Behavioral Issues in B2C E-commerce

Abstract

B2C e-commerce is one of the fastest growing industries worldwide. A lot of studies were carried out by both scholars and practitioners in order to assess online consumers’ behavioral issues in B2C e-commerce platforms. This paper aims to organize and classify the accumulated literature on B2C e-commerce in order to determine less-researched areas and provide future research directions. For that purpose 208 peer-reviewed articles from 71 journals published between 2005 and 2014 were retrieved and analyzed. The findings of the studies are discussed within the scope of developed framework.

Keywords

B2C e-commerce online shopping Internet shopping consumer behavior

Introduction

Electronic commerce or e-commerce refers to any type of commercial transaction conducted over computer networks such as the Internet. Business-to-Consumer (B2C) is one of the most common types of e-commerce and it has penetrated businesses in many ways. In a B2C e-commerce model, businesses provide goods and services to individual consumers over the Internet. Online shopping, Internet banking and online travel services can be given as examples of the B2C e-commerce model. According to the survey conducted by Pew Research Center [1], online shopping is one of the fastest growing Internet activities. Many consumers prefer online shopping due to several reasons, such as convenience, wide variety of products, easy price comparisons, discounted products etc. More and more businesses also prefer to have online presence since it will enable them to reach diverse customers in different locations who are otherwise impossible to reach through traditional business channels. According to the reports of Internet World Stats [2], the number of Internet users has increased nearly eight-fold from 2000 to 2014 and it continues to increase every year. A continuous increase in the number of worldwide Internet users provides opportunity for e-commerce transactions to expand even larger.

E-commerce can be considered as one of the important and successful implementations of information technology in the business world. Its economic impact is very huge and increasing continuously every passing year. According to eMarketer [3] worldwide B2C e-commerce sales will be 1.7 trillion dollars in 2015 and this figure is estimated to reach 2.4 trillion dollars in 2018. Due to its economic importance, B2C e-commerce related issues have been subject to many researches of both practitioners and scholars. Online consumer-oriented and online vendor-oriented studies were carried out in order to determine critical factors which might increase the rate of online shopping.

The aim of this paper is to organize and classify the accumulated knowledge on B2C e-commerce in order to assess the level of knowledge and developments in this field and provide direction to the future researches. Based on accumulated literature, a classification framework focusing on online consumer and e-vendor related factors was developed and discussed. The rest of this paper is structured as follows. Firstly, methodology of the research is explained along with the classification framework. Secondly, contents of the articles are discussed based on the classification framework. Thirdly, common limitations of the identified articles are presented. Finally, conclusion and future research directions are given.

Research Methodology

It is possible to come across B2C e-commerce articles in various journals in many disciplines such as marketing, management, business, psychology, information technology etc. In order to retrieve relevant articles, the following databases were utilized: Emerald, IEEE Xplore, Sage Journals Online, Science Direct, Springer Link and Wiley Online Library. While conducting the literature search, the following keywords were utilized: “B2C ecommerce”, “online shopping”, “internet shopping”, “internet retailing”, “web shopping” and “consumer behavior”. Only peer-reviewed journals published between 2005 and 2014 were taken into consideration in this study. Full text of each retrieved article was reviewed in order to eliminate the ones that were not related to online consumer’s behavioral issues on B2C e-commerce platforms. Articles that were not included for further research were mainly focused on the technical aspects of B2C e-commerce and its related technologies. After the extensive review process, 208 articles were retained from 71 journals.

The distribution of the articles by year is given in Figure 1. As shown in the figure, there is a growing trend in the number of published articles related to B2C e-commerce. From 2005 to 2014 the number of published articles increased nearly twofold.

Figure 1.

Distribution of articles by year.

The list of journals that published more than three articles related to B2C e-commerce is given in Table 1. Journal of Business Research, Electronic Commerce Research and Applications, Computers in Human Behavior are the top three journals that published approximately one fifth of the retrieved articles.

Table 1.

Journals with more than three articles on B2C e-commerce.

Journal Name	# of Articles	Cumulative #	Cumulative %
Journal of Business Research	18	18	8,7
Electronic Commerce Research and Applications	14	32	15,4
Computers in Human Behavior	11	43	20,7
International Journal of Retail & Distribution Management	11	54	26,0
Journal of Retailing and Consumer Services	10	64	30,8
Electronic Commerce Research	8	72	34,6
Information & Management	8	80	38,5
Electronic Markets	7	87	41,8
International Journal of Information Management	7	94	45,2
Information Systems and e-Business Management	5	99	47,6
Internet Research	5	104	50,0
Journal of Fashion Marketing and Management	4	108	51,9
Decision Support Systems	4	112	53,8
Direct Marketing: An International Journal	3	115	55,3
International Journal of Consumer Studies	4	119	57,2
Journal of Consumer Behaviour	4	123	59,1
Journal of Research in Interactive Marketing	4	127	61,1
Journal of Retailing	4	131	63,0
Journal of Services Marketing	4	135	64,9
Psychology and Marketing	4	139	66,8
Technovation	4	143	68,8

Retrieved articles were classified according to their research focus, aim and perspective. Figure 2 shows the classification framework and the number of the articles in each research area. The purpose of this classification is to show the focus of existing studies, to highlight less researched areas and to give direction to the future studies. Even though this classification is subjective in nature, the content of the articles sufficiently reflects each research area. B2C e-commerce research can be broadly classified as online consumer related and e-vendor related studies. Consumer related studies focus on online consumers’ behavioral issues and online consumer segmentation. In an attempt to understand consumers’ decision-making processes in and attitudes and intentions toward online shopping, behavioral studies take various psychological, cognitive and demographic factors into consideration. E-vendor related studies concentrate on online store features, online shopping tools, online store credibility and reputation. The numbers in each box represent the numbers of published articles related to the given research area. Most of the retrieved articles examined both consumer and e-vendor related issues; therefore, these papers appeared in more than one research area.

Figure 2.

Classification framework of B2C e-commerce studies.

The distribution of the articles by research focus and year is given in Table 2. According to Table 2, while online consumers’ behavioral issues and e-store features are the most commonly researched areas, online shopping tools is the research area that received little attention in the B2C e-commerce domain.

Table 2.

Distribution of articles by focus and year.

Focus area	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	Total
Online consumer behavioral issues	8	9	8	9	8	13	9	10	17	16	107
Online store features	4	6	1	3	10	6	2	9	5	9	55
Online consumer segmentation	2	1	2	2	5	6	4	5	10	8	45
Online Store / Vendor credibility and reputation	0	4	2	3	1	1	0	2	4	3	20
Online shopping tools	0	0	1	1	2	2	3	2	2	2	15

Framework

Online Consumer Related Studies

Online Consumer Behavioral Issues

Articles related to online consumer behavioral issues in B2C e-commerce platforms attempt to determine various critical factors influencing consumers’ attitudes and behavioral intentions toward online shopping. Factors identified under this category includes consumers’ Internet usage and previous online shopping experiences, shopping motivation, personal traits, risk and benefit perceptions, trust perception, subjective norms and perceived behavioral control. The following paragraphs discuss each factor respectively.

Two types of online consumer shopping motivation that are utilitarian and hedonic have been examined quite extensively in the relevant literature [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. While utilitarian shopping motivation is defined as goal oriented and mission critical, hedonic motivation refers to consumers’ shopping behaviors that focus on enjoyment, satisfaction, happiness and sensuality [7]. Utilitarian and hedonic shopping motivation has been found to be positively associated with customer satisfaction and purchase intention [11, 13, 14]. These two motivational factors also affect consumers’ intention to spread positive word-of-mouth information on the web about online retailers [8]. Several studies assessed the influence of emotional factors such as satisfaction [9, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27], enjoyment [9, 28, 29, 12] and loyalty [25, 26, 30, 31] on consumers’ online shopping behavior. Results of the studies showed that consumer satisfaction with the online retailer’s website is positively related to consumer’s loyalty to it [9, 16, 17, 19] and loyalty has been found to increase the actual website buying frequency [30]. However, shopping enjoyment was not found to influence either online store loyalty [9] or online shopping continuance [12, 29].

