Consumer Perceptions Of User Experience And Risk: A Research On Online Shopping

Abstract

Providing consumers with a web site that leads consumers to perceive less risk, have a satisfying experience, and trigger consumers to purchase online are crucial for e-retailers. Online consumer reviews can be affected by risk perceptions toward a product or e-retailer. Moreover designing a friendly web site can lead consumers to purchase online. In this vein, this study aims to examine the effect of consumer reviews on perceived risk and the mediating role of user experience on the relationship between perceived risk and online purchase intention. 345 fully answered online surveys were collected from participants who purchased single-purchase items via online channels and structural equation modelling was performed for analyses. The findings state that consumer reviews do not have effect on financial risk; however, it is found that consumer reviews reduce perceived convenience risk and increase product risk, and convenience risk affects the tendency for shopping online. Finally, user experience positively mediates the relationship between convenience risk and online purchase intention.

Keywords: Online consumer reviewsonline purchase intentionperceived riskuser experience

Introduction

Within the rapid advancement of World Wide Web, online platforms provide an opportunity for consumers to get information about products and services and to compare them. In addition to this, number of consumers who share their personal experiences and opinions on products and services they buy increases day by day. These giant information sources about products and services are offered for consumer using by online retailers. Online shopping has some advantages such as comparing products, accessibility to products for buying whenever consumers want, etc. However, despite the advantages offered, the fact that consumers do not have the chance to examine products, they do not trust online seller, etc. and these negative situations are perceived as risk by consumers. When online reviews are considered in the context of perceived risk, it can be said that consumers use online consumer reviews for eliminating risks that stem from online shopping. Moreover, perceived risk has a key role in online shopping behavior which was proved by previous studies (East, Hammond, & Lomax, 2008; Pavlou, 2003; Vázquez-Casielles, Suárez-Álvarez, & Del Rio-Lanza, 2013).

Considering that purchasing is a process affected by several variables, perceived risk alone would not be enough to explain the intention of the purchase. Research reveales that online purchase intention is affected by community building, satisfaction, switching barriers (Tsai & Huang, 2007), gender and online trust (Thamizhvanan & Xavier, 2013), personality (Bosnjak, Galesic, & Tuten, 2007), web site quality (Hsu, Chang, & Chuang, 2015), perceived value, perceived ease of use, perceived usefulness, firm reputation, privacy, trust, reliability and functionality (Har Lee, Cyril Eze, & Oly Ndubisi, 2011), and web satisfaction (Shukla, Sharma, & Swami, 2010; Park & Kim, 2003). It can be said that some variables which affect online purchase intention such as web site quality and web satisfaction are related to online purchasing platforms. In this context, it is required that characteristics of online platforms and their effect on purchase intention should be examined in detail. When considered from this point of view, user experience is one of the website characteristics. Although various studies examined the direct effect of user experience on purchase intention, the number of mediating effect of user experience studies is quite limited.

The aim of this study is to determine the effect of usefulness of online consumer reviews on perceived risk, the effect of perceived risk on online purchase intention, and finally the mediating effect of user experience on the relationship between perceived risk and online purchase intention by considering the points that are considered incomplete in the literature. Due to the nature of single purchase items, the investigations were conducted in the context of single purchase items.

Literature Review and Theoretical Framework

Consumer Reviews

With the rapid expansion of online shopping, online product reviews have become an effective resource for both customers and retailers (Engler, Winter, & Schulz, 2015). These assessments often include product information and recommendations made by previous users. According to Chen and Xie (2008), evaluations help eliminate product uncertainties for potential buyers. Users can be evaluated in two different ways, such as rating and interpretation (Tang & Guo, 2015). The rating helps quantify the customer's approach to the product. In contrast, customers with written comments share their positive or negative opinions in detail and are difficult to analyse in terms of vendors (Netzer, Feldman, Goldenberg, & Fresko, 2012). Web retailers can convince customers in two ways: product information written by themselves and experiences shared by users. In the studies conducted, it was concluded that the experiences and recommendations shared by the consumers were more reliable than the information shared by the sellers (Dellarocas, 2003). One possible reason for this could be the more real and neutral perception of consumer assessments by potential buyers.

