By: Weny Cheng, SE (Alumni of International Marketing) and Annetta Gunawan, SE, MM (Faculty Member of International Marketing)
The continued development of the internet led to more and more users take advantage from such situation by using it as a venue to run the desired business or transactions. Peter & Olson (2010) stated that electronic commerce, or e-commerce, is the process where buyers and sellers exchange information, money, and goods through the electronics, i.e., mainly on the internet. E-commerce sales will always increase every year, Statista (2013) has predicted that in 2016, 2017, and 2018, e-commerce will increase. Internet users in Indonesia also follow the global developments which are always increasing every year. According to Statista (2013), the average time spent by internet users in Indonesia was 5.1 hours per day, with popular online activities included mobile messaging and social media. According to Nielsen (2014), Indonesia is one of the countries with the most frequent usage the mobile phones for online shopping in the Southeast Asia. Figure below portrays the purchase statistic carried out by Indonesians.
Based on those data, we can conclude that there are as many as 55% of all online shoppers in Indonesia bought airline tickets online. Some of the big players in online travel industry are Traveloka, Tiket.com, NusaTrip.com, yuktravel.com, and Pegipegi.com. However, it seems that the market potential of Indonesia is still wide open, where the mainstream market has not yet been widely touched by the online travel industry players.
In order to prove the initial hypothesis of this study concerning the lack of strength of behavioral intention of the Indonesian market as a whole to buy airline ticket online, initial survey of consumer experience in buying airline ticket online was conducted to 57 respondents. From 57 respondents that represent different age groups and occupations who usually buy airline ticket, 63% of them (36 respondents) had never bought airline ticket online or through a website, and the remaining of 37% hadpurchased airline ticket online. From this initial survey results, it can be seen that there is big potential for e-commerce market in the field of airline ticket by increasing behavioral intention of travel consumers, in this case the airline tickets.
Ajzen in Lim, et.al. (2011) stated that the antecedents of behavioral intention consist of three variables, namely attitude, subjective norm, and perceived behavioral control. Added by Mosavi, et.al. (2012),behavioral intention in the context of internet shopping is also driven by promotion, price, brand image, and word-of mouth. A study by Chen, et.al. (2011) also have shown the effect of perceived usefulness, perceived ease of use, product feature, and quality of mind-stimulating playfulness to behavioral intention, while Wu, et.al. (2015) confirms the effect of perceived risk and personal innovativeness on the interest of users for online shopping.
Regarding consumer behavioral intention in the context of technology product, Moore (2014) revised the model of Technology Adoption Life Cycle (TALC), which consists of Innovators, Early adopters, Early Majority, Late Majority and Laggards, by identifying Chasm between early adopters and early majority segments. Chasm is a gap which becomes an important key in addressing the differences on behavioral intention between early adopters and early majority segments. In online travel market, particularly airline ticket, this model is applied, whereas not all consumers can directly adopt the way of online shopping in buying airline ticket. Although it has become a habit for some people, but the mainstream market yet have sufficient of behavioral intention, proven by the pre-test results above.
In order to increase behavioral intention of online travel market, especially in Jakarta, it needs to be confirmed whether the determinants factors of behavioral intention is significant in affecting user’s interest to buy airline ticket online. Then, the characteristics of each existing market segment can be identified based on such behavioral intention factors with TALC model.
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