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Technology
Procedia Technology 8 (2013) 435 - 444 ^^^^^^^^^^^^^^
6th International Conference on Information and Communication Technologies in Agriculture, Food and Environment (HAICTA 2013)
Predicting Consumer's Perceptions in On-line Shopping
A. Mandilasa*, A. Karasavvogloua, M. Nikolaidisa, L. Tsourgiannisa
aKavala Institue of Technology, Deparment of Accountancy, Kavala, 65404, Greece
Abstract
The diffusion of the e-commerce is now well-known in all of its types of activities. The four types that described in this paper is the most common and shows how people nowadays that is familiar with the Internet, tend to adopt more easily than the previous years. The results will indicate which are the major concerns for people in order to adopt one of those activities. Also, even now that internet has been so widely spread and used, people are so pessimistic in e-commerce adoption because of the risk or not. Of course the research is by its nature web-based so the appropriate data collection is via e-mail. The findings of the survey might be useful for companies and businesses which are active in this field. © 2013TheAuthors.PublishedbyElsevierLtd.
Selectionandpeer-reviewunderresponsibilityofThe HellenicAssociation for Information and Communication Technologies in Agriculture
Food and Environment (HAICTA)
Keywords: E-Commerce; TAM; Consumers perceptions
1. Introduction
The rapid diffusion of the Internet retailers the previous decade, was too high for the business to customer (B2C) commerce. Only in United States, for the fiscal year 2001, total retail sales was 3.50 trillion dollars while in ecommerce retail sales was 32.57 billion dollars. Those numbers indicate that B2C commerce is still growing up, and that the traditional retailers are not in danger of being replaced by electronic commerce. There is the possibility though that the e-commerce might be the new retail medium for supplementing, complementing and even replace other media [59].
* Corresponding author. tel +30-2510-462-222, fax +30-2510-462-224. E-mail address: smand@teikav.edu.gr
2212-0173 © 2013 The Authors. Published by Elsevier Ltd.
Selection and peer-review under responsibility of The Hellenic Association for Information and Communication Technologies in Agriculture Food and Environment (HAICTA) doi: 10.1016/j.protcy.2013.11.056
The Internet evolution had significantly changed the operations of retail business worldwide. United States for instance, witnessed larger hypermarkets which centralized their operations in order to provide the increased demand for their offerings. As a result, those hypermarkets were able to provide a cheaper and wider range of products while smaller retailers were struggling to be competitive against them. Because of the huge size of those hypermarkets, they need sufficient population to support them in order to operate normally. With the Internet's evolution, it is obvious that there will be an increase in those hypermarket sales as an alternative distribution [46].
2. Literature Review
2.1. Technology Acceptance Model (TAM)
Many researches which surround the customer's behavior to adopt in particular technologies had been taken from the Technology Acceptance Model (TAM). Modifications of the TAM model are many, from ERP system implementation [34] to mobile services [33].
Up to now, the rivalry of the actual usage versus prediction can be easily noticed by comparing different studies of the technology. For example, previous studies about personal computer had investigated the actual usage [45] while the new technological acceptances like banking Salancik [52], set aside the actual usage and focused on the criterion of the intention alone. However, since the adoption of the Internet purchase is still in its new forms in Greece, with this study the measurement focuses on the intention of the online shopper rather than the actual purchase.
The behavioral intention had been used to predict the actual usage successfully until now. Behavioral intention is the "degree to which a person has formulated conscious plans to perform or not perform some specified future behavior" [60]. This one comes across with the Theory of Reasoned Action [18] and his theory of planned behavior [1], which supports that the behavioral intention is for the behavior a good predictor. In Information systems (IS), TAM was used widely by many studies in order to predict the behavioral intention for the information technology [36]. While past researches [14] found no connectivity with the subjective norm and the behavioral intention, which had as a result not to include it on their TAM model, however Brown et al. [9], found that the norm does influence the behavioral intention.
Davis firstly introduced TAM, and his goal was to explain and predict user's IT acceptance over his workplace. According to Davis [13], perceived usefulness "is the prospective user's subjective probability that using a specific application system will increase his or her job performance within an organizational context" while ease of use "refers to the degree to which the prospective user expects the system to be free from effort".
