Factors Affecting Airlines E-Ticket Purchase Intent in Covid-19 Pandemic

Multicultural Education Volume 11, Issue 12, 2025 _______________________________________________________________________________________ 32 Factors Affecting Airlines E-Ticket Purchase Intent in Covid-19 Pandemic Atif Ullah, Sheikh Raheel Manzoor, Sadia Aziz, Muhammad Abdullah Khan Niazi Article Info Abstract Article History Received: September 07,2025 This study aims to examine the factors that influence the airline electronic (e-ticket) purchase intention and the mediating effect of customer satisfaction (CS) in Covid-19 pandemic. The collected sample involved passengers whom booked the e-ticket service for traveling from the top-five well-known airlines of Pakistan's namely, Airblue, PIA, Shaheen Airline, Serene Air and Askari Aviation. The results found that the online shopping factors (OSF), website functionality (WF) has a significant positive influence on the online e-tickets PI whereas, the perceived risk (PR) factors has an insignificant negative effect on the online e-tickets PI. The result also revealed that the CS partially mediates in a relationship with OSF, WF, and online e-ticket PI, whereas, the CS insignificantly mediates in a relationship with PR and online e-ticket PI in the pandemic in Pakistan. The study will aid airlines in identifying key success characteristics that build passengers or customer confidence in developing countries, and also focusing efforts in the proper direction to reduce risks and convert traditional purchasing methods to online purchasing patterns. Accepted: December 08,2025 Keywords : Online-Shopping-Factors, Online Purchase Intent, Customer Satisfaction, Perceived Risk, Website Functionality DOI: 10.5281/zenodo.17868697 Introduction The COVID-19 epidemic is wreaking havoc on the world's market (McKibbin & Fernando, 2021). The stock markets are crashing all around the world (Kaplan et al., 2020). As the pandemic spreads, global conferences, sports, fashion, and other events are also being cancelled. The tourism sector has also been affected adversely due to travel bans, the closing of public places, and a government imposition of the travel ban. Numerous airlines have cancelled their scheduled flights due to lower demand, whereas, the British local airline Flybe crumbled (Parnell-Turner et al., 2020). The cruise line industry was hard hit and several train stations have also been closed (Parnell-Turner et al., 2020). International mail between some countries stopped or was delayed due to reduced transportation between countries or suspension of domestic service (Baker et al., 2020). As a pandemic began the online shopping was increased gradually with stay-at-home orders in place around the globe (Ecola et al., 2020). The recent increase in the e-business domain has also been observed and customers have concentrated on a multitude of activities such as buying online, clothes, shoes, groceries, furniture, beauty items, and the airline electronic ticket (e-ticket) (Tandon et al., 2018). The rapid growth in online shopping has also been observed in the subcontinent (Tandon et al., 2018). According to the report of Global Internet Statistics (GIS), (2019), in Pakistan, there were 44.61 million internet users. This shows an insignificant figure, because if we compare this figure with the entire population of Pakistan i.e. 200.81 million, depicting 22.51% of internet users. The airline e-ticket business has also drastically affected all around the world and particularly, in Pakistan due to the epidemic situation arising from the Corona virus. This pandemic has also put substantial influence upon the online shopping (OS) or online purchase intent (OPI) of the customers (Baker et al., 2020). The purchase intent (PI) is defined as a measure of the strength of one's intention to perform a specific behavior or make the decision to buy a product or service (Lesmono et al., 2020). Research studies reported that the PI is influenced by customer satisfaction (CS) (Yussoff & Nayan, 2020). The CS is defined as how happy customers are with the company's products, services, and capabilities (Basari & Shamsudin, 2020). In the online shopping environment the CS dependent upon some substantial antecedents, these antecedents are a) online shopping factors (OSF) (ordering ease, price-value, social influence, performance expectancy), b) perceived risk (PR) (financial, time, security, privacy) and c) website functionality (WF) (security, navigation, web-design) (Tandon et al., 2018). The consumer satisfaction can have an immense persuasion in online shopping, both pleasant and unpleasant. The literature also acknowledges the importance of customer satisfaction (CS) (Tandon et al., 2016; Guo et al., 2012; Athanasopoulou, 2009). But the fact is that fewer research models and paradigms exist that analyze the airline companies’ online engagement with the satisfaction of passengers precisely, in Western Europe and the US (Loo, 2020; Palvia, 2013; Waheeduzzaman et al., 2011). The Tandon et al. (2018) also reported that there 33 exist few studies that measure the empirical