Determining the Effects of Perceived Utilitarian and Hedonic Value on Online Shopping Intentions

In today’s digital world, the Internet is having vigorous and transformational effects on consumer’s behavior. Over the past ten years, consumers all over the world have increasingly used the Internet as an efficient medium in their shopping experience. Online retailers are trying to influence consumers shopping attitude and behavior by creating renewed shopping experiences in order to sustain their business under the catastrophic destructive competition among online and offline retailers. In the catastrophic destructive rivalry environment, it is vital for retailers to understand online consumers’ beliefs, attitudes, shopping intentions and behavior toward online shopping. Therefore, this study was designated to clarify consumers’ online shopping intentions within the online shopping environment. This study extends the technology acceptance model (TAM) and consumer perceived value theory. In the data gathering process, we used convenience sampling and face-to-face interviews techniques. The 400 valid questionnaires were gathered from the Internet shoppers who voluntarily participated with in our research in Osmaniye, Turkey. In order to test the research model, we used Partial Least Squares (PLS-PM) analysis method. The analysis results provide strong support for the research model. Particularly, perceived usefulness, hedonic value, and online shopping satisfaction dimensions have statistically positive effect on online shopping intentions. The findings suggest that perceived usefulness and positive online shopping attitude plays a significant role in increasing both perceived utilitarian and hedonic online shopping value. In addition, online shopping satisfaction and hedonic value have a significant effect on consumer online shopping intentions. Finally, analysis results give some useful insights into the consumers’ online shopping intentions.


Introduction
There is no doubt that the average people living in developed or developing countries have less free time than before as a result fast living conditions.Technological advances lead people to look for new methods for daily life.Human beings found themselves as performing fundamental changes in any aspect of their lifestyles.At this point, the Internet emerged as a transformational tool of life styles and changed people life style from conventional to post modern life styles.Forsythe et al. (2006, p. 56) define the Internet as "a tool of information search and products and services purchase".Kozinets (2002) emphasize the impact of the Internet as informational base and communication media on consumption decisions.According to Casalo et al. (2007), the Internet has become one of the most important communication channels and it motivates some changes in purchasing patterns.The Internet provides consumers much more information about products and services and offers the opportunity of effortless and quickly comparison.In addition, marketers have the opportunity of gathering more data about customers.This changing marketing environment could be called as "new age" in marketing management (Reedy et al., 1999).E-commerce has brought remarkable benefits for vendors and consumers and changed the way of doing transactions activities (Schneider, 2007).
Traditional shopping involves many time-consuming activities (arriving to store by private vehicle or public transportation, finding park space, waiting for others at the check-out line or changing room etc.) and not efficient when it is about checking for alternative goods and prices.Information search and alternative comparison process needs considerable time due to huge number of alternatives.The money and time saving potential of Internet mostly enables consumers to feel better and consumers have a tendency of saving time or/and saving money (Horrigan, 2008).Yu (2006) suggests that Internet enabling a "frictionless" business environment by removing the barriers (geographic, communicative, spatial).Yu (2006) accepts, the Internet as a global medium that helps to simplify business operations and expresses the importance on internationalizing the firm activities.When taking into account the excessive global competition, being in Internet is beyond an option for businesses, but rather an obligation (Lee et al., 2011, p. 201).
Online shopping is the business transactions over the Internet in real time and without any intermediary service (Demangeot & Broderick, 2007).Online shopping has some advantages for both consumer and businesses.When compared with conventional brick-and-mortar stores online shopping has advantages as being greater flexibility, greater convenience, lower cost structure, greater customization and privacy, faster transactions, greater variety of products and services.Srinivasan et al. (2002, p. 41) emphasize the importance of Internet on reduction of information asymmetries between sellers and buyers, Kuttner (1993, p. 20) accepts internet as a medium which is nearly perfect market because it provides instantaneous information for buyers who can compare the offerings of sellers worldwide.Consumers found online formats as more objective information sources when they need to make a purchase decision on a given product or service.The interactive and cost-effective nature of the new technologies has changed the way of shopping in different ways.Some characteristics of online shopping as being; "time saving", "less taxes", "easy to compare prices", "no crowd", "more variety", "spend less on delivery", "less prices", "privacy protection", "reachable any time/anywhere" and etc. make it popular.
The emergence of the Internet has created business-to-business (b2b) and business-to-consumer (b2c) opportunities for enterprises to stay in marketplace (Lee et al., 2011, p. 200).The potential profits of electronic commerce have attracted firms to this medium and firms started to develop and use online opportunities on maintaining better and long-lasting relationships with target consumers in order to enhance the loyalty and conduct the sustainability of their businesses.According to Reichheld & Sasser (1990) customer relationships costly when acquiring new customers and unprofitable during early transactions and the cost of serving loyal customers falls during later transactions.And the Internet is a unique medium to maintain customer loyalty with lower budgets.Growing the Internet usage triggers some radical changes in the consumer purchasing process.With the growth of online shopping, it has become an important transaction channel, which provides enormous benefits of Internet to both vendors and consumers.Online sales are growing rapidly (more than 19 % annually) however it still appear as a small segment of conventional retailing (estimated almost $1.4 trillion by 2015) (DesMarteau, 2004;Wagoner, 2014).
Equal access to new information technologies has changed the use of the Internet independent from the income level of consumers but still some segments of society (depending on age and education level) have been adapting slowly to use the Internet (Moss & Mitra, 1998).Depending on some variables such as cultural, economic, societal and political factors patterns of online shopping will not be identical across countries.Many consumers go online sites with intentions to purchase but do not complete the transaction because of technical problems, including computer freezes, disconnections, or service interruptions or some customers use the online store for gathering information before purchase the product in a brick-and-mortar store (Tedeschi, 1999).And some of customers leave the cart and the site without the transaction although they add item(s) their online cart (Fram & Grandy, 1997).So it is possible to divide online shoppers in two groups according to their intention of making online purchase.And it is important to define the reasons of leaving the cart and the site without completing purchasing.Some studies (e.g., Kim et al., 2003;Lee & Johnson, 2002) tried to determine the reasons and major findings were technical problems, negative Internet shopping experiences, unacceptable delivery fees and methods, slow download speeds, difficulty in finding specific products, lack of return policies/personal service/credit card security and privacy protection.
Consumers are increasingly utilizing Internet as an effective medium in their shopping experience.The widespread utilization of online shopping has altered the styles and patterns of consumption and more people started to prefer electronic shopping platforms rather than physical stores.Customers' online shopping intention is a critical measure of success factor to the online business.Therefore, the present study was designated to clarify consumer online shopping intentions within the online shopping context.In order to determine the effects of consumer' perceived online shopping beliefs, online shopping attitudes, perceived utilitarian and hedonic value, and satisfaction dimensions on consumer online shopping intentions, by including consumer perceived utilitarian and hedonic value, and online shopping satisfaction dimensions.

