research paper on impact of online shopping

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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ORIGINAL RESEARCH article

The impact of online reviews on consumers’ purchasing decisions: evidence from an eye-tracking study.

• 1 School of Business, Ningbo University, Ningbo, China
• 2 School of Business, Western Sydney University, Penrith, NSW, Australia

This study investigated the impact of online product reviews on consumers purchasing decisions by using eye-tracking. The research methodology involved (i) development of a conceptual framework of online product review and purchasing intention through the moderation role of gender and visual attention in comments, and (ii) empirical investigation into the region of interest (ROI) analysis of consumers fixation during the purchase decision process and behavioral analysis. The results showed that consumers’ attention to negative comments was significantly greater than that to positive comments, especially for female consumers. Furthermore, the study identified a significant correlation between the visual browsing behavior of consumers and their purchase intention. It also found that consumers were not able to identify false comments. The current study provides a deep understanding of the underlying mechanism of how online reviews influence shopping behavior, reveals the effect of gender on this effect for the first time and explains it from the perspective of attentional bias, which is essential for the theory of online consumer behavior. Specifically, the different effects of consumers’ attention to negative comments seem to be moderated through gender with female consumers’ attention to negative comments being significantly greater than to positive ones. These findings suggest that practitioners need to pay particular attention to negative comments and resolve them promptly through the customization of product/service information, taking into consideration consumer characteristics, including gender.

Introduction

E-commerce has grown substantially over the past years and has become increasingly important in our daily life, especially under the influence of COVID-19 recently ( Hasanat et al., 2020 ). In terms of online shopping, consumers are increasingly inclined to obtain product information from reviews. Compared with the official product information provided by the sellers, reviews are provided by other consumers who have already purchased the product via online shopping websites ( Baek et al., 2012 ). Meanwhile, there is also an increasing trend for consumers to share their shopping experiences on the network platform ( Floh et al., 2013 ). In response to these trends, a large number of studies ( Floh et al., 2013 ; Lackermair et al., 2013 ; Kang et al., 2020 ; Chen and Ku, 2021 ) have investigated the effects of online reviews on purchasing intention. These studies have yielded strong evidence of the valence intensity of online reviews on purchasing intention. Lackermair et al. (2013) , for example, showed that reviews and ratings are an important source of information for consumers. Similarly, through investigating the effects of review source and product type, Bae and Lee (2011) concluded that a review from an online community is the most credible for consumers seeking information about an established product. Since reviews are comments from consumers’ perspectives and often describe their experience using the product, it is easier for other consumers to accept them, thus assisting their decision-making process ( Mudambi and Schuff, 2010 ).

A survey conducted by Zhong-Gang et al. (2015) reveals that nearly 60% of consumers browse online product reviews at least once a week and 93% of whom believe that these online reviews help them to improve the accuracy of purchase decisions, reduce the risk of loss and affect their shopping options. When it comes to e-consumers in commercial activities on B2B and B2C platforms, 82% of the consumers read product reviews before making shopping choices, and 60% of them refer to comments every week. Research shows that 93% of consumers say online reviews will affect shopping choices, indicating that most consumers have the habit of reading online reviews regularly and rely on the comments for their purchasing decisions ( Vimaladevi and Dhanabhakaym, 2012 ).

Consumer purchasing decision after reading online comments is a psychological process combining vision and information processing. As evident from the literature, much of the research has focused on the outcome and impact of online reviews affecting purchasing decisions but has shed less light on the underlying processes that influence customer perception ( Sen and Lerman, 2007 ; Zhang et al., 2010 ; Racherla and Friske, 2013 ). While some studies have attempted to investigate the underlying processes, including how people are influenced by information around the product/service using online reviews, there is limited research on the psychological process and information processing involved in purchasing decisions. The eye-tracking method has become popular in exploring and interpreting consumer decisions making behavior and cognitive processing ( Wang and Minor, 2008 ). However, there is very limited attention to how the emotional valence and the content of comments, especially those negative comments, influence consumers’ final decisions by adopting the eye-tracking method, including a gender comparison in consumption, and to whether consumers are suspicious of false comments.

Thus, the main purpose of this research is to investigate the impact of online reviews on consumers’ purchasing decisions, from the perspective of information processing by employing the eye-tracking method. A comprehensive literature review on key themes including online reviews, the impact of online reviews on purchasing decisions, and underlying processes including the level and credibility of product review information, and processing speed/effectiveness to drive customer perceptions on online reviews, was used to identify current research gaps and establish the rationale for this research. This study simulated a network shopping scenario and conducted an eye movement experiment to capture how product reviews affect consumers purchasing behavior by collecting eye movement indicators and their behavioral datum, in order to determine whether the value of the fixation dwell time and fixation count for negative comment areas is greater than that for positive comment area and to what extent the consumers are suspicious about false comments. Visual attention by both fixation dwell time and count is considered as part of moderating effect on the relationship between the valence of comment and purchase intention, and as the basis for accommodating underlying processes.

The paper is organized as follows. The next section presents literature reviews of relevant themes, including the role of online reviews and the application of eye movement experiments in online consumer decision research. Then, the hypotheses based on the relevant theories are presented. The research methodology including data collection methods is presented subsequently. This is followed by the presentation of data analysis, results, and discussion of key findings. Finally, the impact of academic practical research and the direction of future research are discussed, respectively.

Literature Review

Online product review.

Several studies have reported on the influence of online reviews, in particular on purchasing decisions in recent times ( Zhang et al., 2014 ; Zhong-Gang et al., 2015 ; Ruiz-Mafe et al., 2018 ; Von Helversen et al., 2018 ; Guo et al., 2020 ; Kang et al., 2020 ; Wu et al., 2021 ). These studies have reported on various aspects of online reviews on consumers’ behavior, including consideration of textual factors ( Ghose and Ipeirotiss, 2010 ), the effect of the level of detail in a product review, and the level of reviewer agreement with it on the credibility of a review, and consumers’ purchase intentions for search and experience products ( Jiménez and Mendoza, 2013 ). For example, by means of text mining, Ghose and Ipeirotiss (2010) concluded that the use of product reviews is influenced by textual features, such as subjectivity, informality, readability, and linguistic accuracy. Likewise, Boardman and Mccormick (2021) found that consumer attention and behavior differ across web pages throughout the shopping journey depending on its content, function, and consumer’s goal. Furthermore, Guo et al. (2020) showed that pleasant online customer reviews lead to a higher purchase likelihood compared to unpleasant ones. They also found that perceived credibility and perceived diagnosticity have a significant influence on purchase decisions, but only in the context of unpleasant online customer reviews. These studies suggest that online product reviews will influence consumer behavior but the overall effect will be influenced by many factors.

In addition, studies have considered broader online product information (OPI), comprising both online reviews and vendor-supplied product information (VSPI), and have reported on different attempts to understand the various ways in which OPI influences consumers. For example, Kang et al. (2020) showed that VSPI adoption affected online review adoption. Lately, Chen and Ku (2021) found a positive relationship between diversified online review websites as accelerators for online impulsive buying. Furthermore, some studies have reported on other aspects of online product reviews, including the impact of online reviews on product satisfaction ( Changchit and Klaus, 2020 ), relative effects of review credibility, and review relevance on overall online product review impact ( Mumuni et al., 2020 ), functions of reviewer’s gender, reputation and emotion on the credibility of negative online product reviews ( Craciun and Moore, 2019 ) and influence of vendor cues like the brand reputation on purchasing intention ( Kaur et al., 2017 ). Recently, an investigation into the impact of online review variance of new products on consumer adoption intentions showed that product newness and review variance interact to impinge on consumers’ adoption intentions ( Wu et al., 2021 ). In particular, indulgent consumers tend to prefer incrementally new products (INPs) with high variance reviews while restrained consumers are more likely to adopt new products (RNPs) with low variance.

Emotion Valence of Online Product Review and Purchase Intention

Although numerous studies have investigated factors that may influence the effects of online review on consumer behavior, few studies have focused on consumers’ perceptions, emotions, and cognition, such as perceived review helpfulness, ease of understanding, and perceived cognitive effort. This is because these studies are mainly based on traditional self-report-based methods, such as questionnaires, interviews, and so on, which are not well equipped to measure implicit emotion and cognitive factors objectively and accurately ( Plassmann et al., 2015 ). However, emotional factors are also recognized as important in purchase intention. For example, a study on the usefulness of online film reviews showed that positive emotional tendencies, longer sentences, the degree of a mix of the greater different emotional tendencies, and distinct expressions in critics had a significant positive effect on online comments ( Yuanyuan et al., 2009 ).

Yu et al. (2010) also demonstrated that the different emotional tendencies expressed in film reviews have a significant impact on the actual box office. This means that consumer reviews contain both positive and negative emotions. Generally, positive comments tend to prompt consumers to generate emotional trust, increase confidence and trust in the product and have a strong persuasive effect. On the contrary, negative comments can reduce the generation of emotional trust and hinder consumers’ buying intentions ( Archak et al., 2010 ). This can be explained by the rational behavior hypothesis, which holds that consumers will avoid risk in shopping as much as possible. Hence, when there is poor comment information presented, consumers tend to choose not to buy the product ( Mayzlin and Chevalier, 2003 ). Furthermore, consumers generally believe that negative information is more valuable than positive information when making a judgment ( Ahluwalia et al., 2000 ). For example, a single-star rating (criticism) tends to have a greater influence on consumers’ buying tendencies than that of a five-star rating (compliment), a phenomenon known as the negative deviation.

Since consumers can access and process information quickly through various means and consumers’ emotions influence product evaluation and purchasing intention, this research set out to investigate to what extent and how the emotional valence of online product review would influence their purchase intention. Therefore, the following hypothesis was proposed:

H1 : For hedonic products, consumer purchase intention after viewing positive emotion reviews is higher than that of negative emotion ones; On the other hand, for utilitarian products, it is believed that negative comments are more useful than positive ones and have a greater impact on consumers purchase intention by and large.

It is important to investigate Hypothesis one (H1) although it seems obvious. Many online merchants pay more attention to products with negative comments and make relevant improvements to them rather than those with positive comments. Goods with positive comments can promote online consumers’ purchase intention more than those with negative comments and will bring more profits to businesses.

Sen and Lerman (2007) found that compared with the utilitarian case, readers of negative hedonic product reviews are more likely to attribute the negative opinions expressed, to the reviewer’s internal (or non-product-related) reasons, and therefore, are less likely to find the negative reviews useful. However, in the utilitarian case, readers are more likely to attribute the reviewer’s negative opinions to external (or product-related) motivations, and therefore, find negative reviews more useful than positive reviews on average. Product type moderates the effect of review valence, Therefore, Hypothesis one is based on hedonic product types, such as fiction books.

Guo et al. (2020) found pleasant online customer reviews to lead to a higher purchase likelihood than unpleasant ones. This confirms hypothesis one from another side. The product selected in our experiment is a mobile phone, which is not only a utilitarian product but also a hedonic one. It can be used to make a phone call or watch videos, depending on the user’s demands.

Eye-Tracking, Online Product Review, and Purchase Intention

The eye-tracking method is commonly used in cognitive psychology research. Many researchers are calling for the use of neurobiological, neurocognitive, and physiological approaches to advance information system research ( Pavlou and Dimoka, 2010 ; Liu et al., 2011 ; Song et al., 2017 ). Several studies have been conducted to explore consumers’ online behavior by using eye-tracking. For example, using the eye-tracking method, Luan et al. (2016) found that when searching for products, customers’ attention to attribute-based evaluation is significantly longer than that of experience-based evaluation, while there is no significant difference for the experiential products. Moreover, their results indicated eye-tracking indexes, for example, fixation dwell time, could intuitively reflect consumers’ search behavior when they attend to the reviews. Also, Hong et al. (2017) confirmed that female consumers pay more attention to picture comments when they buy experience goods; when they buy searched products, they are more focused on the pure text comments. When the price and comment clues are consistent, consumers’ purchase rates significantly improve.

Eye-tracking method to explore and interpret consumers’ decision-making behavior and cognitive processing is primarily based on the eye-mind hypothesis proposed by Just and Carpenter (1992) . Just and Carpenter (1992) stated that when an individual is looking, he or she is currently perceiving, thinking about, or attending to something, and his or her cognitive processing can be identified by tracking eye movement. Several studies on consumers’ decision-making behavior have adopted the eye-tracking approach to quantify consumers’ visual attention, from various perspectives including determining how specific visual features of the shopping website influenced their attitudes and reflected their cognitive processes ( Renshaw et al., 2004 ), exploring gender differences in visual attention and shopping attitudes ( Hwang and Lee, 2018 ), investigating how employing human brands affects consumers decision quality ( Chae and Lee, 2013 ), consumer attention and different behavior depending on website content, functions and consumers goals ( Boardman and McCormick, 2019 ). Measuring the attention to the website and time spent on each purchasing task in different product categories shows that shoppers attend to more areas of the website for purposes of website exploration than for performing purchase tasks. The most complex and time-consuming task for shoppers is the assessment of purchase options ( Cortinas et al., 2019 ). Several studies have investigated fashion retail websites using the eye-tracking method and addressed various research questions, including how consumers interact with product presentation features and how consumers use smartphones for fashion shopping ( Tupikovskaja-Omovie and Tyler, 2021 ). Yet, these studies considered users without consideration of user categories, particularly gender. Since this research is to explore consumers’ decision-making behavior and the effects of gender on visual attention, the eye-tracking approach was employed as part of the overall approach of this research project. Based on existing studies, it could be that consumers may pay more attention to negative evaluations, will experience cognitive conflict when there are contradictory false comments presented, and will be unable to judge good or bad ( Cui et al., 2012 ). Therefore, the following hypothesis was proposed:

H2 : Consumers’ purchasing intention associated with online reviews is moderated/influenced by the level of visual attention.

To test the above hypothesis, the following two hypotheses were derived, taking into consideration positive and negative review comments from H1, and visual attention associated with fixation dwell time and fixation count.

H2a : When consumers intend to purchase a product, fixation dwell time and fixation count for negative comment areas are greater than those for positive comment areas.

Furthermore, when consumers browse fake comments, they are suspicious and actively seek out relevant information to identify the authenticity of the comments, which will result in more visual attention. Therefore, H2b was proposed:

H2b : Fixation dwell time and fixation count for fake comments are greater than those for authentic comments.

When considering the effect of gender on individual information processing, some differences were noted. For example, Meyers-Levy and Sternthal (1993) put forward the selectivity hypothesis, a theory of choice hypothesis, which implies that women gather all information possible, process it in an integrative manner, and make a comprehensive comparison before making a decision, while men tend to select only partial information to process and compare according to their existing knowledge—a heuristic and selective strategy. Furthermore, for an online product review, it was also reported that gender can easily lead consumers to different perceptions of the usefulness of online word-of-mouth. For example, Zhang et al. (2014) confirmed that a mixed comment has a mediating effect on the relationship between effective trust and purchasing decisions, which is stronger in women. This means that men and women may have different ways of processing information in the context of making purchasing decisions using online reviews. To test the above proposition, the following hypothesis was proposed:

H3 : Gender factors have a significant impact on the indicators of fixation dwell time and fixation count on the area of interest (AOI). Male purchasing practices differ from those of female consumers. Male consumers’ attention to positive comments is greater than that of female ones, they are more likely than female consumers to make purchase decisions easily.

Furthermore, according to the eye-mind hypothesis, eye movements can reflect people’s cognitive processes during their decision process ( Just and Carpenter, 1980 ). Moreover, neurocognitive studies have indicated that consumers’ cognitive processing can reflect the strategy of their purchase decision-making ( Rosa, 2015 ; Yang, 2015 ). Hence, the focus on the degree of attention to different polarities and the specific content of comments can lead consumers to make different purchasing decisions. Based on the key aspects outlined and discussed above, the following hypothesis was proposed:

H4 : Attention to consumers’ comments is positively correlated with consumers’ purchasing intentions: Consumers differ in the content of comments to which they gaze according to gender factors.

