Indonesia experienced an increase in the number of passengers of international and domestic aircraft in the period from January to June 2017, the number of passengers of international flight increased 13.54% compared to the previous year while domestic flight increased by 10.22% (Badan Pusat Statistik, 2017). Indonesia is also expected to be included in the top ten global aviation market by 2020 and will become the world’s top five global aviation market by 2034 (International Air Transport Association, 2016).
With the development of Information and Communication Technology (ICT), mobile devices such as mobile phones allow consumers to access the Internet to retrieve information on the variety of services that support the online travel industry. In the period of 2014 to 2020, online travel sales in Indonesia projected to increase by an average of 16% per year and sales are expected to reach 10.49 billion US dollars by 2020. Currently, Indonesians tend to use mobile applications compared to other devices in travel browsing (41%) and booking (48%) (Criteo dan Euromonitor International, 2017).
Despite the growing use of mobile applications in Indonesia, users tend to still have various transaction-related constraints on mobile applications. Constraints faced by customers on mobile applications, such as security concerns, product details, complicated navigation, the difficulty of compare and input details, and the length of time access (ComScore, 2016; PATA, 2015). Furthermore, the percentage of bad reviews on airline mobile applications tend to be larger than mobile applications owned by the online travel agent (Google Playstore, 2017).
Unsatisfied customers on the quality of mobile app are likely to provide poor reviews for companies, such reviews can be disseminated to family or friends, through their social media (Google, 2014; Dynatrace, 2015). Therefore, more attention to the quality of the application to intention to use required. This study aims to obtain factors that influence intention to use airline flight booking applications with regard to quality factors.
2. Literature Review
Operations management in manufacturing and service companies has grown rapidly over the years due to changes in market demand. The application of information technology/information systems in operations management has significantly changed the strategy, technique, and technology of operations management itself (Gunasekaran & Ngai, 2012).
Typically, information systems and operations management work together to design an information network. The information system should be able to accommodate the needs of operations management since it changes in response to market demands. Subsequently, to improve the operations management functions, it is left to the information system to bring the latest capabilities in information technology to the organization (Reid & Sanders, 2011).
Technology acceptance model has been the most interesting model in the field of information systems among many other models used to explain and predict system usage. Thus, it is important for the company to gain an understanding of the technology acceptance model by learning user acceptance of technology (Chuttur, 2009).
2.1. Technology Acceptance Model
The technology acceptance model proposed by Fred D. Davis is a proven model for analyzing the acceptability of a technology (Liu & Yu, 2017). TAM which was an expansion of TRA (theory of reasoned action) has been widely used in information system field to learn about user behavior related to information technology (Nyoro, Kamau, Wanyembi, Titus, & Dinda, 2015). TAM is also considered in the e-commerce website research of online shopping sites (Dachyar & Banjarnahor, 2017) and on cloud computing with factors influencing are cost-effectiveness, need factor, security, and reliability of applied technology (Dachyar & Prasetya, 2012). Technology Acceptance Model consists of three dimensions: perceived ease of use, perceived usefulness, and intention to use (Chen & Tsai, 2017).
Usage intention is an important part of the system’s success because psychologically users will not use a system if they have no intention to use it before (Mardiana, Tjakraatmadja, & Aprianingsih, 2015). Behavioral intentions contain motivational factors that influence behavior with stronger intentions, the more likely it is to actual use. Factors which influence behavior are an indication of how hard people are planning to try and how much effort they put to do such behavior (Ajzen, 1991). TAM variables (perceived ease of use and perceived usefulness) are stronger predictors for measuring intention to use than actual use (Turner, Kitchenham, Brereton, Charters, & Budgen, 2010).