Theoretical Framework and Hypotheses
Many scholars explained online shopping intentions by using different theories.Davis (1986;1989) introduced the Technology Acceptance Model (TAM), which is based on Theory of Reasoned Action (TRA).The Technology Acceptance Model (TAM) assumes technological acceptance/adoption/intention of individuals could be analyzed by two key variables, perceived ease of use and perceived usefulness.Some scholars (e.g., Lee et al., 2003;Sun & Zhang, 2006) accept TAM as one of the most successful theories for analyzing technology acceptance of individuals.Many of the studies in this field (Childers et al., 2002;O'Cass & Fenech, 2003;Van DerHeijden & Verhagen, 2004;Ha & Stoel, 2009) have used the TAM by extending the theory with new dimensions.

Perceived Ease of Use
Perceived ease of use referred in the literature as "the degree to which a consumers believes that using online shopping would be free of effort" (Chiu et al., 2009); "the motivational aspect inherent to the interaction between the user and the computer" (Davis, 1989); "perception of the effort level needed to complete a transaction by the individual when using a system" (Venkatesh & Davis, 1996) and believed that easy use of a technology (online shopping in this research) more likely to be accepted by consumers.
Customer oriented online features (web site/content design) make easier to complete a transaction and hence, more likely have an impact on customer positive attitude (Kim et al., 2009).Pearson et al. (2007) emphasize that the level of consumers' computer usage needs to consider when the assessing perceived ease of use the website.Consumers' computer usage level is ranging from novice to expert (Pearson et al., 2007).Therefore, some users could find difficult to use a web site where others could claim clear the content of the same site.If customer has a negative perception on the ease of use, they are more likely to continue purchasing using conventional channels instead of online shopping (Hsieh & Liao, 2011, p. 1272).In literature, there are many studies found a positive effect of ease of use on online shopping intention but some other studies accepted ease of use as an indirect mediating factor of perceived usefulness (Igbaria et al., 1995).Prevention of a negative attitude toward online shopping depends on the elimination of barriers (e.g., poor web site design, complex content, complicate information search, long download times, unrelated info or categories, complex payment process) that reduce perceptions of ease of use (Hsieh & Liao, 2011, p. 1272).According to literature if the level of required effort made by the online shoppers on performing tasks to complete a purchasing transaction decreases it is more likely online shopping activity will finalize as a purchase and affect the intention to repurchase in future.Therefore, there is enough evidence to suggest the following hypotheses: H 1 : Perceived ease of use has a statistically significant effect on perceived usefulness.
H 2 : Perceived ease of use has a statistically significant effect on attitude toward online shopping H 3 : Perceived ease of use has a statistically significant effect on hedonic online shopping value.

Perceived Usefulness
The perceived usefulness has been defined by different scholars as, "the prospective user's subjective probability that using a specific application system will increase his or her job performance within an organizational context" (Davis et al., 1989); "the belief that using the application will increase one's performance" (Davis, 1989); "the extent to which a consumer believes that online shopping will enhance his or her transaction performance" (Chiu et al., 2009); "the utility that a user gets from adopting or using a technology" (Eri et al., 2011).Teo et al. (1999) indicate that motives and the perceived usefulness as the key factors of adopting the behavioral intention of the Internet, it means whether a customer perceived more demands on a specific objective it is possibly raise the intensity of the motive.Moon & Kim (2001) report that perceived usefulness has a significantly positive influence on behavioral intentions.Chiu et al. (2009) state that if a customer perceives useful the shopping task of product acquisition he/she will be more likely to show repurchase intentions.Thus, we set forth the following hypotheses.
H 4 : Perceived usefulness has a statistically significant effect on utilitarian online shopping value H 5: Perceived usefulness has a statistically significant effect on attitude toward online shopping