Thus, the framework of the current study is shown in Figure 1 .

Figure 1 . Conceptual framework of the study.

Materials and Methods

The research adopted an experimental approach using simulated lab environmental settings for collecting experimental data from a selected set of participants who have experience with online shopping. The setting of the task was based on guidelines for shopping provided on Taobao.com , which is the most famous and frequently used C2C platform in China. Each experiment was set with the guidelines provided and carried out for a set time. Both behavioral and eye movement data were collected during the experiment.

Participants

A total of 40 healthy participants (20 males and 20 females) with online shopping experiences were selected to participate in the experiment. The participants were screened to ensure normal or correct-to-normal vision, no color blindness or poor color perception, or other eye diseases. All participants provided their written consent before the experiment started. The study was approved by the Internal Review Board of the Academy of Neuroeconomics and Neuromanagement at Ningbo University and by the Declaration of Helsinki ( World Medical Association, 2014 ).

With standardization and small selection differences among individuals, search products can be objectively evaluated and easily compared, to effectively control the influence of individual preferences on the experimental results ( Huang et al., 2009 ). Therefore, this research focused on consumer electronics products, essential products in our life, as the experiment stimulus material. To be specific, as shown in Figure 2 , a simulated shopping scenario was presented to participants, with a product presentation designed in a way that products are shown on Taobao.com . Figure 2 includes two segments: One shows mobile phone information ( Figure 2A ) and the other shows comments ( Figure 2B ). Commodity description information in Figure 2A was collected from product introductions on Taobao.com , mainly presenting some parameter information about the product, such as memory size, pixels, and screen size. There was little difference in these parameters, so quality was basically at the same level across smartphones. Prices and brand information were hidden to ensure that reviews were the sole factor influencing consumer decision-making. Product review areas in Figure 2B are the AOI, presented as a double-column layout. Each panel included 10 (positive or negative) reviews taken from real online shopping evaluations, amounting to a total of 20 reviews for each product. To eliminate the impact of different locations of comments on experimental results, the positions of the positive and negative comment areas were exchanged, namely, 50% of the subjects had positive comments presented on the left and negative comments on the right, with the remaining 50% of the participants receiving the opposite set up.

Figure 2 . Commodity information and reviews. (A) Commodity information, (B) Commodity reviews. Screenshots of Alibaba shopfront reproduced with permission of Alibaba and Shenzhen Genuine Mobile Phone Store.

A total of 12,403 product reviews were crawled through and extracted from the two most popular online shopping platforms in China (e.g., Taobao.com and JD.com ) by using GooSeeker (2015) , a web crawler tool. The retrieved reviews were then further processed. At first, brand-related, price-related, transaction-related, and prestige-related contents were removed from comments. Then, the reviews were classified in terms of appearance, memory, running speed, logistics, and so on into two categories: positive reviews and negative reviews. Furthermore, the content of the reviews was refined to retain the original intention but to meet the requirements of the experiment. In short, reviews were modified to ensure brevity, comprehensibility, and equal length, so as to avoid causing cognitive difficulties or ambiguities in semantic understanding. In the end, 80 comments were selected for the experiment: 40 positive and 40 negative reviews (one of the negative comments was a fictitious comment, formulated for the needs of the experiment). To increase the number of experiments and the accuracy of the statistical results, four sets of mobile phone products were set up. There were eight pairs of pictures in total.

Before the experiment started, subjects were asked to read the experimental guide including an overview of the experiment, an introduction of the basic requirements and precautions in the test, and details of two practice trials that were conducted. When participants were cognizant of the experimental scenario, the formal experiment was ready to begin. Participants were required to adjust their bodies to a comfortable sitting position. The 9 points correction program was used for calibration before the experiment. Only those with a deviation angle of less than 1-degree angle could enter the formal eye movement experiment. In our eye-tracking experiment, whether the participant wears glasses or not was identified as a key issue. If the optical power of the participant’s glasses exceeds 200 degrees, due to the reflective effect of the lens, the eye movement instrument will cause great errors in the recording of eye movements. In order to ensure the accuracy of the data recorded by the eye tracker, the experimenter needs to test the power of each participant’s glasses and ensure that the degree of the participant’s glasses does not exceed 200 degrees before the experiment. After drift correction of eye movements, the formal experiment began. The following prompt was presented on the screen: “you will browse four similar mobile phone products; please make your purchase decision for each mobile phone.” Participants then had 8,000 ms to browse the product information. Next, they were allowed to look at the comments image as long as required, after which they were asked to press any key on the keyboard and answer the question “are you willing to buy this cell phone?.”

In this experiment, experimental materials were displayed on a 17-inch monitor with a resolution of 1,024 × 768 pixels. Participants’ eye movements were tracked and recorded by the Eyelink 1,000 desktop eye tracker which is a precise and accurate video-based eye tracker instrument, integrating with SR Research Experiment Builder, Data Viewer, and third-party software tools, with a sampling rate of 1,000 Hz. ( Hwang and Lee, 2018 ). Data processing was conducted by the matching Data Viewer analysis tool.

The experiment flow of each trial is shown in Figure 3 . Every subject was required to complete four trials, with mobile phone style information and comment content different and randomly presented in each trial. After the experiment, a brief interview was conducted to learn about participants’ browsing behavior when they purchased the phone and collected basic information via a matching questionnaire. The whole experiment took about 15 min.

Figure 3 . Experimental flow diagram. Screenshots of Alibaba shopfront reproduced with permission of Alibaba and Shenzhen Genuine Mobile Phone Store.

Data Analysis

Key measures of data collected from the eye-tracking experiment included fixation dwell time and fixation count. AOI is a focus area constructed according to experimental purposes and needs, where pertinent eye movement indicators are extracted. It can guarantee the precision of eye movement data, and successfully eliminate interference from other visual factors in the image. Product review areas are our AOIs, with positive comments (IA1) and negative comments (IA2) divided into two equal-sized rectangular areas.

Fixation can indicate the information acquisition process. Tracking eye fixation is the most efficient way to capture individual information from the external environment ( Hwang and Lee, 2018 ). In this study, fixation dwell time and fixation count were used to indicate users’ cognitive activity and visual attention ( Jacob and Karn, 2003 ). It can reflect the degree of digging into information and engaging in a specific situation. Generally, a more frequent fixation frequency indicates that the individual is more interested in the target resulting in the distribution of fixation points. Valuable and interesting comments attract users to pay more attention throughout the browsing process and focus on the AOIs for much longer. Since these two dependent variables (fixation dwell time and fixation count) comprised our measurement of the browsing process, comprehensive analysis can effectively measure consumers’ reactions to different review contents.

The findings are presented in each section including descriptive statistical analysis, analysis from the perspective of gender and review type using ANOVA, correlation analysis of purchasing decisions, and qualitative analysis of observations.

Descriptive Statistical Analysis

Fixation dwell time and fixation count were extracted in this study for each record. In this case, 160 valid data records were recorded from 40 participants. Each participant generated four records which corresponded to four combinations of two conditions (positive and negative) and two eye-tracking indices (fixation dwell time and fixation count). Each record represented a review comment. Table 1 shows pertinent means and standard deviations.

Table 1 . Results of mean and standard deviations.

It can be noted from the descriptive statistics for both fixation dwell time and fixation count that the mean of positive reviews was less than that of negative ones, suggesting that subjects spent more time on and had more interest in negative reviews. This tendency was more obvious in female subjects, indicating a role of gender.

Fixation results can be reported using a heat mapping plot to provide a more intuitive understanding. In a heat mapping plot, fixation data are displayed as different colors, which can manifest the degree of user fixation ( Wang et al., 2014 ). Red represents the highest level of fixation, followed by yellow and then green, and areas without color represent no fixation count. Figure 4 implies that participants spent more time and cognitive effort on negative reviews than positive ones, as evidenced by the wider red areas in the negative reviews. However, in order to determine whether this difference is statistically significant or not, further inferential statistical analyses were required.

Figure 4 . Heat map of review picture.

Repeated Measures From Gender and Review Type Perspectives—Analysis of Variance

The two independent variables for this experiment were the emotional tendency of the review and gender. A preliminary ANOVA analysis was performed, respectively, on fixation dwell time and fixation count values, with gender (man vs. woman) and review type (positive vs. negative) being the between-subjects independent variables in both cases.

A significant dominant effect of review type was found for both fixation dwell time ( p 1  < 0.001) and fixation count ( p 2  < 0.001; see Table 2 ). However, no significant dominant effect of gender was identified for either fixation dwell time ( p 1  = 0.234) or fixation count ( p 2  = 0.805). These results indicated that there were significant differences in eye movement indicators between positive and negative commentary areas, which confirms Hypothesis 2a. The interaction effect between gender and comment type was significant for both fixation dwell time ( p 1  = 0.002) and fixation count ( p 2  = 0.001). Therefore, a simple-effect analysis was carried out. The effects of different comment types with fixed gender factors and different gender with fixed comment type factors on those two dependent variables (fixation dwell time and fixation count) were investigated and the results are shown in Table 3 .

Table 2 . Results of ANOVA analysis.

Table 3 . Results of simple-effect analysis.

When the subject was female, comment type had a significant dominant effect for both fixation dwell time ( p 1  < 0.001) and fixation count ( p 2  < 0.001). This indicates that female users’ attention time and cognitive level on negative comments were greater than those on positive comments. However, the dominant effect of comment type was not significant ( p 1  = 0.336 > 0.05, p 2  = 0.43 > 0.05) for men, suggesting no difference in concern about the two types of comments for men.

Similarly, when scanning positive reviews, gender had a significant dominant effect ( p 1  = 0.003 < 0.05, p 2  = 0.025 < 0.05) on both fixation dwell time and fixation count, indicating that men exerted longer focus and deeper cognitive efforts to dig out positive reviews than women. In addition, the results for fixation count showed that gender had significant dominant effects ( p 1  = 0.18 > 0.05, p 2  = 0.01 < 0.05) when browsing negative reviews, suggesting that to some extent men pay significantly less cognitive attention to negative reviews than women, which is consistent with the conclusion that men’s attention to positive comments is greater than women’s. Although the dominant effect of gender was not significant ( p 1  = 0.234 > 0.05, p 2  = 0.805 > 0.05) in repeated measures ANOVA, there was an interaction effect with review type. For a specific type of comment, gender had significant influences, because the eye movement index between men and women was different. Thus, gender plays a moderating role in the impact of comments on consumers purchasing behavior.

Correlation Analysis of Purchase Decision

Integrating eye movement and behavioral data, whether participants’ focus on positive or negative reviews is linked to their final purchasing decisions were explored. Combined with the participants’ purchase decision results, the areas with large fixation dwell time and concerns of consumers in the picture were screened out. The frequency statistics are shown in Table 4 .

Table 4 . Frequency statistics of purchasing decisions.

The correlation analysis between the type of comment and the decision data shows that users’ attention level on positive and negative comments was significantly correlated with the purchase decision ( p  = 0.006 < 0.05). Thus, Hypothesis H4 is supported. As shown in Table 4 above, 114 records paid more attention to negative reviews, and 70% of the participants chose not to buy mobile phones. Also, in the 101 records of not buying, 80% of the subjects paid more attention to negative comments and chose not to buy mobile phones, while more than 50% of the subjects who were more interested in positive reviews chose to buy mobile phones. These experimental results are consistent with Hypothesis H1. They suggest that consumers purchasing decisions were based on the preliminary information they gathered and were concerned about, from which we can deduce customers’ final decision results from their visual behavior. Thus, the eye movement experiment analysis in this paper has practical significance.

Furthermore, a significant correlation ( p  = 0.007 < 0.05) was found between the comments area attracting more interest and purchase decisions for women, while no significant correlation was found for men ( p  = 0.195 > 0.05). This finding is consistent with the previous conclusion that men’s attention to positive and negative comments is not significantly different. Similarly, this also explains the moderating effect of gender. This result can be explained further by the subsequent interview of each participant after the experiment was completed. It was noted from the interviews that most of the male subjects claimed that they were more concerned about the hardware parameters of the phone provided in the product information picture. Depending on whether it met expectations, their purchasing decisions were formed, and mobile phone reviews were taken as secondary references that could not completely change their minds.

Figure 5 shows an example of the relationship between visual behavior randomly selected from female participants and the correlative decision-making behavior. The English translation of words that appeared in Figure 5 is shown in Figure 4 .

Figure 5 . Fixation count distribution.

The subjects’ fixation dwell time and fixation count for negative reviews were significantly greater than those for positive ones. Focusing on the screen and running smoothly, the female participant decided not to purchase this product. This leads to the conclusion that this subject thought a lot about the phone screen quality and running speed while selecting a mobile phone. When other consumers expressed negative criticism about these features, the female participant tended to give up buying them.

Furthermore, combined with the result of each subject’s gaze distribution map and AOI heat map, it was found that different subjects paid attention to different features of mobile phones. Subjects all had clear concerns about some features of the product. The top five mobile phone features that subjects were concerned about are listed in Table 5 . Contrary to expectations, factors, such as appearance and logistics, were no longer a priority. Consequently, the reasons why participants chose to buy or not to buy mobile phones can be inferred from the gazing distribution map recorded in the product review picture. Therefore we can provide suggestions on how to improve the design of mobile phone products for businesses according to the features that users are more concerned about.

Table 5 . Top 5 features of mobile phones.

Fictitious Comments Recognition Analysis

The authenticity of reviews is an important factor affecting the helpfulness of online reviews. To enhance the reputation and ratings of online stores, in the Chinese e-commerce market, more and more sellers are employing a network “water army”—a group of people who praise the shop and add many fake comments without buying any goods from the store. Combined with online comments, eye movement fixation, and information extraction theory, Song et al. (2017) found that fake praise significantly affects consumers’ judgment of the authenticity of reviews, thereby affecting consumers’ purchase intention. These fictitious comments glutted in the purchasers’ real ones are easy to mislead customers. Hence, this experiment was designed to randomly insert a fictitious comment into the remaining 79 real comments without notifying the participants in advance, to test whether potential buyers could identify the false comments and find out their impact on consumers’ purchase decisions.

The analysis of the eye movement data from 40 product review pictures containing this false commentary found that only several subjects’ visual trajectories were back and forth in this comment, and most participants exhibited no differences relative to other comments, indicating that the vast majority of users did not identify the lack of authenticity of this comment. Moreover, when asked whether they had taken note of this hidden false comment in interviews, almost 96% of the participants answered they had not. Thus, Hypothesis H2b is not supported.

This result explains why network “water armies” are so popular in China, as the consumer cannot distinguish false comments. Thus, it is necessary to standardize the e-commerce market, establish an online comment authenticity automatic identification information system, and crack down on illegal acts of employing network troops to disseminate fraudulent information.

Discussion and Conclusion

In the e-commerce market, online comments facilitate online shopping for consumers; in turn, consumers are increasingly dependent on review information to judge the quality of products and make a buying decision. Consequently, studies on the influence of online reviews on consumers’ behavior have important theoretical significance and practical implications. Using traditional empirical methodologies, such as self-report surveys, it is difficult to elucidate the effects of some variables, such as review choosing preference because they are associated with automatic or subconscious cognitive processing. In this paper, the eye-tracking experiment as a methodology was employed to test congruity hypotheses of product reviews and explore consumers’ online review search behavior by incorporating the moderating effect of gender.

Hypotheses testing results indicate that the emotional valence of online reviews has a significant influence on fixation dwell time and fixation count of AOI, suggesting that consumers exert more cognitive attention and effort on negative reviews than on positive ones. This finding is consistent with Ahluwalia et al.’s (2000) observation that negative information is more valuable than positive information when making a judgment. Specifically, consumers use comments from other users to avoid possible risks from information asymmetry ( Hong et al., 2017 ) due to the untouchability of online shopping. These findings provide the information processing evidence that customers are inclined to acquire more information for deeper thinking and to make a comparison when negative comments appear which could more likely result in choosing not to buy the product to reduce their risk. In addition, in real online shopping, consumers are accustomed to giving positive reviews as long as any dissatisfaction in the shopping process is within their tolerance limits. Furthermore, some e-sellers may be forging fake praise ( Wu et al., 2020 ). The above two phenomena exaggerate the word-of-mouth effect of negative comments, resulting in their greater effect in contrast to positive reviews; hence, consumers pay more attention to negative reviews. Thus, Hypothesis H2a is supported. However, when limited fake criticism was mixed in with a large amount of normal commentary, the subject’s eye movements did not change significantly, indicating that little cognitive conflict was produced. Consumers could not identify fake comments. Therefore, H2b is not supported.

