Hussam Al Halbusi
Khalid Al-Sulaiti2
Jaffar Abbas- 1Management Department, Ahmed Bin Mohammed Military College (ABMMC), Doha, Qatar
- 2Al Rayyan International University College in partnership with University of Derby UK, Doha, Qatar
- 3Antai College of Economics and Management, School of Media and Communication, Shanghai Jiao Tong University, Shanghai, China
- 4Newcastle Business School, Northumbria University, Northumbria University, Newcastle upon Tyne, United Kingdom
The COVID-19 pandemic developed new challenges for global consumers. In response to this disaster, digital technology users have faced the necessity to adopt and use specific technology apps for online shopping. This article examines how contingencies disrupt existing theoretical models and their implications for the post-COVID-19 era for online purchases. Customers prefer apps to use on the websites for search and purchase amid the COVID-19 crisis. The websites offer competitive advantages to apps for branding and CRM prospects. This motive keeps customers happy and satisfied with the website offers. This study focuses on consumer electronics and observes the comparative influence of fundamental elements (i.e., hedonic motivation, habits, perceived risk, technological trust, and technological awareness) on purchasing customer satisfaction. The study further examines the impact of customer satisfaction with online purchases with website continuance intention (WCI). Notably, this study explores the moderating effect of word-of-mouth (WOM) on the relationship between customer satisfaction with online purchases and website continuance intention. This study designed a web-based survey and recruited frequent visitors including international and citizens of Qatar for data collection. The study employed a purposive sampling technique and used three standardized psychological tools to obtain the data set needed to measure customer satisfaction with online purchases. The survey used a web link, distributed 600 questionnaires via email and social media, and received only 468 responses. After screening, only 455 were valid responses. The study showed a response rate of 75.83%. The study results showed that hedonic motivation, habits, perceived risk, and technological awareness were positively related to customer satisfaction with online purchasing. Besides, customer satisfaction with subsequent online purchases is also positively associated with website continuance intention (WCI). The results revealed that this relationship remained stronger when word-of-mouth (WOM) was higher. Hence, this shows that online shopping is seen as a vital and interesting activity in the Qatari context. The findings provide useful insights for future studies to explore the effects of COVID-19 on online purchase intentions.
1 Introduction
The E-commerce market is growing at the fastest rate in the world. Consumer electronics is the most popular category, accounting for 48% of total e-commerce sales in this profitable industry (Wang et al., 2019; Trivedi & Sama, 2020). The active participation of millennial consumers, who make up the largest consumer demographic online, is driving the online retail movement (Edeling, & Himme, 2018; Zhao et al., 2021). As millennials lose interest in traditional advertising, consumer electronics category marketers turn to social media influencers to develop an engaging relationship with them (Cooley & Parks-Yancy, 2019; Bazi et al., 2020; Al Halbusi et al., 2021).
The COVID-19 pandemic developed new challenges for global consumers (Abbas et al., 2019a; Aqeel et al., 2021b; Li et al., 2021; Aqeel et al., 2022; NeJhaddadgar et al., 2022). In response to this disaster, digital technology users have faced the necessity to adopt and use specific technology apps for online shopping (Aqeel et al., 2021a; Abbas et al., 2021; Paulson et al., 2021; Farzadfar et al., 2022; Ge et al., 2022). Customers choose apps to use on the websites for search and purchase amid the COVID-19 crisis (Mubeen et al., 2021; Sarfraz et al., 2021; Aman et al., 2022; Liu et al., 2022; NeJhaddadgar et al., 2022). Disease outbreaks and epidemics have changed online shopping behaviors throughout human recorded history, transformed societies, affected personal relationships, and transformed world paradigms (Moradi et al., 2021; Wang et al., 2021; Zhou et al., 2021; Fu & Abbas, 2022; Rahmat et al., 2022). The coronavirus (COVID-19) virus has severely influenced humans’ way of life (Abbas, 2020; Aman et al., 2021; Mubeen et al., 2021; Soroush et al., 2021; Mamirkulova et al., 2022). Governments have imposed several lockdown restrictions that directly affect how people buy stuff online, and businesses function (Pouresmaeil et al., 2019; Khazaie et al., 2021; Lebni et al., 2021; Shoib et al., 2021; Yoosefi Lebni et al., 2021). The response to the pandemic (COVID-19) has led to overnight shifts in people’s daily lives, the day-to-day operations of companies, and online buying behaviors’ that would otherwise never happen (Abbas et al., 2019a; Abbas et al., 2019b; Moradi et al., 2020; Su et al., 2021a; Su et al., 2021b). Consumers have been inclined to change their preferences and behavioral patterns (Abbas et al., 2019a; Shuja et al., 2020a; Shuja et al., 2020b; Yoosefi Lebni et al., 2020; Azadi et al., 2021; Azizi et al., 2021; Maqsood et al., 2021). It includes shifting to online purchase needs and substitute pickups and delivery options (Uzir et al., 2021a; Uzir et al., 2021b; Ghazali et al., 2022).