In the related literature, the ratings and written evaluations of the consumers online were evaluated as Electronic Word of Mouth (E-WOM). Klein (1998) stated that WOM is an effective solution when consumers cannot experience the product before the purchase. Contrary to the time and energy loss in traditional WOM communication, today's use of E-WOM has provided fast and reliable information sharing. In the studies conducted, it was concluded that consumer evaluations had a great effect on the increase of sales of the products in question (Chevalier & Mayzlin, 2006; Ghose & Ipeirotis, 2006; Liu, 2006). In this study, the authors examine the impact of an E-WOM type of communication, which is an E-WOM communication type, on the purchase intention of potential buyers in the context of products such as books, music, movies, and computer games.

Perceived Risk of Online Shopping

Perceived risk refers to the possible losses that may occur during or after the transaction of the goods and services that the customers intend to purchase (Chen & He, 2003). Keh and Sun (2008) defined the concept of risk as the purchased product does not meet expectations and cause disappointment. Online buyers cannot physically examine, smell or try products, as opposed to traditional purchases. As a result, more risk perceptions are likely to occur than physical stores. The reasons behind the high risk perception of consumers are product instability and financial losses (Barnes, Bauer, Neumann, & Huber, 2007). Unlimited information sharing, which is one of the advantages of the internet today, brings together the problem of trust. In this case, retailers should focus on the risks that adversely affect the purchasing intentions of customers and make improvements to reduce e-commerce (Garbarino & Strahilevitz, 2004).

In this study, the three dimensions of the perceived risk in online purchases were discussed. Financial Risk , which is proven to be a major obstacle to online purchases, is defined as a potential loss of money by consumers (Maignan & Lukas, 1997). Today, consumers who do not want to share their credit card information and personal information may lose their confidence in buying because of low trust. Product Risk that arises from the inability of consumers to inspect the products they want to buy is one of the issues that is worried about for online purchases. Such risks are defined as the product obtained after the process does not show the expected performance (Kim, Ferrin, & Rao, 2008). Time Risk refers to the loss of time when the product is sent, changed or returned by the consumer (Ko, Jung, Kim, & Shim, 2004). In addition, long-term orders on websites are thought to cause a loss of motivation.

User Experience

Evaluations toward web sites and mobile applications have emerged with the extensive usage of electronic devices such as personal computers, smart phones, tablets, etc. These evaluations are entitled as user experience in aesthetic, design and marketing literature. User experience can be defined in the context of consumer behaviour as all features of usability and desirability of a product or web site (Lorenz, Pascual, Blankertz, & Vidaurre, 2014). User experience concept adapts a holistic view of the relationship between humans and systems (Hassenzahl, Platz, Burmester, & Lehner, 2000; Hassenzahl, 2001). Paredes and Hernandez (2017) state that besides the interaction between user and system, context is another important determinant for user experience.

User experience quality is a crucial indicator for e-retailers. Online purchasing behaviour, repurchase intention and recommending website to others can be affected by user experience quality. Therefore, firms can gain a competitive advantage by designing user experience (Paredes & Hernandez, 2017). On the other hand, digital literacy lead users have an expectancy that can be summarized as getting high satisfaction from their interaction with system, web site or interface (Schrepp, Cota, Gonçalves, Hinderks, & Thomaschewski, 2017) and this expectancy is reflected by firms as an obligation for designing web sites strategically (Paredes & Hernandez, 2017).

User experience is related to functional design (Alkadeem, 2012) and psychological design (Norman, 1990). The functional aspect refers to efficiency, learnability, memorability, satisfaction, and effectiveness and psychological aspect of design is related to emotions. In this vein, it can be said that user experience has a key role in proposing superior value to consumers and understanding online consumer behaviours.

Online Purchase Intention

Consumers' desire to make purchases by contacting web retailers online is defined as the intention to buy online (Pavlou, 2003). In order for online sellers to maintain their competitive advantage in their current position, it is vital that they encourage customers to purchase behavior and reduce risks. In addition, efforts to increase online purchasing intention can help web retailers improve their existing marketing and customer relationships (Van der Heijden, Verhagen, & Creemers, 2003). Consumers are required to use technology during online shopping while communicating with sales consultants in traditional purchases. It is important that the website used in this context is understandable, easy and reliable by consumers. In addition, factors such as the impact of the brand on the consumer and the previous shopping experience with the same website have a positive effect on the buying intention (Blackwell, Miniard, & Engel, 2001). Hong and Cha (2013) stated that financial performance and psychological risks have a negative impact on consumers' buying intentions in their research on the subject.