Attitude toward Online Shopping
The contemporary definition of an "attitude is relatively enduring overall evaluations of objects, products, services, issues, behavior, or people" (Babin & Harris, 2014, p. 121).According to Boone and Kurtz (2015, p. 186), "attitudes are consumer's enduring favorable or unfavorable evaluations, emotions, or action tendencies toward some object, idea, or behavior".Attitude towards a behavior defined as "a person's evaluation of a specified behavior involving an object or outcome" (Fishbein & Ajzen, 1975); "a predisposition to respond in a consistent manner to a particular situation" (Hansen et al., 2004); "a person's relatively consistent evaluations, feelings and tendencies toward an object or idea" (Ellen & De Lima-Turner, 1997); "predispositions to respond in a particular way towards a particular object or class of objects in a consistently favorable or unfavorable way" (Rosenberg, 1960).Huang and Liaw (2005) define online shopping attitude as "an individual's overall evaluation of online shopping as a way of shopping."As similar general attitude definition, Chiu et al. (2005) define attitude towards online shopping as "a consumer's positive or negative evaluations, emotions, or action tendencies related to toward accomplishing the purchasing behavior on the internet."Yang et al. (2007) indicate attitude towards online shopping is a significant predictor of online purchase intentions.
According to the both theory of reasoned action and theory of planned behavior (Ajzen, 2005), behavioral intentions are a function of three basic determinants: the individual's attitude toward the behavior, the person's perception of social pressure to perform or not perform the behavior, and the ability to perform the behavior of interest (p.117).According to Ajzen (1991, p. 188), "attitude toward the behavior refers to the degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior in question."In the TRA and TPB model, attitude toward the behavior is determined by a person's beliefs about the results of the behavior (Ajzen, 2005, p. 123).TRA and TPB assume that a person who believes that performing a given behavior will lead to mostly positive outcomes will hold a favorable attitude toward performing the behavior (Ajzen, 2005, p. 124).Therefore, consumers who believe that shopping from online will lead to most positive outcomes will hold a favorable attitude toward online shopping.
According to the Technology Acceptance Model (TAM), is introduced by Davis (1986), behavioral intentions are a function of three basic determinants.The first determinant of behavioral intentions is perceived usefulness, which is defined as "the degree to which individuals believe that use of the system will improve their performance."The second determinant of behavioral intention is the perceived ease of use, which is "the degree to which individuals believe that the system will be easy to learn and use".Finally, the third determinant of behavioral intentions is individual's attitude toward the system.In the TAM, individual's attitude toward the system mediates belief-intention relationship (Davis et al., 1989, p. 994).Therefore, there is enough evidence to suggest the following hypotheses: H 6 : Online shopping attitude has a statistically significant effect on utilitarian online shopping value.
H 7 : Online shopping attitude has a statistically significant effect on hedonic online shopping value.
H 8 : Online shopping attitude has a statistically significant effect on online shopping satisfaction.

Perceived Utilitarian Value
In a regular purchase process customers faced with cost and value.In online shopping "cost" covers monetary payments and non-monetary sacrifices (e.g., stress experienced, time consumption, energy consumption), and "value" includes rewards associated with offering.Customer perceived value defined as "the consumer's overall assessment of the utility of a product based on perceptions of what is received and what is given " Zeithaml (1988); "customer's perceived net trade-off received from all relevant benefits and costs or sacrifices delivered by a product or service or supplier and its use" (Snoj et al., 2004); "customer evaluation of what is fair, right, or deserved for the perceived cost of the offering" (Bolton & Lemon, 1999); "a function of a 'get' component and a 'give' component in acquiring the offering" (Parasuraman & Grewal, 2000); "difference between benefits and costs" (Kleijnen et al., 2007); "efficient and timely service delivery in general" (Childers et al., 2002).Perceived utilitarian value is defined as "an overall assessment (judgment) of functional benefits and sacrifices" (Overby & Lee, 2006, p. 1161).Utilitarian value is relevant for task-specific use of online shopping, such as economic "value for the money" (Zeithaml, 1988) and judgments of convenience and timesavings (Teo et al., 1999).
In online shopping literature, "perceived utilitarian value" is an important variable that affects online shopping intentions.Many of researchers (Dodds et al., 1991;Parasuraman & Grewal, 2000;Chiu et al., 2005;Hume, 2008) indicate that perceived utilitarian value has a positive relationship with intention to purchase/repurchase. Pura (2005, p. 537) emphasizes the importance of "getting what customer need in a certain situation".Moliner et al. (2007) accepts perceived value as a major element of relationship marketing.Extended perceived utilitarian value will reduce an individual's need to seek alternatives but when the perceived value is low, customers will switch to other product/service providers (Anderson & Srinivasan, 2003;Chang, 2006).If a purchase offers a high level of perceived utilitarian value, this would improve the purchase and repurchase.Therefore, we offer the following hypotheses.
H 9 : Utilitarian online shopping value has a statistically significant effect on online shopping satisfaction H 11 : Utilitarian online shopping value has a statistically significant effect on online shopping intention.

Perceived Hedonic Value
Online shopping provides an optimal environment for customers by providing fast and low cost of searching and comparison opportunities.Thus, customers gained the ability of access to required (relevant) and accurate info and protection from information asymmetry.But the utilities of online shopping that provides cost advantage for customer mostly will not enough for a purchase or repurchase.For businesses establishing good relations with customers in long-term depends on if they serve hedonic experiences in addition to their other better product and low price offers.Consumers often use the Internet for entertainment (Mathwick et al., 2001).Entertainment is a factor in marketing applications (Wolf, 1999) especially in promotion activities that offer people to have a unique experience.Entertainment is a hedonic element of online shopping (Luo, 2002;Wolfinbarger & Gilly, 2001).E-factor is being used in online shopping with the purpose of keeping the attention of customer and creating a positive perception of the brand/products.
There are many studies in the marketing literature have analyzed the relationship between online shopping experience and hedonic value.Hedonic pleasure enhances an online shopper's satisfaction toward a web site and so, customers spend more time browsing for other items (Wolfinbarger & Gilly, 2001;Seock & Bailey, 2008).Providing consumers a means to experience enjoyment can enhance marketing effectiveness and can make customers open to promotional incentives (Menon & Kahn, 2002).And many studies (Hirschman & Holbrook, 1982;Davis et al., 1989;Koufaris, 2002;Bart et al., 2005) argue that the customer's higher level hedonic value perceptions may lead to high levels of customer shopping intentions.Thus, the following hypotheses are proposed: H 10 : Hedonic online shopping value has a statistically significant effect on online shopping satisfaction H 12 : Hedonic online shopping value has a statistically significant effect on online shopping intention.