Although the dominant effect of gender was not significant on the indicators of the fixation dwell time and fixation count, a significant interaction effect between user gender and review polarity was observed, suggesting that consumers’ gender can regulate their comment-browsing behavior. Therefore, H3 is partly supported. For female consumers, attention to negative comments was significantly greater than positive ones. Men’s attention was more homogeneous, and men paid more attention to positive comments than women. This is attributed to the fact that men and women have different risk perceptions of online shopping ( Garbarino and Strahilevitz, 2004 ). As reported in previous studies, men tend to focus more on specific, concrete information, such as the technical features of mobile phones, as the basis for their purchase decision. They have a weaker perception of the risks of online shopping than women. Women would be worried more about the various shopping risks and be more easily affected by others’ evaluations. Specifically, women considered all aspects of the available information, including the attributes of the product itself and other post-use evaluations. They tended to believe that the more comprehensive the information they considered, the lower the risk they faced of a failed purchase ( Garbarino and Strahilevitz, 2004 ; Kanungo and Jain, 2012 ). Therefore, women hope to reduce the risk of loss by drawing on as much overall information as possible because they are more likely to focus on negative reviews.

The main finding from the fixation count distribution is that consumers’ visual attention is mainly focused on reviews containing the following five mobile phone characteristics: running smoothly, battery life, fever condition of phones, pixels, and after-sales service. Considering the behavior results, when they pay more attention to negative comments, consumers tend to give up buying mobile phones. When they pay more attention to positive comments, consumers often choose to buy. Consequently, there is a significant correlation between visual attention and behavioral decision results. Thus, H4 is supported. Consumers’ decision-making intention can be reflected in the visual browsing process. In brief, the results of the eye movement experiment can be used as a basis for sellers not only to formulate marketing strategies but also to prove the feasibility and strictness of applying the eye movement tracking method to the study of consumer decision-making behavior.

Theoretical Implications

This study has focused on how online reviews affect consumer purchasing decisions by employing eye-tracking. The results contribute to the literature on consumer behavior and provide practical implications for the development of e-business markets. This study has several theoretical contributions. Firstly, it contributes to the literature related to online review valence in online shopping by tracking the visual information acquisition process underlying consumers’ purchase decisions. Although several studies have been conducted to examine the effect of online review valence, very limited research has been conducted to investigate the underlying mechanisms. Our study advances this research area by proposing visual processing models of reviews information. The findings provide useful information and guidelines on the underlying mechanism of how online reviews influence consumers’ online shopping behavior, which is essential for the theory of online consumer behavior.

Secondly, the current study offers a deeper understanding of the relationships between online review valence and gender difference by uncovering the moderating role of gender. Although previous studies have found the effect of review valence on online consumer behavior, the current study first reveals the effect of gender on this effect and explains it from the perspective of attention bias.

Finally, the current study investigated the effect of online reviews on consumer behavior from both eye-tracking and behavioral self-reports, the results are consistent with each other, which increased the credibility of the current results and also provides strong evidence of whether and how online reviews influence consumer behavior.

Implications for Practice

This study also has implications for practice. According to the analysis of experimental results and findings presented above, it is recommended that online merchants should pay particular attention to negative comments and resolve them promptly through careful analysis of negative comments and customization of product information according to consumer characteristics including gender factors. Based on the findings that consumers cannot identify false comments, it is very important to establish an online review screening system that could automatically screen untrue content in product reviews, and create a safer, reliable, and better online shopping environment for consumers.

Limitations and Future Research

Although the research makes some contributions to both theoretical and empirical literature, it still has some limitations. In the case of experiments, the number of positive and negative reviews of each mobile phone was limited to 10 positive and 10 negative reviews (20 in total) due to the size restrictions on the product review picture. The number of comments could be considered relatively small. Efforts should be made in the future to develop a dynamic experimental design where participants can flip the page automatically to increase the number of comments. Also, the research was conducted to study the impact of reviews on consumers’ purchase decisions by hiding the brand of the products. The results would be different if the brand of the products is exposed since consumers might be moderated through brand preferences and brand loyalty, which could be taken into account in future research projects.

Data Availability Statement

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

Author Contributions

TC conceived and designed this study. TC, PS, and MQ wrote the first draft of the manuscript. TC, XC, and MQ designed and performed related experiments, material preparation, data collection, and analysis. TC, PS, XC, and Y-CL revised the manuscript. All authors contributed to the article 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.

Acknowledgments

The authors wish to thank the Editor-in-Chief, Associate Editor, reviewers and typesetters for their highly constructive comments. The authors would like to thank Jia Jin and Hao Ding for assistance in experimental data collection and Jun Lei for the text-polishing of this paper. The authors thank all the researchers who graciously shared their findings with us which allowed this eye-tracking study to be more comprehensive than it would have been without their help.

Ahluwalia, R., Burnkrant, R., and Unnava, H. (2000). Consumer response to negative publicity: the moderating role of commitment. J. Mark. Res. 37, 203–214. doi: 10.2307/1558500

CrossRef Full Text | Google Scholar

Archak, N., Ghose, A., and Ipeirotis, P. (2010). Deriving the pricing power of product features by mining. Con. Rev. Manag. Sci. 57, 1485–1509. doi: 10.1287/mnsc.1110.1370

Bae, S., and Lee, T. (2011). Product type and consumers’ perception of online consumer reviews. Electron. Mark. 21, 255–266. doi: 10.1007/s12525-011-0072-0

Baek, H., Ahn, J., and Choi, Y. (2012). Helpfulness of online consumer reviews: readers’ objectives and review cues. Int. J. Electron. Commer. 17, 99–126. doi: 10.2753/jec1086-4415170204

Boardman, R., and McCormick, H. (2019). The impact of product presentation on decision making and purchasing. Qual. Mark. Res. Int. J. 22, 365–380. doi: 10.1108/QMR-09-2017-0124

Boardman, R., and Mccormick, H. (2021). Attention and behaviour on fashion retail websites: an eye-tracking study. Inf. Technol. People . doi: 10.1108/ITP-08-2020-0580 [Epub ahead of print]

Chae, S. W., and Lee, K. (2013). Exploring the effect of the human brand on consumers’ decision quality in online shopping: An eye-tracking approach. Online Inf. Rev. 37, 83–100. doi: 10.1108/14684521311311649

Changchit, C., and Klaus, T. (2020). Determinants and impact of online reviews on product satisfaction. J. Internet Commer. 19, 82–102. doi: 10.1080/15332861.2019.1672135

Chen, C. D., and Ku, E. C. (2021). Diversified online review websites as accelerators for online impulsive buying: the moderating effect of price dispersion. J. Internet Commer. 20, 113–135. doi: 10.1080/15332861.2020.1868227

Cortinas, M., Cabeza, R., Chocarro, R., and Villanueva, A. (2019). Attention to online channels across the path to purchase: an eye-tracking study. Electron. Commer. Res. Appl. 36:100864. doi: 10.1016/j.elerap.2019.100864

Craciun, G., and Moore, K. (2019). Credibility of negative online product reviews: reviewer gender, reputation and emotion effects. Comput. Hum. Behav. 97, 104–115. doi: 10.1016/j.chb.2019.03.010

Cui, G., Lui, H.-K., and Guo, X. (2012). The effect of online consumer reviews on new product sales. International. J. Elect. Com. 17, 39–58. doi: 10.2753/jec1086-4415170102

Floh, A., Koller, M., and Zauner, A. (2013). Taking a deeper look at online reviews: The asymmetric effect of valence intensity on shopping behaviour. J. Mark. Manag. 29:646670, 646–670. doi: 10.1080/0267257X.2013.776620

Garbarino, E., and Strahilevitz, M. (2004). Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation. J. Bus. Res. 57, 768–775. doi: 10.1016/S0148-2963(02)00363-6

Ghose, A., and Ipeirotiss, P. G. (2010). Estimating the helpfulness and economic impact of product reviews: mining text and reviewer characteristics. IEEE Trans. Knowl. Data Eng. 23:188. doi: 10.1109/TKDE.2010.188

GooSeeker (2015), E. coli . Available at: http://www.gooseeker.com/pro/product.html , (Accessed January 20, 2020).

Google Scholar

Guo, J., Wang, X., and Wu, Y. (2020). Positive emotion bias: role of emotional content from online customer reviews in purchase decisions. J. Retail. Consum. Serv. 52:101891. doi: 10.1016/j.jretconser.2019.101891

Hasanat, M., Hoque, A., Shikha, F., Anwar, M., Abdul Hamid, A. B., and Huam, T. (2020). The impact of coronavirus (COVID-19) on E-Business in Malaysia. Asian J. Multidisc. Stud. 3, 85–90.

Hong, H., Xu, D., Wang, G., and Fan, W. (2017). Understanding the determinants of online review helpfulness: a meta-analytic investigation. Decis. Support. Syst. 102, 1–11. doi: 10.1016/j.dss.2017.06.007

Huang, P., Lurie, N., and Mitra, S. (2009). Searching for experience on the web: an empirical examination of consumer behavior for search and experience goods. J. Mark. Am. Mark. Assoc. 73, 55–69. doi: 10.2307/20619010

Hwang, Y. M., and Lee, K. C. (2018). Using an eye-tracking approach to explore gender differences in visual attention and shopping attitudes in an online shopping environment. Int. J. Human–Comp. Inter. 34, 15–24. doi: 10.1080/10447318.2017.1314611

Jacob, R., and Karn, K. (2003). “Eye tracking in human-computer interaction and usability research: ready to deliver the promises,” in The mind’s eye North-Holland (New York: Elsevier), 573–605.

Jiménez, F. R., and Mendoza, N. A. (2013). Too popular to ignore: the influence of online reviews on purchase intentions of search and experience products. J. Interact. Mark. 27, 226–235. doi: 10.1016/j.intmar.2013.04.004

Just, M., and Carpenter, P. (1980). A theory of reading: from eye fixations to comprehension. Psychol. Rev. 87, 329–354. doi: 10.1037/0033-295X.87.4.329

PubMed Abstract | CrossRef Full Text | Google Scholar

Just, M., and Carpenter, P. (1992). A capacity theory of comprehension: individual differences in working memory. Psychol. Rev. 99, 122–149. doi: 10.1037/0033-295x.99.1.122

Kang, T. C., Hung, S. Y., and Huang, A. H. (2020). The adoption of online product information: cognitive and affective evaluations. J. Internet Commer. 19, 373–403. doi: 10.1080/15332861.2020.1816315

Kanungo, S., and Jain, V. (2012). Online shopping behaviour: moderating role of gender and product category. Int. J. Bus. Inform. Syst. 10, 197–221. doi: 10.1504/ijbis.2012.047147

Kaur, S., Lal, A. K., and Bedi, S. S. (2017). Do vendor cues influence purchase intention of online shoppers? An empirical study using SOR framework. J. Internet Commer. 16, 343–363. doi: 10.1080/15332861.2017.1347861

Lackermair, G., Kailer, D., and Kanmaz, K. (2013). Importance of online product reviews from a consumer’s perspective. Adv. Econ. Bus. 1, 1–5. doi: 10.13189/aeb.2013.010101

Liu, H.-C., Lai, M.-L., and Chuang, H.-H. (2011). Using eye-tracking technology to investigate the redundant effect of multimedia web pages on viewers’ cognitive processes. Comput. Hum. Behav. 27, 2410–2417. doi: 10.1016/j.chb.2011.06.012

Luan, J., Yao, Z., Zhao, F., and Liu, H. (2016). Search product and experience product online reviews: an eye-tracking study on consumers’ review search behavior. Comput. Hum. Behav. 65, 420–430. doi: 10.1016/j.chb.2016.08.037

Mayzlin, D., and Chevalier, J. (2003). The effect of word of mouth on sales: online book reviews. J. Mark. Res. 43:409. doi: 10.2307/30162409

Meyers-Levy, J., and Sternthal, B. (1993). A two-factor explanation of assimilation and contrast effects. J. Mark. Res. 30, 359–368. doi: 10.1177/002224379303000307

Mudambi, S., and Schuff, D. (2010). What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Q. 34, 185–200. doi: 10.1007/s10107-008-0244-7

Mumuni, A. G., O’Reilly, K., MacMillan, A., Cowley, S., and Kelley, B. (2020). Online product review impact: the relative effects of review credibility and review relevance. J. Internet Commer. 19, 153–191. doi: 10.1080/15332861.2019.1700740

Pavlou, P., and Dimoka, A. (2010). NeuroIS: the potential of cognitive neuroscience for information systems research. Inform. Sys. Res. Art. Adv. 19, 153–191. doi: 10.1080/15332861.2019.1700740

Plassmann, H., Venkatraman, V., Huettel, S., and Yoon, C. (2015). Consumer neuroscience: applications, challenges, and possible solutions. J. Mark. Res. 52, 427–435. doi: 10.1509/jmr.14.0048

Racherla, P., and Friske, W. (2013). Perceived “usefulness” of online consumer reviews: an exploratory investigation across three services categories. Electron. Commer. Res. Appl. 11, 548–559. doi: 10.1016/j.elerap.2012.06.003

Renshaw, J. A., Finlay, J. E., Tyfa, D., and Ward, R. D. (2004). Understanding visual influence in graph design through temporal and spatial eye movement characteristics. Interact. Comput. 16, 557–578. doi: 10.1016/j.intcom.2004.03.001

Rosa, P. J. (2015). What do your eyes say? Bridging eye movements to consumer behavior. Int. J. Psychol. Res. 15, 1250–1256. doi: 10.1116/1.580598

Ruiz-Mafe, C., Chatzipanagiotou, K., and Curras-Perez, R. (2018). The role of emotions and conflicting online reviews on consumers’ purchase intentions. J. Bus. Res. 89, 336–344. doi: 10.1016/j.jbusres.2018.01.027

Sen, S., and Lerman, D. (2007). Why are you telling me this? An examination into negative consumer reviews on the web. J. Interact. Mark. 21, 76–94. doi: 10.1002/dir.20090

Song, W., Park, S., and Ryu, D. (2017). Information quality of online reviews in the presence of potentially fake reviews. Korean Eco. Rev. 33, 5–34.

