Online shopping is growing rapidly and increasing its share of retail sales these days. An online store website, as a connection channel to customers, plays an important role in customer acquisition and retention. The first encounter of a customer to an online retailer usually happens through the website of that online store. The ability of a website to draw visitors’ attention and changing them to customers has a great importance for its success. Online store website could have some features, which each of them might have an impact on customer purchase decision. These features are even more significant when the online store is newly-established and seeking its first customers. By studying research papers and books in this research, dimensions of these features and elements of each dimension were extracted. The level of importance of these dimensions and their elements were questioned from 288 students of Kharazmi University and their responses were clustered using k-means algorithm. Each cluster members are a group of customers, which share some similar characteristics. Five clusters were generated after applying clustering algorithm. The attributes of each cluster give a good insight to the owners of online web stores about the needs of that cluster members. The first 2 clusters which each of them has domination in one gender, are more beneficial than the others in this research.
Keywords: Online store, Purchase decision, Website features, Clustering
In recent years, online shopping has grown dramatically throughout the world. For example, the Alibaba.com website had a daily sales record of $5.75 billion coming from 402 million buyers (11 November, 2013). In the United States, customers set a new domestic record of $1.2 billion for single day online shopping on Black Friday 2013 (Schultz & Block, 2015). Given the significant growth in online retailing, the online retailer needs to understand the particular reasons as to why consumers choose to shop online (Rohm & Swaminathan, 2004). Global online retail sales was $1.3 trillion in 2014, which attributed 5% of total global retail sales. In spite that, these numbers shows sales opportunities for online stores, but only 4% of all visitors will purchase a product eventually. For mobile devises, Conversion rate is not promising at all and reduces to 1.2%. Researches indicate that website design is an important factor contributing to the conversion of visitors to customers. As people spend more time in online activities any rise in the converted victors to shoppers will increase online stores profits. (McDowell, Wilson, & Kile, 2016).
Since traditional shopping experience is not present in online shopping, Website appearance like images and information content are determinant in purchase from online stores. Online shopping has many similarities to catalog shopping where in both of them some senses like touching and smelling are absent (Park & Kim, 2010).
Perceived risk and uncertainty are higher when customers want to buy from an online store. As trust toward unfamiliar online stores determine whether a customer do a purchase or not, the destiny of these businesses is shaped by trust-building activities (Harrison, McKnight, Choudhury, & Kacmar, 2002).
Purchase behavior of customers at online stores is fundamentally different from their purchase behavior at traditional physical stores. Accordingly, many different theories have been developed concerning this behavioral difference. The purchase behavior at online stores strongly depends on the degree of interactivity of online store websites. Interactive user interface is a unique tool in the hand of online webstores differentiating them from traditional ones (Haubl & Trifts, 2000).
Effective website design plays a critical role in attracting and retaining customers. Websites are the major and sometimes the only channel to communicate with Web stores. Effective website design requires a profound understanding of the impact of different website design elements (e.g., customer reviews and marketing promotions) on online customers (Song & Zahedi, 2005). One of the important differences between online stores and physical stores is that customers can change their targeted online store quite quickly. In fact, in online shopping, customers can choose the competing store just by a few clicks. Thus, understanding customers’ different needs and priorities is particularly vital (Nazari, Hajiheydari, & Nasri, 2013).
Many studies have been conducted on purchase intention. However, researchers have rarely examined first time purchase intention, commonly known as the ice-breaker of business-to-consumer (B2C) relationships. In addition, despite the considerable growth of online stores in Iran and the global growth of B2C relationships, the number of studies investigating this new shopping environment is quite small. Relying on previous studies on online and physical purchase intention and different papers examining the characteristics of online stores, the present research aims at investigating costumers’ first time purchase from online stores. The results will have a fundamental impact on many start-ups, which based on online sale. Clustering helps us find customer groups with members that have similar interest to features of online stores. This method provides online vendors with information on customers’ wide range of tastes. Accordingly, online stores can develop personalized marketing strategies for each cluster.
In an online shopping experience, customers’ perceptions of the values offered by online store’s features are categorized into two groups. These include hedonic values and utilitarian values (Overby & Lee, 2006). Hedonic values deal with entertaining features of a product, such as connectivity to social networks and gamification (Bilgihan & Bujisic, 2014). Utilitarian values are customers’ overall assessment of a product’s functional benefits to what they sacrifice (e.g., the ratio of a product’s efficiency to its price) (Novak & Hoffman, 1996) and as it can be seen in figure 1 at the end the perceived value is the root of the decision for their purchase (Zeithaml 1988)
Benefits expected from purchase
Cost of purchase
Figure 1: Purchase intention based on perceived value from the point of Zeithaml
From among the different features of a website, those features that offer customers both hedonic and utilitarian values shape the image of online store in customers mind and ultimately, influence customers’ purchase intention (Chang & Tseng, 2013).
