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Mobile Application's Acceptance A survey on user acceptance of online taxi
applications in Pakistan MS Thesis
Thesis · January 2019
DOI: 10.13140/RG.2.2.31813.60644
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Mobile Application’s Acceptance
A survey on user acceptance of online taxi applications in Pakistan
MS Thesis
Submitter: Jay Kumar
Supervisor: Dr. Jalaluddin Qureshi
Department of Computer Science, NUCES, Karachi Campus
Acknowledgements
First of all I would like to gratefully thank my supervisor Dr. Jalaluddin Qureshi for this excellent
guidance throughout the thesis process. Without his incredible mentoring skills, this thesis would
not have been possible. I would like to thank my friends and colleagues who participated in interview
process and for pilot study. I would also like to express deepest gratitude to the participating
respondents of the survey.
In addition, I would like to thank my family for supporting me throughout writing this thesis and my
life in general.
2
Abstract
Online taxis have brought a major change in the individual transportation sector, and such
services has entered in Pakistan like Uber, Careem, Limofied, Shahi Sawari, Paxi and Uride [1]. As
per the official downloads of the applications it appears that Uber and Careem have been great
successful and used by many users in Pakistan, while Limofied, Shahi Sawari, Paxi and Uride are
struggling [2], [3], [4], [5], [6], [7], so the purpose of this study is to find the factors which motivate
users to use online taxis by using UTATUT2 model. The UTAUT2 model consists of factors related
to user acceptance, such as Performance Expectancy, Effort Expectancy, Social Influence,
Facilitating Conditions, Hedonic Motivation, Price Value and Habit. Questionnaire was created on
the basis of these factors in context of online taxis and survey was conducted in Pakistan. Simple
linear regression was applied on the responses collected to find out the factors which affect user
acceptance, and from results it appeared that all factors except Social Influence affect User
Acceptance. After analysis of the results, research and practical implications are provided, through
which online taxis can increase their user acceptance.
Keywords:
Ride Hailing Apps; Online Taxi; Adoption of technology; UTATUT; UTATUT2
3
Table of Contents
1
2
Introduction: ............................................................................................................................................ 7
1.1
Background....................................................................................................................................... 7
1.2
Problem Statement .......................................................................................................................... 9
1.3
Research Gap.................................................................................................................................... 9
1.4
Research Purpose ............................................................................................................................. 9
1.5
Research Questions .......................................................................................................................... 9
Literature Review ..................................................................................................................................... 9
2.1
User Acceptance............................................................................................................................... 9
2.2
Information Systems Acceptance Models...................................................................................... 10
2.2.1
Theory of Reasoned Action (TRA) ........................................................................................ 10
2.2.2
Theory of Planned Behavior (TPB)........................................................................................ 11
2.2.3
Technology Acceptance Model (TAM) ................................................................................. 12
2.2.4
Model of PC Utilization (MPCU) .......................................................................................... 13
2.2.5
Innovation Diffusion Theory (IDT) ....................................................................................... 14
2.2.6
Motivational Model (MM) ..................................................................................................... 14
2.2.7
Social Cognitive Theory (SCT).............................................................................................. 15
2.2.8
Combined TAM & TPB (C-TAM-TPB) ................................................................................ 16
2.2.9
Unified Theory of Acceptance and Use of Technology (UTAUT) ........................................ 16
2.2.10
Consumer Acceptance and Use of Information Technology (UTAUT2) .............................. 18
2.3
3
User Adoption in context of Online taxis ....................................................................................... 21
Research Methodology .......................................................................................................................... 22
3.1
Research Approach ........................................................................................................................ 22
3.2
Data Collection Method ................................................................................................................. 22
3.2.1
Primary Data .......................................................................................................................... 22
3.2.2
Sampling Strategy .................................................................................................................. 23
3.2.3
Secondary Data ...................................................................................................................... 23
3.3
Questionnaire Design ..................................................................................................................... 23
3.3.1
Factors .................................................................................................................................... 24
3.3.2
Scale ....................................................................................................................................... 24
3.3.3
Pilot Test ................................................................................................................................ 24
3.3.4
Final Questionnaire ................................................................................................................ 25
4
3.4
Questionnaire Data Analysis .......................................................................................................... 25
3.4.1
Descriptive Analysis .............................................................................................................. 25
3.4.2
Reliability Analysis ................................................................................................................ 25
3.4.3
Bivariate Analysis .................................................................................................................. 26
3.4.4
Credibility of the study ........................................................................................................... 26
4
Proposed Model ..................................................................................................................................... 27
5
Results .................................................................................................................................................... 30
6
7
5.1
Descriptive Analysis........................................................................................................................ 30
5.2
Reliability Results ........................................................................................................................... 32
5.3
Hypotheses Test Results................................................................................................................. 33
5.4
Correlation of factors ..................................................................................................................... 39
5.5
Relationships between factors and UA .......................................................................................... 40
Discussion ............................................................................................................................................... 46
6.1
Implications for research................................................................................................................ 46
6.2
Implications for practice................................................................................................................. 47
Conclusion and Future works ................................................................................................................. 48
References........................................................................................................................................................ 50
Appendix ........................................................................................................................................................ 55
Figures
Figure 1. Theory of Reasoned Action (TRA)................................................................................................. 11
Figure 2. Theory of Planned Behavior (TPB) ................................................................................................ 12
Figure 3.Technology Acceptance Model (TAM) ........................................................................................... 13
Figure 4.Model of PC Utilization (MPCU) .................................................................................................... 14
Figure 5.Social Cognitive Theory (SCT) ....................................................................................................... 15
Figure 6.Combined TAM & TPB (C-TAM-TPB) ......................................................................................... 16
Figure 7.Unified Theory of Acceptance and Use of Technology (UTAUT) ................................................. 18
Figure 8.Consumer Acceptance and Use of Information Technology (UTAUT2) ........................................ 20
Figure 9.Proposed Model ............................................................................................................................... 29
Figure 10.Correlation of factors ..................................................................................................................... 40
Figure 11.Relationships between factors and UA .......................................................................................... 41
Figure 12.Relationship between PE and UA .................................................................................................. 42
Figure 13.Relationship between EE and UA.................................................................................................. 42
Figure 14.Relationship between SI and UA .................................................................................................. 43
Figure 15.Relationship between FC and UA.................................................................................................. 44
Figure 16.Relationship between HM and UA ................................................................................................ 44
5
Figure 17.Relationship between PV and UA ................................................................................................. 45
Figure 18.Relationship between H and UA.................................................................................................... 46
Tables
Table 1.Questionnaire Period ........................................................................................................................ 25
Table 2.Hypotheses ........................................................................................................................................ 29
Table 3.Gender Distribution ........................................................................................................................... 30
Table 4.Age Distribution ................................................................................................................................ 30
Table 5.Occupation Distribution .................................................................................................................... 31
Table 6.Location Distribution ........................................................................................................................ 31
Table 7.Online Taxis used.............................................................................................................................. 32
Table 8.Online Taxi experience ..................................................................................................................... 32
Table 9.Reliability Results ............................................................................................................................. 33
Table 10.PE Responses .................................................................................................................................. 34
Table 11.EE Responses .................................................................................................................................. 35
Table 12.SI Responses ................................................................................................................................... 36
Table 13.FC Responses .................................................................................................................................. 36
Table 14.HM Responses ................................................................................................................................ 37
Table 15.PV Responses .................................................................................................................................. 38
Table 16.Habit Responses .............................................................................................................................. 39
Table 17.UA Responses ................................................................................................................................. 39
6
1
Introduction:
1.1 Background
As usage of smartphone is increasing continuously in worldwide from 2014 to 2018, and it is
expected that it will continue to raise in the period of 2018 to 2020 [8]. According to Pakistan
Telecommunication Authority, Pakistan has 161 million mobile phone users in April 2019 [9].
Mobile phone industry is one of the fastest growing industries of consumption goods. Its users are
multiplying with each day passing and it encompasses almost all type of customer segments
including not only students but professionals as well. The global demand for mobile applications
also has been going up dramatically. Mobile applications download reached to 149.3 billion in 2018,
and it is expected that number will reach to 352.9 billion in 2021 [10]. In order to fulfill this demand,
there are many companies working in this industry and getting great opportunities to make revenue.