Tupikovskaja-Omovie, Z., and Tyler, D. (2021). Eye tracking technology to audit google analytics: analysing digital consumer shopping journey in fashion m-retail. Int. J. Inf. Manag. 59:102294. doi: 10.1016/j.ijinfomgt.2020.102294

Vimaladevi, K., and Dhanabhakaym, M. (2012). A study on the effects of online consumer reviews on purchasing decision. Prestige In. J. Manag. 7, 51–99. doi: 10.1504/IJIMA.2012.044958

Von Helversen, B., Abramczuk, K., Kopeć, W., and Nielek, R. (2018). Influence of consumer reviews on online purchasing decisions in older and younger adults. Decis. Support. Syst. 113, 1–10. doi: 10.1016/j.dss.2018.05.006

Wang, Y., and Minor, M. (2008). Validity, reliability, and applicability of psychophysiological techniques in marketing research. Psychol. Mark. 25, 197–232. doi: 10.1002/mar.20206

Wang, Q., Yang, S., Cao, Z., Liu, M., and Ma, Q. (2014). An eye-tracking study of website complexity from cognitive load perspective. Decis. Support. Syst. 62, 1–10. doi: 10.1016/j.dss.2014.02.007

World Medical Association (2014). World medical association declaration of Helsinki: ethical principles for medical research involving human subjects. J. Am. Coll. Dent. 81, 14–18. doi: 10.1111/j.1447-0756.2001.tb01222.x

Wu, Y., Liu, T., Teng, L., Zhang, H., and Xie, C. (2021). The impact of online review variance of new products on consumer adoption intentions. J. Bus. Res. 136, 209–218. doi: 10.1016/J.JBUSRES.2021.07.014

Wu, Y., Ngai, E., Pengkun, W., and Wu, C. (2020). Fake online reviews: literature review, synthesis, and directions for future research. Decis. Support. Syst. 132:113280. doi: 10.1016/j.dss.2020.113280

Yang, S. F. (2015). An eye-tracking study of the elaboration likelihood model in online shopping. Electron. Commer. Res. Appl. 14, 233–240. doi: 10.1016/j.elerap.2014.11.007

Yu, X., Liu, Y., Huang, X., and An, A. (2010). Mining online reviews for predicting sales performance: a case study in the movie domain. IEEE Trans. Knowl. Data Eng. 24, 720–734. doi: 10.1109/TKDE.2010.269

Yuanyuan, H., Peng, Z., Yijun, L., and Qiang, Y. J. M. R. (2009). An empirical study on the impact of online reviews sentimental orientation on sale based on movie panel data. Manag. Rev. 21, 95–103. doi: 10.1007/978-3-642-00205-2_9

Zhang, K., Cheung, C., and Lee, M. (2014). Examining the moderating effect of inconsistent reviews and its gender differences on consumers’ online shopping decision. Int. J. Inf. Manag. 34, 89–98. doi: 10.1016/j.ijinfomgt.2013.12.001

Zhang, J., Craciun, G., and Shin, D. (2010). When does electronic word-of-mouth matter? A study of consumer product reviews. J. Bus. Res. 63, 1336–1341. doi: 10.1016/j.jbusres.2009.12.011

Zhong-Gang, Y., Xiao-Ya, W., and Economics, S. O. J. S. E. (2015). Research progress and future prospect on online reviews and consumer behavior. Soft Science. 6:20. doi: 10.3760/cma.j.cn112137-20200714-02111

Keywords: online reviews, eye-tracking, consumers purchasing decisions, emotion valence, gender

Citation: Chen T, Samaranayake P, Cen X, Qi M and Lan Y-C (2022) The Impact of Online Reviews on Consumers’ Purchasing Decisions: Evidence From an Eye-Tracking Study. Front. Psychol . 13:865702. doi: 10.3389/fpsyg.2022.865702

Received: 30 January 2022; Accepted: 02 May 2022; Published: 08 June 2022.

Reviewed by:

Copyright © 2022 Chen, Samaranayake, Cen, Qi and Lan. 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: XiongYing Cen, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Evaluating the impact of social media on online shopping behavior during COVID-19 pandemic: A Bangladeshi consumers’ perspectives ☆

Md rukon miah.

a Department of Marketing, Comilla University, Cumilla, Bangladesh

Afzal Hossain

b Department of Business Administration, Trust University, Barishal, Bangladesh

Rony Shikder

Meher neger, associated data.

Data will be made available on request.

Background of the study

Nowadays, the business pattern is changing globally. The business organization is influenced customers to purchase their necessary goods and services from online businesses. The online-based business takes promotional activities through social media platforms like Facebook, Twitter, Instagram, and Pinterest.

The aim of the research was to investigate the impact of social media on online shopping behavior during the COVID-19 pandemic in the context of Bangladeshi consumers.

Research methods

Quantitative type research was applied and the study used descriptive research design. A standardized questionnaire was used to collect 350 data points from Bangladeshi consumers using an online purposive sampling method. A partial least square structural equation modeling (PLS-SEM) approach was used to evaluate the data and test the hypotheses.

PLS-SEM analysis method demonstrated that celebrity endorsement, promotional tools, and online reviews had a positive significant impact on online shopping behavior during the COVID-19 pandemic in the perspective of Bangladesh.

The research paper provides practical guidelines for online-based business organizations on how to effectively use social media platforms for business target advertising and promotional activities. The customers are also motivated to purchase through social media because of positive online reviews and trustworthy celebrity endorsements.

Online shopping; Social media, Bangladeshi consumers, COVID-19 pandemic, PLS-SEM.

1. Introduction

With the expansion and spread of the 2019 novel coronavirus (2019-nCoV), also known as the severe acute respiratory syndrome coronavirus 2, a new public health crisis is threatening the world (SARS-CoV-2). In December 2019, the virus was revealed in bats and conveyed to humans via anonymous intermediary species in Wuhan, Hubei Province, China. To date (05/03/2020), there have been roughly 96,000 recorded cases of coronavirus disease 2019 (COVID-2019) and 3300 recognized deaths. The disease is spread through inhalation or contact with polluted droplets, with a 2 to 14-day incubation period. Fever, cough, sore throat, dyspnea, weariness, and malaise are common symptoms. Most people have a minor case of the common symptoms. Most people have a minor case of the condition. However, certain people (typically the elderly and those with comorbidities) may develop complications ( Singhal, 2020 ). The global proliferation of coronavirus has had a number of negative effects on human health ( Jajodia et al., 2020 ; Rajendran et al., 2020 ). Most enterprises have been adversely impacted by COVID-19, and as a consequence, they have been compelled to implement multiple measures to limit the proliferation of the coronavirus while also harming their organizational performance and effectiveness ( Bartik et al., 2020 ; Donthu and Gustafsson, 2020 ; Sohrabi et al., 2020 ). To contain the spread, people should exercise social detachment, self-isolation, and reduce travel, which also led to a significant decrease in institutional and business output ( Nicola et al., 2020 ). The global COVID-19 epidemic has severely affected societies and economies around the world and has hit various sectors of society in various ways. This unprecedented situation has far-reaching consequences for consumers’ daily lives and has dramatically changed how businesses operate and how consumers behave ( Donthu and Gustafsson, 2020 ; Yuen et al., 2020 ). The current situation, after the first wave and the beginning of the second wave of the COVID-19 epidemic in Europe, has forced many consumers to reconsider their established shopping and shopping habits or even learn new ones ( Sheth, 2020 ). Nowadays, social media is playing a significant role in the online marketing environment for buying products from online stores rather than traditional themed stores with the help of an internet connection. In the current situation, social media is a relatively new trend. The most popular social networking sites like Facebook, Twitter, LinkedIn, Pinterest, and Google contribute to the majority of activities such as messaging, chatting, gambling, and blogging. Consumers typically participate actively on social media and spend long hours on Facebook and Twitter, creating content and sharing it. Companies that are aware of these issues are moving towards various activities to attract customers, increase their level of awareness and make the most of the opportunities offered through social media. Accordingly, firms conduct strategic campaigns that overlap with customer structures and brand values to increase the level of social brand recognition. Digital and social media marketing allows companies to accomplish their marketing aims at relatively low cost ( Ajina, 2019 ; Yadav, 2016 ).

Individuals and families who buy a company's goods for personal consumption are denoted as consumers ( Kotler, 2004 ). Consumer behavior refers to the actions that consumers participate in when buying, consuming, and disposing of products and services. Consumer behavior is the study of how people shop, what they shop for, when they shop, and why they shop. When a customer needs to make a purchase, they will go through the steps of acknowledgement, information search, evaluation, purchase, and feedback ( Blackwell et al., 2006 ). Finally, the customer will select a product or brand to consume from a variety of options available in the market. These factors, on the other hand, have an impact on consumer purchasing behavior. When it comes to consumer buying choice behavior, it's critical to identify the many sorts of consumers who have different buying decision behaviors based on their level of involvement and capacity to discern significant differences between brands. The term “buying participation” is defined by Hawkins and Mothersbaugh (2010) as the level of interest a buyer has in purchasing a product or service. Retail managers and marketers must keep records of shifts in consumer buying behavior and attitudes in order to identify which strategies they should implement ( Verma and Gustafsson, 2020 ). Pantano et al. (2020) argue that customers have re-examined their buying habits even while recognizing advantages from previously unknown services. On the one hand, social media is a rich source of information about a company's consumer views; on the other hand, it promotes social interaction among consumers, which results in increased trust and, thus, changes in customer preferences' purchasing behavior ( Hajli, 2014 ).

Online shopping behavior involves the process of purchasing goods and services through the internet ( Sun et al., 2019 ). After collecting product information, the consumer selects an item according to its requirements and transaction criteria for the selected product, evaluates the product along with other available options, and gains post-press experience ( Kotler, 2000 ). Online shopping behavior is related to the psychological state of the customer buying online ( Li and Zhang, 2002 ). Social networking sites have been widely used by people for their professional and personal use in the era of global communication. According to E-marketer (2013) , companies for various marketing activities such as marketing research, branding, customer relationship management, sales promotion, and service and service delivery have gradually adopted various studies as well as social networking sites that ensure the positive effects of social development in marketing strategy media.

The World Wide Web has persuaded people around the world to make small changes in their behavior and attitudes. Because of these blessings, online shopping has emerged, which affects the lives of ordinary citizens. Online shopping has started in Bangladesh, but consumers are still not very accustomed to shopping online. Customers are becoming familiar with the internet and its benefits. Online shopping is becoming popular and a priority among a group of customers to get better quality offers related to information, benefits, and cost choice. Like other young Asians, Bangladeshi youth are experimenting with new ways of shopping that have led to the rise and popularity of online shopping in Bangladesh.

Nowadays, customers' purchasing patterns are changing globally, and they are purchasing goods and services through online shopping. Customers were heavily influenced by social media to shop online. During COVID-19, customers didn't go to shopping malls frequently because of lockdown, isolation, and fear of being affected by the coronavirus ( Eger et al., 2021 ). Business organizations can motivate customers to purchase through online shopping via social media platforms like Facebook, Twitter, Instagram, and Pinterest. Marketers have a great advantage on social media because they can influence or create awareness about goods and services and motivate them to purchase via online shopping. Business organizations can use social media platforms to influence their existing and potential customers to purchase their necessary goods and services through online shopping or online business platforms ( Chaturvedi and Gupta, 2014 ). Customers have been influenced by organizations via live streaming, celebrity endorsements, online reviews of customers, and promotional tools like target advertising ( Geng et al., 2020 ; Schouten et al., 2020 ). During the corona pandemic, the marketers took home delivery services to the customers ( Wang et al., 2021 ). Good online reviews have influenced potential customers to purchase through online shopping ( Mo et al., 2015 ). Online shopping behavior will benefit both customers and marketers ( Berman, 2012 ). Nowadays, in our society, some customers are so busy that they don't have the available time to purchase their necessary products or services. That's why they are not able to go to the market practically within a short time. They prefer to order any kind of commodity or service via online shopping. At present, customers want a relaxed environment on social media for shopping. Marketers provide target advertising via social media like Facebook, Twitter, and so on ( Luo et al., 2019 ). Thus, social media marketing tools are more useful than other marketing communication mixes. Word of mouth from celebrities and positive customer reviews encourages other customers to shop online.

This study was conducted on social media due to several factors that influence buying behavior. Purchasing online remittances has become an interesting and new topic for researchers around the world. People's buying patterns are changing. Online social media is a tool that has only recently developed and developed rapidly in the last few years, and it might have the problem of a lack of studies in all countries since it is at an early stage in the field of social commerce ( Huang and Benyoucef, 2015 ; Hossain et al., 2019 ). There are a lot of social media users in Bangladesh and they prefer to shop online, but there is still a lack of research on the trend of social media impact when buying a product online. Thus, by doing this research, marketers can focus on the areas that have the most impact on their online buying behavior. The purpose of the study is to understand the buying behavior of online shoppers.

After reviewing most of the related literature on social media that influences online shopping, it is clear that most researchers tried to assess the influence of social media (live streaming, celebrity endorsements, promotional tools, and online reviews) on buying behavior, purchase intention, purchase decision, customer satisfaction, and online shopping behavior from the perspectives of customers all over the world, but this research has been tried to focus on investigating the influence of social media on online shopping behavior during the COVID-19 pandemic from the perspectives of Bangladesh, which remained an unexplored field. This research provides an insight on the influence of live streaming, celebrity endorsements, promotional tools, and online reviews on online shopping behavior during the COVID-19 pandemic of citizenship customers' level in eminent Bangladeshi purchasers' and sellers' experiences, which will help policy makers and stakeholders formulate better digital marketing strategies in Bangladesh, as well as the research field in the perspectives of the COVID-19 pandemic.

The broad objective of the research was to investigate the influence of social media on online shopping behavior during the COVID-19 pandemic in the context of Bangladeshi consumers. Specific objectives are: to assess the behavior pattern of consumers towards online platforms; to explore the impacts of the COVID-19 pandemic on buying behavior; and to study the effect of live streaming, celebrity endorsements, promotional tools, and online reviews on the online shopping behavior of consumers during the coronavirus pandemic in the context of Bangladesh.

The theory behind the study and the terminology and propositions that will be used to achieve the research objective will be explained. Furthermore, the interrelated association of dependent and independent variables will also be deliberated upon following past studies. The key research questions of the study are stated as follows: Is there any significant relationship between live streaming and online shopping behavior?; How is celebrity endorsement relevant to online shopping behavior?; How are promotional tools relevant to online shopping behavior?; and what are the relationships between online reviews and online shopping behavior?

The research paper is allocated into several sections. Initially, the literature review is provided based on a past study. Secondly, the conceptual model and hypotheses developed have been demonstrated. Thirdly, research methodologies that are applied to the current research are described. Fourthly, the paper is presented with the results and interpretations. Fifthly, the discussions, conclusion, and implications sections incorporate the consequences of the present research and its linkups with the previous studies. At the end of the segment, the shortcomings and potential directions of the research are stated.

2. Literature review

2.1. theoretical background, 2.1.1. social influential theory.

According to Kelman (1958) , SIT (Social Influential Theory) is defined as individuals' beliefs, attitudes, and consequent activities or manners that are impacted on other people over three procedures: compliance, identification, and internalization. Persuasion is expected to happen when people receive influence and accept the persuaded conduct to increase rewards and evade punishments. Hence, satisfaction resulting from compliance is because of the social effect of acquiescent influence. Identification might be said to occur when individuals embrace persuasion with the purpose of making or sustaining a preferred and useful connection to other people or a group. Internalization is expected to happen when individuals receive influence and later observe that the gratified of the persuaded performance is pleasing in which the content designates the attitudes as well as actions of others. Influencers perform their functions as a third party who can meaningfully form the company's purchasers' opinions, choices, and actions. Any person can be an influencer by influencing customers to purchase goods and services within a community ( Gillin, 2007 ). Information transferred from one person to another person influences customers through word of mouth. Celebrity people's behavior influences customers through talking about the company ( Sernovitz et al., 2012 ).

2.1.2. Information processing theory

How people collect, illustrate, and use information to make decisions is the main concept of Human Information Processing Theory ( Newell and Simon, 1958 ; Norman, 1968 ; Reitman, 1965 ). Information process theory conceptualizes how individuals take care of ecological occasions, encode data to be learned, relate it to what they know, store new information in their memory, and retrieve it depending on the situation ( Shuell, 1986 ), cited in Schunk (2012) . Researchers have shown that buyers' decisions are formed by the manner in which humans' process information ( Huber and Seiser, 2001 ). In this study, online shopping behavior also depends on the buyer's decision. Information is one of the most important things that influences the consumer's purchasing pattern. When consumers gather or collect information from online reviews and celebrity endorsements, they will be motivated to purchase the products or services.