Online Shopping Satisfaction
Consumers' expectations toward online shopping experience influence their attitudes/intentions for shopping at a particular web site.Purchasing behavior of consumers depends on previous experience about a web site whether it confirm his/her expectations.If expectations are not met low degree of satisfaction influences the customer's buying behavior negatively (Jahng et al., 2001).Satisfaction defined as "the summary psychological state resulting when the emotion surrounding disconfirmed expectations is coupled with a consumer's prior feelings about the consumer experience" Oliver (1997); "an attitude construct that affects consumers' behavioral intention" (Devaraj et al., 2002); "perception of pleasurable fulfillment of a service, and loyalty as deep commitment to the service provider" (Shankar et al., 2002, p. 2); "users' general feelings about past online shopping experience" (Bhattacherjee, 2001).
A dissatisfied customer is more likely to continue searching for alternatives, review previous relations, dependence, and resist communication efforts of current vendor (Anderson & Srinivasan, 2003, p. 125).Shankar et al. (2002, p. 2) propose that loyalty of customers depends on to overall satisfaction level with the product/service provider and customers' high loyalty can provide resistance to counter-persuasion/adverse expert opinion, willingness to pay more/to recommend to other consumers.Devaraj et al. (2003) claim satisfaction as an important predictor of continuance intention.Therefore, we set forth the following hypothesis.
H 13 : Online shopping satisfaction has a statistically significant effect on online shopping intention.

Online Purchasing Intentions
The volume of online shoppers and product/service types offered are skyrocketing and this has resulted in intense competition and lower profitability (Brown & Jayakody, 2009).At this business environment keeping into account the importance of customer retention than more costly new customer acquisition (Crego & Schiffrin 1995;Parthasarathy & Bhattacherjee, 1998), which requires loyalty and continued purchasing (Shankar, Smith, & Rangaswamy, 2003).Howard and Sheth (1969) defines purchase intention as" a cognitive state reflecting the consumer's plan to buy in a specified time period" in a similar way Bigne-Alcaniz et al. ( 2008) claim as "a mental state that reflects the consumer's decision to acquire a product or service in the immediate future".Repurchase intention defined by Hellier et al. (2003) as "a customer's willingness to make another purchase from the same firm, based on previous experiences".

Research Methodology
During the research process, we used convenience sampling method and face-to-face interviews technique to collect data.Analysis was performed by using Partial Least Squares.The SmartPLS 3.0 software (Ringle et al., 2015) was used to assess the measurement and structural model.The following sections describe sampling and data collection process, questionnaire design and measurement instrument and analysis method.

Sampling and Data Collection
Theoretically, the population of this study consists of the Internet users who are over 18 years old and who had made an online purchase experience in the last six months.Because, there does not exist such a list of the Internet shoppers, it is not possible to arrange our sampling frame.Therefore, respondents were selected using convenience-sampling method at public facilities (university campus, local mall, three of the busiest streets of the city).Before data collection, we trained six MBA student interviewers by explaining the purpose and content of the survey.Trained interviewers approached the participants and first asked if they are 18 years old or over and if they had purchased products online in the last six months, and only those who are 18 years old and had online purchase experience were asked to participate our survey.The 400 valid questionnaires were collected between April and June 2015, via face-to-face interviews from the Internet shoppers who volunteer to participate our research in Osmaniye, Turkey.

Questionnaire Design and Measures
The questionnaire for this study consists of two main sections.The first section contains questions directed to the scale items (indicators), selected to measure each construct based on existing measures (Table.1).Measurement items were adapted from the technology acceptance model, customer perceived shopping value, and online shopping intention literature.The items for measuring perceived ease of use (PEOU) and perceived usefulness (PU) constructs were adapted from Davis (1989), Venkatesh and Davis (2000), Pavlou (2003) and Chiu et al. (2009).Measures for online shopping attitude (AOS) construct were adapted from Ajzen and Fishbein (1980), Amaro and Duarte (2015).The items for measuring customer online shopping satisfaction construct were adapted from Anderson and Srinivasan (2003), Ha and Perks (2005), and Chang and Chen (2008).Online shopping satisfaction construct items were included respondent's general feeling, happiness, and overall satisfaction about the website.The items for measuring hedonic online shopping value (HOSV) and utilitarian online shopping value (UOSV) constructs were adapted from Babin et al. (1994), andO'Brien (2010).Finally, measures for online shopping intentions construct were adapted from Dodds et al. (1991), Sweeney et al. (1999), Pavlou (2003), and Chiu et al. (2009).In our survey instrument, each of the construct measures is designed to be reflective and all items are assessed by using a seven-point Likert scale, ranging from 1 meaning (strongly disagree) to 7 (strongly agree).

Variable
Indicators Survey Items References Perceived ease of use (PEOU)

PEOU1
The web site is easy to use Davis (1989), Venkatesh and Davis (2000), Pavlou (2003), Chiu et al. (2009) PEOU2 Learning to operate the web site is easy PEOU3 It is easy to become skillful at using the web site PEOU4 The web site is flexible to interact with PEOU5 My interaction with the web site is clear and understandable Perceived usefulness (PU)

PU2
The web site makes it easier to search for and purchase goods PU3 The web site improves my performance when searching for and purchasing goods PU4 The web site increases my productivity when searching for and purchasing goods PU5 The web site enhances my effectiveness in goods searching and purchasing Online shopping attitude (AOS)

AOS1
Generally, online shopping is a good idea Ajzen and Fishbein (1980) It is likely that I will continue to purchase products from the website in the future OSI4 I expect to purchase through this website in the future The final section of the questionnaire contains questions about respondents' socio-demographic characteristics; such as, age, gender, education level, and income level.In this section, respondents were also asked to internet usage and online shopping behaviors; such as, internet usage experience, average daily time spent on the internet, the frequency of online shopping, favorite online shopping sites, and the most frequently bought items from the online retailers.