In the literature, several articles assessed the influence of Internet usage [32, 33, 34] and previous online shopping experience [29, 35, 36, 37, 38, 39] on online consumer shopping behavior. Findings of the studies showed that the amount of consumers’ Internet surfing hours is positively associated with consumers’ online shopping activity [33, 40] and consumers with high Internet experience are more likely to prefer online shopping channels over offline channels [34]. It was also found that consumers who have previous online shopping experience would perceive less risk than those who have never shopped online before [38]. In addition, higher prior online shopping experience leads to higher online purchase intention [35, 36, 37].

The relationship between online shopping behavior and personal traits such as innovativeness, active involvement, agreeableness, conservation, self-enhancement, self-confidence, openness to experience, individualism, collectivism and risk aversion was assessed by several studies [40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]. Active involvement, agreeableness and openness to experience were found to have a significant influence on consumers’ willingness to shop online [42]. Innovativeness in new technology was also found to affect attitude and intention toward online shopping positively [40, 44, 46]. Consumers exhibiting higher levels of horizontal individualism and vertical collectivism are more likely to exhibit higher level of perceptions about the integrity and responsibility of the retailer. Consumers’ perception level, in turn, plays an important role in building loyalty toward online retailer’s services [47]. Zhou et al. [48] found that individualism positively affects not only consumers’ initial trust in an online seller but also their online shopping intention. Furthermore, Yoon [45] showed that the higher the degree of uncertainty avoidance, the lower the effects of trust and perceived usefulness on intention to shop over the Internet.

Consumers’ risk and benefit perceptions were examined in a number of studies by taking their online shopping behavior into consideration. Privacy risk, security risk, financial risk, product delivery risk, product performance risk, psychological risk, social risk and refund risk are among the risk factors whose influence on online shopping behavior were researched quite extensively in the literature [14, 22, 28, 30, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72]. Wang et al. [30] and Lee et al. [70] showed that there is a negative relationship between consumers’ perceived risk and their attitude and loyalty toward retailers’ online store. Hong and Cha [51] found that consumers’ perceived financial, performance, psychological and social risks are negatively related to online shopping intention. Furthermore, Huang et al. [67] showed that consumers with previous online shopping experience perceive less risk toward online shopping than consumers who have never shopped online before. On the other hand, researchers analyzed perceived usefulness, perceived ease of use, online product quality, money-saving, time-saving and effort-saving capabilities of online stores as perceived benefit factors [28, 44, 45, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90]. Some of the findings are as follows. Perceived ease of online shopping use is positively related to perceived usefulness of online shopping [73, 78, 80]. Perceived usefulness, in turn, has a positive impact on consumers’ intention to use online stores [44, 45, 83, 91]. It was also found that product quality and time and effort saving features of online shopping significantly influences consumers’ online shopping intention [28].

Researchers assessed the relationship between consumers’ perceived trust and their online shopping behavior and their study results showed that consumers’ trust in online shopping environment originates from various sources. Transaction security and privacy [23, 92, 93, 94, 95, 96, 97], situational abnormalities [98], information and service quality [26, 91, 99] provided by the online retailer significantly influences consumers’ trust level. It was also found that there is a positive relationship between reputation of the company and consumers’ initial trust in online retailer [89]. Consumers’ trust level, in turn, was found to play an important role in influencing online shopping rate. It was found that trust in online retailers positively and significantly influences consumers’ attitudes and intentions toward shopping in online stores [35, 85, 86, 93, 100, 101, 102, 103, 104]. Furthermore, Kim and Jones [105] found that there is a positive relationship between consumers’ offline brand trust and their intention to shop online. E-trust also leads to positive online word-of-mouth advertising; that is, satisfied consumers tell other online consumers how much they liked particular retailer’s products and services [21].

Several studies assessed the influence of subjective norms [46, 54, 86, 106, 107, 108, 109] and perceived behavioral control [46, 54, 107, 109, 110] on consumers’ willingness to shop online. Subjective norm is defined as consumers’ perceptions about online shopping which is mainly influenced by the judgment of parents, friends, spouse, news and media. Subjective norm comes out from two underlying factors that are the normative beliefs the consumer relates with significant referents and the motivation to act according to these referents’ thoughts. Findings of the studies showed that there is a positive and significant relationship between subjective norms and online consumer shopping intention [46, 54, 108]. On the other hand, perceived behavioral control refers to individual’s perceptions of their ability to perform a particular behavior. Studies conducted in Thailand by Laohapensang [107] and in Sweden by Hansen [109] showed that perceived behavioral control on Internet shopping positively influences consumers’ online shopping intention. However, studies conducted in Spain did not find any significant relationship between these factors [46, 54]. These contradictory results might be due to cultural factors. A few studies showed that factors influencing consumers’ intention to shop online varies depending on the nationality of the consumer [130, 132].

Online Consumer Segmentation

Studies classified under this category attempt to segment online consumers based on their demographic, psychographic and behavioral characteristics in order to help online retailers to tailor their products and services according to each segments needs and requirements. The goal of these studies is to show that online consumers are not composed of a single market; instead they are composed of heterogonous groups whose members have different needs and expectations and respond differently to the marketing efforts.

Segmenting and assessing the behavior of online consumers according to their demographic characteristics is one the most commonly followed strategies in the literature. Age, gender, marital status, income and occupation are some of the characteristics upon which grouping is carried out. The influence of consumers’ age [33, 54, 84, 108, 111, 112, 113, 114, 115, 116, 117], gender [11, 13, 22, 23, 33, 54, 84, 96, 108, 111, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,126], marital status [33, 84, 108], income [84, 108, 112, 115, 117, 127], education [54, 84, 108, 112], occupation [54, 108, 115] and culture [26, 45, 128, 129, 130, 131, 132, 133] on online shopping behavior have been examined by many studies. Some findings from the selected articles are as follows. Gong et al. [84], Clemes et al. [108] and Bhatnagar [115] found that the younger the consumers are, the more likely that they will shop online. Individual’s gender also was found to influence online shopping intention significantly [33, 108, 122]. When it comes to marital status, while the study conducted in China by Clemes et al. [108] indicated that single consumers are more likely to shop online, the study conducted in Israel by Liebermann and Stashevsky [33] found no significant relationship between marital status and online shopping rate. These contradictory results show that caution should be exercised while interpreting research findings to different cultural contexts. Study findings of Gong et al. [84] and Bhatnagar [115] showed that the frequency of online shopping is positively related to the income level of the consumer. It was also found that there is a positive relationship between consumer’s education level and online shopping adoption [54, 84, 108]. While Clemes et al. [108] showed that occupation has a positive impact on online shopping adoption, Crespo and Bosque [54] did not find any significant relationship between them. Several other studies found that factors influencing consumers’ intention to shop online vary depending on the nationality of the consumer [130, 132, 133].

