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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.

Reviewed by:

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

A study on factors limiting online shopping behaviour of consumers

Rajagiri Management Journal

ISSN : 0972-9968

Article publication date: 4 March 2021

Issue publication date: 12 April 2021

This study aims to investigate consumer behaviour towards online shopping, which further examines various factors limiting consumers for online shopping behaviour. The purpose of the research was to find out the problems that consumers face during their shopping through online stores.

Design/methodology/approach

A quantitative research method was adopted for this research in which a survey was conducted among the users of online shopping sites.

As per the results total six factors came out from the study that restrains consumers to buy from online sites – fear of bank transaction and faith, traditional shopping more convenient than online shopping, reputation and services provided, experience, insecurity and insufficient product information and lack of trust.

Research limitations/implications

This study is beneficial for e-tailers involved in e-commerce activities that may be customer-to-customer or customer-to-the business. Managerial implications are suggested for improving marketing strategies for generating consumer trust in online shopping.

Originality/value

In contrast to previous research, this study aims to focus on identifying those factors that restrict consumers from online shopping.

• Online shopping

Daroch, B. , Nagrath, G. and Gupta, A. (2021), "A study on factors limiting online shopping behaviour of consumers", Rajagiri Management Journal , Vol. 15 No. 1, pp. 39-52. https://doi.org/10.1108/RAMJ-07-2020-0038

Emerald Publishing Limited

Copyright © 2020, Bindia Daroch, Gitika Nagrath and Ashutosh Gupta.

Published in Rajagiri Management Journal . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Today, people are living in the digital environment. Earlier, internet was used as the source for information sharing, but now life is somewhat impossible without it. Everything is linked with the World Wide Web, whether it is business, social interaction or shopping. Moreover, the changed lifestyle of individuals has changed their way of doing things from traditional to the digital way in which shopping is also being shifted to online shopping.

Online shopping is the process of purchasing goods directly from a seller without any intermediary, or it can be referred to as the activity of buying and selling goods over the internet. Online shopping deals provide the customer with a variety of products and services, wherein customers can compare them with deals of other intermediaries also and choose one of the best deals for them ( Sivanesan, 2017 ).

As per Statista-The Statistics Portal, the digital population worldwide as of April 2020 is almost 4.57 billion people who are active internet users, and 3.81 billion are social media users. In terms of internet usage, China, India and the USA are ahead of all other countries ( Clement, 2020 ).

The number of consumers buying online and the amount of time people spend online has risen ( Monsuwe et al. , 2004 ). It has become more popular among customers to buy online, as it is handier and time-saving ( Huseynov and Yildirim, 2016 ; Mittal, 2013 ). Convenience, fun and quickness are the prominent factors that have increased the consumer’s interest in online shopping ( Lennon et al. , 2008 ). Moreover, busy lifestyles and long working hours also make online shopping a convenient and time-saving solution over traditional shopping. Consumers have the comfort of shopping from home, reduced traveling time and cost and easy payment ( Akroush and Al-Debei, 2015 ). Furthermore, price comparisons can be easily done while shopping through online mode ( Aziz and Wahid, 2018 ; Martin et al. , 2015 ). According to another study, the main influencing factors for online shopping are availability, low prices, promotions, comparisons, customer service, user friendly, time and variety to choose from ( Jadhav and Khanna, 2016 ). Moreover, website design and features also encourage shoppers to shop on a particular website that excite them to make the purchase.

Online retailers have started giving plenty of offers that have increased the online traffic to much extent. Regularly online giants like Amazon, Flipkart, AliExpress, etc. are advertising huge discounts and offers that are luring a large number of customers to shop from their websites. Companies like Nykaa, MakeMyTrip, Snapdeal, Jabong, etc. are offering attractive promotional deals that are enticing the customers.

Despite so many advantages, some customers may feel online shopping risky and not trustworthy. The research proposed that there is a strong relationship between trust and loyalty, and most often, customers trust brands far more than a retailer selling that brand ( Bilgihan, 2016 ; Chaturvedi et al. , 2016 ). In the case of online shopping, there is no face-to-face interaction between seller and buyer, which makes it non-socialize, and the buyer is sometimes unable to develop the trust ( George et al. , 2015 ). Trust in the e-commerce retailer is crucial to convert potential customer to actual customer. However, the internet provides unlimited products and services, but along with those unlimited services, there is perceived risk in digital shopping such as mobile application shopping, catalogue or mail order ( Tsiakis, 2012 ; Forsythe et al. , 2006 ; Aziz and Wahid, 2018 ).

Literature review

A marketer has to look for different approaches to sell their products and in the current scenario, e-commerce has become the popular way of selling the goods. Whether it is durable or non-durable, everything is available from A to Z on websites. Some websites are specifically designed for specific product categories only, and some are selling everything.

The prominent factors like detailed information, comfort and relaxed shopping, less time consumption and easy price comparison influence consumers towards online shopping ( Agift et al. , 2014 ). Furthermore, factors like variety, quick service and discounted prices, feedback from previous customers make customers prefer online shopping over traditional shopping ( Jayasubramanian et al. , 2015 ). It is more preferred by youth, as during festival and holiday season online retailers give ample offers and discounts, which increases the online traffic to a great extent ( Karthikeyan, 2016 ). Moreover, services like free shipping, cash on delivery, exchange and returns are also luring customers towards online purchases.

More and more people are preferring online shopping over traditional shopping because of their ease and comfort. A customer may have both positive and negative experiences while using an online medium for their purchase. Some of the past studies have shown that although there are so many benefits still some customers do not prefer online as their basic medium of shopping.

While making online purchase, customers cannot see, touch, feel, smell or try the products that they want to purchase ( Katawetawaraks and Wang, 2011 ; Al-Debei et al. , 2015 ), due to which product is difficult to examine, and it becomes hard for customers to make purchase decision. In addition, some products are required to be tried like apparels and shoes, but in case of online shopping, it is not possible to examine and feel the goods and assess its quality before making a purchase due to which customers are hesitant to buy ( Katawetawaraks and Wang, 2011 ; Comegys et al. , 2009 ). Alam and Elaasi (2016) in their study found product quality is the main factor, which worries consumer to make online purchase. Moreover, some customers have reported fake products and imitated items in their delivered orders ( Jun and Jaafar, 2011 ). A low quality of merchandise never generates consumer trust on online vendor. A consumer’s lack of trust on the online vendor is the most common reason to avoid e-commerce transactions ( Lee and Turban, 2001 ). Fear of online theft and non-reliability is another reason to escape from online shopping ( Karthikeyan, 2016 ). Likewise, there is a risk of incorrect information on the website, which may lead to a wrong purchase, or in some cases, the information is incomplete for the customer to make a purchase decision ( Liu and Guo, 2008 ). Moreover, in some cases, the return and exchange policies are also not clear on the website. According to Wei et al. (2010) , the reliability and credibility of e-retailer have direct impact on consumer decision with regards to online shopping.

Limbu et al. (2011) revealed that when it comes to online retailers, some websites provide very little information about their companies and sellers, due to which consumers feel insecure to purchase from these sites. According to other research, consumers are hesitant, due to scams and feel anxious to share their personal information with online vendors ( Miyazaki and Fernandez, 2001 ; Limbu et al. , 2011 ). Online buyers expect websites to provide secure payment and maintain privacy. Consumers avoid online purchases because of the various risks involved with it and do not find internet shopping secured ( Cheung and Lee, 2003 ; George et al. , 2015 ; Banerjee et al. , 2010 ). Consumers perceive the internet as an unsecured channel to share their personal information like emails, phone and mailing address, debit card or credit card numbers, etc. because of the possibility of misuse of that information by other vendors or any other person ( Lim and Yazdanifard, 2014 ; Kumar, 2016 ; Alam and Yasin, 2010 ; Nazir et al. , 2012 ). Some sites make it vital and important to share personal details of shoppers before shopping, due to which people abandon their shopping carts (Yazdanifard and Godwin, 2011). About 75% of online shoppers leave their shopping carts before they make their final decision to purchase or sometimes just before making the payments ( Cho et al. , 2006 ; Gong et al. , 2013 ).

Moreover, some of the customers who have used online shopping confronted with issues like damaged products and fake deliveries, delivery problems or products not received ( Karthikeyan, 2016 ; Kuriachan, 2014 ). Sometimes consumers face problems while making the return or exchange the product that they have purchased from online vendors ( Liang and Lai, 2002 ), as some sites gave an option of picking from where it was delivered, but some online retailers do not give such services to consumer and consumer him/herself has to courier the product for return or exchange, which becomes inopportune. Furthermore, shoppers had also faced issues with unnecessary delays ( Muthumani et al. , 2017 ). Sometimes, slow websites, improper navigations or fear of viruses may drop the customer’s willingness to purchase from online stores ( Katawetawaraks and Wang, 2011 ). As per an empirical study done by Liang and Lai (2002) , design of the e-store or website navigation has an impact on the purchase decision of the consumer. An online shopping experience that a consumer may have and consumer skills that consumers may use while purchasing such as website knowledge, product knowledge or functioning of online shopping influences consumer behaviour ( Laudon and Traver, 2009 ).