relationship amongst the OSF, perceived risk (PR) factors, website functionality (WF), and CS. It is obvious that in an online shopping environment the CS has become a critical feature and customer satisfaction substantively depends on the perceived risk (PR) factors that are associated with the product. These perceived risk factors can vary following the country’s culture and situation and can increase or decrease the CS level and the frequency of OPI (Tandon et al., 2018). So, there is a need to study the persuasion of PR factors on CS level and the frequency of OPI. Secondly, the customer purchase choices are influenced by several different factors, particularly when it comes to online shopping since the intangibility of items that reduce instant confidence is one of the unfavorable aspects of online marketing because some customers want to see and feel the item to say if it is good or bad. Therefore, to achieve a greater level of customer satisfaction, a vital mix of website features is mandatory to avoid the risk associated with the product/service purchased. Moreover, as per the report of GIS (2019) in Pakistan, only 22.51% of internet users buy products/services online, and the remaining 77.49% buy goods/services traditionally (Rehman, 2018). So there exists a substantive gap to study the influence of OSF, PR, and WF on the CS and PI of airline e-ticket in Pakistan. Furthermore, Venkatesh et al. (2003) represents the Unified theory of Acceptance & Use of Technology (UTAUT). The goal of UTAUT is to explain why people want to use an information technology and how they utilize it. The concept was postulated via an assessment and integration of the constructs of diverse model that had previously been used to elucidate information systems, behavioral intention (theory of planned behavior, acceptance and use of technology model, motivational model, theory of reasoned action, a merged theory of planned behavioral acceptance model, and a consolidated theory of planned behavior technology adoption). Venkatesh et al. (2003) observed in a retrospective study that UTAUT accounted for 70% of variability in Behavior Intention in online usage and nearly about 50% of variation in actual usage. So, the model proposed in this study was based on the UTAUT model that strengthens our understanding of the OSF, web-functionality drivers, and perceived risk associated with the service / product purchase and their effect on CS and OPI precisely of airline e-tickets within the Covide-19 pandemic in Pakistan. Therefore, the objectives of this investigation are a) to measure the effect of OSF, PR, and WF upon the airline e-ticket purchase intent (PI), and b) to measure the mediating influence of CS in a relationship with OSF, PR, WF and airline e-ticket PI in the contemporary COVID-19 pandemic in Pakistan. This study is imperative because firstly, this study gave an insight into the passengers online purchase intent within the contemporary COVID-19 pandemic situation in Pakistan. Secondly, the study gave detailed knowledge of the markets, scholars, and expert bodies about the association and mediating influence of CS in a relationship with OSF, PR, WF and the customer online purchase intent within a contemporary epidemic situation in Pakistan. Literature Review and Hypothesis Development In Indian, the effect of online shopping factors (OSF) on online purchase (travel) intent was investigated by taking (price-value, ordering-ease, social-influence, performance-expectancy), as the facets of OSF. The consequences discovered that the OSF viz. (price-value, ordering-ease, social-influence, performanceexpectancy), has a substantial influence upon online purchase intent (Panda & Swar, 2016). Another investigation was conducted in Jordan upon online (airline) PI. The consequences endorse that electronic-wordof-mouth influences trust (online) and online (airline) PI. The verdicts deliver value statistics to forthcoming investigators and airline corporation managers who engage in booking (online) systems (El-Said, 2020). Additional investigation was conducted in Denmark on OSF to measure and its effect on CS and online purchase intent. The findings shown that the OSF considerably predicts the online PI (Alam et al., 2020). Venkatesh et al. (2003) represents the Unified theory of Acceptance & Use of Technology (UTAUT). The UTAUT comprised of performance expectation, facilitating condition, social influence and price value. The customers must consider these factors while doing online shopping (Venkatesh et al., 2003). Based on UTAUT, the Tandon et al. (2018) also found that the OSF significantly predicts CS and online PI. Thus, underneath hypothesis is tested. H1: OSF has a significant effect on the CS on online PI The studies were carried out in the United States to measure the perceived risk (PR) factors by taking (time, security, financial, privacy) as PR facets to measure its effect on online (e-ticket, travel) PI. The consequences discovered the PR factors have a significant negative influence on the customers’ online (ticket) intent of purchase (Suh et al., 2015; Kim et al., 