Technology took internet just four years to reach an audiences of 50 million and this mean that the internet was the fastest-growing tool of communication ever than radio and television broadcasters which took about 38 years and 13 years respectively to reach the similar number of audiences (Women’s Learning Partnership, 2009). IDC Malaysia Telecommunications also expects that internet business-to-consumer market which includes online purchase of goods and services bill payments to RM15. 3 billion at 2009 (Lee and Leong, 2009).
According to the survey conducted by A. C Nielsen Company among 500 Malaysian internet users, in 2008, online shopping habits reveal that about 30% of Malaysian internet users have experienced online purchases (Lee and Leong, 2009). Airline tickets and reservations (55%), tour or holiday reservations (41%), computer hardware (22%), online books (21%) and event tickets (18%) were found to be the most commonly items purchased online by Malaysian internet shoppers.
Although purchasing via online is an increasingly common practice for consumers in Malaysia, a great segment of consumers still do not prefer online purchasing. They prefer to buy something only when they can see the item in front of them rather than giving out their credit cards number online. They feel insecure to buy the item online than buy the item at the store (Lee and Leong, 2009).
On the other hand, although the burst of internet bubble left many people thinking that electronic commerce is dead, the actual picture is actually better (Chang, Cheung and Lai, 2005). The growth of interest in the internet as a shopping medium is extremely interesting for practitioners and researchers alike. Moreover, many well-established companies operated the shops by using the internet as a real world business nowadays (Zhou, Dai and Zhang, 2007). The internet exerts an increasingly strong influence on people’s everyday life.
Although there many research have been conducted on online purchasing behavior, there is a necessity for additional research (i. e., this study) to understand more about this topic. For example, Lee and Leong (2009) found that up to 80% of Malaysian internet users have purchased travel items online but only 69% of Malaysian internet users have shopping for books. Furthermore, according to Rhee, Riggins and Kim (2009), product variety is widely noted as an important variable for e-business success. The consumers shop online because of the variety of products. It means that the characteristics of products will influence the consumers’ choice of the shopping channel. Thus, to have a better understanding, this study will examines whether or not the characteristics of product provides in website that will influence the online purchase intention.
Along with this line, Vijayasarathy (2002) also argues that the consumers’ decision to repeat the internet purchases is actually differ from the initial intention when they want to do the online purchasing. This is due to the fact that the repetition behavior can be influenced by the consumers’ experiences on previous transactions on the web. Thus, there is also a need to compare the online purchasing intention among the internet buyers and potential internet buyers.
Background of Study
The framework of this study suggests that a variety of product characteristics, i. e., i) low cost and tangible products; ii) low cost and intangible products; iii) high cost and tangible products; and iv) high cost and intangible products will affect the consumers’ online purchase intention. According to Peterson, Balasubramaniam and Bronnenberg (1997), products can be categorized along three dimensions which are cost/frequency of purchase, value and proposition, and degree of differentiation.
In this study, two of the dimensions, i. e., cost and value proposition were included in the classification scheme. The first dimension refers to the cost of a product, and can be distinguished between low cost products (e. g., milk) and high cost products (e. g., refrigerator). Similarly, value proposition, which is an indication of the tangibility of a product can also be differentiated at two levels, i. e., tangible (e. g., clothing) and intangible (e. g., insurance). Combining the levels of the two dimensions yielded the four product types mentioned earlier. Meanwhile, the online purchase intention which is referring to the consumers’ willingness to make purchases in an internet store (Crespo, Bosque and Sanchez, 2009) and to return for additional purchases at the online store (Li and Zhang, 2002) was measured in terms of the consumers’ intention to buy the classification of different types of products and services through internet.
The consumer choice between a retail store and internet shopping mall may be influenced by the type of products and services offered (Girard, Koeganonkar and Silverblatt, 2003; Phau and Poon, 2002). That is, these characteristics are important factors that influence consumers’ internet purchases of the shopping channel and influence the consumers’ choice of specific products. Similarly, Kacen, Hess and Walker (2008) found that product characteristics have a fifty percent greater influence on impulse purchasing than do merchandising factors.
Influences of Product Characteristic on Online Purchase Intention
Previous research suggests that consumers’ purchasing behavior can vary according to product characteristics (Girard et al., 2003; Vijayasarathy, 2002; Yeh, Wang and Huang, 2007). Based on the dimensions of product classification schemed by Peterson et al. (1997), Phau and Poon (2000) concluded in their study that products and services that have a low cost, are frequently purchased; intangible and relatively high in differentiation are more suitable to be purchased over the internet. For example, online newspaper and magazines are more likely to be purchased online than some grocery products, car loans, and insurance products. Similarly, books and gifts cause less worry among consumers when compared with clothes because these products would not require fitting and size specifications (Wai, Chang, Moon, and Hsia, 2004; Vijayasarathy, 2002).