Few studies, nevertheless, have attempted to comprehend the impact of influencer marketing on numerous aspects of customer behavior (Lou, & Yuan, 2019; Argyris et al., 2020). Choosing between topic expert influencers and also proven to be a challenge for marketers (Campbell, & Farrell, 2020; Theocharis & Papaioannou, 2020). Few academic research has sought to compare the efficacy of the two methods; thus, this is a crucial addition to the field (Schouten et al., 2020; Trivedi & Sama, 2020). In addition, the impact of influencer marketing on the consumer-brand connection has received less attention. In todays market, such research is critical when marketers attempt to build an emotional link with their customers. Because the impact of different types of endorsers varies depending on the product they promote, finding the right influencer is more complex (De Veirman et al., 2017; Schweitzer et al., 2019). Thus, e-commerce has become a crucial aspect of today’s business environment since technological advancements have increased and the proliferation of the Internet (Kabango & Asa, 2015; Leung et al., 2019). Because of its ease of use, comfort, and cost-efficiency, e-commerce has become an essential buying tool for customers worldwide. Different facets, such as the social nature of e-commerce, appear to influence online purchase intentions (Chawla & Kumar, 2021; Hassan et al., 2022).
Nonetheless, e-commerce has changed the way consumers purchase products and services. With the growing importance of online sales and the rise in the number of online shoppers, marketers and academics have focused their interest on better understanding online purchases behaviorally, and there is a significant opening that requires deliberation (Kumar, & Ayodeji, 2020; Tokar et al., 2021). E-commerce is more economical and convenient than offline shopping (Chawla, & Kumar, 2021). Therefore, several issues need to be highly concerned. A combination of fundamental elements (i.e., hedonic motivation, habits, perceived risk, technological trust, and technological awareness) could significantly advance knowledge as these components are critical for several reasons. For instance, hedonic motivation means a person’s pleasure and pain receptors on their willingness to move toward a goal or away from a threat (Khatimah et al., 2019). In addition, habit is another element that people do often and regularly, sometimes without knowing that he/she is doing it. The perceived risk could be assessed as a potentiality in pursuing one’s desired consequence or resulting in digital technology utilization (Featherman and Pavlou, 2003). Perceived risk is consumer utilization of novel digital technologies having an unfortunate upshot. Besides, consumer inclination to accept risk on the propensity of favorable presumption on behavior in a digital platform could be regarded as a technology of trust (Ennew & Sekhon, 2007; Uzir et al., 2021a). Technology trust as a variable is recently applied in adopting digital medication studies. Technology trust is essential in predicting and examining online behavioral intention in medical sciences. Concerning the technology, awareness reflects the individual’s values and beliefs on adjacency and alignment with the new technology. One can see others using the new technological system in an organization (Venkatesh et al., 2003). Thus, this study intends to contribute to the knowledge by examining the mentioned factors toward customer satisfaction with online purchasing and subsequently testing the consumers’ satisfaction with online purchasing on website continuance intention (WCI).
Notably, the strength of this study is that we advanced the theory by analyzing the moderating role of Recommendation Words of Mouth (WOM) on the relationship between customer satisfaction with online purchasing and website continuance intention (WCI). WOM is particularly important in marketing and e-commerce since intangible products are difficult to judge before consumption (Reza Jalilvand & Samiei, 2012). They provide more information about the product to help make a more informed decision (Hammood et al., 2020; Talwar et al., 2021). According to Park and Nicolau (2015), they are seen to be crucial empirical information cues. More delight and satisfaction came from higher review ratings than moderate or lower ones.
At present, academia has applied various theoretical frameworks to explore consumer attitudes toward online purchases through website continuance intention (WCI), including the Theory of Planned Behaviour (TPB), the Technology Acceptance Model (TAM), and the Unified Theory of Acceptance. However, different studies have examined the value-attitude-behavior (VAB) model in research on green purchase intention through online shopping (Alalwan et al., 2018), green shopping decisions (Alkailani & Abu-Shanab 2021), and online purchases (Al-Khalaf & Choe 2020). In the literature, to our knowledge, past studies have ignored VAB models linking online purchase intentions. In addition, technology adoption, awareness, and technology trust are critical to customer satisfaction for online purchases through Website Persistent Intent (WCI). In this regard, word-of-mouth (WoM) recommendation plays a crucial role in the technological adoption of online shopping, suggesting that consumer behavior research has not received any attention in the existing literature. Therefore, this study aims to address this identified gap by using the VAB model to identify factors that promote online purchases with website continuance intention (WCI).
The literature shows that few studies have explored employee attitudes and values in various domains (Chen et al., 2004; Cheng & Lee 2011; Chen & Chang 2013; Chawla & Kumar 2021). Still, to the best of the researchers, their application in online purchasing decision research is lacking. These sites provide a competitive advantage for brands and CRM prospecting applications. This motivation makes customers feel satisfied and satisfied with the service provided by the website. This study focuses on consumer electronics and observes the comparative impact of fundamental elements (hedonic motivation, habits, perceived risk, technological trust, and technological awareness) on purchasing customer satisfaction. The study further examines the impact of customer satisfaction with online purchases and website persistence intent (WCI). Notably, this study explored the moderating effect of word of mouth (WOM) on the relationship between customer satisfaction with online purchases and continued website usage intentions. Several studies have examined various factors influencing online purchasing and consumer cognitive attitudes. It shows significant predictors of online purchases in fast food consumption (Chevalier & Mayzlin 2006). Furthermore, testing in different contexts (especially online purchases) is needed to demonstrate the predictability of emotional attitudes to online purchases. Again, in the context of online purchase intentions, past research has failed to incorporate values and perspectives into a single model, leading to a gap in searching for better explanations.