From the various findings and viewpoints of the previous researchers, the present study identifies the complications online shoppers face during online transactions, as shown in Figure 1 . Consumers do not have faith, and there is lack of confidence on online retailers due to incomplete information on website related to product and service, which they wish to purchase. Buyers are hesitant due to fear of online theft of their personal and financial information, which makes them feel there will be insecure transaction and uncertain errors may occur while making online payment. Some shoppers are reluctant due to the little internet knowledge. Furthermore, as per the study done by Nikhashem et al. (2011), consumers unwilling to use internet for their shopping prefer traditional mode of shopping, as it gives roaming experience and involves outgoing activity.

Several studies have been conducted earlier that identify the factors influencing consumer towards online shopping but few have concluded the factors that restricts the consumers from online shopping. The current study is concerned with the factors that may lead to hesitation by the customer to purchase from e-retailers. This knowledge will be useful for online retailers to develop customer driven strategies and to add more value product and services and further will change their ways of promoting and advertising the goods and enhance services for customers.

Research methodology

This study aimed to find out the problems that are generally faced by a customer during online purchase and the relevant factors due to which customers do not prefer online shopping. Descriptive research design has been used for the study. Descriptive research studies are those that are concerned with describing the characteristics of a particular individual or group. This study targets the population drawn from customers who have purchased from online stores. Most of the respondents participated were post graduate students and and educators. The total population size was indefinite and the sample size used for the study was 158. A total of 170 questionnaires were distributed among various online users, out of which 12 questionnaires were received with incomplete responses and were excluded from the analysis. The respondents were selected based on the convenient sampling technique. The primary data were collected from Surveys with the help of self-administered questionnaires. The close-ended questionnaire was used for data collection so as to reduce the non-response rate and errors. The questionnaire consists of two different sections, in which the first section consists of the introductory questions that gives the details of socio-economic profile of the consumers as well as their behaviour towards usage of internet, time spent on the Web, shopping sites preferred while making the purchase, and the second section consist of the questions related to the research question. To investigate the factors restraining consumer purchase, five-point Likert scale with response ranges from “Strongly agree” to “Strongly disagree”, with following equivalencies, “strongly disagree” = 1, “disagree” = 2, “neutral” = 3, “agree” = 4 and “strongly agree” = 5 was used in the questionnaire with total of 28 items. After collecting the data, it was manually recorded on the Excel sheet. For analysis socio-economic profile descriptive statistics was used and factors analysis was performed on SPSS for factor reduction.

Data analysis and interpretation

The primary data collected from the questionnaires was completely quantified and analysed by using Statistical Package for Social Science (SPSS) version 20. This statistical program enables accuracy and makes it relatively easy to interpret data. A descriptive and inferential analysis was performed. Table 1 represents the results of socio-economic status of the respondents along with some introductory questions related to usage of internet, shopping sites used by the respondents, amount of money spent by the respondents and products mostly purchased through online shopping sites.

According to the results, most (68.4%) of the respondents were belonging to the age between 21 and 30 years followed by respondents who were below the age of 20 years (16.4%) and the elderly people above 50 were very few (2.6%) only. Most of the respondents who participated in the study were females (65.8)% who shop online as compared to males (34.2%). The respondents who participated in the study were students (71.5%), and some of them were private as well as government employees. As per the results, most (50.5%) of the people having income below INR15,000 per month who spend on e-commerce websites. The results also showed that most of the respondents (30.9%) spent less than 5 h per week on internet, but up to (30.3%) spend 6–10 h per week on internet either on online shopping or social media. Majority (97.5%) of them have shopped through online websites and had both positive and negative experiences, whereas 38% of the people shopped 2–5 times and 36.7% shopped more than ten times. Very few people (12%), shopped only once. Most of the respondents spent between INR1,000–INR5,000 for online shopping, and few have spent more than INR5,000 also.

As per the results, the most visited online shopping sites was amazon.com (71.5%), followed by flipkart.com (53.2%). Few respondents have also visited other e-commerce sites like eBay, makemytrip.com and myntra.com. Most (46.2%) of the time people purchase apparels followed by electronics and daily need items from the ecommerce platform. Some of the respondents have purchased books as well as cosmetics, and some were preferring online sites for travel tickets, movie tickets, hotel bookings and payments also.

Factor analysis

To explore the factors that restrict consumers from using e-commerce websites factor analysis was done, as shown in Table 3 . A total of 28 items were used to find out the factors that may restrain consumers to buy from online shopping sites, and the results were six factors. The Kaiser–Meyer–Olkin (KMO) measure, as shown in Table 2 , in this study was 0.862 (>0.60), which states that values are adequate, and factor analysis can be proceeded. The Bartlett’s test of sphericity is related to the significance of the study and the significant value is 0.000 (<0.05) as shown in Table 2 .

The analysis produced six factors with eigenvalue more than 1, and factor loadings that exceeded 0.30. Moreover, reliability test of the scale was performed through Cronbach’s α test. The range of Cronbach’s α test came out to be between 0.747 and 0.825, as shown in Table 3 , which means ( α > 0.7) the high level of internal consistency of the items used in survey ( Table 4 ).

Factor 1 – The results revealed that the “fear of bank transaction and faith” was the most significant factor, with 29.431% of the total variance and higher eigenvalue, i.e. 8.241. The six statements loaded on Factor 1 highly correlate with each other. The analysis shows that some people do not prefer online shopping because they are scared to pay online through credit or debit cards, and they do not have faith over online vendors.

Factor 2 – “Traditional shopping is convenient than online shopping” has emerged as a second factor which explicates 9.958% of total variance. It has five statements and clearly specifies that most of the people prefer traditional shopping than online shopping because online shopping is complex and time-consuming.

Factor 3 – Third crucial factor emerged in the factor analysis was “reputation and service provided”. It was found that 7.013% of variations described for the factor. Five statements have been found on this factor, all of which were interlinked. It clearly depicts that people only buy from reputed online stores after comparing prices and who provide guarantee or warrantee on goods.

Factor 4 – “Experience” was another vital factor, with 4.640% of the total variance. It has three statements that clearly specifies that people do not go for online shopping due to lack of knowledge and their past experience was not good and some online stores do not provide EMI facilities.

Factor 5 – Fifth important factor arisen in the factor analysis was “Insecurity and Insufficient Product Information” with 4.251% of the total variance, and it has laden five statements, which were closely intertwined. This factor explored that online shopping is not secure as traditional shopping. The information of products provided on online stores is not sufficient to make the buying decision.

Factor 6 – “Lack of trust” occurred as the last factor of the study, which clarifies 3.920% of the total variance. It has four statements that clearly state that some people hesitate to give their personal information, as they believe online shopping is risky than traditional shopping. Without touching the product, people hesitate to shop from online stores.

The study aimed to determine the problems faced by consumers during online purchase. The result showed that most of the respondents have both positive and negative experience while shopping online. There were many problems or issues that consumer’s face while using e-commerce platform. Total six factors came out from the study that limits consumers to buy from online sites like fear of bank transaction and no faith, traditional shopping more convenient than online shopping, reputation and services provided, experience, insecurity and insufficient product information and lack of trust.

The research might be useful for the e-tailers to plan out future strategies so as to serve customer as per their needs and generate customer loyalty. As per the investigation done by Casalo et al. (2008) , there is strong relationship between reputation and satisfaction, which further is linked to customer loyalty. If the online retailer has built his brand name, or image of the company, the customer is more likely to prefer that retailer as compared to new entrant. The online retailer that seeks less information from customers are more preferred as compared to those require complete personal information ( Lawler, 2003 ).

Online retailers can adopt various strategies to persuade those who hesitate to shop online such that retailer need to find those negative aspects to solve the problems of customers so that non-online shopper or irregular online consumer may become regular customer. An online vendor has to pay attention to product quality, variety, design and brands they are offering. Firstly, the retailer must enhance product quality so as to generate consumer trust. For this, they can provide complete seller information and history of the seller, which will preferably enhance consumer trust towards that seller.

Furthermore, they can adopt marketing strategies such as user-friendly and secure website, which can enhance customers’ shopping experience and easy product search and proper navigation system on website. Moreover, complete product and service information such as feature and usage information, description and dimensions of items can help consumer decide which product to purchase. The experience can be enhanced by adding more pictures, product videos and three-dimensional (3D), images which will further help consumer in the decision-making process. Moreover, user-friendly payment systems like cash on deliveries, return and exchange facilities as per customer needs, fast and speedy deliveries, etc. ( Chaturvedi et al. , 2016 ; Muthumani et al. , 2017 ) will also enhance the probability of purchase from e-commerce platform. Customers are concerned about not sharing their financial details on any website ( Roman, 2007 ; Limbu et al. , 2011 ). Online retailers can ensure payment security by offering numerous payment options such as cash on delivery, delivery after inspection, Google Pay or Paytm or other payment gateways, etc. so as to increase consumer trust towards website, and customer will not hesitate for financial transaction during shopping. Customers can trust any website depending upon its privacy policy, so retailers can provide customers with transparent security policy, privacy policy and secure transaction server so that customers will not feel anxious while making online payments ( Pan and Zinkhan, 2006 ). Moreover, customers not only purchase basic goods from the online stores but also heed augmented level of goods. Therefore, if vendors can provide quick and necessary support, answer all their queries within 24-hour service availability, customers may find it convenient to buy from those websites ( Martin et al. , 2015 ). Sellers must ensure to provide products and services that are suitable for internet. Retailers can consider risk lessening strategies such as easy return and exchange policies to influence consumers ( Bianchi and Andrews, 2012 ). Furthermore, sellers can offer after-sales services as given by traditional shoppers to attract more customers and generate unique shopping experience.