Revenue from mobile applications was 88.3 billion U.S dollars in 2016, more than 20 billion U.S
dollars compared to 2015 [11].
Moving around in big cities becomes extremely suitable to depend on public transport and
rented cars because of heavy traffic and few parking places. This allows people to take advantage of
comfortable ride rather than using personal vehicles and security troubles. In the past, people used
to catch a taxi from street, then it changed into the call-center taxi service. Nowadays consumers
rely on their smartphones for the variety of needs. So, the idea of using applications to locate a taxi
is a smooth transition. Mobile app-based taxi services have become a right choice as the efficient
service is available all the time. Online taxis are advanced mobile service applications that enable
requests for transportation services via Internet and geo-location by using information systems and
telephone calls and tracking the service provided and the payment due.
Online taxis make use of several technological developments in order to better match
passengers with available vehicles, such as, for instance: GPS services, ubiquitous cellular and WiFi access, mobile device-embarked sensor platforms, navigation services based on commercial or
open-source digital maps, credit card processing and payment systems, pricing and dispatching
algorithms, data logging and big data analytics [12]. All of these technologies enable clients and
drivers who have downloaded the app and who are registered with the centralized platform to be put
into touch with each other when the former requests a trip and the latter is nearby and accepts the
request [12] .
7
Prospective passengers can specify their destination and can evaluate the exact or estimated
fare –including any potential surcharges for peak period travel. [12]. Once a ride request is matched
by the platform, the prospective passenger can monitor the progress of the vehicle to the pick-up
point on-screen alongside the name of the driver and certain identifying features like the make of
the vehicle and its license plate number. Information regarding the estimated arrival time and route
is displayed in real-time to the passenger while underway and once the vehicle arrives at its
destination, the passenger exits without handling any money since the platform automatically
processes the bill to the driver taking a percentage of the overall fare. After the transaction, both
driver and passenger rate each other and each has a reputational score associated with their profile
that is visible to subsequent drivers or passengers. In addition to data regarding registered clients,
drivers and vehicles, the platform also stores trip data including pick-up and drop-off locations,
travel route and timing, and information on the driver providing the service as well as the passenger
[12].
Uber is perhaps the best-known online taxi, and certainly the best capitalized with a market
valuation of over US$ 82 billion in May, 2019 [12]; however, it is not alone. Several strong regional
competitors are emerging with similar services and business models – including Lyft in the United
States, Cabify in Spain and South America, Limofied, Shahi Sawari, Paxi and Uride in Pakistan.
Online taxi market was nearly $56.828 billion in 2019 in three core regions – China, Europe, and
the United States [13] and it should continue to experience impressive annual growth rates in the
future. Currently (2019), China and US are the two largest markets for shared mobility, at US$35
billion and US$18 billion, respectively, being both markets dominated by e-hailing players that hold
market shares that exceed 80% in each country [13].
The global market of smartphones has become increasingly diverse due to sophisticated
mobile applications i.e. apps. These apps are pieces of software installed onto personal phones to
attain the services like communication, entertainment, transportation, mapping, shopping, etc. There
are a number of mobile apps-based taxi services offering their services to connect smartphone users
seeking taxi services in their locality. Online taxi applications are nowadays available via
smartphone and available for both the Android and iOS platforms. It is becoming popular in the
Pakistan as the drivers linked to these services are more committed than typical who tend to decline
passengers during rush hours or sometimes quote for unfair fixed rate fare. Online taxi applications
8
which currently provide service in Pakistan are Uber, Carem, Limofied, Shahi Sawari, Paxi and
Uride.
1.2 Problem Statement
As per official downloads of all the online taxi applications from the Android and iPhone
stores, we can see that Uber and Careem are more successful in Pakistan, while Limofied, Shahi
Sawari, Paxi and Uride are struggling [2], [7], [5], [6], [3], [4]. We have little idea behind the success
and failures of these apps and what are the reasons/factors that influence users to user acceptance of
online taxis.
1.3 Research Gap
As the countless of studies done in the field of information technology and adaptation of
technology, none of the studies have tried to link the user theory of acceptance and use of technology
(UTAUT) [14] to Pakistani consumers in the context of Online taxi applications. Therefore, this
research study will try to explore whether, [14] theory can be applied and it can fit the Pakistan
context of study.
1.4 Research Purpose
The research purpose of this study is to investigate the user acceptance of online taxis in Pakistan.
1.5 Research Questions
What are the factors and how do these factors influence the user acceptance of online taxis
in Pakistan?
2
Literature Review
2.1 User Acceptance
User acceptance is as equivalent with technology acceptance in terms of the same explanatory
ability on perceptual and emotional aspects which result in a person to finally accept a mobile app,
9
digital service or other form of technological products. Hence user (technology) acceptance equals
technology acceptance (by users).
User acceptance is derived from the willingness of a person to use a new technology according
to his or her perception, expectation and intention of the actual behavior to [14], [15]. User
acceptance research has been one of most important topics within information system field, along
with development of modern technologies, innovations or services such as mobile services, mobile
commerce, E-services, wearable technologies or social network sites [16], [17], [18], [19] .Prior user
acceptance researches build on constructs mostly from study field of sociology and psychology [20],
[21], [22], [23].A most consistent and significant construct among is namely behavioral intention
which is also verified in the information system field as a predictor of acceptance as well as a
determinant that directly link to actual technology use. A significant amount of existing research has
focused on identifying behavioral intention [24], [25], [14], [26] and technology use [27].
Information technology use as the acceptance of an IT system by individuals is of importance in the
information system field [27]. That is to say, technology use, also equivalently known as technology
implementation [28] and technology adoption [29], is fundamentally crucial in achieving
information system success. Technology use has been conceptualized and operationalized [27] as
extent of use [30], breadth of use [28], variety of use [31], users’ cognitive absorption into the system
[32].
2.2 Information Systems Acceptance Models
Reasons behind user adoption of an IS have been investigated by the researchers and
practitioners of Information Systems (IS) [33]. Numerous strategies have been used to assess
information systems in order to predict how users will respond to them, so as to improve their use.
Among these approaches, one can highlight the Theory of Reasoned Action (TRA), the Theory of
Planned Behavior (TPB) and the Technology Acceptance Model (TAM) with its various variants,
as explained below
2.2.1
Theory of Reasoned Action (TRA)
10
The Theory of Reasoned Action (TRA), with its roots in the Social Psychology setting, seeks to
identify backgrounds of intentional and conscious behavior [23]. The TRA assumes that people
evaluate what they can gain or lose through their attitudes. Therefore, their ideas, personal goals,
values, beliefs and attitudes influence their behavior.
Since TRA is so easy to generalize and can be applied into diverse theoretical views of
Psychology [15], its use is appropriate in studies about the critical success factors associated with
the use of computers and information systems (IS) as well. When applied in this context, the TRA
points out that a person’s attitude in relation to the use of an information system, besides peer
pressure, might influence their intentions to use the IS, as well as IT in general.
Figure 1. Theory of Reasoned Action (TRA)
2.2.2
Theory of Planned Behavior (TPB)
There was potential flaw in TRA in cases where individuals are not fully aware of their
behavior. The Theory of Planned Behavior (TPB) was developed from TRA by adding one more
concept to the intention of using IS, namely perceived behavioral control (PBC) [24]. This construct
is defined as the personal perception about the resources, available opportunities and information
that might hamper or enable the behavior under analysis [26], [34].While the TRA has been largely
used to study user acceptance to computers and information systems, other theoretical perspectives
were also proposed and applied in this realm. Due to this, the TPB has been applied in empirical
research on the acceptance of sundry computational systems [26], [35].
11
Figure 2. Theory of Planned Behavior (TPB)
2.2.3
Technology Acceptance Model (TAM)
Technology Adoption Model (TAM) has been widely used in technology adoption studies. The
technology acceptance model explain the primary factors for acceptance and rejection of new
technology in the information technology field. The strength of the model lies in its simplicity as it
has only two constructs, namely, "perceived usefulness" and "perceived ease of use" for predicting
extent of adoption of new technologies at individual level. Perceived usefulness is defined as the
extent to which users believe that the use of a system will improve their performance. As to perceived
ease of use is defined as the extent to which an individual believes that the use of a system is
effortless. These two perceptions ensure a favorable disposition or positive intention to use an
Information System. Thus, the TAM infers that individuals will use an IS if they believe its use will
bring them positive results in the form of perceived ease of use and usefulness [36].