Hypothesis Development

Positive and negative e-wom can provide information about perceived quality for customers in online shopping. In addition to this, negative e-wom increases consumers’ perceived risk level toward online shopping (Lee, Rodgers, & Kim, 2009). Lin, Jones, and Westwood (2009) stated that online consumer reviews are more important for consumers in purchasing process when the perceived risk level is considered high. Another approach to the relationship between online consumer reviews and perceived risk is that perceived risk could be decreased by giving importance to online consumer reviews (Zhu & Zhang, 2010). In this context H1 has been proposed;

H 1: Usefulness of online consumer reviews has an effect on pervaded risk toward online shopping.

In recent years, a considerable increase in the number of studies on online consumer reviews has been observed. Various studies showed that negative consumer reviews affect purchase intention (East, Hammond, & Lomax, 2008; Vázquez-Casielles, Suárez-Álvarez, & Del Rio-Lanza, 2013). Pavlou (2003) suggested that the level of perceived risk (high or low) is an important determinant for purchasing in online channels. Similarly, perceived risk toward online shopping leads to repurchase behaviors of consumers. According to Hsin Chang, and Wen Chen (2008), if the perceived risk can be decreased, consumers will have more tendency to purchase online. In this context H2 has been proposed;

H 2: Perceived risk toward online shopping has an effect on online purchase intention.

Thorelli, Lim, and Ye (1988) stated that perceived risk is a significant determinant for explaining consumers’ purchase intention. In this vein, previous researches revealed that perceived risk in online shopping affects consumer online purchase behaviour and intention (Hsin Chang & Wen Chen 2008; Gefen, 2002; Kim, Kim, & Leong, 2005; Pavlou, 2003; Sweeney, Soutar, & Johnson, 1999). However, it can be said that various variables may have a mediating effect on this direct relationship. If an evaluation is made based on user experience, according to Shukla et al., (2010) website satisfaction mediates consumers’ purchase intention and ease of use, perceived usefulness, information and joyful aspects of website are the two of the important determinant of online satisfaction. On the other hand, interface quality (Park & Kim, 2003), readability and accessibility (Bauer, Grether, & Leach, 2002) are remarkable antecedents for user commitment. Davis, Bagozzi, and Warshaw (1989) implied that easily navigated websites impact website usage level and satisfaction. Finally, it is clear there is a positive relationship between repurchase intention and customer satisfaction (Patterson & Spreng, 1997). Considering the above information it can be said that user experience has an important role in the relationship between perceived risk and online purchase intention. In this context H3 has been proposed;

H 3: User experience mediates the effect of perceived risk on online purchase intention.

Research Method

The present study aims to investigate the effect of perceived usefulness of consumer reviews on perceived risk toward online shopping and online purchase intention. On the other hand, the authors aim to investigate the mediating effect of user experience on the relationship between perceived usefulness of consumer reviews and perceived risk toward online shopping.

Sample and Data Collection

The focal point of this study is single-purchase items. Single-purchase items considered as experience goods (Nelson, 1970) and books, movies, video games, music, etc. are some examples of this product type. The main feature of these items is that it is difficult to observe product characteristics before consuming them. In this vein, online consumer reviews can be helpful for avoiding or decreasing purchasing risk when making a purchasing decision on this type of products (Zhu & Zhang, 2010). The population of the study consists of individuals who purchase single-purchase items at least once in Istanbul. An online survey was designed and shared on different social media platforms such as Facebook, Twitter, and Instagram in order to collect data. 463 individuals participated in the survey, however, only 345 of them state that they have purchased single-purchase items via online channels. Therefore, the sample size of the research was constituted by 345 surveys and the data was collected between the 6th of March and 27th of March 2019.

Research Design

For the purposes of the study, a comprehensive literature review was performed and the most proper scales were tried to be chosen for collecting data. In this direction, 4 items scale for perceived usefulness of online consumer reviews adapted from Davis, Bagozzi, and Warshaw (1989); the scale for perceived risk toward online shopping was contained 16 items was adapted from Forsythe, Liu, Shannon, and Gardner (2006); 8 items user experience scale was adapted from Schrepp, Hinderks, and Thomaschewski (2017); and finally 3 items online purchase intention scale was adapted from Chen and Barnes (2007).