Data Analysis
We analyzed our research hypotheses by using partial least squares in structural equations modeling (PLS-SEM/PLS-PM), which is a second-generation structural equation modeling technique (Hair et al., 2014, p. 3;Vinzi et al., 2010, p. 48).PLS-SEM was developed by Wold (1974Wold ( , 1982Wold ( , and 1985) ) for the analysis of high dimensional data in a low structure environment (Henseler et al., 2009).The estimation procedure for PLS-SEM is an ordinary least squares regression method rather than the maximum likelihood estimation procedure.PLS-SEM uses available data to estimate the path relationships in the model with the objective of minimizing the error terms of the endogenous constructs.Therefore, PLS-SEM estimates coefficients that maximize the explained variance of target endogenous constructs.For this reason, PLS-SEM is regarded as variance-based approach to structural equations modeling (Hair et al., 2014, p. 14).
PLS-SEM has major advantages over other SEM techniques.First, PLS-SEM works efficiently with small sample sizes and complex models and makes no distributional assumptions (normal distribution) about underlying data.Second, PLS-SEM can easily handle reflective and formative measurement models, as well as single-item constructs, with no identification problems.Third, PLS-SEM provides the more accurate estimates of mediation effects.Finally, PLS-SEM has greater statistical power in parameter estimation than other structural equations modeling techniques (Chin, 1998;Henseler et al., 2009).For this mentioned features, we applied PLS-SEM analysis method to test research hypotheses in our research model.

Demographic Characteristics of the Respondents
A total of 400 respondents participated in the study.The demographic profile and online shopping behavior of respondents is summarized in Table 2.Among the survey respondents, 53% were male, 53% of respondents were married.In terms of age level, with the most significant number of responses was the age level 30-39, with 48.2% of the total of responses.According to the survey, the sample seems to be composed by highly educated individuals, with 44% of the respondents indicated that they had completed an undergraduate degree.In terms of the average income, group with the most significant number of responses was the income 2.001-4.000,with 47.2% of the total of responses.According to the survey, approximately 50% of respondents gave their occupation as government employees.Among the survey respondents, 45% of the respondents reported that they shop online once per month, nearly 64% of the respondents indicated that they use the internet 7 to more than 10-year.According to the survey, 30% of the respondents reported that they spent their time with the internet average 1-2 hours per day, approximately 33% of the respondents reported that they spent their time to shop online an average of 31-45 minutes.Among the respondents, 18% of the respondents reported hepsiburada.comas their favorite online shopping site, nearly 38% of the respondents reported that their online shopping experience level was average.Finally, when asked what products they had bought online within the last 6 months, 35% of respondents stated that they had purchased consumer electronics, mobiles, and accessories on the internet, 28% of respondents had purchased apparel, accessories, and footwear, approximately 14% of respondents had purchased computer hardware and software products on the Internet.

Measurement Model Assessment
PLS Path Modeling is a component-based estimation method (Tenenhaus, 2008).PLS path models are formally defined by two sets of linear equations: the inner model and the outer model.The inner model specifies the relationships between unobserved or latent variables, whereas the outer model specifies the relationships between a latent variable and its observed or manifest variables.Reliable and valid outer model estimations allow us an evaluation of the inner path model estimates.Therefore, the first step in a PLS analysis is the analysis of the measurement (outer) model (Henseler et al., 2009, p. 284).In order to evaluate the psychometric properties of the multiple item scales used in our reflective research model, we follow the procedures suggested by Vinzi et al. (2010) and Hair et al. (2014).Unidimensionality, convergent validity, composite reliability, and average variance extracted (AVE), and discriminant validity were evaluated for the measurement models.Vinzi et al. (2010, p. 50) suggest that reflective measurement construct should be homogenous and unidimensional.In order to evaluate unidimensionality, we conducted principle component analysis with varimax rotation (by using PASW Statistics18) for each of the one exogenous and six endogenous latent constructs.For all six constructs, unidimensionality is evidenced as the first eigenvalue (λ>1) of the variables exceeds one and the second eigenvalue (λ<1) is smaller than one.Based on the principle component analysis results each of the seven latent constructs was considered as unidimensional (see PCA/ eigenvalue column in Table 3).The measurement model for constructs with reflective measures is assessed by looking at individual item reliability.The individual item reliability is evaluated by examining the loadings of the measures with the construct they intend to measure.High indicator's outer loading on constructs represents how much of the variation in an item is explained by the construct and is described as the variance extracted from the item.Using the rule of thumbs of accepting items with loadings of 0.708 or more, this implies that the variance shared between the construct and its indicator is larger than the measurement error variance (Hair et al., 2014, p. 103).
As shown in Table 3, the standardized outer loadings of the reflective constructs are large (>0.70) and statistically significant (all the outer loadings t-values >2.58; significance level 1%) on their respective constructs.PLS-PM analysis results reveal that within-method convergent validity is evidenced by the large (>0.708) and statistically significant item loadings on their respective constructs.
Furthermore, construct convergent validity assessment build on the AVE value as the evaluation criterion.In the measurement model, as shown Table 3, the AVE values of 0.670 (perceived ease of use), 0.653 (perceived usefulness), 0.703 (online shopping attitude), 0.676 (online shopping satisfaction), 0.702 (hedonic online shopping value), 0.682 (utilitarian online shopping value), and 0.843 (online shopping intentions) are above the required minimum level of 0.50 (Hair et al., 2014, p. 103).Thus, PLS-PM analysis results reveal that the measure of the seven reflective constructs have high levels of convergent validity.
The internal consistency was examined by using Cronbach's alpha coefficient and composite reliability index.Cronbach's alpha coefficient is the traditional criterion for internal consistency, which provides an estimate of reliability based on the inter-correlations of the observed indicator variables.A construct is considered homogenous if Cronbach's alpha coefficient is larger than 0.70 for confirmatory studies (Vinzi et al., 2010, p. 50;Hair et al., 2014, p. 101).Table 3 shows, PLS-PM analysis results indicate that the Cronbach's Alpha values of constructs were above the minimum threshold level of 0.70.The Cronbach's alpha values of 0.877 (perceived ease of use), 0.867 (perceived usefulness), 0.894 (online shopping attitude), 0.838 (online shopping satisfaction), 0.952 (hedonic online shopping value), 0.907 (utilitarian online shopping value), and 0.938 (online shopping intentions) demonstrate that all constructs have high level of internal consistency.In our model, as shown Table 3, the composite reliability value for all constructs exceeds the minimum acceptable value of 0.70 (Hair et al., 2014, p. 102).The composite reliability values of 0.910 (perceived ease of use), 0.904 (perceived usefulness), 0.922 (online shopping attitude), 0.892 (online shopping satisfaction), 0.959 (hedonic online shopping value), 0.928 (utilitarian online shopping value), and 0.955 (online shopping intentions) demonstrate that all reflective constructs have high levels of internal consistency reliability.
Finally, in order to evaluate construct's discriminant validity, the Fornell and Larcker (1981) criterion and cross-loading criterion were used.According to the Fornell and Larcker criterion, the square root of the AVE of each construct should be higher than the construct's highest correlation with any other construct in the model.Table 4 shows the results of the Fornell and Larcker criterion assessment with the square root of the reflective constructs' AVE on the diagonal and the correlations between the constructs in the lower left triangle.The logic of this method is based on the idea that a construct shares more variance with its associated indicators than with any other constructs (Hair et al., 2014, p. 105).Overall, the square roots of the AVEs for the reflective constructs 0.819 (perceived ease of use), 0.808 (perceived usefulness), 0.839 (online shopping attitude), 0.838 (hedonic online shopping value), 0.826 (utilitarian online shopping value), 0.822 (online shopping satisfaction), and 0.918 (online shopping intentions) are all higher than the correlations of the constructs with other latent variables in the path model.In addition the Fornell and Larcker criterion, we also examined the cross loadings to evaluate constructs' discriminant validity.Discriminant validity is established when an indicator' loading on a construct is higher than all of its cross loading with other constructs (Hair et al., 2014, p. 105).Table 5 shows the loadings and cross loadings for every indicator.Comparing the loadings across the columns, in all cases an indicator's loadings on its own construct are higher than all of its cross-loadings with other constructs, thus, the results indicate there is discriminant validity between all the constructs.Overall, the Fornell and Larcker criterion as well as cross loading provide evidence for the constructs' discriminant validity.Bold values are constructs loading for each item that are above the recommended value of 0.70; an item's loadings on its own variable are higher than all of its cross loadings with other variable.
The goal of reflective measurement model assessment is to ensure unidimensionality, reliability, and validity of the constructs measures.The analysis results provide support for the overall quality of the reflective constructs' measures and analysis result implies that our data and measurement model are sufficient for hypothesis testing.