Segmentation according to psychographic and behavioral characteristics considers factors such as online consumers’ lifestyle, attitudes, expectations, shopping activities, shopping motivation and orientation. By using e-store attribute importance and online shopping motivation measures, Ganesh et al. [134] found six and seven different online consumer segments respectively. In their study Pradas et al. [135] classified non-shoppers based on the barriers which discourage them to shop online and the drivers which might encourage them to make online purchases. The results of the study gave four different non-shopper segments based on the barriers for online shopping and six different segments based on the drivers to start purchasing on the Internet. By using factors such as web-usage-related lifestyle, themes of Internet usage, demographics and Internet attitude, Brengman et al. [136] determined four online shopper segments (tentative shoppers, suspicious learners, shopping lovers, and business users) and four non-shopper segments (fearful browsers, positive technology muddlers, negative technology muddlers, and adventurous browsers). In their study Liu et al. [137] determined six different online consumer typologies by using real online retail transaction data. These segments are economical purchasers, active-star purchasers, direct purchasers, high-loyalty purchasers, risk-averse purchasers and credibility-first purchasers. Later, they assessed each online consumer group’s sensitivity to three different promotion strategies, which are discount, advertising and word-of-mouth. Results of the study showed that each consumer group responds differently to three different promotion strategies. Other studies determined different online consumer segments based on consumer characteristics [47], shopping motivation [138], shopping orientation [139], consumers’ underlying cognitive style and involvement [140], Internet usage pattern [141], computer expertise and preference structure (i.e., e-store attributes and features) [142] and decision making style [143]. Results of these studies showed that each of the determined consumer groups have different expectations, attitudes and behavioral intentions toward online shopping.

Online Vendor / Store Related Studies

Online Store Features

Studies classified under this category attempt to assess the relationship between online store features (i.e., design, functionality and content) and online consumer shopping behavior.

Studies that focus on online store design factors evaluate online stores’ general design and visual aspects [21, 77, 99, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154], ambience and atmosphere [155, 156, 157] and customer interface features [17, 153,158]. Liao et al. [77] showed that the appearance of an online store positively affects the consumers’ perceptions about online stores' usefulness. It was found that the better consumers perceive online store design to be, the more they get enjoyment and satisfaction [21, 147, 150]. Furthermore, Mummalaneni [155] showed that online store ambient factors positively and significantly influence consumers’ pleasure and arousal levels. Background music tempo within online stores was also found to influence arousal level of consumers positively [156].

Studies that focus on online store functionality evaluate online stores from the perspective of convenience [17, 21, 108, 144, 150, 154, 159, 160], customization [17, 161, 162, 163, 164], technical adequacy [77, 144, 165], usability [166, 167, 168], interactivity [12, 17, 69, 169, 170], fulfillment [87, 171], efficiency [171], complexity [172], navigation [147] and transaction speed [150, 160] and education and entertainment-focused in-store events [173]. It was found that when consumers find the online transaction completion process as inconvenient, they are more likely to abandon the online shopping cart [160]. Online stores that exhibit a high degree of convenience in terms of transaction and search processing positively influence consumers’ online shopping intention and loyalty [17, 159]. Customization was found to positively influence online consumers’ satisfaction, loyalty and behavioral intentions toward online shopping [17, 161]. Customization also makes consumers’ positive emotions stronger in retailers’ online stores [164]. Websites with a high degree of interactivity not only positively influence consumers’ satisfaction and loyalty toward online retailers [17], but also lead to forming initial trust in online retailers [69]. No significant relationship was found between rapid response time in online stores and online consumer satisfaction level [150]. Duration of waiting time to finalize a transaction in online stores was not found to influence the rate of consumers shopping cart abandonment [160].

Studies that focus on online store content assess online stores in terms of general content quality [25, 26, 77, 99, 102, 150, 170, 174, 175, 176, 177, 178, 179, 180], content quantity [178, 181, 182, 183, 184], content presentation [175, 184, 185], informativeness [21, 144, 186, 187] and product variety [108, 150]. The content quality of an online store positively influences consumer’s perceptions about its usefulness [77]. Higher levels of information quality were found to increase consumers’ satisfaction levels and loyalty intentions toward online retailers [150, 174]. It was also found that the higher perceived information quality is, the more likely consumers will choose that online store to shop among competing websites [175]. The degree of perceived information overload increases with the amount of information presented to consumers within online store [181]. An increase in information load leads to a reduction in the consumers’ product consideration set sizes, which are the products consumers seriously consider for purchasing [183]. It was also found that product variety within online stores positively influences consumers’ satisfaction levels and their online shopping adoption [108, 150]. However, information presentation quality was not found to influence consumers’ likelihood to choose a particular online store among competing alternatives [175].

Online Store Credibility and Reputation

Studies classified under this category assess the influence of perceived online store credibility and online seller reputation on consumers’ attitudes and behavioral intentions toward online shopping. Credibility of the online stores was assessed from various aspects such as existence of sound privacy policies [95, 188, 189, 190,191, 192, 193], effective security measures [94, 95, 97, 191,192, 194,195], reasonable delivery services [196] and fair return policies [94, 195, 196, 197]. A number of studies investigated the relationship between online consumers’ attitudes toward making purchases over Internet and the reputation of an online retailer [89, 99, 190, 198, 199, 200, 201, 202].

If consumers do not perceive online stores credibly due to privacy and security issues they are more likely to buy online cart contents from physical stores [191]. Findings of the studies showed that privacy policies and security measures within online stores positively influence perceived trustworthiness of online stores [95, 188]. Perceived fairness of the return policy is positively associated with both consumers’ trust in online retailers and their intention to make purchases from their online stores [197]. It was also found that free delivery and return policies increase the number of order made over online stores [196]. The study conducted in Singapore by Xie et al. [190] showed that online consumers tend to provide accurate personal information in the presence of privacy notices within online stores. Even though they found positive relationship between the presence of privacy notices within online stores and consumers’ intention to provide accurate personal information, they stressed that the nature of this relationship may vary according to the political, economic, legal and cultural contexts [190].

It was found that reputation of the company or online store is positively associated with consumers’ provision of accurate personal information [190]. While Wang and Lang [89] found that company’s reputation affects consumers’ initial trust belief toward online seller, Utz et al. [198] did not find any significant relationship between these factors. Kim and Lennon [199] showed that when online sellers’ reputation is high, consumers’ hold more positive emotions and less perceived risk toward online sellers.

Online Shopping Tools

Studies classified under this category attempt to assess the influence of online shopping tools on online consumer shopping behavior. Shopping tools assessed by researchers includes recommender agents [16, 18, 203, 204, 205], avatars [206], image interactive technologies [70, 207, 208], social presence tools [209, 210], search tools [181, 182] and communication tools such as online consumer reviews [211]. Recommender agents are information filtering technology which provides high-quality product recommendations for the online consumers. There are several types of recommender agents, namely, collaborative filtering, knowledge-based, content-based, demographic, hybrid, etc. Yoon et al. [16] and Hostler et al. [204] showed that the use of collaborative filtering systems positively influences product recommendation quality, product promotion effectiveness and product search effectiveness. Recommendation quality and product promotion effectiveness, in turn, have a positive effect on consumers’ satisfaction with their shopping experiences at retailers' online stores. Furthermore, Lin and Chuan [170] found that knowledge-based recommender agents improve the decision making process by reducing decision making duration and effort in online stores. Keeling et al. [206] utilized avatars (i.e., interactive animated characters) to play the social role of a sales assistant. Their study tested whether avatars with social- or task-oriented communication styles contribute to consumer trust and patronage intentions. Results of the study showed that both types of communication styles directly and positively related to consumers trust and patronage intention toward online merchants’ websites. Bae and Lee [211] showed that online consumer reviews can be utilized as a powerful communication tool to reduce consumers’ uncertainty and perceived risk of buying products from online retailers.