The online questionnaire included 38 questions and 2 parts. Firstly, the participants were asked whether they bought a single-purchase item before. The participants who answered “yes” to this question were able to see other parts of the survey. The second part of the questionnaire was constituted by 31 questions and 23 of these questions were measured using 7-point Likert scale (1: Strongly Disagree; 7: Strongly Agree) and 8 questions (user experience scale) were measured via semantic differential scale. Finally, the third part of the survey was designed for gathering demographic data about participants.

Findings

Firstly, the demographic characteristics of the participants were revealed. Demographic findings reveal that 60,6% of participants are female, 65,8% of participants are single, 32,5% of participants are 25 and 30 years old, 31,6% of participants earn 2020 TL and less in a month, 51,9% of participants have a bachelor degree, 45,5% of participants are employed at private sector and finally 34,8% of participants use online channels to purchase products 11 times and more per a year.

Table 1 shows the mean and standard deviation values of items and Cronbach’s alpha values of the variables. The item “Online consumer reviews are useful to me.” (4,49) has the highest mean in the usefulness of online consumer reviews scale and the item “Online consumer reviews make me a smarter shopper.” (5,28) has the lowest mean. The item “May purchase something by accident.” (2,99) has the lowest mean in perceived risk scale and the item “Inability to touch and feel the item.” (5,60) has the highest mean.

Table 1 -
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The item “Inefficient – Efficient” (5,53) has the highest mean in user experience scale and the item “Usual – Leading Edge” (4,14) has the lowest mean. Finally, the highest mean was acquired by “Given the chance, I predict that I should use this retailer’s web site in the future.” (5,18) in online purchase intention scale and the lowest mean was acquired by “It is likely that I will transact with this web retailer in the near future.” (5,03).

In order to reveal sub dimensions of the scales, exploratory factor analysis was performed. Kaiser-Mayer-Olkin values were 0,929, 0,878 and 0,914 and Bartlett Test significant level was 0,000 so that the sample was both adequate and sufficient for the factor analysis (see Table 2 ).

Table 2 -
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According to the results of exploratory factor analyses, the usefulness of consumer reviews and online purchase intention scales were singled out in one factor group. On the other hand, PR3 and PR9 items in perceived risk scale were moved from analyses because PR3 item had a lower value (0,483) than 0,50 and PR9 item was nearest values (0,587 – 0,573) at Rotated Component Matrix. After these processes exploratory factor analysis re-conducted for perceived risk scale. The results revealed that perceived risk scale had three sub dimensions. These dimensions entitled as Financial Risk (PR1, PR2, PR4, PR5, PR6, PR7), Product Risk (PR8, PR10, PR11, PR12, PR13,), Convenience Risk (PR14, PR15, PR16). The other multidimensional scale is user experience and exploratory factor analysis revealed that UX1, UX2, UX3, and UX4 constituted first dimension (Ease of Use Experience) of user experience; UX5, UX6, UX7, UX8 constituted second dimension (Feeling of Use Experience) of user experience. The items of perceived risk scale were renamed after exploratory factor analysis for the structural equation model (see Table 1 SEM Code Column). Reliability of new sub dimensions were calculated as follows; Financial Risk (0,849), Product Risk (0,833), Convenience Risk (0,792), Ease of Use Experience (0,843), Feeling of Use Experience (0,732). The research model and hypotheses were revised according to the result of factor analyses (see Figure 1 );

H 1a: Usefulness of online consumer reviews has an effect on financial risk toward online shopping.

H 1b: Usefulness of online consumer reviews has an effect on product risk toward online shopping.

H 1c: Usefulness of online consumer reviews has an effect on convenience risk toward online shopping.

H 2a: Financial risk toward online shopping has an effect on online purchase intention.

H 2b: Product risk toward online shopping has an effect on online purchase intention.

H 2c: Convenience risk toward online shopping has an effect on online purchase intention.

H 3a: Ease of user experience mediates the effect of financial risk on online purchase intention.

H 3b: Ease of user experience mediates the effect of product risk on online purchase intention.

H 3c: Ease of user experience mediates the effect of convenience risk on online purchase intention.

H 3d: Feelings of user experience mediates the effect of financial risk on online purchase intention.

H 3e: Feelings of user experience mediates the effect of product risk on online purchase intention.

H 3f: Feelings of user experience mediates the effect of convenience risk on online purchase intention.