Those two variables are the major factors which are expected to influence someone's attitude for using a specific system, which in turn with the perceived usefulness, are expected to explain any possible intension of using the system.
2.2. Prior research on TAM
According to Davis [13] "the proposed future technology acceptance research need to address other variables that could affect the TAM". Thus, it is useful to extent the original TAM model so that we can explore how it is reacted
The two basic constructs of the TAM model is perceived usefulness and perceived ease of use and the HCI researches after investigation, they agreed that those constructs are valid in predicting the information technology systems [39]. There is also the need for additional variables apart from those two, depending on the specific technology which is being used [41]. According to Dillon and Morris [15] "the demonstrable willingness within a user group to employ information technology for the tasks it is designed to support". Other researches indicated that the possible perception of the information technologies can easily be influenced by the type of the technology and
the interaction among users [35]. An example is the perception from the user for a system which is influenced by people who use and evaluate the system [50]. As a result, social bonds which connect user with the other parties, can be predicted as essential for the user's technology acceptance adoption [54]. The study of Salancik and Pfeffer [52], the perceptions of a specific technology from a user can be influenced by the people that surround him, from their opinions and behaviors towards it. Moreover Lee et al. [35], found that the technology acceptance is mostly influenced by social interactions and not by objectives from the technical characteristics.
2.3. Beliefs about online shopping Enjoyment
Perceived enjoyment is one of the most important factors for the Internet shopping acceptance. Shopping enjoyment is defined as "the extent to which one believes that shopping will provide reinforcements in its own right, going beyond performance consequences and such enjoyment extends to the online channel" [5]. Studies for consumers reached to the point that people have a wide range of different motivations and different shopping approaches which trigger their behavior [53]. Moreover, studies for the motives of Internet shopping include not only the advantages which consist of convenience, financial benefits and great information accessing but also hedonic aspects of e-commerce like enjoyment, normative beliefs and self-efficacy [27].
Perceived enjoyment is a strong predictor for the acceptance of a new technology [10]. Davis et al. [14] wanted to extend their original TAM model including the aspect of perceived enjoyment as an additional factor which may affect the technology acceptance. Studies for the Internet shopping found that the factor of enjoyment is a strong predictor for the acceptance of the Internet shopping and that its role is being distinct from the roles of PU and EOU [11].
According to [11] "perceived usefulness is the final outcome resulting from a chain of shopping activities while consumers associate ease of use and enjoyment with one's shopping process and one's intrinsic perception of e-shopping leading to the consequent perception."
2.4. Beliefs about online shopping Risk
One of the most important barriers for the adoption and online purchase is the perceived risk. Perceived risk is defined as the extent for a consumer's belief about the potential uncertain negative outcomes from the online transaction. The study of Jacoby and Kaplan [61], identified seven different types of risk: psychological, social, time, financial, performance, physical and opportunity cost risk. However, in the Internet shopping three types of risk were found to be important: financial risk, information and product risk [7].
The perceived risk for the online purchase adoption was found to influence the consumer's online intention [3]. Usually customers are reluctant when they have to take a decision for an online transaction because the sense of risk was found to be higher compared to the traditional marketing channels. The above mentioned barriers make the consumer to be attentive to risk with the online transactions and they are probably influenced on their decision about purchasing or not from an online vendor [30].
2.5. Shopping Orientation
The drawback of the TAM model is its treatment towards the Internet shopping as an outset of the technology. This means that TAM treats Internet shopping as a medium to communicate with purchasing without taking in mind the disposition of any possible consumer. Here comes the Theory of Planned Behavior and the Theory of Reasoned Action which explains someone's relationship between belief and attitude towards the intention in Internet shopping.
3. Research model and hypotheses
Here is our modified TAM model about the intention for the Internet shopping in Greece. Except from the perceived usefulness and the ease of use which are the main TAM factors, we include the perceived risk, perceived enjoyment, subjective norms and self efficacy.
3.1. Perceived risk
According to Bauer, [4] "Consumer behavior involves risk in the sense that any action of a consumer will produce consequences that he cannot anticipate with anything approximating certainty, and some of which are likely to be unpleasant".
H1: Perceived risk will have a negative impact on the customer's intention to shop online.
