Predicting Internet Usage for Travel Bookings in China Li Li Dimitrios Buhalis School of Management University of Surrey, United Kingdom
[email protected] [email protected] Abstract Businesses around the world face the challenge of identifying, attracting and retaining customers in the cybermarket, as well as converting lookers to bookers. Most of the consumer behaviour research has been conducted for European and American on-line segments. As these markets approach on-line maturity, marketers seek new and emerging markets to expand their offering. China is one of these markets, due to its population and economic growth. However, studies about Chinese eCustomers seem to be very limited. This paper aims to identify the determinants of booking travel on-line in China. A predictive model was developed by utilising binary logistic regression analysis. This research quantified the effects of identified predictors. Its results reaffirmed innovation diffusion theory, theory of reasoned actions and theory of planned behaviour. Keywords: tourism, eCommerce, Internet, prediction; eCustomer, China.
1
Introduction: eCommerce in China
Many studies have provided an insight into the adoption of eShopping and the prediction of commercial usage of the Internet. Intention-based theories and innovation diffusion theory are valuable tools because they are empirically validated. A number of cultural and economic factors determine the level of eCommerce in different regions and researchers need to appreciate the factors that influence its adoption. Tourism is developing rapidly as one of the key sectors of eCommerce (Buhalis, 2003). Increased from 620 thousand Internet users in 1997, there are almost 80 million in mainland China in 2003, representing an annual growth rate of 35% (CNNIC, 1998, 2004). This is only 6% of the Chinese population, demonstrating the potential for growth. The large on-line population provides an opportunity for eCommerce. According to CNNIC, over 40% of Chinese Internet users have purchased goods and services through on-line websites in 2003 (CNNIC, 2004a). However, there is little known about eShopping adoption by Chinese eCustomers, especially in the tourism perspective (Ma, Buhalis and Song, 2003). Around 10% of the Chinese cyberbuyers
have purchased tourism products in 2003 (CNNIC, 2004). They often book on-line and then purchase off-line or pay on delivery. This paper examines Chinese Internet users adopting eShopping for travel products by looking into seven independent variable sets. These are socio-demographics, travelrelated behaviours, Internet usage patterns, perception of the Internet, customer domain-specific innovativeness (DSI) and self-efficacy (Anckar and Walden, 2000; Chau, Cole, Massey, Montoya-Weiss and O’Keefe, 2002; Christou and Kassianidis, 2002; Citrin, Sprott, Silverman & Stem, 2000; Morrison, Jing, O’Leary & Cait, 2001; Vijayasarathy, 2004). In addition, Guanxi is also examined as it influences Chinese consumer behaviour (Efendioglu and Yip, 2004; Luk, Fullgrabe and Li, 1999; Merrilees and Miller, 1999). It discusses the theories and studies that explain and predict Internet/eCommerce adoption, and identifies the predictors of Chinese eCustomers booking travel on-line based on the results of an on-line survey with over 800 replies.
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Towards Adopting eShopping
The adoption of eShopping by customers can be explained and predicted by using the innovation diffusion theory (IDT), the theory of reasoned actions (TRA), and the theory of planned behaviour (TPB). IDT postulates that five perceived characteristics of an innovation affect adopter’s attitude, which can be favourable or unfavourable and leads to a decision of adopting or refusing the innovation. These are relative advantage, compatibility, complexity, trialability and observability (Rogers, 1995). TRA holds that an individual’s behaviour is determined by his/her behavioural intention, which is governed by individual attitudes towards performing the behaviour and subjective norms. The attitudes are based on the evaluation of consequences of engaging in the behaviour, which are typically represented as product attributes or brand equity in marketing (Fishbein & Ajzen, 1975). Based on TRA, TPB (Ajzen and Madden, 1986) introduces perceived behavioural control (PBC) as a determinant of behaviour. According to TPB, PBC is governed by control beliefs, which relate to perceptions of the availability of skills, resources and opportunities, and perceived facilitation. Both TRA and TPB are widely used for “predicting or explaining cognitive and affective behavior using the belief-attitude-intention-behavior relationship in social psychology” (Shih, 2004:352). Many predictive models are developed based on these theories. The technology acceptance model (TAM) (Davis, 1986), which is built upon TRA, postulates that while perceived ease of use (EOU) influences perceived usefulness (U), they both predict attitude (A). U and A influence one’s intention to use a given technology; the intention then predicts its actual use. U and EOU represent IDT’s constructs of relative advantage and complexity respectively (Vijayasarathy, 2004). While most of predictive models tend to disregard cultural difference in eShopping adoption, Park and Jun (2003) observe that Korean Internet users show higher perceived risks on
privacy, security and product than American users, but still purchase goods on-line frequently. They use the cushion effect (Hsee and Weber, 1999) to explain this. Hsee et al (1999) claim that in collectivist cultures, like China and Korea, family and other members will offer help when anyone in the group suffers losses after selecting a risky option; whereas people in individualist culture are expected to bear the consequences of their own decisions. The primary research adopts the conceptual model presented in Morrison’s et al (2001) study. As the model is tourism-focused, it provides a clear blueprint of the development of Internet travellers to guide the research. Internet travel lookers (hereafter referred as to “lookers”) are those who retrieve information about travel products via the Net. When lookers purchase tourism products on-line, they become Internet travel bookers (hereafter referred as to “bookers”).
