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Factors that Affecting Behavioral Intention in Online Transportation Service: Case study of GO-JEK
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Available online at www.sciencedirect.com Procedia Computer Science 00 (2018) 000–000
ScienceDirect
www.elsevier.com/locate/procedia
Procedia Computer Science 124 (2017) 504–512
4th Information Systems International Conference 2017, ISICO 2017, 6-8 November 2017, Bali, Indonesia
Factors that Affecting Behavioral Intention in Online Transportation Service: Case study of GO-JEK Rizky Septiani, Putu Wuri Handayani, Fatimah Azzahro* Faculty of Computer Science, Universitas Indonesia, Depok, 16424, Indonesia
Abstract This study aims to analyze factors that affect the user's behavioral Intention on one of online transportation services in Indonesia: GO-JEK. These factors are derived from several IT adoption theories, namely, Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Diffusion of Innovation (DOI) theory. This study uses a quantitative approach with a total of 1,2792 collected respondents. The data gathered within this study is being analyzed using covariance based Structural Equation Model (CB-SEM) method using AMOS 22.0 software. This study found that the factors of internal perception (perceived ease of use), external influences (subjective norm), innovation characteristic (compatibility), perceived enjoyment and variety of service influence the behavioral intention of users on online transportation service in Indonesia. © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 4th Information Systems International Conference 2017. Keywords: Behavioral intention; Indonesia; m-commerce; online transportation; GO-JEK
1. Introduction According to Southeast Asia's MasterCard president, Matthew Driver, Indonesia has the largest customer base for e-commerce in Asia-Pacific1. According to Featherman et al., m-commerce is considered more potential than ecommerce due to the exponential growth of mobile devices2. The development of m-commerce is also experienced a rapid increase in Indonesia. This phenomenon is supported by the facts issued by the social marketing agency, We Are Social, which reports that there are 326.3 million sim-card registered on mobile devices in 2016, an amount that exceeds the total population of Indonesia itself1. It is also evidenced by the high number of mobile device users in Indonesia that reach 85%. Among them, there are 43% that use smartphone devices. Supporting previous evidence, a survey conducted by APJII also found that there are 63.1 million or about 47.6% of the total internet users in
*
Corresponding author. Tel.: +6281231350303 E-mail address: [email protected]
1877-0509 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 4th Information Systems International Conference 2017. 1877-0509 © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 4th Information Systems International Conference 2017 10.1016/j.procs.2017.12.183
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Indonesia who uses smartphones on daily basis. (Hew, 2016). Obviously, the phenomenon opens great opportunities for companies to expand their business to m-commerce. The growth of m-commerce resulted in intensive competition between m-commerce3. To stay competitive, developers need to pay attention to user acceptance of m-commerce which is one of the keys to successful mcommerce development4. Additionally, m-commerce developers also need to understand the factors that can interest users to use it5. The ability to understands these factors would provide insight to developers in determining the right strategy to be able to compete in developing m-commerce6. One example of m-commerce is online transportation services that can be accessed via mobile phone, such as Uber and Lyft. For the past two years, online transportation services in Indonesia are having substantial growth. Several apps dominate the online transportation market, namely, Grab, Uber and GO-JEK. While Grab and Uber are created from other countries, GO-JEK is quite special since it was co-founded by Indonesian7. Furthermore, according to result research Tania (2017) about online ride booking apps preference, GO-JEK ranks first to become the most favorite online transportation services in and outside Java island in Indonesia8. Therefore, this study only focus on GO-JEK. For the past few decades, research on user acceptance becomes a hot topic in IS studies 9,10. Various theories are introduced, such as Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), Theory of Reasoned Action (TRA), Unified Theory of Acceptance (UTAUT) and Diffusion of Innovation (DOI). Although many theory can be used to predict user acceptance, there are some issues need to be resolved4. First, according to Min et al., many IT adoption theories use different terminology on acceptance factors but have similar meanings4. Second, there is no theory that can cover all the factors that can explain user adoption on new technologies. Thus, it can be concluded that each theory has its own weaknesses. Therefore, it is necessary to integrate theories that can provides a suitable model to determine factors that affect the user's behavioral intention on the online transportation services in Indonesia 4. Furthermore, according to Chong et al., factors that may interest users to use m-commerce differs in each country. This study aligns with Dai & Palvi’s research that highlight the difference between factor that interest users in China, Malaysia, and United States11,12. Hence, this research tries to integrate several theories to explain the factors that influence the behavior of the user (behavioral intention) on the online transportation service in Indonesia. 2. Literature Study 2.1. IT adoption in m-commerce User acceptance is one of the obstacles to the successful implementation of Information System (IS) and IT 5. As m-commerce grow rapidly, the company that own m-commerce needs to clearly understand consumer perception and acceptance towards m-commerce5. According to Min et al., user acceptance is one of the key success of mcommerce4. A lack of understanding of user needs and technology infrastructure will hamper the success of mecommerce5. By understanding users, m-commerce developers may provide product or services that satisfy user’s needs11. In addition, understanding user acceptance can also provide insight to the management in developing effective strategies, so that m-commerce is able to compete with other competitors5. Besides understanding user acceptance, m-commerce developers need to understand the factors that can attract user’s interest (behavioral intention) to use m-commerce5. Behavioral intention can be defined as the power of one's interest in performing certain behaviors13. In m-commerce, behavioral intention is considered as user subjectivity on their possibility of using a particular m-commerce6. There are several well-known IT adoption theory such as TRA, TAM, extended TAM (TAM2), TPB, and UTAUT4. However, TAM, DOI, UTAUT, and Task Technology Fit (TTF) are the most often theory used to analyze user behavior and factors that influence their decision to adopt new technology 6. TAM was developed by Davis, after adopting TRA theory6. TAM consists of several components, namely, perceived ease of use, perceived usefulness, attitude toward using, behavioral intention to use, and actual use. In TAM, perceived ease of use and perceived usefulness are considered the two most critical internal belief that affects user acceptance13. While TAM theory is more focused on user’s internal perception, TPB theory is more focused on external factors that affects user acceptance13. TPB was developed from TRA theory and consist of several factors: attitude, subjective norm, and perceived behavioral control. When new technology emerges, one may have limited knowledge on internal and
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external perception. Thus, according to Zhang et al., innovativeness factor should be considered when measuring user intention to use new technology13. Therefore, this research use Diffusion of Innovation theory (DOI), a wellknown theory that introduced by Rogers in 1995 that explains adoption pattern and methods while assisting in predicting the success of new technology adoption5,12. DOI theory has innovation attributes that help improving adoption rate, such as relative advantage, compatibility, complexity, trialability, and observables14. 2.2. Online Transportation Services Currently, all activities of various sectors are heavily dependent on the existence of technology, both directly and indirectly, including the transportation services sector. A public transportation service project that is being rampant is the modernization of traditional public transport services through the use of IT, known as an online transport service. According to Jenita, online transportation service is a transportation service which all transactions are done online by using smartphone, related application, and internet15. The need for online transport services is influenced by various factors such as cost, quality of service, income, and ownership of the mode of transportation used 16. In addition, Van et al. argue that psychological factors also affect the selection of online transportation services in various Asian countries such as Japan, Thailand, China, Vietnam, Philippines, and Indonesia16. The presence of online transportation services has changed the lifestyle of the people to obtain easy transportation services that only utilize the smartphone without having a fuss17. Users do not need to bargain the fare because the apps will provide real-time fare calculation. Moreover, users can also find the critical driver information which encourages safety. Thus, online transportation systems become more popular due to its benefits. 3. Conceptual Model and Research Hypotheses The current study seeks to develop a research framework of mobile payment adoption by drawing on the extant literature on TAM, TBP and DOI theory. The present study focuses on 8 factors that may affects user interest on using new technology, namely, trust, perceived enjoyment, variety of service, perceived usefulness, perceived ease of use, subjective norm, innovativeness, and compatibility. Those factors are derived from the ranking of factors that have been widely used by previous studies. Fig. 1 presents the research model as well as the proposed hypotheses.