Common Limitations of Existing Studies

The most commonly encountered limitations in the retrieved articles are given in Table 3. Top limitations are as follows. A large majority of the studies utilized convenience sampling method (96.15%) and cross-sectional study design (95.19%). That is, participants of these studies were selected based on their ease of access and these studies were carried out over a short period. A number of studies were conducted in a single country context (94.71%); therefore, caution is required while generalizing the results of these studies to other cultural contexts. A great deal of studies utilized self-report questionnaires (94.23%) to gather research data. Due to its nature, self-report measures might not reflect consumers’ actual behaviors. A number of studies utilized university students as study participants (45.19%). Student samples have generalizability issues; that is, they have limited power to represent the population of interest. A lot of studies utilized online surveys for data collection (27.88%) which is limited in terms of sampling and respondent availability.

Table 3.

Common limitations of retrieved articles.

Limitation	Frequency	Percentage
Convenience sampling method	200	96.15%
Cross-sectional study	198	95.19%
Single country context	197	94.71%
Self-reports	196	94.23%
Student participants	94	45.19%
Online survey	58	27.88%
Simulated shopping task on a fictitious website	38	18.27%
Participants with online shopping experience	30	14.42%
Focus on certain type of product(s)	19	9.13%
Hypothetical buying scenario on real website	9	4.33%
Small sample size	8	3.85%
Focus on certain type of website(s)	5	2.40%

Conclusion

Literature review showed that online consumer segmentation based on psychographic and behavioral characteristics is one of the areas that need further investigation. Fewer studies carried out such segmentation analysis and their results showed online consumers are not a single market segment; rather they are composed of heterogeneous groups whose members have different needs and expectations. Results of these studies have invaluable business implications. Companies with online presence can utilize segmentation strategies in order to improve their marketing efforts for each online customer group.

Online shopping tools are another research area which has received little attention in the relevant literature. Conducted studies showed that online shopping tools such as recommender agents and avatars play an important role in developing consumers’ initial trust in online vendors by playing the role of sales assistants in psychical stores. Online retailers are recommended to integrate various online shopping tools into their web-based stores in order to facilitate consumer decision making process.

Literature review also showed that a great many studies utilize consumer self-reports to predict their shopping behavior. Self-report data have several potential limitations such as overestimation and inaccurate declarations. Future studies are recommended to carry out direct observation of the consumers’ actual behavior in order to make more accurate inferences about their online shopping behavior.

References

Purcell

(2011) Search and email still top the list of most popular online activities. Available at: http://www.pewinternet.org/2011/08/09/search-and-email-still-top-the-list-of-most-popular-online-activities/ (accessed 6 February 2015)

Internet World Stats (2014) Internet Usage Statistics. Available at: http://www.internetworldstats.com/stats.htm (accessed 1 January 2014)

eMarketer (2014) Worldwide ecommerce sales to increase nearly 20% in 2014. Available at: http://www.emarketer.com/Article/Worldwide-Ecommerce-Sales-Increase-Nearly-20-2014/1011039 (accessed 6 February 2014)

Overby

Lee

(2006) The effects of utilitarian and hedonic online shopping value on consumer preference and intentions. Journal of Business Research 59: 1160–1166.

Kinney

(2010) Beyond buying: Motivations behind consumers’ online shopping cart use. Journal of Business Research 63: 986–992.

Bridges

Florsheim

(2008) Hedonic and utilitarian shopping goals: The online experience. Journal of Business Research 61: 309–314.

Liao

Lin

(2007) Shopping motivations on Internet: A study based on utilitarian and hedonic value. Technovation 27: 774–787.

Scarpi

Pizzi

Visentin

(2014) Shopping for fun or shopping to buy: Is it different online and offline? Journal of Retailing and Consumer Services 21: 258–267.

Shun

Yunjie

(2006) Effects of outcome, process and shopping enjoyment on online consumer behaviour. Electronic Commerce Research and Applications 5: 272–281.

10.

López

Ruiz

(2011) Explaining website effectiveness: The hedonic–utilitarian dual mediation hypothesis. Electronic Commerce Research and Applications 10: 49–58.

11.

Kim

Galliers

Shin

Ryoo

Kim

(2012) Factors influencing Internet shopping value and customer repurchase intention. Electronic Commerce Research and Applications 11: 374–387.

12.

Lee

Fiore

Kim

(2006) The role of the technology acceptance model in explaining effects of image interactivity technology on consumer responses. International Journal of Retail & Distribution Management 34(8): 621–644.

13.

Davis

Lang

Diego

(2014) How gender affects the relationship between hedonic shopping motivation and purchase intentions? Journal of Consumer Behaviour 13: 18–30.

14.

Chiu

Wang

ETG

Fang

Huang

(2014) Understanding customers’ repeat purchase intentions in B2C e-commerce: The roles of utilitarian value, hedonic value and perceived risk. Information Systems Journal 24: 85–114.

15.

Park

Cho

Rao

(2012) The effect of pre- and post-service performance on consumer evaluation of online retailers. Decision Support Systems 52: 415–426.

16.

Yoon

Hostler

Guo

Guimaraes

(2013) Assessing the moderating effect of consumer product knowledge and online shopping experience on using recommendation agents for customer loyalty. Decision Support Systems 55: 883–893.

17.

Chang

Chen

(2008) The impact of customer interface quality, satisfaction and switching costs on e-loyalty: Internet experience as a moderator. Computers in Human Behavior 24: 2927–2944.

18.

Hostler

Yoon

Guimaraes

(2012) Recommendation agent impact on consumer online shopping: The Movie Magic case study. Expert Systems with Applications 39: 2989–2999.

19.

Pontevia

AFA

N’Goala

Poncin

(2013) A good deal online: The Impacts of acquisition and transaction value on e-satisfaction and e-loyalty. Journal of Retailing and Consumer Services 20: 445–452.

20.

Gounaris

Dimitriadis

Stathakopoulos

(2010) An examination of the effects of service quality and satisfaction on customers’ behavioral intentions in e-shopping. Journal of Services Marketing 24(2): 142–156.

21.

Chung

Shin

(2010) The antecedents and consequents of relationship quality in internet shopping. Asia Pacific Journal of Marketing and Logistics 22(4): 473–491.

22.

Roman

(2010) Relational consequences of perceived deception in online shopping: The moderating roles of type of product, consumer’s attitude toward the Internet and consumer’s demographics. Journal of Business Ethics 95: 373–391.

23.

Martín

Jiménez

(2011) Online buying perceptions in Spain: Can gender make a difference? Electronic Markets 21: 267–281.

24.

Chou

Hsu

(2015) Understanding online repurchase intention: Social exchange theory and shopping habit. Information Systems and E-Business Management. DOI:10.1007/s10257-015-0272-9.

25.

Chen

Cheng

(2013) How online and offline behavior processes affect each other: Customer behavior in a cyber-enhanced bookstore. Quality & Quantity Journal 47: 2539–2555.

26.

Chen

Yen

Pornpriphet

Widjaja

(2014) E-commerce web site loyalty: A cross cultural comparison. Information Systems Frontiers. DOI: 10.1007/s10796-014-9499-0.

27.

Perks

(2005) Effects of consumer perceptions of brand experience on the web: Brand familiarity, satisfaction and brand trust. Journal of Consumer Behaviour 4(6): 438–452.

28.

Broekhuizen

Huizingh

EKRE

(2009) Online purchase determinants Is their effect moderated by direct experience? Management Research News 32(5): 440–457

29.

Liu

Forsythe

(2010) Post-adoption online shopping continuance. International Journal of Retail & Distribution Management 38(2): 97–114.

30.

Wang

Pallister

Foxall

(2006) Innovativeness and involvement as determinants of website loyalty: II. Determinants of consumer loyalty in B2C e-commerce. Technovation 26: 1366–1373.

31.