Structural Model Assessment
As mentioned above, having the measurement model has been confirmed as reliable and valid, then, the next step is to evaluate the structural model results, which involves examining the model's predictive capabilities and the relationships between the constructs.According to Hair et al. (2014, p. 169), the key criteria for evaluating the structural model in PLS-PM are the significance of path coefficient, the level of R 2 values, the f 2 effect size, the predictive relevance (Q 2 ), and q 2 effect size.

Figure 2. Results of the structural path model
The assessment of the structural model builds on the results from the standard model estimation, the bootstrapping, and the blindfolding procedure (Hair et al., 2014, p. 170).After running the PLS-PM algorithm, the path coefficients estimates (see Figure 2) were obtained for the structural model relationships, which represent the hypothesized relationships between the reflective constructs.The path coefficients statistical significance were obtained by means of the bootstrapping routine (5,000 subsample and 400 bootstrap cases).In addition, in order to assess model's predictive relevance, Stone-Geisser's Q 2 values were also obtained by using the blindfolding procedure.Table 6 and 7 show the results of the hypothesis testing, structural relationships, P-value, and Stone-Geisser's Q 2 values.According to PLS-PM analysis result, as can be seen in Table 6, the perceived ease of use construct has a positive and statistically significant effect on the perceived usefulness construct (β=0.708,P<0.01).This result empirically supports Hypothesis 1. Also, the perceived ease of use construct has a positive and statistically significant effect on online shopping attitude construct (β=0.280,P<0.01).This result empirically supports Hypothesis 2.Moreover, the perceived ease of use construct has a positive and statistically significant effect on perceived hedonic value construct (β=0.110,P<0.05).This result empirically supports Hypothesis 3.
PLS-PM analysis reveals that the perceived usefulness construct has a positive and statistically significant effect on perceived utilitarian value construct (β=0.350,P<0.01).This result empirically supports Hypothesis 4. In addition, the perceived usefulness construct has a positive and statistically significant effect on online shopping attitude construct (β=0.553,P<0.01).This result empirically supports Hypothesis 5.
Furthermore, analysis result reveals that the online shopping attitude construct has a positive and statistically significant effect on perceived utilitarian value construct (β=0.265,P<0.01).This result empirically supports Hypothesis 6.Also, the online shopping attitude construct has a positive and statistically significant effect on perceived hedonic value construct (β=0.302,P<0.01).This result empirically supports Hypothesis 7. In addition, the online shopping attitude construct has a positive and statistically significant effect on online shopping satisfaction construct (β=0.147,P<0.01).This result empirically supports Hypothesis 8.
According to PLS-PM analysis result, the perceived utilitarian value construct has a positive and statistically significant effect on online shopping satisfaction construct (β=0.108,P<0.05).This result empirically supports Hypothesis 9. Also, the perceived hedonic value construct has a positive and statistically significant effect on online shopping satisfaction construct (β=0.565,P<0.01).This result empirically supports Hypothesis 10.In addition, the perceived utilitarian value construct has a positive and statistically significant effect on online shopping intentions construct (β=0.224,P<0.01).This result empirically supports Hypothesis 11.Furthermore, the perceived hedonic value construct has a positive and statistically significant effect on online shopping intentions construct (β=0.251,P<0.01).This result empirically supports Hypothesis 12. Finally, the online shopping satisfaction construct has a positive and statistically significant effect on online shopping intentions construct (β=0.373,P<0.01).This result empirically supports Hypothesis 13.
According to Hair et al. (2014), the most commonly used measure to evaluate the structural model is the coefficient of determination (R 2 ) value.This coefficient is a measure of the model's predictive accuracy.The R 2 value represents the amount of explained variance of the endogenous constructs in the structural model.The R 2 value ranges from 0 to 1 with higher levels indicating higher levels of predictive accuracy.In general, the R 2 values of 0.75, 0.50, and 0.25 for the endogenous constructs can be considered substantial, moderate, and weak respectively (p. 186).Assessing the structural model's predictive accuracy, we examined the R 2 values of endogenous latent variables, which are shown in Table 7.The R 2 values of, Online Shopping Attitude (0.60), Perceived Usefulness (0.50), Online Shopping Intentions (0.48), Online Shopping Satisfaction (0.47), and Utilitarian Online Shopping Value (0.33) was considered moderate, while the R 2 value of Hedonic Online Shopping Value (0.14) was slightly weak.The R 2 values of endogenous latent variables were range from 0.14 to 0.60, which indicates model's predictive accuracy.
