This research tries to proof that information which UBER provides regarding its
service by IT mechanisms can increase trust, further increase users’ participating intention.
This concept is proofed after statistically examining the hypothesizes. Below are the
findings through the results.
Overviewing the result of people who had used UBER before, information quality
can indirectly increase participating intention. Interestingly, the only path that information
quality can increase people’s intention is to increase trust on the UBER platform, and
then increase the trust on drivers, and finally increase their intention. Two conclusions are
made through the finding of the only path.
First, information quality can definitely increase trust, but more importantly,
institutional-based trust plays as the key factor that cause the trust on drivers is mainly
increased through information quality by the mediator, the trust on the UBER. This proofs
this study’s argument that while the trustor and the trustee are strangers, which means the
relationship between passengers and drivers, a third party is needed to create a trustworthy
environment. UBER uses IT mechanisms to provide information and build trust between
users and itself first, and through the information which UBE R provides by the IT
mechanisms, passengers can further know more about the drivers though they are
strangers before, so they are more able to perceive benevolence, integrity, and ability from
the drivers, thereby increase the trust on the drivers. In this process, UBER successfully
anchors itself as an third party information provider.
Second, trust on UBER cannot directly increase passengers’ participating intention,
but through their trust on drivers instead. This research considers that this is because
drivers are the actual service providers who literally carry passengers to destinations.
While this sample contain only passengers who had used UBER before, they know more
clearly that their using experience are strongly influenced by the service from the drivers.
People’s trust on UBER persuade them more willing to trust drivers, but the trust that the
drivers would fulfill their expectation of the driving services and improve the experiences
of the trip will be the key determinant to use UBER. Thus, the trust on drivers become
the mediator between the trust on UBER and participating intention.
Therefore, to sum up, information quality will increase people’s trust on UBER first,
and then increase trust on drivers. And when users indeed trust the drivers, they would
finally become more willing to use the UBER service.
Comparing with the sample which participants had used UBER before, people who
had never used UBER before present few different behaviors. Similar to the previous
sample, information quality can increase trust on UBER, and the trust on UBER fully
mediate the effect between information quality and the trust on drivers. However, it is
found that the trust on UBER can directly increase intention, and the trust on drivers do
not have positive effect on intention. This research considers that prior knowledge may
be the main reason. This research have proposed that when the passengers have higher
trust, they will have higher participating intention because they consider their expectation
can be fulfilled. However, things go different when people are unexperienced of taking
UBER. People’s trust of UBER can increase their intention because they’ve at least
already heard of UBER, and this research have revealed the information UBER provided
again, so they have a clearer image of what UBER platform can help reaching their
expectation. However, while they had no actual interacting experience with drivers before,
they do not clearly know what the drivers can do for them additionally, which means they
have no actual expectation, and this leads to that the trust on drivers do not have the power
to influence their participating intention. Thus, in this sample, information quality would
increase the trust on UBER, and further increase the trust on drivers and the intention
respectively, but not through the trust on drivers.
The theoretical contribution of this research is proofing that IT mechanism can be
regarded as an innovative way to increase trust by facilitating information sharing. In the
past, information sharing between peers is not easy, and Burt proposed the social network
as a solution. He considered that a social network which is closed enough and hard to
escape can force people to behave well, because bad reputation will spread from the
connections of the social network, and trust will increase through information sharing in
this network. But nowadays, in this research, IT mechanism is proofed to be an alternative
way to build an environment with trust by providing high quality information.
Information do not necessary to be shared through social networks. Instead, IT
mechanisms become another efficient information discloser. Take UBER as an example,
passengers and drivers are not formed to be in a social network, and most of them are
strangers to each other. Trust is being built through the information UBER provide, and
they are still willing to participate in this sharing economy program. This research argues
that precisely because of the IT mechanisms which can facilitate information sharing, new
business models like UBER and Airbnb are able to rise. Users’ willingness to engage in
such sharing economy programs are based on the high quality information, which is
provided by the IT mechanisms. Therefore, this research believes that new opportunities
with innovative sharing types will be invented based on the contributions of information
provided by the IT mechanisms.
Two managerial implications can be derived from this research. First, it is found that
UBER cannot to be disintermediated between passengers and drivers. Through the
models of this study contain sample that had used or never used UBER before, the path
to increase users’ intention are both pass through the trust on UBER. This demonstrates
the value of UBER itself. A trustful environment is built by UBER as a third party, which
is completely different from traditional taxi that is less trust as foundation of interactions.
Second, by segmenting target customers into two groups which are respectively
customers who had used UBER before, and customers who had never used UBER before,
UBER should develop different strategies. For people who had used UBER before, UBER
should put more effort on increasing people’s trust on drivers, because their continue
using intention in mainly depend on their trust on drivers. To increase people’s trust on
drivers, Uber should increase their perception of the drivers’ benevolence, integrity, and
competence. For example, some innovative picking up platform encourages the
passengers to reveal their tendency of chatting in the car, and the drivers will decide
whether to talk to the passengers according to their record. This policy will make the
drivers being regarded more benevolent to the passenger. In contrast, for people who had
no using experience of UBER, UBER should persuade them to trust more on UBER itself,
which can attract them more willing to give a first try on UBER service. For example, it
may be a good option for UBER to post ads and demonstrate their benevolence, integrity,
and ability to the potential passengers. Thus, UBER should position itself as an
disintermediated third party service provider, and set different strategies on different types
of people.