Fig. 1 Research model and hypotheses.
Based on the figure above, the research hypotheses proposed in this study are as follows: Hypothesis 1. Perceived enjoyment positively affects behavioral intention on the use of GO-JEK. Hypothesis 2. Subjective norm positively affects behavioral intention on the use of GO-JEK. Hypothesis 3. Perceived usefulness positively affects behavioral intention on the use of GO-JEK. Hypothesis 4. Compatibility positively affects behavioral intention on the use of GO-JEK. Hypothesis 5. Perceived ease of use positively affects behavioral intention on the use of GO-JEK.
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Hypothesis 6. Innovativeness positively affects behavioral intention on the use of GO-JEK. Hypothesis 7. Variety of service positively affects behavioral intention on the use of GO-JEK. Hypothesis 8. Trust positively affects behavioral intention on the use of GO-JEK. Hypothesis 9a. Subjective norm positively affects perceived usefulness on the use of GO-JEK. Hypothesis 9b. Compatibility positively affects perceived usefulness on the use of GO-JEK. Hypothesis 9c. Perceived ease of use positively affects perceived usefulness on the use of GO-JEK. Hypothesis 10. Innovativeness positively affects perceived ease of use on the use of GO-JEK.
3.1. Perceived Enjoyment and Behavioral Intention Enjoyment is an intrinsic reward derived from the use of technology that has been learned 11,13. Enjoyment is used to capture the hedonism dimension towards user consumption and measure how far users find the service fun, convenient, and entertaining to be used 11. According to past study, perceived enjoyment plays an important role in defining user acceptance towards new technology12. In their study, Cheong and Park prove that there is a strong relationship between perceived enjoyment and user behavior in using m-commerce13. Later, it is supported by research conducted by Ha et al., (2007) and Teo (2001) who also found a positive relationship between perceived enjoyment and user behavior in using m-commerce2. 3.2. Subjective Norm and Behavioral Intention According to Lu et al. (2003), subjective norm is a person’s belief whether others should be involved in a particular activity or not14. Subjective norm is examined in TRA theory and TPB theory as a social factor that explain technology adoption. Subjective norms also refer to perceived social pressure for a person to behave 11. Davis's research (1989) suggests that a person will use a technology to meet the advice of others compared to his feelings and beliefs11. In addition, Fan et al. (2005) also argue that users are more likely to recommend a service to other users if they are satisfied with the service14. 3.3. Perceived Usefulness and Behavioral Intention Perceived usefulness is defined by Davis (1989) as the level of user’s belief that the use of technology can improve their performance6. Perceived usefulness is one of TAM's theoretical factors that has a significant influence on technology. Perceived usefulness emphasizes the task accomplishment and describes the user's desire to engage with technology as a result of the external award earned 2. Perceived usefulness is not only used to assess extrinsic characteristics of m-commerce but also how m-commerce can help users to accomplish task-related goals such as emphasizing effectiveness and efficiency. 3.4. Compatibility and Behavioral Intention Compatibility is part of Diffusion of Innovation theory (DOI) that defines to what extent the innovation consistent with a person’s values, experiences, and needs5. Compatibility also refers to whether the user feels an innovation compatible with their needs and lifestyle11. The higher the level of compatibility should result a higher user’s interest to adopt new technology5. 3.5. Perceived Ease of Use and Behavioral Intention Perceived ease of use can be defined as the level of user trust in using m-commerce which requires minimal effort to operate2. Although mobile phone penetration rates in developing countries are considered high, m-commerce still considers as a new technology by its users6. Despite many people found that m-commerce is pretty useful, they also feel that m-commerce is rather difficult to use14. Therefore, perceived ease of use should be an important factor in adopting m-commerce6. 3.6. Innovativeness and Behavioral Intention Innovativeness can be defined as the level of a person's desire to adopt new ideas compared to other users 13. Innovativeness is a personality factor that can be used to predict the user's innovative tendency to adopt various technological innovations11. The higher the level of innovativeness a user has, the higher the likelihood of the user to adopt the latest technology13.