Although nowadays, most of the vendors do give plenty of offers in form of discounts, gifts and cashbacks, but most of them are as per the needs of e-retailers and not customers. Beside this, trust needs to be generated in the customer’s mind, which can be done by modifying privacy and security policies. By adopting such practices, the marketer can generate customers’ interest towards online shopping.

Conceptual framework of the study

Socioeconomic status of respondents

KMO and Bartlett’s test

Cronbach’s α

Agift , A. , Rekha , V. and Nisha , C. ( 2014 ), “ Consumers attitude towards online shopping ”, Research Journal of Family, Community and Consumer Sciences , Vol. 2 No. 8 , pp. 4 - 7 , available at: www.isca.in/FAMILY_SCI/Archive/v2/i8/2.ISCA-RJFCCS-2014-017.php

Akroush , M.N. and Al-Debei , M.M. ( 2015 ), “ An integrated model of factors affecting consumer attitudes towards online shopping ”, Business Process Management Journal , Vol. 21 No. 6 , pp. 1353 - 1376 , doi: 10.1108/BPMJ-02-2015-0022 .

Alam , M.Z. and Elaasi , S. ( 2016 ), “ A study on consumer perception towards e-shopping in KSA ”, International Journal of Business and Management , Vol. 11 No. 7 , p. 202 .

Alam , S. and Yasin , N.M. ( 2010 ), “ What factors influence online brand trust: evidence from online tickets buyers in Malaysia ”, Journal of Theoretical and Applied Electronic Commerce Research , Vol. 5 No. 3 , pp. 78 - 89 , doi: 10.4067/S0718-18762010000300008 .

Al-Debei , M.M. , Akroush , M.N. and Ashouri , M.I. ( 2015 ), “ Consumer attitudes towards online shopping: the effects of trust, perceived benefits, and perceived web quality ”, Internet Research , Vol. 25 No. 5 , pp. 707 - 733 , doi: 10.1108/IntR-05-2014-0146 .

Aziz , N.N.A. and Wahid , N.A. ( 2018 ), “ Factors influencing online purchase intention among university students ”, International Journal of Academic Research in Business and Social Sciences , Vol. 8 No. 7 , pp. 702 - 717 , doi: 10.6007/IJARBSS/v8-i7/4413 .

Banerjee , N. , Dutta , A. and Dasgupta , T. ( 2010 ), “ A study on customers’ attitude towards online shopping-An Indian perspective ”, Indian Journal of Marketing , Vol. 40 No. 11 , pp. 36 - 42 .

Bianchi , C. and Andrews , L. ( 2012 ), “ Risk, trust, and consumer online purchasing behaviour: a Chilean perspective ”, International Marketing Review , Vol. 29 No. 3 , pp. 253 - 275 , doi: 10.1108/02651331211229750 .

Bilgihan , A. ( 2016 ), “ Gen Y customer loyalty in online shopping: an integrated model of trust, user experience and branding ”, Computers in Human Behavior , Vol. 61 , pp. 103 - 113 , doi: 10.1016/j.chb.2016.03.014 .

Casalo , L. , Flavián , C. and Guinalíu , M. ( 2008 ), “ The role of perceived usability, reputation, satisfaction and consumer familiarity on the website loyalty formation process ”, Computers in Human Behavior , Vol. 24 No. 2 , pp. 325 - 345 , doi: 10.1016/j.chb.2007.01.017 .

Chaturvedi , D. , Gupta , D. and Singh Hada , D. ( 2016 ), “ Perceived risk, trust and information seeking behavior as antecedents of online apparel buying behavior in India: an exploratory study in context of Rajasthan ”, International Review of Management and Marketing , Vol. 6 No. 4 , pp. 935 - 943 , doi: 10.2139/ssrn.3204971 .

Cheung , C.M. and Lee , M.K. ( 2003 ), “ An integrative model of consumer trust in internet shopping ”, ECIS 2003 Proceedings , p. 48 .

Cho , C.H. , Kang , J. and Cheon , H.J. ( 2006 ), “ Online shopping hesitation ”, Cyberpsychology and Behavior , Vol. 9 No. 3 , pp. 261 - 274 , doi: 10.1089/cpb.2006.9.261 .

Clement , J. ( 2020 ), “ Worldwide digital population as of April 2020 ”, available at: www.statista.com/statistics/617136/digital-population-worldwide/ ( accessed 18 June 2020 ).

Comegys , C. , Hannula , M. and Váisánen , J. ( 2009 ), “ Effects of consumer trust and risk on online purchase decision-making: a comparison of Finnish and United States students ”, International Journal of Management , Vol. 26 No. 2 , available at: www.questia.com/library/journal/1P3-1874986651/effects-of-consumer-trust-and-risk-on-online-purchase

Forsythe , S. , Liu , C. , Shannon , D. and Gardner , L.C. ( 2006 ), “ Development of a scale to measure the perceived benefits and risks of online shopping ”, Journal of Interactive Marketing , Vol. 20 No. 2 , pp. 55 - 75 , doi: 10.1002/dir.20061 .

George , O.J. , Ogunkoya , O.A. , Lasisi , J.O. and Elumah , L.O. ( 2015 ), “ Risk and trust in online shopping: experience from Nigeria ”, International Journal of African and Asian Studies , Vol. 11 , pp. 71 - 78 , available at: https://iiste.org/Journals/index.php/JAAS/article/view/23937

Gong , W. , Stump , R.L. and Maddox , L.M. ( 2013 ), “ Factors influencing consumers’ online shopping in China ”, Journal of Asia Business Studies , Vol. 7 No. 3 , pp. 214 - 230 , doi: 10.1108/JABS-02-2013-0006 .

Huseynov , F. and Yildirim , S.O. ( 2016 ), “ Internet users’ attitudes toward business-to-consumer online shopping: a survey ”, Information Development , Vol. 32 No. 3 , pp. 452 - 465 , doi: 10.1177/0266666914554812 .

Jadhav , V. and Khanna , M. ( 2016 ), “ Factors influencing online buying behavior of college students: a qualitative analysis ”, The Qualitative Report , Vol. 21 No. 1 , pp. 1 - 15 , available at: https://nsuworks.nova.edu/tqr/vol21/iss1/1

Jayasubramanian , P. , Sivasakthi , D. and Ananthi , P.K. ( 2015 ), “ A study on customer satisfaction towards online shopping ”, International Journal of Applied Research , Vol. 1 No. 8 , pp. 489 - 495 , available at: www.academia.edu/download/54009715/1-7-136.pdf

Jun , G. and Jaafar , N.I. ( 2011 ), “ A study on consumers’ attitude towards online shopping in China ”, International Journal of Business and Social Science , Vol. 2 No. 22 , pp. 122 - 132 .

Karthikeyan ( 2016 ), “ Problems faced by online customers ”, International Journal of Current Research and Modern Education (IJCRME) , Vol. 1 No. 1 , pp. 166 - 169 , available at: http://ijcrme.rdmodernresearch.com/wp-content/uploads/2015/06/23.pdf

Katawetawaraks , C. and Wang , C.L. ( 2011 ), “ Online shopper behavior: influences of online shopping decision ”, Asian Journal of Business Research , Vol. 1 No. 2 , pp. 66 - 74 , available at: https://ssrn.com/abstract=2345198

Kumar , M. ( 2016 ), “ Consumer behavior and satisfaction in e-commerce: a comparative study based on online shopping of some electronic gadgets ”, International Journal of Research in Commerce and Management , Vol. 7 No. 7 , pp. 62 - 67 , available at: https://ijrcm.org.in/article_info.php?article_id=6785

Kuriachan , J.K. ( 2014 ), “ Online shopping problems and solutions ”, New Media and Mass Communication , Vol. 23 No. 1 , pp. 1 - 4 , available at: www.academia.edu/download/34229456/Online_shopping_problems_and_solutions

Laudon , K.C. and Traver , C.G. ( 2009 ), E-Commerce Business. Technology. Society , 5th ed ., Prentice Hall .

Lawler , J.P. ( 2003 ), “ Customer loyalty and privacy on the web ”, Journal of Internet Commerce , Vol. 2 No. 1 , pp. 89 - 105 , doi: 10.1300/J179v02n01_07 .

Lee , M.K. and Turban , E. ( 2001 ), “ A trust model for consumer internet shopping ”, International Journal of Electronic Commerce , Vol. 6 No. 1 , pp. 75 - 91 , doi: 10.1080/10864415.2001.11044227 .

Lennon , S.J. , et al. ( 2008 ), “ Rural consumers’ online shopping for food and fiber products as a form of outshopping ”, Clothing and Textiles Research Journal , Vol. 27 No. 1 , pp. 3 - 30 , doi: 10.1177/0887302X07313625 .

Liang , T.P. and Lai , H.J. ( 2002 ), “ Effect of store design on consumer purchases: an empirical study of on-line bookstores ”, Information and Management , Vol. 39 No. 6 , pp. 431 - 444 , doi: 10.1016/S0378-7206(01)00129-X .

Lim , P.L. and Yazdanifard , R. ( 2014 ), “ Does gender play a role in online consumer behavior? ”, Global Journal of Management and Business Research , Vol. 14 No. 7 , pp. 48 - 56 , available at: https://journalofbusiness.org/index.php/GJMBR/article/view/1570

Limbu , Y.B. , Wolf , M. and Lunsford , D.L. ( 2011 ), “ Consumers’ perceptions of online ethics and its effects on satisfaction and loyalty ”, Journal of Research in Interactive Marketing , Vol. 5 No. 1 , pp. 71 - 89 , doi: 10.1108/17505931111121534 .