2.1.3. Social exchange theory

SET was developed initially to investigate human behavior ( Homans, 1958 ) and was later applied to comprehend hierarchical behavior ( Blau, 1964 ; Emerson, 1962 ). The Social Exchange Theory states that individuals and organizations are assisted to maximize their rewards and limit their expenses ( Salam et al., 1998 ). Individuals regularly anticipate proportional advantages, like individual warmth, trust, appreciation, and monetary return, at the point when they act as indicated by social norms. Accordingly, relational cooperation from a money-saving perspective is an exchange where actors obtain benefits. From a cost-benefit perspective, they communicate individually, which aids in exchange where the actor gains an opportunity ( Blau, 1964 ). In the present day, SET has been adopted in social networking research. So, this theory is suitable for this study because it depends on online shopping behavior. Based on psychology, SET accepts the fundamental ideas of modern economics as a foundation for analyzing human behavior and connections in order to determine the complexity of social structures. At the time of promoting, companies require a cost to get a customer's attractions in order to retain the customer. Hence, if the research is used promotional tools more, such as advertising, personal selling, and sales promotion, as a result, it's possible to get customer attention whenever they are motivated or influenced, at which time they will purchase goods and services online. Promotional tools and live streaming are both related to human behavior and easily affect online shopping behavior.

2.2. Live streaming

The coronavirus pandemic calamity knocked out the world and affected all sides of our lives, including customers' preferences, habits, and shopping behaviors. During the corona pandemic times, e-shops were stimulated on social media ( Ali et al., 2021 ). Day by day, live streaming has been popular. Numerous merchants on social commerce display places have embraced it because of its ability to increase their company's sales performance. Live streaming shopping is a new form of social commerce that has already been developed and implemented by social commerce merchants ( Adoeng et al., 2019 ; Taobangdan and Taobao, 2019 ). The live presentation helps a businessman influence the online customer to purchase products. Live streaming has transformed the out-of-date social business model in different ways. In outdated online shopping, customers can only know about goods and services via text and pictures. Otherwise, live streaming allows online sellers to show real-time videos of the products and also let customers know about the product's overall features and quality ( Wongkitrungrueng and Assarut, 2018 ). In traditional social commerce, shoppers could only ask about product-related topics, but in modern times, consumers can ask the question via screen and streamers can give the answer in real-time ( Wongkitrungrueng and Assarut, 2018 ). Live streaming shopping creates a real-time stream between sellers and buyers. Online shoppers can watch the live presentations of products that influence customers to purchase that product. Customers' any confusion about products can be reduced through visual presentations of products ( Chen et al., 2017 ; Kim and Park, 2013 ; Zhou et al., 2018 ). The increasing popularity of visual presentations highly influences customers to buy the products ( Yu et al., 2018 ). While customers' engagement with live presentations of products is positively impacted on customer minds about products, it is also a stimulus to shop for those products ( Wongkitrungrueng and Assarut, 2018 ). Despite the fact that buyer commitment has been identified as a significant antecedent persuading purchaser buying in online spending ( Prentice et al., 2019 ), only a few studies have measured the previous circumstances and outcomes of purchaser assignation according to live streaming shop. Live streaming broadcasting makes use of one or more pieces of equipment that can instantly show images and sounds to other locations, allowing users to observe their existence ( Chen and Lin, 2018 ). Live streaming shopping is a new social media form with a high HCI that raises customer awareness of products. Preceding live-streaming lessons have chiefly concentrated on video games and e-sports ( Cheung and Huang, 2011 ; Sjoblom and Hamari, 2017 ). Many customers increase their capacity to buy through live streaming shopping by gaining new perspectives and asking pertinent questions ( Lu et al., 2018 ). Live streaming can show images as well as sounds from one place to a different place instantly ( Chen and Lin, 2018 ). Live streaming purchasing is an extremely noticeable form of merchandise demonstration through online videos. When customers make purchase decisions, they need clear information about products and also want to see the products visibly through the live presentation. It gives the clients an intellect of engagement. Besides, the richness of live streaming spending makes it stress-free to fascinate buyers. Consequently, consumers observe immersion ( Yim et al., 2017 ). Besides, live presentations can communicate complete videos to consumers, as well as the sellers can show how to use the merchandise through live streaming, which permits the product to be visualized ( Li, 2019 ; Javadi et al., 2012 ). In live presentations, sellers and customers interact with each other through live streaming, and customers watch the seller's voice, movement, and product features. So, customers know that the sellers are real people because of the live presentation via social media. Live streaming allows companies to broadcast their products' different items via live presentations. Furthermore, live presentations can prompt captivation, which can lead to a logic of immersion ( Shin, 2017 ). Online shopping and e-commerce have developed an innovative and lucrative business model. Here, buyers and sellers are both connected with live presentations, with buyers asking product-related questions to sellers and also watching the product and product features ( Attfield et al., 2011 ). Visual presentation shopping is being subjected to extraordinary growth. On the other hand, interest in the live-stream market is in its embryonic stage. Different celebrities talk about products and motivate them through live presentations ( Ma, 2021 ). Day by day, with the increase of online shopping, many companies provide live help or visual presentations through test chatting, instant messaging, and live product presentations. Businesses and customers can conduct real-time human-to-human communications for e-commerce Web sites ( Qiu and Benbasat, 2005 ). E-retailers are taking on innovative arithmetic advertising tactics to deliver more accurate information to their consumers. In real-time business, live video streaming allows sellers and consumers to interact ( Zhang et al., 2019 ). Nowadays, consumers have become familiar with visual presentations and product features online and have finally purchased those products that they like. Consumers are motivated to purchase products through live presentations ( Yin, 2020 ).

2.3. Celebrity endorsement

There are many social media platforms, for instance, Facebook, Twitter, Snapchat, and Instagram. Day by day, social media continues to rise speedily in popularity. Celebrity people are using different social media platforms and distributing different information about products to customers. The celebrity of Instagram is influencing consumers' online purchasing behaviors ( Gupta et al., 2020 ). Through social media, online information sharing in the communal sphere has not only promoted the customers' buying choices. Celebrity people provide information about goods and services to actual and potential customers ( Lee et al., 2008 ; Ashfaq and Ali, 2017 ). Along with the diverse investigators, the practice of celebrity endorsements supports in structure the products' identification as well as generates optimistic insolence ( Petty et al., 1983 ), improves the prospect of buying ( Friedman and Friedman, 1979 ), nurtures trademark trustworthiness, and completely influences positive word of mouth ( Bush et al., 2004 ). Celebrity endorsements have a significant impact on consumers' purchase decisions ( Ohanian, 1990 ). In the same way, Instagram celebrity has a momentous impact on consumers' online shopping behaviors ( Kutthakaphan and Chokesamritpol, 2013 ). Most celebrities have a more positive impact on consumers' minds about the products than less credible celebrities. Credible celebrity people influence consumers' online shopping behavior ( Aziz et al., 2013 ). Celebrity people created a brand different from another one because consumers can easily select their preferred products. Through social media advertisements ( Meng et al., 2020 ). celebrity endorsements have an effect on customers' buying behavior. Celebrity images might have an effect on positive and negative consumer attitudes. A celebrity's usefulness depends on their trustworthiness and credibility in an online advertisement. A celebrity's good image can have a positive effect on product acceptance ( Ibok, 2013 ). A celebrity can easily motivate consumers towards purchasing products because people believe infamous people. Through social media, a famous personality created awareness about products with customers. They can positively influence customers' opinions of the brand ( Rai and Sharma, 2013 ). Celebrity endorsement is one kind of promotional activity that attracts customers to specific products. Different companies use different celebrities to promote the awareness of their products to customers, and customers might be motivated to purchase those products. Customers purchased products based on the credibility of celebrities ( Khatri, 2006 ). The influence of superstars' post-legitimacy, observational learning, sentimentality polarization, and impulse purchasing propensity reins in the dormant state-trait theory. Security is influencing consumers' online shopping behavior through social media ( Zafar et al., 2021a ). Normally, followers consider that celebrity posts are authentic; that's why they easily influence consumers to make online purchases ( Wilcox and Stephen, 2013 ). On social media, celebrities share their opinions and advertisements that highly stimulate potential buyers to purchase products ( Chung and Cho, 2017 ; Xiang et al., 2016 ). Celebrity advertisements have so many advantages and disadvantages. Celebrity advertisements can be used to achieve a company's competitive advantage ( Han and Yazdanifard, 2015 ). With regard to a celebrity's values, occupation, ethnicity, and other characteristics, the customer ought to never be curious about why this star is certifying the merchandise ( Meng et al., 2021 ; Gan and Wang, 2015 ). Generally, the research should be focused on celebrities' groups or pages where customers are replaying or commenting on celebrities' posts as well as their peers' social communication. Some celebrities have a large number of followers; they maintain an online community. Business organizations give priority to social media celebrities in their marketing strategy to motivate online shopping behavior ( Pemberton, 2017 ). Consumers follow the celebrity's posts and pursue their lifestyle, with clothing, makeup, fashion, the destination of holidays, even restaurant choice. Organizations try to use such celebrities for effective social media marketing promotions ( Hennig-Thurau et al., 2013 ; Kumar and Mirchandani, 2013 ). Celebrity followers always enquire for recommendations from business organizations. Celebrities' any business-related posts that stimulate consumers' online purchasing behavior ( Wilcox and Stephen, 2013 ).

2.4. Promotional tools

Technological changes are occurring in eye flashes and values are changing over time. Customers' buying habits change rapidly, and the fortunes of different companies vary. Online marketing has been seen as a new form of marketing and has given companies new opportunities to do business. According to Dehkordi et al. (2012) , e-commerce and e-marketing show that internet marketing is easier than conventional marketing ( Dehkordi et al., 2012 ). Leena Jeenefa noted that there are several notable relationships between purchasing behavior and the effects of media advertising ( Jenefa, 2017 ). Reza Jalilvand and Samiei (2012) evaluates how advertisers use social media to make their products popular. The reason for the promotional price promotion is that the consumer does not have the rational mindset to think about whether it is worth buying more at that moment, and this also increases online purchasing behavior ( Agyeman-Darbu, 2017 ). Some social media stated that if consumers buy two, they will get one free, and this also leads to the consumer having a strong positive feeling. Ibok (2013) found that young people feel more comfortable when choosing and buying products online than in physical shopping options. Social media helps them save time and effort examining product information. Privacy, trust, and protection play an important role in social media networking sites. Online advertising businesses use electronic marketing tools to create marketing strategies, advertising theories, and customer buying behavior due to potential market segmentation. According to Eyre et al. (2020) , online advertising includes contextual ads on examining banner ads, rich media ads, social network advertising, online classified advertising, and marketing email like spam. Advertising is defined as the definition of any personal meaning related to product ideas and information in the media to create a brand image ( Kotler and Amstrong, 2010 ). For many years, television, radio, newspapers, and magazines were the only means and channels of advertising, but nowadays, online advertising is becoming the main driving force in many advertising initiatives and efforts ( Kotler and Amstrong, 2010 ). Content is one of the most important features of e-advertisement. It delivers written information regarding particular products or services to online users. Customers are rapidly adopting online shopping day by day due to a busy lifestyle. Undoubtedly, as a developing country, Bangladesh has a lot of potential customers for online businesses. Bangladesh is one of the countries that uses social media the most. It is important to know what causes online buying behavior on social media.

2.5. Online reviews

Purchase intention can be used to measure the possibility of a consumer buying a certain product. When deciding to buy a product, most customers are influenced by comments and ratings from online reviews, and they take a positive or negative view of the product. Social media enabled through mobile devices can be accessed everywhere, instead of not only increasing access to information but also allowing people to create content and strengthen their voices around the world ( Labrecque et al., 2013 ). Social media is playing a crucial role in sharing opinions and product knowledge with consumers and, as a result, having an impact on other consumers ( Lim et al., 2016 ). According to Zhang et al. (2019) , the availability of online reviews plays an important role in online shopping behavior compared to other things. The availability of online reviews refers to the large number of online reviews that are sufficiently available online for the consumer's decision-making process ( Zhang and Zhu, 2010 ). Social media users have realized that a good number of online reviews point to online shopping behavior among customers. Good online shops create an opportunity to search for any product ( Zhang and Zhu, 2010 ). Furthermore, the availability of online reviews makes online shopping appreciate the quality and motivates the customer to try it for the first time ( Cui et al., 2010 ). A good number of customer reviews will have a positive impact on other users on social media, and it can be effective for the online shopping industry to increase sales volume through social media reviews ( Geetha et al., 2018 ). In addition, many researchers have found that a large number of online reviews can influence a potential customer when they choose a product through social media. Significantly, if consumers respond positively to a good number on social media sites, they are more likely to choose their favorite product than cheap ones ( Geng et al., 2020 ). For example, the availability of online reviews on social media should create an opportunity to try a new product, and potential customers may be the priority in their selection criteria ( Geetha et al., 2018 ). Numerous empirical studies across different industries have already investigated the influence of the number of review attributes from a variety of perspectives. For example, the number of reviews ( Dellarocas et al., 2007 ; Ghose and Ipeirotis, 2010 ), the response to negative reviews for online product management ( Kim et al., 2015 ), the positive online product reviews ( Ye et al., 2009 ), and the overall valence of a set of reviews of a product ( Spark & Browning, 2011 ). Consumers consider the internet as a tool to obtain information as a part of the decision-making process before purchasing products. The number of online reviews needs to have a positive impact on potential customers of unfamiliar products ( Zhang and Zhu, 2010 ). As a result, the brand availability of online-spread products increases because customers share their experiences on social media pages. A product review site assesses consumers on their own and how they feel about product quality, service systems, and their overall environment. For this reason, the behavioral motive of the customer should change when they decide to choose a product from the review site ( Gan and Wang, 2015 ). An online review is similar to a traditional face-to-face communication messenger. It is considered a new form of recommendation ( Helm et al., 2010 ). Zhang and Zhu (2010) indicate that the reviews' perceptual information and reasoning power are an important determinant of customer behavioral will, although the source is not credible. So online review materials still play an important role in consumer decision-making because good reviews from one customer can lead to another customer purchasing the product. Additionally, many prior studies have examined whether the availability of online reviews has a significant influence on consumers' product selection when they search for other reviews on social media ( Zhang et al., 2019 ; Cui et al., 2010 ). It has also been noted that the availability of online reviews has been verified as an effective tool for conducting research questions on consumer product selection ( Zhang et al., 2019 ).

2.6. Online shopping behavior

Businesses turned to alternatives and took up online marketing because of COVID-19 pandemic. Online marketing is a significant method for streamlining business processes, reducing managerial costs and turnaround time, maintaining social distance, staying at home, protecting against viruses, and illuminating associations with customers and business partners ( Hossain, et al., 2022 ; Hossain and Khan, 2018 ). At present, online shopping is becoming more popular all over the world, especially for retailers and customers. Online shopping creates opportunities for both online retailers and customers ( Kuester and Sabine, 2012 ; Hossain et al., 2018b ). Customer research has shown that customer assessments dispatched online and the allotment of information or particular views have become enormously influential means of communication. Online reviews have taken over business organizations through social media (Facebook, Snapchat, Twitter, and Instagram) ( Doh and Hwang, 2009 ; Lee et al., 2011a ; Jalilvand and Samiei, 2012 ; Huete-Alcocer, 2017 ). Different types of online reviews have improved the customers' internet shopping performance. Satisfied customers are giving online reviews through social media that influence other consumers' online shopping ( Fu et al., 2020 ). Nowadays, several customers are purchasing social media. Many business organizations have opted to take advantage of opportunities obtainable through social media networks to gain more consumers ( Kaplan and Haenlein, 2014 ). Live streaming stimuli motivate consumer cognitive states that influence consumer online shopping behavior ( Xu et al., 2020 ). The business organization has promoted social media advertising to attract online shoppers to purchase products online ( Mumtaz et al., 2011 ). Targeted advertising by businesses on social media (Facebook, Instagram, and so on). Business organizations know about customers' choices, preferences, and information through social media. They are doing e-advertising based on customers' preferable products and are influencing customers to purchase those products. An organization is able to run different advertising for different categories of customers, and an organization can set their target price ( Iyer et al., 2005 ). Companies can transfer information about products through online advertising. Consumers can visually watch their preferred products via advertising. Entrepreneurs use celebrity endorsements to promote their company's products, and it is increasing consumer purchase intentions. Consumers purchase products online and the created appeal of a statement by a celebrity might influence a customer's product image ( Wang et al., 2013 ).

