customer satisfaction online shopping thesis

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Customer Satisfaction in Online Shopping: a study into the reasons for motivations and inhibitions

This study endeavours to understand customer satisfaction in online shopping while investigating the major reasons that motivated customers' decision-making processes as well as inhibitions of online shopping. The Kotler and Killers (2009) Five Stage Buying Process Model was chosen as the basis of framework of this study to explain customer satisfaction through their motivations to buy products online. The existing literature was reviewed to discover reasons that would influence customers positively or negatively towards shopping online. Surveys were conducted by distributing questionnaires in the Wrexham area (North Wales) to gather data for this research. SPSS software package was used to present research data graphically and to test research hypothesis. From the findings, it was discovered that respondents use internet to purchase products through online because they believe it is convenience to them and the term convenient includes elements such as time saving, information availability, opening time, ease of use, websites navigation, less shopping stress, less expensive and shopping fun. In contrast, along with respondents' mind-sets, online payment security, personal privacy and trust, unclear warranties and returns policies and lack of personal customer service are the foremost barriers of online shopping. Furthermore, the result of hypotheses established that even though online shopping is convenient to all consumers, online payment system and privacy or security anxieties have significant impact on online shopping. Finally, some recommendations have been offered for online retailers to take initiatives for making online shopping more admired and trustworthy.

Related Papers

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UGC Approved Journal of Marketing Strategy(JMS)

Buying on the Internet is one of the most rapidly growing modes of shopping demonstrating a double-digit annual increase in sales in recent years. Reasons for such growth seem to arise from its advantages such as convenience, the ability to be seen as a leisure activity, savings of time and effort, and its 24 hours a day and 7 days a week access. Although Internet buying has shown rapid growth, it also has been hampered by the real or perceived perceptions of consumers that it lacks privacy and security while also suffering from issues in product delivery and returns and tactility. The primary purpose of the study is to explore the profile of Internet buyers and compare them to the non-buyers in terms of demographic characteristics, technology experiences, and his or her attitudes towards consumer and marketing issues. Such information will help e-tailers as they work to develop more effective and efficient online retail outlets.

ORIGINAL RESEARCH article

Online consumer satisfaction during covid-19: perspective of a developing country.

• 1 Antai College of Economics & Management, Shanghai Jiao Tong University, Shanghai, China
• 2 School of Management, Zhejiang Shuren University, Hangzhou, China
• 3 Faculty of Management Sciences, Riphah International University, Faisalabad Campus, Punjab, Pakistan
• 4 Department of Professional Psychology, Bahria University, Islamabad, Pakistan

A conceptual model based on the antecedents and consequences of online consumer satisfaction has been proposed and empirically proved in this study. Data were collected during Smart Lockdown of COVID-19 from 800 respondents to observe the difference between perceived and actual, and direct and indirect e-stores. Confirmatory factor analysis was used to observe the validity of the data set. The structural equation modeling technique was used to test the hypotheses. The findings indicated that consumers feel more satisfied when they shop through direct e-store than indirect e-store, whereas their perception and actual experience are different. Implications have also been added to the study.

Introduction

Online shopping is the act of buying a product or service through any e-stores with the help of any website or app. Tarhini et al. (2021) stated that shopping through online channels is actively progressing due to the opportunity to save time and effort. Furthermore, online shopping varies from direct e-store and indirect e-store about their perception against the actual experience. Developing countries still face various conflicts and issues while promoting and utilizing e-commerce to the maximum compared with the developed countries ( Rossolov et al., 2021 ). In the developing countries, the difference between the perception and actual experience of the consumers varies when buying from indirect e-store compared to the direct e-store. On the contrary, as the world has been suffering from the COVID-19 pandemic, it has brought drastic changes globally in many sectors, business being one of them. De Vos (2020) stated that a large-scale lockdown was imposed worldwide to prevent the virus from spreading.

To survive, switching traditional shopping or trade toward digital was one factor that captured the attention across the globe on a larger scale. In April 2020, Walmart reported a 74% increase in online sales even though they faced a low customer walk-in at stores ( Nassauer, 2020 ; Redman, 2020 ). This upsurge of swift adoption of online channels has led this research to ask a few questions. First, what will be the difference between the perceived and the actual product purchased online? A recent study has documented that consumers bear actual risk after shopping through online channels ( Yang et al., 2020 ). Research reported that 30% of the products through online channels get returned and are not according to their perception ( Saleh, 2016 ). The same author also showed that the return and complaint rates are getting higher when consumers shop through an online channel.

Second, is there any difference between the perceived and the actual product purchase online from a direct e-store or an indirect e-store? Direct e-store means the online brand store, for example, Walmart, and indirect e-store means third-party stores such as Amazon, Alibaba, Jingdong (JD), and Daraz. The direct e-store strives hard to maintain a clear, potent perception in the mind of its buyer ( Grewal et al., 2009 ). Kumar and Kim (2014) stated that a brand strengthening its relationship with its consumer satisfies its needs through the actual product or services. In the literature ( Olotewo, 2017 ; Rossolov et al., 2021 ), it is stated that the shopping patterns of buyers from direct and indirect e-stores vary greatly, especially in the developing countries. In this way, when shopping through a direct e-store, consumers may easily recognize the difference in buying from a direct and indirect e-stores ( Mendez et al., 2008 ).

Third, a conceptual framework from a consumer perspective, antecedents and consequences of customer satisfaction, has been proposed and empirically proved. The literature ( Alharthey, 2020 ) discussed different risk types in online shopping. Three main types of risk, perceived uncertainty, perceived risk, and price, are addressed in this model. To the best of the knowledge of the authors, no such investigation directed specific circumstances, particularly in the developing countries. Therefore, it is necessary to look for the antecedents and consequences of customer satisfaction to promote online shopping in the developing countries. The degree of consumer satisfaction defines his/her experience and emotions about the product or service purchased through the online channel. Recent studies ( Guzel et al., 2020 ; Mamuaya and Pandowo, 2020 ) stated that the intention of the consumers to repurchase and their electronic-word-of-mouth (e-WOM) depends on their degree of satisfaction. In light of these heavy investments in online shopping, there is an exciting yet unexplored opportunity to comprehend better how the purchasing experiences of consumers through online channels influence their satisfaction level.

The study contributed to the current marketing literature in several ways. First, this study has highlighted that the perceived risk is very high when shopping through online channels, mainly the indirect e-stores. Therefore, the managers should develop strategies that reduce the perceived risk for the online consumer to shop more. Second, the study also disclosed that the perceived uncertainty in shopping through the online channel is high. While shopping online, the website design, graphics, and color scheme make the product more attractive than the actual one. Therefore, the managers must balance the visual appearance of the product on the website with the actual appearance of the product. This would increase the confidence and satisfaction of the consumer. Third, this study has also revealed that people are more satisfied while shopping from direct e-stores than indirect e-stores. Because the focal brands officially sponsor the direct e-stores, they pay more attention to their quality to retain consumers and maintain their brand reputation. Fourth, an indirect e-store works as a third party or a retailer who does not own the reputation of the product. This study exhibited the difference between the perception of the consumer being very high and the actual experience of using that product being quite different when shopping from the indirect channel. Last but not the least, this study is the first to report pre- and post-purchase consumer behavior and confirmed the perceived and the actual quality of a product bought from (i) direct e-store and (ii) indirect e-store.