TAM in a workplace environment and found factors of perceived usefulness and perceived ease
of use directly influencing the attitude of employees towards use of a new technology, and
furthermore, the behavioral intention to actual use [15]. The factors of TAM and their relationships
are shown in the Figure 3. The theory has been widely supported as fundamental research for usage
behavior of technology.
12
Figure 3.Technology Acceptance Model (TAM)
2.2.4
Model of PC Utilization (MPCU)
This model presents a competitive perspective to that proposed by TRA and TPB. MPCU is
largely derived from theory of human behavior [37]. According to this theory "Behavior is
determined by what people would like to do (attitudes), what they think they should do (social
norms), what they have usually done (habits), and by the expected consequences of their behavior".
Intentions and habits are direct antecedents of behavior and both are further affected by factors such
as norms, roles, emotions, attitude etc [37]. Theory of interpersonal behavior was applied in the field
of information technology by using the model to investigate personal computer utilization [38]. It
was found that the theory is much adaptive in exploring acceptance and use of information
technology [38]. However, the focus in MPCU is on exploring the usage behavior rather than the
intention. Factors influencing utilization of PC is shown in figure 4. They are job-fit, complexity,
long-term consequences, affect towards use, social factors and facilitating conditions. This theory
primarily deals with extent of utilization of a PC by a worker where the use is not mandated by the
organization but is contingent on the option of the user. In such a setting, the theory posits that the
use of computer by the worker is likely to be influenced by several factors such as his feelings
(affect) toward using PCs, prevalent social norms regarding use of PC at the workplace, general
habits related to use of the computer, consequences expected by the user by using the PC and extent
of conditions that are present at the work place for facilitating use of PC.
13
Figure 4.Model of PC Utilization (MPCU)
2.2.5
Innovation Diffusion Theory (IDT)
This theory was proposed to study a wide range of innovations [39]. Generally innovation is
defined as something that is perceived as new for an individual or a social system. Goal of this theory
is to explain the process by which technological innovations are adopted and circulated by users.
IDT considers the following factors to be antecedents of diffusion of large-scale innovation: relative
advantage, compatibility, complexity, trainability, and observability [39]. However, from these
attributes only relative advantage, compatibility and complexity appear to be consistently associated
with the adoption of technological innovation [40].
During research into IT adoption, this theory should be taken into consideration as it can add
more explanatory power to the original TAM [40] , [41], [42]. Furthermore, several studies have
applied the IDT successfully to ascertain the antecedents to the adoption of Internet and mobile
systems [43].
2.2.6
Motivational Model (MM)
Motivational theory was applied to study information technology adoption and use [15]. The
main propositions of the Motivation Model is that there are two types of motivations that shape the
behavior of the user, which are extrinsic and intrinsic. Extrinsic motivation is defined as the
14
perception that users want to perform an activity "because it is perceived to be instrumental in
achieving valued outcomes that are distinct from the activity itself, such as improved job
performance, pay, or promotions" [15]. Examples of extrinsic motivation are perceived usefulness,
perceived ease of use, and subjective norm. On the other hand, intrinsic motivation is defined as
performing an activity leads to a feeling of pleasure and results in satisfaction for the individual [44].
An examples of intrinsic motivation is the extent of enjoyment that a person derives from playing
with a computer [15], [30].
2.2.7
Social Cognitive Theory (SCT)
Social cognitive theory is one of the most powerful theories of human behavior and have
been widely adopted [21]. It is learning theory based on the ideas that people learn by watching
others do (observation) within the context of social interactions and experience. Theory for the
acceptance of technology was applied and extended in Canada in the field of Information technology
[45]. In SCT following factors have been test to use a significant influence on using technology are
outcome expectations – performance, outcome expectations – personal, self-efficacy, affect and
anxiety. The relationship among the five factors is shown in figure 5, which evidently leads toward
usage. Outcome expectations refers to related result on both personal and working aspect of the
behavior. Self-efficacy refers judgement of one’s ability to use a technology to accomplish a
particular job or task [46]. Affect refers to a person’s preference for a specific behavior. Anxiety
means that anxious reactions is triggered when it comes to acting a behavior.
Figure 5.Social Cognitive Theory (SCT)
15
2.2.8
Combined TAM & TPB (C-TAM-TPB)
Combined TAM and TPB model (C-TAM-TPB) was proposed after comparing the models TAM
and TPB, to evaluate which is better for predicting use of information technology [26]. After a
twelve-week longitudinal study on users from a computer resource center, the author concluded that
both TAM and TPB had equal importance in understanding the behavioral intention and actual use.
Replacing the factor ‘attitude towards using’ from TRA with ‘perceived usefulness’ and ‘perceived
ease of use’ from TAM, C-TAM-TPB was created as below (see figure 6). The integrated model is
believed to offer more explanatory power together than each model independently and offer
significant improvement based on each model [47].
Figure 6.Combined TAM & TPB (C-TAM-TPB)
2.2.9
Unified Theory of Acceptance and Use of Technology (UTAUT)
Unified theory of acceptance and use of technology was proposed in [14] for the purpose of
creating a combined perspective toward the user acceptance of information technology. Eight prior
important theories of behavioral intention and user behavior were used to form UTATUT. New
model was formed by integrating the overlapping the theoretical interpretation of the factors
inevitably existed in previous research, such as subjective norm in TPB, TRA, C-TAM-TPB and
even TAM2, social factors in MPCU. This idea was endorsed and acknowledged by many
researchers [15], [38], [26], [46]. The resulting UTAUT model is described below, consists of four
core factors which influence behavioral intention and technology use. The factors are performance
16
expectancy, effort expectancy, social influence and facilitating conditions. In addition, those factors
are mediated by moderators like gender, age, experience and voluntariness of use.
Managers acknowledged UTAUT as a useful tool to evaluate the acceptance possibility of
implementing a new technology in an organization. It also facilitates in predicting the specific factors
that might influence the implementation of a new technology, that is to say, appropriate functionality
can be accurately developed in favor of actual needs through the application of UTAUT.
2.2.9.1 Performance Expectancy
The definition of performance expectancy is the “degree to which a person believes that
using the system will help him or her to achieve gains in working performance” [14]. Performance
expectancy is derived from the proposed five constructs in previous studied, which are “extrinsic
motivation” in MM [48], “perceived usefulness” in TAM [15], “relative advantage” in IDT [49],
“job-fit” in MPCU [38], and “outcome expectations” in SCT [25] [50].
2.2.9.2 Effort Expectancy
The definition of effort expectancy is the “degree of ease related with the use of the system”
[14]. Effort expectancy is derived from the proposed three constructs in previous studied, which are
“complexity” in MPCU [38], “perceived ease of use” in TAM [15], and “ease of use” in IDT [49].
2.2.9.3 Social Influence
The definition of social influence is the “degree to which a person perceives that significant
others believe he or she should use the new system” [14]. It contains prior factors such as “subjective
norm” in TRA, TAM2, TPB and C-TAM-TPB [24], [23], [26], “social factors” in MPCU [38], and
“image” in IDT [49]. It was observed by many researchers that social influence has significant
relationship with behavioral intention.
2.2.9.4 Facilitating Conditions
The definition of facilitating conditions is “the degree to which a person believes that an
organizational and technical infrastructure exists to support use of the system” [14]. Facilitating
conditions also was built upon findings from prior researches – “perceived behavioral control” in
TPB and C-TAM-TPB [23], [26], “facilitating conditions” in MPCU [38]and “compatibility” in IDT
[49].
