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

Ghosh, Munmun Article Meta-analytic review of online purchase intention: conceptualising the study variables Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Ghosh, Munmun (2024) : Meta-analytic review of online purchase intention: conceptualising the study variables, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-35, https://doi.org/10.1080/23311975.2023.2296686 This Version is available at: https://hdl.handle.net/10419/325948 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Business & Management ISSN: 2331-1975 (Online) Journal homepage: www.tandfonline.com/journals/oabm20 Meta-analytic review of online purchase intention: conceptualising the study variables Munmun Ghosh To cite this article: Munmun Ghosh (2024) Meta-analytic review of online purchase intention: conceptualising the study variables, Cogent Business & Management, 11:1, 2296686, DOI: 10.1080/23311975.2023.2296686 To link to this article: https://doi.org/10.1080/23311975.2023.2296686 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group View supplementary material Published online: 31 Jan 2024. Submit your article to this journal Article views: 7054 View related articles View Crossmark data Citing articles: 9 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 Marketing | reVieW artiCLe Cogent Business & ManageMent 2024, VoL. 11, no. 1, 2296686 Meta-analytic review of online purchase intention: conceptualising the study variables Munmun ghosh symbiosis institute of Media & Communication, symbiosis international (Deemed) university, Pune, Maharashtra, india ABSTRACT this meta-analytic review conceptualises and synthesises the study factors related to this phenomenon to effectively comprehend online purchasing intention. this study’s "online purchase intention" concerns consumers’ propensity or likelihood to purchase via online channels. this review develops a conceptual framework that clarifies the aspects influencing online purchase intention by analysing pertinent data. the variables under investigation encompass usefulness, ease of use, congruence, trust, security, e-WOM and many other demographics and contextual factors. the potential moderators were also identified, and the moderating effect is validated in the study variable as framed in the conceptual framework. this review contributes to the field by offering valuable insights into the determinants of online purchase intention, enabling marketers and researchers to devise effective strategies to optimise consumer behaviour in the online shopping domain. 1. Introduction the growing popularity of online platforms and shopping has rapidly increased with time. the proliferation of the internet and mobile phones has completely changed the online scenario. the internet’s rapid penetration has massively turned around business activities, and internet-enabled business has become a new phenomenon (ruiz Mafé & Sanz Blas, 2006). Considering the massive set of advantages that the business offers online, consumers have also shifted their base towards online shopping and started exploring the diverse avenues of the business. Online shopping is one aspect the present-day customer is hooked on (Deng etal., 2021). the variety of offers in the forms of coupons, sales, lucrative discounts, and features of instantaneity, localisation, and super-personalisation have attracted many customers to these platforms and to shop online (Pan et al., 2017). Online shopping is not restricted to electronics, apparel, or basic needs. the periphery of online shopping has expanded its limits to every avenue. From basic needs to food, travel, bookings, and other desires, humans can think can be done and preferred online (teo, 2002; Wani & Wajid, 2016). the benefits, the flexibility, and the broader possibilities have made this the most feasible option among the consumer. the recent COViD-19 pandemic has also enhanced online shopping owing to increased sales (Soares et al., 2023). the spending and purchase habits have changed after the massive pandemic hit (alvarez-risco et al., 2022; truong & truong, 2022). However, along with the wide range of apparent benefits of internet-enabled online transactions, it also counters the fear of trust, anxiety, risk, and fraud, which results in an unwillingness. it refrains consumers from making that final purchase online. Hence, the consumer becomes reluctant to engage in online transactions (Jaradat etal., 2018). Owing to the advent of online platforms, the risk of fraudulence has also increased; hence, consumers usually search for goods and services online to understand the scenario while refraining from the final purchase (Bauman & Bachmann, 2017). as a result, it is now crucial for academics and practitioners to examine the aspects that impact the choice to purchase anything online. the meta-analytic review in this article is focused on comprehending the study factors © 2024 the author(s). Published by informa uK Limited, trading as taylor & Francis group CONTACT Munmun ghosh [email protected] supplemental data for this article can be accessed online at https://doi.org/10.1080/23311975.2023.2296686 https://doi.org/10.1080/23311975.2023.2296686 this is an open access article distributed under the terms of the Creative Commons attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. the terms on which this article has been published allow the posting of the accepted Manuscript in a repository by the author(s) or with their consent. ARTICLE HISTORY received 24 July 2023 revised 12 September 2023 accepted 14 December 2023 KEYWORDS Online purchase intention; meta-analysis; consumer behaviour; technology, online REVIEWING EDITOR Hamida Skandrani, University of Manouba, the Higher institute of accounting and Business administration, tunisia SUBJECTS testing, Measurement and assessment; Consumer Psychology; Marketing; information technology 2 M. gHOSH related to online purchasing intention. as a statistical technique for blending the findings of multiple investigations, meta-analysis is an excellent tool to compile and analyse a considerable body of data systematically and impartially (amos et al., 2014). this review tries to provide a thorough knowledge of the underlying elements influencing customers’ desire to purchase online by looking at a wide range of studies. this study aims to offer a thorough assessment of the existing knowledge base about purchase intention research in the context of online shopping and to provide a thorough grasp of the research that has happened so far in this field. the research attempts to collate and segregate all the previous studies and perform a meta-analysis to help understand the antecedents and the determining factors that finally govern the online purchase intention studies. additionally, the current meta-analysis, apart from investigating the distinct roles of the impactful variables, in coherence with the argument put forward by previous researchers, also put forth an exclusive grouping of the prevalent study variables which were earlier studied in silos. this research fills