This research has been prepared during COVID-19. In the research has applied three types of theories, such as social influence theory, information processing theory, and social exchange theory. In previous research, researchers have used online reviews as well as celebrity endorsements as factors under both social influence theory and information processing theory. For the first time at COVID-19, the research has applied these factors under the social influence theory and information processing theory, which have never been used before. The research paper has used social exchange theory. This theory identifies that promotional tools influence customers to buy their necessary goods and services through online shopping. The previous researchers didn't show social media impacts on online shopping behavior during COVID-19. The research has applied those factors during the COVID-19 time period, which made research paper unique from previous research. During COVID-19, The research was used technical tools that had never been applied to that type of theory before. The research paper has analyzed by SmartPLS version 3.0 and used a structural equation model..

3. Conceptual model and hypotheses development

According to Zhang et al. (2019) , by reducing psychological distance and perceived uncertainty, a live streaming strategy can improve a customer's online purchase intention. Chandrruangphen et al. (2022) find out vendors to concentrate on significant live streaming attributes to develop trust with their clients and increase their customers' intentions to watch and buy. The literature and researcher findings suggest that offering live presentations enables sellers to introduce items in a novel way, which might improve customers' moods and sentiments towards the product. So, customers should feel more confidence in the seller and his/her items because of live streaming. Thus, it is expected that:

Hypothesis 1 (H1) : Live streaming has a significant impact on online shopping behavior.

Park and Lin (2020) develop and test an integrative model of online celebrity endorsement by exploring compatibility impacts on customers. Meng et al. (2021) find that the feelings of audiences towards online celebrities can influence a buyer's willingness to buy products suggested by the online superstar. The literature and researcher findings suggest that celebrity endorsements represent attractiveness, believability, and celebrity-product compatibility, which have positive effects on a buyer's attitude towards products and brands as well as purchase intention. As a result, celebrity endorsement may increase users' desire to purchase any product. Therefore, it is expected that:

Hypothesis 2 (H2) : Celebrity endorsement has a positive influence on online shopping behavior.

Ashraf et al. (2014) found that sales promotion played a more significant role in the development of consumer buying behavior. Yahya et al. (2019) and Shamout (2016) revealed in their study that coupons, discounts, free delivery, and other promotional tools have a positive impact on consumer buying decisions. The literature and researcher findings suggest that sales promotion has a huge impact on consumer buying behavior, such as purchase time, product brand, product quantity, brand switching, and so on. Again, sales promotion can be used by marketers to create a long-term customer relationship, which can help them increase their sales. Based on the previous discussion, it is expected that promotional tools will have a positive relationship with purchase intention ( Siddique and Hossain, 2018 ). Thus, it is expected that:

Hypothesis 3 (H3) : Promotional tools have a positive influence on online shopping behavior.

According to Nuseir (2019) and Ventre and Kolbe (2020) , organizations should seek to increase customers' sharing of their positive online opinions in order to improve attachment and encourage online shopping. When the reviews contain detailed information about the product, consumers deem online reviews to be more credible ( Jimenez and Mendoza, 2013 ). The literature and researcher findings suggest that consumer opinion and peer reviews are among the top factors to consider for online shopping behavior. Thus, online sentimental reviews grab more attention from consumers and affect them positively when purchasing products. Therefore, it is expected that:

: Online reviews have a significant impact on online shopping behavior.

In this study, four independent variables (live streaming celebrity endorsements, promotional tools, and online reviews) and one dependent variable (online shopping behavior) have been recognized. Based on the previous literature and discussions, the conceptual framework ( Figure 1 ).

Research model.

4. Research methods

4.1. research design.

The research design was applied when the collection of data and analysis of data processed by combining them were used in the research ( Jahoda et al., 1951 ). This study is based on the quantitative survey method, with data collected using a structural questionnaire. To test the hypothesis, the study was conducted based on an online convenience and judgmental sampling survey. This study applied a descriptive study and collected respondents' attitudes and behaviors about social media's impact on online shopping behavior.

4.2. Methods of research data collection

The study collected data from respondents in written form. The study confirmed that informed consent was obtained from all participants for our research paper. The research paper applied primary and secondary data to prepare the study and make it more presentable. Primary data was collected via a survey and developed questionnaire. Business market research might use a questionnaire technique to collect consumer and customer opinions ( Wang and Feng, 2012 ). Online surveys are used to learn about the impact of social media on internet shopping behavior. Primary data was collected from respondents by developing a Google form and sharing that form with other respondents via Facebook, WhatsApp, e-mail, and so on. In particular, the questionnaire was developed for those people who connected with social media like Facebook, Twitter, Pinterest, YouTube, WhatsApp, and so on.

This research paper also used secondary data that was collected from different articles, books, and newspapers. The research was collected secondary data by penetrating electronic databases, including Research Gate, Google Scholars, and Emerald Insight. The research was collected secondary data by searching top journals like the Journal of Marketing Analytics, the Journal of Business Research, the Journal of Consumer Research, and so on.

4.3. Method of sampling

4.3.1. sampling unit.

People who have the equivalent attitudes and behavior in the direction of an entire group of people ( Sekaran and Bougie, 2016 ). These people use social media and their age is above 15 years old. They are considered the population of this study. So, the population is unfamiliar with this research paper. For this research paper, there is no earmarked sampling unit among the total population. In this study, the population is considered students, managerial-level people, businessmen, and teachers.

4.3.2. Sampling technique

Respondents for this study were chosen using an online purposive sampling technique and non-probability sampling methods. This research data was collected during the corona pandemic. The researcher collected data by distributing the questionnaire through Google Form Link and sharing this link with different convenient people. Non-probability sampling has been used because it is less time-consuming and less costly to prepare a sampling frame. Among the numerous ways of non-probability sampling, purposive sampling technique has been used because they are cheerfully available, generate a relatively low cost, and are convenient.

4.3.3. Sample size

The purposive sampling method is applied to collect (N = 350) respondents' opinions through a developed questionnaire. The sample (N = 350) was collected from the Dhaka, Sylhet, Khulna, and Chattogram divisions among eight divisions of Bangladesh.

4.4. Measurement scale of dependent and independent variable

The study used the Likert Scale (5 ratings). The Likert Scale is used for individual responses and measures the dependent variable and independent variable about the impact of social media on online shopping behavior during the coronavirus pandemic. The Likert Scale has five stages, and each statement in the form was measured by 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree.

4.4.1. Measurement instruments

As illustrated in Table 1 , the study used four constructs of social media to examine online shopping behavior during the COVID-19 pandemic. Live streaming factors include social sharing, hedonic consumption, cognitive assimilation, and impulsive consumption. The celebrity endorsement factor includes the number of shares, authenticity, positive sentiments, and recognizable celebrity. Promotional tools factor includes price discount, sales promotion, buy one get one, surroundings influence. Online review factors include the reviewer's reputation, the review's reliability, good customer rating, and argument quality.

Table 1

Origin of constructs and measured variables.

4.5. Data analysis

The smartPLS software version 3.0 was applied to examine the data collected via questionnaire. The conceptual model of the study was verified using structural equation modeling (SEM). For sample distribution, percentile measures and frequency distribution were primarily used in this study. The study's descriptive statistics were tested using mean as well as standard deviation measures. Collinearity statistics were used to test for multicollinearity among the independent variables. Besides, the reliability of the data or scale items was ascertained using Cronbach's alpha coefficients and composite reliability (CR). Discriminant validity was also used to test the Fornell-Larcker Criterion and the Heterotrait-Monotrait ratio (HTMT) among the independent and dependent variables.

4.6. Quality of data assurance

Enumerators and overseers were knowledgeable about this research objective, scale, data collection technique, and questionnaire. On a daily basis, the data collected is appropriately administered by superintendents and the data comprehensiveness and reliability are tested before the data is input to SmartPLS version 3.0 for more treatment as well as analysis.

5. Results and interpretations

5.1. descriptive analysis.

The study used mean and standard deviation scores to explore all of the aspects. The constructs were ranked in accordance with their enumerated mean standards. As shown in Table 2 , online reviews had the highest mean score (M = 4.1164), while celebrity endorsements had the lowest mean score (M = 3.4829). Most of the factors produced medium mean scores. Therefore, the factor mean scores recommend that among all perspectives, there be no higher variation.

Table 2

Descriptive statistics analysis.

5.2. Multicollinearity test

The study used a multicollinearity test to measure the independent variables that were highly correlated among themselves. The estimated path coefficients were affected by the predictor constructs of collinearity. Tolerance values below 0.10 and variance inflation facet values above 5 specify the existence of inter predictor constructs' collinearity ( Hair et al., 2019 ). As illustrated in Table 3 , all tolerance and VIF values have an acceptable range in collinearity statistics. So, it was recommended that multicollinearity wouldn't affect the independent variable's capability to take to mean the outcome variable.

Table 3

Multicollinearity test.

5.3. Measurement model analysis (outer model)

Hair et al. (2019) define "the measurement model as a constituent of a theoretical path model that holds the pointers and their associations with the factors; also called the outer model in PLS-SEM." In this study, confirmatory factor analysis (CFA) is applied to square in the event the variables are loaded on their relevant constructs ( Hair et al., 2019 ). In this study, SmartPLS software version 3.0 was applied to conduct structural equation modelling ( Ringle et al., 2015 ).

5.3.1. Unidimensionality

In the present constructs, the unidimensionality component designates that every measurement item has a satisfactory equal factor loading according to the corresponding latent construct. Hair et al. (2019) claim that each factor has a measurement variable with a least factor loading of 0.70. According to Table 4 , online reviews (OR1) and online shopping behavior (OSB6) have factor loadings of 0.674 and 0.663, respectively. However, OR1 and OSB6 factor loading values are close to 0.70. So, the research can be recommended that the unidimensionality measurement model has been recognized.

Table 4

Measurement model summary.

5.3.2. Construct reliability tests

The researcher used Cronbach's alpha and composite reliability (CR) to test the internal consistency. The recommended values of composite reliability (CR) and Cronbach's alpha are equal to or greater than 0.70, which is considered satisfactory to good for research ( Hair et al., 2019 ). As illustrated in Table 4 , all of the CR and Cronbach's alpha values have a satisfactory level. So, the researcher recommended that the constructs be reliable for further research.

5.3.3. Convergent validity tests

The average variance extracted (AVE) is 0.50 or greater than 0.50, assuring the convergent validity of the latent constructs ( Hair et al., 2019 ). As illustrated in Table 4 , all the average variance extracted (AVE) values are greater than 0.50 in this study because of the appropriateness of the constructs for further research.

5.3.4. Discriminant validity tests

Discriminant validity implies that each construct is empirically distinct from the other cross-loading that exists among the latent constructs. The correlation coefficients and square root of average variance extracted (AVE) among the constructs are associated to create discriminant validity ( Hair et al., 2019 ). According to Table 5 , the diagonal numbers are higher than the inter-construct resemblances presented off-diagonally. However, the discriminant's legitimacy is gained for the research constructs.

Table 5

Discriminant validity: Fornell-Larcker Criterion.

5.4. Measurement model analysis (Inner model)

The study measurement model recommended that all the measurement models be valid, then analyze the structural model relationship ( Hair et al., 2019 ). The researcher makes an assessment which one accepts and rejects via significant and insignificant relationships that can be identified by structural model analysis. Besides, the researchers used a bootstrapping procedure with a subsample of 500 to assess the size of the path coefficients ( Ringle et al., 2015 ).

Figure 2. Structural model.

The structural model analysis includes the paths, path coefficients, t values, p values, and path coefficient results. A two-tailed t-test with a level of significance of 5% was used to test the hypotheses that had been developed. The coefficients are statistically significant if the measured t-value is greater than the critical value of 1.96. According to Table 6 and Figure 2 , the path coefficients of three latent constructs, including celebrity endorsement, promotional tools, and online reviews, had a positive and significant association with online shopping behavior at p < 0.05. Here, the researchers mention that hypotheses H2, H3, and H4 are accepted. However, hypothesis H1 has no significant and positive relationship with online shopping behavior. Accordingly, H1 live streaming was rejected. According to Table 6 and Figure 2 , the celebrity endorsement perspective's highest path coefficient (β2 = 0.452) specifies that if celebrity endorsement were to grow by one standard deviation unit, online shopping behavior could increase by 0.452 standard deviation units if all other independent perspectives continued constant.

Table 6

Structural model estimates.

Note: p∗< 0.05, based on the two-tailed test; t = 1.96.

6. Discussions

In the Bangladeshi setting, the research aimed at understanding the impact of social media on online shopping behavior during the COVID-19 pandemic. It has been found that most researchers explored the influence of social media on purchase intention, behavioral intention, satisfaction, purchase decision, and loyalty ( Hossain et al., 2020 ; Gupta et al., 2020 ; Fu et al., 2020 ; Zhou et al., 2018 ; Jenefa, 2017 ). However, there was less focus and thus fewer studies into the impact of social media on online shopping behavior during the COVID-19 pandemic in the context of Bangladeshi consumers.

According to the findings of the above analysis, three social media factors out of four had a significant and positive impact on online shopping behavior during the COVID-19 pandemic from the perspective of Bangladeshi consumers. Besides, the rest of the factors of social media have no significant positive relationship with the online shopping behavior of consumers during the COVID-19 pandemic in the country. The celebrity endorsement factor (β2 = 0.452, t = 10.233), promotional tools factor (β3 = 0.215, t = 3.809), and online reviews factor (β4 = 0.207, t = 4.901) are significantly and positively related to the online shopping behavior of Bangladeshi consumers during the COVID-19 pandemic at p < 0.05.

From the above findings, the study found that those three independent variables, like celebrity endorsements, promotional tools, and online reviews, have a significant positive relationship with the dependent variable, online shopping behavior. Based on the analysis, the researcher found that the independent variable live streaming has no significant positive relationship with the dependent variable online shopping behavior. Here, the live streaming was not supported at a significant value of 0.380, which is higher than the p value of 0.05. The study recommended that live streaming has no significant positive relationship with online shopping behavior. Based on the research, celebrity endorsement's significant value was notated at 0.000, which is lower than the p-value of 0.05. This indicates that celebrity endorsement has a significant positive relationship with consumers' online shopping behavior. Xiang et al. (2016) ; Zafar et al., 2021a ; and Ahmed et al. (2015) , also supported that celebrity endorsement has a positive impact on consumers' online shopping behavior. Based on the analysis, the researchers found that promotional tools have a positive connection with consumers' online shopping behavior. Here, the significant value of 0.00 is lower than the p-value of 0.05. Based on the study, online reviews were significant at a significant value of 0.00, which is smaller than the p-value of 0.05. This suggests that online reviews have a significant positive relationship with customers' online shopping behavior. According to Zhang and Zhu (2010) ; Fu et al. (2020) , also supported that online reviews have a strong relationship with customers' online shopping behavior.

7. Conclusion and implications

During the COVID-19 pandemic, customers are purchasing their necessary products through an online platform. Customers are learning about new products being launched in the market through social media. Customers are safely purchasing their products through online shopping behavior during the corona pandemic. The study has been conducted with the objective of exploring the impact of social media on online shopping behavior during the COVID-19 pandemic from the perspective of Bangladeshi consumers. Different aspects of social media are important tools to guide consumers' online shopping behavior during the coronavirus pandemic in Bangladesh. This research studies the influence of live streaming, celebrity endorsements, promotional tools, and online reviews on consumers’ online shopping behavior during the coronavirus pandemic in the context of Bangladesh. The results of the research has revealed that celebrity endorsement, promotional tools, and online reviews had a positive significant impact on online shopping behavior in the perspectives of Bangladesh. In contrast, live streaming had no significant positive relationship with the online shopping behavior of consumers during the COVID-19 pandemic. The research paper provides practical guidelines for online-based business organizations on how to effectively use social media platforms for business target advertising and promotional activities. Customers are also motivated to purchase through social media because of positive online reviews and trustworthy celebrity endorsements.