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3.7. Variety of Service and Behavioral Intention According to Chong et al., m-commerce do not offer services as much as e-commerce do12. There are limited number of services available in mobile purchasing, mobile advertisement, and m-learning. Therefore, it becomes mcommerce’s challenge to attract users if they are only offer limited services 2. According to Chong, m-commerce providers need to understand and provide attractive services in order to retain their customers. This due to fact that the service variety affects user behavior in using m-commerce2,12. 3.8. Trust and Behavioral Intention As a new technology, m-commerce are still overshadowed by user’s doubts regarding its level of trust, security, and privacy6. Trust on m-commerce is defined as the trust of users against the safety of m-commerce and free from privacy threats6. Several past studies argue that trust is one of the important and complex factors in the world of commerce4,6,14. According to Luarn and Lin (2005), lack of trust in m-commerce is mostly due to online transactions being performed by sellers and buyers in the absence of face-to-face contact. Thus, buyers are concerned about the possibility that seller may give their personal information and money to other people without their consent14. Hence, lack of trust may results in users doubt and being reluctant to buy products or services from online sources6. 3.9. Perceived Ease of Use, Compatibility, Subjective Norm and Perceived Usefulness Perceived usefulness is an internal belief that is critical to the use of technology5,13. Research conducted by Zhang et al. states that perceived usefulness is influenced by how high perceive ease of use the user has13. The more users believe that to use innovation the only requires minimum effort, the higher their level of trust that their performance can be improved by using the technology. In addition, perceived usefulness is also influenced by the level of compatibility perceived by the user. If the user feels that the existing technology is incompatible with the value they have, then, it is unlikely that the user will perceive the technology to improve their performance 5. Furthermore, subjective norms can also affect perceived usefulness. If the subjective norm experienced by the user does not support the adoption of existing technology, then users will less likely adopt the technology. In addition, it also affects the level of user trust that the technology offered can improve their performance 18. 3.10. Innovativeness and Perceived Ease of Use Higher perceived innovativeness leads to a more positive IT adoption behavior. Consequently, the person who is more innovative will find a new technology is easy to use compared to a person who has low innovativeness. According to Citrin et al. (2000) and Hung et al. (2003), innovativeness affects perceived ease of use19 4. Methodology A quantitative study was conducted to measure the relationships between variables identified in the prior section. Questionnaires were distributed among GO-JEK users in Indonesia by using purposive sampling as the method for data collection. The data collection was conducted using online survey and disseminated through various social media such as Facebook, Twitter, Path, Linkedin, Instagram, and Line Messager. The measurement items were formulated as Likert-type statements anchored by a five-point scale, ranging from 1 (‘‘strongly disagree”) to 5 (‘‘strongly agree”). Table 1 shows the example of questionnaire items. To examine the latent variables within their causal structure, we applied covariance-based structural equation modeling (CB-SEM) using AMOS 22.0 software. At the end of the data collection period, 1.279 usable responses were received to be analyzed further. Table 2 presents the demographic statistics for data collected. Table 1. Example of questionnaire items Construct
Perceived Usefulness
Items
Sources
Using online transportation services would increase my productivity when traveling. For example, I can read books or news as I travel.
Wu and Wang (2005)
Using online transportation services would enhance my effectiveness in being mobile.
Wu and Wang (2005)
I think using online transportation service is very useful
Wu and Wang
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for me to be mobile.
(2005)
I think using online transportation services would save my time.
Tsu et al. (2009)
509
Table 2. Demographics of respondents. Demographic Variable Gender Age
Frequency of using GO-JEK per week
Period of m-payment use per month
Frequency
Percent
Female
844
66%
Male
435
34%
< 20
205
16%
20 - 25
972
76%
26 -30
64
5%
>30
38
3%
1-5 times
921
72%
6-10 times
205
16%
>10 times
153
12%
<3months
77
6%
3-7 months
256
20%
8-12 months
294
23%
> 12 months
652
51%
5. Research Results 5.1. Measurement model The measurement model was assessed by using confirmatory factor analysis (CFA). The first step is examining measurement model by testing the construct reliability and validity to assess the internal consistency of the construct measurement. The construct reliability and validity were measured using loading factors, Cronbach’s Alpha (CA), Composite Reliability (CR), and Average Variance Extracted (AVE). A factor is considered reliable and valid if its loading factor, CA and CR are more than 0,7 while its AVE is more than 0.5 20–22. Table 3 provides a list of all measurement items and the results of validity and reliability analysis. Table 3. Construct Validity and Reliability Variable Trust Variety of Service Innovativeness Perceived Enjoyment Behavioral Intention Subjective Norm Compatibility Perceived Ease of Use
Items TR1 TR2 VOS1 VOS2 VOS3 IN2 IN3 IN4 PE1 PE2 BI1 B14 SN1 SN2 SN3 CO2 CO3 PEOU1 PEOU2
Composite
Cronbach’s
Reliability
Alpha
0.776
0.873
1.789
0.650
0.847
0.843
0.979
0.993
0.772
0.794
0.885
0.884
0.615
0.761
0.769
0.983
0.994
0.720
0.965
0.982
0.716
0.617
0.865
0.860
Loading Factors
AVE
0.903 0.858 0.819 0.844 0.754 0.993 0.987 0.989 0.901 0.881 0.746 0.821 0.991 0.992 0.992 0.983 0.982 0.716 0.720
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510
Perceived Usefulness
PEOU3 PEOU4 PU2 PU3
0.876 0.820 0.855 0.893
0.764
0.866
7
0.880
5.2. Structural Model The next step is estimating the model’s overall goodness-of-fit as shown in Table 4. The actual values of all fit indices were better than the recommended values, which demonstrate a good fit between the model and data 23. Furthermore, R2 value was examined to see the strength of structural prediction model. Table 5 shows R2 value for all endogen variables. BI has R2 value of 0.577 that shows that BI variable can be explained by its exogenous factors up to 57.7% and the rest of it explained by other factors outside model. Then, PU variable has R2 value of 0.44 while PEOU has R2 value of 0.117. Therefore, one can conclude that only BI variable has strong correlation with its exogenous variables. Table 4. Fit indices for measurement model. Fit index Recommended value Model value
Chi Square >0.05 376.289
RMSEA < 0,07 0.03
GFI >0.90 0.972
CFI >0.90 0.987
NFI >0.90 0.974