the gap by putting forth this unique categorisation of the study variables by bringing them together and further establishing their conceptual distinctions among the constructs. this unique categorisation of the study variables has emerged from past studies of diverse temporal trends in geographical areas and widely considered theoretical frameworks. Hence, identifying and validating them in this unique standpoint will offer a distinctive direction to future studies in online purchase intention. the research article follows the subsequent structure. the first section of this article highlights the increasing significance of comprehending the factors that shape consumers’ decision-making processes in the online environment. the crucial factors examined concerning online purchase intention are included in the following sections. this review explores these variables’ relative significance and the potential mechanisms that affect online purchase intention. then, in the next step, the study identified potential moderators from several research, offering insightful information that might affect the relationship between the study variables. this review presented a nuanced knowledge of the existing literature in the subsequent section by presenting various methodological criteria, such as sample characteristics, measurement scales, and data coding methods. the results of this meta-analytic review are provided with probable managerial and conceptual implications. Finally, the study’s limitations and future directions are discussed for greater comprehension. the insight gained from this review has significant implications for academics and business, enabling a more profound comprehension of online customer behaviour and guiding evidence-based choices in the ever-changing e-commerce environment. 2. Literature review and hypothesis development Previous studies in this area have used several theories, majorly including the technology adoption Model (taM), theory of reasoned action (tra), theory of Planned Behaviour (tPB), and Unified theory of acceptance and Use Of technology (UtaUt) and its derivatives (akhlaq & ahmed, 2015; Har Lee etal., 2011; isaac et al., 2017; kaur & thakur, 2019; keisidou et al., 2011; Law et al., 2016; ranaweera et al., 2008; Venkatesh & Morris, 2000). as antecedents of online purchase intention, previous researchers have looked mainly at the technological acceptance criteria such as perceived usefulness, perceived ease of use, perceived behavioural control, perceived risk, subjective norms, enjoyment, attitude, and perceived risk. this derivative has been looked at frequently, along with a few additional derivatives of external factors such as e-WOM, cultural norms, platform diversity, social influence, and innovations. apart from all these, past studies have also identified a few mediators and moderators that play a crucial role in driving purchase intention online. For this meta-analysis study, the frequently and repeatedly used study variables were segregated to put together in groups and then showcased in a well-structured conceptual framework to understand the body of work that has happened so far and which also gives a clear and expressive dimension in context to online purchase intention. the study variables were put together considering their coherence, and they established their role in online purchase intention. the identified study variables from the past studies were clubbed into three categories: Perceived Utility (PUt), reliability and reputation (rr), and Contextual Factors (CF) that lead to Online Purchase intention (OPi). additionally, the mediators/moderators considered in the past studies were categorised into two categories – Personal factors (PF) and Perceived Value (PV) driving the purchase intention online. COgent BUSineSS & ManageMent 3 2.1. Perceived utility (PUT) 2.1.1. Perceived usefulness taM states that a technology’s intended adoption is based on how useful people perceive it (Fortes & rita, 2016). the subjective probability that an individual will think shopping online is more practical than in-person shopping can be used to determine the perceived usefulness of online buying (Chiu et al., 2014; koufaris, 2002; Law et al., 2016). Perceived usefulness assesses how much internet usage will enhance purchasing power (e.g., convenience, agility, and time). according to the literature, perceived usefulness positively correlates with purchase intention (Law etal., 2016). e-commerce is viewed as more affordable and practical than in-person transactions (Ventre & kolbe, 2020). the customer will likely have a better opinion of the website and return to it if they feel that using it and that particular site to make their purchases is beneficial (koufaris, 2002; Moslehpour et al., 2018). Unlike offline purchases, online platforms typically offer various products, increasing the likelihood that customers will find the desired item (Chiu et al., 2014). the internet’s frequency, duration, and use increased when users perceived it as a helpful tool, changing the platform on which customers purchase (isaac et al., 2017). internet use is positively impacted by perceived usefulness (isaac etal., 2017), and prior research has found substantial positive relations between perceived usefulness and attitudes toward online shopping (Celik & Yilmaz, 2011; Chiu et al., 2005; Law et al., 2016; Moslehpour et al., 2018; Sukno & Pascual del riquelme, 2019). in this approach, the hypothesis framed is as follows: H1a: Perceived usefulness significantly and positively impacts OPi. 2.1.2. Perceived ease of use the taM dimension, "perceived ease of use," measures how much a person thinks utilising a particular technology will be effortless (Davis & Venkatesh, 1996). according to the reviewed studies, a person’s attitude toward and desire to use a particular technology are influenced by how simple it is (Law et al., 2016). the literature also highlights how perceived ease of use affects utility, contending that the more a given technology is viewed as valuable by a consumer, the more user-friendly it is (isaac et al., 2017). nevertheless, compared to other studies (Chiu etal., 2005; Law etal., 2016), this research aims to determine the range and validity of the framework in which the perceived ease of purchase is expressed, which affects the dimension’s focal point. as a result, the belief that an online purchase will require less effort than an offline physical transaction constitutes the perceived ease of purchase (koufaris, 2002; Law et al., 2016). Because online users are becoming more accustomed to using online platforms easily, the dimension can be considered an extension of the previous construct. Celik and Yilmaz (2011) reported that innovative and effective online purchasing techniques, such as apps, have been widely adopted due to the internet and e-commerce. therefore, the ease of use of technology affects the user’s perception of its usefulness and tendency to use it more frequently (Sukno & Pascual del riquelme, 2019). as a result, the perceived ease of use is crucial to the online shopping