7.1. Theoretical implications

Day by day, people are becoming more accustomed to online shopping during the corona pandemic. Most people have connected with social media like Facebook, Twitter, Pinterest, YouTube, WhatsApp, and so on. Social media has a positive impact on online shopping behavior. Customers are watching different advertisements via social media, and they are motivating consumers to shop online. The study has proven that celebrity endorsements, promotional tools, and online shopping have a significant positive impact on online shopping behavior. In the meantime, with the development of social media, the influences on online shopping are increasing. During the coronavirus pandemic, social media-based marketing has also attracted the attention of enterprises. However, there has recently been little research studying the relationship between social media and online shopping behavior. To compensate for the gap, this research has been based on the impact of social media on online shopping behavior. Live streaming has no significant relationship with online shopping during the COVID-19 pandemic. On the other hand, celebrity endorsement has a significant positive connection with online shopping behavior. Besides, promotional tools and online reviews have a positive impact on online shopping behavior during the corona pandemic. Business organizations are highly focused on social media-based promotional activities. Consumers have adjusted their online shopping behavior during the COVID-19 pandemic.

7.2. Practical implications

Introducing celebrity endorsements, promotional tools, and online reviews of social media constructs have a positive connection with online shopping behavior during a COVID-19 pandemic. The research paper yields several practical suggestions for social commerce sellers and e-commerce-based organizations. First, the research results illustrated that celebrity endorsements have a positive relationship with customers' online shopping behavior, which includes attractive celebrities, celebrities, and recognizable celebrities. Hence, social commerce sellers who have not until now accepted celebrity endorsements for promotion should adopt celebrity endorsements that help increase the consumer's online shopping behavior. When famous or attractive celebrities talk about products and live streaming products, then customers are stimulated to purchase those products through the online market. Celebrity endorsers should have clear knowledge about product features before motivating them to purchase those products via online shopping.

Second, the research results showed that promotional tools constructed by social media have a significant positive connection with online shopping behavior. E-commerce sellers should promote promotional activities to increase the sales volume of online shopping. Besides, they should have used re-targeting advertising via social media to enhance online shopping behavior.

Third, the study also found that online reviews have a significant positive relationship with online shopping behavior during the corona pandemic. Potential customers' positive reviews or good ratings influence potential customers’ online shopping behavior. To connect with current and potential customers, e-commerce business sellers should have Facebook pages, Twitter accounts, Instagram accounts, and so on. The social media seller requests that customers give reviews about their product features, price, and quality via social media. Actual customers' positive reviews are highly motivated by other actual and potential customers' purchases through an online business.

8. Limitations and future research

In the study, the main objective was to investigate the major influencing factors that impact consumers' online shopping behavior during COVID-19 outbreaks. The research paper has several limitations. For instance, in the literature, there are several antecedents of the impact of social media on online shopping behavior, but in this study, the researchers only used four antecedents, like live streaming, celebrity endorsement, promotional tools, and online reviews. Future research should add more antecedents in their research paper with four antecedents. Second, this study used an online purposive sampling technique to investigate the impact of social media on consumers' online shopping behavior. The research will be recommended that for future research, they should use experimental methods to measure customers’ online buying behavior through social media. Third, due to the COVID-19 pandemic outbreaks, data was collected from respondents through an online survey using a self-administered questionnaire. For that reason, in some cases, it was not possible to know more properly about the respondents. Field-level surveys and face-to-face interview methods should be used to collect data for further research to address the problem of false information and data. Fourth, current research is based on quantitative information but may differ in results when applying qualitative information. Future research should apply a combination of quantitative and qualitative data analysis.

Declarations

Author contribution statement.

Md Rukon Miah: Conceived and designed the experiments; Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Afzal Hossain: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper; and Corrected proof.

Rony Shikder: Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Tama Saha: Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Meher Neger, PhD: Conceived and designed the experiments; Analyzed and interpreted the data; Overall Supervision of the Study.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability statement

Declaration of interest’s statement.

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

☆ This article is a part of the "Business and Economics COVID-19 Special Issue.