Ranganathan and Ganapathy (2002) described the key dimensions of B2C websites. In a survey of 214 online websites’ customers, they identified four key dimensions of B2C websites, i.e., information content, design, security, and privacy. From among these dimensions, security was found to have the greatest impact on purchase intention.
Scheffelmaier and Vinsonhaler (2003) synthesized 59 studies on characteristics of successful e-commerce websites. They examined old studies and extracted phrases referring to the success of e-commerce websites. Next, they organized the phrases into 12 groups along with the number of citations mentioning each phrase. This study became a significant basis for future research.
Cao et al. (2005) used the technology acceptance model (TAM) to examine the quality of e-commerce websites. They explored website quality based on four different factors. These factors included system quality, information quality, service quality, and attractiveness. System quality measures the functionality of a website, including usability, availability, and response time. Information quality deals with the content available on a website. Service quality represent the total support offered by the online store website and it contains trust. Attractiveness shows whether or not webpages are pleasing to read.
In a conjoint study, Chen et al. (2010) studied the website attributes that boost consumer purchase intention and categorized them into three groups: technology factors, shopping factors, and product factors. Technology factors consisted of security, privacy, and usability. Shopping factors comprised convenience, trust, and delivery. Finally, product factors included product value and merchandizing. Using K-means algorithm they clustered the respondents of questionnaire and identify each cluster’s important features.
Karimov et al. (2011) presented a framework in which, three dimensions, namely, visual design, social cue, and content design affect initial trust building and purchase intention.
In order to understand the first purchase concept in online shopping, Kim (2012) created an integrated model of initial trust and technology acceptance Model (TAM). Variables like company reputation, structural assurance, and trusting stance build initial trust. Structural assurance means the degree a shopper sees the online environment technologically and legally safe and secure for online business. Trusting stance is a measure that determines how much a customer believes in reliability of online stores regardless of a particular store.
Chang and Tseng (2013) studied the impact of online store image on purchase intention with an emphasis on the perceived value as a mediator. They defined store image as a multidimensional construct with tangible and intangibles attributes that create consumers’ image of the online store. Thus, a store image is how a customer outlines a store in his/her mind. Accordingly, they proposed the following relationship for an online purchase with the perceived risk acting as a moderator:
Store image Perceived value Purchase intention
Wu et al. (2013) studied the effects of online store design on consumers’ purchase intention. By surveying 626 internet users and employing a structural model, the relationships between an online store’s design and atmospheric attraction from one hand and consumers’ emotion and attitude towards the website from the other hand was studied. They utilised the stimulus-organism-response (S-O-R) model. The first stage is stimulus (S), which includes store design and atmospheric elements. The second stage, organism (O), examine the psychological effects of these features on consumers. Here, we are dealing with consumers’ emotional stimulations and attitude towards the online store. Response (R), is the last stage and the consumer responds through his/her purchase intention. The results showed that online store atmosphere significantly influences emotional arousal and attitude towards the website. In other words, a good layout coupled with a warm and happy environment has a arouse consumers’ emotions and attitudes towards the website, increasing the likelihood of purchase.
In a longitudinal approach, Curty and Zhang (2013) studied five online stores (Amazon, eBay, Wal-Mart, Target, and Overstock) to investigate the effects of social commerce on their websites over the time. They created a conceptual framework, which had three emphases of e-commerce: transactional, relational, and social then they used the framework to analyze the webpages of these five online stores in their life long. Using this method, they identified 174 technical features.
Diaz and Koutra (2013) in their study of the persuasive features of hotel chains’ websites clustered websites based on six persuasive features. The features they examined are informativeness, usability, credibility (privacy/security), inspiration (aesthetics), involvement (interactivity), and reciprocity (The ability of costumer to send information in addition to receiving). Based on the elements’ of these features they clustered hotel chains’ websites into four groups: interactive websites, informative websites with marketing promotion offers, hardly persuasive websites, and credible and easy-to-navigate websites. The findings can help hotel managers identify the strengths and weaknesses of their hotels’ websites.
In order to answer this question that when a customer purchases from an online store, the online behavior model of customers and the place of web site features must be examined. In figure 2 all of the factors that influence customer purchase can be seen. As it is obvious from the figure, purchase intention is shaped by some demographic foreground factors and some other intervening elements (Laudon and Traver 2016).
Figure2: Factors influencing customer purchase from the view of Laudon & Traver
In their study, McKechnie and Nath (2016) examined the effects of new-to-market online store’s features on first time browsers. They studied the effects interactivity and customization features on the purchase intention of 273 participants in four experimental websites. The researchers defined interactivity as a feature that permits users to choose the type of information they are seeking for and the time and order of the information availability when they are searching in website. At the same time, customization is defined as an adaptation of the marketing mix for each individual customer based on the marketer’s information about that customer. They concluded that the website features with high levels of interactivity and customization impact customers’ perceptions of trust and decision satisfaction positively.