AMOS 22.0 is utilized to perform one-tailed hypothesis testing. The significant path coefficients (p ≤ 0.05) appear to support the proposed model. Table 6 shows the result of hypothesis testing. From 12 initial hypotheses, 4 of them are rejected while the rest are accepted. Table 5. R2 value for endogen variables Endogen Variable R2 Behavioral Intention
0,577
Perceived Usefulness
0,440
Perceived Ease of Use
0,117
Table 6 Hypotheses testing results HypoEstimate Attributes theses H1 0.45 PE → BI H2 -0.043 SN → BI H3 H4
p
p/2
Results
0.021
0.0105
Accepted
0.037
0.0185
Accepted
PU → BI
0.308
0.19
0.095
Rejected
CO → BI
0.223
0.001
0.0005
Accepted
H5
PEOU → BI
-0.245
0.002
0.001
Accepted
H6
IN → BI
0.012
0.568
0.284
Rejected
H7
VOS → BI
0.137
0.075
0.0375
Accepted
H8
TR → BI
-0.035
0.459
0.2295
Rejected
H9a
SN → PU
0.05
0.002
0.001
Accepted
H9b
CO → PU
0.045
0.224
0.112
Rejected
H9c
PEOU → PU
0.859
0.001
0.0005
Accepted
H10
IN → PEOU
0.342
0.002
0.001
Accepted
6. Discussion In this empirical study, we analyzed users’ behavioral intention using GO-JEK, a mobile online transportation service, as the case study. Our results show that perceived ease of use has positive impacts on behavioral intention. This may due to easiness and user’s knowledge on GO-JEK application make them feel more competent. Hence, they ignore perceived usefulness in the use of GO-JEK apps2. This result aligns with Chong et al.’s study that found that perceived usefulness does not affect user intention to use m-commerce. The study also found that compatibility does not affect perceived usefulness. Thus, one can conclude that user needs and lifestyle do not affect how they perceived usefulness of GO-JEK apps. Meanwhile, this study also found that innovativeness factor does not positively affect behavioral intention. Although this result does not align with several previous studies, it fits Indonesian users’ characteristics that mainly
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have consumptive behavior and less innovative mindset24. Furthermore, it is noted that trust factor does not affect behavioral intention of GO-JEK users. The majority of users believe that GO-JEK services are safe to be used25. This phenomenon amplified by the transparency that GO-JEK offered to get sufficient information regarding user’s order, their GO-JEK virtual wallet, and others. This transparency is significantly decreasing user’s doubt when using GO-JEK as their online transportation services. 7. Conclusion This study shows that user’s behavioral intention to use GO-JEK is affected by internal perception factors (perceived ease of use), external factors (subjective norm), innovation characteristics (compatibility) and other factors (perceived enjoyment and variety of service). However, innovativeness, trust, and perceived usefulness do not affect user’s behavior intention to use Go-JEK in Indonesia. Furthermore, this study found that compatibility does not perceived usefulness felt by users. This study has several limitations. First, we did not incorporate actual usage behavior into the proposed model. However, substantial empirical study exists regarding the causal link between behavioral intention and usage behavior10. Second, the demography of respondents did not cover all cities that in GO-JEK coverage. Thus, the results of this study may have not represents GO-JEK users’ behavioral intention in general. Further data collection need to be conducted in order to improve the results of this study. Acknowledgements We want to convey our gratitude to the Directorate of Research and Community Engagement Universitas Indonesia for the Program Hibah Publikasi Internasional Terindeks untuk Tugas Akhir Mahasiswa (PITTA), grant No. 416/UN2.R3.1/HKP.05.00/2017. References 1 Sagari N. Pengaruh Kualitas Layanan dan Kepuasan Pelanggan terhadap Minat Beli Ulang pada Situs Mobile Commerce. 2016. 2 Chong AYL. A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Syst Appl 2013; 40: 1240–1247. 3 Lee WO, Wong LS. Determinants of Mobile Commerce Customer Loyalty in Malaysia. Procedia - Soc Behav Sci 2016; 224: 60–67. 4 Min Q, Ji S, Qu G. Mobile Commerce User Acceptance Study in China: A Revised UTAUT Model. Tsinghua Sci Technol 2008; 13: 257–264. 5 Wu J-H, Wang S-C. What drives mobile commerce? Inf Manag 2005; 42: 719–729. 6 Liebana-Cabanillas F, Marinkovic V, Kalinic Z. A SEM-neural network approach for predicting antecedents of m-commerce acceptance. Int J Inf Manage 2017; 37: 14–24. 7 Sukma D. Perbandingan Tarif & Layanan 9 Ojek Online di Indonesia. Arenalte.com. 2017.https://arenalte.com/berita/industri/tarif-ojek-onlinedi-indonesia/ (accessed 14 Feb2017). 8 Tania S. Online Ride Booking Apps Preference - Survey Report – JAKPAT. JAKPAT. 2017.https://blog.jakpat.net/online-ride-booking-appspreference-survey-report/ (accessed 1 May 2017). 9 Benbasat I, Barki H. Quo vadis, TAM? J Assoc Inf Syst 2007; 8: 211–218. 10 Venkatesh V, Thong J, Xu X. Consumer acceptance and user of information technology: Extending the unified theory of acceptance and use of technology. MIS Q 2012; 36: 157–178. 11 Dai H, Palvi PC. Mobile commerce adoption in China and the United States: a cross-cultural study. ACM SIGMIS Database 2009; 40: 43–61. 12 Chong AYL, Chan FTS, Ooi KB. Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia. Decis. Support Syst. 2012; 53: 34–43. 13 Zhang L, Zhu J, Liu Q. A meta-analysis of mobile commerce adoption and the moderating effect of culture. Comput Human Behav 2012; 28: 1902–1911. 14 Tsu Wei T, Marthandan G, Yee‐Loong Chong A, Ooi K, Arumugam S. What drives Malaysian m‐commerce adoption? An empirical analysis. Ind Manag Data Syst 2009; 109: 370–388. 15 Jenita. Transportasi Online. 2012.https://prezi.com/hqywaio5gn1k/transportasi-online/ (accessed 1 Apr2017). 16 Narayanaswami S. Urban transportation: innovations in infrastructure planning and development. Int J Logist Manag 2017; 28: 150–171. 17 Daily Octagon. Bagaimana Perkembangan Transportasi Online Menurut Pakar? 2015.https://daily.oktagon.co.id/bagaimana-perkembangantransportasi-online-menurut-pakar/ (accessed 1 Apr2017). 18 Shankar V, Urban GL, Sultan F. Online trust: a stakeholder perspective, concepts, implications, and future directions. J Strateg Inf Syst 2002; 11: 325–344. 19 Yang KCC. Exploring factors affecting the adoption of mobile commerce in Singapore. Telemat Informatics 2005; 22: 257–277. 20 Lance CE, Butts MM, Michels LC. The Sources of Four Commonly Reported Cutoff Criteria. Organ Res Methods 2006; 9: 202–220. 21 Hair JF, Ringle CM, Sarstedt M. PLS-SEM: Indeed a Silver Bullet. J. Mark. Theory Pract. 2011; 19: 139–152.
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22 Hair JF, Gabriel M, Patel V. AMOS Covariance-Based Structural Equation Modeling (CB-SEM): Guidelines on Its Application as a Marketing Research Tool. 2014.https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2676480 (accessed 15 Jun2017). 23 Doloi H, Iyer KC, Sawhney A. Structural equation model for assessing impacts of contractor’s performance on project success. Int J Proj Manag 2011; 29: 687–695. 24 Audia L. Perilaku Konsumtif Akibat Pengaruh Hedonisme di Kalangan Mahasiswa Jurusan Geografi Universitas Negeri Malang. Academia.edu. 2017. 25 Benas A. Ride Booking App Trend in Jakarta - Survey Report - JAKPAT. JAKPAT. 2017.