17
Figure 7.Unified Theory of Acceptance and Use of Technology (UTAUT)
2.2.10 Consumer Acceptance and Use of Information Technology (UTAUT2)
After 9 years of UTATUT, UTAUT2 was proposed [35] as an extension of UTAUT to
particularly study the acceptance and use of technology in the mobile application context from a
consumer perspective. There was an increasing need for UTAUT to increase its theoretical capacities
and functionalities to address the new technology. Three new factors were added to UTAUT to form
UTAUT2, which include hedonic motivation, price value and habit as additional factors believing
to have direct or indirect impact on behavioral intention and use behavior. Hedonic motivation, also
known as perceived enjoyment, is found to have significant influence on technology usage according
to a variety of prior information system acceptance research. Price value is important because
consumers, unlike employees, have to undertake the cost of buying information system or
technology by themselves. The situation is often on the contrary in a workplace. Regarding habit,
the aim to take it in as factor is to reinforce the generalizability of UTAUT2. The UTAUT2 (see
figure 8) factors are moderated by age, gender and experiences. Besides, the prior moderator
voluntariness of use in UTAUT is discarded by establishing a new link between facilitating
conditions and behavioral intention. In a nutshell, compared to UTAUT, UTAUT2 has evidently
18
more explanatory power on behavioral intention and technology usage as UTAUT2 not only
inherited the main structure from UTAUT, but also added new factors and relationships. Due to its
expansibility, future research can extend the UTAUT2 in different countries, age group or
technologies.
2.2.10.1 Hedonic Motivation
The term hedonic originates from the word hedonism which was used to represent the
doctrine that “pleasure or happiness is the chief good in life” [51]. Hedonic motivation was
conceptualized often as perceived enjoyment in prior researches. Perceived enjoyment centers on
intrinsic motivation are as well important determinants of behavioral intention for using a hedonic
information system [52]. Therefore, perceived enjoyment can be considered as a vital role in
predicting user acceptance.
2.2.10.2 Price Value
When UTAUT [14] did not take into consideration users’ perception toward the cost of a
technology, as the context is situated in workplace scenarios and usually the organizational
employees tend to be quite insensitive to the monetary cost. Bearing that in mind, Venkatesh et al.
incorporated price value as a factor in UTAUT2 [35] and testified price value indeed had a
significant influence on behavioral intention when “the benefits of using a technology are perceived
to be greater than the monetary cost”. IT services providers or developers should take into
consideration what the most valuable point in the system is providing for customers.
2.2.10.3 Habit
Habit is defined as a repetitive behavioral pattern that takes place automatically beyond the
pale of the conscious awareness [37]. Previous research suggested two types of understanding of
habit in information system field. On one hand, habit was refereed as equivalently to automaticity
[53] and is in consistent with the term of “habitual goal directed consumer behavior” and “goaldependent automaticity” from prior IS researches [29], [54]. On the other hand, it was defined as
habit as the “degree to which people tend to perform behaviors automatically” [55]. Although it
looks similar in both conceptualizations, two authors had put the factor of habit into different
practice. In [53] habit as prior behavior and thus found that habit is a significant antecedent for
technology use. However, [55] measured habit as the “extent to which a person believes the behavior
to be automatic”. Subsequently, such measurement of habit has also demonstrated that there is a
19
positive relationship between habit and technology use as well as habit and behavioral intention [55].
As a result, both conceptualization and operationalization of habit are cooperating in predicting
behavioral intention and use of technology. Therefore, habit was incorporated as a determinant into
UTAUT2. Additionally, it was suggested that, in the consumer context, habit plays a significant role
on personal technology use especially under the circumstances which is miscellaneous and everchanging [35].
Figure 8.Consumer Acceptance and Use of Information Technology (UTAUT2)
Since UTAUT was highly appreciated in IS field, many researchers begun to adopt UTAUT
and UTAUT2 to investigate user acceptance worldwide. For instance, in [56] UTAUT was applied
to identify the drivers and moderator of user acceptance of tablets in advance education context.
Their findings confirmed performance expectancy (PE) as the most important driver for tablet
acceptance. However, effort expectancy (EE) and social influence (SI) were not significant and
facilitating condition (FC) was not even measurably significant. Similarly in [16] UTAUT was
applied to investigate the user acceptance of mobile devices in Finland, and found that PE and EE
were significant except for SI and FC. In addition, in terms of user acceptance of information and
20
communication technology and services in e-government settings in India. It was found that PE, EE,
SI and FC were all positive factors of technology use [57]. There are also several studies applied
UTAUT2 in researching user acceptance of mobile payment and banking. In [58] it was found that
PE, EE, hedonic motivation (HM) and price value (PV) were crucial factors in affecting mobile bank
in Jordan. This result is in line with the findings of [59] which conducted in wide range of Arica
countries. Besides, UTAUT2 is used as main theoretical model to study a variety of new technologies
or services in many countries like Portugal, China, Spain, Malaysia etc. [60], [61], [62], [63].
2.3 User Adoption in context of Online taxis
Research was conducted in China to explore the factors affecting the user adoption of Call
Taxi App [64] using "attitude-intention-behavior" models. Results showed that perceived ease of
use, perceived usefulness and compatibility have an indirect positive impact on people’s attitude
toward using CTA; subjective norm has a positive influence on Behavioral Intention; perceived risk
has a negative impact on behavioral intention; perceived price level has a negative impact on both
behavioral intention and attitude toward using.
Another study was conducted in Brazil [65] on adoption of E-applications in Brazil by
developing meta-model via a theoretical background based on theories of information system
adoption, diffusion of innovation, trust in virtual environments and user satisfaction, as well as
research hypotheses. It was established that perceived utility, compatibility, relative advantage and
trust are antecedents of user satisfaction with EHA, this factor being an antecedent of the intention
to use the system. Lastly, it was found that subjective norms have a direct and statistically significant
impact on the intention of use of EHA.
An empirical investigation [66] was done to determine the factors leading to the adaptation
of Taxi Hailing Mobile Apps among Malaysian consumers using the UTAUT, and from findings it
appeared that Performance expectancy, social influence and behavior intension has positive
influence on user behavior while effort expectancy and behavioral intension has no influence.
Another study was done on the user acceptance of sharing economy (Ride hailing
applications) by using UTAUT2 model [67]. Result showed that all seven factors of UTAUT2 such
as performance expectancy, effort expectancy, social influence, hedonic motivation, price value and
habit are influencing user acceptance intermediately positive.
21
3
Research Methodology
3.1 Research Approach
There are three types of research purposes – exploratory research, descriptive research and
explanatory research [68]. Exploratory research is a research conducted for a problem that has been
not studies clearly, and it intends to see what is happening and to gain new insights. Then descriptive
or explanatory research is used to study research phenomena further. Descriptive research focus on
describing the characteristics of the population or phenomena that is being discussed. This
methodology focus on what of the research subject than why of the research subject. Explanatory
research is termed as an attempt to examine cause and effect relationships, meaning that researchers
want to explain what is going on between dependent and independent variables which have been
formed on the basis of prior researches [68]. Descriptive research can tell that 20% of the students
are failed in exam. Explanatory research can tell us what the reason behind this failure. The research
purpose of this study is to find out the factors which affect user acceptance of online taxis in Pakistan,
so this is the cause and effect relationship between UTATUT2 factors and user acceptance, therefore
it will be called explanatory study.
Deductive approach is followed in this research by using the UTATUT2 model. Deductive
approach is concerned with developing a hypotheses based on theory [69] and then designing a
research strategy to test the hypotheses’. By using UTATUT2 model, seven hypotheses were
developed on the basis of UTATUT2 factors. Survey Questionnaire was created on the basis of
factors and the responses were analyzed through statistical analysis to test the hypotheses.
3.2 Data Collection Method
3.2.1
Primary Data
Primary data is data that is collected by a researcher for the first time from first-hand
resources using methods like experiments, observations, interviews and [70]. In this research,
primary data was collected through questionnaire, because empirical data is essential for this paper
research approach. A questionnaire is serious of questions that are asked from respondents for the
purpose of gathering information. These questions can be close-ended or open-ended and questions
should be unbiased. Questionnaires are conducted for explanatory and descriptive research to gather
samples for executing a quantitative analysis [68].
22
Questionnaire have advantages over interviews as they are cheap, and don’t require much
time like conducting interviews, for interviews we need to meet with each person, which will take
time and will be expensive if travelling is required. Questionnaire can be created and distributed
through online tools like Google forms. Google forms are easy to use and free having many features
like providing charts of the results. Google form also provides feature to share survey through email
with your respondents. It also generates link (URL), which can be distributed to respondents and
respondents can fill when they get time. So it is very cheap and don’t requires surveyors time.