The pandemic (COVID-19) influenced consumer buying behavior worldwide. The pandemic has changed purchase patterns and popular online purchases in response to the COVID-19 protective measures. According to the statistics given by Forbes (2020), in 2019, almost 81% of American consumers never bought groceries by using online platforms. In contrast, the COVID-19 pandemic outbreak has changed the situation and reversed buying patterns. Almost 79% of consumers in the United States purchased groceries and other stuff during 2020 amid COVID-19. US consumers’ have purchased groceries through online shopping, and it increased from 1.20 US dollars in August 2019 to 7.20 US dollars by June 2020 (Forbes, 2020). McKinsey (2020) reports that 15% of European consumers have already adopted new online grocery services and 12% have even switched to new grocery stores after home delivery or click-and-collect services. These new customer segments appear to be continuing to use online grocery services even after the first peak of the pandemic (Uzir et al., 2021a; Uzir et al., 2021b; Tyrvainen et al., 2022). The pandemic has changed online shopping worldwide, including Qatar and other Gulf countries.
In this context, while investigation provides various insights to fill the gaps in consumers’ purchasing behavior, further research with limited and targeted perspectives is required. Such a study can concentrate on the most recent trends, changes, and transformations in the globalized market regarding consumer behavior and the elements that influence it. Further research is required, particularly in the Middle East region, where online shopping has become increasingly popular. In particular, this research focuses on the Qatari context. With an estimated value of $ 1.2 billion, the e-commerce market in Qatar represents an ideal context for this research, especially since Qatar was ranked the seventh largest e-commerce market in the MENA region in 2015 (Alkailani & Abu-Shanab, 2021). The Qatari market is a favorable setting for e-commerce adoption for varied purposes. The first is the vast amount of discretionary income available to its citizens. Second, Qatar’s population is oriented toward younger urban residents who can afford online shopping. Lastly, Qatar provides high-speed internet with fixed connectivity (Al-Sulaiti et al., 2006; Al-Khalaf, & Choe, 2020; Khatoon et al., 2020). All of this points to Qatar as a suitable location for e-commerce development. In essence, this study examines the direct effect of the hedonic motivation, habit, perceived risk, technology trust, and technology awareness toward customer satisfaction with online purchasing and, subsequently, the effect of customer satisfaction with online purchasing on the website continuance intention (WCI). Significantly, this study analyzed the moderating role of recommendation words of mouth (WOM).
2 Theory and Hypotheses
2.1 The Relation of Hedonic Motivation With Customer Satisfaction of Online Purchasing
Hedonic motivation refers to a consumer’s view of how enjoyable it is to use a modern technology system (Venkatesh et al., 2012). Adoption was primarily motivated by internal values and utilitarian considerations when most user IS were created to be essentially task-oriented (Thong et al., 2006). In particular, Venkatesh et al. (2012, p. 161) defined it as “the fun or pleasure derived from using technology. It has been shown to play an important role in determining technology acceptance, and it is quite similar to ‘perceived enjoyment’ (Thong et al., 2006). In IS studies, it was revealed to be influential in predicting the intention to implement technology (Venkatesh et al., 2012). As IS designers realized that customers would use information systems to complete tasks and entertainment, they have modified design ideas accordingly (Al Sulaiti et al., 2005; Morosan & Defranco, 2016). Accordingly, it was adapted and added as a construct into the established technology adoption model (Venkatesh et al., 2012). Thus, value in the shopping process raises the pleasure and emotional involvement provided by the bargaining process (Uzir et al., 2021a; Hassan et al., 2022; Naveed et al., 2022). According to Venkatesh et al. (2012), the pleasure and satisfaction generated by the bargaining process is a kind of hedonic shopping value. However, these shoppers love to shop because they enjoy the shopping process (Atulkar, & Kesari, 2017; Al Halbusi et al., 2020; Tyrväinen et al., 2020). Venkatesh et al. (2012) identified the self-gratifying benefits of shopping, which make the shopper feel better during the process of shopping by reducing stress or tensions.
Researchers showed that some shoppers enjoy socializing (Hassan et al., 2021; Thaichon, 2017; Hoyer et al., 2020) with others while shopping and that shopping gives them a chance to bond with other shoppers (Hoyer et al., 2020). Hedonic shopping value is the perception that a customer perceives during shopping, generates greater values by eliminating the disturbance, and helps customers focus on their shopping activities (Jones et al., 2006; An & Han, 2020). Some consumers may enjoy the latest trends in fashion, styling, or innovations, which motivates consumers to browse retail stores (Silva & Bonetti, 2021). In addition, consumer involvement, freedom, fantasy fulfillment, and escapism enhance the hedonic aspect of shopping (Scarpi et al., 2014; Hoyer et al., 2020). Therefore, retailers today invest a massive amount of money in designing hypermarkets to fulfill the needs of the global brand. With a relaxing and valuable retail environment, all of these attempts are to gain customer satisfaction from purchasing. Therefore, we theorized the following hypothesis:
Hypothesis 1. Hedonic motivation is positively correlated with customer satisfaction with online purchases.
2.2 The Relation of Habit With Customer Satisfaction of Online Purchasing
A study by Venkatesh et al. (2012) stated the idea that consumers’ automatic behaviors outside of the task context influence their behavior. They comprised habit, which reflects the extent to which consumers are likely to perform automatic behaviors due to learning (Venkatesh et al., 2012), which are put in motion after some amount of repetition (Orbell et al., 2001). The habit was conceptualized separately from behavior in several models (Khalifa & Liu, 2007). However, it was established as a predictor of behavioral intentions (Featherman and Pavlou, 2003; Hassan et al., 2021; Hassan et al., 2022) and continuing usage of IS (Khalifa and Liu, 2007). Lankton et al. (2010) mentioned that consumers involved in commercial tasks spanning from need analysis to product consumption/evaluation go through a series of repeating actions, which, in the context of e-commerce, could lead to habit formation (Venkatesh et al., 2012; Hsu et al., 2015).