Hassan (2016) investigated the impact of website features on customer irritation. The perceived irritation affects consumer purchase behavior. In this study, the effects of visual, navigational, and informational features on consumers’ perceived irritation and purchase behavior were studied. An experimental store was created to analyze the effects of each feature on consumers’ perceived irritation. The research findings were consistent with the findings of previous researches, which investigated the impacts of the same features on consumers’ perceived irritation in physical stores. For example, in both online and physical stores, customers get irritated when they cannot find what they are looking for.
In studying the effects of online store’s website quality on consumers’ purchase intention, Lee et al. (2016) compared the four main tools used to measure e-service quality at online stores. These tools included e-SERVQUAL, e-TailQ, WebQual, and SiteQual. They chose e-TailQ as the study’s measurement tool. E-SERVQUAL usually deals with customers’ interactions with websites like purchase effectiveness, purchase efficiency, and delivery. This tool involves some basic criteria, including efficiency, fulfillment, privacy, and availability. Other criteria include refund, responsiveness, compensation, and contact.
In their study of the role of website features in building trust with consumers Toufaily and Pons (2017) believe that many of the previous relevant studies put excessive emphasis on the functional features (e.g., security, aesthetics, and ease of use) of e-commerce websites, while relational features or Web 2.0 play a significant role in establishing human and social relationships. Thus, in this research, e-commerce websites’ relational features (customization, social presence, virtual communities, and quality of support) were studied along their functional features.
Koo and Park (2017) investigated the role of critical atmospheric cues in designing online stores. They believe that social cues have been ignored in a great number of researches that studied online store websites. Both social and atmospheric cues cause pleasure and attract consumers to online stores’ websites. Atmospheric cues include visual, information and navigation cues. Visual cues do not have enough power to generate pleasure in customers and need to be coupled with social cues.
In this research, 14 papers on website design and their different dimensions and elements were reviewed to obtain a relatively comprehensive image of all the features deemed important by prominent scholars in this field. There are numerous similarities between these identified features. However, over time, social dimension and Web 2.0 have succeeded in attracting much more attention. Table (1) lists all the identified dimensions along the names of the scholars who have used them in their studies.
Table (1): A Brief List of Dimensions and Scholars Mentioned In the Literature Review
|Dimension||No. of Scholars||Scholars|
|Information Content||7||Ranganathan and Ganapathy (2002), Cao et al. (2005), Karimov et al. (2011), Curty and Zhang (2013), Diaz and Koutra (2013), Hassan (2016), Koo et al. (2017)|
|Design and Navigation||11||Ranganathan and Ganapathy (2002), Karimov et al. (2011) Wu et al. (2013), Diaz and Koutra (2013), Hassan (2016), Lee et al. (2016), Tantini (2016), Toufaily et al. (2017), Koo et al. (2017), Chang and Tseng (2013), Cao et al. (2005)|
|Security/Privacy||7||Ranganathan and Ganapathy (2002), Chen et al. (2010), Cao et al. (2005), diaz and Koutra (2013), Lee et al. (2016), Tantini (2016), Toufaily et al. (2017)|
|Entertainment||2||Cao et al. (2005), Chang and Tseng (2013)|
|Product||2||Chen et al. (2010), Chang and Tseng (2013)|
|Social Cues||4||Karimov et al. (2011), Curty and Zhang (2013), Toufaily et al. (2017), Koo et al. (2017)|
|Interactivity||4||Diaz and Koutra (2013), McKechnie and Nath (2016), Tantini (2016), Toufaily et al. (2017)|
|Customization||4||Diaz and Koutra (2013), McKechnie and Nath (2016), Tantini (2016), Toufaily et al. (2017)|
|Support Quality||1||Toufaily et al. (2017)|
Content involves information. Information displayed on a website should be able to reduce ambiguities faced by customers and improve their pictures of the offered products and services in the shortest time possible (Hernández, Jiménez, & Martín, 2009). One of factors that encourages customers to revisit a website is their sense of curiosity and interest in encountering new contents. Thus, regular updates and new contents motivates customers to revisit websites (Reynolds & Mofazali, 2004).
Although information content plays a significant role in online shopping, content should be both adequate and relevant so that customers do not feel lost in irrelevant information (McDowell, Wilson, & Kile, 2016). Furthermore, the quality of information offered on online stores is of utmost importance. Up-to-date and targeted content contribute to information quality (Hassan & Abuelrub, 2011).The readability and understandability of contents displayed in website can cause high quality and comprehensible information (Barnes and Vidgen, 2000).