3.2. Perceived usefulness
According to Davis [13] "perceived usefulness is the extent to which a person believes that using a particular technology will enhance his or her job performance". This performance should be centered with the benefits through Internet purchasing adoption minus the normal retailing.
For the needs of this research, perceived usefulness is the extent to which a person believes that by adopting on Internet purchase, will create value for him.
H2: Perceived usefulness will have a positive impact on the customer's intention to shop online.
3.3. Perceived ease of use:
This has to be about the level of effort someone needs in order to make an e-commerce adoption. The higher the effort, the easier user will abandon the system [56]. Perceived ease of use is one of the two strong factors, which play a significant role in Internet shopping, like perceived usefulness. Thus, in this research perceived ease of use is when the user believes that any possible Internet purchase, will be free from effort Davis [13]. H3: Perceived ease of use will have a positive impact on the customer's intention to shop online.
3.4. Perceived Enjoyment:
According to Reid and Brown [49], "there are many motivational reasons that govern individual's intention to shop, which includes overcoming boredom, peer group influence and status consciousness." Bellenger and Kargoankar [6] divide the potential customers into two categories: economic and recreational shoppers. Also Reid and Brown [6] stated for economic customers that "they are more often than not to recluse themselves from unnecessarily engaging in the shopping experience."
Past studies for the demographic variables, indicate that mostly teenagers are in great knowledge of the Internet tools [12]. Also, the factor of enjoyment could not be discounted by the shopping orientation. Jarvenpaa and Todd [25], found that convenience is a significant factor for the adoption of the Internet purchasing.
Satisfaction can be interpreted by the means of elation and pleasure which are relevant with the user's Internet purchasing. Thus, perceived enjoyment has a positive impact on the online shopping intention. H4: Perceived enjoyment will have a positive impact on the customer's intention to shop online.
3.5. Subjective norms
Subjective norm is a construct which is similar to the social influence and it was firstly presented in the theories of planned behavior [1] and of reasoned action [18]. For the current study, subjective norms are used instead of social influence for out extended TAM model, because it is a more widely accepted construct in the e-commerce literature. According to Fishbein and Ajzen [18], "the person's perception that most people who are important to him think he should or should not perform the behavior in question". Previous studies have found out that the subjective norm tend to influence the user's intention and adoption [24, 57].
4. Data collection and analysis
Primary data was used in this research in order to find out the intention in online shopping in Greece. Data was collected through the summer of 2012 and the targeted population was more than 100 users; the final number was 124 respondents. E-mails were sent to randomly selected people in Greece through Internet (e-mails, Facebook etc).
The questionnaire was divided into the 4 parts: The first part is the demographic variables. The second part is about the Internet usage questions. Third is the part which are included the items of the research and the final category is the questions for the dependent variable.
5. Results
5.1. Demographic results
The total data sample was consisted of 124 people who were selected through the convenience method sampling. Because of the nature of the research, the majority of the people were exposed to the Internet services and shopping also. The demographic results are indicated in the following table.
From the total sample, 57,3% where male respondents while 42,7% where female. The age group of 21-30 was by far first with 50,8% of the total respondents. The majority of the respondents where holding a University (46,8%) or Masters Degree (28,2%). However, the economic recession in Greece can be displayed by the results in occupation, with most of them being Unemployed (27,4%), and with monthly income below 800€ in most of the cases (43,5%).
Table 1. Demographic Variables
Variable Frequency Percentage
Gender Male 53 57,3
Female 71 42,7
Occupation Public servant 16 12,9
Private employee 31 25,0
Student 14 11,3
Freelancer 21 16,9
Unemployed 34 27,4
Pensioner 8 6,5
Age Group <20 14 11,3
21-30 63 50,8
31-40 21 16,9
41-50 12 9,7
>50 14 11,3
Educational Level Secondary high School 24 19,4
University Degree 58 46,8
Masters Degree 35 28,2
PhD 7 5,6
Monthly Income <800€ 54 43,5
800-1200€ 48 38,7
1200-1400€ 13 10,5
>1400€ 9 7,3
5.2. Internet Usage results
Internet usage questions were also made, to test the overall Internet adoption of the respondents. As the table 2 indicates Internet usage had been very widely accepted in those years. The majority of the respondents (79,8%) were using Internet services more than 3 years while a third out of four are connected on a daily basis. As for the time spent connected, 70,2% are connected more than 10 hours per week while 18,5% are connected 4-10 hours per week. Lastly, the Internet usage seem to have an impact to the online purchases because 9/10 of the correspondents had already made an online purchase (88,7%).
