Finally, we would like to thank all participating respondents in Malaysia and Singapore by making an effort to complete the Google online questionnaire. Without cooperation and feedback from the respondents, it will be impossible to complete the survey. In the current era, digitization and mobile technology are resulting in a boom in the sharing economy.
This research investigated the determinants influencing the behavioral intention of e-hailing users in Malaysia and Singapore.
RESEARCH OVERVIEW
- Research Background
- Introduction to E-Hailing
- E-Hailing in Malaysia
- E-Hailing in Singapore
- Risk in E-Hailing Applications
- Research Problems
- Research Objectives
- Research Questions
- Research Significance
To identify the factors influencing consumer behavioral intention to use an e-hailing application in Malaysia and Singapore. This research shows how some factors influence the user's intention to use an e-reporting application. What are the factors that influence a person's intention to use an e-hailing application in Malaysia and Singapore.
How marketers can improve user intent to adopt an e-hailing app in Malaysia and Singapore.
LITERATURE REVIEW
- Overview on Sharing Economy
- Background and Definition of Sharing Economy
- Background and Definition of E-Hailing Application
- Global Trends in the Sharing Economy
- Reduction of Private Vehicle Ownership
- Underlying Theory
- Theoretical Framework Review
- Review of Theoretical Framework Proposed by
- Review of Theoretical Framework Proposed by Tossy
- Proposed Theoretical Framework
- Review of Variables
- Dependent Variable
- Independent Variables
- Summary of Hypotheses
The UTAUT suggested that effort expectancy (EE), facilitating conditions (FC), performance expectancy (PE), and social influence (SI) are the 4 main determinants influencing behavioral intention (BI) and technology use behavior (Venkatesh et al., 2003). As a result, effort expectation, performance expectation and also social influence significantly influence the behavioral intention to adopt mobile payment. It was suggested by Venkatesh et al. 2003) that an individual's behavioral intention to adopt a technology has a significant impact on usage behavior.
2003) defined enabling conditions as “the extent to which an individual believes that an organizational and technical infrastructure exists to support use of the system”.
METHODOLOGY
- Research Design
- Sampling Design
- Target Population
- Sampling Frame
- Survey Technique
- Sampling Size
- Data Collection Methods
- Survey Strategy
- Primary Data
- Secondary Data
- Questionnaire Design
- Proposed Data Analysis Tool
- Data Analysis
- Descriptive Analysis
- Reliability Analysis
- Inferential Analysis
- Pilot Test
A small part of the target population is used to infer the statistical population, since the initial number of the target population is too large, so the implementation of the census will be difficult and impractical (Saunders et al., 2009). There is no sample framework because the list of user names of any e-calling application is not accessible as it is confidential information held by the e-calling companies. An advanced statistical platform such as SPSS can be used to collect and calculate data, thus facilitating the process of studying the relationship between UTAUT determinants and e-calling application usage behavior.
Due to the limited financial resources and the familiarity of the respondents, a Google form will be used in the survey. This is due to its international status as a language and it is a language widely used by the people of Malaysia and Singapore. In this research, we used SPSS to determine the significance of the relationship between the independent variables (performance expectancy, effort expectancy, social influence, facilitating conditions, perceived risk) and the dependent variable (behavioral intention).
One of the main reasons for using the SPSS instrument in this research was that it is highly comparable, as it is widely used in the social behavioral sciences (Landau, . 2007). It is a tool for determining the internal consistency of how closely the answers are related to the rating scale of the measurement items in the questionnaire (UCLA, 2012). Only when the alpha value exceeds 0.7 can this study be considered acceptably reliable; otherwise, the internal consistency of measurement items will be questionable or unreliable.
The measurement items of the constructs were on a Likert scale, and the Likert scale is an ordinal scale. Before proper data collection is done on the actual results of the survey, a pilot test is essential to establish the reliability of the proposed online survey.
DATA ANALYSIS
- Analysis on Section A
- Analysis on Section B
- Reliability Analysis
- Reliability Analysis for Malaysia Responses
- Reliability Analysis for Singapore Responses
- Correlation Analysis
- Correlation Analysis for Malaysia Responses
- Correlation Analysis for Singapore Responses
- Inferential Analysis
- Inferential Analysis for Malaysia Responses
- Inferential Analysis for Singapore Responses
Respondents' demographic and general information about e-mail use was collected in section B of the online questionnaire. On the other hand, most of the respondents from Singapore fell under three age groups. Most of the respondents in Malaysia (32.1%) have less than 2 years of experience using email applications, and those with less than 1 year of experience accounted for 24.4%.
Only a small percentage of respondents from Malaysia (10.4%) and Singapore (5.8%) did not voluntarily use the e-hailing application. For the responses collected in Malaysia, the reliability test was conducted to identify the reliability of the variables and the internal consistency of a measurement. The R Square value obtained was 0.530, there is 53.0% of the dependent variable that can be explained by the independent variables.