The meta-analysis study by Jeyaraj (2022) reported that the choice environment remains relatively consistent in situations where the behavior is frequently practiced (daily to several times a week). The frequency of past behavior has a more substantial direct effect on future behavior than the cognitive-based intention to perform the behavior (Viswesvaran and Ones, 1995). In those cases, the individual’s prevalence of previous behavior might be a good predictor of habit formation, commonly referred to as habit formation (Ajzen, 2002; Hsu et al., 2015). Therefore, habit is a factor that will favorably influence customer satisfaction with online purchases. According to Khalifa and Liu (2007) and Kim & Kim (2019), if customers are equally satisfied with an online store, clients with high levels of habit are more likely to repurchase from the same online business. Therefore, the study formulated the following hypothesized statement:
Hypothesis 2. Habit is positively related to customer satisfaction with online purchasing.
2.3 The Relation of Perceived Risk With Customer Satisfaction of Online Purchasing
The concept of perceived risk is that customers interacting with virtual retailers, which have larger unpredictability than traditional businesses, is a significant challenge for internet commerce (Shiau et al., 2017; Wu et al., 2020). Online commerce has less verification and control for a simultaneous exchange of products and money (Cheng & Lee, 2011; Marakanon & Panjakajornsak, 2017). When there is a high amount of perceived risk, consumers might use risk-reduction methods, including warranties, trustworthy suggestions, a solid reputation, and supporting information (Chen & Chang, 2013; Sharma et al., 2021). Consumers would be hesitant to use online purchasing if there was no system-based technique to limit transactional risk from the e-vendors’ undesired behavior. As a result, perceived risk is a significant stumbling block for online shoppers making purchasing decisions. The term “perceived risk” refers to a consumer’s belief in the possibility of unfavorable consequences from an online transaction (Kim et al., 2008; Wu et al., 2020). The term “perceived risk” has been used extensively in marketing literature. According to an early definition, there are several sorts of risk: financial, performance (product), physical, psychological, social, time, and opportunity cost (Wu et al., 2020). Traditional shopping is dominated by two categories of risk: financial and product risk (Bhatnagar et al., 2000; Wu et al., 2020).
Because these are critical issues in internet-based communications, the information-based risk is a specific worry in online buying regarding uncertainties related to vendors, such as suggestions, security, and privacy (Chiu et al., 2014). In online businesses, this study presents perceived risk as a single construct with the four qualities of financial, product, suggestion, and security, when privacy is considered part of security. Thus, the role of perceived risk in the poor perception of shopping at e-stores has been highlighted in previous studies (Shaw & Sergueeva, 2019). To better understand the uptake of services like online banking and portfolio management, a study model combining the technology acceptance model (TAM) and perceived risk has been developed (Hwang & Choe, 2020). Perceived risk is described in that study by several factors, including financial, performance, psychological, social, and so on. Perceived danger had an adverse impact on numerous elements due to its uncertainty (Horst et al., 2007; Wu et al., 2020). Accordingly, this study proposed the following hypothesis:
Hypothesis 3. Perceived risk is negatively related to customer satisfaction with online purchasing.
2.4 The Relation of Technology Trust With Customer Satisfaction of Online Purchasing
Trust is described as an individual’s willingness to accept vulnerability based on optimistic expectations about the motives or behavior of others in an environment marked by interdependence and risk (Ennew & Sekhon, 2007; Uzir et al., 2021b). Technology adoption research often uses trust as a variable. It was found to be a significant predictor of behavioral intent (Venkatesh et al., 2016). There are also studies on mobile banking (Alalwan et al., 2018), e-learning (Tarhini et al., 2017), and online information services (Oh & Yoon, 2014). Security and trust issues when using the system will dominate the application of trust value in user decision-making. The significant unpredictability, intangibility, heterogeneity, and vagueness related to Internet use and technology may explain the interest in the concept. (Gefen et al., 2003; Barua et al., 2018; Ul Hassan et al., 2020).
The presence of trust is a prerequisite for any transaction to be completed successfully. Thus, technology trust gives predominant leverage to allow them to coproduce the services (Collier & Sherrell, 2010; Pappas, 2016; Alsaad et al., 2017; Alnoor et al., 2022). Nevertheless, there are some solid theoretical bases. For example, researchers report that technical reliability improves trustworthiness (Skard, & Nysveen, 2016; Barua et al., 2018), or trust is customers’ perception of credibility and reliability for customer perception (Ashraf et al., 2014; Leung & Ma, 2020). In addition, Kim et al. (2013) suggest that when the users perceive that a system is reliable, that assists in fostering trust in the system. Therefore, the study postulates the following hypothesis:
Hypothesis 4. Technology trust is positively related to customer satisfaction with online purchasing.