• Adoeng W., Kalangi J.B., Wangke S.J. A comparative analysis of E-advertisement between jd. Id and shopee customers in Manado. Jurnal EMBA: Jurnal Riset Ekonomi, Manajemen, Bisnis dan Akuntansi. 2019; 7 (3):3379–3388. [ Google Scholar ]
• Agyeman-Darbu K. Retailing of Consumer Goods in Kumasi Metropolis. The Mediating Effect of Customer Service (Doctoral Dissertation) 2017. The impact of sales promotion on consumer purchasing behaviour. [ Google Scholar ]
• Ahmed R., Seedani S., Ahuja M., Paryani S. 2015. Impact of celebrity endorsement on consumer buying behavior. Available at SSRN 2666148. [ Google Scholar ]
• Ajina The perceived value of social media marketing: an empirical study of online word-of-mouth in Saudi Arabian context. Entrepreneurship Sustain. Issues. 2019; 6 (3):1512–1527. [ Google Scholar ]
• Ali Taha V., Pencarelli T., Škerháková V., Fedorko R., Košíková M. The use of social media and its impact on shopping behavior of Slovak and Italian consumers during COVID-19 pandemic. Sustainability. 2021; 13 (4):1710. [ Google Scholar ]
• Ashfaq M., Ali M. Impact of celebrity endorsement on consumer buying behavior in FMCG sector of Pakistan. Oman Chap. Arab. J. Busin. Manag. Rev. 2017; 34 (5627):1–12. [ Google Scholar ]
• Ashraf M.G., Rizwan M., Iqbal A., Khan M.A. The promotional tools and situational factors’ impact on consumer buying behaviour and sales promotion. J. Publ. Adm. Govern. 2014; 4 (2):179–201. [ Google Scholar ]
• Attfield S., Kazai G., Lalmas M., Piwowarski B. WSDM Workshop on User Modelling for Web Applications. 2011, February. Towards a science of user engagement (position paper) pp. 9–12. [ Google Scholar ]
• Aziz S., Ghani U., Niazi A. Impact of celebrity credibility on advertising effectiveness. Pakistan J. Comm. Social Sci. (PJCSS) 2013; 7 (1):107–127. [ Google Scholar ]
• Bakewell C., Mitchell V. Generation Y female consumer decision-making styles. Int. J. Ret. Distrib. Manag. 2003; 31 (2):95–106. [ Google Scholar ]
• Blackwell R.D., Miniard P.W., Engel J.F. tenth ed.s. Thomson/Sount; Masao, OH: 2006. Consumer Behavior. [ Google Scholar ]
• Bartik A., Bertrand M., Cullen Z., Glaeser E.L., Luca M., Stanton C. How are Small businesses adjusting to COVID-19? Early evidence from a survey. SSRN Electron. J. 2020; 20 (12):1–36. [ Google Scholar ]
• Berman S.J. Digital transformation: opportunities to create new business models. Strategy & Leadership. 2012; 40 (2):16–24. [ Google Scholar ]
• Blau P. John Wiley & Sons; New York: 1964. Exchange, and Power in Social Life. [ Google Scholar ]
• Bush A.J., Martin C.A., Bush V.D. Sports celebrity influence on the behavioral intentions of generation Y. J. Advert. Res. 2004; 44 (1):108–118. [ Google Scholar ]
• Chandrruangphen E., Assarut N., Sinthupinyo S. The effects of live streaming attributes on consumer trust and shopping intentions for fashion clothing. Cogent Busin. Manag. 2022; 9 (1) [ Google Scholar ]
• Chaturvedi D., Gupta D. Sachin, Effect of Social Media on Online Shopping Behaviour of Apparels in Jaipur City-An Analytical Review. 2014. Effect of Social Media on Online Shopping Behaviour of Apparels in Jaipur City-An Analytical Review. March 2014) [ Google Scholar ]
• Chen A., Lu Y., Wang B. Customers’ purchase decision-making process in social commerce: a social learning perspective. Int. J. Inf. Manag. 2017; 37 (6):627–638. [ Google Scholar ]
• Chen C.C., Lin Y.C. What drives live-stream usage intention? The perspectives of flow, entertainment, social interaction, and endorsement. Telematics Inf. 2018; 35 (1):293–303. [ Google Scholar ]
• Cheng Y.H., Ho H.Y. Social influence's impact on reader perceptions of online reviews. J. Bus. Res. 2015; 68 (4):883–887. [ Google Scholar ]
• Cheung G., Huang J. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2011, May. Starcraft from the stands: understanding the game spectator; pp. 763–772. [ Google Scholar ]
• Chung S., Cho H. Fostering par asocial relationships with celebrities on social media: implications for celebrity endorsement. Psychol. Market. 2017; 34 (4):481–495. [ Google Scholar ]
• Cui B., Tung A.K., Zhang C., Zhao Z. Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data. 2010, June. Multiple feature fusion for social media applications; pp. 435–446. [ Google Scholar ]
• Dehkordi G.J., Rezvani S., Rahman M.S., Nahid F.F.N., Jouya S.F. A conceptual study on E-marketing and its operation on firm's promotion and understanding customer's response. Int. J. Bus. Manag. 2012; 7 (19):114. [ Google Scholar ]
• Dellarocas C., Zhang X.M., Awad N.F. Exploring the value of online product reviews in forecasting sales: the case of motion pictures. J. Interact. Market. 2007; 21 (4):23–45. [ Google Scholar ]
• Doh S.J., Hwang J.S. How consumers evaluate eWOM (electronic word-of-mouth) messages. Cyberpsychol. Behav. 2009; 12 (2):193–197. [ PubMed ] [ Google Scholar ]
• Donthu N., Gustafsson A. Effects of COVID-19 on business and research. J. Bus. Res. 2020; 117 :284–289. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Eger L., Komárková L., Egerová D., Mičík M. The effect of COVID-19 on consumer shopping behaviour: generational cohort perspective. J. Retailing Consum. Serv. 2021; 61 [ Google Scholar ]
• E-marketer E-marketer, eMarketer in Review – Key 2013 Trends, Coverage Areas and Platform Growth. 2013. https://www.emarketer.com/newsroom/index.php/emarketer-review-key-2013-trends-coverage-areas-platform-growth
• Emerson R.M. Power- dependence relations. American Sociology Review. 1962; 27 (1):31–41. [ Google Scholar ]
• Eyre R., De Luca F., Simini F. Social media usage reveals recovery of small businesses after natural hazard events. Nat. Commun. 2020; 11 (1):1–10. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Friedman H.H., Friedman L. Endorser effectiveness by product type. J. Advert. Res. 1979; 19 (5):63–71. [ Google Scholar ]
• Fu H., Manogaran G., Wu K., Cao M., Jiang S., Yang A. Intelligent decision-making of online shopping behavior based on internet of things. Int. J. Inf. Manag. 2020; 50 :515–525. [ Google Scholar ]
• Gan C., Wang W. Uses and gratifications of social media: a comparison of microblog and WeChat. J. Syst. Inform. Technol. 2015; 17 (4):351–363. [ Google Scholar ]
• Geetha V., Rajkumar V.S., Arunachalam L. Impact of social media sites on students purchase intention in online shopping: an empirically study. Int. J. Mech. Prod. Eng. Res. Dev. 2018; 8 :927–938. [ Google Scholar ]
• Geng R., Wang S., Chen X., Song D., Yu J. Content marketing in e-commerce platforms in the internet celebrity economy. Indust. Manag. Data Syst. 2020; 120 (3):464–485. [ Google Scholar ]
• Ghose A., Ipeirotis P.G. Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Trans. Knowl. Data Eng. 2010; 23 (10):1498–1512. [ Google Scholar ]
• Gillin P. Linden Publishing; 2007. The New Influencers: A Marketer’s Guide to the New Social media. [ Google Scholar ]
• Gupta Y., Agarwal S., Singh P.B. To study the impact of instafamous celebrities on consumer buying behavior. Acad. Market. Stud. J. 2020; 24 (2):1–13. [ Google Scholar ]
• Hair J.F., Black W.C., Babin B.J., Anderson R.E. Cengage Learning EMEA- United Kingdom; 2019. Multivariate Data Analysis: A Global Perspective. [ Google Scholar ]
• Hajli N. A study of the impact of social media on consumers. Int. J. Mark. Res. 2014; 56 (3):387–404. [ Google Scholar ]
• Han O.W., Yazdanifard R. The review of the effectiveness of celebrity advertising that influence consumers perception and buying behavior. Global J. Manag. Bus. 2015 [ Google Scholar ]
• Hawkins D.I., Mothersbaugh D.L. eleventh ed. McGraw-Hill Irwin; New York, NY: 2010. Consumer Behavior: Building Marketing Strategy. [ Google Scholar ]
• Helm S., Eggert A., Garnefeld I. Handbook of Partial Least Squares. Springer; Berlin, Heidelberg: 2010. Modeling the impact of corporate reputation on customer satisfaction and loyalty using partial least squares; pp. 515–534. [ Google Scholar ]
• Hennig-Thurau T., Hofacker C.F., Bloching B. Marketing the pinball way: understanding how social media change the generation of value for consumers and companies. J. Interact. Market. 2013; 27 (4):237–241. [ Google Scholar ]
• Homans G. Social behavior as exchange. Am. J. Sociol. 1958; 63 (6):597–606. [ Google Scholar ]
• Hossain A., Chowdhury M.H.K., Shamsuzzaman H.S., Fahim M., Khan M.Y.H. Banking service in Bangladesh: the impact of service marketing Mix on purchase intention of university students. Strat. Change. 2020; 29 (3):363–374. [ Google Scholar ]
• Hossain A., Hasan S., Begum S., Sarker M.A.H. Consumers’ Online Buying Behaviour during COVID-19 Pandemic Using Structural Equation Modeling. Transntl. Market. J. 2022; 10 (2):311–334. [ Google Scholar ]
• Hossain A., Jamil M.A., Rahman M.M. Exploring the key factors influencing consumers’ intention, satisfaction and loyalty towards online purchase in Bangladesh. Int. J. Econ. Finan. Res. 2018; 4 (7):214–225. [ Google Scholar ]
• Hossain A., Khan M.Y.H. Green marketing mix effect on consumers buying decisions in Bangladesh. Mark. Manag. Innov. 2018; 10 (4):298–306. [ Google Scholar ]
• Hossain A., Neger M., Chowdhury M.H.K. Analyzing the impact of social media, promotional efforts and reference groups on consumers buying behavior of eco-friendly products in Bangladesh. Int. J. Sci. Bus. 2019; 3 (1):126–135. [ Google Scholar ]
• Hossain A., Rahman, Md. L. & Hasan, M.M. Consumers’ internet shopping decision toward fashion apparels and its impact on satisfaction in Bangladesh. Bus. Ethics Leadership. 2018; 2 (4):74–82. [ Google Scholar ]
• Huang Z., Benyoucef M. User preferences of social features on social commerce websites: an empirical study. Technol. Forecast. Soc. Change. 2015; 95 :57–72. [ Google Scholar ]
• Huber O., Seiser G. Accounting and convincing: the effect of two types of justification on the decision process. J. Behav. Decis. Making. 2001; 14 (1):69–85. [ Google Scholar ]
• Huete-Alcocer N. A literature review of word of mouth and electronic word of mouth: implications for consumer behavior. Front. Psychol. 2017; 8 :1256. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Ibok N.I. Factors determining the effectiveness of celebrity endorsed advertisements: the case of Nigerian telecommunication industry. Am. J. Bus. Manag. 2013; 2 (3):233–238. [ Google Scholar ]
• Iyer G., Soberman D., Villas-Boas J.M. The targeting of advertising. Market. Sci. 2005; 24 (3):461–476. [ Google Scholar ]
• Jahoda M., Deutsch M., Cook S.W. Vol. 1, Basic processes. Vol. 2, Selected techniques. Dryden Press 1; 1951. Research methods in social relations with special reference to prejudice. [ Google Scholar ]
• Jajodia A., Ebner L., Heidinger B., Chaturvedi A., Prosch H. Imaging in corona virus disease 2019 (COVID-19)—a Scoping review. Europ. J. Radiol. Open. 2020; 7 (1):1–6. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Jalilvand M.R., Samiei N. The effect of electronic word of mouth on brand image and purchase intention: an empirical study in the automobile industry in Iran. Market. Intell. Plann. 2012 [ Google Scholar ]
• Javadi M.H.M., Dolatabadi H.R., Nourbakhsh M., Poursaeedi A., Asadollahi A.R. An analysis of factors affecting on online shopping behavior of consumers. Int. J. Market. Stud. 2012; 4 (5):81. [ Google Scholar ]
• Jenefa L. Impact of digital advertisement on garments buying behavior. Int. J. Transform. Operat. Market. Manag. 2017; 1 (1) [ Google Scholar ]
• Jiménez F.R., Mendoza N.A. Too popular to ignore: the influence of online reviews on purchase intentions of search and experience products. J. Interact. Market. 2013; 27 (3):226–235. [ Google Scholar ]
• Kaplan A., Haenlein M. Collaborative projects (social media application): about Wikipedia, the free encyclopedia. Bus. Horiz. 2014; 57 (5):617–626. [ Google Scholar ]
• Kelman H.C. Compliance, identification, and internalization three processes of attitude change. J. Conflict Resolut. 1958; 2 (1):51–60. [ Google Scholar ]
• Khatri P. Celebrity endorsement: a strategic promotion perspective. Indian Media Stud. J. 2006; 1 (1):25–37. [ Google Scholar ]
• Kim S., Park H. Effects of various characteristics of social commerce (s-commerce) on consumers’ trust and trust performance. Int. J. Inf. Manag. 2013; 33 (2):318–332. [ Google Scholar ]
• Kim W.G., Lim H., Brymer R.A. The effectiveness of managing social media on hotel performance. Int. J. Hospit. Manag. 2015; 44 :165–171. [ Google Scholar ]
• Kotler P. millennium edition. 2000. Marketing Management. Boston. [ Google Scholar ]
• Kotler P. John Wiley & Sons; 2004. Ten Deadly Marketing Sins: Signs and Solutions. [ Google Scholar ]
• Kotler P., Armstrong G. Pearson education; 2010. Principles of Marketing. [ Google Scholar ]
• Kuester S. Vol. 110. University of Mannheim; 2012. MKT 301: Strategic Marketing & Marketing in Specific Industry Contexts; pp. 393–404. [ Google Scholar ]
• Kumar V., Mirchandani R. Increasing the ROI of social media marketing. IEEE Eng. Manag. Rev. 2013; 41 (3):17–23. [ Google Scholar ]
• Kutthakaphan R., Chokesamritpol W. 2013. The Use of Celebrity Endorsement with the Help of Electronic Communication Channel (Instagram): Case Study of Magnum Ice Cream in Thailand. [ Google Scholar ]
• Labrecque L.I., vor dem Esche J., Mathwick C., Novak T.P., Hofacker C.F. Consumer power: evolution in the digital age. J. Interact. Market. 2013; 27 (4):257–269. [ Google Scholar ]
• Lee J., Park D.H., Han I. The effect of negative online consumer reviews on product attitude: an information processing view. Electron. Commer. Res. Appl. 2008; 7 (3):341–352. [ Google Scholar ]
• Lee J., Park D.H., Han I. The different effects of online consumer reviews on consumers' purchase intentions depending on trust in online shopping malls: an advertising perspective. Intern. Res. 2011 [ Google Scholar ]
• Li C.Y. How social commerce constructs influence customers' social shopping intention? An empirical study of a social commerce website. Technol. Forecast. Soc. Change. 2019; 144 :282–294. [ Google Scholar ]
• Li N., Zhang P. Consumer online shopping attitudes and behavior: an assessment of research. AMCIS 2002 proceedings. 2002; 74 https://aisel.aisnet.org/amcis2002/74 [ Google Scholar ]
• Lim Y.J., Osman A., Salahuddin S.N., Romle A.R., Abdullah S. Factors influencing online shopping behavior: the mediating role of purchase intention. Procedia Econ. Finance. 2016; 35 :401–410. [ Google Scholar ]
• Lu Z., Xia H., Heo S., Wigdor D. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 2018, April. You watch, you give, and you engage: a study of live streaming practices in China; pp. 1–13. [ Google Scholar ]
• Luo J., Pan X., Wang S., Huang Y. Identifying target audience on enterprise social network. Indust. Manag. Data Sys. 2019; 119 (1):111–128. [ Google Scholar ]
• Ma Y. To shop or not: understanding Chinese consumers’ live-stream shopping intentions from the perspectives of uses and gratifications, perceived network size, perceptions of digital celebrities, and shopping orientations. Telematics Inf. 2021; 59 [ Google Scholar ]
• Meng L.M., Duan S., Zhao Y., Lü K., Chen S. The impact of online celebrity in live streaming E-commerce on purchase intention from the perspective of emotional contagion. J. Retailing Consum. Serv. 2021; 63 [ Google Scholar ]
• Meng X., Zhang W., Li Y., Cao X., Feng X. Social media effect, investor recognition and the cross-section of stock returns. Int. Rev. Financ. Anal. 2020; 67 [ Google Scholar ]
• Mo Z., Li Y.F., Fan P. Effect of online reviews on consumer purchase behavior. J. Serv. Sci. Manag. 2015; 8 (3):419. [ Google Scholar ]
• Momtaz H., Islam M.A., Ariffin K.H.K., Karim A. Customers’ satisfaction on online shopping in Malaysia. Int. J. Bus. Manag. 2011; 6 (10):162. [ Google Scholar ]
• Neger M., Uddin B. Factors affecting consumers’ internet shopping behavior during the COVID-19 Pandemic: evidence from Bangladesh. Chin. Bus. Rev. 2020; 19 (3):91–104. [ Google Scholar ]
• Newell A.J.C.S., Simon H.A. Elements of a theory of human problem-solving. Psychol. Rev. 1958; 65 :151–166. [ Google Scholar ]
• Nicola M., Alsafi Z., Sohrabi C., Kerwan A., Al-Jabir A., Iosifidis C., Agha M., Agha R. The socio-economic implications of the coronavirus pandemic (COVID-19): a review. Int. J. Surg. 2020; 78 (1):185–193. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Norman D.A. Toward a theory of memory and attention. Psychol. Rev. 1968; 75 :522–536. [ Google Scholar ]
• Nuseir M.T. The impact of electronic word of mouth (e-WOM) on the online purchase intention of consumers in the Islamic countries–a case of (UAE) J. Islam. Market. 2019; 10 (3):759–767. [ Google Scholar ]
• Ohanian R. Construction and validation of a scale to measure celebrity endorsers' perceived expertise, trustworthiness, and attractiveness. J. Advert. 1990; 19 (3):39–52. [ Google Scholar ]
• Pantano E., Pizzi G., Scarpi D., Dennis C. Competing during a pandemic? Retailers’ ups and downs during the COVID-19 outbreak. J. Bus. Res. 2020; 116 :209–213. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Park H.J., Lin L.M. The effects of match-ups on the consumer attitudes toward internet celebrities and their live streaming contents in the context of product endorsement. J. Retailing Consum. Serv. 2020; 52 [ Google Scholar ]
• Park S., Nicolau J.L. Asymmetric effects of online consumer reviews. Ann. Tourism Res. 2015; 50 :67–83. [ Google Scholar ]
• Pemberton C. 2017. Energize Your Influencer Marketing - Smarter with Gartner. https://www.gartner.com/smarterwithgartner/energize-your-influencer-marketing (Accessed 5 12 2018) [ Google Scholar ]
• Petty R.E., Cacioppo J.T., Schumann D. Central and peripheral routes to advertising effectiveness: the moderating role of involvement. J. Consum. Res. 1983; 10 (2):135–146. [ Google Scholar ]
• Prentice C., Han X.Y., Hua L.L., Hu L. The influence of identity-driven customer engagement on purchase intention. J. Retailing Consum. Serv. 2019; 47 :339–347. [ Google Scholar ]
• Qiu L., Benbasat I. Online consumer trust and live help interfaces: the effects of text-to-speech voice and three-dimensional avatars. Int. J. Human-Comp. Inter. 2005; 19 (1):75–94. [ Google Scholar ]
• Rai S.K., Sharma A.K. Celebrity attributes and influence on consumer behaviour: a study of Shekhawati region of Rajasthan. Pacific Bus. Rev. Int. 2013; 5 (11):57–64. [ Google Scholar ]
• Rajendran D.K., Rajagopal V., Alagumanian S., Santhosh Kumar T., Sathiya Prabhakaran S.P., Kasilingam D. Systematic literature review on novel corona virus SARS-CoV-2: a threat to human era. Virus Disease. 2020; 31 (2):253–261. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Reitman W.R. John Wiley & Sons; New York: 1965. Cognition and Thought: an Information-Processing Approach. [ Google Scholar ]
• Reza Jalilvand M., Samiei N. The effect of electronic word of mouth on brand image and purchase intention: an empirical study in the automobile industry in Iran. Market. Intellig. Plan. 2012; 30 (4):460–476. [ Google Scholar ]
• Ringle C.M., Wende S., Becker J.M. SmartPLS GmbH; Boenningstedt: 2015. SmartPLS.“SmartPLS 3. [ Google Scholar ]
• Salam A.F., Rao H.R., Pegels C.C. Paper Presented at Americas Conference on Information Systems. 1998. An investigation of consumer- perceived risk on electronic commerce transactions: the role of institutional trust, and economic incentive in a social exchange framework. Baltimore, MD, USA. [ Google Scholar ]
• Schouten A.P., Janssen L., Verspaget M. Celebrity vs. Influencer endorsements in advertising: the role of identification, credibility, and Product-Endorser fit. Int. J. Advert. 2020; 39 (2):258–281. [ Google Scholar ]
• Schunk D.H. 2012. Learning Theoriesan Educational Perspective. [ Google Scholar ]
• Sekaran U., Bougie R. Research methods for business: a skill building approach. John Wiley & Sons. 2016 [ Google Scholar ]
• Sernovitz A. How Smart Companies Get People Texas; 2012. Word of Mouth Marketing. [ Google Scholar ]
• Shamout M.D. The impact of promotional tools on consumer buying behavior in retail market. Int. J. Bus. Soc. Sci. 2016; 7 (1):75–85. [ Google Scholar ]
• Sheth J. Impact of Covid-19 on consumer behavior: will the old habits return or die? J. Bus. Res. 2020; 117 :280–283. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Shin D.H. The role of affordance in the experience of virtual reality learning: technological and affective affordances in virtual reality. Telematics Inf. 2017; 34 (8):1826–1836. [ Google Scholar ]
• Shuell T.J. Cognitive conceptions of learning. Rev. Educ. Res. 1986; 56 :411–436. [ Google Scholar ]
• Siddique Z.R., Hossain A. Sources of consumers awareness toward green products and its impact on purchasing decision in Bangladesh. J. Sustain. Dev. 2018; 11 (3):9–22. [ Google Scholar ]
• Singhal T. A review of coronavirus disease-2019 (COVID-19) Indian J. Pediatr. 2020; 87 (4):281–286. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sjöblom M., Hamari J. Why do people watch others play video games? An empirical study on the motivations of Twitch users. Comput. Hum. Behav. 2017; 75 :985–996. [ Google Scholar ]
• Sohrabi C., Alsafi Z., O’Neill N., Khan M., Kerwan A., AlJabir A., Iosifidis C., Agha R. World Health Organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19) Int. J. Surg. 2020; 76 (1):71–76. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sparks B.A., Browning V. The impact of online reviews on hotel booking intentions and perception of trust. Tourism Manag. 2011; 32 (6):1310–1323. [ Google Scholar ]
• Sun Y., Shao X., Li X., Guo Y., Nie K. How live streaming influences purchase intentions in social commerce: an IT affordance perspective. Electron. Commer. Res. Appl. 2019; 37 [ Google Scholar ]
• Taobangdan, Taobao Taobao Live Streaming Ecological Development Report. Report. 2019 [ Google Scholar ]
• Ventre I., Kolbe D. The impact of perceived usefulness of online reviews, trust and perceived risk on online purchase intention in emerging markets: a Mexican perspective. J. Int. Consum. Market. 2020; 32 (4):287–299. [ Google Scholar ]
• Verma S., Gustafsson A. Investigating the emerging COVID-19 research trends in the field of business and management: a bibliometric analysis approach. J. Bus. Res. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Wang J.S., Cheng Y.F., Chu Y.L. Effect of celebrity endorsements on consumer purchase intentions: advertising effect and advertising appeal as mediators. Human Fact. Ergon. Manuf. Service Indust. 2013; 23 (5):357–367. [ Google Scholar ]
• Wang Y., Feng H. Customer relationship management capabilities: Measurement, antecedents and consequences. Manag. Decis. 2012; 50 (1):115–129. [ Google Scholar ]
• Wang X.C., Kim W., Holguín-Veras J., Schmid J. Adoption of delivery services in light of the COVID pandemic: who and how long? Transport. Res. Pol. Pract. 2021; 154 :270–286. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Wilcox K., Stephen A.T. Are close friends the enemy? Online social networks, self-esteem, and self-control. J. Consum. Res. 2013; 40 (1):90–103. [ Google Scholar ]
• Wongkitrungrueng A., Assarut N. The role of live streaming in building consumer trust and engagement with social commerce sellers. J. Bus. Res. 2020; 117 :543–556. [ Google Scholar ]
• Xiang L., Zheng X., Lee M.K.O.O., Zhao D. Exploring consumers’ impulse buying behavior on social commerce platform: the role of parasocial interaction. Int. J. Inf. Manag. 2016; 36 (3):333–347. [ Google Scholar ]
• Xu X., Wu J.H., Li Q. What drives consumer shopping behavior in live streaming commerce? J. Electron. Commer. Res. 2020; 21 (3):144–167. [ Google Scholar ]
• Yadav B.S. The role of social media communication in the branding of educational hubs. IUP Journal of Soft Skills. 2016; 10 (4):51. [ Google Scholar ]
• Yahya S.F.H., Hashim N.A., Bahsri N., Dahari N.A. The effect of sales promotion strategy on online fashion shopping behavior among employee of Sahawan Sdn bhd. Global Bus. Manag. Res. 2019; 11 (2):1–13. [ Google Scholar ]
• Ye Q., Law R., Gu B. The impact of online user reviews on hotel room sales. Int. J. Hospit. Manag. 2009; 28 (1):180–182. [ Google Scholar ]
• Yim M.Y.C., Chu S.C., Sauer P.L. Is augmented reality technology an effective tool for e-commerce? An interactivity and vividness perspective. J. Interact. Market. 2017; 39 :89–103. [ Google Scholar ]
• Yin S. International Conference on Human-Computer Interaction. Springer; C ham: 2020, July. A study on the influence of E-commerce live streaming on consumer’s purchase intentions in mobile internet; pp. 720–732. [ Google Scholar ]
• Yu E., Jung C., Kim H., Jung J. Impact of viewer engagement on gift-giving in live video streaming. Telematics Inf. 2018; 35 (5):1450–1460. [ Google Scholar ]
• Yuen K.F., Wang X., Ma F., Li K.X. The psychological causes of panic buying following a health crisis. Int. J. Environ. Res. Publ. Health. 2020; 17 (10):3513. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Zafar A.U., Qiu J., Li Y., Wang J., Shahzad M. The impact of social media celebrities' posts and contextual interactions on impulse buying in social commerce. Comput. Hum. Behav. 2021; 115 [ Google Scholar ]
• Zhang M., Qin F., Wang G.A., Luo C. The impact of live video streaming on online purchase intention. Serv. Ind. J. 2019; 40 (9–10):656–681. [ Google Scholar ]
• Zhang X.M., Zhu F. Impact of online consumer reviews on sales: the moderating role of product and consumer characteristics. J. Market. 2010; 74 (2):133–148. http://www.hbs.edu/faculty/Pages/item. aspx?num=45146 05 17th 2016. [ Google Scholar ]
• Zhou L., Wang W., Xu J.D., Liu T., Gu J. Perceived information transparency in B2C e-commerce: an empirical investigation. Inf. Manag. 2018; 55 (7):912–927. [ Google Scholar ]