Doong

Wang

Foxall

(2011) An investigation of consumers’ webstore shopping: A view of click-and-mortar company. International Journal of Information Management 31: 210–216.

32.

Suki

(2013) Consumer shopping behaviour on the Internet: Insights from Malaysia. Electronic Commerce Research 13:477–491.

33.

Liebermann

Stashevsky

(2009) Determinants of online shopping: Examination of an early-stage online market. Canadian Journal of Administrative Sciences 26(4): 316–331.

34.

Frambach

Roest

HCA

Krishnan

(2007) The impact of consumer internet experience on channel preference and usage intentions across the different stages of the buying process. Journal of Interactive Marketing 21(2): 26–41.

35.

Thamizhvanan

Xavier

(2013) Determinants of customers’ online purchase intention: An empirical study in India. Journal of Indian Business Research 5(1): 17–32.

36.

Riley

Scarpi

Manaresi

(2009) Purchasing services online: A two-country generalization of possible influences. Journal of Services Marketing 23(2): 93–103.

37.

Spake

Finney

Joseph

(2011) Experience, comfort, and privacy concerns: Antecedents of online spending. Journal of Research in Interactive Marketing 5(1): 5–28.

38.

Park

Stoel

(2005) Effect of brand familiarity, experience and information on online apparel purchase. International Journal of Retail & Distribution Management 33(2): 148–160.

39.

Jeffrey

Hodge

(2007) Factors influencing impulse buying during an online purchase. Electronic Commerce Research 7: 367–379.

40.

Blake

Neuendorf

Valdiserri

(2005) Tailoring new websites to appeal to those most likely to shop online. Technovation 25: 1205–1214.

41.

Riquelme

Roman

(2014) The relationships among consumers’ ethical ideology, risk aversion and ethically-based distrust of online retailers and the moderating role of consumers’ need for personal interaction. Ethics and Information Technology 16: 135–155.

42.

Bosnjak

Galesic

Tuten

(2007) Personality determinants of online shopping: Explaining online purchase intentions using a hierarchical approach. Journal of Business Research 60: 597–605.

43.

Xia

(2010) An examination of consumer browsing behaviors. Qualitative Market Research: An International Journal 13(2): 154–173.

44.

Alcaniz

Mafe

Manzano

Sanz-Blas

(2008) Influence of online shopping information dependency and innovativeness on internet shopping adoption. Online Information Review 32(5): 648–667.

45.

Yoon

(2009) The effects of national culture values on consumer acceptance of e-commerce: Online shoppers in China. Information & Management 46: 294–301.

46.

Crespo

Bosque

(2008) The effect of innovativeness on the adoption of B2C e-commerce: A model based on the Theory of Planned Behaviour. Computers in Human Behavior 24: 2830–2847.

47.

Chang

(2013) Online shoppers’ perceptions of e-retailers’ ethics, cultural orientation, and loyalty: An exploratory study in Taiwan. Internet Research 23(1): 47–68.

48.

Zhou

Wang

(2014) Examining online consumers’ initial trust building from an elaboration likelihood model perspective. Information Systems Frontiers. DOI: 10.1007/s10796-5.

49.

Hsu

Chang

Chen

(2012) Flow experience and Internet shopping behavior: Investigating the moderating effect of consumer characteristics. Systems Research and Behavioral Science 29: 317–332.

50.

Cai

Liu

(2011) Online shopping among Chinese consumers: An exploratory investigation of demographics and value orientation. International Journal of Consumer Studies 35: 458–469.

51.

Hong

Cha

(2013) The mediating role of consumer trust in an online merchant in predicting purchase intention. International Journal of Information Management 33: 927–939.

52.

Lian

Lin

(2008) Effects of consumer characteristics on their acceptance of online shopping: Comparisons among different product types. Computers in Human Behavior 24: 48–65.

53.

Lian

Yen

(2013) To buy or not to buy experience goods online: Perspective of innovation adoption barriers. Computers in Human Behavior 29: 665–672.

54.

Crespo

Bosque

(2010) The influence of the commercial features of the Internet on the adoption of e-commerce by consumers. Electronic Commerce Research and Applications 9: 562–575.

55.

Liang

Lee

Tung

(2014) The role of sunk costs in online consumer decision-making. Electronic Commerce Research and Applications 13: 56–68.

56.

Liao

Keng

(2013) Online shopping delivery delay: Finding a psychological recovery strategy by online consumer experiences. Computers in Human Behavior 29: 1849–1861.

57.

Soopramanien

DGR

Robertson

(2007) Adoption and usage of online shopping: An empirical analysis of the characteristics of ‘‘buyers’’ ‘‘browsers’’ and ‘‘non-internet shoppers’’. Journal of Retailing and Consumer Services 14: 73–82.

58.

Teo

TSH

(2005) Online buying behavior: A transaction cost economics perspective. Omega 33: 451–465.

59.

Liu

Brock

Shi

Chu

Tseng

(2013) Perceived benefits, perceived risk, and trust: Influences on consumers’ group buying behaviour. Asia Pacific Journal of Marketing and Logistics 25(2): 225–248.

60.

Zhang

Liu

(2011) Effects of influential factors on consumer perceptions of uncertainty for online shopping. Nankai Business Review International 2(2): 158–171.

61.

Korgaonkar

Karson

(2007) The influence of perceived product risk on consumers’ e-tailer shopping preference. Journal of Business and Psychology 22: 55–64.

62.

Keating

Quazi

Kriz

(2009) Financial risk and its impact on new purchasing behavior in the online retail setting. Electronic Markets 19: 237–250.

63.

Buttner

Goritz

(2008) Perceived trustworthiness of online shops. Journal of Consumer Behaviour 7: 35–50.

64.

Forsythe

Liu

Shannon

Gardner

(2006) Development of a scale to measure the perceived benefits and risks of online shopping. Journal of Interactive Marketing 20(2): 55–75.

65.

Soopramanien

(2011) Conflicting attitudes and scepticism towards online shopping: The role of experience. International Journal of Consumer Studies 35: 338–347.

66.

Punj

(2012) Consumer decision making on the web: A theoretical analysis and research guidelines. Psychology and Marketing 29(10): 791–803.

67.

Huang

Schrank

Dubinsky

(2006) Effect of brand name on consumers’ risk perceptions of online shopping. Journal of Consumer Behaviour 4(1): 40–50.

68.

Stern

Royne

Stafford

Bienstock

(2008) Consumer acceptance of online auctions: An extension and revision of the TAM. Psychology & Marketing 25(7): 619–636.

69.

(2010) Effects of perceived interactivity, perceived web assurance and disposition to trust on initial online trust. Journal of Computer-Mediated Communication 16: 1–26.

70.

Lee

Kim

Fiore

(2010) Affective and cognitive online shopping experience: Effects of image interactivity technology and experimenting with appearance. International Textile & Apparel Association 28(2): 140–154.

71.

Lee

Damhorst

(2012) Exploring multidimensions of product performance risk in the online apparel shopping context: Visual, tactile, and trial risks. Clothing and Textiles Research Journal 30(4): 251–266.

72.

Huseynov

Yıldırım

(2014) Internet users’ attitudes toward business-to-consumer online shopping: A survey. Information Development. DOI: 10.1177/0266666914554812.

73.

Stoel

(2009) Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research 62: 565–571.

74.

Chen

Cheng

(2014) Perceived value, transaction cost, and repurchase-intention in online shopping: A relational exchange perspective. Journal of Business Research 67: 2768–2776.

75.

Andrews

Bianchi

(2013) Consumer internet purchasing behavior in Chile. Journal of Business Research 66: 1791–1799.

76.

Pozzi

(2013) E-commerce as a stockpiling technology: Implications for consumer savings. International Journal of Industrial Organization 31: 677–689.