2.5 The Relation of Technology Awareness With Customer Satisfaction of Online Purchasing
Technology awareness represents an individual’s attitudes and views about the new technology’s applicability and alignment. The extent to which others in an organization use a new technical system (Venkatesh et al., 2003). Compared to other constructs, technological awareness has received less attention in the research. More precisely, awareness in connection with technology refers to awareness and comprehension of a specific technological product or service (Mofleh et al., 2008). Per the description, technological awareness is the knowledge of how to utilize and features of a particular technology or technological component (Lingmont & Alexiou, 2020). Awareness of any e-services in the context of this definition can be highly beneficial in improving their use (Huang et al., 2019). Several studies have stated that technological awareness is crucial for many perspectives. For example, Top et al. (2011) and Belanche et al. (2014) have stated that having a good understanding of technology can help people use e-services more effectively. Lee and Wu (2011) and Naveed et al. (2022) have stated that a target audience will only embrace a technical innovation if they are sufficiently aware of it. It was also said that when individuals are informed of the most recent feedback, they have more trust in implementing it. Therefore, Individuals need to understand and comprehend the latest technological advancements to enhance their usability (Bamberg, & Möser, 2007; Huang et al., 2019). Thus, based on these explanations, the current study hypothesized this statement:
Hypothesis 5. Technology awareness is positively related to customer satisfaction with online purchasing.
2.6 The Relation of Customer Satisfaction of Online Purchasing With Website Continuance Purchasing
Customer satisfaction allows businesses to improve sales income and obtain a competitive advantage over competitors (Lewin, 2009; Charoensukmongkol, & Sasatanun, 2017), as well as gain customer satisfaction, which leads to long-term benefits (Wirtz, 2003; Yi, & Nataraajan, 2018). Thus, customer satisfaction stems from the awareness that businesses must interface with dynamic environments in ways compatible with customer behavior to remain competitive (Smith et al., 1996). Customer satisfaction may have a role in the work’s success and continuance (Sadowski, 2017; Amin et al., 2020). Hsu et al. (2015) and Khatoon et al. (2020) mentioned the effects of perceived playfulness. They perceived flow on customer satisfaction and purchase intentions using playfulness and perceived flow as an outcome (Uzir et al., 2021a). The findings revealed that the customer’s perception of playfulness and flow is influenced by the quality of the website (Amin et al., 2020; Ashfaq et al., 2020). Therefore, Participating users create online social networks in e-commerce systems/sites by establishing social relationships with their peers, such as real-world acquaintances, online acquaintances, or like-minded individuals (Sherchan et al., 2013; Khare et al., 2020). Users can share, analyze, and find relevant content using the online social networks that have been built in this way (Shao et al., 2020). Positive referrals within social networks, in particular, boost cognitive trust in the service provider (Kuan, & Bock, 2007; Arora et al., 2017). According to Möllering (2002), cognitive trust comes before emotional trust, and emotional trust leads to establishing positive or negative expectations about the trustee. Liu and Park (2015) discuss the importance of reviewers’ identity and reputation of the vendor as critical in encouraging customers to purchase services online. Therefore, the study proposed the following hypothesis:
Hypothesis 6. Customer satisfaction with online purchasing is positively related to Website continuance purchasing.
2.7 The Moderating Role of Recommendation Word-of-Mouth
A large body of literature investigates the impact of WOM on online consumer behavior, with the valence and volume of WOM being the most widely explored (Dellarocas et al., 2007; Duan et al., 2008a; Bulut & Karabulut, 2018). Nevertheless, some studies displayed that WOM volume positively affects subsequent sales (Duan et al., 2008b; Amblee & Bui, 2011; Al Halbusi & Tehseen, 2018), a sales rank of electronic products (Cui et al., 2012; Gu et al., 2012), books (Chen et al., 2004), and online purchase intention (Park et al., 2007). Some studies find WOM valence has a positive impact on sales rank of electronics (Archak et al., 2011), books and movie box-office performance (Chevalier, & Mayzlin, 2006; Chintagunta et al., 2010), sales of cellphones (Gopinath et al., 2014), consumer package goods (Maslowska et al., 2017), and beer (Clemons et al., 2006). Therefore, this study has identified WOM as the contingent role and a boundary condition concerning customer satisfaction with online purchasing and website continuance purchasing. It is because of the following reasons: because WOM is a significant influencing factor of online driving behavior, WOM can affect online consumers’ choices through two effects, namely, awareness effects and persuasive effects (Duan et al., 2008b; Liu et al., 2017). The presence of the product is communicated by WOM, which places it in the choice set of online consumers. The persuasive effects, on the other hand, affect online consumers’ views and evaluations of the goods, influencing their decision (Reza Jalilvand et al., 2012).
A study by Lee & Youn (2009) and Qiu et al. (2012) have stated that researchers used attribution theory to understand how WoM influences online customer behavior. Attribution theory examines how people draw causal inferences about why a communicator advocates a particular viewpoint or acts in a particular manner. People frequently attribute compelling information about a stimulus to the stimulus and non-stimulus variables provided by the communicator. However, for many consumers, online or oral reviews have become essential reasoning to develop intentions; the influence of recommendation “word-of-mouth” could be exacerbated by mouth (Arli, & Dietrich, 2017; Pourfakhimi et al., 2020). In the proposed model, the word-of-mouth recommendation is predictable to the model’s structure (Liu et al., 2019). It would be one of the moderating constructs being tested to provoke how this variable can augment the relationship between customer satisfaction with online purchasing and Website continuance purchasing online adoption. Word-of-mouth is the most restricted approach to expressing user satisfaction. However, it is powerful and influential as it is a very personal approach, which results in superior in defining the intention of using online technology for purposes (Website continuance purchasing). Nevertheless, to distinguish the recommendation of the word-of-mouth variable as a moderator. It clearly shows the impact of social influence, a common term that describes all types of external influences that affect the customer’s perception of using technology for their purchases in the Qatar context:
Hypothesis 7. The recommendation word-of-mouth moderated the relationship between customer satisfaction of online purchasing and website continuance purchasing.This relationship will be stronger when recommendation word-of-mouth is high than low (see Figure 1).