Liu , C. and Guo , Y. ( 2008 ), “ Validating the end-user computing satisfaction instrument for online shopping systems ”, Journal of Organizational and End User Computing , Vol. 20 No. 4 , pp. 74 - 96 , available at: www.igi-global.com/article/journal-organizational-end-user-computing/3849

Martin , J. , Mortimer , G. and Andrews , L. ( 2015 ), “ Re-examining online customer experience to include purchase frequency and perceived risk ”, Journal of Retailing and Consumer Services , Vol. 25 , pp. 81 - 95 , doi: 10.1016/j.jretconser.2015.03.008 .

Mittal , A. ( 2013 ), “ E-commerce: it’s impact on consumer behavior ”, Global Journal of Management and Business Studies , Vol. 3 No. 2 , pp. 131 - 138 , available at: www.ripublication.com/gjmbs_spl/gjmbsv3n2spl_09.pdf

Miyazaki , A.D. and Fernandez , A. ( 2001 ), “ Consumer perceptions of privacy and security risks for online shopping ”, Journal of Consumer Affairs , Vol. 35 No. 1 , pp. 27 - 44 , doi: 10.1111/j.1745-6606.2001.tb00101.x .

Monsuwe , T.P.Y. , Dellaert , B.G.C. and Ruyter , K.D. ( 2004 ), “ What drives consumers to shop online? A literature review ”, International Journal of Service Industry Management , Vol. 15 No. 1 , pp. 102 - 121 , doi: 10.1108/09564230410523358 .

Muthumani , A. , Lavanya , V. and Mahalakshmi , R. ( 2017 ), “ Problems faced by customers on online shopping in Virudhunagar district ”, International Journal of Science Technology and Management (IJSTM) , Vol. 6 No. 2 , pp. 152 - 159 , available at: www.ijstm.com/images/short_pdf/1486214600_S184_IJSTM.pdf .

Nazir , S. , Tayyab , A. , Sajid , A. , Ur Rashid , H. and Javed , I. ( 2012 ), “ How online shopping is affecting consumers buying behavior in Pakistan? ”, International Journal of Computer Science Issues (IJCSI) , Vol. 9 No. 3 , p. 486 .

Nikhashem , S.R. , Yasmin , F. , Haque , A. and Khatibi , A. ( 2011 ), “ Study on customer perception towards online-ticketing in Malaysia ”, In Proceedings For 2011 International Research Conference and Colloquium , Vol. 1 , No. 1 , pp. 320 - 338 .

Pan , Y. and Zinkhan , G.M. ( 2006 ), “ Exploring the impact of online privacy disclosures on consumer trust ”, Journal of Retailing , Vol. 82 No. 4 , pp. 331 - 338 , doi: 10.1016/j.jretai.2006.08.006 .

Roman , S. ( 2007 ), “ The ethics of online retailing: a scale development and validation from the consumers’ perspective ”, Journal of Business Ethics , Vol. 72 No. 2 , pp. 131 - 148 , doi: 10.1007/s10551-006-9161-y .

Sivanesan ( 2017 ), “ A study on problems faced by customers in online shopping with special reference to Kanyakumari district ”, International Journal of Research in Management and Business Studies , Vol. 4 No. 3 , pp. 22 - 25 , available at: http://ijrmbs.com/vol4issue3SPL1/sivanesan.pdf

Tsiakis , T. ( 2012 ), “ Consumers’ issues and concerns of perceived risk of information security in online framework. The marketing strategies ”, Procedia – Social and Behavioral Sciences , Vol. 62 No. 24 , pp. 1265 - 1270 , doi: 10.1016/j.sbspro.2012.09.216 .

Wei , L.H. , Osman , M.A. , Zakaria , N. and Bo , T. ( 2010 ), “ Adoption of e-commerce online shopping in Malaysia ”, In 2010 IEEE 7th International Conference on E-Business Engineering , IEEE , pp. 140 - 143 .

Yazdanifard , R. and Godwin , N.W. ( 2011 ), “ Challenges faced by customers: Highlighting E-shopping problems ”, Paper presented at international Conference on Economics, Business and Marketing Management (CEBMM 2011) , Shanghai, China , available at: http://www.researchgate.net/profile/Assc_Prof_Dr_Rashad_Yazdanifard/publication/268507745_Challenges_faced_by_customers_Highlighting_E-shopping_problems/links/546d4ade0cf26e95bc3cb0a1/Challenges-faced-by-customers-Highlighting-E-shopping-problems.pdf ( accessed 20 March 2020 ).

Further reading

Grabner-Kräuter , S. and Kaluscha , E.A. ( 2003 ), “ Empirical research in on-line trust: a review and critical assessment ”, International Journal of Human-Computer Studies , Vol. 58 No. 6 , pp. 783 - 812 .

Nurfajrinah , M.A. , Nurhadi , Z.F. and Ramdhani , M.A. ( 2017 ), “ Meaning of online shopping for indie model ”, The Social Sciences , Vol. 12 No. 4 , pp. 737 - 742 , available at: https://medwelljournals.com/abstract/?doi=sscience.2017.737.742

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International Journal of Frontiers in Sociology , 2022, 4(4); doi: 10.25236/IJFS.2022.040407 .

Consumer Online Shopping Satisfaction Evaluation Research

Hongshou Chen

Business School, Graduate University of Mongolia, Ulaanbaatar, Mongolia

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The innovation of Internet technology and the popularity of mobile network make online shopping gradually develop into a new sales model. In the increasingly fierce market competition, in order to perceive the needs of consumers online shopping, timely analysis of the key factors affecting consumer online shopping satisfaction, and construct the evaluation model of consumer satisfaction index system (4 first-level indicators, 7 second-level indicators and 17 tertiary indicators) by using fuzzy comprehensive evaluation method. Through the model Through the model can see that all kinds of products and 24-hour shopping services are the most important factors for consumers, on this basis, put forward measures and suggestions to improve consumer satisfaction.

Consumer, Online Shopping, Satisfaction Evaluation, Countermeasure Analysis

Cite This Paper

Hongshou Chen. Consumer Online Shopping Satisfaction Evaluation Research. International Journal of Frontiers in Sociology (2022), Vol. 4, Issue 4: 38-42. https://doi.org/10.25236/IJFS.2022.040407.

[1] L. J. King, “Analysis of the Impact of New Consumption Based on Online Shopping,” Journal of Modern Shopping Malls, no. 6, pp. 6-8, 2021.

[2] Y. Y. Chen, and S. Gao, “Empirical Study on the Impact of Customer Experience on Consumers' Willingness to Buy,” Journal of Productivity Research, no. 6, pp. 118-122+149, 2020.

[3] X. P. Liang, “The Exploration and Innovation of E-Commerce-Based Enterprise Network Marketing Model,” Journal of Modern Shopping Malls, no. 16, pp. 72-73, 2015.

[4] W. B. Zhang, and J. Yan, “Basic Tutorial on SPSS Statistical Analysis,” Monograph of Beijing: Higher Education Press, 2004.

[5] Y. Y. Gu, W. Q. Huang, Y. Tian and L. Jiang, “Research on Online Shopping Evaluation Based on Fuzzy Level Analysis,” Journal of Modern Industrial Economy and Informatization, no. 12, pp. 96-97+100, 2016.

[6] Y. P. Li, and P. Hu, “Study on the Influence Factors of Customer's Online Shopping Satisfaction,” Journal of Business Research, no. 1, pp. 160-165, 2015.

[7] R. Jiang, “Study on the Construction of Consumer's Online Shopping Satisfaction Evaluation System,” Journal of Market Forum, no. 8, pp. 70-73, 2016.

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A study on relationship between customer satisfaction and loyalty in online shopping

Online shopping is an important revolution of the Internet retail marketing in this new digital era. It has attracted many users to try online shopping as a preferred shopping channel. The number of consumers preferring internet for their shopping needs has been constantly gaining importance mainly because of its easy convenience and availability at all time. As modern day consumers are running short of time many consumers do their transactions online. Internet online shopping is seen as a boon to manage both shopping and work as consumers can look at the online stores websites and purchase products in comfort of their home. This study is to identify the relationship between customer satisfaction and loyalty in online shopping and five hypothesis formulated. Total Participation was from 194 respondents and the samples were selected by convenience sampling method Results indicate that when consumers perceive brick and motar shopping as inconvenient, their intention to shop online is greater, the online shopping intention, attitude and loyalty are not only due to ease of use alone, but also by factors like trust, product quality, Service quality, previous online shopping Satisfaction experiences and easy access in online shopping. The correlation and regression test was used to identify the relationships. Findings suggest the there is significant relationship between customer satisfaction and loyalty in online shopping.

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Research Report Online Shopping & Customer’s Satisfaction

I feel immense interest at preparing this report. This report has given me the opportunity to practically work on research report. When you are going to read the proposal, undoubtedly you will be pleased to see that all parts of report are clearly described and pointed out. The simple explanation is that this is one of those instances in which all the pertinent data lead down to simplification. I have tried my best to follow the guidelines prescribed by you. I am very much grateful to you that you have given me the opportunity to prepare this research report and hope that this report will meet the standard of your judgment.

Sincerely yours, Acknowledgements The supreme power almighty ћAllah” made it possible for me to study and write this thesis. I am very much indebted to Dry. Md. Shari Allah, the supervisor of my research work for his continuous support and guidance. His valuable suggestions gave me the strength to complete my project. I owe my deepest gratitude to my friends and respondents who gave their valuable time to help me response many questions. I would like to dedicate this project to my friends and family members who have given me the strength to prepare this research.