There are different opinions among researchers in defining website design. However, all the definitions include three key elements, i.e., visual design, technology, and network delivery. Visual design deals with the layout of the website, technology includes the coding techniques, and network delivery is about web pages download speed and reliability when accessed on internet (Powell, 2011).
Turban et al. (2015) define information security this way: “Information security refers to a variety of activities and methods that protect information systems, data, and procedures from any action designed to destroy, modify, or degrade the systems and their operations.” Computer security include measures taken to protect data, networks, computer programs, and other computer processing elements. Computer security is a very broad concept and should be divided into two subcategories. The first subcategory deals with general issues (e.g., encryption) found in all information systems. The second subcategory covers issues related to e-commerce. Cyberattacks on online stores, identity theft, and online scam techniques are, all, included in the second subcategory (Turban et al., 2015).
The degree to which a website is entertaining and is considered attractive by consumers significantly affects purchase intention. Playfulness is one of the elements used in measuring a website’s attractiveness (Cao, Zhang, & Seydel, 2005). Gamification is adding game components in non-game situations. In fact, gamification is “the process of adding game mechanics to processes, programs and platforms that wouldn’t traditionally use such concepts” (Swan, 2012). One of the ways to add gamification to online stores is rewarding consumers after doing some defined activities (Partners et al., 2012).
The features associated with a product presented on a website (e.g., price, assortment) are the website’s features as well. The customers’ perceptions of the price of a product is extremely important. The customer sees this perceived price as an external cue and the most important information influencing his/her purchase intention (Beneke, 2013). The value a costumer receives in a purchase is the product quality divided by price. Since customers cannot touch items in an online store, product quality and the website’s ability in conveying product quality to consumers significantly influence the purchase intention (Kim, Krishnan, & Vogt, 2007).
Social commerce has made e-commerce more socially oriented and changed its direction toward an atmosphere that customers determines its path. Since customers are the heart of social commerce, online stores should add new features to their websites and structures to give them this ability of being in the epicentre. Web 2.0 techniques should be implemented in websites to make information exchange and social interaction possible. This will transform websites into empowering tools serving social commerce (Curty, and Zhang, 2013).
Interactivity is the degree to which a user can change the shape and content of a mediatory environment in real-time. Interactivity has three dimensions: two-way communication, concurrency, and user control. Two-way communication is the two-way information flow which enables users to respond. Responsiveness measures how fast a website responds, immediate feedback, and speed of doing transactions. User control is the degree of control the users have on timing, content, and order of communication (Abdullah, Jayaraman, & Kamal, 2016).
Customization can change the relationship between an online business and its customers. Customization means that online businesses can create personalized online experiences based on the information they have about different individuals or groups and their behavioral characteristics. In other words, customization is a type of customer relationship management individually tailored for each customer. It makes the customer feel that the website is created for him/her exclusively, making it easier for the online business to persuade the customer to stay longer on a website or make a purchase. Information obtained by using customization features can also be employed in data mining (Reynolds & Mofazali, 2004).
Support quality is defined as the quality of the service and the subsequent actions that a website takes when a customer do a purchase or after that purchase. It includes complaint follow-up, claims management, and technical support. As support can also be provided before the purchase, it can obviously affect customers’ purchase intention. In an online store, online support and call center are two key styles of providing support to customers (Toufaily & Pons, 2017).
In this research, desk studies were conducted first followed by data collection using questionnaires. The questionnaire has two parts: a) General questions, which include demographic and general questions on internet usage b) Website features questions, in this part 61 questions measure 9 dimensions of website features variable. The dimensions were measured using a 7-points Likert scale, which is one of the commonly used measurement methods. Below the structure and its spectrum can be seen.
|Respondent’s attitude||critical||very important||important||no idea||unimportant||very unimportant||no importance|
The questions of this survey are extracted from the literature review and researchers have already examined their validity, also some experts, including research supervisor and consultant have validated the questionnaire. In this research, re-test method has been used for the reliability of the questionnaire. Reliability assures us that in equal conditions in what degree the results are the same. In the re-test method, a test runs two times for a test group in the same condition. To examine the difference between the two tests, the “Paired sample T test and the Pearson correlation coefficient” have been used. In this test, research questions were asked from 30 respondents with a 20-day interval and the difference between the two tests was analyzed using SPSS Statistics software. The results showed that there was no significant difference between the 2 tests.
Statistical population (N) are students of management faculty in Kharazmi Univerity. According to the university website, the number of students is about 1000 people. Sampling means choosing a part of society as representative of that society. The sample should be selected in a way that the result of research could be generalized to the society. The random sampling method is used in this research. The Cochran formula is used to determine the sample size. After calculation, the number 277 was obtained as the sample size. To analyze the research data, descriptive statistical method was used to describe demographic variables such as age, gender, Education, Internet usage, and so on. In this method, parameters like mean, standard deviation and variance were calculated, also, IBM SPSS Statistics ver. 24 was used to describe data and check reliability.