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ScienceDirect
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Procedia Computer Science 124 (2017) 504–512
4th Information Systems International Conference 2017, ISICO 2017, 6-8 November 2017, Bali, Indonesia
Factors that Affecting Behavioral Intention in Online Transportation Service: Case study of GO-JEK Rizky Septiani, Putu Wuri Handayani, Fatimah Azzahro* Faculty of Computer Science, Universitas Indonesia, Depok, 16424, Indonesia
Abstract This study aims to analyze factors that affect the user's behavioral Intention on one of online transportation services in Indonesia: GO-JEK. These factors are derived from several IT adoption theories, namely, Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Diffusion of Innovation (DOI) theory. This study uses a quantitative approach with a total of 1,2792 collected respondents. The data gathered within this study is being analyzed using covariance based Structural Equation Model (CB-SEM) method using AMOS 22.0 software. This study found that the factors of internal perception (perceived ease of use), external influences (subjective norm), innovation characteristic (compatibility), perceived enjoyment and variety of service influence the behavioral intention of users on online transportation service in Indonesia. © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 4th Information Systems International Conference 2017. Keywords: Behavioral intention; Indonesia; m-commerce; online transportation; GO-JEK
1. Introduction According to Southeast Asia's MasterCard president, Matthew Driver, Indonesia has the largest customer base for e-commerce in Asia-Pacific1. According to Featherman et al., m-commerce is considered more potential than ecommerce due to the exponential growth of mobile devices2. The development of m-commerce is also experienced a rapid increase in Indonesia. This phenomenon is supported by the facts issued by the social marketing agency, We Are Social, which reports that there are 326.3 million sim-card registered on mobile devices in 2016, an amount that exceeds the total population of Indonesia itself1. It is also evidenced by the high number of mobile device users in Indonesia that reach 85%. Among them, there are 43% that use smartphone devices. Supporting previous evidence, a survey conducted by APJII also found that there are 63.1 million or about 47.6% of the total internet users in
*
Corresponding author. Tel.: +6281231350303 E-mail address: [email protected]
1877-0509 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 4th Information Systems International Conference 2017. 1877-0509 © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 4th Information Systems International Conference 2017 10.1016/j.procs.2017.12.183
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Indonesia who uses smartphones on daily basis. (Hew, 2016). Obviously, the phenomenon opens great opportunities for companies to expand their business to m-commerce. The growth of m-commerce resulted in intensive competition between m-commerce3. To stay competitive, developers need to pay attention to user acceptance of m-commerce which is one of the keys to successful mcommerce development4. Additionally, m-commerce developers also need to understand the factors that can interest users to use it5. The ability to understands these factors would provide insight to developers in determining the right strategy to be able to compete in developing m-commerce6. One example of m-commerce is online transportation services that can be accessed via mobile phone, such as Uber and Lyft. For the past two years, online transportation services in Indonesia are having substantial growth. Several apps dominate the online transportation market, namely, Grab, Uber and GO-JEK. While Grab and Uber are created from other countries, GO-JEK is quite special since it was co-founded by Indonesian7. Furthermore, according to result research Tania (2017) about online ride booking apps preference, GO-JEK ranks first to become the most favorite online transportation services in and outside Java island in Indonesia8. Therefore, this study only focus on GO-JEK. For the past few decades, research on user acceptance becomes a hot topic in IS studies 9,10. Various theories are introduced, such as Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), Theory of Reasoned Action (TRA), Unified Theory of Acceptance (UTAUT) and Diffusion of Innovation (DOI). Although many theory can be used to predict user acceptance, there are some issues need to be resolved4. First, according to Min et al., many IT adoption theories use different terminology on acceptance factors but have similar meanings4. Second, there is no theory that can cover all the factors that can explain user adoption on new technologies. Thus, it can be concluded that each theory has its own weaknesses. Therefore, it is necessary to integrate theories that can provides a suitable model to determine factors that affect the user's behavioral intention on the online transportation services in Indonesia 4. Furthermore, according to Chong et al., factors that may interest users to use m-commerce differs in each country. This study aligns with Dai & Palvi’s research that highlight the difference between factor that interest users in China, Malaysia, and United States11,12. Hence, this research tries to integrate several theories to explain the factors that influence the behavior of the user (behavioral intention) on the online transportation service in Indonesia. 2. Literature Study 2.1. IT adoption in m-commerce User acceptance is one of the obstacles to the successful implementation of Information System (IS) and IT 5. As m-commerce grow rapidly, the company that own m-commerce needs to clearly understand consumer perception and acceptance towards m-commerce5. According to Min et al., user acceptance is one of the key success of mcommerce4. A lack of understanding of user needs and technology infrastructure will hamper the success of mecommerce5. By understanding users, m-commerce developers may provide product or services that satisfy user’s needs11. In addition, understanding user acceptance can also provide insight to the management in developing effective strategies, so that m-commerce is able to compete with other competitors5. Besides understanding user acceptance, m-commerce developers need to understand the factors that can attract user’s interest (behavioral intention) to use m-commerce5. Behavioral intention can be defined as the power of one's interest in performing certain behaviors13. In m-commerce, behavioral intention is considered as user subjectivity on their possibility of using a particular m-commerce6. There are several well-known IT adoption theory such as TRA, TAM, extended TAM (TAM2), TPB, and UTAUT4. However, TAM, DOI, UTAUT, and Task Technology Fit (TTF) are the most often theory used to analyze user behavior and factors that influence their decision to adopt new technology 6. TAM was developed by Davis, after adopting TRA theory6. TAM consists of several components, namely, perceived ease of use, perceived usefulness, attitude toward using, behavioral intention to use, and actual use. In TAM, perceived ease of use and perceived usefulness are considered the two most critical internal belief that affects user acceptance13. While TAM theory is more focused on user’s internal perception, TPB theory is more focused on external factors that affects user acceptance13. TPB was developed from TRA theory and consist of several factors: attitude, subjective norm, and perceived behavioral control. When new technology emerges, one may have limited knowledge on internal and
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external perception. Thus, according to Zhang et al., innovativeness factor should be considered when measuring user intention to use new technology13. Therefore, this research use Diffusion of Innovation theory (DOI), a wellknown theory that introduced by Rogers in 1995 that explains adoption pattern and methods while assisting in predicting the success of new technology adoption5,12. DOI theory has innovation attributes that help improving adoption rate, such as relative advantage, compatibility, complexity, trialability, and observables14. 2.2. Online Transportation Services Currently, all activities of various sectors are heavily dependent on the existence of technology, both directly and indirectly, including the transportation services sector. A public transportation service project that is being rampant is the modernization of traditional public transport services through the use of IT, known as an online transport service. According to Jenita, online transportation service is a transportation service which all transactions are done online by using smartphone, related application, and internet15. The need for online transport services is influenced by various factors such as cost, quality of service, income, and ownership of the mode of transportation used 16. In addition, Van et al. argue that psychological factors also affect the selection of online transportation services in various Asian countries such as Japan, Thailand, China, Vietnam, Philippines, and Indonesia16. The presence of online transportation services has changed the lifestyle of the people to obtain easy transportation services that only utilize the smartphone without having a fuss17. Users do not need to bargain the fare because the apps will provide real-time fare calculation. Moreover, users can also find the critical driver information which encourages safety. Thus, online transportation systems become more popular due to its benefits. 3. Conceptual Model and Research Hypotheses The current study seeks to develop a research framework of mobile payment adoption by drawing on the extant literature on TAM, TBP and DOI theory. The present study focuses on 8 factors that may affects user interest on using new technology, namely, trust, perceived enjoyment, variety of service, perceived usefulness, perceived ease of use, subjective norm, innovativeness, and compatibility. Those factors are derived from the ranking of factors that have been widely used by previous studies. Fig. 1 presents the research model as well as the proposed hypotheses.