experience and is closely tied to the intentions to purchase (Moslehpour et al., 2018). additionally, numerous research studies have already examined and determined the connection between perceived utility and perceived ease of use, as well as how those two factors relate to the intention to purchase (Celik & Yilmaz, 2011; Chopdar etal., 2022; kim, 2012; Manis & Choi, 2019; Sukno & Pascual del riquelme, 2019) which brings us to frame the hypothesis of the study as: H1b: Perceived ease of use significantly and positively impacts OPi. 2.1.3. Congruence Fit and congruence are separate things, as Bezes (2013) explains. Congruence is based on impressions of similarities between ideas about two different things. if there is a high level of congruence between the customer brand expectations about the new channel, then consumers will rely on a more thorough approach to evaluate the new channel, and they may even mimic their behaviours in the new online context, including loyalty patterns. this processing leads to quicker transmission attitudes (Wang & 4 M. gHOSH Herrando, 2019). nevertheless, what if there is a disparity? (i.e., perceived incongruence). in that instance, this direct and comprehensive transfer might not occur, resulting in a more involved and time-consuming evaluation procedure and, possibly, a more unfavourable inclination from the consumer’s standpoint (Badrinarayanan et al., 2014). Most studies have examined how well offline and online retailers’ images align (Wu etal., 2018) or conceived congruity as a one-dimensional concept (Badrinarayanan etal., 2014; Wang etal., 2009). a few studies have also compared congruity attribute by attribute (e.g., Badrinarayanan etal., 2014; Verhagen & van Dolen, 2009). Badrinarayanan etal. (2014), as a reference work, conceptualised as a higher-order factor with the following sub-dimensions: environment, service, pricing orientation, security, transaction convenience, and aesthetic appeal for a multichannel retailer’s online and physical storefronts. these characteristics can be contrasted between online and brick-and-mortar retailers, including experiential (visual appeal, atmosphere) and functional (navigation, convenience) elements. Verhagen and van Dolen (2009) revealed that a critical predictor of consumers’ intention to make an online purchase is the atmosphere of the online store (as assessed by fun, pleasure, and attractiveness). H1c: Congruence significantly and positively impacts OPi. 2.2. Reliability and reputation (RR) 2.2.1. Security through website features that offer accurate product details, transactional and competent service quality and delivery capacity, customers can learn about the value of products. Without adequate knowledge of the security measures, purchase intent will be deterred. according to azizi and Javidani (2010), disclosure of financial details, such as credit card numbers, account numbers, and Pins, is associated with security. Security concerns are acknowledged as a hindrance to internet shopping (teo, 2002). although consumers can buy and use things more easily online, the lack of security measures will negatively impact consumers’ purchase intentions (Meskaran et al., 2013; tsai & Yeh, 2010). Consumers are reluctant to make online purchases, enter their credit card information, and even ship information (Leeraphong & Mardjo, 2013). However, when customers shop online, it is necessary to provide more personal data, such as the delivery address, personal preferences, and numerous other specifics (Dai et al., 2014). according to Hsu and Bayarsaikhan (2012), security issues negatively affect online purchase plans. Customers uncomfortable with an online site will refrain from providing their personal information and are more likely to offer inaccurate or erroneous information (kayworth & Whitten, 2010). Online purchase chance correlates significantly with security issues (teo & Liu, 2007). according to Martín and Camarero, (2009), consumers avoid internet shopping because they are concerned about having their personal information stolen, not because it is inconvenient. thus, they conclude that security risk significantly affects the intention to shop online. adnan (2014) said that privacy measures are necessary to lower customers’ perceived security risk and increase their propensity to purchase online clothing. Considering the preceding discussion, the hypothesis is suggested: H1d: Security significantly and positively impacts OPi. 2.2.2. Fulfilment reliability according to Wolfinbarger and gilly (2003) definition of fulfilment reliability, this involves both the timely delivery of the appropriate goods within the predetermined time range as well as the accuracy of product descriptions offered on the website so that customers may acquire what the online retailer promised them for their order. although it is appropriate for online shopping, customers wait a few days or weeks before receiving their purchases once they place orders (kautish et al., 2021). When establishing a long-term relationship with an e-tailer, customers are most considerate about fulfilment reliability (kautish etal., 2021; keeling etal., 2013). therefore, a necessary condition to ensure fulfilment reliability is to fulfil the product assurances, service promises, and customers’ orders following the product/service information displayed. Customers’ positive responses concerning aspects of shopping assistance are positively COgent BUSineSS & ManageMent 5 impacted by putting more effort into performance and focusing on the quality of e-tail services within a reasonable time frame, receiving the right product ordered, and receiving the product in its proper form (Peinkofer et al., 2016). Customers’ attitudes toward online purchases were strongly affected by prompt, encouraging, and time-bound responses to their queries (Mishra, 2017; Pahari et al., 2023). the findings of Wolfinbarger and gilly (2003) and Peinkofer et al. (2016) state that Fulfillment reliability was determined to be the most significant factor in referring to favourable customer replies and in online purchases, according to study results from Wolfinbarger and gilly (2003) and Peinkofer etal. (2016). thus, fulfilment reliability was the most crucial element driving online purchases based on references to favourable customer reviews (Peinkofer etal., 2016; Wolfinbarger & gilly, 2003). given the discussion above, the subsequent hypotheses were proposed: H1e: Fulfilment reliability significantly and positively impacts OPi. 2.2.3. Trust Online trust is the most critical component of corporate strategy, according to Bauman and Bachmann (2017), as it reduces perceived risk and generates favourable word of mouth. Predictability, dependability, and fairness are the three critical components of the word "trust," in which the values are examined by comparing the expenses of establishing and maintaining the relationship with their real value to the client (Yuen et al., 2018). in their study, kim and Park (2013) discussed that "perceived ability, perceived benevolence/integrity, perceived critical mass, and trust in