3
Primary Research
Research Model: The proposed research model takes into account DSI, perception of the Internet, self-efficacy, Guanxi, socio-demographics, Internet usage patterns and travel-related variables. Customer Domain-Specific Innovativeness - It is “important to consider consumer’s domain-specific innovativeness when trying to understand and predict a consumer’s propensity to adopt the Internet for shopping in relationship to their prior Internet usage” (Citrin et al. 2000:298). A looker is likely to be an experienced Internet user, and be innovative within the domain of the Internet and Web. In this research, DSI is defined as customers’ innovation within the domain of travel websites. For instance, a person who is innovative within the domain of travel websites will visit a new tourism company’s website even if he/she has not heard of it before. Perception of the Interne - Customers’ perceptions of the Internet, as an information and reservation tool for travel bookings, have direct relationships with their intention of eShopping (Christou et al. 2002). Perceived compatibility refers to the extent to which a consumer believes that shopping tourism products matches his/her lifestyle, needs and shopping preference. Perceived relative advantage refers to the degree to which consumers perceive eShopping to “be superior to in-store traditional travel agent shopping”, and perceived complexity is the degree to which consumers find eShopping “difficult to understand and use in practice” (Christou et al. 2002:96). Perception of relative advantage and compatibility of electronic travel shopping with their lifestyle positively influence their intention to adopt eShopping, whereas complexity reduces the intention. Self-efficacy - Self-efficacy is the “consumer’s self-assessment of his/her capabilities to shop on-line”, and represents a positive relationship with the intention to purchase goods from the Net (Vijayasarathy, 2004:5). In essence, the construct represents TPB’s perceived behavioural control (Anckar et al. 2000). In this research self-
efficacy is defined as an Internet traveller’s self-assessment of his/her capabilities to purchase travel on-line. Guanxi - Representing an aspect of Chinese cultural values, Guanxi shapes Chinese consumers’ buying behaviours. The concept is defined as “a network of relationships embedded with mutual obligations through a self-conscious manipulation of “face”, “Renqing” (favour) and related symbols” (Wong and Tam, 2000:58). Renqing is a set of social norms, meaning that when accepting a favour, the recipient owes Renqing to the person and is obliged to pay back the debt of gratitude to him/her (Luk et al. 1999). Ganqing implies expectations and obligations of getting/granting favourable responses from/to one’s friends (Luk et al. 1999). Merrilees et al (1999) and Luk et al (1999) disclose that salespersons who utilised Guanxi generated higher sales records than those who did not use. Guanxi’s cultural characteristics are also seen in the current transaction systems in China’s eCommerce. Efendioglu et al (2004:55) detect that Chinese eRetailers capture customers’ “moral obligation to return a favour” to encourage on-line sales. Focusing on Renqing and Ganqing, the research endeavours to examine whether Guanxi will affect customer’s adoption of eShopping. Socio-demographics - Morrison et al (2001) point out that education level, age, income and occupation are considerably different among bookers, lookers and other Internet users. This type of data is useful as knowledge about Chinese eCustomers is very limited in the academic field. Internet Usage Patterns - A “wired lifestyle (measured by months/years of Internet experience, what is being bought and why)” is vital to predicting Internet shopping (Chau et al. 2002:139). The more frequently customers use the Internet, the more likely they become eShoppers (Sexton, Johnson and Hignite, 2002). Weber and Roehl (1999) discover that bookers spend more time on-line than those booking offline. Also, instead of principals’ websites, travellers often search those of on-line travel services to compare travel prices (Morrison et al. 2001). Travel-Related Variables - There are few studies (Bonn, Furr and Susskind 1999; Furr, Bonn and Hausman, 1999) about travel-related behaviour of Internet travellers. However, frequency of travel and number of trips yearly are important indicators of lookers’ probability of being bookers. Membership of Frequent Flyer Programmes can also partially predict lookers’ likelihood of booking on-line. Hypotheses Set: Based on these variables emerging from the literature review, seven hypotheses are set as follows: H1-There is a relationship between DSI and lookers’ likelihood of booking travel on-line. H2-There is a relationship between perception of the Internet and lookers’ likelihood of booking travel on-line.
H3-There is a relationship between self-efficacy and lookers’ likelihood of booking travel on-line. H4-There is a relationship between Guanxi and lookers’ likelihood of booking travel on-line. H5-There is a relationship between socio-demographics and lookers’ likelihood of booking travel on-line. H6-There is a relationship between Internet usage patterns and lookers’ likelihood of booking travel on-line. H7-There is a relationship between travel-related variables and lookers’ likelihood of booking travel on-line. Methodology: Thanks to eLong.com’s assistance, an on-line questionnaire in Chinese was emailed to 103,000 randomly selected registered customers of the company in June 2004. 872 electronic replies were then received, which represented a response rate of less than 1%. Despite the low response rate, the sampling method was considered good. As a leading on-line travel company in China, eLong.com acted as a good vehicle for reaching the Chinese who were Internet users and generally interested in travel activities. To predict a binary dependent variable (DV) from independent variables (IDV), logistic regression analysis was used. It overcame the problem of violating the assumption of linearity between variables by expressing the “multiple linear regression equation in logarithmic terms” (Field, 2000:164). Since most of the variables in this study were dichotomous, or with restricted ranges, it was not realistic to assume the IDVs were multivariate normal, which excluded use of discriminate analysis in this research. Logistic regression equation was presented as (Field, 2000) P(Y) = 1 / (1+e-(β0+β1X1+…+βnXn+εi))
(1)
where P(Y) – the probability of lookers being bookers Y – predicted from a combination of each predictive variable (X) multiplied by its respective regression coefficient (β) e – the base of natural logarithms ε – a residual term Basically, running a binary logistic regression analysis in SPSS would produce a selected model that could best fit the sample data. Consequentially, the predictors that significantly contributed to the equation could be identified by reading their respective p values and βs. Each predictor’s effect on the probability of Chinese booking online could then be quantified according to its Exp(β) values.
Perception of the Internet, self-efficacy, DSI and Guanxi were measured on a sevenpoint Liker scale with a number of statements, which were developed based on reviewed literature. Table 1 illustrates some of the items in the measurements. Table 1. Concepts Operationalisation Concept Perception of the Internet selfefficacy DSI Guanxi
Some of the Items in Instruments I do not feel safe to use credit card online. I feel it is not easy to book travel online. I am proficient in using the Internet for shopping travel products. I feel confident that I can use the Internet for shopping travel products. I am the first in my circle of friends to know of any new travel websites. If I heard that a new travel website was available on the Internet, I would be interested enough to shop from it. Sometimes, I have to purchase tour package from the person/party whom I have owed Renqing to even though the same tour or a similar one can be bought from a travel website. When I intent to purchase a product presented on a travel website, I would ask a friend of mine who may be able to get a discounted rate to book it for me.
Limitations: Given the large on-line population in China, the non-probability sampling with the relatively small sample size would limit the ability to generalise findings as representative to the population. Also, one should be cautious with applying the developed model for other samples, because a model resulted from logistic regression analysis always fits better to a particular sample than to the population (Norušis, 1993).
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Findings
There is not a serious collinearity problem in the research data based on computed Variance Inflation Factor and tolerance values for all the IDVs. Table 2 suggests that all the variable sets, with the exemption of Guanxi, have some predictive powers in explaining the probability of lookers booking travel on-line – accepting H1, H2, H3, H5, H6 and H7. Guanxi does not determine Chinese Internet users adopting eShopping in these data. Maybe, this is caused by the low level of personal interaction between customers and eRetailors in on-line transactions. The increased information transparency on the Internet may also attribute to the reduced dependence on personal networks.
Table 2. Predictive Model – Likelihood of Lookers Booking Travel On-line Variables in the Equation
β
Household size (HSH) Membership of frequent flyer programme (FFP) Length of time using the Internet (LUS) Frequency of using the Internet for travel information a year (FRE) Commercial travel website visited most often (WEB) I am proficient in using the Internet for shopping travel product/services. (EFF1) I feel confident that I can use the Internet for shopping travel product/services. (EFF2) If I heard that a new travel website was available on the Internet, I would be interested enough to shop from it. (DSI2) Compared to my friends, I seek out relatively more information over the Web. (DSI3) I will visit a new tourism company’s website even if I have not heard of it before. (DSI5) I don’t feel safe to use credit card on-line. (PRA1) I would buy discounted travel products on-line. (PRA3) I feel I am not clear with on-line reservation procedure. (PCX1) Using the Internet to shop travel product/services fits with my lifestyle. (CMP2) Constant
-0.7648 0.60851 -0.99056
0.050 0.080 0.060
Exp (β) 0.47 1.84 0.37
0.004453
0.117
1.00
-0.9789
0.008
0.38
1.241151