Confirmatory factor analyses were performed after the exploratory factor analysis in order to determine the validity of the scales by using the AMOS 25. Scale fit indices were stated in Table 3 . In order to acquire the required scale fit indices some modifications performed. These processes listed below.

Figure 1: Revised Research Model
Revised Research Model
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In order to obtain the required scale fit indices PR1 was moved and between PR2 and PR7 items covariance correlation was established; between PR12 and PR13 items covariance correlation was established; between UX1 and UX3 items covariance correlation was established and finally, covariance correlation was established between UX5 and UX8 items. On the other hand, reliability (CR) and AVE values of scales show that CR values are higher than 0,70 and AVE values (except Feeling of Use Experience) are higher than 0,50. These findings state that the scales are valid and reliable (Hair, Black, Babin, & Anderson 2010). Hypothesis tests were conducted via structural equation models.

Table 3 -
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Table 4 includes the model fit indices for the first structural equation model. In order to acquire the required model fit values covariance correlation established between error terms e10 and e20; between e10 and e16. The model fit indices are within the required values so it can be said that the model is structurally valid.

Table 4 -
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The standardized β coefficients, standard error, critical ratio, p and R2 statistics of the model in question are shown in Table 5 . The findings reveal that the perceived usefulness of online consumer reviews has an effect on product risk (β=0,342; p<0,05) and convenience risk (β= -0,213; p<0,05) . Another finding is that product risk (β=0,206; p<0,05) and convenience risk (β= -0,283; p<0,05) have effect on online purchase intention. It can be said that H1c and H2c are supported; H1a, H1b, H2a, and H2b are not supported.

Table 5 -
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In order to investigate the mediating effect of user experience on the relationship betweenperceived usefulness of consumer reviews and perceived risk, three conditions’ existence were investigated. Baron and Kenny (1986) listed these requirements as below;

  • Independent variables (Perceived Risk Dimensions) must effect dependent variable (Online Purchase Intention) see Table 5 ,

  • Independent variables (Perceived Risk Dimensions) must effect mediator variable (User Experience Dimensions),

  • Mediator variable (User Experience Dimensions) must effect dependent variable (Online Purchase Intention).

In this context, the mediating effect of user experience investigated only for the relationship between product risk and online purchase intention, the relationship between convenience risk and online purchase intention. Because of the insignificant effect of financial risk on online purchase intention, the mediating effect of user experience on this relation could not be revealed. The structural model for condition 2 and 3 can be seen in Figure 2 . Model fit indices for Figure 2 are shown in Table 6 .

Table 6 -
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The standardized β coefficients, standard error, critical ratio, p and R2 statistics of the model in question are shown in Table 7 . The findings reveal that financial risk has effect on ease of use experience (β= -0,178; p<0,05) and feeling of use experience (β= -0,305; p<0,05); product risk has effect on ease of use experiences (β= 0,302; p<0,05) and feeling of use experience (β= 0,184; p<0,05); convenience risk has effect on ease of use experience (β= 0,099; p>0,05) and does not have significant effect on feeling of use experience (β= 0,302; p<0,05). Another ease of use experience (β= 0,314; p<0,05) and feeling of use experience (β= 0,223; p<0,05) has effect on online purchase intention.

Table 7 -
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These findings indicate that the mediating role of user experience on the relationship between product risk and online purchase intention, convenience risk, and online purchase intention can be investigated. According to the findings, it can be said that partial mediating effects of user experience dimensions’ on product risk and convenience risk were observed. It can be said that for the mediating effect of user experience only H3c is supported.

Figure 2: Second Structural Equation Model
Second Structural Equation Model
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Conclusion and Discussions

The usage of the internet by consumers in order to obtain information and share experiences about products and services enable usage of online platforms for supporting their purchase decision. During online purchase, process consumers are exposed to some kind of risks and these risk types can be considered as key determinants of purchase intention. For decreasing perceived risks toward online shopping, consumers use online consumer reviews and get information about products or services. Moreover, decreasing perceived risk with the help of consumer reviews is inadequate for making purchase decisions. Online retailers can help consumers to make an easier purchase online via providing unique user experience. In this vein, it can be said that designing a web site that is in accordance with consumers’ needs and expectations leads them to be more inclined to online purchasing. Starting from these arguments, the present study aims to investigate the effect of perceived usefulness of consumer reviews on perceived risk and the mediating effect of user experience on the relationship between perceived risk and online purchase intention.