a website were the four major antecedents of trust" relating to social networking site product recommendations. Prior studies have also pointed out that online businesses try to reduce risk at first, which enhances customer trust and subsequently raises purchase intention for online goods and services. Other essential components of trust in online buying include shared values, website privacy, and security features (katta & Patro, 2017; Mukherjee & nath, 2007). the significance of trust in online purchases is a crucial predictor of a person’s attitude and purchase intention (ashraf et al., 2014; Hassanein & Head, 2007; Hsu et al., 2013; Lin, 2011). Online buying is thought to have higher risks for customers because there is no direct contact or engagement (O'Cass & Carlson, 2012; Pavlou et al., 2007). this signifies that perceived trust is the main factor influencing online shoppers’ sentiments about a product or service (Van der Heijden et al., 2003). in this regard, Lin (2011) suggests that attitudes regarding e-shopping were significantly influenced by online trust due to the increasing level of uncertainty and dynamic nature of cyberspace. therefore, the following hypothesis is: H1f: trust significantly and positively impacts OPi. 2.2.4. Uncertainty avoidance according to nath and Murthy (2004), those with high levels of uncertainty avoidance consider online platforms as a riskier process, which causes them to reject them. On the other hand, people who do not perceive risk in these transactions are more likely to innovate and adopt them (agarwal & Wu, 2018). Uncertainty avoidance appears to be the individual aspect that is most pertinent to consumer acceptance and use of online shopping (Magnusson etal., 2014). the uncertainty-avoidance dimension is also the most frequently used in the research on online consumer behaviour. this is due to the simplicity with which it can be interpreted in the context of the online market, as well as the fact that the existing research indicates that personality traits like perceived risk and trust are among the most significant factors influencing consumers’ purchasing decisions (Cheung et al., 2005). according to other researchers working in the digital and online context, uncertainty is always associated with ambiguity about how a decision will turn out (Hyun etal., 2022; Jordan etal., 2018). it is broadly recognised that the probability of an online transaction success is low as the uncertainty level increases. it is common to think of uncertainty avoidance in terms of the probability of avoiding the risk of getting a negative outcome. H1g: Uncertainty avoidance significantly and positively impacts OPi. 6 M. gHOSH 2.2.5. Perceived risk in the terminology of Schierz et al. (2010), the expectation of losses is perceived risk. according to Laroche et al. (2005), perceived risk refers to the unfavourable perceptions of unpredictable and changing outcomes from purchased products. Consumer purchase intentions are significantly influenced by perceived risk. according to Lee and Lin (2005), customers who perceive more significant risks are less inclined to make online purchases. (kim and Park (2013) argued that the consumer’s online purchase intentions are weaker the higher the perceived risk of shopping at online retailers. akhlaq and ahmed (2015) highlighted that customer intentions to make online purchases are negatively impacted by perceived risk. this shows that when consumers learn that an online transaction is risky, their desire to purchase is suppressed. Previous findings show a negative relationship between online purchasing intentions and perceived risk (akhlaq & ahmed, 2015; Zhao et al., 2017). Performance, financial, time, safety, social, and psychological risks are all included in perceived risk, as per Featherman and Pavlou (2003). in contrast, Bhukya and Singh (2015) findings on purchase intention looked at four categories of perceived hazards: functional, financial, physical, and psychological. Han and kim (2016) investigated a multidimensional perceived risk in the setting of an online marketplace, including financial, privacy, product, security, social/psychological, and time dimensions. Çera et al. (2020) underline that product, financial, and security concerns are the most significant in the perceived risk category, with the framework of online purchasing being more intensive than other dimensions. H1h: Perceived risk significantly and positively impacts OPi. 2.3. Contextual factors (CF) 2.3.1. e-WOM erkan and evans (2016) stated that online reviews play an important role in covering the anonymity issue of online reviews on shopping websites. See-to and Ho (2014) mentioned that electronic word of mouth (e-WOM) is one of the inexpensive online reviews which affect the purchase intention in social network sites (SnSs). Previous research stated that positive e-WOM increases purchase intention, and negative e-WOM reduces purchase intention. according to Chan and ngai (2011) and See-to and Ho (2014), e-WOM influences purchase intention through the impact of e-WOM on consumers’ trust and value co-creation. the findings showed that the impact of e-WOM in SnS is an essential proposition for marketers, which can help marketers design a better method to propagate the marketing messages through SnS and develop positive e-WOM for the firms and the products and services. as more people use social media and the internet to find relevant information, e-WOM has become increasingly popular. Online users think online opinions are trustworthy and credible (Dwidienawati et al., 2020; Ventre & kolbe, 2020). Previous research on online shopping emphasised the importance of e-WOM in building online trust (awad & ragowsky, 2008; Dwidienawati et al., 2020; Wang et al., 2009). Consumers develop credibility in e-WOM platforms and the content they offer by reading and frequently interacting with e-WOM sources like blogs and websites. trust formation is also greatly influenced by prior interactions (Hsu etal., 2013) in online platforms. Online reviews and recommendations are crucial for consumers looking for new details about goods and services and information on service quality (Chevalier & Mayzlin, 2006). as a result, e-WOM significantly influences online consumers’ attitudes and perceptions (rana et al., 2023; Ventre & kolbe, 2020). Positive e-WOM improves online shoppers’ attitudes and trust by lowering their perceived risk and uncertainty (rana etal., 2023; Ventre & kolbe, 2020). Considering the discussion above, the following hypotheses are put forth: H1i: e-WOM significantly and positively impacts OPi. 2.3.2. Consumer innovativeness Consumer innovativeness is "the degree to which an individual can embrace new knowledge and make innovative decisions without the influence of others," according to Midgley and Dowling (1978, p. 3). Consumer innovativeness was similarly defined by goldsmith and Hofacker (1991) as "consumers’ COgent BUSineSS & ManageMent 7 inclination to acquaint and accept a new product, procedure, or a delivery channel. “Consumer innovativeness may be innate or