Fig. 1 Research model and hypotheses.
Based on the figure above, the research hypotheses proposed in this study are as follows: Hypothesis 1. Perceived enjoyment positively affects behavioral intention on the use of GO-JEK. Hypothesis 2. Subjective norm positively affects behavioral intention on the use of GO-JEK. Hypothesis 3. Perceived usefulness positively affects behavioral intention on the use of GO-JEK. Hypothesis 4. Compatibility positively affects behavioral intention on the use of GO-JEK. Hypothesis 5. Perceived ease of use positively affects behavioral intention on the use of GO-JEK.
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Hypothesis 6. Innovativeness positively affects behavioral intention on the use of GO-JEK. Hypothesis 7. Variety of service positively affects behavioral intention on the use of GO-JEK. Hypothesis 8. Trust positively affects behavioral intention on the use of GO-JEK. Hypothesis 9a. Subjective norm positively affects perceived usefulness on the use of GO-JEK. Hypothesis 9b. Compatibility positively affects perceived usefulness on the use of GO-JEK. Hypothesis 9c. Perceived ease of use positively affects perceived usefulness on the use of GO-JEK. Hypothesis 10. Innovativeness positively affects perceived ease of use on the use of GO-JEK.
3.1. Perceived Enjoyment and Behavioral Intention Enjoyment is an intrinsic reward derived from the use of technology that has been learned 11,13. Enjoyment is used to capture the hedonism dimension towards user consumption and measure how far users find the service fun, convenient, and entertaining to be used 11. According to past study, perceived enjoyment plays an important role in defining user acceptance towards new technology12. In their study, Cheong and Park prove that there is a strong relationship between perceived enjoyment and user behavior in using m-commerce13. Later, it is supported by research conducted by Ha et al., (2007) and Teo (2001) who also found a positive relationship between perceived enjoyment and user behavior in using m-commerce2. 3.2. Subjective Norm and Behavioral Intention According to Lu et al. (2003), subjective norm is a person’s belief whether others should be involved in a particular activity or not14. Subjective norm is examined in TRA theory and TPB theory as a social factor that explain technology adoption. Subjective norms also refer to perceived social pressure for a person to behave 11. Davis's research (1989) suggests that a person will use a technology to meet the advice of others compared to his feelings and beliefs11. In addition, Fan et al. (2005) also argue that users are more likely to recommend a service to other users if they are satisfied with the service14. 3.3. Perceived Usefulness and Behavioral Intention Perceived usefulness is defined by Davis (1989) as the level of user’s belief that the use of technology can improve their performance6. Perceived usefulness is one of TAM's theoretical factors that has a significant influence on technology. Perceived usefulness emphasizes the task accomplishment and describes the user's desire to engage with technology as a result of the external award earned 2. Perceived usefulness is not only used to assess extrinsic characteristics of m-commerce but also how m-commerce can help users to accomplish task-related goals such as emphasizing effectiveness and efficiency. 3.4. Compatibility and Behavioral Intention Compatibility is part of Diffusion of Innovation theory (DOI) that defines to what extent the innovation consistent with a person’s values, experiences, and needs5. Compatibility also refers to whether the user feels an innovation compatible with their needs and lifestyle11. The higher the level of compatibility should result a higher user’s interest to adopt new technology5. 3.5. Perceived Ease of Use and Behavioral Intention Perceived ease of use can be defined as the level of user trust in using m-commerce which requires minimal effort to operate2. Although mobile phone penetration rates in developing countries are considered high, m-commerce still considers as a new technology by its users6. Despite many people found that m-commerce is pretty useful, they also feel that m-commerce is rather difficult to use14. Therefore, perceived ease of use should be an important factor in adopting m-commerce6. 3.6. Innovativeness and Behavioral Intention Innovativeness can be defined as the level of a person's desire to adopt new ideas compared to other users 13. Innovativeness is a personality factor that can be used to predict the user's innovative tendency to adopt various technological innovations11. The higher the level of innovativeness a user has, the higher the likelihood of the user to adopt the latest technology13.
