiz Mafé and Sanz Blas (2006)
10	Expectation Disconfirmation Theory	Uzir et al. (2021), Liu et al. (2016)
11	Uncertainty-Avoidance Theory & Collectivism Theory	Rosillo-Díaz et al. (2020), Jun et al. (2010), Rosenbaum (2005)
12	Social Exchange Theory	San-Martin et al. (2020), Cuevas et al. (2021)
13	Perceived value Theory, Heuristics Framework, Domain-specific innovativeness	San-Martin et al. (2020), Jebarajakirthy et al. (2021), Lee and Huddleston (2010)

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