2005). The study conducted in Malaysia, to investigate the link amongst attributions for air service failures and consumer satisfaction. According to the findings the customer satisfaction appears to be inversely associated with switching intents (Nikibin, Ismail & Marimuthu, 2012).Further, in Italy, Wei et al., (2018), discovered that the PR factors have a significant adverse effect on the customers’ online (fruit) intent of purchase. Park & Tussyadiah (2017) found that online PR factors negatively affect customer trust, innovation, and perceptibility. Rendering to the Theory of Planned-Behavior (TPB) 34 presented by (Ajzen, 1991) and the Reasoned Action Theory (TRA) presented by (Ajzen & Fishbein, 1975), a person's behavioral intent depends on his mindset and subjective norms. These theories emphasize that if people could see that there would be beneficial consequences (outcomes), they adapt to change their behavior, e.g. if people get benefited from computer and technologies, they learn and change their behavior to learn computer and technology, so they would use computers. The projecting validity of the online PR factors inveterate as it considerably elucidates apparent behavioral intent in the online purchase (travel) intent (Park & Tussyadiah, 2017). The study was conducted in India to examine the influence of PR factors on CS. The statistical consequence discovered that the online PR factors have a substantial influence on the CS (Tandon et al., 2018). The under mentioned hypothesis is, therefore, examined. H2: PR substantively influenced upon the CS and online PI. The investigation was conducted in Canada on online website information search and its persuasion on CS and online PI (travel). The consequences discovered that the internet adoption and usage have a substantial positive influence on the CS and online PI (travel) (Kah et al., 2008). Wei et al. (2018) further stated that in Italy, the website quality has a positive influence on the online PI (fruit). Tandon et al. (2018) carried out the study in India to measure the effect of website functionality (WF) on CS. The consequences discovered that the WF by including its facets i.e. (web-design, navigation, security) positively influenced on the CS. In Malaysia, Hasanov & Khalid, (2015), discovered that the website quality has unintended persuaded upon the online purchase (food) intent via full intermediation of client satisfaction. In the United States, Wen (2010), found that website quality design, customers’ search intent, and customers’ faith stimulate the online PI. In India, Ganguly et al., (2010) discovered that the WF (web-design, navigation, security) has a considerable influence on the online PI, whereas, in Indonesia, Jauhari et al., (2019), discovered that the WF (web-design, navigation, security) has a positive effect upon the CS and online PI of airline e-tickets. Jauhari et al., (2019) further found that the CS has a substantial influence upon the online PI. In Thailand, study examines the influence of CS on online purchase (travel) intent. The consequences discovered the CS positively influence the online purchase and re-purchase (travel) intent (Vuthisopon & Srinuan, 2017; Jiradilok et al., 2014). Therefore, undermentioned hypothesis is tested. H3: WF has a significant effect on online PI. In Turkey, the study examines the association amongst the consumer expectation, loyalty and satisfaction in the airline sector. The findings shown that the consumer satisfaction (CS) was significantly boosted by trustworthiness and facilities and the client pleasure was also discovered to be an important determinant of customers ’ loyalty (Gures, Arslan & Tun, 2014). The Chang & Chao, (2018) led the investigation in Taiwan on shopping values and online purchase of travel products by taking CS as a mediator. The findings of the investigation depicted that CS partially mediate a relationship amongst shopping value, PR, and online purchase of travel products. Moreover, shopping values substantively predicts the online purchase of travel products (Chang & Chao, 2018). In China, the study investigates the service quality of airlines and its impact on customer loyalty and satisfaction. The findings shown that the satisfactory services insignificantly influence the customer loyalty, i.e. (business travelers). Moreover, the ticket price had significant influence on CS and customer loyalty (leisure travelers), but insignificant effect on the CS and customer loyalty (business travelers). Researcher highlighted the gap as limited studies are available pertaining to quality of airline services and CS in Asian context (Jiang & Zhang, 2016). A study was conducted in Malaysian airline industry, based on the justice theory, to look into the significance of sustainable customer service and examine the interrelationships amongst perceived fairness, restoration satisfaction, consumer satisfaction, trust, commitment and also examined the mediating influence of satisfaction and trust. The findings shown that instructional and procedural justice has a considerable impact on recovery satisfaction; procedural and