This research focuses on clustering online store customers so that those customers who have similar preferences in terms of website features are placed in the same cluster and their needs are analyzed. To find a group of customers who have same features of a website, same importance for them, K-means clustering algorithm is used. The purpose of data mining is to find patterns in the data with the least human intervention. In this study, it is tried to use this technique to segment customers from the point of their expectations from an online store. In spite of being simple, k-means is the most popular clustering algorithm. This algorithm, without the need of supervision, create clusters using self-replication. The reason for using this algorithm in addition to being popular and easy to use, which made it the most popular clustering method in researches, is that when the number of variables is high this algorithm makes denser clusters (A. E. Barnes, Laughlin, and Graphics 2002).
In this research IBM SPSS Modeler ver. 18 is utilized for clustering. To determine the number of clusters Silhouette coefficient method is used. In this method, the mean distance of each cluster member from other members of that cluster is compared with the mean distance of that member to other clusters. This coefficient is between -1 and 1. The higher the Silhouette coefficient, the better the clusters.
Clustering performed on 9 dimensions which in the questionnaire relevant questions were asked about them alongside several demographic elements. Among the demographic elements three components of gender, the amount of interest in Internet shopping and Internet shopping tools are included. The age and income level elements were not considered due to the high degree of similarity among the statistical community who are students of Kharazmi University. In order to find the optimal number of clusters, the silhouette coefficient was used. The silhouette coefficient was calculated from 3 to 7 clusters.The result is shown in Fig.2.
Figure 2: Silhouette coefficient for clustering from 3 to 7 clusters
As Fig.2. Shows if the number of clusters is 4 or 5, the Silhouette coefficient has the highest value. Since number 5 creates more separated clusters, 5 is used as the number of clusters in k-means algorithm in this research. After clustering with the input of k=5 as the number of clusters, 5 clusters were obtained, the largest cluster with 144 members includes 49.7% respondents in it and the smallest of them with 9 members, constitutes 3.1% of respondents.
Among all clusters, the 3 dimensions of entertainment, social cues and customization are of the lowest importance. Unwillingness to social cues is partly due to unavailability of social networks such as Facebook and Twitter in IRAN and partly due to lack of proper usage of this dimension in online stores that have a large portion of online retailing in Iran, such as Digikala. Such stores shape online sale behavior and culture of customers. The absence of these features results in lack of experience and awareness of the customers of this social dimension’s elements. In terms of customization, from the viewpoint of customers, considerations such as personal account and personalization of marketing promotions may not be a top priority for them, but it should be noted that customization provides valuable information for the online stores, which could direct customers unconsciously towards shopping. Entertainment dimension like social cues and customization didn’t have a high value in comparison with other dimensions in this research. Since purchasing is a serious process with financial transactions, it is natural that this aspect is not the main concern of the customers.
The first cluster of the 5 obtained clusters from the clustering algorithm has the highest number of members and is the most important cluster. Most of the members of this cluster are men whose preference for online shopping is higher than traditional shopping. As these people do most of their purchases online, all the features of the online store are important to them. The support is the most important dimension for these people. This indicates that, despite the availability of all online features for an expert buyer in online purchasing, there is still needs to get more information from a human being. The interactivity dimension has the second importance for this group due to the importance of the ability to compare between products for this group who are expert in online shopping. In addition to these 3 dimensions, information content, design / navigation, security / privacy and product, which are the main dimensions of website features, have near to each other and high importance. Entertainment, social cues and customization have lower significant than other dimensions. The main purchase tool for this group is a personal computer and, to a lesser extent, smart phone. The second major group is women who sometimes make online purchases. This indicates that men tend to buy more online, and women online purchases are occasional. The members of this cluster use equally smartphones as well as personal computers for shopping, so it is obvious proper display of website in smartphone is more important for this group. For members of this group, except for 3 dimensions which have low importance for all groups, the rest of the dimensions have near equal value; therefore, it can be said that for the majority of women, it is important that online store website has a proper level of key features for their purchase.
The third group is made of men who sometimes make online purchases, although this male group has fewer members than the first group and its members are about one third of the first group, but still accounts for about 15% of respondents. The interesting thing is that personal computer is still the most important online shopping tool for men. Information content, Security / privacy, and partly the Product features have a higher significance for this male group. High security / privacy importance is understandable because their lower experience in online shopping makes them feel more insecure and they need to be reassured by stronger features. With regard to product feature, since this group members are still interested in traditional shopping those features which present in both styles of shopping and give them the ability to compare online with traditional shopping such as price are important to them. Since this group sometimes does online purchases, elements of information content dimension like decision support tools and the comprehensiveness of information help them more in online shopping to compare products with those present in traditional stores.