77.

Liao

Palvia

Lin

(2006) The roles of habit and web site quality in e-commerce. International Journal of Information Management 26: 469–483.

78.

Shang

Chen

Shen

(2005) Extrinsic versus intrinsic motivations for consumers to shop on-line. Information & Management 42: 401–413.

79.

Lee

MKO

Shi

Cheung

CMK

Lim

Sia

(2011) Consumer’s decision to shop online: The moderating role of positive informational social influence. Information & Management 48: 185–191.

80.

Lin

(2007) Predicting consumer intentions to shop online: An empirical test of competing theories. Electronic Commerce Research and Applications 6: 433–442.

81.

Mosteller

Donthu

Eroglu

(2014) The fluent online shopping experience. Journal of Business Research 67: 2486–2493.

82.

Ramus

Nielsen

(2005) Online grocery retailing: What do consumers think? Internet Research 15(3): 335–352.

83.

Suki

Ramayah

Suki

(2008) Internet shopping acceptance: Examining the influence of intrinsic versus extrinsic motivations. Direct Marketing: An International Journal 2(2): 97–110.

84.

Gong

Stump

Maddox

(2013) Factors influencing consumers’ online shopping in China. Journal of Asia Business Studies 7(3): 214–230.

85.

Benamati

Fuller

Serva

Baroudi

(2010) Clarifying the integration of trust and TAM in e-commerce environments: Implications for systems design and management. IEEE Transactions on Engineering Management 57(3): 380–393.

86.

Holsapple

Sasidharan

(2005) The dynamics of trust in B2C e-commerce: A research model and agenda. Information Systems and E-Business Management 3: 377–403.

87.

Liao

Palvia

Lin

(2010) Stage antecedents of consumer online buying behavior. Electronic Markets 20: 53–65.

88.

Kim

(2012) An investigation of the effect of online consumer trust on expectation, satisfaction, and post-expectation. Information Systems and E-Business Management 10: 219–240.

89.

Kim

(2012) An empirical study on consumer first purchase intention in online shopping: Integrating initial trust and TAM. Electronic Commerce Research 12: 125–150.

90.

Capece

Calabrese

Pillo

Costa

Crisciotti

(2013) The impact of national culture on e-commerce acceptance: The Italian case. Knowledge and Process Management 20(2): 102–112.

91.

Chen

Teng

(2013) A comprehensive model of the effects of online store image on purchase intention in an e-commerce environment. Electronic Commerce Research 13: 1–23.

92.

Flavian

Guinaliu

(2006) Consumer trust, perceived security and privacy policy: Three basic elements of loyalty to a web site. Industrial Management & Data Systems 100(5): 601–620.

93.

Mukherjee

Nath

(2007) Role of electronic trust in online retailing: A re-examination of the commitment-trust theory. European Journal of Marketing 41(9/10): 1173–1202.

94.

Sahney

Ghosh

Shrivastava

(2013) Conceptualizing consumer ‘‘trust’’ in online buying behaviour: An empirical inquiry and model development in Indian context. Journal of Asia Business Studies 7(3): 278–298.

95.

Riquelme

Roman

(2014) Is the influence of privacy and security on online trust the same for all type of consumers? Electronic Markets 24: 135–149.

96.

Kim

Lee

Chung

(2013) Investigating the role of trust and gender in online tourism shopping in South Korea. Journal of Hospitality & Tourism Research 37(3): 377–401.

97.

Jiang

Jones

Javie

(2008) How third-party certification programs relate to consumer trust in online transactions: An exploratory study. Psychology & Marketing 25(9): 839–858.

98.

Moody

Galletta

Lowry

(2014) When trust and distrust collide online: The engenderment and role of consumer ambivalence in online consumer behavior. Electronic Commerce Research and Applications 13: 266–282.

99.

Bartikowski

Singh

(2014) Doing e-business in France: Drivers of online trust in business-to-consumer websites. Global Business and Organizational Excellence: 28–36.

100.

McCole

Ramsey

Williams

(2010) Trust considerations on attitudes towards online purchasing: The moderating effect of privacy and security concerns. Journal of Business Research 63: 1018–1024.

101.

Kim

Gupta

(2012) Which is more important in Internet shopping, perceived price or trust? Electronic Commerce Research and Applications 11: 241–252.

102.

Gregg

Walczak

(2010) The relationship between website quality, trust and price premiums at online auctions. Electronic Commerce Research 10: 1–25.

103.

Becerra

Korgaonkar

(2011) Effects of trust beliefs on consumers’ online intentions. European Journal of Marketing 45(6): 936–962.

104.

Limbu

Wolf

Lunsford

(2012) Perceived ethics of online retailers and consumer behavioral intentions: The mediating roles of trust and attitude. Journal of Research in Interactive Marketing 6(2): 133–154.

105.

Kim

Jones

(2009) Online shopping and moderating role of offline brand trust. Direct Marketing: An International Journal 3(4): 282–300.

106.

Lueg

Ponder

Beatty

Capella

(2006) Teenagers’ use of alternative shopping channels: A consumer socialization perspective. Journal of Retailing 82(2): 137–153.

107.

Laohapensang

(2009) Factors influencing internet shopping behaviour: A survey of consumers in Thailand. Journal of Fashion Marketing and Management 13(4): 501–513.

108.

Clemes

Gan

Zhang

(2014) An empirical analysis of online shopping adoption in Beijing, China. Journal of Retailing and Consumer Services 21: 364–375.

109.

Hansen

(2008) Consumer values, the theory of planned behaviour and online grocery shopping. International Journal of Consumer Studies 32: 128–137.

110.

Kim

Park

(2005) A consumer shopping channel extension model: Attitude shift toward the online store. Journal of Fashion Marketing and Management 9(1): 106–121.

111.

Lian

Yen

(2014) Online shopping drivers and barriers for older adults: Age and gender differences. Computers in Human Behavior 37: 133–143.

112.

Punj

(2011) Effect of consumer beliefs on online purchase behavior: The influence of demographic characteristics and consumption values. Journal of Interactive Marketing 25: 134–144.

113.

Sorce

Perotti

Widrick

(2005) Attitude and age differences in online buying. International Journal of Retail & Distribution Management 33(2): 122–132.

114.

Kwon

Noh

(2010) The influence of prior experience and age on mature consumers’ perceptions and intentions of internet apparel shopping. Journal of Fashion Marketing and Management 14(3): 335–349.

115.

Bhatnagar

(2007) Do determinants of online shopping differ for personal shoppers and professional shoppers? EuroMed Journal of Business 2(1): 87–102.

116.

Wan

Nakayama

Sutcliffe

(2012) The impact of age and shopping experiences on the classification of search, experience, and credence goods in online shopping. Information Systems and E-Business Management 10: 135–148

117.

Norum

(2008) Student Internet purchases. Family and Consumer Sciences Research Journal 36(4): 373–388.

118.

Zhang

KZK

Cheung

CMK

Lee

MKO

(2014) Examining the moderating effect of inconsistent reviews and its gender differences on consumers’ online shopping decision. International Journal of Information Management 34: 89–98.

119.

Hasan

(2010) Exploring gender differences in online shopping attitude. Computers in Human Behavior 26: 597–601.

120.

Chen

Yan

Fan

Gordon

(2015) The joint moderating role of trust propensity and gender on consumers’ online shopping behavior. Computers in Human Behavior 43: 272–283.

121.

Liu

Lin

Lee

Deng

(2013) On gender differences in consumer behavior for online financial transaction of cosmetics. Mathematical and Computer Modelling 58: 238–253.

122.

Jayawardhena

Wright

Dennis

(2007) Consumers online: Intentions, orientations and segmentation. International Journal of Retail & Distribution Management 35(6): 515–526.