Vijayasarathy (2002) also found that intangible/information products are more suitable to be purchased over the internet. For example, media products including books, music and videos are believed to transfer to online shopping from physical stores more easily than other categories. Consumers also thought that CDs or videos could be shopped online to save time and for ease of making purchases from other countries by this method. Specifically, consumer intentions to shop online for intangible products were higher than their intentions to shop for tangible products (Norazah, 2003; Phau and Poon, 2000). In addition, intangible/information products can be evaluated directly using downloaded information, while information products are conducive for search and browsing because degraded versions of information products can be easily sampled online (Peterson et al., 1997).
Similarly, products with higher differentiation are more suited to be purchase over the internet than products with low differentiation (Phau and Poon, 2000). For example, consumers are able to try out the demonstration version of computer software, or be given trial periods of the online newspapers or videos/music, before making a purchase decision. This reduces uncertainty in purchase decision and stimulates purchases. Thus, based on the findings of previous research and arguments above, this study aims to describe the characteristics of product provides in website which will influence the online purchase intention and to determine the most influential products characteristics that influences the online purchase intention.
Methodology and Measures
A purposive sampling method was used to select two private companies in Petaling Jaya respectively in two different industries. They are Sin Chew daily Press (M) Sdn. Bhd. (major Chinese newspapers in Malaysia) and Sony (M) Sdn. Bhd. (Electric and Electronic company). These two companies were chosen as they represent to distinctively different environment working conditions. A convenience sampling method was then used to select the respondents in both companies since it was difficult to get the company’s list of workers.
The main instrument for this study was a questionnaire, which was divided into four sections, i. e., respondent’s socio demographic background, patterns of using internet and purchasing online, product/service classification, and online purchase intention. The data were analyzed using SPSS and hierarchical multiple regression was used to determine the most influential product characteristic that influence the online purchase intention.
The literature from marketing (e. g., Jarvenpaa and Todd, 1997; Peterson et al., 1997), and information technology and management (e. g., Heidjen and Verhagen, 2004; Klopping and Mckinney, 2004) provided the basis to measure the product/services classification and internet purchase intention, respectively. In this study, the product/service classification was asked in terms of the likeliness of the respondents to purchase each of 22 listed products/services through internet on a five-point Likert scale. The list was adopted from Peterson et al.’s (1997) classification scheme. The products/services were classified into various categories according to costs, purchase frequency and product tangibility. Meanwhile, online purchase intention was measured using the measurement adapted from Jarvenpaa and Todd (1997), Heidjen and Verhagen (2004), and Klopping and Mckinney (2004).
Analysis and Results
The demographic profile of the respondents has been calculated among 120 respondents, consisted of 56. 7% males and the remaining 43. 3% females. It shows that quite a balance gender group between males and females were obtained as the respondents in this study. The results also showed that the highest percentage of the respondents were aged between 25-34 years old (35. 0%), followed by those who were aged between 18-24 years old (23. 3%) and 35-44 years old (23. 3%). Only 3. 3% were aged between 55 years and older. In terms of ethnic groups, almost half of the total respondents were Malays (48. 3%) and Chinese (42. 5%), while the remaining were Indians (9. 2%).
The respondents were also asked about the pattern of their internet usage and purchasing online. The particular internet usage patterns were measured in terms of their experiences of using internet and the duration of internet use in a day. Meanwhile, the information about the experience of purchasing online were used to categorize the respondents into two categories, i. e., internet buyers and non-internet buyers. The results of the current study showed that all of the respondents (100. 0%) had used the internet before and most of them have used the internet for more than four years (75. 8%) and more than four hours a day (64. 2%). Based on the experience of purchasing online, more than half of the respondents were categorized as an internet buyers (59. 2%) and the remaining 40. 8% were non-internet buyers, who had never purchased via online before.
Product Characteristics
The descriptive analysis was used to measure the tendency of the respondents to purchase the 22 listed products/services through internet and the results are summarized in Table 1. The analysis had been divided into three levels; unlikely, neutral and likely. The results showed the more likely to be purchased by the respondents (at least more than 50%) were concert/play tickets services (74. 9%), travel arrangement (72. 1%), online books/journals (66. 0%), online magazines/newspapers (62. 9%), and online videos/movies/music (62. 8%). Conversely, there were also some products found to be significantly unlikely to be purchased by the respondents. They include generic grocery items 995. 9%), soft drinks, wines, cigarettes (93. 8%), vegetables (96. 9%), precious metals (75. 3%), home electronics/appliances (79. 4%), jewellery (86. 6%), and stereo systems (78. 4%).
Based on cost outlay and tangibility, category 1 products, which were those with low cost, are frequently purchased and tangible were unlikely to be purchased by the respondents. This is because there products are easily available in the neighborhood shops at low prices. Moreover, these products are usually bought when the need arises are very little search for the pre-purchase information is needed. For example, consumers are more likely to buy drinks from retail stores than through online sources.
Besides that, category 3, which were those with high cost, are infrequently purchased and tangible also were unlikely to be purchased by the respondents via online. There products require more visual inspection than other products categories and high monetary risks involved in buying these high cost products/services. Thus, among the four categories, category 2 were those with low cost, are frequently purchased and intangible products were likely to be purchased by the respondents. Internet enables online trial of this group of products before purchase (for example, online books/journals can be evaluated using download few pages thereby avoiding costly).
Table 1
Descriptive Analysis of Product Characteristic
Category
No.
Items
Unlikely
(%)
Neutral
(%)
Likely
(%)
Category 1
1
2
3
4
5
6
Generic grocery items (e. g., milks, eggs)
Flowers
Soft drinks, Wines, Cigarettes
Clothing/Shoes
Vegetables
CDs/Albums
95. 9
54. 7
93. 8
47. 4
96. 9
19. 6
4. 1
21. 6
6. 2
33. 0
3. 1
43. 3
0
23. 7
0
19. 6
0
37. 1
Category 2
7
8
9
10
11
Online Videos/Movies/Music
Online Magazines/Newspapers
Concert/Play tickets services
Online Books/Journals
Stock market quotes
7. 2
4. 1
1. 0
4. 1
16. 5
29. 9
33. 0
24. 7
29. 9
47. 4
62. 8
62. 9
74. 3
66. 0
36. 1
Category 3
12
13
14
15
16
Computer hardware
Precious metals (e. g., gold, silver)
Home electronics/appliances
Jewellery
Stereo systems
48. 5
75. 3
79. 4
86. 6
78. 4
40. 2
23. 7
15. 5
12. 4
19. 6
11. 4
1. 0
5. 2
1. 0
2. 1
Category 4
17
18
19
20
21
22
Investment choices (e. g., stocks)
Insurances services
Legal services
Online computer software/games
Real estate
Travel arrangement
38. 1
30. 9
27. 8
13. 4
49. 5
4. 1
47. 4
56. 7
46. 4
40. 2
44. 3
23. 7
14. 4
12. 4
25. 8
46. 4
6. 2
72. 1
Note: Category 1 = Low outlay, frequently purchased and tangible products
Category 2 = Low outlay, frequently purchased and intangible products
Category 3 = High outlay, infrequently purchased and tangible products
Category 4 = High outlay, infrequently purchased and intangible products
Online Purchase Intention
There were total of 12 items used to examine the respondents’ online purchase intention. Table 2 showed the descriptive analysis of each question for online purchase intention. The analysis had been divided into three levels of online purchase intention: agree, neutral and disagree. The results displayed that the difference of percentages between both groups of agree and disagree were not very significant, but the percentages of the respondents who had more intention to purchase online were a bit higher than those who had low online purchase intention. For example, 34. 2% of the respondents agreed that for them, online shopping would be much better in addition to traditional methods versus 27. 5% of the respondents were disagree. Similarly, there were more respondents who will consider their purchasing at the website in the short term (32. 5%) versus 22. 5% of them who will put no consideration on that.
Table 2
Descriptive Analysis of Online Purchase Intention
No.
Statement
Disagree
(%)
Neutral
(%)
Agree
(%)
1.
I intend to purchase using the internet.
18. 3
38. 3
43. 4
2.
I pelan to do more of my purchasing using the internet.
22. 5
38. 3
39. 2
3.
I intent to use the internet to collect information about product.
5. 0
14. 1
80. 9
4.
I think it would be very good to use the internet for my shopping activities in addition to traditional methods.
25. 8
32. 5
41. 7
5.
I think it would be very desirable to use the internet for my shopping activities in addition to traditional methods.
28. 3
32. 5
39. 2
6.
I think it would be much better for me to use the internet for my shopping activities in addition to traditional methods.
27. 5
38. 3
34. 2
7.
The thought of buying at the website is appealing to me.
19. 2
46. 7
34. 1
8.
I will consider the purchasing at the website in the short term.
22. 5
45. 0
32. 5
9.
I will consider the purchasing at the website in the long term.
24. 2
36. 7
39. 1
10.
I will consider the purchasing at the website if I need the product.
6. 7
25. 8
67. 5
11.
Overall, I like to use the internet for my shopping activities.
20. 0
45. 0
35. 0
The results also showed that majority of the respondents were intent to use the internet to collect information about product (80. 9%) and more than half of them will consider the purchasing at the website of they need the product (67. 5%). This is consistent with the argument made by Phau and Poon (2000) that the rapid adoption of internet results internet users more easily to compare similar products across different website with a click of the mouse since the search costs for the product information through the internet are low.
The online purchase intention level was then determined based on the mean scale (mean = 36. 0) of the online purchase intention scales. Respondents who scored equal and above the mean were categorized as having high levels of online purchase intention and respondents who scored below the mean were categorized as having low levels of online purchase intention. Referring to Table 3, the result showed that more than half of the respondents (68. 3%) had high level of online purchase intention; they were more willing to make purchase in the internet store than in future.
Table 3
Online Purchase Intention
Intention Level
%
Low Intention
High Intention
31. 7
68. 3
Independent samples t-test was then performed to examine the difference in online purchase intention among the internet buyers and potential internet buyers. The result found that there was no significant difference in online purchase intention among those two groups (t= 0. 847, p> 0. 05). This result could not be compared with other studies since there were no past studies had addressed the issue of online purchase behaviour among white collar workers. However, when this result is viewed together with the result of the level of purchase intention, it can be said that the intention to do online is high regardless whether the white collar workers are internet buyers or potential internet buyers.
The Influence of Product Characteristics on Online Purchase Intention
Multiple regression analysis was used to determine the unique predictor that will influence the online purchase intention. Table 4 showed the most influential product category that influence the online purchase intention and the result indicated that low cost, frequently purchased and intangible of product had significant influences on online purchase intention (β= 0295, p <0. 01). The result was consistent with the finding in descriptive analysis which indicated that category 2 which was those with low cost, are frequently purchased and intangible products were likely to be purchased by the respondents via the internet. The finding was also consistent with previous research done by Phau and Poon (2000) which found the consumers; intention to shop online for low cost and intangible product/services were higher than their intention to shop for high cost and tangible products/services.
However, the result showed that the adjusted R2 was relatively low which only 13. 8% of the variance in online purchase intention can be explained by the characteristics of products. This means that there are another factors that also can influence the online purchase intention, besides the product characteristics.
Table 4
The Result of Multiple Regression Analysis
Product Categories
Online Purchase Intention
(β)
Category 1:
Low outlay, frequently purchased and tangible products
Category 2:
Low outlay, frequently purchased and intangible products
Category 3:
High outlay, infrequently purchased and tangible products
Category 4:
High outlay, infrequently purchased and intangible products
Adjusted R2 = 0. 138
F = 4. 829***
ns
0. 295**
ns
ns
Note: **p <0. 01; ***p <0. 001
Discussion
Implication of the Study
The results of this study have several marketing implications Firstly, with the online purchasing in Malaysia had been increased in the recent years, it is important to determine the influence of product characteristics on online purchase intention. This research has provided useful information for marketers interested in online marketing. They will have more understanding of the online purchase intention among white collar workers. Knowing the intention and this group of consumer would be beneficial to the marketers since they are one of the groups that use internet heavily while they are in their workplaces.
Secondly, promotions could also be tailored to the this segment. For example, findings from this research indicated that low cost, are frequently purchased and intangible products had significant influences with online purchase intention can give the marketer the view that they are more likely to buy online video/movies/music, online magazines/ concert/play tickets services, online books/journal and travel arrangement.
Limitations and Future Research Directions
The findings of this study must be interpreted in the light of several limitations. These limitations, along with the findings, of this study, also suggest directions for future research. Firstly, as mentioned earlier, only two of the three dimensions which were cost and tangibility suggested by Peterson et al., (1997) were incorporated in distinguishing between product types. Degree of differentiation may also play a vital role and it may have interaction effects with either or both cost and tangibility. Future researchers can examine the relationship between intentions to use online purchasing and product types that are differentiated on all three dimensions employing a more diverse sample.
Secondly, this research had only examines the influence of product characteristics on online purchase intention. Future researchers are suggested to determine other factors that influence online purchase intention such as web design characteristics. This suggestion is based on the result of multiple regression which found that only 13. 8% of the variance in online purchase intention can be explained by the characteristics of products. Therefore, it can help them to understand other factors that may influence the online purchase intention. Finally, future research should also examine the influence of product characteristics on other online purchase behavior, such as online purchase adoption and online purchase continuance.