3.2.2
Sampling Strategy
Sampling is the selection of subset of individuals from the whole population to estimate the
characteristics of the whole population [71]. The advantages of performing sampling are lower cost
and faster data collection than measuring the entire population [68]. The target population in this
survey was the people in the Pakistani who have used Online Taxis. In this research, a nonprobability voluntary sampling was conducted. Voluntary sampling is useful when we want to allow
people to choose to take part in the research on their own will. Survey was distributed though Social
networks like Facebook and WhatsApp and people who interested in the survey took part and
become respondents. This method was selected as it was convenient, accessible and attracts relevant
respondents. The target for responses was set minimum 400, means when we achieve responses
more than 400, survey will be closed.
3.2.3
Secondary Data
Secondary data is the data which is collected previously by someone else other than the
researcher, Common sources of secondary data for social science include surveys, censuses, research
papers etc. [70]. Benefit of using secondary data is that much of the background work needed has
been already carried out, so it is time-saving and cost-efficient. Secondary data was used for
collecting information about the context and research method. Literature was gathered from the
multiple platforms like Google and IEEE Explore. The keywords were used like Ride hailing
application, UTATUT, UTATUT2, User acceptance, Bivariate Analysis, Ride Sharing, Uber,
Careem, Online Taxis in Pakistan etc. For the quantitative research literature from renowned authors
like Saunders et al. is used.
3.3 Questionnaire Design
23
3.3.1
Factors
The questionnaire items were created for the each factor of UTATUT2. All the factors were
mapped in the context of Online Taxis. In each factors questionnaire items were placed in the context
of Online Taxis. Some questions were also picked up from the other surveys conducted on Online
Taxis in other countries like Brazil, Malaysia etc. Own questions were also developed on the basis
of interviews conducted. Different questions under the each construct were asked to gather more
additional information for each factor, and also it was tried to add similar questions to maintain the
reliability of the questionnaire.
3.3.2
Scale
Nominal scales are used in the questionnaire for collecting the information of the respondents
like gender, age, location, occupation, which taxis they have used, and experience with Online taxis,
and ordinal scales are used for the questionnaire items of the factors checking the hypotheses items.
Likert scale is used for the ordinal scales, which include options from totally agree to totally disagree.
For getting the clear response from the respondents, we used six point Likert Scale on the
questionnaire (Totally agree, agree, slightly agree, slightly disagree, disagree, and totally disagree).
Middle option was removed to avoid answering of middle ambiguous option.
3.3.3
Pilot Test
The purpose of Pilot study was to get the feedback for improving the questionnaire. For that
it was conducted on 19th March 2019. This survey was forwarded to some of close friends, colleagues
and teachers for their valuable feedback. The intention of Pilot test was to check the length of the
survey, and understanding of the questions. It was also intention to check whether any question is
being missed or it is properly accessible and visible. Valuable feedback was received which included
spelling mistakes, understanding of questions, grammatical mistakes. It was also reported that that
all questions should be on one page, it is frustrating for the user to press next for each type of
questions means user had to press next button “7” times. It was also suggested to remove some
questions as it had many questions and will be discouraging for users, so some questions were
removed without affecting the quality of survey. Some of the suggested changes were incorporated
to make the survey better.
24
3.3.4
Final Questionnaire
The initial questionnaire was distributed on 6th April, 2019 through Facebook and WhatsApp.
Both are very popular social networking applications in Pakistan. It was also distributed in the office,
my friends and relatives also asked in their circle to fill out the survey form by forwarding URL of
the Google Forms.
The questionnaire was closed on 14th April, 2019 as sufficient responses had been gathered.
By the end of survey 433 responses were gathered, however only 417 remained after deletion of
some invalid responses.
Pilot Test
Improvements
Questionnaire Start
Questionnaire Closing
19th March 2019
1st April, 2019
6th April, 2019
14th April, 2019
Table 1.Questionnaire Period
3.4 Questionnaire Data Analysis
Google forms were used to collect the data. It has the feature to show the graphs for all the
responses and those were used for the Descriptive Analysis. It also provides the feature to export
data. Data was exported from Google forms and then it was imported into the IBM’s SPSS software.
SPSS is a software package used for interactive, or batched, statistical analysis.
3.4.1
Descriptive Analysis
Descriptive analysis is used to describe the basic features of data. In this we will try to explore
the responses which we received in survey and analyze the characteristics of the respondents with
respect to age, gender, location, occupation, experience with online taxis, and which Online Taxis
they have used.
3.4.2
Reliability Analysis
Your analysis will be reliable when the same individual re-takes survey, and result is same
or similar. Cronbach’s Alpha gives us simple way to measure whether or not score is reliable, which
25
can be done by using multiple items measuring the same construct. Cronbach’s Alpha is used to
analyze the reliability of this study. IBM’s SPSS software provides this feature, we can get the
Cronbach’s Alpha by select the questions of same construct.
3.4.3
Bivariate Analysis
Bivariate analysis involves the analysis of two variables for the purpose of determining the
empirical relationship between them. Through this relationship between two variables is explored,
association and the strength of relationship, whether positive or negative. It can be helpful in testing
simple hypotheses of association. We will explore whether our independent variables have no
similarity or strong relationship among them and to test whether they are independent or not. After
it we will check whether our independent variables affect dependent variable positively or
negatively, if it affects positivity then we will say our hypotheses is accepted otherwise rejected.
In this study, Spearmen’s ranked correlation coefficient is used as it is ideal for the ordinal
data from the employed Likert scale. To check the interrelationships between variables matrix was
created in which we calculated the value for each factor against all factors. A correlation coefficient
should not be higher than 0.7, which is the critical point, if it is higher than 0.7 means the factors are
not independent. Then there is need for combination of two factors. After it linear regression simple
linear regression is used to test the relationship between each independent variables of UTAUT2 and
the dependent variable User Acceptance. The standardized coefficient beta value is between -1 to 1.
If it 0 then there is no relationship, if it is positive great than 0, then there is relationship, greater the
value, the stronger relationship.
3.4.4
Credibility of the study
The credibility of this study was ensured by operationalization of two criterions - the
reliability and the validity.
3.4.4.1 Reliability
Survey results will be called reliable if those are same or consistent when conducted
repeatedly [68]. For that Cronbach’s Alpha is used to analyze the level of internal consistency
among the questions based on the each factors of the proposed UTATUT2 model. In this survey,
questions were setup as mandatory, and respondents have to respond to all questions.
26
3.4.4.2 Validity
Validity refers to the accuracy of assessment, whether or not it measures what is supposed to
measure [72]. Even a test is reliable it is not necessary that it is valid. Validity is measure in three
ways which are Content, Criterion, and Construct. Content validity was given highest priority for
the questionnaire items, which was strengthened by pilot test and important literature review.
4
Proposed Model
In the context of online taxis, performance expectancy (PE) could be considered as working
performance. By using the online taxi people can save time by going to street and waiting for the
taxi, so this increases the efficiency of working performance. Therefore, in light of [35],
H1: Performance Expectancy (PE) has a positive effect on the intention to use taxi call app among
Pakistani consumers.
In the context of online taxis, effort expectancy (EE) could be considered as the degree of ease or
struggle people face by using the online taxi from hailing a car to complete the ride when arrived
after paying bill. Therefore, in light of [35],
H2: Effort expectancy (EE) has a positive effect on the intention to use taxi call app among Pakistani
consumers.
In the context of online taxis, social influence (SI) reflects on that individuals seem to be inclined or
concerned to the information and thoughts of their reference group (i.e. parents, relatives, friends
and colleagues) in determining to use online taxi apps and services. Therefore, in light of [35],
H3: Social influence (SI) has a positive effect on the intention to use taxi call app among Pakistani
consumers.
In the context of online taxis, facilitating conditions (FC) exist in a form of compulsory resources
that are requisite for individuals to use online taxi apps and services successfully and effectively.
Online taxis cannot be used if anything is missing such as mobile phones, mobile network, Wi-Fi or
payment methods. Also support is available in case of any issue occurs while hailing a taxi or
instructions to use it. Therefore, in light of [35],
27
H4: Facilitating conditions (FC) has a positive effect on the intention to use taxi call app among
Pakistani consumers.
In the context of online taxi, hedonic motivation (HM) can be regarded as a perception of joy,
entertainment, delight and pleasure that is offered to individuals when using the online taxi apps and
services. Therefore, in light of [35],
H5: Hedonic motivation (HM) has a positive effect on the intention to use taxi call app among
Pakistani consumers.