actualised (Zhang et al., 2020). While actualised innovativeness is related to behavioural manifestations of innovativeness (Zhang etal., 2020), which are reflected through decisions to buy or try new products or technologies (rašković et al., 2016), innate innovativeness is related to personality traits and psychological aspects (Bartels & reinders, 2011; Chang & Chen, 2021). “technological innovativeness” (Zhang etal., 2020) is the expression of innovation (willingness to take risks) when using technology, whereas “product innovativeness” is the reflection of innovation when choosing new products or services (al-Jundi et al., 2019). the current study defines consumer innovativeness as an actualised innovation more specific to a product and technology. Customers’ tendency to acquire or test new products online is, thus, the precise understanding of consumer innovativeness in the current study. Consumer innovativeness affects the decision of which online platform to use and the intention to purchase products (Fatima etal., 2017; Fowler & Bridges, 2010; noh etal., 2014). innovative customers prefer novelty to tradition and are more willing to embrace global trends than the latter (rašković etal., 2016). High-innovative consumers take greater chances to distinguish themselves from low-innovative consumers (Das et al., 2021). Since customers cannot physically see, touch, or feel the products online before buying them, more perceived risks are involved (nawi et al., 2019; thakur & Srivastava, 2015). On the other hand, online methods guarantee quicker availability than offline. as a result, highly innovative clients are expected to choose online purchases over in-store ones, enabling them to distinguish themselves from the rest of society by adopting cutting-edge technologies. H1j: Consumer innovativeness significantly and positively impacts OPi. 3. Potential moderators Based on theoretical arguments, the following possible moderators were found. 3.1. Personal factors (PF) 3.1.1. Demographic variables – gender, age, and income 3.1.1.1. Gender. in marketing, a particular focus has been on how gender affects decision-making and purchase behaviour. it has also been examined in terms of the procedure for accepting new technologies, with the conclusion that depending on the person’s gender, distinct technology adoption features and uses are assessed (gefen & Straub, 1997; Venkatesh & Morris, 2000). in addition, the current study has not revealed any statistically significant variations in internet usage between males and females (Shin, 2009; Zhang, 2005). Men and women exhibit the same interest in adopting technology regarding their degrees of experience (teo, 2002). as a result, gender inequalities become less pronounced due to specialised technological experience or technology adoption (Sobieraj & krämer, 2020). Previous studies have revealed minimal gender-derived differences among a sample of persons who have used technology before (Cai etal., 2017; Shaouf & altaqqi, 2018). 3.1.1.2. Age. Similarly, the traditional literature is reviewed to highlight the significance of users’ ages in understanding their behaviour (Shaouf & altaqqi, 2018; teo, 2002). age has been considered an essential element in many studies to explain internet shopping behaviour (Mummalaneni & Meng, 2009; noh etal., 2014; Shaouf & altaqqi, 2018). according to Venkatesh and Morris (2000), age is closely linked with the difficulty of interpreting inputs and significantly correlated with the amount of time it takes for untrained individuals to get comfortable using online platforms (Hernández et al., 2011; Laroche et al., 2005). age impacts consumers’ first decision to shop online but not their subsequent behaviour, such as the number of transactions or amount spent (McCloskey, 2006). 3.1.1.3. Income. another factor that has gained much academic interest in technology acceptability is income, which may promote or discourage the use of e-commerce (allard et al., 2009; Shin, 2009). numerous research studies have used it to explain shopping behaviour. However, the findings are mixed in their significance (al-Somali etal., 2009; Lu etal., 2003; Miyazaki & Fernandez, 2001; raijas & tuunainen, 2001). internet users with higher incomes perceive fewer implicit risks while making transactions online, 14 M. gHOSH fulfilment reliability and OPi is very high, indicating that it is unlikely that the mean effect sizes for each of these taken-into-consideration factors will be zero. additionally, egger’s regression test (egger et al., 1997) and the rank correlation test (Begg & Mazumdar, 1994, p. 1088) were used to determine objective measures of potential bias. table C above represents the p-values for the tests in each case. For most cases, neither the rank correlation nor egger’s regression test was statistically significant. except for perceived ease of use, egger’s p-value = 0.022 and rank test p-value = 0.047. the extremely low likelihood of publishing bias is implied by the elevated fail-safe n of 12,809. Overall, these tests have not found any significant evidence of publication bias. 8. Moderator analysis Further, the researcher also analysed the variables that might have moderated the impact of each of these antecedents on OPi. 8.1. Univariate meta-regressions Univariate meta-regressions are performed as suggested in earlier meta-analytical investigations (Massey etal., 2018; Wood etal., 2015). For each of the eight remaining antecedents in the two major categories of PF and PV, univariate meta-regressions were used to account for potential moderators that might be used to explain the difference in effect sizes. the moderators tested were (1) Demographic variables, (2) Social variables, (3) technology readiness, (4) attitude, (5) Website Design, (6) Perceived Quality, (7) Price, and (8) Hedonic Benefits. to find additional effects beyond the given hypotheses, each of these moderators was evaluated for the three antecedent groups of Perceived Utility (PUt), reliabilty and reputation (rr) and Contextual Factors (CF). 8.2. Results the outcomes of the univariate meta-regressions are presented in table 2. the findings followed the moderating effects suggested in hypotheses H2, H3, H4, H5, H6, H7, H8, and H9. the summary of supported and non-supported hypotheses is shown in table 2. an interesting finding was identified through the analysis of the study factors that were used as moderators. the Demographic variables, the cumulative aggregation of gender, income and age, are not supported as the moderator. However, other antecedents such as technology readiness, attitude, Perceived Quality, Hedonic Benefit and Website Design appear to be strong moderators that created a more visible impact on the OPi. Moreover, the other moderators, such as Price and Social Variable, which again is a cumulative integration of social identity and social influence, also significantly impact the OPi (table 3). 