Literature Review

Theoretical review.

Literature shows that when consumers get influenced to buy a particular product or service, some underlying roots are based on their behavior ( Wai et al., 2019 ). Appraisal theory significantly explains consumer behavior toward shopping and provides an opportunity to analyze the evaluation process (e.g., Roseman, 2013 ; Kähr et al., 2016 ; Moors et al., 2017 ; Ul Haq and Bonn, 2018 ). This research, aligned with the four dimensions of appraisal theory as the first stage, clearly defines the agency stage that either of the factors is responsible for customer satisfaction. The second stage explains that consumer's degree of satisfaction holds great importance and refers to novelty in the literature. The third stage of the model briefly explains the feelings and emotions of the consumers about the incident, aligning with the certainty phase. The last step explains whether the consumers have achieved their goal or are not aligned with the appetitive purpose.

Cognitive appraisal researchers stated that various emotions could be its root cause ( Scherer, 1997 ); it could be the reaction to any stimulus or unconscious response. On the contrary, four dimensions of appraisal theory are discussed in this research ( Ellsworth and Smith, 1988 ; Ma et al., 2013 ). Agency (considering themselves or objects are answerable for the result of the circumstance) ( Smith and Ellswoth, 1985 ; Durmaz et al., 2020 ); novelty (assessing the difference between the perception of an individual and his actual experience) ( Ma et al., 2013 ); certainty (analysis of the apparent probability of a specific outcome and its effect on the emotions of the buyer) ( Roseman, 1984 ), and appetitive goal (judging the degree to what extent the goal has been achieved) ( Hosany, 2012 ).

Hypotheses Development

Perceived risk and consumer satisfaction.

Perceived risk is the perception of shoppers having unpleasant results for buying any product or service ( Gozukara et al., 2014 ). Consumers who buy a specific product or service strongly impact their degree of risk perception toward buying ( Jain, 2021 ). Buyers who tend to indulge in buying through online channels face perceived risk characterized by their perception compared to the actual uncertainty involved in it ( Kim et al., 2008 ). Literature ( Ashoer and Said, 2016 ; Ishfaq et al., 2020 ) showed that as the risk of buying is getting higher, it influences the degree of consumers about information about their buying, either purchasing from the direct or indirect e-shop. Johnson et al. (2008) stated that consumer judgment that appears due to their experience strongly impacts their satisfaction level. Jin et al. (2016) said that as the ratio of risk perception of their consumer decreases, it enhances customer satisfaction. Thus, from the above arguments, it is hypothesized as follows:

H 1 : Perceived risk has a significant negative impact on consumer satisfaction—direct vs. indirect e-store; perceived vs. actual experience .

Perceived Uncertainty and Consumer Satisfaction

Uncertainty is defined as a time that occurs in the future that comprises the predictable situation due to the asymmetry nature of data ( Salancik and Pfeffer, 1978 ). Consumers may not expect the outcome of any type of exchange conducted as far as the retailer and product-oriented elements are concerned ( Pavlou et al., 2007 ). Therefore, uncertainty initiates that retailers may not be completely predictable; on the contrary, consumers tend to analyze and understand their actions about decision making ( Tzeng et al., 2021 ). Thus, the degree of uncertainty involved in buying through online channels influences that degree of customer satisfaction. In addition, when the performance of any particular product or service matches the degree of expectations, he gets satisfied and, hence, repeats his decision of buying ( Taylor and Baker, 1994 ). But if the product quality fails to meet the requirements, it negatively affects the degree of satisfaction ( Cai and Chi, 2018 ).

H 2 : Perceived uncertainty has a significant negative impact on consumer satisfaction—direct vs. indirect e-store; perceived vs. actual experience .

Price Value and Consumer Satisfaction

Oliver and DeSarbo (1988) suggested that the price value is the proportion of the result of the buyer to the input of the retailer. It is defined as an exchange of products/services based on their quality against a price that is to be paid ( Dodds et al., 1991 ). Consumers look for a higher value in return; consumers are willing to pay a higher price ( Pandey et al., 2020 ). Yet, it leads to higher dissatisfaction when they receive a lower degree of profitable products. Besides, the buyers associate such type of product/service they use as less favorable or not according to their needs and desires. Hence, the buyers regret their decision-making degree for choosing that particular product ( Zeelenberg and Pieters, 2007 ). Aslam et al. (2018) indicated that a product/service price influences the satisfaction of a buyer. Afzal et al. (2013) recommended that the price is among those factors that hold great significance for the degree of satisfaction of the consumer. If the price value of any product/service differs from consumer to consumer, consumers tend to switch brands. Hence, it is hypothesized that:

H3 : Price value has a significant positive impact on consumer satisfaction—direct vs. indirect e-store; perceived vs. actual experience .

Consumer Satisfaction With Consumer Delight, Consumer Regret, and Outrage

Satisfaction is defined as how a consumer is pleased with a particular brand or view about a product/service that matches requirements. It is an essential factor that triggers when the product or service performance exceeds the expectation and perception of the customers ( Woodside et al., 1989 ). The decision of the buyer significantly affects their satisfaction toward the product or service ( Park et al., 2010 ). If buyers are satisfied with the product/service they purchased online, this degree of satisfaction significantly affects their repurchase intention and WOM ( Butt et al., 2017 ). Tandon (2021) stated that a consumer satisfied with the product/service would get delighted. Consumer satisfaction, when exceeding the expectations, leads to consumer delight ( Mikulić et al., 2021 ). Mattila and Ro (2008) recommended that the buyer gets disappointed by anger, regret, and outrage. It also defines that negative emotions have a significant effect on the purchasing intention of the consumers. Oliver (1989) stated that unsatisfied buyers or products that do not fulfill the needs of the customers can create negative emotions. Sometimes, their feelings get stronger and result in sadness and outrage. Bechwati and Xia (2003) recommended that the satisfaction of the consumers influences their behavior to repurchase; outraged consumers due to dissatisfaction sometimes want to hurt the company. Besides deciding to purchase, consumers mostly regret their choices compared to other existing choices ( Rizal et al., 2018 ). Hechler and Kessler (2018) investigated that consumers who are outraged in nature actively want to hurt or harm the company or brand from which they got dissatisfied or hurt. Thus, it is proposed that:

H 4 : Consumer satisfaction has a significant negative impact on (a) consumer delight, (b) consumer regret, (c) consumer outrage—direct vs. indirect e-store; perceived vs. actual experience .

Consumer Delight and E-WOM

Oliver et al. (1997) recommended that a degree of delight in a buyer is termed as a positive emotion. Consumers purchase a product/service that raises their degree of expectation and gets them delighted ( Crotts and Magnini, 2011 ). Delighted buyers are involved in sharing their experiences with their friends and family and spreading positive WOM to others ( Parasuraman et al., 2020 ). Happy buyers generally share their opinions while posting positive feedback through social media platforms globally ( Zhang, 2017 ). A positive WOM of the buyer acts as a fundamental factor in spreading awareness about the product/service and strongly impacts other buyers regarding buying it ( Rahmadini and Halim, 2018 ). Thus, it is proposed that:

H5 : Consumer delight has a significant positive impact on E-WOM—direct vs. indirect e-store; perceived vs. actual experience .