distributive fairness, and also recovery satisfaction, affected total CS; and consumer trust has been impacted by interactional fairness. Moreover, the influence between restoration satisfaction and consumer loyalty is partially mediated by consumer trust and customer entire satisfaction. The researcher also highlighted the gap as, prior studies, specifically in the area of airline service, has overlooked the impact of customer entire satisfaction in mediating the association between restoration customer loyalty (Mohd-Any et al., 2019). In the Emirates, the study investigates the mediating impacts of consumer satisfaction in airline sector perspective. The findings support the mediating role of consumer satisfaction, asserting that consumer satisfaction is critical with an airline to survive in such a competitive industry, and to keep current passengers, attract new ones, and ultimately convert the others into product loyal consumer (Hussain, 2016). In Malaysia, the study was conducted on consumer behavioral intent (Rehman & Shaikh, 2020). The consequence revealed that customers’ behavioral intent was expressively and certainly prejudiced by apparent WF, usefulness, and comfort of usage, although a substantial adverse 35 association was originated amongst customers’ behavioral intent and apparent risk. The verdicts also discovered an intermediating association of CS amongst apparent convenience usage, usefulness, risk, and behavioral intent (Rehman & Shaikh, 2020). Thus underneath hypotheses are examined. H4: CS mediates in a relationship amongst OSF and online PI. H5: CS mediates in a relationship amongst PR and online PI. H6: CS mediates in a relationship amongst WF and online PI. Figure 1: Framework represents endogenous, mediator and exogenous variables Note. Online-shopping-factors, Perceived-Risk & Web Functionality are exogenous, whereas, CS is mediator & purchase intent is response endogenous. Methodology Population, Data Sample For hypotheses assessment and data gathering, the questionnaire survey was used. A pilot survey was conducted on 70 participants, before the actual study commenced. Malmqvist et al. (2019), stated the pilot survey is the randomized trial used to determine the feasibility of a process that would be used in a larger experiment. Fraser et al. (2018) stated that the pilot survey will assess the efficacy of recruitment, retention, randomization, screening, experimental methods, and the introduction of novel interventions. We used probability stratified sampling to choose passengers from the top-five well-known airlines of Pakistan's namely, Airblue, PIA, Shaheen Airline, Serene Air and Askari Aviation. The study population includes both outbound (expatriates) and inbounds (host-country nationals) passengerswhom applied for traveling via e-ticketing services.Around two-months' data about the passengers was taken whom booked the e-ticket service for traveling from aforementioned airlines located in Peshawar, Rawalpindi, Islamabad, Lahore, and Karachi cities of Pakistan. The sample of air travelers was selected from those who agreed to participate in a survey about their intention for using e-ticketing services. Only educated passengers aged 19 to over 40 were included in the sample frame, centered on a pilot study that suggested passengers under the age of 19 had difficulty in answering to survey questions. The size measurement method developed by Soper (2018) is used to decide the proper sample size for the structural model (SEM). Rendering to Soper (2018) estimate the sample of 1094 respondents met the SEM criteria. Professional enumerators administered face-to-face survey via questionnaire. Enumerators further give an explanation to participants and take a verbal permission form stating their desire to participate in the survey. Participants were further assured that no personally identifiable information would've been disclosed in the study report. The enumerators obtained 810 usable questionnaires from 1094 passengers. The percentage of people who responded was 74 percent, representing as good (Sekaran & Bougie, 2016). Table 1 shows the descriptive data of the respondents. Table 1. Demographic Data (n=810) Choices Freq % Gender Male 519 64.7 Female 291 35.9 Ages 19-29 years 498 61.4 30-39 years 234 28.8 Above 40 years 78 9.63 Education Masters 472 58.2 36 Choices Freq % Gender Male 519 64.7 Female 291 35.9 Graduates 338 41.7 Total 810 100 Note. Freq: Frequency; %: Percentage Measures The constructs were made up of 42 items that were usually extracted from previous studies, shown in Table 2. A 5-point Likert is a structured scale wherein participants choose one option that best describes their feelings. The 5-point scale (Likert), is often used to gauge the respondents' sentiments by asking how much they disagree or agree with a certain question or viewpoint. Sekaran & Bougie (2016) stated if an issue is particularly sensitive, it might be prudent to preserve the midpoint (average) on a 5-point scale. Furthermore, Simms et al. (2019) stated that these response choices and attributes have an influence mostly on the measure's