123.

Muthaly

(2009) Four alternative models of online purchase behavior in the Asia-Pacific Region: A lesson of gender difference from South Korea. Asia Pacific Journal of Marketing and Logistics 21(2): 267–279.

124.

Prendergast

Lam

(2013) An evolutionary explanation for shopping behavior. Journal of Consumer Marketing 30(4): 366–370.

125.

Ulbrich

Christensen

Stankus

(2011) Gender-specific on-line shopping preferences. Electronic Commerce Research 11: 181–199.

126.

Handa

Gupta

(2009) Gender influence on the innovativeness of young urban Indian online shoppers. VISION-The Journal of Business Perspective 13(2): 25–32.

127.

Punj

(2012) Income effects on relative importance of two online purchase goals: Saving time versus saving money? Journal of Business Research 65: 634–640.

128.

Hwang

Lee

(2012) Investigating the moderating role of uncertainty avoidance cultural values on multidimensional online trust. Information & Management 49: 171–176.

129.

Davis

Wang

Lindridge

(2008) Culture influences on emotional responses to on-line store atmospheric cues. Journal of Business Research 61: 806–812.

130.

Smith

Deitz

Royne

Hansen

Grünhagen

Witte

(2013) Cross-cultural examination of online shopping behavior: A comparison of Norway, Germany, and the United States. Journal of Business Research 66: 328–335.

131.

Park

Lee

(2009) Antecedents of online reviews’ usage and purchase influence: An empirical comparison of U.S. and Korean consumers. Journal of Interactive Marketing 23: 332–340.

132.

Kim

Yang

Kim

(2013) Online retailer reputation and consumer response: Examining cross cultural differences. International Journal of Retail & Distribution Management 41(9): 688–705.

133.

Mee

Huei

(2015) A profile of the Internet shoppers: Evidence from nine countries. Telematics and Informatics 32: 344–354.

134.

Ganesh

Reynolds

Luckett

Pomirleanu

(2010) Online shopper motivations, and e-store attributes: An examination of online patronage behavior and shopper typologies. Journal of Retailing 86(1): 106–115.

135.

Pradas

Miguel

García

ÁH

Peláez

(2013) Barriers and drivers for non-shoppers in B2C e-commerce: A latent class exploratory analysis. Computers in Human Behavior 29: 314–322.

136.

Brengman

Geuens

Weijters

Smith

Swinyard

(2005) Segmenting Internet shoppers based on their Web-usage-related lifestyle: A cross-cultural validation. Journal of Business Research 58: 79– 88.

137.

Liu

Peng

Zhang

(2013) Online purchaser segmentation and promotion strategy selection: Evidence from Chinese E-commerce market. Annals of Operations Research. DOI: 10.1007/s10479-Z.

138.

Hill

Beatty

Walsh

(2013) A segmentation of adolescent online users and shoppers. Journal of Services Marketing 27(5): 347–360.

139.

Gehrt

Rajan

Shainesh

Czerwinski

O’Brien

(2012) Emergence of online shopping in India: Shopping orientation segments. International Journal of Retail & Distribution Management 40(10): 742–758.

140.

Wang

Pallister

Foxall

(2006) Innovativeness and involvement as determinants of website loyalty: I. A test of the style/involvement model in the context of Internet buying. Technovation 26: 1357–1365.

141.

Aljukhadar

Senecal

(2011) Segmenting the online consumer market. Marketing Intelligence & Planning 29(4): 421–435.

142.

Chen

Hsu

Lin

(2010) Website attributes that increase consumer purchase intention: A conjoint analysis. Journal of Business Research 63: 1007–1014.

143.

Rezaei

(2015) Segmenting consumer decision-making styles (CDMS) toward marketing practice: A partial least squares (PLS) path modeling approach. Journal of Retailing and Consumer Services 22: 1–15.

144.

Shin

Chung

Lee

(2013) The effect of site quality on repurchase intention in Internet shopping through mediating variables: The case of university students in South Korea. International Journal of Information Management 33: 453–463.

145.

Ethier

Hadaya

Talbot

Cadieux

(2006) B2C web site quality and emotions during online shopping episodes: An empirical study. Information & Management 43: 627–639.

146.

Koo

(2006) The fundamental reasons of e-consumers loyalty to an online store. Electronic Commerce Research and Applications 5: 117–130.

147.

Floh

Madlberger

(2013) The role of atmospheric cues in online impulse-buying behavior. Electronic Commerce Research and Applications 12: 425–439.

148.

Benn

Webb

Chang

BPI

Reidy

(2015) What information do consumers consider, and how do they look for it, when shopping for groceries online? Appetite.

149.

Wang

Lang

(2015) The effects of special displays on shopping behavior. Journal of Retailing and Consumer Services 23: 125–132.

150.

Liu

Gao

Xie

(2008). An empirical study of online shopping customer satisfaction in China: A holistic perspective. International Journal of Retail & Distribution Management 36(11): 919–940.

151.

Hausman

Siekpe

(2009) The effect of web interface features on consumer online purchase intentions. Journal of Business Research 62: 5–13.

152.

Stoel

(2012) Online apparel retailing: Roles of e-shopping quality and experiential e-shopping motives. Journal of Service Management 23(2): 197–215.

153.

Fasolo

Misuraca

McClelland

Cardaci

(2006) Animation attracts: The attraction effect in an on-line shopping environment. Psychology & Marketing 23(10): 799–811.

154.

(2012) The effects of online shopping attributes on satisfaction–purchase intention link: A longitudinal study. International Journal of Consumer Studies 36: 327–334.

155.

Mummalaneni

(2005) An empirical investigation of web site characteristics, consumer emotional states and on-line shopping behaviors. Journal of Business Research 58: 526–532.

156.

Ding

Lin

(2012) How does background music tempo work for online shopping? Electronic Commerce Research and Applications 11: 299–307.

157.

Lee

Wang

(2014) How can online store layout design and atmosphere influence consumer shopping intention on a website? International Journal of Retail & Distribution Management 42(1): 4–24.

158.

Mazursky

Vinitzky

(2005) Modifying consumer search processes in enhanced on-line interfaces. Journal of Business Research 58: 1299–1309.

159.

Jiang

Yang

Jun

(2013) Measuring consumer perceptions of online shopping convenience. Journal of Service Management 23(2): 191–214.

160.

Rajamma

Paswan

Hossain

(2009) Why do shoppers abandon shopping cart? Perceived waiting time, risk, and transaction inconvenience. Journal of Product & Brand Management 18(3): 188–197.

161.

Tsai

Huang

(2007) Determinants of e-repurchase intentions: An integrative model of quadruple retention drivers. Information & Management 44: 231–239.

162.

Pechpeyrou

(2009) How consumers value online personalization: A longitudinal experiment. Direct Marketing: An International Journal 3(1): 35–51.

163.

Chellappa

Sin

(2005) Personalization versus privacy: An empirical examination of the online consumer’s dilemma. Information Technology and Management 6: 181–202.

164.

Pappas

Kourouthanassis

Giannakos

Chrissikopoulos

(2014) Shiny happy people buying: The role of emotions on personalized e-shopping. Electronic Markets 24: 193–206.

165.

Sia

(2010) Consumer trust and distrust: An issue of website design. International Journal of Human-Computer Studies 68: 913–934.

166.

Chen

Macredie

(2005) The assessment of usability of electronic shopping: A heuristic evaluation. International Journal of Information Management 25: 516–532.

167.

Bai

Law

Wen

(2008) The impact of website quality on customer satisfaction and purchase intentions: Evidence from Chinese online visitors. International Journal of Hospitality Management 27: 91–402.