Consumer Delight and Repurchase Intention

Delighted consumers tend toward brand loyalty; thus, they increase their buying intention of the service or product ( Ludwig et al., 2017 ; Ahmad et al., 2021 ). Customers can understand the objective of loyalty in purchasing a similar product or a new one from the same company. Delighted consumers tend to indulge in a higher degree of an emotional state that leads them to higher purchase intentions; it eliminates the switching of brands ( Parasuraman et al., 2020 ). Kim et al. (2015) stated that consumers delighted with a product or service of a brand become loyal to it, and the possibility of switching brands gets very low. Research ( Loureiro and Kastenholz, 2011 ; Tandon et al., 2020 ) shows that delighted consumers are more eager to purchase the same product again. Hence, it is proposed that:

H6 : Consumer delight has a significant positive impact on his repurchase intention—direct Vs. indirect e-store; Perceived Vs. actual experience

Consumer Regret and E-WOM

Regret is considered a negative emotion in reaction to an earlier experience or action ( Tsiros and Mittal, 2000 ; Kumar et al., 2020 ). Regret is when individuals frequently feel pity, disgrace, shame, or humiliation after acting in a particular manner and afterward try to amend their possible actions or decisions ( Westbrook and Oliver, 1991 ; Tsiros and Mittal, 2000 ). Regret is that specific negative emotion the buyers feel while making a bad decision that hurts them; their confidence level is badly affected. They blame themselves for choosing or creating a terrible decision ( Lee and Cotte, 2009 ). Li et al. (2010) suggested that buyers quickly start regretting and find their way to express their negative emotions. When they feel betrayed, they tend to spread negative WOM (NWOM) as a response to their anxiety or anger. Jalonen and Jussila (2016) suggested that buyers who get dissatisfied with their selections get involved in negative e-WOM about that particular brand/company. Earlier research says that buyers suffering from failure to buy any product/services tend to participate actively and play a role in spreading NWOM due to the degree of regret after making bad choices. Whelan and Dawar (2014) suggested that consumers sense that business has treated them unreasonably, and many consumers complain about their experience, resulting in e-WOM that may reduce consumer repurchase intention. Thus, it can be stated that:

H7 : Consumer regret has a significant negative impact on e-WOM—direct vs. indirect e-store; perceived vs. actual experience .

Consumer Regret and Repurchase Intention

Regret has a substantial influence on the intentions of the consumers to not entirely be measured by their degree of happiness ( Thibaut and Kelley, 2017 ). Results may not be evaluated by matching the structured degree of expectation but are also linked to alternatives reachable in the market. Therefore, such sort of evaluation and assessments will probably influence repurchase intention. For example, suppose the skipped reserve overtakes the picked alternative. In that case, the customer might change the replacement for the future purchase, regardless of whether the individual is profoundly happy with the picked option ( Liao et al., 2017 ). According to the researchers, there is a negative relationship between regret and consumer repurchase intention ( Liao et al., 2017 ; Durmaz et al., 2020 ). Furthermore, Unal and Aydin (2016) stated that perceived risk negatively impacts regret, influencing the repurchase intention of the consumers. Thus, it can be stated that:

H8 : Customer's regret has a significantly negative influence on his repurchase intention—direct vs. indirect e-store; perceived vs. actual experience .

Consumer Outrage and E-WOM

The disappointment of the consumers is a negative response to a product or a service ( Anderson and Sullivan, 1993 ). Outrage is the negative emotion a consumer experience when he purchases something totally against his requirements ( Lindenmeier et al., 2012 ). Besides, when the perception of the buyer is infringed, such behaviors occur. According to Torres et al. (2019) , enraged consumers get involved in communicating their outrage through e-WOM. Outraged consumers actively hurt the firm or brand from which they got hurt ( Hechler and Kessler, 2018 ). Consumers give e-WOM online reviews to decrease the negative emotions from the experiences of the consumer and re-establish a calm mental state to equilibrium ( Filieri et al., 2021 ). Thus, such consumers tend to give negative comments about the brand or product, which failed to match their expectations. NWOM has been characterized as negative reviews shared among people or a type of interpersonal communication among buyers concerning their experiences with a particular brand or service provider ( Balaji et al., 2016 ). Hence, it is hypothesized that:

H9 : Consumer outrage has a significant negative impact on e-WOM—direct vs. indirect e-store; perceived vs. actual experience .

Consumer Outrage and Repurchase Intentions

Repurchase intentions are characterized as the expressed trust of a buyer that they will or will not purchase a specific product and service again in the future ( Malhotra et al., 2006 ). Establishing relations with buyers should result in the repurchase. Negative disconfirmation ensues dissatisfaction or a higher level of outrage ( Escobar-Sierra et al., 2021 ). When a service/product fails and is not correctly addressed, the negative appraisal is overstated. Hence, “it may be more difficult to recover from feelings of victimization than to recover from irritation or annoyance” typically associated with dissatisfaction ( Schneider and Bowen, 1999 , p. 36). Therefore, consumers get outraged from buying such a product that fails to match their perception. When the experience of a consumer prompts a negative disconfirmation, the purchaser will also have a higher urging level through outrage. Therefore, consumers will probably have negative intentions to repurchase and do not want to indgule in making the same decision repeatedly ( Wang and Mattila, 2011 ; Tarofder et al., 2016 ). Therefore, it is proposed that:

H10 : Consumer outrage has a significant negative impact on repurchase intention—direct vs. indirect e-store; perceived vs. actual experience .

Methodology

This research explores the difference between the perception of the consumers and the actual online shopping experience through direct and indirect e-stores. It was an experimental design in which online shopping was studied in the developing countries. Data were collected from those individuals who shop from online channels; direct e-store and indirect e-store. Taking care of COVID-19 standard operating procedures, only 50 respondents were gathered two times, every time in a university auditorium after obtaining the permission from the administration. The total capacity of the auditorium was 500, as the lockdown restrictions were lifted after the first wave of the coronavirus.

Data Collection Tool

A questionnaire was used for the survey. The questionnaire was adapted in English to guarantee that the respondents quickly understood the questions used. A cross-sectional study technique was used for this research. A cross-sectional study helps in gathering the data immediately and collects data from a large sample size. The total number of distributed questionnaires was 1,250, out of which 800 were received in the usable form: 197 incomplete, 226 incorrect, and dubious responses, and 27 were eliminated. Thus, a 64% response rate was reported. Research showed that a 1:10 ratio is accepted ( Hair et al., 1998 ) as far as the data collection is concerned; for that instance, this study data fell in the acceptable range.