The findings imply that consumer reviews do not affect financial risk, however positively affect product risk and convenience risk. These findings suggest that consumers’ usage of online reviews does not decrease the level of perceived product and convenience risks. Moreover, while purchasing single-purchase items, consumer reviews increase the level of perceived product risk and decrease the level of perceived convenience risk. These situations may stem from the nature of single-purchase items. The price of single-purchase items are almost standard in every online retailer. That is why consumer reviews do not have an effect on financial risk toward single-purchase items. However increase in the level of the perceived risk of product can be interpreted as negative reviews on single-purchase items lead consumers to perceive more risk about product. On the other hand, the usefulness of consumer reviews decreases the level of perceived convenience risk. It can be said that consumer reviews help consumers find appropriate websites, and complete order. When the effect of perceived risk dimensions on online purchase intention is considered, it is concluded that financial risk and product risk do not affect online purchase intention neither positively nor negatively. The examination on the relationship between convenience risk and online purchase intention revealed that convenience risk negatively affects consumers’ online purchase intentions. If consumers cannot complete orders easily, they find appropriate websites and web sites have lower level visual efficacy, consumers show less tendency for purchasing online.

Two dimensions of user experience has emerged in this study and they are entitled as “ease of user experience” and “feelings of user experience”. In the context of user experience, findings reveal that ease of user experience affects financial and convenience risks negatively and product risk positively. It can be said that if consumers perceive web site as easily accessible, not confusing, trustworthy, etc., their financial and convenience risk perceptions are reduced. However, product risk increases with the impact of ease of user experience. Reaching easily to web site is a positive factor for consumers but reaching easily does not mean finding enough and correct information about products. It can be said that if consumers reach easily to web sites with inadequate information, perceived risk toward product may be increased. The results show that feelings of user experience have a negative effect on financial risk, positive risk on product risk and no effect on convenience risk. The interpretation of these findings is that web sites that address consumers' feelings help consumers develop a positive attitude and reduce perceived risk. Similarly, with the relationship between ease of use experience and perceived product risk, feelings of user experience increase product risk. It can be said that web sites that are usual, conventional and boring, and do not entertain the consumers increase the product risk. Finally, ease of user experience and feelings of user experience effect online purchase intention positively. When all these findings are evaluated together, that is possible to say user experience has an important role in navigating risk perceptions of consumers and online purchase intention. The emergence of the two dimensions of user experience is another important finding. Online retailers should pay attention when they design their websites.

The examinations for the mediating role of user experience on the relationship between perceived risk and online purchase intention has revealed that ease of user experience decreases the effect of convenience risk on online purchase intention. In other words, when consumers perceive convenience risk toward online shopping, if they are satisfied with the functionality of web site they tend to reduce convenience risk and shop online.

For managerial implications, it should be noted that firms should pay attention to consumer reviews on online platforms. Negative consumer reviews can provoke product risk and due to these reviews, consumers avoid purchasing online. Furthermore, consumer reviews lead individuals to perceive less convenience risk. These findings suggest that e-retailers should serve consumers in order to satisfy them and direct sharing their product evaluations on online platforms. To increase online purchase intention firms should design their website with the aim of reducing convenience risk. Moreover, in order to reduce the risk perception of online shopping and direct consumers to online shopping, emotional and functional aspects of user experience should be regarded while designing web sites.

Limitations of the present study can be listed as follows; the sample of the research consist of single-purchase item buyers, the authors reached the participants on social media for data collection. In this case, individuals who are not social media users but buy single-purchase items could not be included in the sample of the study. Future research can focus on different product or service categories, different aspects of user experience or different business model on the online environment.