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Table of contents Chapter Chapter 1: Introduction Chapter 2: Literature Review Chapter 3: Results & Analysis Chapter 4: Conclusion & Recommendation Appendix Bibliography 1. 2 1. 3 1. 4 1. 5 1. 6 2. 1 Contents Introduction Objectives of the Study Scope for Future Research Significance of the Study Limitations of the study Research Methodology Literature Review Page 2 3 5-9 3. 1 Results & Discussion 10-12 4. 1 4. 2 Conclusion Recommendation Detailed Results Survey Questionnaire Data Set References 14 16-18 19-20 21 List of Tables & Figures Figure 2. : Five stage model of Buying process Figure 3. 1: Satisfaction Level Table 3. 1: One-Sample Statistics Table 3. 2: One-Sample Test Table: Model Summary Table: NOVA Table: Coefficients Table: Correlations 6 11 16 17 Executive Summary Internet has changed the consumption habits and means of satisfying demands for comfort. Today the uses of internet are growing rapidly. People use internet for many different purposes such as shopping, banking, education, airline/railway/bus ticket reservation, training, communication and many other purposes.

Online shopping is a new term in the uses of internet. The term online shopping is come due to making life easy and comfortable. Online shopping is becoming popular because of its simplicity and reduction of efforts and cost. Through Online shopping products can be readily available to customers. Customers are satisfied whenever they get their needed products without making much effort. For most educated people, online purchasing is no longer mysterious and, for many, it is the preferred way to shop.

Customers” expectations are higher than ever before and technology is one of the major drivers behind the change. Online shopping depends on a number of factors such time savings, website quality, riskiest, cost reduction and many other factors. To be successful, online retailers need to provide an outstanding online shopping experience. Whenever a customer feels that all their needs are fulfilled then he/she may be attracted in online shopping. The purpose of this research is to see whether there are any relationship between online shopping and customer satisfaction.

To conduct this research, data are collected from different sources and various statistical techniques such as t-test, regression analysis, and correlation analysis are used to make the decision. This research is conducted on 30 respondents who are strutted a structured questionnaire to make their responses. One of (Time_saving) five independent variables (Time_saving, Riskiest, Information_availability, Broad_product_category, Cost_reduction) is compared to the dependent variable (Satisfaction_level_mum) and found a strong positive correlation which shows that online shopping results in customer satisfaction. . 1 Introduction The trend of e-commerce has been increased rapidly in recent years with the development of internet and due to easy accessibility of internet usage. Easy access to internet has driven consumers to shop online. According to University of California, Los Angles (UCLA) communication policy (2001), online shopping is the third most popular activity on internet after email and web browsing. Globally more than 627 million people have been engaged in online shopping; world”s biggest online shoppers include Germans and British.

Books, airline tickets, apparels, accessories and electronic products are the most popular item purchased on the internet (Canticles Report on Global consumers attitude towards online shopping, 2007). Online shopping is used as a medium for communication and electronic commerce o increase and improve in value, quality and attractiveness of delivering customer benefits and better satisfaction. Risk of credit card fraud, lack of privacy, non-delivery risk, lack of quality of goods and services often deter consumers” from resorting to online shopping.

Concerned authorities are devising policies to minimize the risk involved in online shopping. Online shopping offers wider choice with low transaction cost. Consumers” attitude towards online shopping refers to their psychological state in terms of making purchase over internet. The process of online buying behavior insists of five steps and it is similar to traditional shopping behavior. According to the survey among American students it was concluded that internet knowledge, income and education level are powerful predictors of internet purchase among university students.

An increasing number and variety of firms and organization are exploiting and creating business opportunities on the internet. Fierce competitions among online sellers have forced them to gain the competitive edge in the field of virtual shopping. In order to get the competitive edge in the market, the marketers need to know the consumer behavior in the field of online hopping. So it is important to analyze and identify the factors which influence the consumers to shop online in order to capture the demands of consumers.

The main theme of study is to assess the factors that influence the consumers” attitude and behavior towards online shopping. While it is important to investigate the motivation behind consumer purchasing, it is equally important to find as how the consumers form attitudes and behaviors towards online buying because consumer attitude towards purchasing online is a conspicuous factor affecting actual buying behavior. All the needs are strongly affected by different situational factors which can be cognitive involvement (indicates one”s personal relevance with the Internet as a medium of shopping).

More cognitively involved persons usually believe that the Internet can also raise their shopping efficiency. Affective involvement includes affective factors, such as hedonistic and symbolic expectations, can also influence the personal relevance of a shopping medium. Internet users can be categorized into two Internet Shoppers and Internet Browsers, Internet shoppers are the people who shop inline whereas internet browsers are the people who Just browse the internet other than shopping purpose. 1. Objectives of the Study Therefore, the aim of this research is to explore consumer perceptions of Internet- based retailing and scrutinize the major factors that influence the consumer”s online shopping behavior in decision-making process. The specific objectives of the research are: 1 . To explore the relationship between online shopping and customer”s satisfaction. 2. To explore the reasons why people do online shopping. 3. To discover the individual factors that might, on one hand, prompt a buyer to make n online purchase decision and on the other hand, restrain from such decisions. . Finally to put forward some recommendations to the e-tailors in order to improve the current situation. 1. 3 Scope for Future Research The effect of demographic variables such as race, age, gender and culture on the usages of online shopping was not intensively explored. Some demographic variables have interrelations which might have influence on the usages of online shopping as Lee (2009) stated that the cognitive propensity of individuals to risk differs across culture. This means that the customers” acceptance of online shopping may be influenced by cultural differences.

Nevertheless, the risk and trust factors demand for more intensive analysis to spread online shopping throughout the country. 1. 4 Significance of the Study Very few research worked have been done on the customer satisfaction of online shopping in Bangladesh. The field is new and promising and there is a research gap which motivates to conduct the research on this title. The aim of research is to identify the whether there is any relationship between online shopping and customer satisfaction which will be helpful for the entrepreneurs and managements f this field to design their policies. . 5 Limitations of the study Due to time constraints and lack of prior experience many problems have been faced for making this research. Problems include 1 . Lack of knowledge; 2. Time constraints; 3. Lack of experience; 4. Financial problems; 5. Data may be biased; 6. Information is not sufficient; 7. Limited in specific areas etc. 1. 6 Research Methodology Primary Data Collection: The nature of this research is exploratory and the goal of this research is to explore whether there is any relationship between online shopping and 3 customer satisfaction.

For this reason, a survey was conducted in a specific area of Dacha city to collect primary data by using questionnaire which contains 20 relevant questions regarding online shopping. A simple random process has been used to collect data for this research. A quantitative analysis has been used to analyze the research data. All questions are easy to use, reduce interviewer bias, reduce respondent bias and facilitate coding and tabulation. A Liker scale has been used for the main research questions. Secondary Data Collection: Data from secondary sources have also been collected to complete the research.

Different Journals, books, internet and reports related to online shopping have been used as the important sources of secondary data. Measurement Instrument: The analysis of the data was performed with the help of Statistical Package for Social Sciences (SPAS) version 18. 0. Formulation of Hypotheses: Usually, in quantitative study tests of significance are used to decide whether certain inferences can be drawn regarding any differences or relationships between variables. To do these significant tests SPAS 18. 0 software has been used.

HI : There is a relationship between online shopping and customer satisfaction. 4 Chapter 2: Literature Review 5 2. 1 Literature Review According to Kettle & Keller (2009), a buyer goes through five stages while making a decision to purchase. These stages are best explainable when a buyer goes for buying costly items, such as a house, a car, diamond Jewelry etc. However, in day- to- day purchase, consumers may not go through all these stages, since some commodes do not need information, and based on buyer”s previous experience they will visit a store for the purchase.

These stages are (Figure 2): “problem recognition, information search, evaluation of alternatives, purchase decision, and purchase behavior” (Kettle & Keller 2009). Figure: 2. 1 In addition, many marketing activities (e. G. Relationship marketing, marketing research, data mining, promotion/communications, supply chain management, sales and purchasing and after sales support) are now technology enabled due to the increasing expansion of online marketing via the Internet (O”Connors & Galvan, 2001). Companies are now using the Internet to build closer relationships with consumers and marketing partners.

Besides competing in traditional marketplaces, they now have access to exciting new market spaces. Moreover, through the Internet, consumers can learn about design, order, and pay for products and services, without veer leaving home and then, through the wonders of express delivery, they can receive their purchases in short time. From virtual reality displays that test new products to online virtual stores that sell them, the technology explosion is affecting every aspect of marketing. The Internet has now become a truly global phenomenon (Saurian & Barnes, 2008; Tent et al. 2007). Besides, Awkwardness et al. (2007) in their study considered the purchasing orientation of potential buyers and assessed its impact on purchase behavior, but found that individual orientation is independent of purchasing decision, which reinforces the views of Lie et al. (2008) and Soccer et al. (2005) that every stage of the purchasing decision making cycle may have impact on a potential buyer”s decision to buy online or not. Demanding and Frederick (2007) reported that “information search experiences affect the attitudes towards the site and its brands”.