In the context of online taxis, price value (PV) could be the fact that the online taxi services
provide the estimated price for the ride. Generally charges of the online taxi are balanced and
according to rules, which include the starting price, waiting time, and distance in KM. Online taxi
charges are comparably lower than local taxi. Consequently, in light of [35],
H6: Price value (PV) has a negative effect on the intention to use taxi call app among Pakistani
consumers.
In the context of online taxis, users can be addicted to online taxi as main choice for
transportation and using constantly in daily lives. Therefore, in light of [35],
H7: Habit has a positive effect on the intention to use taxi call app among Pakistani consumers.
28
H1
Performance Expectancy (PE) has a positive effect on the intention to use taxi call app
among Pakistani consumers.
H2
Effort expectancy (EE) has a positive effect on the intention to use taxi call app among
Pakistani consumers.
H3
Social influence (SI) has a positive effect on the intention to use taxi call app among
Pakistani consumers.
H4
Facilitating conditions (FC) has a positive effect on the intention to use taxi call app
among Pakistani consumers.
H5
Hedonic motivation (HM) has a positive effect on the intention to use taxi call app among
Pakistani consumers.
H6
Price value (PV) has a negative effect on the intention to use taxi call app among Pakistani
consumers.
H7
Habit has a positive effect on the intention to use taxi call app among Pakistani consumers.
.
Table 2.Hypotheses
Figure 9.Proposed Model
29
5
Results
5.1 Descriptive Analysis
Descriptive Analysis was conducted to get different set of responses and quality results, for
this questions were added for gender, age, occupation, and location to validate that data is diverse
and generalizable. The total number of respondents was 433. In this survey all questions were set
mandatory to complete the survey. Some invalid responses were removed which included invalid
city names and Online taxis used. After removing invalid responses the total number of valid
responses is 417.
Gender
Responses
Percentage
Male
286
68.6%
Female
130
31.2%
Other/Don’t want to disclose 1
0.2%
417
Total
Table 3.Gender Distribution
The gender distribution within the study was not equal, male respondents were more than double of
female. Male were 68.6%, while female were 31.2%.
Age
Responses
Percentage
Under 18
6
1.4%
18 to 25
195
46.8%
25 to 30
162
38.8%
30 to 40
48
31.2%
Over 45
6
0.2%
Total
417
Table 4.Age Distribution
The age distribution shows that almost half of the respondents were from 18 to 25 year old
category, which is 46.8%. Second major category were from 25 year to 30 year old category, which
was 38.8%. From which it appears that students and employees are highest users of Online Taxis.
Third category of these were 30 to 45 year, which were 11.5%, this shows that in this category there
30
are less number of users in Online Taxis, as these might have their own cars. While there is same
ratio for under 18, and Over 45, which is 1.4%. Under 18 are financially dependents on their parents
and normally use college buses, and while Over 45 people use less technology, and have their own
cars. People with age 18 to 30 year old are normally university students and then new employees
which are heavily using Online Taxis.
Occupation
Responses
Percentage
Salaried class
224
53.7%
Students
153
36.7%
Freelance
18
4.3%
Others
22
5.3%
Total
417
Table 5.Occupation Distribution
Among occupation distribution is shows that highest people using Online Taxis are financial
independent people, which contains employees (53.7%), freelance (4.3%), and others. Student’s
usage is 36.7%.
City
Responses
Percentage
Karachi
309
74.1%
Lahore
7
1.68
Larkana
46
11%
Others
55
13.2%
Total
417
Table 6.Location Distribution
The respondents were from various locations across Pakistan, major respondents were from Karachi,
which is Pakistan biggest city (248 out of 417).
Online taxi used
Responses
Percentage
31
Careem
364
87.3%
Uber
304
72.9%
Uride
12
2.9%
Others
12
2.9%
Total
417
Table 7.Online Taxis used
Online Taxis usage was also asked from respondents, and it appeared that there was highest
usage of Careem in Pakistan, which is 87.3 %( 364 out of 417). Second most used Online Taxi was
Uber, which is 72.9 %( 304 out of 417. While others are not used so much.
Online taxi Experience
Responses
Percentage
Less than 3 months
53
12.7%
3 to 6 months
40
9.6%
6 months to 1 year
78
18.7%
Above 1 year
246
59%
Total
417
Table 8.Online Taxi experience
Experience with Online Taxis was recorded from respondents, and data shows that majority
of the people are using it since more than 1 year (59%), while many are using since last 6 months to
1 year (18.7%).People who have used Online taxis since last 3 months were 12.7%, while 9.6% were
those who are using since last 3 to 6 months.
5.2 Reliability Results
Cronbach’s’ Alpha test was conducted to test the reliability of the factors. If α value is equal
or greater than 0.9 which means an excellent reliability. If the α value is in between 0.7 to 0.8 and
0.8 to 0.9 which means an acceptable and good reliability respectively which is also where majority
of researches ended up. However, if α value is less than 0.5, then the reliability is unacceptable [73].
During test it was found that PE, EE, FC, HM, PV, Habit, and UA ended up equal or higher
than 0.7 in Cronbach’s Alpha test, which means the reliability of those factors are acceptable.
32
However result for SI and FC ended up 0.56 and 0.585 respectively, because these values are above
than 0.5, we consider them are acceptable in reliability.
Factors
Mean
Std. Deviation
Cronbach’s
No. of items
Alpha
PE
5.12
0.919
0.732
4
EE
5.22
0.864
0.7
3
SI
5.21
0.847
0.56
2
FC
5.06
0.936
0.582
2
HM
4.53
1.117
0.836
3
PV
4.57
1.242
0.864
2
Habit
3.82
1.484
0.872
3
UA
4.38
1.416
0.904
2
Table 9.Reliability Results
5.3 Hypotheses Test Results
The factor Performance Expectancy, which consisted of 4 items ended up in average mean
score of 5.12 and standard deviation of 0.919. By looking into each items of PE, we can see mean
score and standard deviation as following “Online taxis helps to take taxi easier” (mean 5.41,
standard deviation 0.67), “Online taxis are more convenient than other channels.” (Mean 5.29,
standard deviation 0.67), “Online taxis helps to take taxi faster” (mean 4.95, standard deviation
0.992), “Online taxis enables me to reach destination more quickly” (mean 4.84, standard deviation
1.066).
Questionnaire Strongly Disagree Slightly
Item
Online taxis
Disagree
Slightly
Agree
Disagree Agree
Strongly
Mean Std.
Agree
Deviation
1
2
3
4
5
6
0
2
4
19
189
203
5.41
0.67
1
1
7
43
178
187
5.29
0.77
helps to take
taxi easier
Online taxis
are more
33
convenient
than other
channels.
Online taxis
1
8
25
82
161
140
4.95
0.992
3
12
27
93
155
127
4.84
1.066
helps to take
taxi faster
Online taxis
enables me to
reach
destination
more quickly
Table 10.PE Responses
The factor Effort Expectancy, which consisted of 3 items have average mean score of 5.22
and standard deviation of 0.864. By looking into each items of EE, we can see mean score and
standard deviation as following “Online taxis are easy to use” (mean 5.27, standard deviation 0.757),
“It doesn’t need to spend too much effort for learning online taxi apps.” (Mean 5.2, standard
deviation 0.868), “I can use Online taxi apps without manual or explanation from the service
provider.” (Mean 5.18, standard deviation 0.954).
Questionnai
Strongl
Disagr
Slightly Slightly Agree
Strongly
re Item
y
ee
Disagre Agree
Agree
Disagre
Mean
Std.
Deviation
e
e
Online taxis
1
2
3
4
5
6
0
2
9
39
191
176
5.27
0.757
1
6
9
50
176
175
5.2
0.868
are easy to
use
It doesn’t
need to
34
spend too
much effort
for learning
Online taxi
apps.
3
I can use
6
12
57
154
185
5.18
0.954
Online taxi
apps
without
manual or
explanation
from the
service
provider.
Table 11.EE Responses
The factor Social Influence, which consisted of 2 items have average mean score of 5.21 and
standard deviation of 0.847. By looking into each items of SI, we can see mean score and standard
deviation as following “Do other people recommend you for Online taxis” (mean 5.02, standard
deviation 0.916), “Many of my friends and fellows are using Online taxis.” (Mean 5.41, standard
deviation 0.722).