9. Discussion the current meta-analysis provides a quantitative consolidation of the antecedents of OPi. Online purchase has become an everyday phenomenon in our daily lives owing to many conveniences offered by various Table 2. Publication bias assessment. s. no Variables egger’s regression Rank correlation 1Perceived usefulness 2.074 (p = 0.038) ns 0.141 (p = 0.401) ns 2Perceived 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 5Fulfilment 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 7uncertainty avoidance 0.558 (p = 0.557) ns 0.667 (p = 0.333) ns 8Perceived 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 COgent BUSineSS & ManageMent 15 online platforms (Deng et al., 2021); a meta-analysis will help to understand and identify the patterns in online consumption. it can act as a symbolic driver, providing a foundation for comprehending heterogeneous online shopping motivations across the globe. Based on 60 selected prior empirical studies on online purchase behaviour/intention influencing factors. this study used a meta-analysis to investigate the 18 key variables influencing online purchasing intent. the identified factors considered and analysed in this meta-analysis study are the factors that emerged multiple times in the prior studies considering this subject. a correlation analysis is performed in the first stage of meta-analysis, where each factor considered in the study’s conceptual framework (as shown in Figure 2) is tested to check the statistical significance with OPi. the result presented in table 1 depicts that each of the factors under the three categories of Perceived Utility (PUt), reliability and reputation (rr) and Contextual Factor (CF) are supported and state that they are positively and significantly impacting the Online Purchase intention (OPi). H1a–H1c considered the overall utility and how a consumer perceives online shopping/purchase as applicable. the factors under this category are perceived usefulness, ease of use and congruence. these factors were sufficiently considered in the prior studies (Badrinarayanan et al., 2014; Law et al., 2016; Moslehpour et al., 2018) and looked for in detail to get an in-depth understanding. the study’s hypotheses’ significant results are consistent with previous studies. Overall, the factors under the Perceived 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. 16 M. gHOSH utility categories are the most fundamental factor that builds up a strong foundation of intention to purchase online. H1d–H1h considered the overall reliability and reputation of the online platform and the online shopping experience. if customers can rely on online platforms regarding security, trust, and risk, they find the platform more reliable. also, factors such as uncertainty avoidance and fulfilment reliability are essential as they generate the impression/reputation of the platform. Suppose the consumer is satisfied with how the online channels mitigate the hazard/threat and how the business can deliver the order accurately and on time. these are crucial determinants in an online business and are an important deciding factor impacting online purchase intention. the significant impact of each factor very neatly depicted the study’s consistent reporting with the prior studies (katta & Patro, 2017; nath & Murthy, 2004; Peinkofer et al., 2016; Zhao et al., 2017). H1i–H1j discusses the external/contextual factors that can effectively contribute to online purchase intention. the factors considered under the Contextual Factors (CF) category are e-WOM and consumer innovativeness. Customer online reviews are critical to creating a perception of the product or the online site (See-to & Ho, 2014). Previous studies have discussed the impact of online reviews or e-WOM on purchase intention (See-to & Ho, 2014; Zhang et al., 2020). Here, it is clubbed together with consumer innovativeness as the e-WOM, in many ways, also establishes the initial trust of the customer in the new product; hence, both these factors act as an external push for the purchase online. Moreover, the hypotheses support that the external push or the contextual factors significantly determine the intent to purchase online, which is also a consistent finding concerning the prior studies. Finally, the moderator analysis is performed to understand and validate the indirect effect of study variables on OPi. Here again, the study’s moderators are also well-established moderators of multiple studies in this domain and hence put together to get a crisp and clear picture. the moderators were divided into Personal Factors (PF) and Perceived Value (PV) categories. in the category of PF, we have again subcategories of Demographic Variables (DV), which comprises gender, age and income, and Social Variables (SV), including social identity and social influence. H2 & H4 test the indirect effect of these factors on the OPi. Here, H4 (Social variables) supports their significant impact indirectly on the OPi, and H2 (Demographic variables) came non-significant. While prior studies (McCloskey, 2006; al-Somali et al., 2009; Shaouf & altaqqi, 2018) have reported the moderating effect of gender, age, and income individually on the OPi, clubbing them together creates a significant difference in the magnitude of the factors. the combined effect size of –0.031 of this study factors signifies that all of them together as DV, not impacting the OPi. H3 & H5, under the category of PF, put forth the technology readiness and attitude of the consumer towards OPi. Both these hypotheses have suggested a significant impact on OPi, and we can easily conclude that technology adaptiveness and attitude indirectly impact the OPi in line with the implications drawn by the previous studies (Lee & Wu, 2017; Parasuraman, 2000). Moving to the category of PV, we have factors such as website design, perceived quality, price, and hedonic benefit under hypotheses H6–H9. all these hypotheses supported the factors’ indirect effect on the OPi. interestingly, in this study, all these factors were categorised into Perceived Value (PV), which is assumed to bring a certain amount of value to the online shopping experience, leading to purchase. So, when a customer thinks about the value of the purchase, then price and quality are significant. However, the experience of the purchase also counts meaningfully. the website design and the pleasure derived through the shopping experience add to the value proposition, fulfilling the OPi significantly. thus, all the study hypotheses have justified their significance and relevance being considered and tested in the study, thus validating the study variables. 