FIGURE 1. Research model.
3 Method and Procedures
For this research, the sample is the regular customers who need to purchase and access online services. Thus, we have set inclusion and filtering criteria for the sample during data collection (Aman et al., 2019a; Aman et al., 2019b). These criteria the participants should be 1) Qatari and foreign citizens living inside Qatar, 2) customers who experienced online purchasing services, 3) age at least 18 years old, and 4) no minimum education level required. Thus, the data was gathered by sending a web link. The web link was circulated to emails and social media like WhatsApp, Facebook, Twitter, Instagram, and LinkedIn (Abdelfattah et al., 2022). Thus, out of 600 questionnaires, only 468 responses were returned from the data collection. Subsequently, from the 468 responses, only 455 were valid, with a response rate of 97%. Notably, 13 questionnaires had irregularities, and the study excluded them.
3.1 Variable Measurement
The measures were adapted from various reliable studies, and three academic experts in related subjects double-checked the questionnaire before the significant data collection phase. Furthermore, we used the cognitive interview approach with fifteen people to assess the questionnaire’s clarity, readability, and applicability, as indicated by Hulland et al. (2018), where only minimal changes were recommended the questionnaire was approved. Nevertheless, the survey questionnaire was translated from English to Arabic because the respondents’ first language was Arabic. Two bilingual speakers translated the items into Arabic following Brislin’s (1980a) back-translation procedure. Then two bilingual speakers compared the translated version to the original version to verify the translation (Brislin, 1980b). The differences were resolved through a cycle of retranslation and review, and then an agreement was reached. All items were measured on a five-point Likert scale ranging from 1 for strongly disagree to 7 for strongly agree.
The study measured the hedonic motivation and adapted 3-items from Venkatesh et al. (2012). Example: “I find the use of online purchasing technology a relaxing experience.” Habit refers to the extent to which people tend to perform behaviors automatically because of learning and habit. Thus, we measured habit 4-items and took them from (Venkatesh et al., 2012; Halbusi et al., 2021). For example, the item, “The use of online purchasing service websites has become a habit for me.” Perceived risk was measured 7-items was adapted from Corbitt et al. (2003). It is emphasized that customers feel worried about performance, financial, and time risks when buying from the internet and less about social and physiological risks. Example of the item “I believe that online purchases are risky because the products/services delivered may be of inferior quality.” The technological trust was assessed with 5-items adapted from Ejdys (2018). The items emphasize trust in technology and show that a person’s belief in the development of technology has an impact on the trust in a particular technology. It is already used or will be used in the future. For example, the item, “I can rely on the web system where I purchase the products.” This study measured technological awareness and adapted 3-items from Collins (2007). For instance, “I have the technical knowledge required for appearing in an online purchasing” We measured customer satisfaction with online purchasing with 4-items taken from Cao et al. (2018). For example, in the item “The online shopping store provides good customer service quality.” Word of mouth (WOM) was measured with 3-items borrowed from Maxham (2001). Typically, WOM provides vital information about a firm, product, and service to consumers that often helps consumers decide whether or not to patronize a firm, product, and service. Example of the item “Given my experience with (online service), I would recommend their service to my friends."
4 Data Analysis and Results
The study applied structural equation modeling (SEM) with partial least squares (PLS) Smart PLS 3.3.3 (Ringle et al., 2015). It is crucial because it is ideal for sophisticated causal analyses, including both first- and second-order ideas, and it does not require severe assumptions regarding the variables (Henseler et al., 2009). (Hair et al., 2017). The study investigated the statistical significance of the path coefficients. The PLS analysis used 5,000 subsamples to construct bootstrap t-statistics with n–1 degree of freedom (where n is the number of subsamples).
4.1 Common Method Bias
The issue of common method bias (CMB) may arise since the independent and dependent variables were collected using the same survey. To solve this challenge, we adopted a two-pronged technique using procedural and statistical approaches (Podsakoff et al., 2003; 2012). We used numerous measuring scales in the research instruments on a procedural level in terms of the procedural element (Afthanorhan et al., 2021). We also told the participants that there were no right or wrong answers and that their identities would be kept private. In addition, we used variance inflation factors (VIFs) in the statistical remedies to achieve a comprehensive collinearity test (Kock, 2015). Kock and Lynn (2012) advocated doing such a test to measure vertical and lateral collinearity. Kock and Lynn (2012) stated that when the VIF is more extensive than 3.3 indicates pathological collinearity, suggesting that CMV may contaminate the model. Nevertheless, as shown in Table 1, this study is considered free of CMV.
TABLE 1. Common method variance assessment via full collinearity estimate criteria.
4.2 Measurement Model Assessment
The measurement model deals with validity and reliability. Therefore, we observed individual item reliability, internal consistency, and convergent and discriminant validity. Concerning item reliability (indictors loading), the results reveal that all items exceed the recommended 0.5 level (Hair et al., 2017) (see Table 2). To assess the constructs’ internal consistency, we employed Cronbach’s Alpha and composite reliability; they ranged from 0.747 to 0.883, higher than the 0.70 cut-offs (Hair et al., 2017). In support of convergent validity, the average variance extracted (AVE) for the constructs ranged from 0.531 to 0.682, above the 0.5 thresholds (Hair et al., 2017) (see Table 2).
TABLE 2. Measurement model, loading, construct reliability, and convergent validity.