Questionnair
Strongl
Disagre
Slightly
Slightly Agre
Strongl
Mea
Std.
e Item
y
e
Disagre
Agree
y Agree
n
Deviatio
Disagre
e
e
n
e
Do other
1
2
3
4
3
5
16
63
5
201
6
129
5.02
0.916
people
recommend
you for
Online taxis
35
Many of my
0
0
9
25
165
217
5.41
0.722
friends and
fellows are
using Online
taxis
Table 12.SI Responses
The factor Facilitating Condition, which consisted of 2 items have average mean score of
5.06, and standard deviation of 0.936. By looking into each items of FC, we can see mean score and
standard deviation as following “I am aware that customer support is available” (mean 5.01, standard
deviation 0.946), “Online taxi apps running smoothly on my smartphone.” (Mean 5.12, standard
deviation 0.926).
Questionnair
Strongl
Disagre
Slightly
Slightly
Agre Strongl
Mea
Std.
e Item
y
e
Disagre
Agree
e
n
Deviatio
Disagre
y Agree
e
n
e
I am aware
1
2
3
4
5
6
2
10
17
52
206
130
5.01
0.946
4
6
10
51
193
153
5.12
0.926
that
customer
support is
available
Online taxi
apps running
smoothly on
my
smartphone
Table 13.FC Responses
The factor Hedonic Motivation, which consisted of 3 items have average mean score of 4.53,
and standard deviation of 1.117. By looking into each items of FC, we can see mean score and
standard deviation as following “I enjoy taking a ride on online taxis.” (Mean 4.8, standard deviation
36
0.84), “The use of Online taxis brings me a lot of fun.” (Mean 4.21, standard deviation 1.189), and
“In general Online taxis applications are interesting.” (Mean 4.59, standard deviation 1.088).
Questionnair
Strongl
Disagre
Slightly
Slightl
Agre
Strongl
Mea
Std.
e Item
y
e
Disagre
y
e
y Agree
n
Deviatio
e
Agree
Disagre
n
e
I enjoy
1
2
3
4
5
6
5
8
20
94
196
94
4.8
0.84
8
34
53
148
117
57
4.21
1.189
4
18
36
110
167
82
4.59
1.088
taking a ride
on Online
taxis.
The use of
Online taxis
brings me a
lot of fun
In general
Online taxis
applications
are
interesting.
Table 14.HM Responses
The factor Price Value, which consisted of 2 items have average mean score of 4.57, and
standard deviation of 1.242. By looking into each items of PV, we can see mean score and standard
deviation as following “Online taxis are cheaper compared to traditional Taxis.” (Mean 4.58,
standard deviation 1.28), “I perceive that Online taxis have a fair pricing.” (Mean 4.56, standard
deviation 1.204).
Questionnair
Strongl
Disagre
Slightly
Slightl
Agre
Strongl
Mea
Std.
e Item
y
e
Disagre
y
e
y Agree
n
Deviatio
e
Agree
n
37
Disagre
e
Online taxis
1
2
3
4
5
6
16
18
32
100
143
108
4.58
1.28
14
15
33
103
164
88
4.56
1.204
are cheaper
compared to
traditional
Taxis
I perceive
that Online
taxis have a
fair pricing
Table 15.PV Responses
The factor Habit, which consisted of 3 items have average mean score of 3.82, and standard deviation
of 1.484. By looking into each items of Habit, we can see mean score and standard deviation as
following “I use online taxis habitually.” (Mean 4.03, standard deviation 1.394), “The service of
Online taxis consists with my life style.” (Mean 4.11, standard deviation 1.34), and “I’m addicted to
use Online taxis.” (Mean 3.32, standard deviation 1.58).
Questionnair
Strongl
Disagre
Slightly
Slightl
Agre
Strongl
Mea
Std.
e Item
y
e
Disagre
y
e
y Agree
n
Deviatio
e
Agree
Disagre
n
e
I use Online
1
2
3
4
5
6
23
49
54
119
112
60
4.03
1.394
17
51
42
122
129
56
4.11
1.34
taxis
habitually
The service
of Online
38
taxis consists
with my life
style
I’m addicted
72
76
56
107
67
38
3.32
1.58
to use Online
taxis.
Table 16.Habit Responses
The factor User Acceptance, which consisted of 2 items have average mean score of 4.38,
and standard deviation of 1.416. By looking into each items of UA, we can see mean score and
standard deviation as following “I use online taxis habitually.” (Mean 4.03, standard deviation
1.393), “The service of Online taxis consists with my life style.” (Mean 4.73, standard deviation
1.351).
Questionnair
Strongl
Disagre
Slightly
Slightly
Agr
Strongl
Mea
Std.
e Item
y
e
Disagre
Agree
ee
y Agree
n
Deviatio
Disagre
e
n
e
I use Online
1
2
3
4
5
6
23
50
51
121
113
59
4.03
1.393
18
35
7
39
199
119
4.73
1.351
taxis
habitually
The service
of Online
taxis consists
with my life
style
Table 17.UA Responses
5.4 Correlation of factors
39
The Spearman's Rank correlation coefficient is a technique which can be used to summarize
the strength and direction (negative or positive) of a relationship between two variables [74]. The
result will always be between 1 and minus 1. It is similar with Pearson correlation coefficient in
terms of two variables. It however measure the linear relationship as compared to Spearmen
correlation coefficient which highlights monotonic relationship. It is necessary to check the possible
existing relationship between factors like PE and EE. We need to check relationship between
variables PE, EE, SI, FC, PV, HM, and H, whether these variables or independent or not. As we can
see in the following table as PE and EE is 1.251, EE and SI is 0.258, SI and FC is 0.267, FC and
HM is 0.267, HM and PV is 0.375, PV and H is 0.343, as all these number are all under the defined
critical point 0.7, we can say that all these factors are independent and positively affect UA.
Figure 10.Correlation of factors
5.5 Relationships between factors and UA
40
The simple linear regression between each factor to user acceptance has been conducted. We
use standardized coefficient beta to examine the strength of relationship and the R square to check
how much of the independent variable i.e. UTAUT2 factors can determine the dependent variable
i.e. User Acceptance The result of regression is shown in figure 22. The regression equation can
have variance that in need of testing in order to confirm the beta value is not in the within the variance
and thus can be confirmed as significant. So as to test for the significance and whether the null
hypotheses can be rejected, T- Tests were used in the research.
Figure 11.Relationships between factors and UA
Hypotheses 1 has Beta-value of 0.214, which confirms that PE is positively interpreter of the
dependent variable UA. Therefore H1 is accepted. Using t-test to verify, a null hypothesis is created
below
41
H10: Performance Expectancy (PE) does not have a positive effect on the intention to use
taxi call app among Pakistani consumers.
H1: Performance Expectancy (PE) has a positive effect on the intention to use taxi call app
among Pakistani consumers.
The T-value is 6.319, which is larger than 6.3138 and thus H10 can be rejected for H1.
Figure 12.Relationship between PE and UA
Hypotheses 2 has Beta-value of 0.217, which confirms that EE is positively interpreter of the
dependent variable UA. Therefore H2 is accepted. Using t-test to verify, a null hypothesis is created
below
H20: Effort expectancy (EE) does not have a positive effect on the intention to use taxi call
app among Pakistani consumers.
H2: Effort expectancy (EE) has a positive effect on the intention to use taxi call app among
Pakistani consumers.
The T-value is 6.418, which is larger than 6.3138 and thus H20 can be rejected for H2.
Figure 13.Relationship between EE and UA
42
Hypotheses 3 has Beta-value of 0.168, which is close to 0 that mean SI is very low positive
interpreter of the dependent variable UA. Using t-test to verify, a null hypothesis is created below
H30: Social influence (SI) does not have a positive effect on the intention to use taxi call app
among Pakistani consumers.
H3: Social influence (SI) has a positive effect on the intention to use taxi call app among
Pakistani consumers.
The T-value is 4.929, which is less than 6.3138 and thus H30 can be accepted, and H3 is rejected.
Figure 14.Relationship between SI and UA
Hypotheses 4 has Beta-value of 0.235, which confirms that FC is positively interpreter of the
dependent variable UA. Therefore H4 is accepted. Using t-test to verify, a null hypothesis is created
below
H40: Facilitating conditions (FC) does not have a positive effect on the intention to use taxi
call app among Pakistani consumers.