Indirect E-Store

Consumers who prefer to shop through online channels were gathered in an auditorium of an institute. Only those consumers were eligible for this experiment, who themselves buy through e-stores. A few products were brought from an indirect e-store, and later on, those products were shown to the respondents from the website of that indirect e-store. After showing products, we asked the respondents to fill the survey as per their perception of the product. Then we asked them to fill out another questionnaire to ascertain the difference between the perception and actual experience when purchasing from an indirect e-store. Once all the respondents completed the survey, we have shown them the actual products they have selected by seeing the website of the indirect e-store.

Direct E-Store

The second experiment was carried out on those consumers who shop from direct e-stores. For that purpose, a few popular reviewed clothing articles were purchased from the e-store. As in the case of an indirect e-store, respondents were also shown these articles from the websites of these direct e-stores. We then asked the respondents to fill the survey to confirm their perception of the products. Once all the respondents completed the survey, we showed them the actual product and asked them to fill out another questionnaire according to their actual purchasing experience from the direct e-store. The primary purpose of this experiment was to compare buying from direct e-store and indirect e-store.

Construct Instruments

The total number of items was 34, which were added in the earlier section of the questionnaire. These items were evaluated with the help of using a five-point Likert scale that falls from strongly disagree (1) to strongly agree (5). The items used in the study were empirically validated. Table 2 carries the details of the items of the questionnaire. The price value was evaluated using three items used by Venkatesh et al. (2012) . The perceived uncertainty was one of the independent variables that carry four items derived from Pavlou et al. (2007) . Perceived risk was the third independent variable used, held three items; thus, its scale was derived from Shim et al. (2001) . Wang (2011) validated consumer satisfaction carrying three items; consumer delight was measured by a 3-item scale proposed by Finn (2012) ; consumer regret was measured by the scale proposed by Wu and Wang (2017) . It carries a three-item scale. Consumer outrage was measured by Liu et al. (2015 ); it has six items. Repurchase intention was measured through a scale adapted from Zeithaml et al. (1996) , which carries four items. e-WOM was validated by the scale adapted from Goyette et al. (2010) ; it has five items.

Demographics of the Respondents

A total of 800 questionnaires were filled, and the respondents expressed their perception and actual experience from direct e-store and indirect e-store. Respondents belonged to different age groups from 18 to 50 years and above. There were 49% women and 51% men who took part in filling this survey. The income level of the respondents was grouped in different categories from “above 10,000 to above 50,000. The majority (56%) of the respondents were single, and 44% were married (Details can be viewed in Figure 1 ; Table 1 ). Data for both direct and indirect e-store was collected equally; 50% each to compare each category better.

Figure 1 . Proposed conceptual framework.

Table 1 . Demographics of the respondents.

Reliability and Validity

Reliability evaluates with the help of composite reliability (CR). All CR values fall into the range of 0.7–0.9, which is acceptable ( Hair et al., 2011 ). Convergent and discriminant validity has been observed through confirmatory factor analysis as recommended by some researchers ( Fornell and Larcker, 1981 ; Hair et al., 2010 ).

Convergent Validity

Convergent validity is evaluated with the help of two standards mentioned in the literature earlier, factor loading and average variance extracted (AVE), both the values should be >0.5 ( Yap and Khong, 2006 ). The values are mentioned in Table 2 .

Table 2 . Reliability and convergent validity.

Discriminant Validity

Discriminant validity is evaluated based on two conditions that are required to evaluate it. First, the correlation between the conceptual model variables should be <0.85 ( Kline, 2005 ). Second, the AVE square value must be less than the value of the conceptual model ( Fornell and Larcker, 1981 ). Table 3 depicts the discriminant validity of the construct of the study.

Table 3 . Discriminant validity.

Multi-Group Invariance Tests

Multi-group confirmatory factor analysis was conducted as the pre-requisites for the measurement model. The multi-group analysis was used to investigate a variety of invariance tests. Different invariance tests were performed to guarantee the items working precisely in the same manner in all the groups. In this research, the following are the model fit indexes, that is, CMIN/dF =2.992 CFI = 0.915, TLI = 0.906, and RMSEA = 0.071. Byrne (2010) and Teo et al. (2009) stated that CFI gives more accurate results, especially when comparing variables in different groups.

Hypotheses Testing

Scanning electron microscope technique was used to run and test the proposed hypotheses for the conceptual model. First, all the hypotheses proposed were checked, from which eight were initially accepted. Later, the multi-group test was utilized to test the proposed hypotheses and compare the shopping experience from direct e-store with indirect e-store and consumer perception with actual experience. Table 4 explains this in detail.

Table 4 . Hypotheses results.

Discussion and Implications

This research offers a remarkable number of facts for practitioners. This study can benefit marketing strategists by reducing the perceived risk, decreasing the intensity of perceived uncertainty, stabilizing the price, enhancing consumer satisfaction, promoting delighting consumers, accepting the negative behavior of the consumers, consumer retention, and establishing a positive e-WOM.

Reducing Risks

Certain factors play a role in antecedents of consumer satisfaction; they are particularly those that resist consumers to shop from any online channel, neither direct e-store nor indirect e-store. Perceived risk, perceived uncertainty, and the price are some of those antecedents that play a significant role in affecting the degree of satisfaction of the consumers, resulting in either to retain a consumer or to outrage a consumer. This study aligns with the existing literature. Tandon et al. (2016) ; Bonnin (2020) and Pandey et al. (2020) showed that consumers seek to shop from an e-store without bearing any risk. Consumers feel more confident about an e-store when the perceived risk is less than shopping from traditional ones as consumers want to feel optimistic about their decision. Second, e-vendors should ensure that the quality of a product is up to the mark and according to the consumer needs. Therefore, vendors should offer complete details about the product/service and its risks to the consumers. Moreover, this study suggests that e-stores must align the visuals of a product with its actual appearance. This would help them to increase customer satisfaction and confidence in the e-store.

Focus on Consumer Satisfaction

Consumer satisfaction is the deal-breaker factor in the online sector. Literature ( Shamsudin et al., 2018 ; Hassan et al., 2019 ) showed that organizations prioritize their consumers by fulfilling their requirements and required assistance. As a result, consumers are more confident and become satisfied consumers in the long run. This study adds to the literature that the degree of satisfaction of the consumers plays an essential role in shopping from an e-store. Consumers feel more confident in shopping from a direct e-store than an indirect e-store as the difference in the perception of consumers and the actual experience varies. Therefore, online vendors should focus on satisfying their consumers as it plays a remarkable role in retaining consumers.

Value Consumer Emotions

Online, retaining, and satisfying consumers are the most vital factor that directly affects the organization. This research aligns with the existing literature ( Jalonen and Jussila, 2016 ; Hechler and Kessler, 2018 ; Coetzee and Coetzee, 2019 ); when the retailer successfully fulfills its requirements, the consumer gets delighted repeating his choice to repurchase. On the other hand, if the online retailer fails to serve the consumer, the consumer regrets and, in extreme cases, becomes outraged about his decision. The negative emotions of the consumers threaten the company from many perspectives, as the company loses its consumer and its reputation in the market is affected. Therefore, first, market practitioners should avoid ignoring the requirements of consumers. Second, online vendors should pay special attention to the feedback of the consumers and assure them that they are valued.