The study analysis identified no problems with discriminant validity; the AVE for each construct was more significant than the variance shared by each construct with the other latent variables (Table 3). (Hair et al., 2017). Henseler et al. (2015) proposed that the Heterotrait-Monotrait ratio (HTMT) of correlations based on a Multitrait-multimethod matrix is more reliable in finding the results. Table 3 indicated that the HTMT values are less than 0.90, demonstrating that each pair of variables has discriminant validity. All HTMT values are significantly different from one (1), and the 95 percent confidence intervals (CI) do not include 1 (Henseler et al., 2015), signifying that each pair of variables has discriminant validity.
TABLE 3. Descriptive statistics, correlation matrix, and discriminant validity.
2.3 Structural Model Assessment (Hypothesis Testing)
This section goes with our direct hypotheses from Hypothesis 1 to Hypothesis 6. According to hypothesis testing, the hedonic motivation was significantly associated with consumer satisfaction with online shopping (= 0.386, t = 6.467, and p 0.000). As a result, Hypothesis H-1 was approved. The second direct effect (Hypothesis 2) of the relationship between habit and customer satisfaction with online purchasing was positively significant with values (β= 0.198, t = 3.019, and p < 0.001), therefore, Hypothesis 2 was also supported. Regarding Hypothesis 3, the relation between perceived risk and customer satisfaction with online purchasing was supported as the perceived risk was negative toward customer satisfaction with online purchasing as per (β= -0.280, t = 3.990, and p < 0.000). The relationship between technological trust and customer satisfaction with online purchasing was insignificant (β= 0.045, t = 0.044, and p < 0.153). Hence, Hypothesis 4 was not supported. Similarly, for Hypothesis 5, technological awareness was significantly related to the customer satisfaction with online purchasing, so Hypothesis 5 was supported as per (β= 0.216, t = 5.020, and p < 0.000). Regarding the final direct effect, Hypothesis 6 showed that customer satisfaction with online purchasing was significantly related to website continuance intention (WCI) with values (β= 0.391, t = 3.166, and p < 0.000). Table 4 shows all the mentioned results.
TABLE 4. Structural path analysis: direct effect and interaction effect.
Following the goals of this study, the moderation test was one of the key contributors to determining if words of mouth recommendation (WOM) moderate the relationship between customer satisfaction of online purchasing and is significantly related to website continuance intention (WCI). Consequently, the interaction between customer satisfaction with online purchasing and words of mouth recommendation (WOM) toward website continuance intention (WCI) revealed a significant interaction, such that (β= 0.231, t = 4.640, and p < 0.000). Hence, Hypothesis 7 was supported (see Table 4). Generally, it is not entirely clear how a moderation analysis differs for high and low interaction. In other words, the size of the precise nature of this effect is not easy to define from the analysis of the coefficient itself (Dawson, 2014). Hence, this study employed an interaction plot for the interactions to look at the gradient of the slopes. As shown in Figure 2, the line labeled ‘high words of mouth recommendation (WOM)’ has a steeper gradient when compared to ‘common words of mouth recommendation (WOM). It indicates that when words of mouth recommendations (WOM) are higher, the positive relationship between customer satisfaction with online purchasing and website continuance intention (WCI) is more substantial (see Figure 2).
FIGURE 2. Interaction plot of customer satisfaction of online purchasing x WOM on the website continuance intention (WCI).
Regarding the model’s explanatory power, R-square values of 0.511 for website continuance intention (WCI) indicated a moderate to large influence (Hair et al., 2017). We have used Stone-Geisser blindfolding sample reuse technique to determine the predictive relevance of the model. It also reveals Q-square values greater than 0, indicating that the research model accurately predicts both customer satisfaction with online purchasing (Q2 = 0.218) and website continuance intention (WCI) (Q2 = 0.245). (Hair et al., 2017). Lastly, the SRMR index value of 0.052 is far below the 0.08 cut-off (Henseler, 2017), and the 95 percent bootstrap quantile is 0.059, or higher than the SRMR value, indicating a good model fit (Hair et al., 2017). Moreover, the discrepancy indices dULS (unweighted least squares discrepancy) and dG (geodesic discrepancy) are both below the bootstrap-based 95 percent percentile (dULS = 1.537; dG = 0.662; HI 95 of dG = 0.981) (Hair et al., 2017). As a result, the difference between the empirical and model-implied correlation matrix is insignificant, and we have no reason to reject the model, which is more likely to be correct (Henseler, 2017).
5 Discussion and Conclusion
This study develops and tests a holistic framework to fill the gaps by integrating the values and attitudes of higher-order constructive formats in the emerging markets context. The current study investigates the antecedents of online purchase intentions during the COVID-19 pandemic. This research will allow online shoppers to understand improved their customers and the factors affecting their online shopping behavior during the pandemic. This study expands the literature on online purchasing by applying the VAB model. It focuses on examining the antecedents of online purchasing in emerging economies during the COVID-19 pandemic. Through this article, online shopping platform providers will be able to prepare for future restrictions and the post-vaccination period by understanding the antecedents of online shopping intentions during quarantine.
This research study explored the reasons that drive emerging-market customers to shop online and website continuance intention (WCI). It considers the elements such as hedonic motivation, habit, perceived risk, technology trust, technology awareness, and customer satisfaction with online purchasing (Alsaad et al., 2017; Ashfaq et al., 2020). Notably, in this study, we have employed recommendation words of mouth contingent on the relationship between customer satisfaction with online purchasing and website continuance intention (WCI). However, our findings confirm the positive and direct effect of the hedonic motivation, habit, perceived risk, and technology awareness on the customer satisfaction of online purchasing and subsequently have a positive effect on the customer satisfaction of online purchasing. Interestingly, recommendation word-of-mouth has significantly moderated the relationship between customer satisfaction with online purchasing and website continuance intention (WCI). The relationship is more robust when WOM is high than low.