H4: Facilitating conditions (FC) has a positive effect on the intention to use taxi call app
among Pakistani consumers.
The T-value is 6.97, which is larger than 6.3138 and thus H40 can be rejected for H4.
43
Figure 15.Relationship between FC and UA
Hypotheses 5 has Beta-value of 0.307, which confirms that HM is positively interpreter of
the dependent variable UA. Therefore H5 is accepted. Using t-test to verify, a null hypothesis is
created below
H50: Hedonic motivation (HM) does not have a positive effect on the intention to use taxi
call app among Pakistani consumers.
H5: Hedonic motivation (HM) has a positive effect on the intention to use taxi call app among
Pakistani consumers.
The T-value is 9.313, which is larger than 6.3138 and thus H50 can be rejected for H5.
Figure 16.Relationship between HM and UA
Hypotheses 6 has Beta-value of 0.335, which confirms that PV is positively interpreter of
the dependent variable UA. Therefore H6 is accepted. Using t-test to verify, a null hypothesis is
created below
44
H60: Price value (PV) does not have a positive effect on the intention to use taxi call app
among Pakistani consumers.
H6: Price value (PV) has a positive effect on the intention to use taxi call app among
Pakistani consumers.
The T-value is 10.248, which is larger than 6.3138 and thus H60 can be rejected for H6.
Figure 17.Relationship between PV and UA
Hypotheses 7 has Beta-value of 0.804, which confirms that Habit is positively interpreter of
the dependent variable UA. Therefore H7 is accepted. Using t-test to verify, a null hypothesis is
created below
H70: Habit does not have a positive effect on the intention to use taxi call app among
Pakistani consumers.
H7: Habit has a positive effect on the intention to use taxi call app among Pakistani
consumers.
The T-value is 39.055, which is larger than 3 and thus H70 can be rejected for H7.
45
Figure 18.Relationship between H and UA
6
Discussion
6.1 Implications for research
Following implications based upon the empirical findings and the existing literature review
on user acceptance are elaborated
Performance Expectancy plays positive role on user acceptance of Online taxis in Pakistan
based on observed findings. Performance Expectancy indicates that “what one can achieve in
working performance depends on whether he believe the application of system will help him” [14].
Effort Expectancy also plays positive role on user acceptance of Online taxis in Pakistan based
on observed findings, but its impact is slightly more than Performance Expectancy. Effort
expectancy as the “degree of ease related with the use of the system” [14].
Social Influence does not play positive role on user acceptance of Online taxis in context of
Pakistan based on observed findings. People define the social influence as the “to what extent a
person thinks that it is significant to apply the new system” [14]. It suggests that Pakistani users are
not motivated by what others are using or what is the current trend, but see who particular thing
benefits them.
Facilitating condition plays positive role on user acceptance of Online taxis in Pakistan based
on observed findings, but its impact is a little more than both Performance Expectancy and Effort
Expectancy. In light of the definition as “the degree to which a person believes that an organizational
and technical infrastructure exists to support use of the system” [14].
46
Hedonic Motivation plays positive role on user acceptance of Online taxis in Pakistan based
on observed findings, but its impact is a more as compared Performance Expectancy. Effort
Expectancy and Facilitating Condition defined hedonic motivation as “the fun or pleasure derived
from using a technology”.
Price Value plays a more positive role on user acceptance of Online taxis in Pakistan based on
observed findings, it appears from the results that Pakistani users are more price conscious and has
a significant effect on user acceptance.
Habit has a very highly positive role on user acceptance of online taxis in Pakistan based on
observed findings. It has been drawn from the results most people who are habitually using the online
taxis will continue to use in future, as it consist with their lifestyle.
6.2 Implications for practice
In regard to our research question on how online taxis can achieve success in Pakistan,
following recommendations are given
From results it appears, that overall online taxis have made people life easier. These are better
as compared to traditional taxis. They are safe, secure, reliable, convenient, comfortable, economical
and easy to use. Online taxis help you to take easy faster and easier. You don’t have to go street and
wait for the taxis this is the key advantage. Algorithm automatically picks up the nearest possible
driver and informs driver about the passenger location and driver can reach quickly. Sometimes it
takes more time for taxi to arrive at pickup location, when user need to reach destination quickly,
there should be improvement in software or there should be more drivers which resolves this issue.
Users feel that online taxi applications are simple, smooth and easy to use, and don’t require
too much to learn it or guidance required. However there should be some improvement regarding
the problem solutions of customer when drivers refuse to go to some destination and don’t want to
cancel the ride. Passengers have to reach to the destination fast, therefore they need to cancel the
ride and book another, in the process charges are applied to passengers and this is happening very
frequently with the users. Passengers some time forget their items in the car and then it’s difficult to
get lost items so there must be some way for recovery of lost items.
47
Passengers feel that online taxis are economical and have a fair pricing, we don’t need to
bargain and it shows the estimated price. However many users also complain that there need
improvement in pricing of the Online taxis. Prices should be more consistent throughout the time,
instead of peak factors at some time, which make it more costly as compared to traditional Taxis.
During peak factors people avoid using it and prefer traditional taxis. Initial Waiting time should be
increased also, which otherwise increases fare. Charging rate during the traffic also becomes
irritating for the user as traditional taxis do not charge for it.
Online taxis are comfortable, but cars should be categorized properly, as many times car that
are assigned in GO category, have no AC working, which has more fare as compared to GO Mini.
Cars in GO Mini category should be better as there are worst old car models are there.
Drivers need to be trained professionally. They also need to have knowledge of using navigation
and reach pick up location by following it instead of asking from passenger. Many drivers do not
want to go users destination and ask users to cancel ride, and it becomes irritating for the user, as
they have to reach destination quickly instead of going to whole process of cancel ride, book another,
wait for another car and also bear the charges of cancelled ride and then contacting support for it.
Many times drivers don’t need reach properly at the pickup location, and wait at some near location,
change status to arrive, which increase the charges for the passenger.
7
Conclusion and Future works
In this study we wanted to understand the factors of user adoption of Online Taxis. To achieve
the purpose of this study, extensive literature review was done. The user acceptance that was required
to investigate for that we worked on the latest framework UTATUT2. Based on the factors of
UTATUT2, which are Performance Expectancy, Effort Expectancy, Social Influence, Hedonic
Motivation, Price Value, Facilitating conditions and Habit, hypotheses were formulated. On the basis
of hypotheses and interviews, questionnaire was created. Questionnaire was distributed online on
widely used social networking platforms in Pakistan like Facebook, and WhatsApp. Valid responses
received were 417 and statistical analyses was performed on the received responses. Cronbach’s
Alpha was done to find out the reliability of responses, Spearmen Correlation to check whether our
independent variables are independent or have any relationship, linear regression for the relationship
between independent variables like Performance Expectancy, Effort Expectancy, Social Influence,
48
Hedonic Motivation, Price Value, Facilitating conditions and Habit and dependent variable User
Acceptance.
After statistical analysis it was found that 6 factors out of 7 are positively influencing User
Acceptance, that are Performance Expectancy, Effort Expectancy, Hedonic Motivation, Price Value,
Facilitating conditions and Habit, while Social Influence has not positive effect on User Acceptance.
After analyzing the results both research and practical implication are presented.
As online taxis are new field of technology, it is recommended to understand how they affect
our society. We can explore the social, political and economic impacts that the technology has
caused. In addition, future studies should focus on the legal implication that the adoption of the
application has raised. Since some of the taxis are unregulated and unlicensed at least not in the
traditional fashion in which taxis were registered.
It is recommended to replicate this study among different countries and cultures to get insights
of culture or industries on the UTATUT2. Factors like risk and privacy are recommended to be
included in further studies on UTATUT2, as these have an impact on user acceptance for some users.
We have applied UTATUT2 model in context of online taxi in Pakistan, it can be applied to other
industries as well like food delivery, social media, shopping etc.
In this research, only passengers were seen as the users of online taxis, and the drivers were
considered as the service providers of the system. We can also conduct another research from the
driver’s point of view, and find out the factors for their adoption. It will be interested for future
research.
.
49
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Appendix
Complete Questionnaire
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