Table 7. PLS results for endogenous latent constructs R 2 and Q 2 a Assessing predictive relevance (Q 2 ) value of the effect size: 0.02= Small, 0.15= Medium, 0.35= Large.
According to Hair et al. (2014, p. 195), after evaluating the R 2 values, the Stone-Geisser's Q 2 values were also examined to assess the model's predictive relevance.Q 2 value is an indicator of the model's predictive relevance and Q 2 value bigger than zero for a certain reflective endogenous latent variable indicates the path model's predictive relevance for a particular construct.Table 7 shows the results of the Stone-Geisser's Q 2 value of all endogenous constructs.In our path model, the predictive relevance Q 2 values of Online Shopping Attitude (0.42) and Online Shopping Intentions (0.40) were considered large effect size, Perceived Usefulness (0.32), Online Shopping Satisfaction (0.32), and Utilitarian Online Shopping Value (0.22) were considered medium effect size, but the Q 2 value of Hedonic Online Shopping Value (0.10) was considered small effect size.By performing blindfolding procedures, the Q 2 values of endogenous latent variables were all above to zero (ranging from 0.10 to 0.42), which supports the model's predictive relevance for the endogenous construct.Henseler et al. (2009) indicate that the effects in the path model can be evaluated by means of Cohen's (1988) f 2 effect size value.Cohen's f 2 effect size value is a measure used to assess the relative impact of a predictor construct on an endogenous construct.The effect size f 2 allows assessing an exogenous construct's contribution to an endogenous latent variable's R 2 value.According to Cohen (1988), f 2 effect size values of 0.02, 0.15, and 0.35 indicate small, medium, and large effects, respectively.In analogy to the effect size (f 2 ) evaluation, the relative measure of the predictive relevance can be assessed by means of the (q 2 ) values of 0.02, 0.15, and 0.35 indicate a small, medium, or large predictive relevance of an exogenous construct, explaining the endogenous latent variable under evaluation.The (f 2 ) effect size and the (q 2 ) effect size for all the relationships in the model, along with the path coefficients are presented in Table 8.As can be seen in Table 8, the path coefficient from Perceived Ease of Use construct to Perceived Usefulness is 0.708; the f 2 and (q 2 ) effect size is 1.004 and (0.477) respectively.In accordance with the rules of the assessment for the f 2 and (q 2 ), the effect size are large.The path coefficient from Perceived Usefulness to Online Shopping Attitude is 0.553; the f 2 and (q 2 ) effect size is 0.383 and (0.184) respectively.In accordance with the rules of the assessment for the f 2 and (q 2 ), the effect sizes are large to medium.The path coefficient from Online Shopping Attitude to Hedonic Online Shopping Value is 0.302; the f 2 and (q 2 ) effect size is 0.059 and (0.039) respectively.In accordance with the rules of the assessment for the f 2 and (q 2 ), the effect sizes are small.The path coefficient from Perceived Usefulness to Utilitarian Online Shopping Value is 0.350; the f 2 and (q 2 ) effect size is 0.080 and (0.046) respectively.In accordance with the rules of the assessment for the f 2 and (q 2 ), the effect sizes are small.The path coefficient from Hedonic Online Shopping Value to Online Shopping Satisfaction is 0.565; the f 2 and (q 2 ) effect size is 0.500 and (0.259) respectively.In accordance with the rules of the assessment for the f 2 and (q 2 ), the effect sizes are large.The path coefficient from Online Shopping Satisfaction to Online Shopping Intentions is 0.373; the f 2 and (q 2 ) effect size is 0.145 and (0.112) respectively.In accordance with the rules of the assessment for the f 2 and (q 2 ), the effect sizes are medium.
According to f 2 and (q 2 ) effect size assessments, customers' perceived utilitarian online shopping value construct has a relatively higher-level effect on online shopping intentions construct then hedonic online shopping value construct.The path coefficient from utilitarian online shopping value construct to online shopping intentions construct is 0.224; the f 2 and (q 2 ) effect size is 0.081 and (0.059) respectively.In accordance with the rules of the assessment for the f 2 and (q 2 ), the effect sizes are small.
In conclusion, the f 2 and (q 2 ) effect size assessments; i) perceived ease of use construct has a relatively larger level effect on hedonic online shopping value construct, however, perceived usefulness construct has a relatively larger level effect on utilitarian online shopping value construct, ii) also, hedonic online shopping value construct has a relatively larger level effect on online shopping satisfaction construct, utilitarian online shopping value construct has a relatively larger level effect on online shopping intentions construct as well.