References

  1. Alkadeem, R. (2012). Physical and interactive product usability method, In Proceeding of the 2012 International Conference on Industrial Engineering and Operation Management. Istanbul, Turkey.
  2. Barnes, S. J., Bauer, H. H., Neumann, M. M., & Huber, F. (2007). Segmenting cyberspace: a customer typology for the internet. European Journal of Marketing, 41(1/2), 71-93.
  3. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173.
  4. Bauer, H. H., Grether, M., & Leach, M. (2002). Building customer relations over the internet. Industrial Marketing Management, 31(2), 155–164.
  5. Blackwell, R. D., Miniard, P. W., & Engel, J. F. (2001). Consumer behavior (9th ed.). Mason, OH., Southwestern Thomas Learning.
  6. Bosnjak, M., Galesic, M., & Tuten, T. (2007). Personality determinants of online shopping: Explaining online purchase intentions using a hierarchical approach. Journal of Business Research, 60(6), 597-605.
  7. Chen, R., & He, F. (2003). Examination of brand knowledge, perceived risk and consumers' intention to adopt an online retailer. Total Quality Management & Business Excellence, 14(6), 677-693.
  8. Chen, Y. H., & Barnes, S. (2007). Initial trust and online buyer behaviour. Industrial Management & Data Systems, 107(1), 21-36.
  9. Chen, Y., & Xie, J. (2008). Online consumer review: Word-of-mouth as a new element of marketing communication mix. Management Science, 54(3), 477-491.
  10. Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345-354.
  11. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982-1003.
  12. Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407-1424.
  13. East, R., Hammond, K., & Lomax, W. (2008). Measuring the impact of positive and negative word of mouth on brand purchase probability. International Journal of Research in Marketing, 25(3), 215-224.
  14. Engler, T. H., Winter, P., & Schulz, M. (2015). Understanding online product ratings: A customer satisfaction model. Journal of Retailing and Consumer Services, 27, 113-120.
  15. Forsythe, S., Liu, C., Shannon, D., & Gardner, L. C. (2006). Development of a scale to measure the perceived benefits and risks of online shopping. Journal of Interactive Marketing, 20(2), 55-75.
  16. Garbarino, E., & Strahilevitz, M. (2004). Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation. Journal of Business Research, 57(7), 768-775.
  17. Gefen, D. (2002). Reflections on the dimensions of trust and trustworthiness among online consumers. Database for Advances in Information Systems, 33(3), 38-53.
  18. Ghose, A., & Ipeirotis, P. (2006). Towards an understanding of the impact of customer sentiment on product sales and review quality. Information Technology and Systems, 12, 1-6.
  19. Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate Data Analysis. London: Pearson Education.
  20. Har Lee, C., Cyril Eze, U., & Oly Ndubisi, N. (2011). Analyzing key determinants of online repurchase intentions. Asia Pacific Journal of Marketing and Logistics, 23(2), 200-221.
  21. Hassenzahl, M., Platz, A., Burmester, M., & Lehner, K. (2000). Hedonic and ergonomic quality aspects determine a softwares appeal. In: Proceedings of the CHI 2000 Conference on Human Factors in Computing Systems, (pp. 201–208). ACM, New York, NY.
  22. Hassenzahl, M. (2001). The effect of perceived hedonic quality on product appealingness. International Journal of Human-Computer Interaction, 13(4), 481-499.
  23. Hong, I. B., & Cha, H. S. (2013). The mediating role of consumer trust in an online merchant in predicting purchase intention. International Journal of Information Management, 33(6), 927-939.
  24. Hsin Chang, H., & Wen Chen, S. (2008). The impact of online store environment cues on purchase intention: Trust and perceived risk as a mediator. Online Information Review, 32(6), 818-841.
  25. Hsu, M. H., Chang, C. M., & Chuang, L. W. (2015). Understanding the determinants of online repeat purchase intention and moderating role of habit: The case of online group-buying in Taiwan. International Journal of Information Management, 35(1), 45-56.
  26. Keh, H. T., & Sun, J. (2008). The complexities of perceived risk in cross-cultural services marketing. Journal of International Marketing, 16(1), 120-146.
  27. Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544-564.
  28. Kim, L. H., Kim, D. J., & Leong, J. K. (2005). The effect of perceived risk on purchase intention in purchasing airline tickets online. Journal of Hospitality & Leisure Marketing, 13(2), 33-53.
  29. Klein, L. R. (1998). Evaluating the potential of interactive media through a new lens: Search versus experience goods. Journal of Business Research, 41(3), 195-203.
  30. Ko, H., Jung, J., Kim, J., & Shim, S. W. (2004). Cross-cultural differences in perceived risk of online shopping. Journal of Interactive Advertising, 4(2), 20-29.