reliability; therefore a 5-point Likert scale response is far less homogeneous. The questionnaire had two sections. Section one needed participants to provide demographic data, while section two requested them to respond to questions concerning their e-ticketing interactions. The investigation contained a questionnaire based on a five-point scale Likert-type (self-valuation measures). For measuring OSF (twelve items, Tandon et al., 2016; Venkatesh et al., 2012; Wolfinbarger & Gilly, 2003); PR (twelve items, Tandon et al., 2016; Zhang et al., 2012); WF (ten items, Wolfinbarger & Gilly, 2003); CS (three items, Tandon et al., 2018); and PI (three items, Nunkoo & Ramkissoon, 2013) were taken respectively. The measurement tool also contained demographic questions regarding age and gender. The measurements are shown in Table 2. The questionnaire was created by first performing a pilot investigation in which we assessed our acquaintances to determine whether the items were acceptable and clear, and afterwards modifying the items depending on the response rate. To measure the Goodness of items: Reliability was estimated via Cronbach’s alpha coefficient. The Table 2 stated the Reliability value as (72.2 for OSF, 67.7 for PR, 62.6 for WF, 67.1 for CS and 78.3 for online PI). Nunnally & Bernstein (1994) stated that reliability-alpha-values more than 0.6, indicating that elements are reliable. Furthermore, to get the fitness of measures (Sekaran & Bougie, 2010), the construct validity was assessed via the cross and respective loadings. In order to deduce the item validation the authors followed, 0.5 standardized threshold values suggested by (Hair et al., 2016). This signifies that a single dimension of either an item should have a value equal or above 0.5. As a consequence, even when an item does have a loadings value greater than 0.5 across multiple dimensions, it's likely that there is indeed a cross-loading problem. The item representing one dimension had a loading (higher) on a certain construct (greater than 0.5) but a lower load on other construct (less than 0.5), indicating construct validity. The convergence validity is assessed by using average variance extraction (AVE) and composite reliability (CR) methods. This assessment is crucial for determining the degree about which several items measuring the same and conform concept. The CR value for OSF (.737), PR (.645), WF (.648), CS (.684), and PI representing (.604) respectively shown in Table 2. These values are higher than 0.7 threshold suggested by (Hair et al., 2016), indicating that the elements have sufficient convergent validity. The AVE value for OSF (.518), PR (.568), WF (.529), CS (.567), and PI representing (.571) respectively shown in Table 2. These values are higher than 0.5 threshold suggested by (Thompson & Daniel, 1996). As a consequence of the convergent validity test, we determine that all five constructs (OSF, PR, WF, CS, and PI) are the true measurements of each respective dimension. Finally, the authors examine the discriminant adequacy to see how well items distinguish between constructs. This analysis is essential to ensure that no constructs are theoretically conflicting. As a consequence, for each construct's squared correlation is far less than the AVE values, suggesting the appropriate discriminant validity representing in Table 2. We utilized multiple regressions to evaluate the mediation effect under the structural model (SEM) method to evaluate the hypotheses. The online PI is the response variable, while OSF, PR, WF is the predictors. Based on the argument the CS is added as a mediator to our framework. Subsequently is the Table 2 representing the detail of the measurement items validity (construct) and the consequence of the EFA. Table 2 Measurement Model Result (EFA) Factors Items Loadings CR AVE R Construct: OSF (CR=.737, AVE=.518, R= 72.2) PV 1. Airline e-ticketing are reasonably priced .754 .742 .510 75.5 2. Airline e-ticketing gives me fair money value .697 3. Airline e-ticketing promotions are attractive & valuable .569 37 PE 1. Airline e-ticketing is beneficial in daily life .559 .746 .547 70.6 2. Airline e-ticketing aids me to get information in a short time span .687 3. Airline e-ticketing is quicker than traditional booking .711 SI 1. People told me to purchase airline e-ticketing online .736 .714 .527 73.4 2. My friends & family thinks that I would buy e-ticketing online .854 3. People whom I value like that I use e-ticketing online .772 OE 1. I can conveniently order to purchase the airline e-ticketing .619 .695 .515 71.6 2. I can conveniently modify the airline e-ticketing orders .587 3. I can conveniently cancel the airline e-ticketing orders .771 Construct: PR (CR=.645, AVE=.568, R=67.7) TR 1. Purchasing an online e-ticket can be a waste of time .561 .702 .582 62.5 2. Slow speed (internet) can waste time when buying e-tickets .644 3. Irrelevant query management leads to time wastage .741 SR 1. Online websites of e-ticketing are not safe .624 .610 .535 72.3 2. Posting private details on website hinders me to e-ticketing .599 3. Online websites don’t have appropriate security