In the fourth group there are women who rarely make online shopping; this group has 3 times more members than the fifth group who are men that rarely do online shopping and as with the comparison between the first two clusters , it shows that men have more desire to buy online. Women who rarely buy online use their smartphones as shopping tools and the features of the online store is not important to them, which is natural as they are not interested in such shopping style. Security/ Privacy has the greatest importance to them than other dimensions.
Men who rarely make online purchases create the fifth group. This group is the smallest cluster with about 3% of the whole sample. For this group, support and product dimensions which also present in traditional shopping have the highest importance for them and their perception of online shopping features is something like traditional stores features. Members of this group make their purchase usually using tablets and other mobile devices, which the reason could be using these devices require less challenges and their convenience.
Table 2: Clusters obtained in this research
|First cluster||Second cluster||Third cluster||Forth cluster||Fifth cluster|
|Size||144 (49.7%)||65 (22.4%)||45 (15.5%)||27 (9.3%)||9 (3.1%)|
|Gender||Mostly men (93.8%)||Mostly women (96.9%)||Men (100%)||Women (100%)||Men (100%)|
|Amount of interest in online shopping||First priority is online shopping (81.3%)||Some of purchases are online (71.4%)||Some of purchases are online (100%)||Rarely do online shopping (100%)||Rarely do online shopping (100%)|
|Online shopping tool||Mostly PC and then smart phone||Mostly PC and then smart phone||PC (100%)||Smartphone (100%)||Tablet and other devices (100%)|
|Importance of online stores features( in order)||1.support
Table 2 shows a summary of the 5 clusters and features with higher importance for each cluster.
Conclusion and Recommendations:
The study found that men who are more interested in online shopping usually do it more with their personal computers and smartphones and for them interactivity and support are more important. This reminds online stores that boosting these 2 features along with having other dimensions to a satisfactory level help them in creating differentiation with their competitors and providing competitive advantage for them. Being interactive gets the website away from being static and dull and provides 2-way communication with the customers. In addition, the importance of support reminds the owners of online stores that computerization of all communications is not appropriate and the customers expect to be able to contact with staff in case of running into a problem or a question. The lack of this kind of support may discourage the customer from first time purchase.
Another thing that can help online sellers is having a proper understanding of women shoppers expectations. This research showed that Women are less willing to buy online and women are more interested in using smartphones as a purchase tool than personal computers. Proper display of the items that women often buy, such as cosmetics on the smartphones and using colors that are more appealing to women in the mobile version of the online store could have more effect on women purchasing decision. Moreover, for women all features have high and near equal importance.
Entertainment, social and customization are 3 dimensions from the 9 studied in this research with the lowest importance. A new online store could fill he gap by using social networks in online shopping and grab a decent portion of online sales for itself. It is natural that entertainment dimension does not have a high priority for customers in their first visit to an online store, but online store designers can cleverly use fun-making techniques to keep customers longer on the website and increase the possibility of their purchase. This dimension could create competitive advantages for stores in comparison to other stores, which do not entertain visitors and have equal strength in other dimensions. Customization, despite having low propriety for the customers, provides valuable information that can be analyzed and ultimately help online stores to improve sales.
This research was conducted on students of Kharazmi University. Doing this study on a larger community gives a better view of the whole online customers and it can be a new research for future researchers. In addition, as the literature review of this research examined most of the last decade published papers studying characteristics of online stores, the extracted dimensions can be very useful for the researchers focused on the characteristics of online stores. This research surveyed views of people without letting them to experience the characteristics of online stores. The opinion of the respondents might be different if they practically experience. Although, Creating an online demo web store and asking customers their opinion after letting them to experience it will cost more but the results will be more reliable.
Abdullah, Dahlan, K. Jayaraman, and Saiful Bahri Mohd Kamal. 2016. “A Conceptual Model of Interactive Hotel Website: The Role of Perceived Website Interactivity and Customer Perceived Value Toward Website Revisit Intention.” Procedia Economics and Finance 37(16): 170–75.
Barnes, Arthur E, Kenneth J Laughlin, and Landmark Graphics. 2002. “Investigation of Methods for Unsupervised Classification of Seismic Data.”
Barnes, Stuart, and Richard Vidgen. 2000. “WebQual : An Exploration of Web-Site Quality.” Communications 1: 298–305. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.107.5463&rep=rep1&type=pdf.
Beneke, Justin. 2013. “The Influence of Perceived Product Quality, Relative Price and Risk on Customer Value and Willingness to Buy: A Study of Private Label Merchandise.” Journal of Product & Brand Management 22(3): 218–28. http://www.emeraldinsight.com/10.1108/JPBM-02-2013-0262.