168.

Zviran

Glezer

Avni

(2006) User satisfaction from commercial web sites: The effect of design and use. Information & Management 43: 157–178.

169.

Yang

Young

(2009) The effects of customized site features on internet apparel shopping. Journal of Fashion Marketing and Management 13(1): 128–139.

170.

Lin

Chuan

(2013) A study on youth online impulsive purchase: The relationship between individual difference, shopping environment, emotion response and purchase. Journal of Creative Communications 8(2/3): 209–229.

171.

Lee

Choi

Kang

(2009) Formation of e-satisfaction and repurchase intention: Moderating roles of computer self-efficacy and computer anxiety. Expert Systems with Applications 36: 7848–7859.

172.

Wang

Yang

Liu

Cao

(2014) An eye-tracking study of website complexity from cognitive load perspective. Decision Support Systems 62: 1–10.

173.

Sands

Oppewal

Beverland

(2015) How in-store educational and entertaining events influence shopper satisfaction. Journal of Retailing and Consumer Services 23: 9–20.

174.

Kim

Niehm

(2009) The impact of website quality on information quality, value, and loyalty intentions in apparel retailing. Journal of Interactive Marketing 23: 221–233.

175.

Ramamurthy

(2009) Consumer selection of e-commerce websites in a B2C environment: A discrete decision choice model. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans 39(4): 819–839.

176.

Lim

Grover

Purvis

(2012) The consumer choice of e-channels as a purchasing avenue: An empirical investigation of the communicative aspects of information quality. IEEE Transactions on Engineering Management 59(3): 348–363.

177.

Hsu

Chang

Chen

(2012) The impact of website quality on customer satisfaction and purchase intention: Perceived playfulness and perceived flow as mediators. Information Systems and E-Business Management 10: 549–570.

178.

Gao

Zhang

Wang

(2012) Understanding online purchase decision making: The effects of unconscious thought, information quality, and information quantity. Decision Support Systems 53: 772–781.

179.

Song

Baker

Lee

Wetherbe

(2012) Examining online consumers’ behavior: A service-oriented view. International Journal of Information Management 32: 221–231.

180.

Lim

Widdows

Hooker

(2009) Web content analysis of e-grocery retailers: A longitudinal study. International Journal of Retail & Distribution Management 37(10): 839–851.

181.

Chen

Shang

Kao

(2009) The effects of information overload on consumers’ subjective state towards buying decision in the internet shopping environment. Electronic Commerce Research and Applications 8: 48–58.

182.

Parra

Ruiz

(2009) Consideration sets in online shopping environments: The effects of search tool and information load. Electronic Commerce Research and Applications 8: 252–262.

183.

Sasaki

Becker

Janssen

Neel

(2011) Does greater product information actually inform consumer decisions? The relationship between product information quantity and diversity of consumer decisions. Journal of Economic Psychology 32: 391–398.

184.

Yoo

Kim

(2012) Online product presentation: The effect of product coordination and a model’s face. Journal of Research in Interactive Marketing 6(1): 59–72.

185.

Huang

Kuo

(2011) An eye-tracking investigation of internet consumers’ decision deliberateness. Internet Research 21(5): 541–561.

186.

Kim

Park

(2010) Consumer perceptions on web advertisements and motivation factors to purchase in the online shopping. Computers in Human Behavior 26: 1208–1222.

187.

Mazaheri

Richard

Laroche

(2012) The role of emotions in online consumer behavior: A comparison of search, experience, and credence services. Journal of Services Marketing 26(7): 535–550.

188.

Pan

Zinkhan

(2006) Exploring the impact of online privacy disclosures on consumer trust. Journal of Retailing 82(4): 331–338.

189.

Smith

Shao

(2007) Privacy and e-commerce: A consumer-centric perspective. Electronic Commerce Research 7: 89–116.

190.

Xie

Teo

Wan

(2006) Volunteering personal information on the internet: Effects of reputation, privacy notices, and rewards on online consumer behavior. Marketing Letters 17: 61–74.

191.

Kinney

(2010) The determinants of consumers’ online shopping cart abandonment. Journal of the Academy of Marketing Science 38: 240–250.

192.

Cheung

CMK

Lee

MKO

(2006) Understanding consumer trust in internet shopping: A multidisciplinary approach. Journal of The American Society For Information Science and Technology 57(4): 479–492.

193.

Wirtz

Lwin

Williams

(2007) Causes and consequences of consumer online privacy concern. International Journal of Service Industry Management 18(4): 326–348.

194.

Sha

(2009) Types of structural assurance and their relationships with trusting intentions in business-to-consumer e-commerce. Electronic Markets 19: 43–54.

195.

Noort

Kerkhof

Fennis

(2008) The persuasiveness of online safety cues: The impact of prevention focus compatibility of web content on consumers’ risk perceptions, attitudes, and intentions. Journal of Interactive Marketing 22(4): 58–72.

196.

Lantz

Hjort

(2013) Real e-customer behavioural responses to free delivery and free returns. Electronic Commerce Research 13: 183–198.

197.

Pei

Paswan

Yan

(2014) E-tailer’s return policy, consumer’s perception of return policy fairness and purchase intention. Journal of Retailing and Consumer Services 21: 249–257.

198.

Utz

Kerkhof

Bos

(2012) Consumers rule: How consumer reviews influence perceived trustworthiness of online stores. Electronic Commerce Research and Applications 11: 49–58.

199.

Kim

Lennon

(2013) Effects of reputation and website quality on online consumers’ emotion, perceived risk and purchase intention: Based on the stimulus-organism-response model. Journal of Research in Interactive Marketing 7(1): 33–56.

200.

Pan

Kuo

Pan

(2013) Antecedent of purchase intention: Online seller reputation, product category and surcharge. Internet Research 23(4): 507–522.

201.

Kim

(2008) Order effect and vendor inspection in online comparison shopping. Journal of Retailing 84(4): 477–486.

202.

Korgaonkar

Silverblatt

Girard

(2006) Online retailing, product classifications, and consumer preferences. Internet Research 16(3): 267–288.

203.

Lee

Kwon

(2008) Online shopping recommendation mechanism and its influence on consumer decisions and behaviors: A causal map approach. Expert Systems with Applications 35: 1567–1574.

204.

Hostler

Yoon

Guo

Guimaraes

Forgionne

(2011) Assessing the impact of recommender agents on on-line consumer unplanned purchase behavior. Information & Management 48: 336–343.

205.

Huseynov

Ozkan

(2014) The influence of knowledge-based e-commerce product recommender agents on online consumer decision-making. Information Development. DOI: 10.1177/0266666914528929.

206.

Keeling

McGoldrick

Beatty

(2010) Avatars as salespeople: Communication style, trust, and intentions. Journal of Business Research 63: 793–800.

207.

Kim

Fiore

Lee

(2007) Influences of online store perception, shopping enjoyment, and shopping involvement on consumer patronage behavior towards an online retailer. Journal of Retailing and Consumer Services 14: 95–107.

208.

Shim

S I

Lee

(2011) Consumer’s perceived risk reduction by 3D virtual model. International Journal of Retail & Distribution Management 39(12): 945–959.

209.

Ogonowski

Montandon

Botha

Reyneke

(2014) Should new online stores invest in social presence elements? The effect of social presence on initial trust formation. Journal of Retailing and Consumer Services 21: 482–491.

210.

Chattaraman

Kwon

Gilbert

(2012) Virtual agents in retail web sites: Benefits of simulated social interaction for older users. Computers in Human Behavior 28: 2055–2066.

211.

Bae

Lee

(2011) Product type and consumers’ perception of online consumer reviews. Electronic Markets 21: 255–266.