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3.7. Variety of Service and Behavioral Intention According to Chong et al., m-commerce do not offer services as much as e-commerce do12. There are limited number of services available in mobile purchasing, mobile advertisement, and m-learning. Therefore, it becomes mcommerce’s challenge to attract users if they are only offer limited services 2. According to Chong, m-commerce providers need to understand and provide attractive services in order to retain their customers. This due to fact that the service variety affects user behavior in using m-commerce2,12. 3.8. Trust and Behavioral Intention As a new technology, m-commerce are still overshadowed by user’s doubts regarding its level of trust, security, and privacy6. Trust on m-commerce is defined as the trust of users against the safety of m-commerce and free from privacy threats6. Several past studies argue that trust is one of the important and complex factors in the world of commerce4,6,14. According to Luarn and Lin (2005), lack of trust in m-commerce is mostly due to online transactions being performed by sellers and buyers in the absence of face-to-face contact. Thus, buyers are concerned about the possibility that seller may give their personal information and money to other people without their consent14. Hence, lack of trust may results in users doubt and being reluctant to buy products or services from online sources6. 3.9. Perceived Ease of Use, Compatibility, Subjective Norm and Perceived Usefulness Perceived usefulness is an internal belief that is critical to the use of technology5,13. Research conducted by Zhang et al. states that perceived usefulness is influenced by how high perceive ease of use the user has13. The more users believe that to use innovation the only requires minimum effort, the higher their level of trust that their performance can be improved by using the technology. In addition, perceived usefulness is also influenced by the level of compatibility perceived by the user. If the user feels that the existing technology is incompatible with the value they have, then, it is unlikely that the user will perceive the technology to improve their performance 5. Furthermore, subjective norms can also affect perceived usefulness. If the subjective norm experienced by the user does not support the adoption of existing technology, then users will less likely adopt the technology. In addition, it also affects the level of user trust that the technology offered can improve their performance 18. 3.10. Innovativeness and Perceived Ease of Use Higher perceived innovativeness leads to a more positive IT adoption behavior. Consequently, the person who is more innovative will find a new technology is easy to use compared to a person who has low innovativeness. According to Citrin et al. (2000) and Hung et al. (2003), innovativeness affects perceived ease of use19 4. Methodology A quantitative study was conducted to measure the relationships between variables identified in the prior section. Questionnaires were distributed among GO-JEK users in Indonesia by using purposive sampling as the method for data collection. The data collection was conducted using online survey and disseminated through various social media such as Facebook, Twitter, Path, Linkedin, Instagram, and Line Messager. The measurement items were formulated as Likert-type statements anchored by a five-point scale, ranging from 1 (‘‘strongly disagree”) to 5 (‘‘strongly agree”). Table 1 shows the example of questionnaire items. To examine the latent variables within their causal structure, we applied covariance-based structural equation modeling (CB-SEM) using AMOS 22.0 software. At the end of the data collection period, 1.279 usable responses were received to be analyzed further. Table 2 presents the demographic statistics for data collected. Table 1. Example of questionnaire items Construct
Perceived Usefulness
Items
Sources
Using online transportation services would increase my productivity when traveling. For example, I can read books or news as I travel.
Wu and Wang (2005)
Using online transportation services would enhance my effectiveness in being mobile.
Wu and Wang (2005)
I think using online transportation service is very useful
Wu and Wang
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for me to be mobile.
(2005)
I think using online transportation services would save my time.
Tsu et al. (2009)
509
Table 2. Demographics of respondents. Demographic Variable Gender Age
Frequency of using GO-JEK per week
Period of m-payment use per month
Frequency
Percent
Female
844
66%
Male
435
34%
< 20
205
16%
20 - 25
972
76%
26 -30
64
5%
>30
38
3%
1-5 times
921
72%
6-10 times
205
16%
>10 times
153
12%
<3months
77
6%
3-7 months
256
20%
8-12 months
294
23%
> 12 months
652
51%
5. Research Results 5.1. Measurement model The measurement model was assessed by using confirmatory factor analysis (CFA). The first step is examining measurement model by testing the construct reliability and validity to assess the internal consistency of the construct measurement. The construct reliability and validity were measured using loading factors, Cronbach’s Alpha (CA), Composite Reliability (CR), and Average Variance Extracted (AVE). A factor is considered reliable and valid if its loading factor, CA and CR are more than 0,7 while its AVE is more than 0.5 20–22. Table 3 provides a list of all measurement items and the results of validity and reliability analysis. Table 3. Construct Validity and Reliability Variable Trust Variety of Service Innovativeness Perceived Enjoyment Behavioral Intention Subjective Norm Compatibility Perceived Ease of Use
Items TR1 TR2 VOS1 VOS2 VOS3 IN2 IN3 IN4 PE1 PE2 BI1 B14 SN1 SN2 SN3 CO2 CO3 PEOU1 PEOU2
Composite
Cronbach’s
Reliability
Alpha
0.776
0.873
1.789
0.650
0.847
0.843
0.979
0.993
0.772
0.794
0.885
0.884
0.615
0.761
0.769
0.983
0.994
0.720
0.965
0.982
0.716
0.617
0.865
0.860
Loading Factors
AVE
0.903 0.858 0.819 0.844 0.754 0.993 0.987 0.989 0.901 0.881 0.746 0.821 0.991 0.992 0.992 0.983 0.982 0.716 0.720
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510
Perceived Usefulness
PEOU3 PEOU4 PU2 PU3
0.876 0.820 0.855 0.893
0.764
0.866
7
0.880
5.2. Structural Model The next step is estimating the model’s overall goodness-of-fit as shown in Table 4. The actual values of all fit indices were better than the recommended values, which demonstrate a good fit between the model and data 23. Furthermore, R2 value was examined to see the strength of structural prediction model. Table 5 shows R2 value for all endogen variables. BI has R2 value of 0.577 that shows that BI variable can be explained by its exogenous factors up to 57.7% and the rest of it explained by other factors outside model. Then, PU variable has R2 value of 0.44 while PEOU has R2 value of 0.117. Therefore, one can conclude that only BI variable has strong correlation with its exogenous variables. Table 4. Fit indices for measurement model. Fit index Recommended value Model value
Chi Square >0.05 376.289
RMSEA < 0,07 0.03
GFI >0.90 0.972
CFI >0.90 0.987
NFI >0.90 0.974