Consumer Retention

The ultimate goal is to retain its consumers, but e-vendors should make proper strategies to satisfy their consumers as far as the online sector is concerned. The earlier studies of Zhang et al. (2015) and Ariffin et al. (2016) contributed to the literature that consumer satisfaction is a significant aspect in retaining a consumer. This research has also suggested that the satisfaction of the consumers plays a vital role in retaining them. Moreover, online shoppers provide the fastest spread of the right WOM about the product/ service. Second, consumers should feel valued and committed to vendors.

Pre- and Post-buying Behavior

This study contributed to a conceptual model that deals with consumer pre- and post-purchase behavior from the direct and indirect e-stores. With the help of experimental design, this study has reported its finding, highlighted how a satisfied customer is delightful and shares e-WOM, and showed repurchase intention. However, if the customer is not satisfied with the flip of a coin, he may feel regretted or outraged and cannot share e-WOM or have a repurchase intention.

Conclusions

This research concludes that online shopping has boomed during this COVID-19 pandemic period, as the lockdown prolonged in both the developed and the developing countries. The study further supports the difference between shopping from a direct e-store and an indirect e-store. The perception of the consumers shopping from direct e-store is more confident, and their degree of satisfaction is much higher, as the actual experience of the consumers aligns with their perceptions. Instead, consumers feel dissatisfied or outraged to choose an indirect e-store for shopping. Indirect e-store makes false promises and guarantees to its buyers, and eventually, when the consumers experience the product, it is against their perception.

This research fills the literature gap about the antecedents that lead to online shopping growth in the developing countries. This study aligns with Hechler and Kessler's (2018) earlier research, which stated that dissatisfied consumers threaten the reputation of the organization. Furthermore, Klaus and Maklan (2013) , Lemon and Verhoef (2016) suggested that handling the experience and satisfaction of the buyers plays a significant role in surviving among its competitors. Grange et al. (2019) recommended that e-commerce develops and attracts consumers by fulfilling their needs and requirements quickly. This study aligned with the existing literature by adding factors influencing the shopping preferences of the consumers from an e-store.

Limitations and Future Research

Despite its significant findings, this research has some limitations and scope for future research. First, this research only examined a few risks involved in online shopping. Future research studies should analyze other risks, for example, quality risk and privacy risk. Second, this study focused on shopping through direct e-stores and indirect e-stores. Future research can implement a conceptual model of a specific brand. Third, this study can be implemented in other sectors, for example, tourism, and hospitality. Fourth, it may be fascinating to look at other fundamentals, such as age, gender, education, relation with the retailer, or the degree of involvement with online shopping to differentiate other factors.

The proposed framework can be utilized in other developing countries, as every country faces different problems according to its growth and development. The model can be examined among specific direct e-stores to compare new customers and loyal customers. Future studies can explore indirect relationships along with adding mediators and moderators in the proposed model.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by This study involving human participants was reviewed and approved by the Ethics Committee of the Department of Management Sciences, Riphah International University, Faisalabad Campus, Faisalabad, Pakistan. The participants provided their written informed consent to participate in this study. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

AS contributed to the conceptualization and writing the first draft of the research. JU contributed to visualizing and supervising the research. All authors who contributed to the manuscript read and approved the submitted version.

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.

Afzal, S., Chandio, A. K., Shaikh, S., Bhand, M., and Ghumro, B. A. (2013). Factors behind brand switching in cellular networks. Int. J. Asian Soc. Science 3, 299–307.

Google Scholar

Ahmad, W., Kim, W. G., Choi, H. M., and Ul Haq, J. (2021). Modeling behavioral intention to use travel reservation apps: a cross-cultural examination between US and China. J. Retail. Consum. Serv. 63:102689. doi: 10.1016/j.jretconser.2021.102689

CrossRef Full Text | Google Scholar

Alharthey, B. (2020). The role of online trust in forming online shopping intentions. Int. J. Online Market. 10, 32–57. doi: 10.4018/IJOM.2020010103

Anderson, E. W., and Sullivan, M. W. (1993). The antecedents and consequences of customer satisfaction for firms. Market. Sci. 12, 125–143. doi: 10.1287/mksc.12.2.125

Ariffin, S., Yusof, J. M., Putit, L., and Shah, M. I. A. (2016). Factors influencing perceived quality and repurchase intention towards green products. Proc. Econ. Finan. 37, 391–396. doi: 10.1016/S2212-5671(16)30142-3

Ashoer, M., and Said, S. (2016). “The impact of perceived risk on consumer purchase intention in Indonesia; a social commerce study,” in Proceeding of the International Conference on Accounting, Management, Economics and Social Sciences . 1–13.

Aslam, W., Arif, I., Farhat, K., and Khursheed, M. (2018). The role of customer trust, service quality and value dimensions in determining satisfaction and loyalty: an Empirical study of mobile telecommunication industry in Pakistan. Market-TrŽište 30, 177–194. doi: 10.22598/mt/2018.30.2.177

Balaji, M. S., Khong, K. W., and Chong, A. Y. L. (2016). Determinants of negative word-of-mouth communication using social networking sites. Inform. Manage. 53, 528–540. doi: 10.1016/j.im.2015.12.002

Bechwati, N. N., and Xia, L. (2003). Do computers sweat? the impact of perceived effort of online decision aids on consumers' satisfaction with the decision process. J. Consum. Psychol. 13, 139–148. doi: 10.1207/S15327663JCP13-1andamp;2_12

Bonnin, G. (2020). The roles of perceived risk, attractiveness of the online store and familiarity with AR in the influence of AR on patronage intention. J. Retail. Consum. Serv. 52:101938. doi: 10.1016/j.jretconser.2019.101938

Butt, M. M., Rose, S., Wilkins, S., and Haq, J. U. (2017). MNCs and religious influences in global markets: drivers of consumer-based halal brand equity. Int. Market. Rev . 12:277. doi: 10.1108/IMR-12-2015-0277

Byrne, B. M. (2010). Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming , 2nd Edn. New York, NY: Routledge.

Cai, R., and Chi, C. G. Q. (2018). The impacts of complaint efforts on customer satisfaction and loyalty. Serv. Industr. J. 38, 1095–1115. doi: 10.1080/02642069.2018.1429415

Coetzee, A., and Coetzee, J. (2019). Service quality and attitudinal loyalty: the mediating effect of delight on retail banking relationships. Glob. Bus. Econ. Rev. 21, 120–138. doi: 10.1504/GBER.2019.096856

Crotts, J. C., and Magnini, V. P. (2011). The customer delight construct: is surprise essential? Ann. Tourism Res. 38, 719–722. doi: 10.1016/j.annals.2010.03.004

De Vos, J. (2020). The effect of COVID-19 and subsequent social distancing on travel behavior. Transport. Res. Interdisciplin. Perspect. 5:100121. doi: 10.1016/j.trip.2020.100121

PubMed Abstract | CrossRef Full Text | Google Scholar

Dodds, W. B., Monroe, K. B., and Grewal, D. (1991). Effects of price, brand, and store information on buyers' product evaluations. J. Market. Res. 28, 307–319. doi: 10.1177/002224379102800305

Durmaz, Y., Demira,g, B., and Çavuşoglu, S. (2020). Influence of regret and regret reversing effort on dissatisfaction and repurchase intention after purchasing fashion products. Preprints. doi: 10.20944/preprints202003.0280.v1

Ellsworth, P. C., and Smith, C. A. (1988). Shades of joy: patterns of appraisal differentiating pleasant emotions. Cogn. Emot. 2, 301–331. doi: 10.1080/02699938808412702

Escobar-Sierra, M., García-Cardona, A., and Vera Acevedo, L. D. (2021). How moral outrage affects consumer's perceived values of socially irresponsible companies. Cogent Bus. Manage. 8:1888668. doi: 10.1080/23311975.2021.1888668

Filieri, R., Galati, F., and Raguseo, E. (2021). The impact of service attributes and category on eWOM helpfulness: an investigation of extremely negative and positive ratings using latent semantic analytics and regression analysis. Comput. Human Behav. 114:106527. doi: 10.1016/j.chb.2020.106527

Finn, A. (2012). Customer delight: distinct construct or zone of nonlinear response to customer satisfaction? J. Serv. Res. 15, 99–110. doi: 10.1177/1094670511425698

Fornell, C., and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. J. Market. Res. 18, 39–50. doi: 10.1177/002224378101800104

Goyette, I., Ricard, L., Bergeron, J., and Marticotte, F. (2010). e-WOM Scale: word-of-mouth measurement scale for e-services context. Can. J. Admin. Sci. 27, 5–23. doi: 10.1002/cjas.129

Gozukara, E., Ozyer, Y., and Kocoglu, I. (2014). The moderating effects of perceived use and perceived risk in online shopping. J. Glob. Strateg. Manage. 16, 67–81. doi: 10.20460/JGSM.2014815643

Grange, C., Benbasat, I., and Burton-Jones, A. (2019). With a little help from my friends: Cultivating serendipity in online shopping environments. Inf. Manage . 56, 225–235.

Grewal, D., Levy, M., and Kumar, V. (2009). Customer experience management in retailing: An organizing framework. J. Retail. 85, 1–14. doi: 10.1016/j.jretai.2009.01.001

Guzel, M., Sezen, B., and Alniacik, U. (2020). Drivers and consequences of customer participation into value co-creation: a field experiment. J. Product Brand Manage. doi: 10.1108/JPBM-04-2020-2847

Hair, J. F., Anderson, R. E., Tatham, R. L., and Black, W. C. (1998). Multivariate Data Analysis . 5th ed., Hoboken, NJ: Prentice-Hall.

Hair, J. F., Celsi, M., Ortinau, D. J., and Bush, R. P. (2010). Essentials of Marketing Research , Vol. 2. New York, NY: McGraw-Hill/Irwin.

Hair, J. F., Ringle, C. M., and Sarstedt, M. (2011). PLS-SEM: indeed a silver bullet. J. Market. Theor. Pract. 19, 139–152. doi: 10.2753/MTP1069-6679190202

Hassan, S., Shamsudin, M. F., and Mustapha, I. (2019). The effect of service quality and corporate image on student satisfaction and loyalty in TVET higher learning institutes (HLIs). J. Tech. Educ. Train. 11:4.

Hechler, S., and Kessler, T. (2018). On the difference between moral outrage and empathic anger: anger about wrongful deeds or harmful consequences. J. Exp. Soc. Psychol. 76, 270–282. doi: 10.1016/j.jesp.2018.03.005

Hosany, S. (2012). Appraisal determinants of tourist emotional responses. J. Trav. Res. 51, 303–314. doi: 10.1177/0047287511410320

Ishfaq, M., Nazir, M. S., Qamar, M. A. J., and Usman, M. (2020). Cognitive bias and the Extraversion personality shaping the behavior of investors. Front. Psychol. 11:556506. doi: 10.3389/fpsyg.2020.556506

Jain, S. (2021). Examining the moderating role of perceived risk and web atmospherics in online luxury purchase intention. J. Fash. Market. Manage. Int. J. 05:89. doi: 10.1108/JFMM-05-2020-0089

Jalonen, H., and Jussila, J. (2016). “Developing a conceptual model for the relationship between social media behavior, negative consumer emotions and brand disloyalty,” in Conference on e-Business, e-Services and e-Society (Cham: Springer), 134–145. doi: 10.1007/978-3-319-45234-0_13

Jin, N., Line, N. D., and Merkebu, J. (2016). The impact of brand prestige on trust, perceived risk, satisfaction, and loyalty in upscale restaurants. J. Hospital. Market. Manage. 25, 523–546. doi: 10.1080/19368623.2015.1063469

Johnson, M. S., Sivadas, E., and Garbarino, E. (2008). Customer satisfaction, perceived risk and affective commitment: an investigation of directions of influence. J. Serv. Market. 5:120. doi: 10.1108/08876040810889120

Kähr, A., Nyffenegger, B., Krohmer, H., and Hoyer, W. D. (2016). When hostile consumers wreak havoc on your brand: the phenomenon of consumer brand sabotage. J. Mark. 80, 25–41. doi: 10.1509/jm.15.0006

Kim, D. J., Ferrin, D. L., and Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decis. Support Syst. 44, 544–564. doi: 10.1016/j.dss.2007.07.001

Kim, M., Vogt, C. A., and Knutson, B. J. (2015). Relationships among customer satisfaction, delight, and loyalty in the hospitality industry. J. Hospital. Tourism Res. 39, 170–197. doi: 10.1177/1096348012471376

Klaus, P. P., and Maklan, S. (2013). Towards a better measure of customer experience. Int. J. Market Res. 55, 227–246. doi: 10.2501/IJMR-2013-021

Kline, R. B. (2005). Principles and Practice of Structural Equation Modeling , 2nd Edn. New York, NY: Guilford Press.

Kumar, A., Chaudhuri, D., Bhardwaj, D., and Mishra, P. (2020). Impulse buying and post-purchase regret: a study of shopping behaviour for the purchase of grocery products. Int. J. Manage. 11:57. doi: 10.34218/IJM.11.12.2020.057

Kumar, A., and Kim, Y. K. (2014). The store-as-a-brand strategy: the effect of store environment on customer responses. J. Retail. Consum. Serv. 21, 685–695. doi: 10.1016/j.jretconser.2014.04.008

Lee, S. H., and Cotte, J. (2009). Post-purchase Consumer Regret: Conceptualization and Development of the PPCR Scale . ACR North American Advances.

Lemon, K. N., and Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. J. Market. 80, 69–96. doi: 10.1509/jm.15.0420

Li, S., Zhou, K., Sun, Y., Rao, L. L., Zheng, R., and Liang, Z. Y. (2010). Anticipated regret, risk perception, or both: which is most likely responsible for our intention to gamble? J. Gambl. Stud. 26, 105–116. doi: 10.1007/s10899-009-9149-5

Liao, C., Lin, H. N., Luo, M. M., and Chea, S. (2017). Factors influencing online shoppers' repurchase intentions: the roles of satisfaction and regret. Inform. Manage. 54, 651–668. doi: 10.1016/j.im.2016.12.005

Lindenmeier, J., Schleer, C., and Pricl, D. (2012). Consumer outrage: emotional reactions to unethical corporate behavior. J. Bus. Res. 65, 1364–1373. doi: 10.1016/j.jbusres.2011.09.022

Liu, M. W., and Keh, H. T. (2015). Consumer delight and outrage: scale development and validation. J. Serv. Theory Pract . 25, 680–699. doi: 10.1108/JSTP-08-2014-0178

Loureiro, S. M. C., and Kastenholz, E. (2011). Corporate reputation, satisfaction, delight, and loyalty towards rural lodging units in Portugal. Int. J. Hospital. Manage. 30, 575–583. doi: 10.1016/j.ijhm.2010.10.007

Ludwig, N. L., Heidenreich, S., Kraemer, T., and Gouthier, M. (2017). Customer delight: universal remedy or a double-edged sword? J. Serv. Theory Pract . 8:197. doi: 10.1108/JSTP-08-2015-0197

Ma, J., Gao, J., Scott, N., and Ding, P. (2013). Customer delight from theme park experiences: the antecedents of delight based on cognitive appraisal theory. Ann. Tour. Res. 42, 359–381. doi: 10.1016/j.annals.2013.02.018

Malhotra, N., Hall, J., Shaw, M., and Oppenheim, P. (2006). Marketing Research: An Applied Orientation . Melbourne, VC: Pearson Education Australia.

Mamuaya, N. C., and Pandowo, A. (2020). Determinants of customer satisfaction and its implications on word of mouth in e-commerce industry: case study in Indonesia. Asia Pacific J. Manage. Educ. 3, 16–27. doi: 10.32535/apjme.v3i1.740

Mattila, A. S., and Ro, H. (2008). Discrete negative emotions and customer dissatisfaction responses in a casual restaurant setting. J. Hosp. Tour. Res. 32, 89–107. doi: 10.1177/1096348007309570

Mendez, J. L., Oubina, J., and Rubio, N. (2008). Expert quality evaluation and price of store vs. manufacturer brands: an analysis of the Spanish mass market. J. Retail. Consum. Serv. 15, 144–155. doi: 10.1016/j.jretconser.2007.11.003

Mikulić, J., Kreši,ć, D., and Šerić, M. (2021). The factor structure of medical tourist satisfaction: exploring key drivers of choice, delight, and frustration. J. Hospital. Tour. Res. 1177:1096348020987273. doi: 10.1177/1096348020987273

Moors, A., Boddez, Y., and De Houwer, J. (2017). The power of goal-directed processes in the causation of emotional and other actions. Emot. Rev. 9, 310–318. doi: 10.1177/1754073916669595

Nassauer, S. (2020). Walmart sales surge as Coronavirus drives Americans to stockpile. Wall Street J . Availale online at: https://www.wsj.com/articles/walmart-sales-surge-as-coronavirus-drivesamericans-to-stockpile-11589888464?mod=hp_lead_pos5 (accessed on May 18, 2020).

Oliver, R. L. (1989). Processing of the satisfaction response in consumption. J. Consum. Satisfact. Dissatisfact. Complain. Behav. 2, 1–26.

Oliver, R. L., and DeSarbo, W. S. (1988). Response determinants in satisfaction judgments. J. Consum. Res. 14, 495–507. doi: 10.1086/209131

Oliver, R. L., Rust, R. T., and Varki, S. (1997). Customer delight: foundations, findings, and managerial insight. J. Retail. 73:311. doi: 10.1016/S0022-4359(97)90021-X

Olotewo, J. (2017). Examining the antecedents of in-store and online purchasing behavior: a case of Nigeria. J. Market. Res. Case Stud. 15, 1–16. doi: 10.5171/2017.668316

Pandey, N., Tripathi, A., Jain, D., and Roy, S. (2020). Does price tolerance depend upon the type of product in e-retailing? role of customer satisfaction, trust, loyalty, and perceived value. J. Strateg. Market. 28, 522–541. doi: 10.1080/0965254X.2019.1569109

Parasuraman, A., Ball, J., Aksoy, L., Keiningham, T. L., and Zaki, M. (2020). More than a feeling? toward a theory of customer delight. J. Serv. Manage . 3:34. doi: 10.1108/JOSM-03-2019-0094

Park, E. O., Chung, K. H., and Shin, J. I. (2010). The relationship among internal marketing, internal customer satisfaction, organizational commitment and performance. Product. Rev. 24, 199–232. doi: 10.15843/kpapr.24.2.201006.199

CrossRef Full Text

Pavlou, P. A., Liang, H., and Xue, Y. (2007). Understanding and mitigating uncertainty in online exchange relationships: a principal-agent perspective. MIS Q. 105–136. doi: 10.2307/25148783

Rahmadini, Y., and Halim, R. E. (2018). The “Influence of social media towards emotions, brand relationship quality, and word of Mouth (WOM) on Concert's Attendees in Indonesia,” in MATEC Web of Conferences (EDP Sciences) , 05058.

Redman, R. (2020). “Online grocery sales to grow 40% in 2020,” in Supermarket News . Available online at: https://www.supermarketnews.com/onlineretail/online-grocery-sales-grow-40-2020 (accessed on May 21, 2020).

Rizal, H., Yussof, S., Amin, H., and Chen-Jung, K. (2018). EWOM towards homestays lodging: extending the information system success model. J. Hosp. Tour. Technol. doi: 10.1108/JHTT-12-2016-0084

Roseman, I. J. (1984). Cognitive determinants of emotion: a structural theory. Rev. Person. Soc. Psychol. 5, 11–36.

Roseman, I. J. (2013). Author reply: on the frontiers of appraisal theory. Emot. Rev. 5, 187–188. doi: 10.1177/1754073912469592

Rossolov, A., Rossolova, H., and Holguín-Veras, J. (2021). Online and in-store purchase behavior: shopping channel choice in a developing economy. Transportation 20, 1–37. doi: 10.1007/s11116-020-10163-3

Salancik, G. R., and Pfeffer, J. (1978). Uncertainty, secrecy, and the choice of similar others. Soc. Psychol. 23, 246–255. doi: 10.2307/3033561

Saleh, M. A. H. (2016). Website design, technological expertise, demographics, and consumer's e-purchase transactions. Int. J. Market. Stud. 8, 125–138. doi: 10.5539/ijms.v8n1p125

Scherer, K. R. (1997). The role of culture in emotion-antecedent appraisal. J. Pers. Soc. Psychol. 73:902. doi: 10.1037/0022-3514.73.5.902

Schneider, B., and Bowen, D. E. (1999). Understanding customer delight and outrage. Sloan Manage. Rev. 41, 35–45. doi: 10.1016/S0022-4359(01)00035–45

Shamsudin, M. F., Razak, A. A., and Salem, M. A. (2018). The role of customer interactions towards customer satisfaction in theme parks experience. Opcion 34, 546–558.