In terms of the theoretical implications, this study makes a significant contribution to the knowledge by examining the factors like hedonic motivation, habit, perceived risk, technology trust, technology awareness, and customer satisfaction of online purchasing toward website continuance intention (WCI). Significantly, this also analyzed the moderating role of recommendation words of mouth (WOM) on the relationship between customer satisfaction with online purchasing and website continuance intention (WCI). It is because WOM is particularly important in marketing and e-commerce since intangible products are difficult to judge prior to consumption. They give more information about the product to help make a more informed decision. They will feel delighted and satisfied with higher reviews than with moderate or lower ratings.
In terms of policy implications, the current findings of this study have ramifications for policymakers, marketing managers, and academics concerned with Qatar as a developing market. For public authorities to reduce their negative views of online shopping, they must first understand the challenges that online shoppers face, such as a lack of trust (Al-Khulaifi et al., 2001). Therefore, governments can concentrate their efforts on enhancing security and data privacy legislation to maximize the benefits of internet shopping. The degree of uncertainty, risk, and complexity in establishing internet channels is higher in an emerging-market scenario. As consumers in emerging nations become more aware of the advantages of online shopping, online businesses must work to reduce risk perceptions. Consumers believe that the information provided by online reviews is useful. As a result, businesses can focus on creating marketing tactics that encourage customers to provide information that positively influences their buy intentions via web pages. Online retailers should pay attention to online reviews of their products and services to foster the development of trust and encourage their consumers to contribute qualified information. Online retailers should improve the usability of online reviews to encourage consumers to post high-quality online reviews (Luo & Ye, 2019; Uzir et al., 2020). In addition, online merchants can use better and more secure transaction mechanisms, such as mobile wallets, internet security protocols, or secure approval signs to minimize risk perception and encourage individuals to buy online (Ventre & Kolbe, 2020).
A fundamental limitation of this study is that the data came from a single source. However, the study used two surveys and models of direct and moderated variables less likely to be affected by common method bias (Podsakoff et al., 2012; Al Halbusi et al., 2020; Halbusi et al., 2021; Alnoor et al., 2022; Abdelfattah et al., 2022), so CMB cannot be eliminated. In addition to the potential impact of CMB, the consistency of empirical results may have been exposed as self-reported data were used to measure intent, which can be a complex issue. Therefore, socially expected response bias cannot be completely ruled out. Future research should collect data from as many sources as possible, such as the sell-side and buy-side. A second limitation is that the results of this study are based on samples from Middle Eastern cultural contexts such as Qatar. Perhaps, specific cultural characteristics of this context, which include strong adherence to values and different cultural perspectives and structures (Moaddel, 2010), may have influenced the results of this study. Indeed, word-of-mouth (WOM) differences are likely to be replicated in cultural contexts (Triandis et al., 1988). However, it can be seen that further research is needed to assess the contextual sensitivity of these findings (Whetten, 2009) by analyzing other cultures where the importance of religious beliefs and cultural characteristics is similar to those applicable to other settings such as the West. Third, this study did not consider other external factors as it sought to explain continuation intentions. The intention is a complex observation influenced by many organizational, personal, and external variables. Therefore, caution must be exercised when making inferences from this study to the extent that a simplified version of the continuation intent is provided by focusing on a few variables, such as Arabic culture (Al Halbusi et al., 2022).
Additionally, a second limitation is that the study highlights Qatar’s online buying behavior. Upcoming research should compare the findings with other emerging markets in the Gulf. We also recommend looking at other aspects to decide online purchases, as other important variables may influence the Qatari website’s stay-on-line (WCI) intent. Therefore, we recommend evaluating other structures, such as social support or social commerce, for future research. In terms of perceived harm, a better understanding of behavioral elements in emerging markets is needed, as well as identifying possible modifiers, such as the frequency of online purchases. Finally, this survey is primarily quantitative; however, given the wide range of personalized customer buying experiences, qualitative research can better understand the underlying psychosocial and contextual structures that can influence behavioral intentions across different customer segments. Additionally, research on website interactivity, quality of service, privacy, and tour package customization can be conducted to understand better, how these factors affect customers’ willingness to use the website.
Data Availability Statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
Ethics Statement
Ethics review and approval/written informed consent was not required as per local legislation and institutional requirements.
Author Contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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Keywords: COVID-19, online purchasing, customer satisfaction, words of mouth (WOM), website continuance intention, technology innovation
Citation: Al Halbusi H, Al-Sulaiti K, Abbas J and Al-Sulaiti I (2022) Assessing Factors Influencing Technology Adoption for Online Purchasing Amid COVID-19 in Qatar: Moderating Role of Word of Mouth. Front. Environ. Sci. 10:942527. doi: 10.3389/fenvs.2022.942527
Received: 12 May 2022; Accepted: 16 June 2022;
Published: 25 July 2022.
Edited by:
Vishal Dagar, Great Lakes Institute of Management, IndiaReviewed by:
Muhammad Sibt E. Ali, Zhengzhou University, ChinaDr Vikas Poonia, Indian Institute of Science (IISc), India
Copyright © 2022 Al Halbusi, Al-Sulaiti, Abbas and Al-Sulaiti. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jaffar Abbas, dr.abbas.jaffar@outlook.com