AMOS 22.0 is utilized to perform one-tailed hypothesis testing. The significant path coefficients (p ≤ 0.05) appear to support the proposed model. Table 6 shows the result of hypothesis testing. From 12 initial hypotheses, 4 of them are rejected while the rest are accepted. Table 5. R2 value for endogen variables Endogen Variable R2 Behavioral Intention
0,577
Perceived Usefulness
0,440
Perceived Ease of Use
0,117
Table 6 Hypotheses testing results HypoEstimate Attributes theses H1 0.45 PE → BI H2 -0.043 SN → BI H3 H4
p
p/2
Results
0.021
0.0105
Accepted
0.037
0.0185
Accepted
PU → BI
0.308
0.19
0.095
Rejected
CO → BI
0.223
0.001
0.0005
Accepted
H5
PEOU → BI
-0.245
0.002
0.001
Accepted
H6
IN → BI
0.012
0.568
0.284
Rejected
H7
VOS → BI
0.137
0.075
0.0375
Accepted
H8
TR → BI
-0.035
0.459
0.2295
Rejected
H9a
SN → PU
0.05
0.002
0.001
Accepted
H9b
CO → PU
0.045
0.224
0.112
Rejected
H9c
PEOU → PU
0.859
0.001
0.0005
Accepted
H10
IN → PEOU
0.342
0.002
0.001
Accepted
6. Discussion In this empirical study, we analyzed users’ behavioral intention using GO-JEK, a mobile online transportation service, as the case study. Our results show that perceived ease of use has positive impacts on behavioral intention. This may due to easiness and user’s knowledge on GO-JEK application make them feel more competent. Hence, they ignore perceived usefulness in the use of GO-JEK apps2. This result aligns with Chong et al.’s study that found that perceived usefulness does not affect user intention to use m-commerce. The study also found that compatibility does not affect perceived usefulness. Thus, one can conclude that user needs and lifestyle do not affect how they perceived usefulness of GO-JEK apps. Meanwhile, this study also found that innovativeness factor does not positively affect behavioral intention. Although this result does not align with several previous studies, it fits Indonesian users’ characteristics that mainly
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have consumptive behavior and less innovative mindset24. Furthermore, it is noted that trust factor does not affect behavioral intention of GO-JEK users. The majority of users believe that GO-JEK services are safe to be used25. This phenomenon amplified by the transparency that GO-JEK offered to get sufficient information regarding user’s order, their GO-JEK virtual wallet, and others. This transparency is significantly decreasing user’s doubt when using GO-JEK as their online transportation services. 7. Conclusion This study shows that user’s behavioral intention to use GO-JEK is affected by internal perception factors (perceived ease of use), external factors (subjective norm), innovation characteristics (compatibility) and other factors (perceived enjoyment and variety of service). However, innovativeness, trust, and perceived usefulness do not affect user’s behavior intention to use Go-JEK in Indonesia. Furthermore, this study found that compatibility does not perceived usefulness felt by users. This study has several limitations. First, we did not incorporate actual usage behavior into the proposed model. However, substantial empirical study exists regarding the causal link between behavioral intention and usage behavior10. Second, the demography of respondents did not cover all cities that in GO-JEK coverage. Thus, the results of this study may have not represents GO-JEK users’ behavioral intention in general. Further data collection need to be conducted in order to improve the results of this study. Acknowledgements We want to convey our gratitude to the Directorate of Research and Community Engagement Universitas Indonesia for the Program Hibah Publikasi Internasional Terindeks untuk Tugas Akhir Mahasiswa (PITTA), grant No. 416/UN2.R3.1/HKP.05.00/2017. References 1 Sagari N. Pengaruh Kualitas Layanan dan Kepuasan Pelanggan terhadap Minat Beli Ulang pada Situs Mobile Commerce. 2016. 2 Chong AYL. A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Syst Appl 2013; 40: 1240–1247. 3 Lee WO, Wong LS. Determinants of Mobile Commerce Customer Loyalty in Malaysia. Procedia - Soc Behav Sci 2016; 224: 60–67. 4 Min Q, Ji S, Qu G. Mobile Commerce User Acceptance Study in China: A Revised UTAUT Model. Tsinghua Sci Technol 2008; 13: 257–264. 5 Wu J-H, Wang S-C. What drives mobile commerce? Inf Manag 2005; 42: 719–729. 6 Liebana-Cabanillas F, Marinkovic V, Kalinic Z. A SEM-neural network approach for predicting antecedents of m-commerce acceptance. Int J Inf Manage 2017; 37: 14–24. 7 Sukma D. Perbandingan Tarif & Layanan 9 Ojek Online di Indonesia. Arenalte.com. 2017.https://arenalte.com/berita/industri/tarif-ojek-onlinedi-indonesia/ (accessed 14 Feb2017). 8 Tania S. Online Ride Booking Apps Preference - Survey Report – JAKPAT. JAKPAT. 2017.https://blog.jakpat.net/online-ride-booking-appspreference-survey-report/ (accessed 1 May 2017). 9 Benbasat I, Barki H. Quo vadis, TAM? J Assoc Inf Syst 2007; 8: 211–218. 10 Venkatesh V, Thong J, Xu X. Consumer acceptance and user of information technology: Extending the unified theory of acceptance and use of technology. MIS Q 2012; 36: 157–178. 11 Dai H, Palvi PC. Mobile commerce adoption in China and the United States: a cross-cultural study. ACM SIGMIS Database 2009; 40: 43–61. 12 Chong AYL, Chan FTS, Ooi KB. Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia. Decis. Support Syst. 2012; 53: 34–43. 13 Zhang L, Zhu J, Liu Q. A meta-analysis of mobile commerce adoption and the moderating effect of culture. Comput Human Behav 2012; 28: 1902–1911. 14 Tsu Wei T, Marthandan G, Yee‐Loong Chong A, Ooi K, Arumugam S. What drives Malaysian m‐commerce adoption? An empirical analysis. Ind Manag Data Syst 2009; 109: 370–388. 15 Jenita. Transportasi Online. 2012.https://prezi.com/hqywaio5gn1k/transportasi-online/ (accessed 1 Apr2017). 16 Narayanaswami S. Urban transportation: innovations in infrastructure planning and development. Int J Logist Manag 2017; 28: 150–171. 17 Daily Octagon. Bagaimana Perkembangan Transportasi Online Menurut Pakar? 2015.https://daily.oktagon.co.id/bagaimana-perkembangantransportasi-online-menurut-pakar/ (accessed 1 Apr2017). 18 Shankar V, Urban GL, Sultan F. Online trust: a stakeholder perspective, concepts, implications, and future directions. J Strateg Inf Syst 2002; 11: 325–344. 19 Yang KCC. Exploring factors affecting the adoption of mobile commerce in Singapore. Telemat Informatics 2005; 22: 257–277. 20 Lance CE, Butts MM, Michels LC. The Sources of Four Commonly Reported Cutoff Criteria. Organ Res Methods 2006; 9: 202–220. 21 Hair JF, Ringle CM, Sarstedt M. PLS-SEM: Indeed a Silver Bullet. J. Mark. Theory Pract. 2011; 19: 139–152.
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22 Hair JF, Gabriel M, Patel V. AMOS Covariance-Based Structural Equation Modeling (CB-SEM): Guidelines on Its Application as a Marketing Research Tool. 2014.https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2676480 (accessed 15 Jun2017). 23 Doloi H, Iyer KC, Sawhney A. Structural equation model for assessing impacts of contractor’s performance on project success. Int J Proj Manag 2011; 29: 687–695. 24 Audia L. Perilaku Konsumtif Akibat Pengaruh Hedonisme di Kalangan Mahasiswa Jurusan Geografi Universitas Negeri Malang. Academia.edu. 2017. 25 Benas A. Ride Booking App Trend in Jakarta - Survey Report - JAKPAT. JAKPAT. 2017.