  31. Lee, M., Rodgers, S., & Kim, M. (2009). Effects of valence and extremity of eWOM on attitude toward the brand and website. Journal of Current Issues & Research in Advertising, 31(2), 1-11.
  32. Lin, P. J., Jones, E., & Westwood, S. (2009). Perceived risk and risk-relievers in online travel purchase intentions. Journal of Hospitality Marketing & Management, 18(8), 782-810.
  33. Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74-89.
  34. Lorenz, R., Pascual, J., Blankertz, B., & Vidaurre, C. (2014). Towards a holistic assessment of the user experience with hybrid BCIs. Journal of Neural Engineering, 11(3), 1-10.
  35. Maignan, I., & Lukas, B. A. (1997). The nature and social uses of the Internet: A qualitative investigation. Journal of Consumer Affairs, 31(2), 346-371.
  36. Meydan, C. H., & Şeşen, H. (2011). Yapısal Eşitlik Modellemesi AMOS Uygulamaları “Structural Equation Modeling AMOS Applications”. Ankara: Detay Yayıncılık.
  37. Nelson, P. (1970). Information and Consumer Behavior, Journal of Political Economy, 78 (2), 311–29.
  38. Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521-543.
  39. Norman, D. A. (1990). The psychology of everyday things. New York: Basic Books.
  40. Paredes, R. K., & Hernandez, A. A. (2017). Measuring the quality of user experience on web services: A case of university in the Philippines. In 2017 IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (pp. 1-6). Manila, Philippines.
  41. Park, C. H., & Kim, Y. G. (2003). Identifying key factors affecting consumer purchase behavior in an online shopping context. International Journal of Retail & Distribution Management, 31(1), 16-29.
  42. Patterson, P. G., & Spreng, R. A. (1997). Modelling the relationship between perceived value, satisfaction and repurchase intentions in a business-to-business, services context: an empirical examination. International Journal of Service Industry Management, 8(5), 414-434.
  43. Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134.
  44. Schrepp, M., Cota, M. P., Gonçalves, R., Hinderks, A., & Thomaschewski, J. (2017). Adaption of user experience questionnaires for different user groups. Universal Access in the Information Society, 16(3), 629-640.
  45. Schrepp, M., Hinderks, A., & Thomaschewski, J. (2017). Design and evaluation of a short version of the user experience questionnaire (UEQ-S). IJIMAI, 4(6), 103-108.
  46. Shukla, A., Sharma, N. K., & Swami, S. (2010). Website characteristics, user characteristics and purchase intention: mediating role of website satisfaction. International Journal of Internet Marketing and Advertising, 6(2), 142-167.
  47. Sweeney, J. C., Soutar, G. N., & Johnson, L. W. (1999). The role of perceived risk in the quality-value relationship: A study in a retail environment. Journal of Retailing, 75(1), 77-105.
  48. Tang, C., & Guo, L. (2015). Digging for gold with a simple tool: Validating text mining in studying electronic word-of-mouth (eWOM) communication. Marketing Letters, 26(1), 67-80.
  49. Thamizhvanan, A., & Xavier, M. J. (2013). Determinants of customers' online purchase intention: an empirical study in India. Journal of Indian Business Research, 5(1), 17-32.
  50. Thorelli, H. B., Lim, J. S., & Ye, J. (1988). Relative importance of country-of-origin, warranty and retail store image on product evaluations. International Marketing Review, 6(1), 35-44.
  51. Tsai, H. T., & Huang, H. C. (2007). Determinants of e-repurchase intentions: An integrative model of quadruple retention drivers. Information & Management, 44(3), 231-239.
  52. Van der Heijden, H., Verhagen, T., & Creemers, M. (2003). Understanding online purchase intentions: contributions from technology and trust perspectives. European Journal of Information Systems, 12(1), 41-48.
  53. Vázquez-Casielles, R., Suárez-Álvarez, L., & Del Rio-Lanza, A. B. (2013). The word of mouth dynamic: How positive (and negative) WOM drives purchase probability: An analysis of interpersonal and non-interpersonal factors. Journal of Advertising Research, 53(1), 43-60.
  54. Zhu, F., & Zhang, X. (2010). Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of Marketing, 74(2), 133-148.

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30 October 2019

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71

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Business, innovation, Strategic management, Leadership, Technology, Sustainability

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Erdil, T. S., Orcan, Ö., & Ayar*, B. (2019). Consumer Perceptions Of User Experience And Risk: A Research On Online Shopping. In & M. Özşahin (Ed.), Strategic Management in an International Environment: The New Challenges for International Business and Logistics in the Age of Industry 4.0, vol 71. European Proceedings of Social and Behavioural Sciences (pp. 45-60). Future Academy. https://doi.org/10.15405/epsbs.2019.10.02.5