Chapter 6. Limitations and Future Research
There are few limitations in this research. First, while this research is conducted in
the campus, the percentage of students are relatively high, and this is why this research
also conducted the ks-test and tried to eliminate the sample bias. Second, UBER is the
only sharing economy platform selected as the research target, and future research can
consider to expand the scope to other platforms with various service models and other
industries. Third, the group of people this research focuses on is only the passenger. While
the roles in the UBER platform contain drivers and passengers, the sample of drivers are
more difficult to reach because of the budget limitation and its scarcity comparing with
the passengers. Future research can focus more on investigating the behaviors of the
service provider side in the sharing economy programs.
Therefore, future research can expand to few directions. First, as previous mentioned,
researchers can consider to put more emphasize on other types of sharing economy
platforms and different roles within the platforms. Second, while this research suspects
that prior knowledge and experience may influence people’s trust toward their
participating intention, future research could consider to verify this argument. At last,
while friends and families surrounded an individual may have different opinion to specific
sharing economy platforms, these tendency and attitude of the opinions can also be taken
into considerations as another way of information sharing. Future research can discuss
more about how theses opinion influence trust, participating intention, or even having
impact on the effects discussed in this model.
REFERENCE
Belk, R. (2010). Sharing. Journal of Consumer Research, 36(5), 715-734.
doi:10.1086/612649
Belk, R. (2014). You are what you can access: Sharing and collaborative consumption online. Journal of Business Research, 67(8), 1595-1600.
doi:10.1016/j.jbusres.2013.10.001
Bock, G.-W., Lee, J., Kuan, H.-H., & Kim, J.-H. (2012). The progression of online trust in the multi-channel retailer context and the role of product uncertainty. Decision Support Systems, 53(1), 97-107.
Burt, R. S. (2007). Closure and stability: Persistent reputation and enduring relations among bankers and analysts. The Missing Links: Formation and Decay of Economic Networks, 100-143.
Cohen, P., West, S. G., & Aiken, L. S. (2014). Applied multiple regression/correlation analysis for the behavioral sciences: Psychology Press.
Ert, E., Fleischer, A., & Magen, N. (2016). Trust and reputation in the sharing economy:
The role of personal photos in Airbnb. Tourism Management, 55, 62-73.
doi:10.1016/j.tourman.2016.01.013
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with
unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Hair, J., Anderson, R., Tatham, R., & Black, W. (1998). Multivariate Data Analysis 7th ed, Upper Saddle River, NJ. Retrieved, June, 11, 2013.
Hamari, J., Sjöklint, M., & Ukkonen, A. (2016). The sharing economy: Why people participate in collaborative consumption. Journal of the Association for
Information Science and Technology, 67(9), 2047-2059. doi:10.1002/asi.23552 Klopping, I. M., & McKinney, E. (2004). Extending the technology acceptance model
and the task-technology fit model to consumer e-commerce. Information Technology, Learning & Performance Journal, 22(1).
McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and Validating Trust Measures for e-Commerce: An Integrative Typology. Information Systems Research, 13(3), 334-359. doi:10.1287/isre.13.3.334.81
Pavlou, P. A., & Gefen, D. (2004). Building Effective Online Marketplaces with Institution-Based Trust. Information Systems Research, 15(1), 37-59.
doi:10.1287/isre.1040.0015
Appendix A: Informed Consent
共享經濟參與行為之研究
感謝您的熱心參與！
這是一份學術研究問卷，目的是瞭解您的共享經濟參與行為。
您的填答對我們的研究非常重要，所以請按實際狀況作答。
填答問卷過程，全程使用無痕視窗填答，
且您所填寫的任何資料僅用於學術研究，絕對保密。
因此，絕不可能將您的個人資料洩漏予第三人，亦不會移作任何商業 使用。
未來資料的結果也將以集體數據的方式呈現，以保護您個人的隱私。
敬請安心依照您的個人狀況完整填寫。
最後非常感謝您的參與，有任何問題歡迎您隨時與我們聯繫。
指導教授 國立臺灣大學資管系 吳玲玲 教授
指導學生 國立臺灣大學資管系 碩士生 林彥礦 敬上
Appendix B: Survey items
Item Statement Adapted From
Intention of keeping
(Integrity)
(Ability)
一般而言，UBER 對於叫車服務非 常了解
(Ability)
Information quality UBER 提供了充分的服務訊息 (Bock et al., 2012) UBER 提供了準確的服務訊息
UBER 即時地提供服務訊息
UBER 提供的服務資訊十分有幫助
Appendix C: Demographic Information
自僱者