10.Theoretical and managerial implications 10.1. Theoretical implications Consumer behaviour researchers from all around the globe have paid close attention to studies on online purchases/buying. in the current meta-analysis research on online purchase intention, the online COgent BUSineSS & ManageMent 17 scenario was primarily emphasised, and only a few studies included the distinctive features of comparison with the offline scenario. this study looks into the gap in the literature by concentrating on the factors that can be considered as possible antecedents that influence the intention to make an online purchase. Meanwhile, this study has broadened OPi's range of influences. this study included 60 empirical papers in a meta-analysis. to remove the research bias brought on by conflicting findings in the current research results on online purchase intention, it suggested a comprehensive framework of the influencing elements of online purchase intention based on quantitative statistical analysis. eventually, the criteria were narrowed to 18 concepts, and the study variables were those examined three or more times in the research articles. these variables were further classified into five categories: Perceived Utility (PUt), reliability and reputation (rr), Contextual Factors (CF), Personal Factors (PF) and Perceived Value (PV). the meta-analysis examining online purchasing intention offers a fresh approach to the studies in this domain. this study explores studies from researchers across the globe with a large sample size with an average sample of 486. it helps understand in-depth the implication of the study variables in detail. this meta-analysis helps identify the critical theories and constructs used in various studies on online purchase intention. it segregates them, giving them a unique structure and also helps to identify them. Conceptualising the study variables and studying them helps us comprehend that most studies have used taM, tPB, tra and UtaUt as their foundational theories (Fogel & Schneider, 2010; keisidou et al., 2011; akhlaq & ahmed, 2015; Dewi et al., 2019; kaur & thakur, 2019). although few studies have also discussed the amalgamation of some other theories along with these theories (Bianchi & andrews, 2012; Wen, 2012; San-Martín et al., 2020), they form the core of most studies researching the OPi. in recent times, where artificial intelligence (ai) based technology has been the core of online platforms and enhancing customer experiences, it is imperative to assess and relook at the variables. On top of that, consumers of recent times are also more experienced with online platforms, owing to the pandemic; hence, relooking the existing frameworks has been of utmost importance. this study has aggregated the widely used variables of recent times that govern the OPi and recalibrated them to provide a more robust framework. the framework tested and established in this study will add to the existing theories, helping the researchers get a more holistic understanding due to a new normal period. trust, risk, perceived value, ease of use, usefulness, attitude, easy website navigation, social references and many more have always been the driving force towards OPi. However, classifying them as homogenous categories and validating them with appropriate quantitative methods is an innovative perspective of this study. these categorisations of variables as PUt, rr, CF, PF and PV are also relevant in the era of smart technologies and add more relevance to the post-pandemic times. existing studies and theories have investigated the interactive effects individually. Contrarily, the fine-tuned aspects of the variables used in this meta-analysis study acknowledge the complexity of the consumer decision-making process and support the cumulative impact of the identified categories on present-day consumers and their purchase intentions. these concepts can serve as a foundation for creating modern scales. the current paper also provides definitions of perceived utility, reliability and reputation, contextual factors, perceived value, and personal aspects that may help future research have greater conceptual clarity, adding value to the prior conceptual and empirical findings. 10.2. Managerial implication this study identifies the key global factors and offers in-depth insights into the variances in consumer online shopping behaviours. For brand managers, market segmentation is a crucial subject of interest. Managers can use the current meta-analytic evaluation to guide market segmentation and identify the symbolic factors influencing online purchasing intentions. Such expertise is more important than ever, given that markets are expanding globally, businesses are adapting in the wake of the pandemic, and the internet has become essential to every consumer’s life. Marketing managers can adapt or standardise their worldwide marketing strategy by being thoroughly aware of the factors that drive online purchasing habits across growing and developed regions. this study’s key points and conclusions may have various managerial implications for online businesses. Here are some significant ramifications: 18 M. gHOSH a. enhancing user experience: Understanding the characteristics that affect online purchase intention, such as usability and engagement, can be accomplished by meta-analysing several research. Managers can use these results to enhance the user experience overall and influence purchase intention. b. Building credibility: Online purchase decisions are heavily influenced by dependability and reputation variables, including security, trust, fulfilment reliability, risk avoidance, and uncertainty avoidance. in the current era of innovative technologies like ai-embedded services, the study proposed a category of reliability and reputation that can assist firms in identifying the credibility-building tactics most effectively influencing purchase intention. reliability concerns can aggravate concerns about security, trust, and consumer expectations, just like with ai-based smart services. Businesses can use these insights to improve website credibility and strengthen purchase intent. c. targeting marketing efforts: the meta-analysis review identified variables, such as online reviews or e-WOM and consumer innovativeness tactics while handling the product in an online platform, that substantially impact online purchase intention. Marketers can use these contextual variables to create specialised social media marketing promotions and discounts. By concentrating on the tactics that have the most significant influence on purchase intention, this information can assist managers in more effectively allocating marketing resources. d. Personalisation and Customisation: Businesses tailor and customise their services depending on client preferences by understanding the elements that drive online purchase intention. in the age of smart technologies, where these factors can be replicated to build and integrate hyper-personalisation, the meta-analysis review helped identify specific attributes or features that drive purchase intention, allowing managers to tailor their products or services more specifically and increase the likelihood of conversion. e. Managing customer expectations: the dimension of perceived value identified in this study can assist businesses in aligning their offerings with customer expectations and, in turn, exceed those expectations by delivering high-quality goods and services, enhancing the pleasure and joy of the online shopping experience, and optimising delivery processes. f. Mobile optimisation: as more people use mobile devices for online purchasing, meta-analysis can shed light on the elements affecting consumers’ decision-making, specifically on mobile platforms. Based on these findings, businesses can develop mobile-specific apps or optimise their websites for mobile users to improve the mobile shopping experience and strengthen purchase intent. in general, managers can benefit from a meta-analysis study on online purchase intention to make wise choices on website design, trust-building tactics, marketing initiatives, personalisation, customer expectations, influencer marketing, and mobile optimisation. Businesses may improve their online presence and marketing strategy to boost client engagement and conversion rates by comprehending the significant factors influencing purchase intention. 