measures .601 PR 1. While buying e-tickets the hackers might steal my information .661 .612 .551 63.8 2. My personal detail might be unveiled to others when e-ticketing .755 3. My contact number might be ill-treated by others when e-ticketing .769 FR 1. I may get overpriced if I buy e-tickets .814 .630 .544 65.9 2. I may not trust on online e-tickets companies .608 3. While e-ticketing the personal info might be embezzled by others .589 Construct: CS (CR=.648, AVE=.529, R=67.1) 1. I am satisfied (truly) by buying e-tickets online .690 .648 .529 67.1 2. I am satisfied with my decision of buying e-tickets online .709 3. I am pleased with my purchase choice of online e-ticketing .515 Construct: WF (CR=.684, AVE=.567, R=62.6) WD 1. The online layout of the websites enables airline e-ticketing .611 .741 .661 65.5 2. The website has a striking color outline that aids e-ticketing .587 3. The website graphics are convenient to do airline e-ticketing .631 Nav 1.The website search function is helpful .557 .569 .540 63.1 2. The website conveniently allow return to the prior pages display .669 WS 1. The security landscapes are accepted on this website .754 .677 .515 60.2 2. Complaints are responded promptly on this website .667 3. The website route & completes the order securely & quickly .590 Cust 1. I can conveniently customize my e-ticketing order .622 .692 .588 61.9 2. Airline e-ticketing websites respond to consumer needs .574 Construct: PI (CR=.604, AVE=.571, R=78.3) .527 PI 1. I anticipate to endure airline e-ticketing .788 .604 .571 78.3 2. I will try always to practice airline e-ticketing .851 3. I plan to do airline e-ticketing regularly. .710 Note. OSF: Online-shopping-factors; PR: Perceived-risk; WF: Website-functionality; CS: Customer-satisfaction; PI: Purchase-intent; PV: Price-value; PE: Performance-expectancy; SI: Social-influence; OE: Ordering-ease; TR: Time-risk; SR: Security-risk; PR: Privacy-risk; FR; Financial-risk; WD: Web-design; Nav: Navigation; WS: Web-security; Cust: Customization;CR: Composite Reliability; AVE: Average Variance Extracted; R: Reliability Results The SEM, a multivariate analytic tool was utilized to evaluate the structural association amongst measured parameters and latent variables. Hair et al. (2016) stated that the SEM combines numerous standard approaches such as hierarchical multiple regression analysis into a single step. The result was evaluated by using the twostep technique. Initially, the demographics, AVE, CR and confirmatory factor assessment (CFA) were assessed. The CFA is the multivariate analysis process for determining whether measured variables accurately indicate the number of components (Hair et al., 2016). The CFA is used to confirm a collection of observable variables' structural model. The researcher used CFA to assess whether there is a link between observable variables and their endogenous latent components. Subsequently, the SEM was employed to evaluate the hypotheses. Approximately 67 percent were male and 35 percent were female respondents between the 19 to 40 plus years of ages. All respondents were well educated see Table 1. The AVE, reliability, CFA and CR coefficients are summarized in Table 2. We further analyzed the AVE square root and its relationship with the other factors to 38 see if it had a concern of discriminating validity. As per Henseler et al. (2015), the AVE square root must be greater than the correlation between all the factors, implying adequate discriminant validity see Table 3. Table 3. Discriminant Validity Variables Mean SD 1 2 3 4 5 1. OSF 3.9 .781 .719 2. PR 4.2 .871 .311 .753 3. WF 3.5 .771 .412 .341 .727 4. CS 3.9 .781 .391 .419 .366 .754 5. PI 3.4 .891 .314 .456 .451 .399 .758 Note. OSF: Online Shopping Factor; PR: Perceive Risk; WF: Website-Functionality; CS: Customer-Satisfaction; PI: Purchase Intention, SD: Std-Deviation The JASP (Jamovi) software package was used to investigate the paths. In adding to either the hypotheses presented in the SEM. All the fit indices (goodness) find as a best fit shown in Table 4. Table 4.Fit Indices Indices Model Value Standardized Value Authors GFI .91 ≥ .9 Lance et al. (2016) X2/df 2.9 ≤ 3 Schumaker (2017) RMSEA .07 ≤ .08 Savalei (2018) AGFI .89 ≥ .8 Lance et al. (2016) CFI .95 ≥ .9 Tabachnick & Fidell (2019) NFI .93 ≥ .9 Kline (2016) RMR .04 ≤ .08 Savalei (2018) TLI .92 ≥ .9 Tabachnick & Fidell (2019) Note. GFI:Goodness-of-fit-Index; RMSEA:Root-means-squares-of-approximations; AGFI: Adjustedgoodness-of-fit-Index; CFI:Comparitive-fit-index; NFI:Normed-fit-index; RMR: Root-meansresidual;TLI: Tucker-Lewis-Index The Hypothesis, one stated that the OSF has a significant effect on the CS on online PI. We found that the OSF has a positive direct effect on online PI with 1% significance level depicting (β=.141,t=4.57,p<.05).It is found that, the OSF leads to greater PI. On the other hand, the diverse consequence is found relating to PR and online PI during pandemic. Hypothesis, two stated that thePR has a significant effect on online PI. We found that the PR has a negative insignificant effect on online PI depicting (β=-.166,t=-1.28,p>.05). Hence, consequence portrays that the PR factors decreases the online PI during pandemic. The hypothesis three stated that website's functionality (WF) has a significant effect on online PI. The statistical result found that the WF has a positive direct effect on online PI with 1% significance level depicting (β=.587,t=20.94,p<.05). On the basis of statistical result, we have found that OSF and WF substantively predicts the online PI whereas, the PR has insignificant negative persuasion on the online PI during pandemic. Hypothesis four, stated that CS mediates in a relationship amongst OSF and online PI. We have found that the CS partially (positively) mediates in an association amongst OSF and online PI with 1% significance level depicting (β=.086,t=6.2,p<.05). Hypothesis five, stated that CS mediates in a relationship amongst PR and online PI. We have found that the CS (negatively) and insignificantly mediates in an association amongst PR and online PI depicting (β=-.052,t=- 1.57,p>.05). Hypothesis six, stated that CS mediates in a relationship amongst WF and online PI. We have found that the CS partially (positively) mediates in an association amongst WF and online PI with 1% significance level depicting (β=.118, t=8.04,p<.05). Overall, it was found that CS partially (positively) mediates the association between OSF, WF and online PI, whereas, CS fully (negatively) mediates the association between PR and online PI. The figure 2 represents the paths consequences and the statistical result of the hypotheses is shown in Table 5. Figure 2: Structure Model 39 Note. Structure model represents exogenous and mediator variable effects on endogenous variables Table 5.Hierarchal Regression Direct Effect Estimate SE CR P OSF → PI 0.227 0.032 7.11 < .05 PR → PI -0.217 0.131 -1.65 > .05 WF → PI 0.706 0.028 25.54 < .05 Indirect Effect Estimate SE CR P OSF → CS → PI 0.086 0.014 6.21 < .05 PR → CS → PI -0.052 0.033 -1.57 > .05 WF → CS → PI 0.118 0.015 8.04 < .05 Note. SE: Standard-error; CR: Critical-ratio; p: Probability-level; OSF: Online-Shopping-Factors; PI: Purchaseintention PR: Perceived-Risk, WF: Website-Functionality; CS: Customer-Satisfaction, R2 = 541, p<.05 Discussion This study measures the intermediating influence of CS in a relationship with OSF, PR factors, WF, and the online PI of airline e-tickets within the existing Covide-19 pandemic, in Pakistan. The two-month' data about the passengers was selected who booked the airline e-tickets for traveling from the airlines of Pakistan. The arithmetic figures depicted that PR insignificantly influences the CS and online PI. The result was matched with prior studies of (Tandon et al., 2017; Park & Tussyadiah, 2016), but dissimilar with studies of (Wei et al., 2018; Suh et al., 2015; Kim, Kim & Leong, 2005). The reason for the dissimilarity in consequences is due to the contemporary pandemic situation, because in this prolonging pandemic situation, both the expatriates (Pakistani nationals living abroad) and the host-country nationals wants to come back their country and meet their relatives. Therefore, the passengers don’t consider and ignore the PR factors associated with airline e-tickets. As a result, expatriates overseas want to return home, and local residents would like to stay with their families as the epidemic continues. Moreover, The OSF substantially influences the CS and online PI. The result was matched with a prior study of (Tandon et al., 2017). The WF substantially influences the CS and online PI. The result was matched with the prior study of (Tandon et al., 2017; Jauhari et al., 2017). Founded upon the consequence it is also originated that CS intermediate in a relationship with online-shopping factors, PR, WF, and online PI. The result was matched with a prior study of (Tandon et al., 2017). Theoretical Implication The research results have deepened awareness about online e-ticketing in Pakistan. Other developing countries should further study the model that emerged from this research to provide thorough consideration of the factors that cause online purchases of e-ticketing. In accordance with UTAUT, the key implication of this study is in the integration of various features of website accessibility, online shopping drivers, and perceived risk, which is a prerequisite for any technology being adopted. As a significant factor, the advent of OSF and WF suggests that consumers tend to acquire product information through graphical components, hyperlinks, and website architecture. Passengers only buy the airline e-tickets if the online website of the airline company is easy to work on and there exist ordering ease, fair price-value, social influence, and better performance expectancy associated with airline e-tickets. Users can find details about the product with an enticing interface and striking graphics, which in turn helps them save time. Possibly this will help to minimize perceived danger. Therefore, to improve website accessibility, a relevant balance of navigation, protection and privacy, and website design 40 for each country must be studied. Perceived risk arose in this research as