Table 2. Internet usage Variables
Variable Frequency Percentage
How long have you been using the Internet Less than a year 4 3,2
services? 1-3 years 21 16,9
More than 3 years 99 79,8
How often are you connected? Once a month 4 3,2
2-3 times a month 12 9,7
4-6 times a week 13 10,5
Daily 95 76,6
Time spent connected. <1 hour per week. 2 1,6
1-4 hours per week 12 9,7
4-10 hours per week 23 18,5
>10 hours per week 87 70,2
Online purchase. Yes 110 88,7
No 14 11,3
5.3. Descriptive analysis
The descriptive analysis indicates that the respondents of the sample, are generally optimistic for the perceived usefulness and the perceived ease of use for the Internet purchase intention. Self-efficacy seems to be slightly lower the PU and EOU, while perceived enjoyment, risk and subjective norms are much lower.
Table 3. Descriptive analyses
Variables Mean Standard Deviation
Perceived Usefulness 4,28 0,58
Perceived Ease of Use 4,16 0,75
Perceived Enjoyment 2,06 0,97
Perceived Risk 3,00 0,77
Self Efficacy 3,97 0,97
Subjective Norms 2,94 1,12
Intention to adopt 3,93 0,90
5.4. Pearson analysis
The Pearson correlation in the following table, indicates that all variables are related to each other apart from the PE variable.
Table 4. Pearson analyses
PU EOU PE PR SE SN Intention
Perceived Usefulness 1,00
Perceived Ease of Use ,500 1,00
Perceived Enjoyment -,005 ,060 1,00
Perceived Risk -,521 -,592 ,013 1,00
Self Efficacy ,506 ,585 ,053 -,583 1,00
Subjective Norms ,257 ,177 -,104 -,209 ,375 1,00
Intention to adopt ,572 ,559 ,102 -,573 ,650 ,258 1,00
The major concern however, is if the variables have the influence we have hypothesized. Multiple regression analysis was used in order to assess the results. The analyses indicated that perceived Risk (H1) has a negative impact upon the intention to shop on-line (p=-0,197) as it was hypothesized. Perceived usefulness (H2) was found to be the strongest predictor for the intention (p=0,381), followed by self-efficacy (H6) which is the second strongest predictor (p=0,319).Perceived ease of use (H3) was found as a slightly lower predictor (p=0,156). On the other hand perceived enjoyment (H4) and subjective norms (H5) indicated only a partial support on our hypotheses (p=0,076 p=0,014). Moreover, all six hypotheses, managed to explain 54% in the Internet shopping intention variance.
Fig.1. The results of the Relationships in the Research Model
6. Conclusions and Implications
6.1. Discussion and Conclusions
Perceived usefulness (PU), was found to be the most important factor which may influence the Internet shopping intention. Customers are mostly influenced by the usefulness of the products instead of its ease of use, and this study validates that. However results may be different depending on the type of the products which is going to be purchased.
Self efficacy was found to have a positive association with the Internet shopping intention and one of the strongest one's.
Perceived ease of use (EOU) had a positive impact to the Internet shopping intention, but not so high like the usefulness predictor. This can be explained that new technologies which help and are easily manipulated nowadays from users, seems not to include the Internet shopping because it is not yet free from effort. Examples like bad design interfaces, bad interaction with the user, information which was outdated, search engines and difficult order procedures may all contribute to the intention of the customer.
Perceived enjoyment (PE) and subjective norms (SN) are also positively associated with the intention but in a partial way. Consumer's whose shopping habits are better served by the conventional shopping, seem not to embrace the on-line shopping environment that easy. However they explore various ways to reduce the consuming of time from activities like shopping and they tend to give at least one chance on the Internet shopping.
Lastly, perceived risk (PR) is negatively associated with the intention. Even now that the technology acceptance for the Internet services have been so widely accepted, there is uncertainty in the majority of the customer's about the willingness to adopt in e-commerce.
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