11. Limitations and future propositions a meta-analytic review can point out critical gaps in the incorporated literature. Several limitations apply to this study, which is detailed in this section. a. although limited by the exclusion of a few studies, considering the strict inclusion requirements, the sample size is equivalent to that in past meta-analysis papers. b. even though other factors may potentially have a substantial impact, this study concentrated on the determinants of online purchase intention because they had been the focus of the majority of studies in the meta-analysis. Future research may examine more factors. c. this study is limited to a few specific quantitative methods. Future research can examine if it would be possible to conduct meta-analyses, especially on experimental studies. More robust evidence on the effects of interventions or manipulations on online purchase intention is provided by experimental designs since they allow for better control of variables and causal conclusions. d. the meta-analysis only used quantitative studies. the incorporation of qualitative studies in weight analysis, which assesses the relationship between antecedents and consequences, should be considered in future research. analysing intricate interactions between variables can be made possible by COgent BUSineSS & ManageMent 19 including structural equation modelling (SeM) in meta-analyses. researchers can explore the underlying mechanisms and pathways that affect online purchase intention using SeM methodologies, giving them more in-depth insights into how various elements interact. e. One significant limitation of this study is that it needs to consider the consequences of cross-platform and multichannel usage. Online shopping activity frequently happens across several platforms and channels. Future meta-analyses can examine the effects of cross-platform and multichannel interactions on the likelihood of online purchasing. By integrating studies that examine the impact of components across various platforms and channels (such as websites and mobile applications), researchers can provide insights into the complexities of the online consumer experience (such as social media and email marketing). f. the fact that this study ignored changes in online purchase intention over time and in response to various stimuli is another one of its limitations. Future meta-analyses can explore the dynamic and time-varying impacts by incorporating articles that look at changes in online purchase intention under various circumstances or interventions. the temporal dynamics and fluctuations in consumer behaviour can be identified using this. g. Future research could analyse how developing technologies affect the desire to purchase online in light of ongoing technological improvements. this involves looking into how technologies like virtual reality, ai, blockchain, and internet of things (iot) gadgets have an impact. in this fast-developing subject, meta-analyses might aid in combining and synthesising the results from various investigations. h. Cultural norms, values, and variables can affect the intention to purchase online. Future meta-analyses incorporating research from other nations and areas can investigate cross-cultural variations. this would give insight into how cultural influences influence the intention to make an online purchase and assist in identifying cultural similarities and variances. i. Online purchasing experiences are significantly influenced by personalisation and recommendation algorithms. Meta-analyses can examine how tailored recommendations, product customisation, and targeted advertising affect the likelihood of an online purchase. this would make it easier to determine how well these strategies work and how they affect customer behaviour. Acknowledgements i am grateful to the journal, editor, reviewers and my organisation for supporting my work and allowing me to conduct my research. Authors’ contributions the whole article is the sole author’s work Disclosure statement no competing interests. Funding the author received no financial support for this article’s research, authorship and publication. Consent for publication i, Munmun ghosh, give my consent for information about myself to be published in the Journal. ORCID Munmun ghosh http://orcid.org/0000-0003-3197-3967 20 M. gHOSH References adnan, H. (2014). an analysis of the factors affecting online purchasing behavior of Pakistani consumers. 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(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. Appendix A. Continued. (Continued) COgent BUSineSS & ManageMent 31 s. no authors sample type of consumer behaviour Method(s) of analysis geographical scope Main findings 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. Appendix A. Continued. (Continued) 32 M. gHOSH s. no authors sample type of consumer behaviour Method(s) of analysis geographical scope Main findings 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 PLsseM 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. Appendix A. Continued. (Continued) COgent BUSineSS & ManageMent 33 s. no authors sample type of consumer behaviour Method(s) of analysis geographical scope Main findings 47 san-Martin et al. (2020) 382 tourists online Purchase intention PLsseM 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 PLsseM 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. Appendix A. Continued. (Continued) 34 M. gHOSH s. no authors sample type of consumer behaviour Method(s) of analysis geographical scope Main findings 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. Appendix A. Continued. COgent BUSineSS & ManageMent 35 Table B. theories used in the study. s. no theories used studies 1taM 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) 2theory 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) 3unified theory of acceptance and use of technology (utaut) amoroso and Mukahi (2013); Dewi et al. (2020) 4Diffusion of innovation theory Bijmolt etal. (2014); Ranaweera et al. (2008); Lee and Huddleston (2010) 5theory 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) 6stimulus-organism-Response theory Chopdar etal. (2022); Hyun etal. (2022) 7Perceived Risk Model Mohseni et al. (2018); Lee and Huddleston (2010) 8theory of Consumer trust Jun etal. (2010), Wen (2011), truong and truong (2022) 9Mass 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)