Conclusions and Implications
In today's digital economy, the Internet has become an important tool for online purchasing.Online retailers are trying to influence consumers' shopping attitude and behavior by creating enhanced shopping experience, beyond the opportunity to shop without any time and space constraints.Therefore, the present study was designated to clarify consumer online shopping intention within the online shopping context.This study extends the technology acceptance model (TAM) and consumer value theory, by including consumer perceived utilitarian and hedonic value, and online shopping satisfaction dimensions in order to determine the effects of consumer' perceived online shopping beliefs, online shopping attitudes, perceived utilitarian and hedonic value, and satisfaction dimensions on consumer online shopping intentions.
The analyses results provide strong support for the proposed research model of online shopping intentions.The current study found that perceived ease of use and perceived usefulness beliefs about online shopping website are significant determinants of consumers' online shopping attitude.In addition, analysis result reveals that the effect of perceived usefulness is relatively higher than perceived ease of use on consumers' online shopping attitude.The results indicate that the usefulness of online shopping website is an important determinant of consumers' online shopping attitude.These results are consistent with the findings of TAM-based previous research on new technology using intentions and customer online shopping intentions (e.g., Davis et al., 1989;Adams et al., 1992;Teo et al., 1999;Childers et al., 2001;Pavlou, 2003;Lin, 2007).Therefore, this study suggests that if the consumers believe that online shopping will enhance their performance and productivity, consumers may have positive attitude toward online shopping.
In addition, analysis result reveals that the perceived usefulness, online shopping attitude, and perceived ease of use are significant determinant of consumer perceived utilitarian and hedonic value.One interesting finding of this study, the perceived usefulness dimension effect is relatively higher than perceived ease of use and online shopping attitude on consumers' perceived utilitarian online shopping value.Another important finding is, online shopping attitude affects hedonic value more than other variables.These results are consistent with the findings of previous research on customer online shopping or repurchasing intentions (e.g., Childers et al., 2001;Overby and Lee, 2006;Teo et al., 2007).Therefore, this study suggests that in order to improve consumer perceptions of utilitarian value, online retailers must provide to consumers a more diversified products selection at a lower cost, same quality products at a lower price, quick access to large volumes of product and service information and a more comfortable and convenient shopping environment.In addition, nowadays, consumers are demanding more pleasure and entertainment from the online retailer beyond the utilitarian value.Hence, this study suggests that in order to improve consumer perceptions of hedonic value, online retailers must provide to consumers a more pleasurable shopping experience.Furthermore, analysis result reveals that the perceived hedonic value, online shopping attitude and perceived utilitarian value are significant determinant of consumer satisfaction.One interesting finding of this study, the hedonic value dimension effect is relatively higher than online shopping attitude and perceived utilitarian value on consumers' online shopping satisfaction.These results are consistent with the findings of previous research on customer online shopping or repurchasing intentions (e.g., Wolfinbarger & Gilly, 2001;Anderson and Srinivasan, 2003;Ha and Perks, 2005;Seock & Bailey, 2008;Chang and Chen 2008).Therefore, this study suggests that online retailers must create a more enjoyable shopping experience in order to increase customers' online shopping satisfaction level.
Finally, analysis result reveals that the online shopping satisfaction, perceived hedonic value, and perceived utilitarian value are significant determinant of online shopping intentions.One interesting finding of this study, while the online shopping satisfaction dimension effect is relatively higher than perceived hedonic and perceived utilitarian value on online shopping intentions, perceived utilitarian and hedonic value dimension have nearly equal effect on consumer online shopping intentions.These results are consistent with the findings of previous research on customer online shopping or repurchasing intentions (e.g., Anderson and Srinivasan, 2003;Shankar et al., 2002;Devaraj et al., 2003;Reynalds and Arnold, 2006).Hence, this study suggests that to generate online shopping intentions and to ensure consumers continue shop from the online retailers must satisfy customers' expectations and generate high-level utilitarian and hedonic value.
In conclusion, this study extends the technology acceptance model (TAM) and consumer value theory.The analyses results provide strong support for the proposed research model of online shopping intentions.As result, analysis results suggest that consumer beliefs, attitudes toward online shopping, perceived hedonic and utilitarian value, and online shopping satisfaction explain consumer online shopping intentions.

Limitations and Future Research
The findings of this study give us some useful insights into the consumers' online shopping intentions.However, the results of this study should be viewed its some limitations.One limitation is that this study uses a non-probability convenience sampling method.This sampling method constrained the application of the study findings to the general population.Therefore, future research should use the probability-sampling method and may retest the research model; thus, their findings could then be applied to the general population.A second limitation is that the data were obtained from only Osmaniye City residents in Turkey, which may lead to sampling bias.Therefore, future research should extend this study and research model to other societies and cultures.Finally, the antecedents of online shopping intentions explained a significant amount of its variance in our research model, but other important factors, which have not been included in the model, may help to better explain online shopping intentions.Such as, consumer perceived risk and trust dimensions might further explain online shopping intentions.In relation to these considerations, the results of this study will provide a useful source for further research work.
This study was designed to clarify consumer online shopping intention within the online shopping context.The foundations of this study are based on the technology acceptance model (TAM) and consumer value theory.In order to determine the effects of consumer' perceived online shopping beliefs, online shopping attitudes, and satisfaction dimensions on consumer online shopping intentions, we included in our research model consumer perceived utilitarian and hedonic value dimensions.In this context, the proposed conceptual research model and hypotheses are shown in Figure1.

Figure
Figure 1.Research model

Table 2 .
Demographic characteristics and online shopping behavior of the sample (n=400)

Table 4 .
Discriminant validity-correlations between latent variablesDiagonal elements (values in parentheses) are the square root of the AVE.

Table 5 .
Discriminant validity-constructs loading and cross loading

Table 6 .
PLS results for structural model and hypothesis testing

Table 8 .
Results of path coefficients f2 and q2 effect size Assessing f 2 and q 2 value of the effect size: 0.02= Small, 0.15= Medium, 0.35= Large. a