a multidimensional construct having an insignificant contributor to the CS and PI respectively. Practical Implication & Recommendations The community awareness campaign should be raised about the utility, convenience, and other benefits of online e-ticketing via companies' web sites. By offering premium advantages to improve CS, they will step up channel pleasure, enthusiasm, and entertainment. The prevalence of the website features as a significant measure contributing to CS highlights the desperate need to make websites more user-friendly with appealing and lively web pages that are hassle-free to upload. For longer periods, happy and pleased passengers will be maintained and this will increase productivity and performance. It would therefore be a win-win situation for both online retailers and consumers. The pricing discounts during festive seasons will draw consumers and thus encourage online purchases. Also, online discussion platforms and a virtual tour through the website will lessen the user’s uneasiness related to online shopping and maintain customer’s trepidation. The study corroborated the perceived risk's negative effect on CS and PI. Online retailers need to provide consumers with social resources to reduce their perceived risk by building confidence in online shopping. Focusing on social, time, safety, product performance, and financial risk will assist in projecting less risky venture online retailing. It is recommended for the airline e-ticketing companies, that to perfectly update the online-shopping factors, including online ordering ease of online e-tickets, lower price-value of online e-tickets, social influence, and better performance expectancy of online e-tickets for the CS and online PI. Moreover, the airline e-ticketing companies must consider the PR factors comprised of financial, time, security, and privacy risks related to online e-tickets for the CS and online purchase intention. Also, the airline e-ticketing companies must deliberate the quality of WF that includes online security, navigation, and web design of the particular airline company from where customers buy e-tickets for travel for the betterment and CS and online PI. Conclusion The study found that the OSF, WF and CS have a substantial influence on the online e-tickets PI. Surprisingly, the PR factors have an insignificant effect on the online e-tickets PI. The airline passengers in Pakistan overlooked such important risk factors, i.e. financial, time, security due to panic situation arose from Covid. On the other side, the CS positively mediates in a relationship with OSF, WF, and online e-ticket PI, whereas, the CS insignificantly mediates in a relationship with PR and online e-ticket PI in the pandemic in Pakistan. The study concluded that due to the contemporary situation of a pandemic, most of the travelers ignore the PR factors while buying online airline e-tickets because of travel bans and cancellation of flights. So, the expatriates who are living abroad wants to come back to their home country and the local nationals also want to stay with their relatives in this prolonging pandemic situation. Therefore, passengers, mostly ignore the PR factors when purchasing online e-tickets in a pandemic situation. Furthermore, the OSF and website integrated development environment, pertaining to an airline e-ticketing, it is necessary to enhance the web design, navigation, safety standards, ordering ease, and privacy functions. Besides that, affirmation notifications must be sent to passengers' mobile devices instantly when they purchase e-tickets online. To deal with financial and privacy risks, transaction outlets via websites should indeed be expanded. Online portal hours for airline offices should indeed be enlarged so that passengers could even reach out to airline authorities at any time. To improve competence in airline e-ticket facilities, strategies should be found immediately in the face of problems or delays with check-in. Limitation and Future Research The present investigation has some shortcomings that need to be addressed. First, in case of online e-ticketing PI, several other general OSF, PR and WF factors might play a crucial role. Second, the sample was drawn only from five airlines companies of Pakistan on a cross-sectional basis. Third, e self-evaluation measures might possibly inflate the study results. Fourth, the generalization (analytical) concern also was a study limitation. Fifth, the analysis is focused on either a quantitative premise that objectives were met, we believe that a qualitative investigation might have reinforced our conclusions. Finally, we did not hypothesize or investigate moderating consequences in terms of the framework.The upcoming research area would be to measure the persuasion of OSF, PR, WF, CS on the online PI with the inclusion of some moderators and inter-mediators variables on longitudinal premise. Instead of using self-evaluation measures the data will be assorted via interviews. Acknowledgements or Notes Please collate acknowledgements or notes in a separate section at the end of the article before the references.