Bilgihan, Anil and Milos Bujisic. 2014. “Electronic Commerce Research and Applications The Effect of Website Features in Online Relationship Marketing : A Case of Online Hotel Booking.” ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 1–11. Retrieved (http://dx.doi.org/10.1016/j.elerap.2014.09.001).
Cao, Mei, Qingyu Zhang, and John Seydel. 2005. “B2C E-Commerce Web Site Quality : An Empirical Examination.” 105(5): 645–61.
Chaffey, Dave. 2015. Digital Business & E-Commerce Management: Strategy Implementation & Practice. Pearson Education.
Chang, En Chi, and Ya Fen Tseng. 2013. “Research Note: E-Store Image, Perceived Value and Perceived Risk.” Journal of Business Research 66(7): 864–70. http://dx.doi.org/10.1016/j.jbusres.2011.06.012.
Chen, Ying Hueih, I. Chieh Hsu, and Chia Chen Lin. 2010. “Website Attributes That Increase Consumer Purchase Intention: A Conjoint Analysis.” Journal of Business Research 63(9–10): 1007–14. http://dx.doi.org/10.1016/j.jbusres.2009.01.023.
Díaz, Estrella, and Christina Koutra. 2013. “Evaluation of the Persuasive Features of Hotel Chains Websites: A Latent Class Segmentation Analysis.” International Journal of Hospitality Management 34(1): 338–47. http://dx.doi.org/10.1016/j.ijhm.2012.11.009.
Gonçalves Curty, Renata, and Ping Zhang. 2013. “Website Features That Gave Rise to Social Commerce: A Historical Analysis.” Electronic Commerce Research and Applications 12(4): 260–79.
Harrison McKnight, D., Vivek Choudhury, and Charles Kacmar. 2002. “The Impact of Initial Consumer Trust on Intentions to Transact with a Web Site: A Trust Building Model.” Journal of Strategic Information Systems 11(3–4):297–323.
Hasan, Bassam. 2016. “Perceived Irritation in Online Shopping: The Impact of Website Design Characteristics.” Computers in Human Behavior 54: 224–30. http://www.sciencedirect.com/science/article/pii/S0747563215300686.
Hasan, Layla, and Emad Abuelrub. 2011. “Assessing the Quality of Web Sites.” Applied Computing and Informatics 9(1): 11–29. http://dx.doi.org/10.1016/j.aci.2009.03.001.
Haubl, Gerald and Valerie Trifts. 2000. “Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids.” Marketing Science 19(1):4–21.
Hernández, Blanca, Julio Jiménez, and M. José Martín. 2009. “Key Website Factors in E-Business Strategy.” International Journal of Information Management 29(5): 362–71.
Karimov, Farhod P., V Brussel, Malaika Brengman, and Leo Van Hove. 2011. “The Effect of Website Design Dimensions on Initial Trust :” Journal of Electronic Commerce Research 12(4): 272–301. http://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=15266133&AN=69885407&h=1DoRTZduxdgDQRDCYBqMkjIeJwSyPsIVrZf7oIU7g9A4/iYBniawFuFQIgYRSOxWexcTnTUnxUDE/VE4GGN+rw==&crl=c.
Kim, Jin Baek. 2012. “An Empirical Study on Consumer First Purchase Intention in Online Shopping: Integrating Initial Trust and TAM.” Electronic Commerce Research 12(2): 125–50.
Kim, Youngsoo, Ramayya Krishnan, and William B Vogt. 2007. “On Product-Level Uncertainty and Online Purchase Behavior : An Empirical Analysis.” New York (Wood 2001): 1–10.
Koo, Wanmo, and Hyejune Park. 2017. “Critical Atmospheric Cues in Designing Online Stores: The Case of Amazon.com.” International Journal of Marketing Studies 9(1): 37. http://www.ccsenet.org/journal/index.php/ijms/article/view/65328.
Laudon, Kenneth C. and Carol Traver. 2016. E-Commerce 2016: Business, Technology, Society. Pearson Higher Ed.
Lee, Tan Shea, Mohd Shoki Ariff, Norhayati Zakuan, and Zuraidah Sulaiman. 2016. “Online Sellers ’ Website Quality Influencing Online Buyers ’ Purchase Intention.” 12014.
McDowell, William C., Rachel C. Wilson, and Charles Owen Kile. 2016. “An Examination of Retail Website Design and Conversion Rate.” Journal of Business Research. http://linkinghub.elsevier.com/retrieve/pii/S014829631630203X.
McKechnie, Sally, and Prithwiraj Nath. 2016. “Effects of New-to-Market E-Store Features on First Time Browsers.” International Journal of Human Computer Studies 90: 14–26. http://dx.doi.org/10.1016/j.ijhcs.2016.03.002.