In the Kook et al. (2008) study, which was conducted on 279 online customers in Korea, it was found that “32. 5 per cent use the internet for news and information, 32. 5 per cent for communication, 7. 5 per cent use it for entertainment while 7. Percent for online shopping”. Furthermore, in consistent with Gay et al. (2007), Yuan and Good (2007), Lee and Line (2005), Raja et al. (2007) , Kit-n and Kit-n (2004) , Ramps and Nielsen (2005) , Lads and Fraternal (2005), Kramer and Kramer (1995), Welch (1995), Earthmen et al. 1998), Johnston (1999), Sings (2004), Smith & Chaffed (2002), and Canaan et al. (2007),: consumer satisfaction and purchase decision on online shopping depends on few more issues. These are: e-store image, delivery and customer services, service quality and purchase behavior, personalization, motivations for online purchase, rust, reliability, privacy, transaction and cost, incentive programs, web-site design, online interactivity, merchandise motivation, assurance, convenience (or Hassle Reduction), pragmatic motivation, responsiveness, consumer risk assessment etc.

E -Commerce E-commerce is the conduct of business via internet which relates to activities of information searching, information sharing, purchasing or exchanging products and services; also maintaining customer relationship without face to face meeting unlike transaction done in traditional way (Kenney, 1999). Often e-commerce is wrongfully received as a way of doing business between web retailers and web end customers but rather commerce encompasses an entire range on conducting online business whether it”s the interaction between business to business, business to customer, and business to government.

Customer Satisfaction Customer satisfaction is when products and services meet the expectation of the consumers (P. Kettle, 2001). It is very important that consumers are content with the products and services provided by the particular website as satisfied customers are likely to be loyal and make repetitive purchases which will increase profitability of hat particular e-commerce company (Rebating, 2002).

In this research, satisfaction which is used in this research will be referred in term of outcome by comparing the prior expectation and the perceived performance for each antecedent factor in order to measure the attitude (satisfaction/pleasing) of the respondents for each of those factors. Purchase Intention According to the model of “The Antifundamentalist View of Attitude” (Lutz, 1981), purchasing intention is the outcome of attitude which refers to the customer”s willingness to buy from a particular e-retailer (McCoy, 2002).

Even though the actual purchase behavior is considerably interesting for the researchers, the purchasing intention is widely used as the representative of the actual purchase behavior especially in consumer behavior researches (Miscarried, 2008) because it is normally not practical or impossible to experimentally study the actual purchase behavior (Rebating, 2002). However, the greater numbers of online retailers do not reveal only the bright side but it also brings about the more intense competition. Therefore, it is imperative to study about the factors leading to success of failure of e-retailers.

Prior Literature of Factors That Impact Customer Satisfaction on Online Purchasing It is imperative to be able to measure customer satisfaction in the context of e- commerce since this will define the success of the vendors (B©longer, 2005). The literature suggests each research is different mainly by the antecedent factors of customer satisfaction since the researchers chose the variables and factors best suit for each circumstance in their perception; thus, the results are varied by time and location. There is no recipe of the antecedent factors used measure satisfaction which will finally leads to purchasing intention.

Christian & France (2005) proves that customers satisfied the most were privacy (Technology factor), Merchandising (Product factor), and convenience (Shopping factor); also followed by trust, delivery, usability, product customization, product quality and security (B©longer, 2005). Surprisingly, security was chosen as the last choice comparing to others. This was assumed that security is perceived as a standard attribute in any websites so other attributes take priority once customers have to choose the site to shop from.

David J. Rebating (2002) has studied about factors attracting customers to the site and factors being able to retain customers by mainly considering the role of price. However, customers tend to shop at other sites unless the vendors provide them good customer service and onetime delivery. Interestingly, e-shopping site using low prices or price promotions to attract customers do mostly tend to draw price- sensitive customers who are well known as having low loyalty (Rebating, 2002).

Factors” motivating the youngsters to shop the commodity product in the cyber-shop examined includes attitudes, demographic, characteristics and purchase decision receptions (Reef, 2004. ). Based on the work of Carven and Todd (1996-97) purchase perceptions or factors influencing online consumer”s purchase decision were grouped into four clusters including: 1 . Product understanding (Product Perception): Price, Product Quality, and Product variety (S. J. Arnold, 1996). 2.

Shopping Experience: Attributes of time, Convenience, and Product availability, Effort, Lifestyle Compatibility and Playfulness or enjoyment of shopping process (Lee, 1995. ). 3. Customer service: Vendor responsiveness, Assurance, and Reliability. 4. Consumer Risk: Economic, Social, Performance, Personal and Privacy risk Carven and Todd (1996-97); park (2001); Simpson & Lanker (1993). Students with experience were more likely to be influenced by Product perception and the Shopping experience more than Customer service and Consumer risk.

This result is consistent with Carven & Todd (1996-97) referring that the sellers should focus on product perception and shopping convenience when building the website. Out of other factors, product perception is considered as the most important among students with higher computer skill levels since they are greatly influenced by price ND quality of products. On the other hand, convenience factor is consistent with the research of Eying and Sean (2002), “Designing Effective cyber store user interface”. Apart from Rodgers et al. 2005), Housemen (2006) has also explained about their investigation of the relationships among Website quality, Customer Value, and Customer that 8 in defining the sub-attributes of each main factor, Web-customer satisfaction can be classified into two distinctive attributes which are Web information quality (Q) referred as “the customers” perception in quality of information presented on a Bessie” and Web system quality (SQ) referred as “the customers” perception of website”s performance in information retrieval and delivery’ (Housemen, 2006).

For customer value side, Quality, Cost-based value, and Performance outcomes are included (Oliver, 1999). In the work of Rodgers, et. Al. (2005), based on the vastly cited works of Delano and McLean (1992) and Pit et al. (1995), there exists the distinctive separation of the antecedent factors into Information quality, System quality, and Service quality (W. Rodgers, 2005). Information quality represents the aspect of quality of information revived on weapon such as timeliness, currency and entertainment of the information.

System quality represents to the engineering performance such as Access and Interactivity. Service quality, often cited by many researchers, represents quality of service provided by the vendors such as Tangibility, Reliability, Responsibility, Assurance, and Empathy.

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Online Consumer Satisfaction During COVID-19: Perspective of a Developing Country

Yonghui rao.

1 Antai College of Economics & Management, Shanghai Jiao Tong University, Shanghai, China

2 School of Management, Zhejiang Shuren University, Hangzhou, China

Aysha Saleem

3 Faculty of Management Sciences, Riphah International University, Faisalabad Campus, Punjab, Pakistan

Wizra Saeed

4 Department of Professional Psychology, Bahria University, Islamabad, Pakistan

Junaid Ul Haq

Associated data.

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

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.

Proposed conceptual framework.

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 .

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.

Discriminant validity.

All diagonal bold values are square root of AVE .

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.

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

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., 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., 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. 10.1016/j.jretconser.2021.102689 [ CrossRef ] [ Google Scholar ]
• Alharthey B. (2020). The role of online trust in forming online shopping intentions . Int. J. Online Market. 10 , 32–57. 10.4018/IJOM.2020010103 [ CrossRef ] [ Google Scholar ]
• Anderson E. W., Sullivan M. W. (1993). The antecedents and consequences of customer satisfaction for firms . Market. Sci. 12 , 125–143. 10.1287/mksc.12.2.125 [ CrossRef ] [ Google Scholar ]
• Ariffin S., Yusof J. M., Putit L., Shah M. I. A. (2016). Factors influencing perceived quality and repurchase intention towards green products . Proc. Econ. Finan. 37 , 391–396. 10.1016/S2212-5671(16)30142-3 [ CrossRef ] [ Google Scholar ]
• Ashoer M., 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. [ Google Scholar ]
• Aslam W., Arif I., Farhat K., 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. 10.22598/mt/2018.30.2.177 [ CrossRef ] [ Google Scholar ]
• Balaji M. S., Khong K. W., Chong A. Y. L. (2016). Determinants of negative word-of-mouth communication using social networking sites . Inform. Manage. 53 , 528–540. 10.1016/j.im.2015.12.002 [ CrossRef ] [ Google Scholar ]
• Bechwati N. N., 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. 10.1207/S15327663JCP13-1andamp;2_12 [ CrossRef ] [ Google Scholar ]
• 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. 10.1016/j.jretconser.2019.101938 [ CrossRef ] [ Google Scholar ]
• Butt M. M., Rose S., Wilkins S., Haq J. U. (2017). MNCs and religious influences in global markets: drivers of consumer-based halal brand equity . Int. Market. Rev . 12 :277. 10.1108/IMR-12-2015-0277 [ CrossRef ] [ Google Scholar ]
• Byrne B. M. (2010). Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming , 2nd Edn. New York, NY: Routledge. [ Google Scholar ]
• Cai R., Chi C. G. Q. (2018). The impacts of complaint efforts on customer satisfaction and loyalty . Serv. Industr. J. 38 , 1095–1115. 10.1080/02642069.2018.1429415 [ CrossRef ] [ Google Scholar ]
• Coetzee A., Coetzee J. (2019). Service quality and attitudinal loyalty: the mediating effect of delight on retail banking relationships . Glob. Bus. Econ. Rev. 21 , 120–138. 10.1504/GBER.2019.096856 [ CrossRef ] [ Google Scholar ]
• Crotts J. C., Magnini V. P. (2011). The customer delight construct: is surprise essential? Ann. Tourism Res. 38 , 719–722. 10.1016/j.annals.2010.03.004 [ CrossRef ] [ Google Scholar ]
• De Vos J. (2020). The effect of COVID-19 and subsequent social distancing on travel behavior . Transport. Res. Interdisciplin. Perspect. 5 :100121. 10.1016/j.trip.2020.100121 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dodds W. B., Monroe K. B., Grewal D. (1991). Effects of price, brand, and store information on buyers' product evaluations . J. Market. Res. 28 , 307–319. 10.1177/002224379102800305 [ CrossRef ] [ Google Scholar ]
• Durmaz Y., Demira,g B., Çavuşoglu S. (2020). Influence of regret and regret reversing effort on dissatisfaction and repurchase intention after purchasing fashion products . Preprints. 10.20944/preprints202003.0280.v1 [ CrossRef ] [ Google Scholar ]
• Ellsworth P. C., Smith C. A. (1988). Shades of joy: patterns of appraisal differentiating pleasant emotions . Cogn. Emot. 2 , 301–331. 10.1080/02699938808412702 [ CrossRef ] [ Google Scholar ]
• Escobar-Sierra M., García-Cardona A., Vera Acevedo L. D. (2021). How moral outrage affects consumer's perceived values of socially irresponsible companies . Cogent Bus. Manage. 8 :1888668. 10.1080/23311975.2021.1888668 [ CrossRef ] [ Google Scholar ]
• Filieri R., Galati F., 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. 10.1016/j.chb.2020.106527 [ CrossRef ] [ Google Scholar ]
• Finn A. (2012). Customer delight: distinct construct or zone of nonlinear response to customer satisfaction? J. Serv. Res. 15 , 99–110. 10.1177/1094670511425698 [ CrossRef ] [ Google Scholar ]
• Fornell C., Larcker D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error . J. Market. Res. 18 , 39–50. 10.1177/002224378101800104 [ CrossRef ] [ Google Scholar ]
• Goyette I., Ricard L., Bergeron J., Marticotte F. (2010). e-WOM Scale: word-of-mouth measurement scale for e-services context . Can. J. Admin. Sci. 27 , 5–23. 10.1002/cjas.129 [ CrossRef ] [ Google Scholar ]
• Gozukara E., Ozyer Y., Kocoglu I. (2014). The moderating effects of perceived use and perceived risk in online shopping . J. Glob. Strateg. Manage. 16 , 67–81. 10.20460/JGSM.2014815643 [ CrossRef ] [ Google Scholar ]
• Grange C., Benbasat I., Burton-Jones A. (2019). With a little help from my friends: Cultivating serendipity in online shopping environments . Inf. Manage . 56 , 225–235. [ Google Scholar ]
• Grewal D., Levy M., Kumar V. (2009). Customer experience management in retailing: An organizing framework . J. Retail. 85 , 1–14. 10.1016/j.jretai.2009.01.001 [ CrossRef ] [ Google Scholar ]
• Guzel M., Sezen B., Alniacik U. (2020). Drivers and consequences of customer participation into value co-creation: a field experiment . J. Product Brand Manage. 10.1108/JPBM-04-2020-2847 [ CrossRef ] [ Google Scholar ]
• Hair J. F., Anderson R. E., Tatham R. L., Black W. C. (1998). Multivariate Data Analysis . 5th ed., Hoboken, NJ: Prentice-Hall. [ Google Scholar ]
• Hair J. F., Celsi M., Ortinau D. J., Bush R. P. (2010). Essentials of Marketing Research , Vol. 2 . New York, NY: McGraw-Hill/Irwin. [ Google Scholar ]
• Hair J. F., Ringle C. M., Sarstedt M. (2011). PLS-SEM: indeed a silver bullet . J. Market. Theor. Pract. 19 , 139–152. 10.2753/MTP1069-6679190202 [ CrossRef ] [ Google Scholar ]
• Hassan S., Shamsudin M. F., 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. [ Google Scholar ]
• Hechler S., 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. 10.1016/j.jesp.2018.03.005 [ CrossRef ] [ Google Scholar ]
• Hosany S. (2012). Appraisal determinants of tourist emotional responses . J. Trav. Res. 51 , 303–314. 10.1177/0047287511410320 [ CrossRef ] [ Google Scholar ]
• Ishfaq M., Nazir M. S., Qamar M. A. J., Usman M. (2020). Cognitive bias and the Extraversion personality shaping the behavior of investors . Front. Psychol. 11 :556506. 10.3389/fpsyg.2020.556506 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• 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. 10.1108/JFMM-05-2020-0089 [ CrossRef ] [ Google Scholar ]
• Jalonen H., 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. 10.1007/978-3-319-45234-0_13 [ CrossRef ] [ Google Scholar ]
• Jin N., Line N. D., 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. 10.1080/19368623.2015.1063469 [ CrossRef ] [ Google Scholar ]
• Johnson M. S., Sivadas E., Garbarino E. (2008). Customer satisfaction, perceived risk and affective commitment: an investigation of directions of influence . J. Serv. Market. 5 :120. 10.1108/08876040810889120 [ CrossRef ] [ Google Scholar ]
• Kähr A., Nyffenegger B., Krohmer H., Hoyer W. D. (2016). When hostile consumers wreak havoc on your brand: the phenomenon of consumer brand sabotage . J. Mark. 80 , 25–41. 10.1509/jm.15.0006 [ CrossRef ] [ Google Scholar ]
• Kim D. J., Ferrin D. L., 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. 10.1016/j.dss.2007.07.001 [ CrossRef ] [ Google Scholar ]
• Kim M., Vogt C. A., Knutson B. J. (2015). Relationships among customer satisfaction, delight, and loyalty in the hospitality industry . J. Hospital. Tourism Res. 39 , 170–197. 10.1177/1096348012471376 [ CrossRef ] [ Google Scholar ]
• Klaus P. P., Maklan S. (2013). Towards a better measure of customer experience . Int. J. Market Res. 55 , 227–246. 10.2501/IJMR-2013-021 [ CrossRef ] [ Google Scholar ]
• Kline R. B. (2005). Principles and Practice of Structural Equation Modeling , 2nd Edn. New York, NY: Guilford Press. [ Google Scholar ]
• Kumar A., Chaudhuri D., Bhardwaj D., 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. 10.34218/IJM.11.12.2020.057 [ CrossRef ] [ Google Scholar ]
• Kumar A., 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. 10.1016/j.jretconser.2014.04.008 [ CrossRef ] [ Google Scholar ]
• Lee S. H., Cotte J. (2009). Post-purchase Consumer Regret: Conceptualization and Development of the PPCR Scale . ACR North American Advances. [ Google Scholar ]
• Lemon K. N., Verhoef P. C. (2016). Understanding customer experience throughout the customer journey . J. Market. 80 , 69–96. 10.1509/jm.15.0420 [ CrossRef ] [ Google Scholar ]
• Li S., Zhou K., Sun Y., Rao L. L., Zheng R., 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. 10.1007/s10899-009-9149-5 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Liao C., Lin H. N., Luo M. M., Chea S. (2017). Factors influencing online shoppers' repurchase intentions: the roles of satisfaction and regret . Inform. Manage. 54 , 651–668. 10.1016/j.im.2016.12.005 [ CrossRef ] [ Google Scholar ]
• Lindenmeier J., Schleer C., Pricl D. (2012). Consumer outrage: emotional reactions to unethical corporate behavior . J. Bus. Res. 65 , 1364–1373. 10.1016/j.jbusres.2011.09.022 [ CrossRef ] [ Google Scholar ]
• Liu M. W., Keh H. T. (2015). Consumer delight and outrage: scale development and validation . J. Serv. Theory Pract . 25 , 680–699. 10.1108/JSTP-08-2014-0178 [ CrossRef ] [ Google Scholar ]
• Loureiro S. M. C., Kastenholz E. (2011). Corporate reputation, satisfaction, delight, and loyalty towards rural lodging units in Portugal . Int. J. Hospital. Manage. 30 , 575–583. 10.1016/j.ijhm.2010.10.007 [ CrossRef ] [ Google Scholar ]
• Ludwig N. L., Heidenreich S., Kraemer T., Gouthier M. (2017). Customer delight: universal remedy or a double-edged sword? J. Serv. Theory Pract . 8 :197. 10.1108/JSTP-08-2015-0197 [ CrossRef ] [ Google Scholar ]
• Ma J., Gao J., Scott N., Ding P. (2013). Customer delight from theme park experiences: the antecedents of delight based on cognitive appraisal theory . Ann. Tour. Res. 42 , 359–381. 10.1016/j.annals.2013.02.018 [ CrossRef ] [ Google Scholar ]
• Malhotra N., Hall J., Shaw M., Oppenheim P. (2006). Marketing Research: An Applied Orientation . Melbourne, VC: Pearson Education Australia. [ Google Scholar ]
• Mamuaya N. C., 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. 10.32535/apjme.v3i1.740 [ CrossRef ] [ Google Scholar ]
• Mattila A. S., Ro H. (2008). Discrete negative emotions and customer dissatisfaction responses in a casual restaurant setting . J. Hosp. Tour. Res. 32 , 89–107. 10.1177/1096348007309570 [ CrossRef ] [ Google Scholar ]
• Mendez J. L., Oubina J., 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. 10.1016/j.jretconser.2007.11.003 [ CrossRef ] [ Google Scholar ]
• Mikulić J., Kreši,ć D., Šerić M. (2021). The factor structure of medical tourist satisfaction: exploring key drivers of choice, delight, and frustration . J. Hospital. Tour. Res. 1177 :1096348020987273. 10.1177/1096348020987273 [ CrossRef ] [ Google Scholar ]
• Moors A., Boddez Y., De Houwer J. (2017). The power of goal-directed processes in the causation of emotional and other actions . Emot. Rev. 9 , 310–318. 10.1177/1754073916669595 [ CrossRef ] [ Google Scholar ]
• 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. [ Google Scholar ]
• Oliver R. L., DeSarbo W. S. (1988). Response determinants in satisfaction judgments . J. Consum. Res. 14 , 495–507. 10.1086/209131 [ CrossRef ] [ Google Scholar ]
• Oliver R. L., Rust R. T., Varki S. (1997). Customer delight: foundations, findings, and managerial insight . J. Retail. 73 :311. 10.1016/S0022-4359(97)90021-X [ CrossRef ] [ Google Scholar ]
• 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. 10.5171/2017.668316 [ CrossRef ] [ Google Scholar ]
• Pandey N., Tripathi A., Jain D., 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. 10.1080/0965254X.2019.1569109 [ CrossRef ] [ Google Scholar ]
• Parasuraman A., Ball J., Aksoy L., Keiningham T. L., Zaki M. (2020). More than a feeling? toward a theory of customer delight . J. Serv. Manage . 3 :34. 10.1108/JOSM-03-2019-0094 [ CrossRef ] [ Google Scholar ]
• Park E. O., Chung K. H., Shin J. I. (2010). The relationship among internal marketing, internal customer satisfaction, organizational commitment and performance . Product. Rev. 24 , 199–232. 10.15843/kpapr.24.2.201006.199 [ CrossRef ] [ Google Scholar ]
• Pavlou P. A., Liang H., Xue Y. (2007). Understanding and mitigating uncertainty in online exchange relationships: a principal-agent perspective . MIS Q. 105–136. 10.2307/25148783 [ CrossRef ] [ Google Scholar ]
• Rahmadini Y., 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. [ Google Scholar ]
• 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., Chen-Jung K. (2018). EWOM towards homestays lodging: extending the information system success model . J. Hosp. Tour. Technol. 10.1108/JHTT-12-2016-0084 [ CrossRef ] [ Google Scholar ]
• Roseman I. J. (1984). Cognitive determinants of emotion: a structural theory . Rev. Person. Soc. Psychol. 5 , 11–36. [ Google Scholar ]
• Roseman I. J. (2013). Author reply: on the frontiers of appraisal theory . Emot. Rev. 5 , 187–188. 10.1177/1754073912469592 [ CrossRef ] [ Google Scholar ]
• Rossolov A., Rossolova H., Holguín-Veras J. (2021). Online and in-store purchase behavior: shopping channel choice in a developing economy . Transportation 20 , 1–37. 10.1007/s11116-020-10163-3 [ CrossRef ] [ Google Scholar ]
• Salancik G. R., Pfeffer J. (1978). Uncertainty, secrecy, and the choice of similar others . Soc. Psychol. 23 , 246–255. 10.2307/3033561 [ CrossRef ] [ Google Scholar ]
• Saleh M. A. H. (2016). Website design, technological expertise, demographics, and consumer's e-purchase transactions . Int. J. Market. Stud. 8 , 125–138. 10.5539/ijms.v8n1p125 [ CrossRef ] [ Google Scholar ]
• Scherer K. R. (1997). The role of culture in emotion-antecedent appraisal . J. Pers. Soc. Psychol. 73 :902. 10.1037/0022-3514.73.5.902 [ CrossRef ] [ Google Scholar ]
• Schneider B., Bowen D. E. (1999). Understanding customer delight and outrage . Sloan Manage. Rev. 41 , 35–45. 10.1016/S0022-4359(01)00035-45 [ CrossRef ] [ Google Scholar ]
• Shamsudin M. F., Razak A. A., Salem M. A. (2018). The role of customer interactions towards customer satisfaction in theme parks experience . Opcion 34 , 546–558. [ Google Scholar ]
• Shim S., Eastlick M. A., Lotz S. L., 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. 10.1016/S0022-4359(01)00051-3 [ CrossRef ] [ Google Scholar ]
• Smith C. A., Ellsworth P. C. (1985). Patterns of cognitive appraisal in emotion . J. Pers. Soc. Psychol . 48 :813. [ PubMed ] [ Google Scholar ]
• Tandon A., Aakash A., 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. 10.1007/s13198-020-00954-3 [ CrossRef ] [ Google Scholar ]
• Tandon U. (2021). Predictors of online shopping in India: an empirical investigation . J. Market. Anal. 9 , 65–79. 10.1057/s41270-020-00084-6 [ CrossRef ] [ Google Scholar ]
• Tandon U., Kiran R., 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. 10.1287/serv.2016.0154 [ CrossRef ] [ Google Scholar ]
• Tarhini A., Alalwan A. A., Al-Qirim N., 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. [ Google Scholar ]
• Tarofder A. K., Nikhashemi S. R., Azam S. F., Selvantharan P., Haque A. (2016). The mediating influence of service failure explanation on customer repurchase intention through customers satisfaction . Int. J. Qual. Serv. Sci. 4 :44. 10.1108/IJQSS-04-2015-0044 [ CrossRef ] [ Google Scholar ]
• Taylor S. A., 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. 10.1016/0022-4359(94)90013-2 [ CrossRef ] [ Google Scholar ]
• Teo T., Lee C. B., Chai C. S., 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. 10.1016/j.compedu.2009.05.017 [ CrossRef ] [ Google Scholar ]
• Thibaut J. W., Kelley H. H. (2017). The Social Psychology of Groups . Routledge. 10.4324/9781315135007 [ CrossRef ] [ Google Scholar ]
• Torres E. N., Milman A., Park S. (2019). Customer delight and outrage in theme parks: a roller coaster of emotions . Int. J. Hospital. Tour. Administr. 16 , 1–23. 10.1080/15256480.2019.1641455 [ CrossRef ] [ Google Scholar ]
• Tsiros M., Mittal V. (2000). Regret: a model of its antecedents and consequences in consumer decision making . J. Consum. Res. 26 , 401–417. 10.1086/209571 [ CrossRef ] [ Google Scholar ]
• Tzeng S. Y., Ertz M., Jo M. S., Sarigöll,ü E. (2021). Factors affecting customer satisfaction on online shopping holiday . Market. Intell. Plann. 8 :346. 10.1108/MIP-08-2020-0346 [ CrossRef ] [ Google Scholar ]
• Ul Haq J., Bonn M. A. (2018). Understanding millennial perceptions of human and nonhuman brands . Int. Hospital. Rev . 9 :14. 10.1108/IHR-09-2018-0014 [ CrossRef ] [ Google Scholar ]
• Unal S., Aydin H. (2016). Evaluation of consumer regret in terms of perceived risk and repurchase intention . J. Glob. Strateg. Manage. 2 , 31–31. 10.20460/JGSM.20161024354 [ CrossRef ] [ Google Scholar ]
• Venkatesh V., Thong J. Y., Xu X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology . MIS Q. 157–178. 10.2307/41410412 [ CrossRef ] [ Google Scholar ]
• Wai K., Dastane O., Johari Z., Ismail N.B. (2019). Perceived risk factors affecting consumers' online shopping behaviour . J. Asian Financ. Econ. Bus. 6 , 246–260. 10.13106/jafeb.2019.vol6.no4.249 [ CrossRef ] [ Google Scholar ]
• Wang C. Y., Mattila A. S. (2011). A cross-cultural comparison of perceived informational fairness with service failure explanations . J. Serv. Market . 25 , 429–439. 10.1108/08876041111161023 [ CrossRef ] [ Google Scholar ]
• Wang X. (2011). The effect of unrelated supporting service quality on consumer delight, satisfaction, and repurchase intentions . J. Serv. Res. 14 , 149–163. 10.1177/1094670511400722 [ CrossRef ] [ Google Scholar ]
• Westbrook R. A., Oliver R. L. (1991). The dimensionality of consumption emotion patterns and consumer satisfaction . J. Consum. Res. 18 , 84–91. 10.1086/209243 [ CrossRef ] [ Google Scholar ]
• Whelan J., Dawar N. (2014). Attributions of blame following a product-harm crisis depend on consumers' attachment styles . Mark. Lett. 27 , 285–294. 10.1007/s11002-014-9340-z [ CrossRef ] [ Google Scholar ]
• Woodside A. G., Frey L. L., Daly R. T. (1989). Linking service quality, customer satisfaction, and behavio . Mark. Health Serv. 9 :5. 10.1016/S0022-4359(01)0009-5 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wu R., 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. 10.1108/EJM-06-2015-0322 [ CrossRef ] [ Google Scholar ]
• Yang Y., Gong Y., Land L. P. W., 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. 10.1016/j.ijinfomgt.2019.102046 [ CrossRef ] [ Google Scholar ]
• Yap B. W., 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. 10.1002/asmb.648 [ CrossRef ] [ Google Scholar ]
• Zeelenberg M., Pieters R. (2007). A theory of regret regulation 1.0 . J. Consum. Psychol. 17 , 3–18. 10.1207/s15327663jcp1701_3 [ CrossRef ] [ Google Scholar ]
• Zeithaml V. A., Berry L. L., Parasuraman A. (1996). The behavioral consequences of service quality . J. Market. 60 , 31–46. 10.1177/002224299606000203 [ CrossRef ] [ Google Scholar ]
• 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). [ Google Scholar ]
• Zhang Z., Ye Q., Song H., 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. [ Google Scholar ]