After adjusting the R Square, 51.7% of the dependent variable can be explained by this model. In addition, when the effort expectation increases by one unit, the level of intention of the Malaysian users to use the e-hailing application increases by 0.339 unit. The R-squared value obtained was 0.754, there is 75.4% of the dependent variable that can be explained by the independent variables.
After adjusting the R-squared, 74.1% of the dependent variable can be explained by this model. Moreover, when the effort expectation increases by one unit, the level of the Singaporean users' intention to use e-hiling application increases by 0.106 units.
DISCUSSION, CONCLUSIONS AND IMPLICATIONS
Performance Expectancy
Performance expectancy was defined as the degree to which an individual felt they would benefit from using a particular system (Venkatesh, 2003). It indicates that both Malaysians and Singaporeans believed they benefited from the e-hailing application by improving their life productivity. Researchers found that performance expectancy is the most dominant predictor of behavioral intention in this model. 2017) supported that performance expectations play a positive role in 361 Chinese user adoption, with user adoption encompassing both intent to use and usage behavior.
Performance expectation also has a positive effect on behavioral intention after collecting data from 400 Indonesian users (Isradila and Indrawati, 2017). Bardhi and Eckhardt (2012) mentioned that one of the important features of the sharing economy is that it provides access to non-owned assets. In our case, this refers to a car and shows that the productivity of an e-hailing application supported this important part of the sharing economy.
Effort Expectancy
A study conducted by Gupta, Dasgupta and Gupta (2008) found that expected effort had influenced the intention of the employees of a government organization in India to adopt a new media system for public service. Since Malaysian users' behavioral intention to use an email app is strongly influenced by the expected effort, it is highly recommended for the email operators in Malaysia to design the app to be more user-friendly so that it can be easily learned by the consumer. Similar to Singapore's result, Isradila et al. 2017) did not find any significant and positive effect of expected effort towards Indonesian behavioral intention to adopt e-hailing application.
Haba and Dastance (2018) also fail to support their hypothesis of expected effort having a significant positive effect on behavioral intentions to adopt an e-calling application. After collecting data from 182 Tanzanian users, Tossy (2014) also found that expected duration of effort did not significantly affect an individual's intention to use a mobile application payment system.
Social Influence
Consequently, the hypothesis that the opinion and suggestion provided by an individual's reference group such as family, friends or colleagues can significantly influence e-hilling users in Malaysia and Singapore to decide whether to use or which e-hilling application to adopt is supported by this study.
Facilitating Condition
Haba and Dastance (2018) are also unable to support his hypothesis that FC has a significant positive impact on BI for e-hilling application adoption.
Perceived Risk
Implications of the Study
In-app features like mobile payments can be similar to other apps to reduce the learning curve. This implies that the ease of use and facilities available for an e-hailing application are not considered important to consumers. A possible reason to explain this is that Singapore is the most tech-savvy country in the international e-government ranking list for 3 consecutive times (Poulami Nag, 2017).
Therefore, the e-hailing company in Singapore can design a more sophisticated application to have more customization features. Artificial intelligence (AI) may be developed in the app in the future to make it more convenient for consumers. Social Influence plays an important role for both Malaysians and Singaporeans to adopt e-hailing application.
The members of society will try to fit into groups in exchange for loyalty. Therefore, the marketers can use the word of mouth or buzz method to spread positive awareness for the e-hailing application.
Limitations of the Study
According to the findings in chapter 4, there is only 53.0% of the variation in the dependent variable that is explained by the independent variables. This means that there may be other variables that will affect Malaysians' behavioral intention for e-hailing application that are not included in this research. Most of the journal articles cited in this research were conducted in another country.
In addition, only a few researchers have adopted the UTAUT model to investigate e-calling application behavior. As a result, it could be a barrier for researchers due to the different cultural background, context and conditions in Malaysia.
Recommendations of the Study
In this research, only passengers are considered as users of the e-hailing application while drivers are considered as providers. Looking at it from another perspective, drivers can also be seen as users of the application.
Conclusion
Retrieved December 8, 2018, from https://www.thestar.com.my/news/nation govt-encouraging-cabbies-to-move-to-e-hailing-services-for-extra-income/. User adoption in the sharing economy: An explanatory study of transportation network companies in China based on UTAUT2. Retrieved from https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/how-shared-mobility-will-change-the-automotive-industry.
Retrieved March 14, 2019, from https://www.itf-oecd.org/app-based-ride-and-taxi-services-principles-regulation. Uber, Go-Jek, Grab: What people in Indonesia really want from ride-hailing apps. Retrieved December 8, 2018, from https://www.jll.com.au/en/trends-and- insights/cities/the-rush-for-rail-in-southeast-asias-fastest-growing-cities The Star Online.
Retrieved March 17, 2019, from https://www.thestar.com.my/news/nation mycar-lodges-police-report-against-driver-offering-sex-service-to-passenger/.
Turnitin Report