Shim, S., Eastlick, M. A., Lotz, S. L., and Warrington, P. (2001). An online prepurchase intentions model: the role of intention to search: best overall paper award—the Sixth Triennial AMS/ACRA Retailing Conference, 2000? J. Retail. 77, 397–416. doi: 10.1016/S0022-4359(01)00051-3

Smith, C. A., and Ellsworth, P. C. (1985). Patterns of cognitive appraisal in emotion. J. Pers. Soc. Psychol . 48:813.

Tandon, A., Aakash, A., and Aggarwal, A. G. (2020). Impact of EWOM, website quality, and product satisfaction on customer satisfaction and repurchase intention: moderating role of shipping and handling. Int. J. Syst. Assur. Eng. Manage. 54, 1–8. doi: 10.1007/s13198-020-00954-3

Tandon, U. (2021). Predictors of online shopping in India: an empirical investigation. J. Market. Anal. 9, 65–79. doi: 10.1057/s41270-020-00084-6

Tandon, U., Kiran, R., and Sah, A. N. (2016). Understanding online shopping adoption in India: unified theory of acceptance and use of technology 2 (UTAUT2) with perceived risk application. Serv. Sci. 8, 420–437. doi: 10.1287/serv.2016.0154

Tarhini, A., Alalwan, A. A., Al-Qirim, N., and Algharabat, R. (2021). “An analysis of the factors influencing the adoption of online shopping,” in Research Anthology on E-Commerce Adoption, Models, and Applications for Modern Business (Pennsylvania: IGI Global), 363–384.

Tarofder, A. K., Nikhashemi, S. R., Azam, S. F., Selvantharan, P., and Haque, A. (2016). The mediating influence of service failure explanation on customer repurchase intention through customers satisfaction. Int. J. Qual. Serv. Sci. 4:44. doi: 10.1108/IJQSS-04-2015-0044

Taylor, S. A., and Baker, T. L. (1994). An assessment of the relationship between service quality and customer satisfaction in the formation of consumers' purchase intentions. J. Retail. 70, 163–178. doi: 10.1016/0022-4359(94)90013-2

Teo, T., Lee, C. B., Chai, C. S., and Wong, S. L. (2009). Assessing the intention to use technology among preservice teachers in Singapore and Malaysia: a multigroup invariance analysis of the technology acceptance model (TAM). Comput. Educ. 53, 1000–1009. doi: 10.1016/j.compedu.2009.05.017

Thibaut, J. W., and Kelley, H. H. (2017). The Social Psychology of Groups . Routledge. doi: 10.4324/9781315135007

Torres, E. N., Milman, A., and Park, S. (2019). Customer delight and outrage in theme parks: a roller coaster of emotions. Int. J. Hospital. Tour. Administr. 16, 1–23. doi: 10.1080/15256480.2019.1641455

Tsiros, M., and Mittal, V. (2000). Regret: a model of its antecedents and consequences in consumer decision making. J. Consum. Res. 26, 401–417. doi: 10.1086/209571

Tzeng, S. Y., Ertz, M., Jo, M. S., and Sarigöll,ü, E. (2021). Factors affecting customer satisfaction on online shopping holiday. Market. Intell. Plann. 8:346. doi: 10.1108/MIP-08-2020-0346

Ul Haq, J., and Bonn, M. A. (2018). Understanding millennial perceptions of human and nonhuman brands. Int. Hospital. Rev . 9:14. doi: 10.1108/IHR-09-2018-0014

Unal, S., and Aydin, H. (2016). Evaluation of consumer regret in terms of perceived risk and repurchase intention. J. Glob. Strateg. Manage. 2, 31–31. doi: 10.20460/JGSM.20161024354

Venkatesh, V., Thong, J. Y., and Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. 157–178. doi: 10.2307/41410412

Wai, K., Dastane, O., Johari, Z., and Ismail, N. B. (2019). Perceived risk factors affecting consumers' online shopping behaviour. J. Asian Financ. Econ. Bus. 6, 246–260. doi: 10.13106/jafeb.2019.vol6.no4.249

Wang, C. Y., and Mattila, A. S. (2011). A cross-cultural comparison of perceived informational fairness with service failure explanations. J. Serv. Market . 25, 429–439. doi: 10.1108/08876041111161023

Wang, X. (2011). The effect of unrelated supporting service quality on consumer delight, satisfaction, and repurchase intentions. J. Serv. Res. 14, 149–163. doi: 10.1177/1094670511400722

Westbrook, R. A., and Oliver, R. L. (1991). The dimensionality of consumption emotion patterns and consumer satisfaction. J. Consum. Res. 18, 84–91. doi: 10.1086/209243

Whelan, J., and Dawar, N. (2014). Attributions of blame following a product-harm crisis depend on consumers' attachment styles. Mark. Lett. 27, 285–294. doi: 10.1007/s11002-014-9340-z

Woodside, A. G., Frey, L. L., and Daly, R. T. (1989). Linking service quality, customer satisfaction, and behavio. Mark. Health Serv. 9:5. doi: 10.1016/S0022-4359(01)0009-5

Wu, R., and Wang, C. L. (2017). The asymmetric impact of other-blame regret versus self-blame regret on negative word of mouth: empirical evidence from China. Eur. J. Market. doi: 10.1108/EJM-06-2015-0322

Yang, Y., Gong, Y., Land, L. P. W., and Chesney, T. (2020). Understanding the effects of physical experience and information integration on consumer use of online to offline commerce. Int. J. Inf. Manage. 51:102046. doi: 10.1016/j.ijinfomgt.2019.102046

Yap, B. W., and Khong, K. W. (2006). Examining the effects of customer service management (CSM) on perceived business performance via structural equation modelling. Appl. Stochast. Models Bus. Indus. 22, 587–605. doi: 10.1002/asmb.648

Zeelenberg, M., and Pieters, R. (2007). A theory of regret regulation 1.0. J. Consum. Psychol. 17, 3–18. doi: 10.1207/s15327663jcp1701_3

Zeithaml, V. A., Berry, L. L., and Parasuraman, A. (1996). The behavioral consequences of service quality. J. Market. 60, 31–46. doi: 10.1177/002224299606000203

Zhang, H. (2017). Understanding the Consumption Experience of Chinese Tourists: Assessing the Effect of Audience Involvement, Flow and Delight on Electronic Word-of-mouth (eWOM) (Doctoral dissertation).

Zhang, Z., Ye, Q., Song, H., and Liu, T. (2015). The structure of customer satisfaction with cruise-line services: an empirical investigation based on online word of mouth. Curr. Issues Tourism . 18, 450–464.

Keywords: consumer perception, online shopping, actual experiences, customer satisfaction, direct shopping, perceived risk, delight, outrage

Citation: Rao YH, Saleem A, Saeed W and Ul Haq J (2021) Online Consumer Satisfaction During COVID-19: Perspective of a Developing Country. Front. Psychol. 12:751854. doi: 10.3389/fpsyg.2021.751854

Received: 02 August 2021; Accepted: 30 August 2021; Published: 01 October 2021.

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Copyright © 2021 Rao, Saleem, Saeed and Ul Haq. 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: Junaid Ul Haq, junaid041@yahoo.com

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