Dataset on factors affecting and technology acceptance requirement of ubiquitous public transport services in Melaka City of Tourist Destination
Description
This dataset contains a data collection results from questionnaire that explores the factors affecting users and drivers of public transport service and factors influencing users' and drivers’ intention towards ubiquitous features for public transport services. The dataset was collected from users and drivers of public transport services in Melaka, a city of tourist destination in Malaysia. Satisfaction was adopted as an “affect” variable and Technology Acceptance Model (TAM) was used to capture factors influencing users in using proposed ubiquitous features for public transport services. For users’ dataset, there are three (3) main sections: 1) Part A that consists of five items (S1 to S5) for collecting factors affecting users on the public transport services, and six items (S6) for collecting the level of users' satisfaction on the existing public transport services, 2) Part B that consists of six items (S7) for collecting users’ knowledge on green technology, and 3) Part C that consists of factors influencing users in using the proposed ubiquitous features for public transport services (six items based on perceived usefulness (S8) and another six items on perceived ease of use (S9)) . Whereas, for the drivers’ dataset, there are also three (3) main sections: 1) Part A that consists of six items (S1 to S6) for collecting factors affecting drivers on providing the public transport services, 2) Part B that consists of six items (S7) for collecting drivers’ knowledge on green technology, and 3) Part C that consists of factors influencing drivers in using the proposed ubiquitous features for public transport services (six items based on perceived usefulness (S8) and another six items on perceived ease of use (S9)). For all bus, taxi and trishaw users, the findings showed they use the transports less than 3 times a week (>=84%), for personal matters (>=72%), because it is more comfortable (>=72%), by getting it at the station (>=84%), and the average waiting time is less than 30 minutes (>=84%). For the level of users satisfaction, timely service, prudent driving, feel safe, and easy to get are led by trishaw with u=3.62,3.77,3.62,3.69 respectively, whereas reasonable fares is led by bus (u=3.40), comfort is led by taxi (u=3.64). For perceived usefulness of the proposed ubiquitous features, provision of proof of payment was strongly agreed by the bus drivers (u=4.56), while provision of travel maps, charge rates and proof of payment were strongly agreed by trishaw users (u=4.23). For perceived ease of use of the proposed ubiquitous features, the service is easily accessible using all types of smart phones was strongly agreed by trishaw passengers (u=4.62), while availability in Malay and English language scored the highest by bus drivers (u=4.24).
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Steps to reproduce
Step 1: Melaka is a city of tourist destination. Linkages were established with transport agencies including Melaka Trishaw Association, and representatives from bus drivers to facilitate data collections. Meetings were held to discuss data collection exercises with representatives from these agencies. Step 2: Instruments were designed for collecting the data from users and drivers of the three types of public transport services (bus, taxi and trishaw). Refer Appendix 1 to 6 for the questionnaires. Step 3: Research assistants were appointed to help with the data collection process. Data collection was conducted in areas of tourist destinations including Banda Hilir, Medan Samudera, Menara Taming Sari, Dataran Pahlawan, Jonker Street, Kota A Famosa and The Stadthhuys, also in Alor Gajah Sentral and Jasin Sentral. Questionnaires were administered face to face and collected after adequate duration given for respondents to respond. Step 4: The raw data (six excel files) were first gone through a cleaning process to identify missing values and rectify it. Then, both the users’ and drivers’ dataset (from Part A, B, and C of the survey forms) were analyzed using frequency analysis to produce descriptive statistics as required.