Nazr, Haji Heydari, Nasri. 2012.” Effects of B2C Retailing Website Features on Customer Online Purchase Intention, Using Conjoint Analysis.” Iranian Business Management 4(14): 127-146
Novak, Thomas P. and Donna L. Hoffman. 1996. “Marketing in Hypermedia Environmen Foundations.” 60(3):50–68.
Overby, Jeffrey W. and Eun-ju Lee. 2006. “The Effects of Utilitarian and Hedonic Online Shopping Value on Consumer Preference and Intentions.” 59:1160–66.
Park, Chung-Hoon and Young-Gul Kim. 2010. “Identifying Key Factors Affecting Consumer Purchase Behavior in an Online Shopping Context.” International Journal of Retail & Distribution Management Iss International Journal of Retail & Distribution Management 3112(6):16–29. Retrieved (http://dx.doi.org/10.1108/09590550310457818%5Cnhttp://dx.doi.org/10.1108/09590550310457818%5Cnhttp://dx.doi.org/10.1108/09590550310457818%5Cnhttp://dx.doi.org/10.1108/09590550310457818).
Partners, Boston Retail et al. 2012. “Gamification and the Online Retail Experience.” International Business & Economics … 11(11): 1223–33. http://www.cluteonline.com/journals/index.php/IBER/article/view/7370%5Cnhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84902478697&partnerID=tZOtx3y1.
Powell, Thomas A. 2001. Web Design: The Complete Reference. McGraw-Hill, Inc.
Ranganathan, C., and Shobha Ganapathy. 2002. “Key Dimensions of Business-to-Consumer Web Sites.” Information and Management 39(6): 457–65.
Reynolds, Janice, and Roya Mofazali. 2004. The Complete E-Commerce Book: Design, Build, and Maintain a Successful Web-Based Business. Cmp Books.
Rohm, Andrew J., and Vanitha Swaminathan. 2004. “A Typology of Online Shoppers Based on Shopping Motivations.” Journal of Business Research 57(7): 748–57.
Scheffelmaier, Gerry W., and John F. Vinsonhaler. 2003. “A Synthesis of Research on the Properties of Effective Internet Commerce Web Sites.” Journal of Computer Information Systems 43(2): 23–30.
Schultz, Don E., and Martin P. Block. 2015. “U.S. Online Shopping: Facts, Fiction, Hopes and Dreams.” Journal of Retailing and Consumer Services 23: 99–106.
Song, Jaeki and Fatemeh ?Mariam? Zahedi. 2005. “A Theoretical Approach to Web Design in E-Commerce: A Belief Reinforcement Model.” Management Science 51(March 2015):1219–35.
Swan, C. (2012). Gamification: A new way to shape behavior. Communication World, 29(3), 13–14.
Toufaily, Elissar, and Frank Pons. 2017. “Impact of Customers’ Assessment of Website Attributes on E-Relationship in the Securities Brokerage Industry: A Multichannel Perspective.” Journal of Retailing and Consumer Services 34(September 2016): 58–69. http://dx.doi.org/10.1016/j.jretconser.2016.09.011.
Turban, Efraim et al. 2015. Electronic Commerce: A Managerial and Social Networks Perspective. Springer.
Wu, Wann-Yih, Chia-Ling Lee, Chen-Su Fu, and Hong-Chun Wang. 2013. “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. http://www.emeraldinsight.com/journals.htm?issn=0959-0552&volume=42&issue=1&articleid=17102311&show=html%5Cnhttp://www.emeraldinsight.com/doi/abs/10.1108/IJRDM-01-2013-0035.
Zeithaml, Valerie A. 1988. “Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence.” Journal of Marketing 52(3):2–22. Retrieved (http://www.jstor.org/stable/10.2307/1251446).
Cite This Work
To export a reference to this article please select a referencing stye below:
Related ServicesView all
Related ContentAll Tags
Content relating to: "Consumer Decisions"
The consumer decision making process involves how consumers identify their needs and gather and process information prior to a purchase. Consumer decisions involve how the emotions and preferences of consumers can impact their buying decisions.
Innovation, Technology and Increased Customer Satisfaction in the Food Industry
Contents EXECUTIVE SUMMARY COMPANY DESCRIPTION AND INDUSTRY ANALYSIS BUSINESS IDEA PESTLE ANALYSIS SWOT ANALYSIS INTERNAL ANALYSIS EXTERNAL ANALYSIS ORGANISATIONAL BUSINESS STRUCTURE LEVEL OF APPROVAL...
Influence of Ethnic Minorities in the Cosmetics Industry
The purpose of this project is to provide an analysis of the consumer behaviour that represent minorities and the impact this creates on the cosmetics industry....
DMCA / Removal Request
If you are the original writer of this dissertation and no longer wish to have your work published on the UKDiss.com website then please: