Journal of Internet Banking and Commerce ISSN: 1204-5357
ISSN: 1204-5357
+44-175-271-2024Fairouz Aldhmour
Department of Management information Systems, Muta'h University Karak, Jordan
Isra Sarayrah
Muta'h University, Jordan
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This study is concerned with investigation of factors that influence consumers' intention to use Online Shopping: An empirical study in South of Jordan. To achieve this aim the researcher investigated set of factors: Technology Acceptance Model (TAM) by Davis [1] was adopted in this study, which consists of two independent variables: perceived Ease of use (PEOU), perceived usefulness (PU). Also Theory of Reason Action (TRA) by Fishbein et al. [2] was adopted by using Subjective Norms (SN). In addition a mediating variable (Attitude) and dependent variable intention also adopted from both theories (TAM &TRA). Finally, Perceived Risk (PR) and Product Involvement (PI) were examined too. A questionnaire was developed and distributed to a sample of (300) respondents to collect primary data, and based on a convenience sample the response rate was about 83%. The study conducted in the context. Furthermore, the findings were analyzed using the Statistical Package for Social Software (SPSS) and Amos program to analyze the path of the independent variables. The results indicated that product involvement have the most significant positive direct impact on consumer attitudes towards online shopping and also have significant positive indirect impact on intention through attitude. Meanwhile PEOU, PU and PR have no direct impact on consumers' attitude, but have indirect impact on intention through attitude. In other hand, the result indicated that SN has positive direct impact on consumers' intention. Finally, the result indicated that consumers attitude have direct impact on consumer intention to use online shopping. Based on the research findings and conclusions, a number of recommendations and future research suggested.
Online Shopping, Intention, Attitude, Perceived Usefulness, Perceived Ease of Use, Subjective Norms, Perceived Risk, Product Involvement
Online Shopping
This concept was first demonstrated before the World Wide Web (WWW) was in use with real time transaction processed from a domestic television. The technology used was called Videotext and was first demonstrated in 1979 by M. Aldrick who designed and installed systems in the United Kingdom. By 1990 T. Berners-Lee created the first WWW server and browser and by 1995 Amazon expanded its online shopping experiences. After that, the next big development was the opening of an online pizza shop by Pizza Hut. In that same year, Netscape introduced SSL encryption to enable encryption over the data transferred online which has become essential for online shopping [1-3].
Forsythe et al. [4] found that Internet shopping has become the fastest-growing use of the Internet. Online shopping is a form of electronic commerce which allows consumers to directly buy goods or services from a seller over the Internet using a web browser. Online shopping is known by many alternative names including ‘e-shop’, ‘e-store’, ‘Internet shop’, ‘web-shop’, ‘web-store’, ‘online store’, and ‘virtual store’. Doherty et al. [5] consider online shopping as a marketing tool for goods and services and view online shopping as a form of direct marketing which requires effective and direct communication between marketers and consumers. They point out that effective and direct communication is essential in order that an intermediary between buyers and sellers is not required. Surech et al. [6] offer another definition stating that online shopping can be understood as being a process which consumers access or go through to purchase products or services over the Internet.
It is important to note that there are positive and negative sides for online shopping. According to Azizi et al. [7], online shopping provides greater ease in searching and finding the proper products and/or services in an inexpensive and expedited manner that typically ensures lower prices for products. In addition, searching and comparing products and services on line functions in providing a person with rich information can help a person make a more informed choice when completing an online purchase. Not only is there voluminous information on products and services online that assist one in purchasing online, one is also able to access free consulting services online to further enable them in obtaining valuable information about a product. And finally, online shopping through the Internet provides more choices of product and services for e-shoppers.
In contrast, not all consumers prefer to do online purchasing transactions. Shankar et al. noted that there were a number of factors that contributed to consumers being reluctant and refusing to engage in online shopping. They found that the lack of privacy and financial security, perceived lack of human contact, failure of technology, unfamiliar service encounter all contributed to consumers not participating in online shopping.
Perceived Ease of Use (PEOU)
As mentioned previously, PEOU is one aspect of the Technology Acceptance Model which was developed by Davis [1]. PEOU is a major determinant that impacts how a particular technology will be embraced and accepted. PEOU can be defined as "the degree to which a person believes that using a particular system would be free from effort" [1]. While users may believe in a certain technology and they may even be aware of the usefulness of that technology, yet they may still resist using that technology if it is perceived as being too difficult to use or understand. “Ease" can be understood as being free from difficulty or not requiring the exertion of great effort. "Effort" is a finite resource that a person may allocate to the various activities for which he or she is responsible. All else being equal, Davis [1] put forth that an application perceived to be easier to use than another is more likely to be accepted by users [1].
The inability to use the Internet, the difficulty in accessing the Internet, the complexity of the technology and the unease of using computers all represent the different barriers that deter many in embracing the Internet and online shopping. In this research, PEOU refers to the consumer's perception that shopping on the Internet will involve the minimum level of effort. Through simplifying the process and steps involved, shopping online using the Internet becomes encouraged and readily accepted. Ease of use translates into non-complexity degree and establishes the extent to which Internet is perceived effortless at best [8]. Furthermore, “Perceived Ease of Use” has a very close relationship with the concept of self-efficacy that is "people's judgments of their capabilities to organize and execute courses of action required to attain designated types of performance". Self-efficacy tries to measure individual perceptions about performing a specific task. On the other hand, self-efficacy deals with the virtual and subjective world, and not the physical or the real world. Self-efficacy is a measure of person’s ability to do a specific task before he/she actually does it. Azizi and Javidani [7] reiterate that self-efficacy measures a person's ability to do a specific task before he/she really does it.
Perceived Usefulness (PU)
According to Davis [1], “Perceived Usefulness” (PU) can be defined as "the degree to which a person believes that using a particular system would enhance his or her job performance" This follows from the definition of the word, “useful”, which is defined as "capable of being used advantageously". A system high in perceived usefulness, is one for which a user believes in the existence of a positive use-performance relationship [1]. This depends on the consumers' expectations of how technology can improve their lives [9]. This factor is well documented and consistently proven in many empirical studies to have a high impact on the behavioral intention to adopt technological products [1]. This construct is a theoretical substitute for the concept of “relative advantage” developed by adoption theory [10]. Relative advantage is the extent to which an innovation is perceived as offering a clear advantage. The concept may involve an economic profit, a social prestige or other benefits. In addition, perceived usefulness has a very close relationship with the concepts of perceived benefits and convenience [7].
Perceived Risk (PR)
Perceived risk was developed through the study of psychology by Bauer [11] where he pointed out that consumer behavior can be regarded as a kind of "risk-taking" because the consumer cannot ensure the results from using the products at the purchasing moment. Thus, in actuality, the consumer bears a certain risk. Bauer [11] considered that perceived risks lie in the notion that consumer behavior is a goal-oriented activity, and purchase of the product is associated with this goal. It’s acceptable expectation level is measured by the degree of achievement to this goal. However, the consumers themselves may not be aware of the purchase goals and the perceived risk but their behavior is deeply affected by the perceived risk. With online purchasing being a relatively new mode in consumer shopping, the traditional consumer who is unfamiliar with online shopping subsequently faces a certain degree of perceived risk when making a purchasing decision. According to Naiyi [12], perceived risk refers to "consumers' uncertainty about loss or gain in a particular transaction". In terms of perceived risk in online shopping, Forsythe et al. [13] defined it as "the subjectively determined expectation of loss by an internet shopping in contemplating a particular online purchase". Consumers will have several buying goals for each purchasing decision and consumers will also have some level of perceived risk in any purchasing decision making situation regardless of the purchase method [14].
Several dimensions of perceived risk have been identified in previous studies, but the consumer perception of these risks varies. The consumer perception of risk depends on the person, the amount of knowledge the individual has about online shopping, the actual shopping situation (for example, traditional brick-and-mortar retail stores, and online catalog) and also related to the specific culture norms of the individual. In this research, the researcher examines several dimensions of perceived risks which include “financial, time, privacy, after sale services, and quality risks.”
First, ‘financial risk’ measures a consumer’s concern about monetary loss when shopping through the Internet [12]. Financial risk is not related to the monetary amount for any given transaction, but it relates rather to the consumer feeling at risk of losing extra money through credit card fraud and extra fees leading to financial loss. Furthermore, the financial risk is compounded with the consumer feeling that they will not be able to get a refund if needed or the fear that they may not be able to reverse the transaction or to stop payment after discovering the mistake [15].
Second, ‘quality risk’ is defined by Sophie [16] as being the disappointment of the buyer in relation to expectations concerning product performance. Still, Naiyi [12] describes quality risk as a consumer’s concern about the product quality, performance, falseness of a product, and product related problem. Quality risk remains a key factor influencing online purchase in many product categories. Therefore, reducing perceptions of product risk may provide a good avenue for Internet retailers to enhance online sales [13].
Third, ‘time risk’ is defined as the consumers' concern about losses that is associated with time expectations and the delivery of a product. Sophia [16] describes it as the time spent for the purchase of a product and the time wasted in the case of a bad purchase. Forthly, ‘privacy risk’ or ‘information risk’ is defined as consumers' concern about the security of personal information" [12]. Consumers may not feel comfortable in providing their personal information via the Internet in general due to the fear that their personal information may not be protected and thus open to identity theft and the potential of a criminal having access to highly sensitive information.
Fifth, ‘after sale risk’ relates to potential loss of after-sale associated with product problems, commercial disputes, and service guarantee [17]. Sparks et al. [18] through their respective research further added two types of services after the sale; ‘anticipated services’ and ‘unanticipated services’. Anticipated services are those services that the customer plans for, such as installation, training, written instructions, maintenance, and upgrading. The maintenance of the sold-out products such as repairs, returns, and replacements are the services that are categorized as unanticipated service [18].
Product Involvement
The concept of ‘product involvement’ was first introduced by Zaichkowsky [19]. He defined product involvement as being an "individual’s perceptions of the relevance of an object based on inherent needs, values, and interest". Since the 1980s, attention has focused on the conceptualization and measurement of involvement in relation to "objects" such as: product, purchase, or activity. Michaelidou et al. [20]; Harari et al. [21] in their research the concept of "product involvement" was introduced to examine "personal involvement". Many studies used product involvement as a substitute for personal involvement [22,23]. Product involvement examines consumer purchasing behaviors by measuring the degree of interest and concern consumer bring into purchasing process [24]. In addition, Antonides et al. [25] found that "involvement" is the level of consumer personal relationship with purchasing including perceived importance value and risk. Zaichkowsky [19] found that the consumer can have a “high product involvement” or a “low product involvement.” Based on Zaichkowsky’s [19] study, consumers with high product involvement tends to be more interested in reading detailed information about the product/services. They also tend to compare product characteristics with other product. In addition, the consumers with high product involvement tend to be concerned about product brand. A research study [26-28] also lends further support to this finding, concluding that there that consumers with greater product involvement were found to be more loyal to a particular brand within the same product category. Moreover, consumers with high product involvement take their time before make the final purchasing decision.
Subjective Norms (SN)
According to the theory of reasoned action [2], subjective norms refer to the perceived social pressures to perform or not perform the behavior. It is the influence of a person’s normative beliefs that guides them in approving or disapproving of a particular behavior [29]. People’s intentions to perform a particular action function in establishing and defining the subjective norm, or their perception that other individuals place great importance in their adhering to subjective norms [2].
Pavlou et al. [30] found that a subjective norm is the influence from individuals’ normative belief that the behavior is accepted, encouraged, and promoted by their circle of influence. Subjective norm can be thought of as being composed of two components: societal norm and social influence. Societal norm refers to the process of adherence to the larger societal fashion. It is also related to the collectivism concept which refers to the extent to which individuals are integrated into groups, forming their judgments based on group norms (and Bond 1988) [31]. In other words, people who place greater emphasis and value on individualism, they tend to view themselves and their immediate family as being relatively more important than the collective. The same logic was observed in study of George [32] where he suggested that in highly collectivist cultures, such as is found in the Arab World, individuals' actions are typically influenced by the expectations of people around them. Social influence refers to the extent to which people accept a hierarchical system with an unequal power distribution, and under this circumstance, the individual is seen to reflect adherence to opinions from family, friends, and peers [30].
Consumers' Attitude towards Online Shopping
Attitudes toward a behavior constitute an individual’s evaluation of a behavior. They are determined by his/her salient beliefs about the benefits and costs of performing the behavior [2]. It refers to feelings of joy, pleasure, happiness, disgust, dislike or hatred towards a given behavior [33,34]. The same logic was supported by Fishbein et al. [2] when they proposed that the attitude toward a behavior is a person’s evaluation of a specified behavior involving an object or an outcome. They found that the attitude towards any behavior is related more strongly to a specified behavior than the attitude toward an object or an outcome. While Rokeach [35,36] found that attitude is a set of interrelated predispositions to action organized around an object or a situation. Social attitudes are assumed to be residues of past experiences that guide the future behavior [37].
An online shopping attitude refers to consumers' psychological state in terms of making purchases on the internet. Consumers’ attitude is a directly influenced factor that affects the consumers’ buying willingness. Therefore, it is thus important to recognize that numerous factors precede attitude formation and change. Therefore, understanding consumer attitudes help marketing managers to predict the online shopping rate and evaluate the future growth of online commerce [38]. In order to investigate consumers’ attitudes, the researcher examined several factors that may influence consumers' attitude towards online shopping. In simple terms, this means that there is no point having an excellent product online if the types of consumers who would buy it are unlikely to be online.
Consumers' Intention to Use Online Shopping
Ajzen [29] found that intentions are assumed to capture the motivational factors that influence a behavior. They are indications of how hard people are willing to try, and of how much of an effort they are planning to exert in order to perform the behavior. As a general rule the stronger the intention to engage in a behavior, the more likely they will perform the behavior [29]. In other words, as Cao et al. [39] noted, an individual may be engaged in a less intended choice due to the presence of some constraints (behavioral control factors). In addition, of course, stated intentions often differ from true intentions due to social desirability bias (the tendency to provide the response that is socially expected) or consistency bias (the need to appear consistent to the analyst, which can result in the stated intentions that are consonant with the previously expressed attitudes, whereas in reality one’s actual behavior will be dissonant from those attitudes). Generally, however, behavioral intention tends to have a positive association with the actual choice of that behavior. That is, the stronger an individual’s behavioral intention, the more likely he/she are to perform the behavior [39].
According to Triandis [33] intentions represent self-instructions to behave in a certain way. It represents desires, wishes or willingness to behave [8]. Taylor et al. [40] state that thanks to the ability of predicting intentions of use, predicting behavior is made possible. Intention to transact is broadly described as the consumer's intent to engage in an electronic exchange relationship with a Web retailer [30].
Perceived Ease of Use (PEOU), Perceived Usefulness (PU) and Intention to use Online Shopping
Beliefs about ease of use and usefulness were posited directly to intention [41]. As for information systems, some researchers empirically tested for ease of use as a direct determinant of attitude [40]. According to TAM, PEOU has a direct influence on consumers' intention to shop online. This direct effect is explained by the fact that in behavioral decision making, consumers attempt to minimize effort in their behaviors. In a study completed by Monsuwe et al., a framework was proposed to increase researchers' understanding of consumers' attitude towards online shopping by using TAM model. The review shows that attitude and intention to use online shopping strongly affected by PEOU and PU. Difficulty of use may create in consumers a negative attitude toward using internet as a shopping tool. The same logic was supported by other researchers who also found that PEOU had a positive influence on consumers' attitude in using the internet to shop online [42]. This is in line with the work of Childers et al. which found that PEOU determines attitude towards interactive shopping. Complexity and low PEOU have a negative relationship with the e-shopping intention. In other studies, they found that PU is the extent to which a consumer believes that using online shopping will enhance his/her transaction performance. Many studies argued that PU significantly influences attitude formation. Furthermore, in a recent study by Premkumar et al., perceived usefulness was found to be the strongest predictor of intention in TAM, and continues to be the strongest predictor of continues intention. Consumers, who believe that online shopping is an advantageous, develop a positive attitude toward online shopping [43]. The same logic was observed by other researchers when found that PU has positive influence on consumers' attitude toward online shopping, and also online shopping intention [41,44]. Azizi et al. [8] further added that perceived e-benefits did not directly affect the shopping intention, but through using mediating variable (e-attitude), they found positive direct and indirect effects of perceived e-ease of use on e-shopping intention. In summary, this means that the higher the perceived e-ease of use, the higher their e-shopping intention is because they possess the adequate knowledge for using websites and know how to shop electronically and this is reflected by the positive impact of attitude toward shopping on the e-shopping intention. Based on this, the researcher proposes to link PU and PEOU with attitude and also with online shopping intention through attitude.
Perceived Risk and Intention to Use Online Shopping
Perceived risk was found in the previous studies to have an effect on customer trust and the intention to buy online [45,46]. In recent study by Ma'ruf et al. [47] compared TAM and TPB, and they then integrated the two models in predicting the behavioral intention to make online purchases among the Internet users. They found that perceived risk is the primary determinant of behavioral intention. Jarvenpaa et al. [48] found that higher consumer trust; this in turn reduces perceived risks associated with Internet shopping and generates more favorable attitudes towards shopping at a particular store, which in turn increases willingness to purchase from that store. The same logic were supported by different researchers, where negative effects from the perception of risk have been found to have a negative impact on shoppers' attitudes towards online shopping [49-51] as well as a negative effect on a person's intention to shop online [52,53]. When a person perceives online shopping as a risky activity, he/she tends to harbor a negative attitude [54]. And also the likelihood of purchasing on the internet decreases with the increases in perceived risk [55].
Product Involvement and Intention to Use Online Shopping
Product involvement can influence the decision-making process regarding a product. More specifically, the extent of the consumer’s search for information about the product, the manner in which the consumer’s attitudes and preferences regarding the product are affected, and the consumer’s perceptions regarding the various alternatives to the same product category [56-58] all influence the decision-making process regarding a product. In a recent study by Keisidou et al. [25], it was found that high product involvement positively affects user attitudes toward online shopping in the context of all employed products or services. Therefore, online shopping can overcome time and spatial barriers and consumers can compare product features and price across sites. Furthermore, the amount of product/service information which can be obtained online also influences a person’s purchasing online. The researcher supposed that consumers with high product involvement could have positive attitude toward online shopping and develop the intention to engage in online shopping.
Subjective Norms and Intention to Use Online Shopping
Previous studies found that consumers may believe that their family, friends, and peers would favor certain online behaviors and this belief tends to influence their intentions and behavior [9,59]. The same logic was supported by Shim et al. [54] where the researchers found subjective norms to be only marginally significant for e-shopping intentions, whereas Foucault et al. [59] found a significant link between talking about e-shopping with friends and intention to e-shop. In a recent study by Howladar et al. [60] the study (2012) found that online purchase intention and its continuity depended on the attitude of the customer that ultimately was influenced by the trust or belief on online shopping. Subjective norms are shaped by society and media, control variables and some support facilities. Moreover, online customers’ intention can become positive towards online buying behavior if there is a social support, positive media role, family support and control of risk variables that cause de-motivation of online purchase. Therefore, the researcher proposed that subjective norms influence consumers' intention to use online shopping.
Attitude and Online Shopping Intention
Attitude has been suggested as influencing behavioral intention in multiple theories, such as the TPB [30], TRA [2] and TAM [1]. Fishbein et al. [2] argued that attitude refers to the degree to which behavior is questioned. Attitude is also understood as it relates to various behaviors and is identified as being one of the primary indicators that makes a significant contribution to the prediction of intention. Understanding the determinants of consumers’ attitude helps predict consumer’s intention. It is argued that this attitude has a strong, direct, and positive effect on consumers’ intentions to actually use the new technology or system [61,62].
There is a strong association between attitude and intention [63]. Attitude has long been identified as a cause of intention [64]. Essentially, attitude was concluded through research findings to be the most significant construct in influencing behavioral intention. In other words, the more positive consumers' attitude toward online shopping, the higher the intention he/she has to engage in online shopping [65,66].
Before examining consumers' attitudes towards online shopping for a particular product or service, it is important to study consumers' attitudes towards online shopping in general. In a study by Karahanna et al. [67] maintained that attitudes dominate one’s intention to using information technology. Hence, a positive intention of using internet to fulfill a transaction is formed by consumers with positive attitudes towards use of internet. Davis et al. [42] concluded that the attitudes towards using technology in general and internet technology in particular was very important. Also, Jahng et al. [68] proposed that attitude is a multidimensional construct, and one such dimension is the consumers' acceptance of the internet as a shopping channel.
Behavioral intentions tend to have a positive association with the actual choice of behavior. That is, the stronger an individual's behavioral intention, the more likely he/she are to perform the behavior [69]. Therefore, the researcher has concentrated in investigating those factors that influence consumers' intention as a determinant of actual use toward online shopping through attitude. The researcher also proposed that consumers' attitude toward online shopping was influenced by Perceived Ease of Use, Perceived usefulness, Perceived Risk, and Product involvement all factors that ultimately lead to the consumers' acting out on their intention towards engaging and using online shopping. A summary of some of the previous studies” factors affecting consumer's intention to use online are listed in the next table (Table 1).
| Authors | Purpose | Data Sources | Major Result |
|---|---|---|---|
| Al-Maghrabi et al. [80] | To clarify theory and identify factors that could explain the level of continuance intention of e-shopping | Online survey questionnaires were received from 465 online shoppers in Saudi Arabia and selected randomly. | Found that perceived usefulness, enjoyment and social pressure are determinants of e-shopping continuance. |
| Kahttab et al. [88] | To explore individualist and collectivist gender moderated differences toward online purchase intentions. | Data were collected via questionnaires from 241 student in Al-Balqa’ Applied University in Jordan. | The results of this study depicted that there was a significant difference between male and female toward online purchase intentions. The study also depicted that, there was a significant difference in the intention to purchase online according to individualist, mixed and collectivist cultural factors. Furthermore, the two-way interaction between gender and individualist, mixed and collectivist groups was significant regarding intention to purchase online. |
| Gong et al. [84] | To investigates the perceptions of online consumers in China and the U.S. toward online shopping. | A total of 503 Chinese consumers participated in a nationwide Internet survey (online questionnaire) in China selected by random sampling. | Findings indicate that Chinese and American consumers hold significantly different perceptions regarding the relative advantage, ease of use, and risk of shopping on the Internet. The results reveal that Chinese consumers perceive online shopping to have more relative advantage, more complex and less risky than do American consumers. |
| Li et al. [89] | To investigating the drivers determining individual consumers’ intention to use online group shopping as well as the relationships between the intention to use online group shopping and the intention to purchase via online group shopping websites. | Questionnaires were collected from 318 Chinese college students. | The main results show that all the individuals’ intention to use online group shopping are motivated by relative advantages, perceived complexity and perceived enjoyment, and their use intention is significantly related to their purchasing intention via online group shopping websites |
| Jusoh et al. [87] | To determine the factors influencing consumers’ attitude towards e-commerce purchases through online shopping. The study also investigate how socio-demographic (age, income and occupation), pattern of online buying (types of goods, e-commerce experience and hours use on internet) and purchase perception (product perception, customers’ service and consumers’ risk) affect consumers’ attitude towards online shopping. | Convenience sampling method was conducted in this study and the sample comparison of 100 respondents. | The findings revealed that there is no significant difference in attitude towards online shopping among age group, but there is a significant difference in attitude towards online shopping among income group. The findings revealed that there is a significant relationship between e-commerce experience, product perception, customers’ service, and attitude towards online shopping among the respondents. |
| Howladar et al. [60] | To develop a conceptual model of developing customers’ intention and motivation for on-line shopping that will be fit with the Bangladesh context. | Surveys for 300 internet users were received. | This study found that online purchase intention and its continuity depend on attitude of customer that ultimately influenced by trust or belief on online shopping and that subjective norms depend on social and media role, control variables and some support facilities. Moreover, online customers intention can be make positive towards online buying behavior if there is social support, positive media role, family support and control of risk variables that caused for de-motivation of online purchase. |
| Hsu et al. [85] | To investigate Mongolian consumer perception of online shopping, as well as the factors influencing on their attitude toward online shopping and their effect on their intention toward online shopping. | Online surveys were received from 107 users. The Study randomly selected consumers | The study found that consumer innovativeness, perceived benefits, and perceived risk are important determining factors influencing on consumer online shopping attitude and online shopping intention. |
| Atilgan-Inan et al. [81] | To identified the factors that give rise to the formation of the behavioral intention of young consumers shopping on the Internet. | 281 university students aged 18-24 and selected on the basis of convenience. | The findings depicted that subjective norms are the most influential predictor variables of behavioral intention among young consumers shopping on the Internet. |
| Yulihasri et al. [43] | To study the factors influencing student’s buying intention through internet shopping in an institution of higher learning in Malaysia. | Convenience sampling was used. The questionnaires were distributed to the respondents to a total of 300. | Technology acceptance model (TAM) has been applied on the study. The results support seven hypotheses from nine. Compatibility, usefulness, ease of use and security has been found to be important predictors toward attitude in on-line shopping. |
| Keisidou et al. [25] | To examine consumers’ attitude when making online purchases in the context of different product types. | A total of 232 internet users were selected to complete a questionnaire. | It has been found that PIIT, perceived security and product involvement have an effect on the attitude towards online shopping, yet the results vary among the different product types. |
| Jun et al. [86] | To examine the level of consumers’ attitude towards online shopping and investigate the factors that could influence the consumers’ attitude to adopting online shopping in China | This research was conducted by using the primary data source, and the survey method was employed in the research. | This research found that there were relationships between the perceived usability, perceived security, perceived privacy, perceived after-sales service, perceived marketing mix, perceived reputation and consumers’ attitude to adopting online shopping in China. However, only marketing mix and reputation were found to significantly influence consumers’ attitude to adopt online shopping. |
| Zhang et al. [91] | To identify the key factors influencing consumer's online shopping behavior. | The data was collected using a convenient sampling approach on a face to face base for 300 internet users. | The findings of this study identified seven important decision factors: Perceived Risk, Consumer Resources, Service Quality, Subjective Norms, Product Variety, Convenience, and Website Factors. All of these factors impact on Chinese consumers’ decisions to adopt online shopping Perceived Risk was the most important factor influencing consumers’ decisions to adopt online shopping. |
| Azizi et al. [8] | To examine the technology acceptance model and its impact on customer attitude toward online shopping. | The sample was drawn from 120 people living in Tehran. The sampling method was disproportionate stratified sampling. Tehran city was divided into four strata: North, south, west and east, and people were chosen randomly | Results indicated that perceived e-benefits did not directly affect the shopping intention, but through using mediating variable (e-attitude), and found positive direct and indirect effects of perceived e-ease of use on e-shopping intention and also showed the positive impact of attitude toward shopping on the e-shopping intention and finally found that e-risks did not affect the e-shopping intention neither directly nor indirectly. |
| Delafrooz et al. [39] | To examines the significance of attitude toward online shopping and to determine relationship between attitude towards online shopping with shopping orientations and perceived benefits scales. | A self-administered questionnaire was developed and a total of 370 post graduate students were selected by random sampling. | The regression analysis demonstrated the determinants of consumers’ attitudes towards online shopping. Additionally, utilitarian orientations, convenience, price, wider selection influenced consumers’ attitudes towards online shopping. |
| To Understand what motivates consumer intention toward online shopping, integrated with Mcleam Model. | Online survey questionnaires were received from 331 respondents. | The main result of this study suggested that the consumer’s intention to use is quite important, and accurately predicts the usage behavior of consumers. In contrast, consumer satisfaction has a significant impact on intention to use but no direct causal relation with actual use. |
Table 1: Summary of Previous Studies.
Online shopping is not a new phenomenon but rather it is a shopping tool that has established itself worldwide for many years now and has also become increasingly familiar for its respective consumers. The rate of consumer accessing online shopping has increased so much so that for many worldwide, online shopping is the preferred shopping method. Notwithstanding the rise in popularity of on-line shopping, there is also contrasting data that suggests that there is resistance and weariness towards engaging with E-commerce. There is not only a large gap in E-commerce usage percentages between developed and developing countries, but surprisingly there is also a disparity between E-commerce usage percentages in developing countries themselves. According to the Arab Advisor Group Survey in 2012, 24.4% of internet users in Jordan used E-commerce and total spending amounted to 370 million dollars. However, Jordan’s E-commerce spending is minimal when compared to 3 billion dollars which was spent in E-Commerce in Saudi Arabia. Saudi Arabia boasts as being the greatest population percentage and amount spent in e-commerce use than any other Arab nation. More specifically, there are 3.5 million users of e-commerce representing 14.26% of Saudi Arabia population.
In spite of the tremendous potential benefits that can be achieved for both E-Shoppers and E-business people, the actual use of online shopping is still very low in Jordan. According to survey report conducted by the ministry of information and communication technology, only 7% of internet users in 2011 made an actual online purchase. Does this percentage suggest that consumers in Jordan have no willingness to use online shopping or does this low percentage indicate that Jordanian internet users would like to engage in on-line shopping but they do not do so due to being unfamiliar and lacking the knowledge with internet shopping? Still, does the lack of Jordanians engaging in purchasing items on line mean that they reject E-commerce all together? The picture is very unclear when it comes to understanding the customers' perspective about purchasing over the Internet. To help provide a clearer understanding of this problem, it's necessary to review the stages of consumer behavior towards online shopping through examining theories of behavior such as TRA (Theory of Reason Action). And TAM (Technology acceptance Model) by exploring and examining such theories, one is better able to understand and appreciate what factors can influence consumer attitudes towards online shopping and assist in better understanding what essentially influences their intention in accessing online shopping.
While there are previous studies that have focused on online shopping on a global level and have examined various factors that influence consumers' actual usage of online shopping to purchase specific products or services, there is still nonetheless a great need for closer examination of online shopping ‘intention’ or willingness (or lack thereof) that influences consumers' actual behavior. In specific countries where there is distinct cultural norms and practices in business and trade such as in Jordan, the intention and willingness to access and engage with online purchasing is greatly impacted by cultural understanding. Acceptance of online shopping in Jordan is still at its infancy stage of development in Jordan and at present, little is known about consumers' intention towards online shopping and the factors which influence their intention. Therefore, this research attempts to explore and assess how consumers form their attitudes in establishing their intention to access or reject online shopping.
The main aim of this research is to answer the following central question, namely;
"What are the Main Factors that Influence Consumers' Intention to Use Online Shopping in South of Jordan?"
To answer the main question, the following sub questions were proposed:
• What are the major factors that influence consumers' attitude towards online shopping?
• What are the main factors that influence consumers' intention to use online shopping though attitude?
• To what extend does attitude influence the consumer intention to use online shopping?
To study the main factors influencing Consumers' Intention to use Online Shopping, the researcher relies on previous studies, the TAM model, TRA and the researcher observations in building the proposed model below.
The importance of this research derives from the following:
• There is a lack of studies that focus on consumers' intention to use online shopping in general in Arab countries, especially in Jordan. As a result, this research serves as being a good starting point for future research projects and studies. Moreover, this research study can also be used as a reference and help in providing other researchers beneficial information and a clearer direction when embarking on future research projects relating to online shopping in Jordan.
• This study is derived from referring to two important theories, more specifically the TRA (Theory of Reason Action) and also TAM (Technology Acceptance Model). These two theories focus on the very important aspect of consumer behavior which is intention; an essential determinant of consumers' actual behavior.
• The findings and recommendations of this research can assist local & foreign retailers in developing appropriate market strategies that can help retain current consumers and also function in attracting new consumers.
A research design is a plan of the research project to investigate and obtain answers to research questions. The research problem is already crystallized and the purposes of the research have been clearly stated such that a descriptive design is the most appropriate research design for this study [70]. Descriptive research helps describe and measure the factors influencing online purchase intention by consumers (Figure 1). A deductive approach was considered appropriate because the information being gathered from this type of research enables the researcher to answer the research questions, objectives, identify the main factors, and depict the influence of these factors influencing of the intention to use online shopping [71].
The design was quantitative because the data took a numerical form. That is, by employing a deductive approach with a quantitative approach method, the researcher has been able to measure and analyze the relationship between influencing factors and consumers' intention to use online shopping. This approach also allows for testing the research hypotheses and generalizing the research findings to the population [71].
Figure 1: The proposed model of the research.
In view of time and cost constraints as well as the large population of Internet users in south of Jordan, and because of the unavailability of the list includes online shopper that involved in online purchases, convenience sampling was used to collect data. It is the best way of getting some basic information quickly and efficiently [70]. The appropriate sample size should be considered in order to make generalizations with confidence about the constructs under investigation. Therefore, the sample statistics need to be reliable and represent the population parameters as close as possible within a narrow margin of error. For factor analysis, the minimum sample size should be at least five times as many observations as the number of variables to be analyzed [72]. Since there are 35 question items to be analyzed in this study, at least 175 usable questionnaires were required. Moreover, Crouch [73] recommends that “minimum sample size for quantitative consumer surveys are of the order of 300 to 500 respondents”. Thus, this study required at least 300 questionnaires. The researcher distributes (300) questionnaires in Karak, Tafelah, Ma'an, and Aqaba, 75 in each. (250) were returned and (230) were valid for analysis. Questionnaires were distributed both by online and by hand to respondents and enough time given to respondents to fill the questionnaire to reduce sampling error. The procedure that is being followed by the researcher to select a sampling unit is a mixed process. A mixed process means distributing survey online as well as in person, in private sector and in public sector, employed and unemployed people.
A Self-administered questionnaire was used for this research in order to obtain data and was conducted in Arabic language and in a simple language in order to reduce the risk of ambiguity. The questionnaire is carefully designed to meet the requirements of the research. It is divided into two sections. The first section covers demographic information (gender, age, marital status, income level, educational level). The second section contains (35) item questions. The questions are taken from previous literature and some of the questions are self-structured to cover the diversity of research problems. The questionnaire comprised from items related to perceived usefulness, perceived ease of use, perceived risk, product Involvement, subjective norms, online shopping attitude, and online shopping intention.
Part 1and part 2 measured perceived usefulness and perceived ease of use with ten item questions developed by Davis [74] and were adapted to meet the research Topic (Online Shopping). Part 3 measured perceived Risk with five dimensions by six item questions adopted from [13,14,18]. Part 4 measured product Involvement with six item questions were derived from the definition of product involvement by Zaichkowsky [20]. Part 5 measured subjective norms with four item questions, and adopted from [75,76]. Part 6 measured consumers' attitude towards online shopping with five item questions developed by Lin [76]. Part 7 measured online shopping intention by four item questions were developed by Zarrad et al. [77]. The five point Likert scale was employed, which ranged from (1) 'Strongly disagree' to (5) 'Strongly agree'.
Construct validity has been assessed by using correlation analysis, the result of testing the validity showed in Table 2. It implies that items that are indicators of a specific construct should converge or share a high proportion of variance [78]. In other words, it assesses the degree to which measures of the same concept are correlated, with high correlation indicating that the scale is measuring its intended concept.
| Factor | Item of Question | Sig (2-tailed) | Correlation of item-to-total |
|---|---|---|---|
| Perceived ease of use | PEU1 | 0.05 | 0.525 |
| PEU2 | 0.05 | 0.602 | |
| PEU3 | 0.05 | 0.533 | |
| PEU4 | 0.05 | 0.605 | |
| PEU5 | 0.05 | 0.355 | |
| Perceived usefulness | PU1 | 0.05 | 0.453 |
| PU2 | 0.05 | 0.459 | |
| PU3 | 0.05 | 0.571 | |
| PU4 | 0.05 | 0.548 | |
| PU5 | 0.05 | 0.444 | |
| Perceived Risk | PR1 | 0.05 | 0.432 |
| PR2 | 0.05 | 0.554 | |
| PR3 | 0.05 | 0.586 | |
| PR4 | 0.05 | 0.584 | |
| PR5 | 0.05 | 0.41 | |
| PR6 | 0.05 | 0.325 | |
| Product Involvement | PI1 | 0.05 | 0.784 |
| PI2 | 0.05 | 0.629 | |
| PI3 | 0.05 | 0.862 | |
| PI4 | 0.05 | 0.858 | |
| PI5 | 0.05 | 0.931 | |
| PI6 | 0.05 | 0.564 | |
| Subjective Norms | SN1 | 0.05 | 0.395 |
| SN2 | 0.05 | 0.54 | |
| SN3 | 0.05 | 0.71 | |
| SN4 | 0.05 | 0.672 | |
| Attitude | ATT1 | 0.05 | 0.649 |
| ATT2 | 0.05 | 0.919 | |
| ATT3 | 0.05 | 0.353 | |
| ATT4 | 0.05 | 0.623 | |
| ATT5 | 0.05 | 0.73 | |
| Intention to use online shopping | IUS1 | 0.05 | 0.315 |
| IUS2 | 0.05 | 0.295 | |
| IUS3 | 0.05 | 0.351 | |
| IUS4 | 0.05 | 0.352 | |
| *Correlation is significant at the 0.05 level (2-tailed) | |||
Table 2: Test of Construct Validity.
The Item-to-total correlations in this research ranged (0.295-0.93) and all items were significant because (Sig <0.05); with each dimension.
The reliability of the scales was established by utilizing Cronbach’s alpha (Table 3). Considering the present research as a whole, Cronbach’s alpha varied from (0.62-0.89), which is considered acceptable for this type of research.
| Model | Collinearity Statistics | |
|---|---|---|
| 1 (Constant) | Tolerance | VIF |
| PEOU | 0.545 | 1.834 |
| Usefulness | 0.539 | 1.856 |
| Risk | 0.89 | 1.124 |
| Involvement | 0.548 | 1.826 |
| Subjective Norms | 0.514 | 1.946 |
| Attitude | 0.449 | 2.228 |
Table 3: Cronbach’s Alpha for the Scales.
| Model | Collinearity Statistics | |
|---|---|---|
| 1 (Constant) | Tolerance | VIF |
| PEOU | 0.545 | 1.834 |
| Usefulness | 0.539 | 1.856 |
| Risk | 0.89 | 1.124 |
| Involvement | 0.548 | 1.826 |
| Subjective Norms | 0.514 | 1.946 |
| Attitude | 0.449 | 2.228 |
Table 4: The Multi -Collinearity Test.
To analyze Multi-Collinearity, two types of measurements can be used: Variation Inflation Factor (VIF) and Tolerance. The VIF, measures the extent the variance of the estimated regression coefficients are inflated as a result of being related to the other independent variables, and Tolerance is the amount of variability of the selected independent variables not explained by other independent variables.
Results in Table 4 shows that VIF for all independent variables ranged between (1.12-2.22), which are less than the limited valued (10) and Tolerance for all independent variables ranged between (0.44-0.89), which are greater than (0.05). This indicates that there was no high correlation among the independent variables (Multi-collinearity).
The ratio of Skewness to its standard error can be used as a test of normality (that is, you can reject normality if the ratio is less than -2 or greater than +2). A large positive value for Skewness indicates a long right tail; an extreme negative value indicates a long left tail" (SPSS Base 16.0 users Guide, 2007: p.276). Table 5 presents the Skewness normality distribution test: The reading of the Skewness test findings, all variables are normally distributed, ranging from (-0.83-0.06) falling within the interval of (+1, -1).
| Variables | Skewness |
|---|---|
| Perceived ease of use -PEOU | -0.47 |
| Perceived usefulness-PU | -0.82 |
| Perceived Risk-PR | -0.83 |
| Product Involvement -PI | 0.06 |
| Subjective Norms -SN | 0.07 |
| Attitude -ATT | -0.71 |
| Intention to use online shopping -IUS | -0.49 |
Table 5: Skewness Coefficients.
Fitness of the model
From the Table 6 the relationships between the independent variable were statistically significant with positive direction, except the relationship between the behavior and personal norms which has a negative direction. After that the fitness indices were tested anther time the results shows chi square (χ2) value was 3.20 (df=5, p=0.66),, comparative fit index (CFI) was 1, and the root mean square residual (REMSA) was 0.00, which indicate good-fit indices so that the null hypotheses are accepted All the goodness of fitness indices are presenting in Table 7. Which all of it within the acceptable range.
| Estimate | S.E. | C.R. | P | |||
|---|---|---|---|---|---|---|
| Involvement | <--> | SN | -0.52 | 0.018 | -7.296 | *** |
| PEOU | <--> | Usefulness | 0.662 | 0.034 | 8.383 | *** |
| Risk | <--> | Involvement | 0.188 | 0.02 | 3.358 | *** |
| Usefulnes | <--> | Risk | 0.249 | 0.026 | 3.827 | *** |
| PEOU | <--> | Risk | 0.218 | 0.023 | 3.381 | *** |
| PEOU | <--> | SN | 0.235 | 0.015 | 4.109 | *** |
| Usefulnes | <--> | SN | 0.221 | 0.016 | 3.892 | *** |
Table 6: Covariance’s: (Group number 1 - Default model).
| Fit Indices | Recommended value | Values obtained |
|---|---|---|
| χ2/d.f | ≤3.00 | 2.51 |
| GFI | ≥0.90 | 0.98 |
| AGFI | ≥0.80 | 0.98 |
| NFI | ≥0.90 | 0.98 |
| CFI | ≥0.90 | 0.98 |
| TLI | ≥0.90 | 0.97 |
| RMSEA | ≥0.08 | 0 |
Table 7: Model-fit indices for modified model.
From the results showed in Table 8.
• The impact of the perceived usefulness on consumers' attitude towards online shopping was 0.11 and the t value was 1.78 with p level 0.08 where (p>0.05) this means there is no significant impact of the perceived usefulness on consumers' attitude towards online shopping.
• The impact of the perceived Ease of use on consumers' attitude towards online shopping was 0.06 and the t value was 0.94 with p level 0.035 where (p>0.05), this means there no significant impact of the ease of use on consumers' attitude towards online shopping.
• The impact of the perceived risk on consumers' attitude towards online shopping was -0.02 and the t value was -0.43 with p level 0.67 where (p>0.05), this means there no significant impact of the perceived risk on consumers' attitude towards online shopping.
• The impact of the product involvement on consumers' attitude towards online shopping was 0.40 and the t value was 7.47 with p level 0.001 where (p<0.05), this means there significant impact of the product Involvement on consumers' attitude towards online shopping.
| Estimate | S.E. | C.R. | P | |||
|---|---|---|---|---|---|---|
| Attitude | <--- | Usefulness | 0.11 | 0.04 | 1.78 | 0.08 |
| Attitude | <--- | PEOU | 0.06 | 0.04 | 0.94 | 0.35 |
| Attitude | <--- | Risk | -0.02 | 0.04 | -0.43 | 0.67 |
| Attitude | <--- | Involvement | 0.4 | 0.04 | 7.47 | 0.001 |
Table 8: Path coefficient and t value for the impact of perceived usefulness, perceived ease of use, perceived risk, product Involvement on consumers' attitude towards online shopping.
From the results showed in Table 9.
• Standardized Indirect the impact of perceived usefulness (PU) on consumers' intention to use online shopping through consumers' attitude was (0.024) which mean that there is statistical significant impact of perceived usefulness on consumers' intention to use online shopping through consumers' attitude.
• Standardized Indirect the impact of perceived ease of use on consumers' intention to use online shopping through consumers' attitude was (0.013) which mean that there is statistical significant impact of perceived ease of use on consumers' intention to use online shopping through consumers' attitude.
• Standardized Indirect the impact of perceived risk of use on consumers' intention to use online shopping through consumers' attitude was (-0.005) which mean that there is statistical significant impact of risk of use on consumers' intention to use online shopping through consumers' attitude.
• Standardized Indirect the impact of impact of product Involvement on consumers' intention to use online shopping through consumers' attitude was (0.091) which mean that there is statistical significant impact of product Involvement on consumers' intention to use online shopping through consumers' attitude.
| Involvement | Risk | Usefulness | PEOU | |
|---|---|---|---|---|
| Attitude | 0 | 0 | 0 | 0 |
| Intention | 0.091 | -0.005 | 0.024 | 0.013 |
Table 9: Standardized Indirect Effects (Group number 1 - Default model).
From the Table 10 the impact subjective norms on consumers' intention to use online shopping was 0.34 and the t value was 6.58 with p level 0.001, this means there significant impact of subjective norms on consumers' intention to use online shopping intention to use online shopping.
| Estimate | S.E. | C.R. | P | |||
|---|---|---|---|---|---|---|
| intention | <--- | Subjective norms | 0.34 | 0.03 | 6.58 | 0.001 |
Table 10: Path coefficient and t value for the impact of subjective norms on consumers' intention to use online shopping.
From the Table 11 the impact subjective norms on consumers' intention to use online shopping was 0.23 and the t value was 4.06 with p level 0.001, this means there significant attitude on consumers' intention to use online shopping.
| Estimate | S.E. | C.R. | P | |||
|---|---|---|---|---|---|---|
| intention | <--- | Attitude | 0.23 | 0.03 | 4.06 | 0.001 |
Table 11: Path coefficient and t value for the impact of attitude on consumers' intention to use online shopping.
Perceived Ease of Use (PEOU), Perceived usefulness (PU), Attitude and Intention
It was found that that (PEOU) and (PU) have no direct impact on consumers' attitude (p0.35 >0.05) and (p0.08 >0.05) respectively and that this is consistent with Davis [1]. However, (PEOU) and (PU) were found to have an indirect impact on intention to use online shopping through attitude with path coefficients (0.24), (0.013) respectively. The same result was recorded by Azizi and Javadani [8] who also found that PU did not directly affect the online shopping intention except through using mediating variable (attitude). In addition, they found that PEOU has a direct and indirect impact on online shopping intention. Due to the fact that people who were surveyed belong to the category of having higher education and being youth consumers, it may be that with good computer knowledge and expertise, this will help them navigate shopping websites easily regardless of the level of sophistication.
The conclusion is further reinforced by individuals who intend to shop and transact electronically would likely search a website for any given product related information in order that they make a purchasing decision. In turn, this justifies the strong relationship between product involvement, attitude, and intention through attitude.
Perceived Risk (PR), Attitude and Intention
It was found that Perceived risk has no direct impact on consumers' attitude (P=0.67 >0.05). In contrast, Shim et al. [54] found that when a person perceived online shopping as a risky activity, he/she tends to hold negative attitude towards online shopping. However, Perceived Risk has an indirect negative impact on consumers' intention to use online shopping through attitude with (-0.005) indirect path coefficient. This finding is similar to the result of Ma'ruf et al. [47] where he found that perceived risk is primary determinant of behavioral intention. With the development and application of high technology, the researcher believes that Jordanian consumers will become more aware of the privacy and security of online shopping offers. Moreover, online shopping services in Jordan have improved significantly in recent years. Consumers are aware that banks in Jordan also provide digital certificates for consumers to enhance the security of transfers. However, most online sellers provide cash on delivery service. When consumers receive the products, they can pay the postman directly. Thereby avoiding transferring money through the Internet. All of the above have contributed to the reduction of consumers’ worries about the perceived risk of online shopping. Thus, the perceived risk of online shopping has not become an important factor impacting on consumers’ attitude in this study but it nonetheless was found to have an indirect impact on intention to use online shopping through attitude.
Product Involvement, Attitude and Intention
It was found that Product involvement has a significant impact of attitude (P=0.001<0.05) and a significant indirect impact on intention through attitude with (0.091) path coefficient. This finding was similar to the research published by Keisidou et al. [25] where they found that high product involvement positively affect users attitude towards online shopping. Such result means that the consumers’ interests and concerns with a product or service impact the likelihood of them purchasing any given item. In other words, when consumers read detailed information about the product that they are considering to buy essentially by choosing a known brand and taking time for examining the product prior to purchasing, he/she consequently develops a positive attitude towards online shopping which then results in increasing their intention in using online shopping in the future. The researcher inferred from the findings that Jordanian consumers are educated consumers, that is that the intention to shop online relies heavily or the extent to which information and knowledge they have concerning the product. Thus, the internet may be said to be valuable source of information to buyers in Jordan. This conclusion highlights that shopping websites should be designed carefully in terms of content and function ability.
Subjective Norms and Intention
In this study it was found that Subjective Norms had a positive direct impact on Intention to use online shopping. This finding is similar with multiple studies [9,59,79]. These results imply that consumers believe that their friends, family, and media can reinforce openness and engaging in online interactions and this belief serves in shaping their intention to use online shopping. It is important to note that typically, Jordanian consumers reflect their collectivism culture and hence they are heavily influenced by the expectations of people around them.
Attitude and Intention
In this study it was found that Attitude had a positive direct impact on Intention to use online shopping. This finding confirms what has been found in several studies [61,65,66]. When consumers have a positive attitude towards online shopping, this results in reinforcing their belief that accessing the internet as a shopping tool is a good, favorable and worthy idea [67-80]. Therefore, this serves ultimately in increasing their intention to use online shopping in the future.
In light of the research results and conclusions the researcher recommends that:
• Retailers should increase the positive perceptions of trust in order make the e-shopping environment more useful and easier to use for consumers. For example, a new customer is more likely to judge a website by its appeal that is that the website reflects quality and trustworthiness rather than simply focusing on usability. Furthermore, to support online shopping intention, system developers for online shopping sites should focus on developing effective user guidance, such as video tutorials and step-by-step visual e-how guides to aid consumers who are new or unfamiliar with online shopping.
• Online retailers can provide interactive features, such as live customer support and chat rooms for shoppers that help in facilitating flexible multiple interactions with consumers and also between consumers and online retailers using multimedia and augmented reality.
• Perceived risk has no direct impact on consumers' attitude but does have an indirect impact on their probability of risk purchasing online. Therefore, retailers and marketers should eliminate the perception of risk and the probability of risk at each and every opportunity by developing the best risk reduction strategy such as: providing a money back guarantee (in the case that they are not satisfied with a product and wish to return the product item high security system) and they should provide an on time delivery guarantee.
• To have a significant effect on e-shopping intentions, any e-shopping environment should encourage a shopping experience that is useful and easy to use. In fact, customers’ involvement in the product design process is likely to be perceived as more enjoyable. For example, Nike online shoppers can customize shoes, colors, styles, and even select a name or message.
• Companies should try to increase the positive attitude of their consumers in order to increase their purchase intention, which in turn will lead to increase of sales of products via Internet. Retailers and site developers should focus on the quality and informative content, more particularly retailers on-line should design a website that best provides a shopping experience that reinforces traditional shopping. Also, a company should establish good brand reputation to the provided product and services. Traditionally, shoppers like to compare the products within the same price range and same quality and they prefer to examine the same product offered by different companies [81-85]. Keeping this strategy in mind, a company’s website should provide a function where customers can compare the products in a virtual traditional shopping environment. Establishing alliances with other similar companies or related product companies can also help the way customers increase in accessing information.
• Retailers or companies have to provide goods and services that are more suitable for internet or online shopping. Based on the research findings, understanding the utilitarian and hedonic roles of e-shopping would enable marketing managers to increase the scope of e-shopping. For example, interactive activities such as an inside look at an airplane, building, and cars in a 360˚ and 3D view, selecting your travel seat using virtual model technology, or trying-on clothing on-line can provide more control over the purchasing process and assist in facilitating product evaluation [86-88]. Sophisticated technologies used in e-shopping can provide e-retailers with many techniques that can increase the feeling of involvement through purchasing for e-customers.
• Online retailers through the ‘positive word of mouth’ can enhance the perceptions of products to friends and family members of current customers. Thus, the ‘positive word of mouth’ serves in increasing perceptions of the product’s trustworthiness. Therefore, retailers should communicate the product’s benefits and values and they should promote spreading the positive ‘buzz’ through the word of mouth---that is that customers recommend the product to others. Through peer social pressures and expectations in purchasing a product, this could encourage consumers to learn and use shopping online and ultimately leading to more intention to e-shop. Use should be made of social networks such as Facebook, Twitter, and many more to enhance the perceptions of family, friends, potential and actual customers of their websites usefulness and trustworthiness.’ This is more important among young consumers as they are more familiar with the virtual online world. Indeed, many through on-line technologies develop personal relationships, play, learn, and share experiences.
Like other researches this research also is not without certain limitations that must be addressed in future research. These limitations do not minimize the significance of the results or findings in this study [89-91]. The below points are mentioned in order to direct the attention of future research identifying and aiding further improvement in this area:
• In this research, the researcher used a single method in data collection (questionnaire) to test a number of hypothesized relationships.
• Most of the respondents were highly educated and well experienced regarding the Internet. Future research might examine more diverse web users such as older, less educated, and less experienced on the Internet to validate the more generalized model.
• The influence of demographics was not investigated in this research. Notably, this research only presented the demographic characteristics of Jordanians. Nevertheless, several differing findings as compared to majority of other electronic commerce research findings were encountered. Hence, the potential outcome of such investigation is likely to be fruitful and highly sought-after as the moderating effects of demographic variables, such as age, gender and income, may provide contrasting responses and further contribute to deeper understanding of consumers’ attitudinal formation and intention to do shopping online.
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